Full text of Economic Report of the President : 2023

The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.

economic
re p ort
of the

president
transmitted to congress | march 2023
together with the annual report
of the council of economic advisers

economic
re p ort
of the

president

transmitted to congress | march 2023
together with the annual report
of the council of economic advisers

Contents
Economic Report of the President. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
The Annual Report of the Council of Economic Advisers. . . . . . . . . . . . . 7
Chapter 1: Pursuing Growth-Enhancing Policies in Today’s
Changing World. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Chapter 2: The Year in Review and the Years Ahead. . . . . . . . . . . . . . . . 51
Chapter 3: Confronting New Global Challenges with Strong
International Economic Partnerships. . . . . . . . . . . . . . . . . . . 93
Chapter 4: Investing in Young Children’s Care and Education. . . . . . . 125
Chapter 5: Building Stronger Postsecondary Institutions.. . . . . . . . . . . 153
Chapter 6: Supply Challenges in U.S. Labor Markets. . . . . . . . . . . . . . 183
Chapter 7: Competition in the Digital Economy: New Technologies,
Old Economics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
Chapter 8: Digital Assets: Relearning Economic Principles.. . . . . . . . . 237
Chapter 9: Opportunities for Better Managing Weather Risk in
the Changing Climate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
References.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
Appendix A: Report to the President on the Activities of the Council
of Economic Advisers during 2022. . . . . . . . . . . . . . . . . . 415
Appendix B: Statistical Tables Relating to Income, Employment,
and Production....................................................................427

____________

*For a detailed table of contents of the Council’s Report, see page 11.

iii

Economic Report
of the
President

Economic Report of the President
To the Congress of the United States:
Our Nation has faced tremendous challenges in recent years. A deadly pandemic and unprovoked war in Ukraine have tested our economy unlike any
time since the Great Depression. When I was sworn into office, COVID-19
was raging and our economy was reeling. Millions of workers were out of
a job, through no fault of their own. Hundreds of thousands of businesses
had closed, our supply chains were snarled, and many schools were still
shuttered. Families across the country were feeling real pain.
Today, two years later, 230 million Americans have been vaccinated,
and COVID no longer controls our lives. We have created a record 12 million jobs, which constitute the strongest two years of job gains on record.
Unemployment is at a more than 50-year low, with near-record lows for
Black and Latino workers, and manufacturing jobs have recovered faster
than in any business cycle since 1953. Growth is up, wages are up, and inflation is coming down. At the same time, a record 10 million Americans have
applied to start small businesses—each of their applications an act of hope.
More Americans have health insurance today than ever before in our history,
and real household wealth is 10 percent above what it was before COVID.
It is safe to say: Our economic plan is working, and American families are
starting to have a little more breathing room.
It is important to remember, however, that the economic anxiety so
many have felt did not start with the pandemic. For decades, the backbone of
America, the middle class, has been hollowed out. Too many American jobs
have been shipped overseas. Unions have been weakened. Once-thriving cities and towns have become shadows of what they used to be, robbing people
of hard-earned pride and self-worth.
I ran for President to rebuild our economy from the bottom up and
middle out, not from the top down—because when the middle class does
well, the poor have a ladder up and the wealthy still do well. We all do
well. And that is what we have been working for. This past year, we made
critical investments to secure America’s future. Together, the Bipartisan
Infrastructure Law, the CHIPS and Science Act, and the Inflation Reduction
Act represent the biggest public investments in our history—expected to
draw more than $3.5 trillion in public and private funding for infrastructure,
the digital economy, and clean energy over the next decade.
First, the Bipartisan Infrastructure Law is an investment in America
and our competitiveness. You cannot be the number one economy in the

Economic Report of the President | 3

world unless you have the best infrastructure in the world. That is why this
once-in-a-generation law is finally rebuilding our roads, bridges, railroads,
ports, airports, and more—to keep our people safe, our goods moving, and
our economy growing. Families across the Nation will have safe drinking
water and high-speed Internet. A network of electric vehicle charging stations will allow more of us to drive cleaner cars. To date, we have funded
over 20,000 construction projects all across the country, creating tens of
thousands of well-paying new jobs. Americans everywhere can take pride
in seeing shovels in the ground.
Second, the CHIPS and Science Act, which I signed in August, will
make sure that America once again leads the world in developing and
manufacturing the semiconductors that power everything from cars to
refrigerators to smartphones. The United States invented these chips; it is
time to again manufacture them at home, and to make sure our economy
never again relies so heavily on foreign chipmakers. Private companies have
already announced more than $300 billion in new investments in American
manufacturing in the last two years, many of them thanks to this law, creating tens of thousands more jobs of the future in every corner of the country.
Third, the Inflation Reduction Act, also enacted last August, takes on
powerful special interests to cut costs for working families. It lowers health
care and prescription drug costs—for example, capping insulin at $35 a
month for seniors on Medicare, and capping drug costs at $2,000 a year for
seniors with Medicare Part D starting in 2025. It extends the Affordable Care
Act’s subsidies, saving families an average of $800 a year. It also makes the
Nation’s most significant investment ever in combating the existential threat
of climate change, investing in everything from climate-smart agriculture to
more resilient electric grids. It builds a new clean energy economy, creating
thousands of green jobs in communities too often left behind, while also
lowering home energy bills for families.
Meanwhile, my Administration has taken wide-ranging executive
actions to help level the playing field and promote competition. From easing
the burden of crippling student debt, to providing relief to families at the gas
pump, to cracking down on unfair junk fees, we are building an economy
that gives everyone a fair shot and a little more breathing room.
Throughout, we have shown that we can invest in our future and be
fiscally responsible at the same time. We are helping to pay for these historic programs by finally making the wealthy and corporations pay their fair
share, without raising taxes on anyone making under $400,000 a year. And
we cut the deficit by $1.7 trillion during my first two years in office, the
largest reduction in history, with more to come.
I have often said that a job is about more than a paycheck; it is about
dignity and respect. This is why we are not only investing in record job
growth; we are also providing historic support for workers and unions at a
4 |

Economic Report of the President

time of big shifts in our workforce. We plan to ban noncompete agreements
for 30 million workers who have been unfairly locked in their jobs, giving
them the right to be paid what they are worth. We have boosted pay and
labor protections for Federal contractors, we have pushed to extend these
same protections to all workers, and we have passed laws to ensure safe
and fair workplaces, including for pregnant and nursing workers and workers who face sexual assault and harassment on the job. We are investing in
job-training programs and registered apprenticeships, which give so many
people a ladder up to well-paying jobs on which they can raise a family
without a college degree.
Now, it is time to finish the job. We have much more to do to build
an economy that benefits everyone—from cracking down on the deadly
fentanyl epidemic and investing in mental health care and recovery, to
fighting for childcare and paid family leave for millions of working families
struggling to care for their loved ones, so no one ever again has to choose
between the paycheck they need and the family they love.
Our Nation is at an inflection point that will determine our future for
decades to come. But today, because of the choices and investments we have
made, jobs are coming back, pride is coming back, and the United States
of America is better positioned to lead than any other country on Earth.
Our blue-collar blueprint to rebuild America is proving that democracy
can deliver, building an economy that is fairer and stronger and leaves no
one behind.

The White House
March 2023

Economic Report of the President | 5

The Annual Report
of the
Council of Economic Advisers

Letter of Transmittal
Council of Economic Advisers
Washington, March 20, 2023

Mr. President:
The Council of Economic Advisers herewith submits its 2023 Annual
Report in accordance with the Employment Act of 1946, as amended by the
Full Employment and Balanced Growth Act of 1978.
Sincerely yours,

Cecilia Elena Rouse
Chair

Jared Bernstein
Member

Heather Boushey
Member

9

Contents
Chapter 1: Pursuing Growth-Enhancing Policies in Today’s
Changing World. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Investing in Production Drives Economic Growth. . . . . . . . . . . . . . 23
The United States’ Economic Growth Over Time.. . . . . . . . . . . . . . 25
The Inputs to U.S. Economic Growth Over Time. . . . . . . . . . . . 29
U.S. Economic Growth in Context. . . . . . . . . . . . . . . . . . . . . . . 34
Sustaining Economic Growth in Today’s Changing World.. . . . . . .
Investing in Human Capital and the Labor Supply: Implications
of More Women Participating in the Labor Force. . . . . . . . . . . .
Investing in Physical Capital: Adapting to the Increasing
Effects of Climate Change.. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Investing in the Economy’s Productivity: The New World of
Digital Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

35
35
41
44

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
Chapter 2: The Year in Review and the Years Ahead. . . . . . . . . . . . . . . . 51
The Year in Review: The Continuing Recovery. . . . . . . . . . . . . . . . 53
Output in 2022: A Return to Near Its Trend. . . . . . . . . . . . . . . . 54
The Historic Strength of Labor Markets in 2022. . . . . . . . . . . . 59
The Cooling of Financial Markets in 2022.. . . . . . . . . . . . . . . . 61
Inflation in 2022.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Factors That Had an Impact on Inflation in 2021–22. . . . . . . . 67
The Forecast for the Years Ahead .. . . . . . . . . . . . . . . . . . . . . . . . . . 84
The Near Term . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
The Long Term . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
Chapter 3: Confronting New Global Challenges with Strong
International Economic Partnerships. . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

11

The United States’ International Trade and Investment in 2022. . . . 94
Pandemic-Related and Macroeconomic Trends Have
Shaped Record Goods Imports. . . . . . . . . . . . . . . . . . . . . . . . . . 98
Geopolitical Shocks and Global Demand Have Shaped
Record Goods Exports.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
International Trade in Services and Digital Trade Have
Been Resilient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Continued Growth for Foreign Direct Investment Despite
Elevated Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
Global Economic Relations Are at a Turning Point. . . . . . . . . . . .
Imperatives of Economic Partnerships in the Changing
Global Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Resilience during Global Supply Shocks. . . . . . . . . . . . . . . . .
Responding to Geopolitical Challenges. . . . . . . . . . . . . . . . . .
Promoting Opportunity and Managing Risks in Digital Trade.

110
112
113
117
119

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Chapter 4: Investing in Young Children’s Care and Education. . . . . . . 125
The Effectiveness of Early Childhood Investments. . . . . . . . . . . . 126
Benefits for Children and Society. . . . . . . . . . . . . . . . . . . . . . . 126
Defining Quality in ECE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
Benefits for Working Parents.. . . . . . . . . . . . . . . . . . . . . . . . . . 129
Challenges in the Market for Early Care and Education.. . . . . . . . 133
Workforce Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The High Costs of High-Quality Care. . . . . . . . . . . . . . . . . . .
ECE Pricing and Price-Sensitive Consumers. . . . . . . . . . . . . .
Business Model Fragility. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Participation in and Availability of ECE. . . . . . . . . . . . . . . . .

134
136
140
141
142

The Role of Subsidies in the Market for Care . . . . . . . . . . . . . . . . 146
International Comparisons. . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
Subsidies in the United States’ ECE Market. . . . . . . . . . . . . . . 147
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
Chapter 5: Building Stronger Postsecondary Institutions.. . . . . . . . . . . 153
The U.S. Postsecondary Institutional Landscape.. . . . . . . . . . . . . . 155

12 |

Annual Report of the Council of Economic Advisers

Institutions Serve a Diverse Student Population.. . . . . . . . . . .
Institutions Vary in Their Prices and Spending on Students.. .
Institutions Vary in Their Student Outcomes. . . . . . . . . . . . . .
Institutions Matter for Student Outcomes. . . . . . . . . . . . . . . . .

156
157
158
160

The Rationale for and Delivery of Public Postsecondary
Investment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162
The Economic Rationale for Public Sector Investment. . . . . . 163
How Public Funds Are Delivered: Student Aid and
Institutional Support .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
The Imperfect Market for Postsecondary Institutions. . . . . . . . . . . 169
Geographic Constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
Informational and Behavioral Constraints. . . . . . . . . . . . . . . . 170
College Expansion Constraints. . . . . . . . . . . . . . . . . . . . . . . . . 171
Institution-Focused Policies That Promote Access to
Postsecondary Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Supporting the Quality of Existing Colleges and Programs.. . 173
Institutional Accountability. . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
Addressing Geographic Barriers to Access. . . . . . . . . . . . . . . 178
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Chapter 6: Supply Challenges in U.S. Labor Markets. . . . . . . . . . . . . . 183
Labor Supply Fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
Trends in U.S. Labor Market Participation. . . . . . . . . . . . . . . 185
Why Worry About Slower Labor Supply Growth?. . . . . . . . . . 186
Causes of U.S. Labor Supply Challenges. . . . . . . . . . . . . . . . . . . . 189
Demographic Trends. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Declining Labor Market Participation Among Men.. . . . . . . .
Female Labor Force Participation: The United States
Falls Behind. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The COVID-19 Pandemic’s Lingering Effects on the
Labor Supply. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

189
191
197
199

Options to Boost the U.S. Labor Supply. . . . . . . . . . . . . . . . . . . . . 201
Increasing Immigration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
Drawing More Adults into the Labor Market . . . . . . . . . . . . . 204
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Contents

| 13

Chapter 7: Competition in the Digital Economy: New Technologies,
Old Economics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
The Benefits of Digital Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Reducing Search Costs .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
Increased Variety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
“Free” Products and Services. . . . . . . . . . . . . . . . . . . . . . . . . 215
How Is Competition Different in Digital Markets?.. . . . . . . . . . . . 217
Big Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Network Effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Multi-Homing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
When Do Markets Tip?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

218
220
223
223

The Role of Law and Regulation in the Digital Market. . . . . . . . . 225
Network Effects Create a Competitive Moat.. . . . . . . . . . . . . .
The Challenge of Preserving Competition in
Digital Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Preventing the Extension of Dominance into
Adjacent Markets.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Preventing the Misuse of Consumer Data. . . . . . . . . . . . . . . .
Monitoring Pricing Algorithms and Collusion. . . . . . . . . . . . .

227
228
229
230
232

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235
Chapter 8: Digital Assets: Relearning Economic Principles.. . . . . . . . . 237
The Perceived Appeal of Crypto Assets. . . . . . . . . . . . . . . . . . . . . 239
Claim: Crypto Assets Could Be Investment Vehicles. . . . . . . .
Claim: Cryptocurrencies Could Offer Money-like Functions
without Relying on a Single Authority . . . . . . . . . . . . . . . . . . .
Claim: Crypto Assets Could Enable Fast Digital Payments ..
Claim: Crypto Assets Could Increase Financial Inclusion . . .
Claim: Crypto Assets Could Improve the United States’
Current Financial Technology Infrastructure. . . . . . . . . . . . . .

244
244
244
245
246

The Reality of Crypto Assets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
Crypto Assets Are Mostly Speculative Investment Vehicles. . . 246
Cryptocurrencies Generally Do Not Perform All the
Functions of Money as Effectively as Sovereign Money,
such as the U.S. Dollar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
Stablecoins Can Be Subject to Run Risk. . . . . . . . . . . . . . . . . . 255
14 |

Annual Report of the Council of Economic Advisers

Crypto Assets Can Be Harmful to Consumers and Investors. . .
There Have Been Limited Economic Benefits from DLT
Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Risks of Financial Innovation. . . . . . . . . . . . . . . . . . . . . .
Other Risks from Crypto Assets. . . . . . . . . . . . . . . . . . . . . . . .

256
258
263
264

Investing in the Nation’s Digital Financial Infrastructure. . . . . . . . 268
The FedNow Instant Payment System. . . . . . . . . . . . . . . . . . . . 268
Central Bank Digital Currencies. . . . . . . . . . . . . . . . . . . . . . . 270
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
Chapter 9: Opportunities for Better Managing Weather Risk in
the Changing Climate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
Economic Principles of Adaptation Policy and Planning. . . . . . . . 277
The Economic Costs and Financial Risks of Climate Change
in the United States. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
The Costs of Climate Change for the United States’
Well-Being and Prosperity.. . . . . . . . . . . . . . . . . . . . . . . . . . . . 280
Climate Change and Financial Stability.. . . . . . . . . . . . . . . . . 284
The Federal Fiscal Implications of Physical Climate Risk. . . . . . . 288
Risk Assumption.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Climate-Exposed Assets.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
The Provision of National Public Goods . . . . . . . . . . . . . . . . .
The Programs of the Social Safety Net. . . . . . . . . . . . . . . . . . .

289
289
291
291

Market Failures and Distortions That Slow Adaptive Adjustments
and Policy Responses.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
Imperfect Information on Physical Climate Risks. . . . . . . . . .
Information Asymmetries.. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Externalities and Public Goods. . . . . . . . . . . . . . . . . . . . . . . .
Credit Constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Moral Hazard. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

293
293
294
295
296

Four Potential Pillars of the Federal Adaptation Strategy and Major
Policy Opportunities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
Producing and Disseminating Knowledge about Climate Risk.297
Long-Term Planning for the Climate Transition.. . . . . . . . . . . 298
Ensuring the Accurate Pricing of Climate Risk. . . . . . . . . . . . 300

Contents

| 15

Protecting the Vulnerable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302
References.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
A.
B.

1-1
1-2
1-3
1-4
1-5
1-6
1-7
1-8
1-9
1-10
1-11
1-12
1-13
1-14
1-15
1-16
1-17
1-18
1-19
2-1

16 |

Appendixes

Report to the President on the Activities of the Council of
Economic Advisers during 2022................................................ 415
Statistical Tables Relating to Income, Employment, and
Production................................................................................... 427

Figures

Average Annual U.S. Real GDP Growth since 1790, by
Decade and Contributor......................................................................26
Miles of U.S. Railroads, 1830–90......................................................27
U.S. Patents Issued, 1790–2000.........................................................28
Contributors to U.S. Real GDP Growth, 1930–50............................29
U.S. Secondary School Enrollment and Graduation Rates,
1890–1991...........................................................................................30
The U.S. Labor Force, 1800–2021....................................................31
U.S. Residences with Electricity, 1930–56........................................31
U.S. Capital Stock, 1925–2021..........................................................32
Total Factor Productivity Growth, 1953–2021..................................33
GDP per Capita for the United States, Argentina, and
Singapore, 1800–2018........................................................................34
Woman’s Labor Force Participation Rate, 1970–2022.....................36
Percentage of Postsecondary Degrees Received by Women,
1980–2020...........................................................................................37
Percentage of Households with a Child under 18 That Have
an Adult over Age 65 Years, 1989–2021...........................................38
Consumption of Nursing Home and Childcare Services and
Women’s Labor Force Participation, 1960–2022..............................39
Share of Private Industry Workers with Access to Benefits.............39
Nonparticipation in the Labor Force, by Reason..............................41
Carbon Dioxide Levels Over Time....................................................42
Number of Billion-Dollar Natural Disasters in the
United States, 1980–2022...................................................................43
Percentage of Adults Who Report Using the Internet Over Time.....45
The U.S. Economy, 2018–22.............................................................51

Annual Report of the Council of Economic Advisers

2-2
2-3
2-4
2-5
2-6
2-i
2-7
2-8
2-9
2-10
2-11
2-12
2-13
2-14
2-15
2-16
2-17
2-18
2-19
2-ii
2-iii
3-1
3-2
3-3
3-i
3-ii
3-iii
3-4
3-5
3-6
3-7
3-8
3-9
3-10

GDP and Trend GDP, 2012–22..........................................................54
The 2019–22 Period Compared with Previous Business Cycles.....55
Job Openings per Unemployed Person, 2000–2022.........................60
The Beveridge Curve at Two Intervals..............................................60
Stock Market and Bond Prices, 2019–22..........................................62
Types of Consumer Price Inflation, 2011–22....................................64
The Expectations-Augmented Phillips Curve at Two Intervals.......65
Decomposition of Inflation, 2019–22................................................66
Global Measures of Consumer Price Inflation..................................67
Supply Chain Pressures and Producer Inflation, 1990–2022...........72
Commodity Pressures and PCE Inflation, 2006–22..........................73
Employment Cost Index and Inflation, 2013–22..............................74
Nominal and Real Measures of the Policy Rate, 2016–22...............76
The Composition of the Federal Reserve’s Balance Sheet,
2007–22...............................................................................................77
The Fiscal Impulse and Inflation, 2012–24.......................................78
OMB’s Primary Deficit Forecast, 2017–33.......................................80
Excess Savings and Inflation, 2016–22.............................................81
Consumer Goods-Services Rotation, 2018–22.................................82
Actual and Expected Inflation, 2012–22...........................................83
The Evolution of the U.S. Population’s Age Composition...............89
Age–Labor Force Participation Rate Profiles in 2019......................89
Real U.S. Trade in Goods and Services, 2012–22............................94
U.S. Trade Balance, 2018–22.............................................................95
Real U.S. Services Trade, 2012–22...................................................95
Federal Reserve Board’s Real Broad Dollar Index, 2016–22..........97
Top Sources of U.S. Goods Imports, 2022........................................99
Top U.S. Goods Export Destinations, 2022......................................99
Real Imports of Consumer Goods, 2018–22...................................100
Real Imports of Capital Goods (Excluding Automobiles),
2018–22.............................................................................................101
Real Exports of Consumer Goods, 2018–22...................................102
U.S. Exports of Liquefied Natural Gas, 2021 and 2022.................103
U.S. Exports of Crude Oil, 2021 and 2022.....................................104
U.S. Trade in Potentially ICT-Enabled Services, 1999–2021.........105
Real U.S. Outward Foreign Direct Investment, by
Destination, 2012–22........................................................................108

Contents

| 17

3-11
3-iv
4-1
4-i
4-2
4-ii
4-iii
4-3
4-4
4-5
4-6
4-7
4-8
5-1
5-2
5-3
5-4
5-5
5-6
6-1
6-2
6-3
6-i
6-4
6-5
6-6
6-ii
7-1

18 |

Real U.S. Inward Foreign Direct Investment, by Source,
2012–22.............................................................................................109
Real Russian Foreign Direct Investment Net Inflows, 2017–22.... 115
Return on Investment in Human Capital, by Age...........................128
Food Insecurity among Households with Young Children,
2000–2021.........................................................................................131
Labor Force Participation Over Time, by Maternal Status.............132
Percent Change in Maternal Employment.......................................135
Racial and Gender Breakdown of Employment..............................137
Formal ECE Consumption, by Income Level.................................138
Average Annual Expenses for Formal ECE as a Proportion of
Income, by Income Level.................................................................139
Ratio of Young Children to Childcare Capacity in 2018................142
Ratio of Infants and Toddlers to Childcare Capacity in 2018........143
Excess Demand by Provider Type...................................................144
Reasons Households Face Difficulty Finding Care........................145
Distribution of Enrollment Across Institution Types, by
Student Characteristics.....................................................................156
Variation in Per-Student Expenditures.............................................157
Variation in Undergraduate Student Outcomes...............................159
Average Public Tuition and Fees and Percentage of Students
Receiving Public Financial Aid—Bachelor’s Degree Programs,
2019–20.............................................................................................166
Distance Between Home and College, 2004–16.............................169
Per-Student State and Local Funding for Public Higher
Education, 1989–2019......................................................................171
U.S. Labor Force Participation Rate, 1948–2022...........................186
Percentage of Total Projected Population That Is Prime Age,
2026–50.............................................................................................190
Prime-Age versus Overall Labor Force Participation,
1990–2022.........................................................................................190
Sources of Annual Income for Prime-Age Workers by Sex and
Labor Force Status, 2022..................................................................192
Prime-Age Male Labor Force Participation, by Race,
1976–2022.........................................................................................193
Prime-Age Female Labor Force Participation, 1984–2021............198
Prime-Age Female Labor Force Participation Rate, 1976–2022.....199
Percent Change in Teacher Employment, 2019–22........................203
Growth in Advertising Revenue by Digital Platform, 2002–21.....215

Annual Report of the Council of Economic Advisers

7-i
7-2
7-3
8-1
8-2
8-3
8-i
8-ii
8-4
8-5
9-1
9-2
9-3

How Data Brokers Aggregate Data from Government,
Commercial, and Publicly Available Sources to Build
In-Depth Profiles of Consumers.......................................................219
Network Effects Are Present in Many Markets—Not
Just Online.........................................................................................221
Completed Acquisitions by Large Tech Firms................................229
A Taxonomy of Digital Assets and Central Bank Money..............239
Market Capitalization of Selected Crypto Assets, 2020–22...........240
Payment Types Used in the United States Over Time....................244
Examples of Hashed Output.............................................................247
Blockchain Blocks Linked by Hashed Values of Their Contents....250
Volatility of Crypto Assets versus Certain Traditional Assets,
2017–22.............................................................................................251
Nominal Cyber Insurance Prices Over Time...................................266
Small Changes in Climate Can Greatly Increase the
Probability of Extreme Weather Events...........................................276
Count of Policies under U.S. Residual Property Insurance
Market, 1990–2021, with Geographic Breakdown for 2021..........285
Governance of Climate Risk Is Complex and Multiscale..............297

Tables

2-1
2-2
2-3
2-4
2-5
5-1
5-2
8-1
8-2

Real GDP Growth and Its Components, 2022..................................57
Selected Legislative and Executive Actions in 2022........................79
Economic Projections, 2021–33.........................................................85
Evolution of Blue Chip Consensus Real GDP Forecast...................86
Supply Side Components of Forecasted Real Output Growth.........88
College Prices and Expenditures by Sector.....................................158
Student Outcomes by Sector............................................................160
Top Ten Crypto Derivative Platforms by Open Interest.................263
Ransomware and Downtime Costs by Country, 2020....................267

1-1
2-1
2-2
2-3
3-1
3-2
3-3

What Is an Aggregate Production Function?.....................................24
Measures of Consumer Price Inflation..............................................63
The Phillips Curve and Other Models of Inflation...........................68
Aging and Growth..............................................................................89
Effects of the Strengthening U.S. Dollar on the U.S. Economy......96
The United States’ Top Goods Trading Partners...............................99
Rising Digital Trade and U.S. Labor Markets.................................107

Boxes

Contents

| 19

3-4
3-5
3-6
3-7
4-1
4-2
4-3
4-4
4-5
5-1
5-2
5-3
5-4
5-5
6-1
6-2
6-3
6-4
6-5
6-6
6-7
7-1
7-2
7-3
7-4
7-5
8-1
8-2
8-3
8-4
9-1
9-2
9-3

20 |

The United States’ New Approach to Economic Partnerships....... 112
Coordination Has Been Critical for the Success of the
Sanctions Policy toward Russia....................................................... 115
The U.S.-EU Energy Partnership Diminishes Russia’s Leverage.. 118
U.S. Digital Trade Initiatives............................................................121
Nutrition Support in Early Childhood.............................................130
The American Rescue Plan and Support for Childcare..................135
Who Works in ECE?.........................................................................137
Federal ECE Investments.................................................................148
New Data and New Methods to Inform Investments in Children...149
The Private and Public Benefits of College....................................164
International Comparison of Income-Driven Student Loan
Repayment.........................................................................................168
Policies Focused on Direct Institutional Support............................175
Gainful Employment and Other Accountability Regulations.........177
Supporting Workforce Training Quality..........................................179
Labor Supply Terminology...............................................................185
Work and Leisure in the United States and Europe........................187
Deaths of Despair in the United States............................................188
On What Income Do Jobless Men Live?.........................................192
The Missing Prime-Age Workers.....................................................200
A Critical Shortfall of Nurses and Physicians.................................202
Staffing Challenges in K-12 Education...........................................203
The Societal Implications of Digital Markets.................................216
Consumer Data as a Business Model..............................................219
Glossary for Describing Digital Markets.........................................222
International and Subnational Efforts at Regulatory Reform.........231
Artificial Intelligence and Digital Markets......................................232
What Are the Functions of Money?.................................................241
How Does Bitcoin Work?.................................................................247
Crypto Asset Mining as a Risk to the Environment........................253
Proposed Uses of Distributed Ledger Technology..........................259
Adaptation and Resilience Investments of the Biden-Harris
Administration...................................................................................278
Climate Change Will Most Likely Interact with and
Exacerbate Existing Inequalities......................................................281
Disaster Insurance in the Changing Climate: Challenges
and Opportunities for Reform..........................................................286

Annual Report of the Council of Economic Advisers

Chapter 1

Pursuing Growth-Enhancing Policies
in Today’s Changing World
Economists often tout the value of economic growth. They argue that as the
size of the economic “pie”—the value of all goods and services produced
in a year—increases, everyone can get a larger slice of it, making them
better off. Of course, growth is not the only economic goal a society may
prioritize. Many societies also have notions of fairness and justice such as
lower poverty and inequality, so they are attentive to how the slices of the
pie are shared. That said, sustained economic growth is an important priority
for most societies and, over long swaths of history, is an indispensable driver
of improvement in human well-being.
Economic growth is constrained, however, when the size of the economy
reaches what economists call “potential gross domestic product (GDP)” or
“capacity.” An economy’s long-run capacity depends on such factors as a
growing and skilled labor force, high-quality physical infrastructure, and
the efficiency of the production process. Actions that affect any of these
factors can either constrain or enhance the capacity of the economy over
time. Investing in increased economic capacity enables the economy to
accommodate more demand in the medium to long run, which can make
it more resilient to economic shocks and minimize the risk of inflationary
episodes. The core of the Biden-Harris Administration’s economic agenda is
building a foundation for steady, sustainable, and shared growth by increasing economic capacity.
Over the last 50 years, the United States’ social and economic context
has changed, leading to both opportunities and challenges for increasing

21

economic capacity. This has been for a variety of reasons, but three important ones are worth noting. First, women have surpassed men in educational
attainment, and they joined the labor force in historic numbers through the
late 1990s, though there has been a slowdown in their labor force participation gains in recent years. Women’s increased participation—and thus an
increase in the size of the labor force generally—helped drive economic
growth in the second half of the 20th century. At the same time, a lack
of public investment in care has challenged workers, especially working
women, with caregiving responsibilities.
Second, soaring carbon emissions in the second half of the 20th century have
exacerbated global warming, and the resulting climate change will increasingly become a barrier to economic growth without effective adaptation.
Interrelated with the climate crisis, the damage to ecosystems continues to
accelerate, creating significant risks for businesses and the wider economy
(World Economic Forum 2020).
Third, computers have entered virtually all aspects of life and can now
perform tasks that previously were thought not able to be automated. The
Internet has changed how people find information, learn, do business,
and communicate with one another. These changes have spurred growth
and helped some industries weather economic shocks like the COVID-19
pandemic better than they otherwise would have. But they have also raised
important issues about how established economic policy can and should
adapt to the new digital world.
To expand the potential growth of the U.S. economy, policymakers need to
adjust how the Nation invests in response to these kinds of changes. This
year’s Report highlights selected areas where the changing economic and
social context calls for a new approach to increasing the capacity for economic growth. The Report discusses how the relevant context in these areas
has changed, analyzes pressing current challenges to sustained economic
growth, and highlights potential strategies to confront these challenges.

22 |

Chapter 1

Investing in Production Drives Economic Growth
The inputs to sustained economic growth can be understood through the
lens of an aggregate production function, which is explained in box 1-1.
According to such a function, an economy’s output depends on its stock
of human and physical capital as well as on a productivity factor that summarizes how efficiently workers, machines, and other types of inputs are put
to use. Thus, sustained output growth relies on continuing private and public
investments in the economy’s workforce, physical capital, and productivity
(Mankiw 2010).
In general, well-functioning markets incentivize households and firms
to make investments that expand the economy, even when these households
and firms do not have the larger economy in mind as they make their
individual decisions. For example, a high school graduate who anticipates
enhanced career opportunities from attaining a higher degree will likely
pursue a college education. A firm that wants to grow but is having trouble
hiring may invest in making its workplace more attractive to potential
employees or pursue a management strategy to improve the efficiency of its
existing workforce. These decisions are made independently throughout the
economy without considering their effect on aggregate production, yet they
jointly increase total economic capacity and growth.
Unlike the private sector, the public sector is designed to invest in
the economy with explicit consideration of the aggregate context. This is
reflected in the types of investments it should ideally make, many of which
are aimed at markets’ overall efficient functioning. The public sector operates the basic institutions that enforce the rule of law and property rights
and thus enable households and firms to engage in the complex market
system. It is also tasked with promoting competition and preventing socially
destructive profit-seeking behavior, ensuring the stability of the financial
infrastructure that greases the economy, and representing the interests of the
U.S. economy in negotiating terms of trade with the rest of the world.
In addition, when the private sector underinvests, the public sector
can step in to invest in human and physical capital. Private underinvestment
occurs for various reasons but often entails some combination of coordination failure, externalities, and credit constraints. For example, although
virtually all firms benefit directly or indirectly from having functional roads
running across the United States, it would be nearly impossible for them to
coordinate a plan of action to build them. Compared with what would be
best for society, firms tend to underinvest in the use of clean energy because
they do not bear the full burden of the cost of pollution (costs are externalized). And information asymmetries in private credit markets can make it
difficult for some entrepreneurs to access funding for upfront investments

Pursuing Growth-Enhancing Policies in Today’s Changing World | 23

Box 1-1. What Is an Aggregate Production Function?
An “aggregate production function” summarizes the process whereby an
economy transforms inputs into goods and services. Consider a tomato
farmer. She needs the input of labor—workers—to run and maintain her
farm; the input of human capital—the education, training, skills, health,
and other valuable resources embodied in a person—to know how to
plant, raise, and harvest the tomatoes; and the input of physical capital—
raw materials like seeds, along with harvesting and hydration equipment.
The farmer uses these inputs to produce tomatoes as her output. Some of
these tomatoes will be sold to consumers at the grocery store or at farmers’ markets as final goods; others will themselves become inputs into
different products like ketchup and pizza sauce. When all the individual
production functions like this farmer’s are combined in an economy, the
result is an aggregate production function, which gives the aggregate
output of the entire economy resulting from all its inputs.
Aggregate output increases when there are more workers with
in-demand skills, when these workers work more hours, and when they
have access to more and better facilities and equipment to help them
effectively perform their jobs. Physical capital also captures the broader
infrastructure of roads, bridges, and broadband that allows goods, services, and information to move throughout the economy; note that this
infrastructure is often public.
An economy also grows when it becomes more efficient at combining labor and capital to produce output—that is, when it can produce
more output with the same amount of inputs. Economists call this
“total factor productivity.” In the tomato farmer example, agricultural
innovations such as better farm management techniques, including crop
rotation, have enabled farmers to grow tomatoes more easily.
When total factor productivity grows, output increases, even if an
economy’s inputs—labor, human capital, and physical capital—are kept
constant, because the economy becomes more efficient at using these
inputs.
Economists tend to discuss total factor productivity in shorthand as
“technology”; but in reality, total factor productivity has many different
drivers and constraints beyond what one might typically think of as technology. For instance, culture and norms can have a substantial impact on
output (Guiso, Sapienza, and Zingales 2006). And corruption holds back
economic growth not just by disincentivizing investment in human and
physical capital, but also by decreasing the amount of economic growth
that an economy sees from a given amount of human and physical capital
(Mauro 1995).
Summarizing aggregate production as a function of physical
capital, human capital, and total factor productivity is a useful abstraction that provides a framework to understand differences in economic

24 |

Chapter 1

activity across time and countries. For certain questions, it makes
sense to extend the basic model. One increasingly relevant extension is
explicitly to include natural capital—the stock of water, land, air, and
renewable and nonrenewable resources—as a distinct production factor
in addition to built or produced physical capital. In the example given
above, factors such as soil quality and quantity, climate, irrigation water,
and the populations of insect pollinators provide important contributions
to the tomato farmer’s output production. As natural capital becomes
an increasingly important driver of variation in economic activity due
to climate change, more questions will benefit from a direct inclusion
of natural capital. Indeed, the Biden-Harris Administration recently
launched a multiyear effort to put nature on the nation’s balance sheet
for the first time (White House 2023).

in their businesses, even when those investments promise private and social
returns in the future, resulting in credit constraints.

The United States’ Economic Growth Over Time
The last two centuries have seen remarkable gains in material well-being
around the world, driven by rapid output growth. The United States is
an excellent example of a country that has experienced this economic
transformation.
Estimates of historical output suggest that in 1800, the United States
was not even among the world’s 10 largest economies (Groningen Growth
and Development Centre, n.d.). But its rapid growth ever since—averaging
about 3.5 percent a year between 1800 and 2021—has turned the United
States into the world’s largest economy in nominal terms. Figure 1-1 decomposes U.S. economic growth since 1800. Total output can be mechanically
separated into (1) labor supply (how many workers there are)—which in
turn depends both on population size and labor force participation—and (2)
how much average output these workers produce. Output per worker reflects
the growth drivers other than labor supply mentioned above: human capital,
physical capital, and total factor productivity.
This decomposition of economic growth highlights how the relative
importance of the American workforce’s size versus its productivity in driving growth has changed over time. Productivity-driven economic growth is
ultimately what spurs sustained growth in output per capita, which better
reflects how growth translates into improved living standards for individuals. In the first half of the 19th century, aggregate growth was driven mainly
by the country’s increasing population and by its larger workforce. This
Pursuing Growth-Enhancing Policies in Today’s Changing World | 25

Figure 1-1. Average Annual U.S. Real GDP Growth since 1790, by Decade
and Contributor
Percent average annual growth over the period
7
6
5

(Note:
Decomposition not
available in
the 1790s)

Post–Civil War boom

World War II /
postwar boom
Rise in
women's
LFPR

4

Output per worker
Reflects human
capital, physical
capital, and TFP

Total
growth

2
1
0

1800-2021

1950s
1960s
1970s

1920s
1930s
1940s

1890s
1900s
1910s

1850s
1860s
1870s
1880s

1820s
1830s
1840s

Civil War / Emancipation
1790s
1800s
1810s

–2

Population

Aging of baby
boomers
1980s
1990s
2000s
2010-21

–1

Reflects labor supply

Labor force
participation

3

Sources: Weiss 1999; Lebergott 1966; Bureau of Economic Analysis; Bureau of Labor Statistics; Census Bureau; CEA calculations.
Note: LFPR = labor force participation rate; TFP = total factor productivity.

implied comparatively more limited gains in material well-being for the
average person. In contrast, aggregate growth in the 20th century was driven
mostly by an increasingly productive workforce and thus translated more
directly into higher individual output and incomes.
Productivity-driven output growth in the United States has been the
result of private and targeted public investment in the skills of its workforce,
its equipment and infrastructure, and the technologies that enable its workers
to most efficiently use their skills.
Two decades in particular—the 1870s and 1940s—stand out for having the highest average growth in American history. These decades serve as
case studies for some of the factors that drive economic expansion.
Economic historians sometimes mark the 1870s as the start of a period
of advancement called the “Second Industrial Revolution” (e.g., DeLong
2022). Some of the investments during this decade were made to repair the
infrastructure that had been damaged or destroyed during the Civil War. But
investment in the 1870s went well beyond replacement. In 1860—on the eve
of the Civil War—there were about 31,000 miles of railroads in operation
in the United States. By 1870, this had increased to 53,000 miles; and by
1880, to 98,000 miles (see figure 1-2). In 1869, Western Union operated
about 105,000 miles of telegraph wires and handled just under 8 million
messages annually. Ten years later, it had doubled the mileage of its wires
and was handling more than three times the number of telegraph messages
(Carter et al. 2006, series Dg 9 and Dg11). And this surge in investment
in the 1870s was not limited to physical infrastructure but also extended
26 |

Chapter 1

Figure 1-2. Miles of U.S. Railroads, 1830–90
Miles operated, thousands
180
160
140
120
100
80
60
40
20
0
1830

1835

1840

1845

1850

1855

1860

1865

1870

1875

1880

1885

1890

Source: Carter et al. 2006, series Df874.

to ideas. The United States issued roughly 72,000 patents for inventions
between 1860 and 1869; the next decade, it issued about 125,000 (see figure
1-3). Moreover, the Nation’s labor supply grew strongly during the 1870s:
the U.S. labor force was about 35 percent larger in 1880 than it was in 1870,
thanks both to natural population growth and immigration (Migration Policy
Institute, n.d.). In comparison, the labor force grew by a total of 5 percent
between 2011 and 2021.
The expansion of physical capital and ideas, combined with the
increasing labor force, corresponded with strong growth in the 1870s and
beyond.
Like the 1870s, the 1940s came on the heels of a catastrophe—in this
case, the Great Depression. However, growth in the 1940s was less about an
increase in the labor supply, given that the labor force grew at about half the
rate it did in the 1870s. Instead, growth in the 1940s was driven by a combination of public investment and greater utilization of a labor force with a
high unemployment rate at the start of the decade: although unemployment
had fallen substantially from its 1932 peak of 22.9 percent, in 1940 it was
still at an elevated rate of 9.5 percent (Carter et al. 2006, series Ba475).1
The United States’ entry into World War II accelerated this growth.
The number of active-duty military personnel grew from just over 300,000
in 1939 to 12 million by the war’s end in 1945 (National World War II
This series treats workers participating in Federal emergency New Deal programs like the Works
Progress Administration and Civilian Conservation Corps as “employed”; official labor market
statistics, which were still in their infancy at the time, classified these workers as “unemployed.”

1

Pursuing Growth-Enhancing Policies in Today’s Changing World | 27

Figure 1-3. U.S. Patents Issued, 1790–2000
Patents issued for inventions, thousands

180
160
140
120
100
80
60
40
20

0
1790 1805 1820 1835 1850 1865 1880 1895 1910 1925 1940 1955 1970 1985 2000
Source: Carter et al. 2006, series Cg30.

Museum, n.d.). This mobilization, in combination with increased private
hiring (driven itself in large part by wartime government orders) pushed the
unemployment rate down to 1.2 percent by 1944 and expanded women’s
labor force participation (Acemoglu, Autor, and Lyle 2004; Carter et al.
2006, series Ba475; National Archives, n.d.).
Public investment in physical capital also skyrocketed. The Federal
Government’s gross investment rose from $12 billion in 1940 to $270 billion in 1944 (inflation-adjusted).2 Growth in government consumption and
investment was responsible for adding 10 percentage points to real GDP
growth in 1941 and an astounding 28 percentage points in 1942 (see figure
1-4).
The end of the war did not mean a return to the prewar economy of the
1930s that was characterized by high unemployment and depressed output.
Demobilization led to a mild recession in 1945, as the United States began
shifting away from the wartime economy, and the unemployment rate crept
back up in the years after the war. But it did not return to its prewar levels
of 9.5 percent and above (Carter et al. 2006, series Ba475).
Even as Federal investment retreated in the years following World
War II, private investment picked up. Between 1944 and 1950, real Federal
defense investment fell by $257 billion (in 2021 dollars). But real private
fixed investment rose by $224 billion over that same period, and real
personal consumption was $444 billion higher. Indeed, increased private
2

This is measured in inflation-adjusted 2021 dollars.

28 |

Chapter 1

Figure 1-4. Contributors to U.S. Real GDP Growth, 1930–50
Percentage points, annual real GDP growth
35
25
15
5
–5
–15
–25
–35

1930

1935
Personal consumption

1940
Private investment

1945
Net exports

Government

1950
Total

Source: Bureau of Economic Analysis.

investment and consumption were even able to substantially offset the
massive 29-percentage-point deduction to GDP growth from the postwar
demobilization in 1946 (see figure 1-4). Government investments during
World War II helped pave the way for private investments that sustained
renewed economic growth throughout the latter half of the 20th century
(Goodwin 2001).

The Inputs to U.S. Economic Growth Over Time
The economic growth of the United States over the last two centuries would
not have happened without investments in the labor force, the physical
capital stock, and total factor productivity. The previous pages of this
section discussed what some of these investments looked like during two
key rapid-growth decades: after the Civil War, and around World War II.
This subsection steps back and considers each of these factors over a wider
span of American history, highlighting key public and private investments
for each one and discussing selected available measures of how they have
evolved over time.
The labor force. Over the past 200 years, both public and private
actors have invested in the skills and size of the labor force. Consider the
key input of education. For centuries, the United States has been a world
leader in public education. Beginning in the 1700s, American communities
began to establish publicly funded or free schools, along with land grants

Pursuing Growth-Enhancing Policies in Today’s Changing World | 29

to support the creation and maintenance of schools (Kober and Rentner
2020). Over time, an array of private and nonprofit institutions—including
private schools, universities, and vocational training programs—have also
become integral to the U.S. educational landscape. Investments in education
transformed the skills of American workers. In the first several decades of
the 20th century, the United States underwent what is now termed the “high
school movement” (see figure 1-5); between 1910 and 1940, the share of
those age 18 years with a high school diploma (from a private or public institution) rose by over 40 percentage points (Goldin and Katz 2009). However,
progress has at times been uneven; segregation and other forms of race and
gender discrimination in the education system have presented barriers to
educational attainment for women and people of color.
The U.S. labor force has also grown—from roughly 1.5 million workers in 1800 to over 160 million today—with particularly rapid growth in the
second half of the 20th century, although there has been a gradual decline in
the growth rate since the 1980s (see figure 1-6).
The physical capital stock. Investment has also focused on physical
capital and productivity. This has included investments by the public sector,
such as under the Rural Electrification Act of 1936, which provided loans
to farmers and investors to expand electricity to rural communities, with
remarkable results, as depicted in figure 1-7 (Sablik 2020). Later in the 20th
century, the United States built out the Interstate Highway System, which
is often described as one of the greatest public works projects in history

Figure 1-5. U.S. Secondary School Enrollment and Graduation Rates,
1890–1991
Percent

100
90
80
70
60
50
40
30
20
10
0

1890

1900

1910

1920

1930

1940

Enrollment

1950

1960

1970

Graduation

Sources: U.S. Department of Education; Goldin and Katz 2009; CEA calculations.
Note: Graduation data were reported every 10 years before 1930; some years are missing after 1930.

30 |

Chapter 1

1980

1990

(Capka 2006; Pfeiffer 2006). In parallel with the public sector’s investments,
the private sector has invested in physical capital, such as by constructing

Figure 1-6. The U.S. Labor Force, 1800–2021
Millions
180
160
140
120
100
80
60
40
20
0
1800

1820

1840

1860

1880

1900

1920

1940

1960

1980

2000

2020

Sources: Weiss 1999; Carter et al. 2006, series ba1-10; Census Bureau; Bureau of Labor Statistics; CEA calculations.

Figure 1-7. U.S. Residences with Electricity, 1920–56
Percent
100
90
80
70

Rural
Electrification
Act

60
50
40
30
20
10
0
1920

1925

1930

1935

1940

Urban and rural nonfarm

1945

1950

1955

Farm

Sources: U.S. Census Bureau 1975, table S 108-119; Sablik 2020.
Note: Data on the percentage of dwelling units with electric service in urban and rural nonfarm settings are only available every
five years.

Pursuing Growth-Enhancing Policies in Today’s Changing World | 31

factories, farms, and office buildings—in 2021, gross private domestic
investment exceeded $4 trillion (FRED 2022).
Public and private investments have combined to facilitate the continued growth of the Nation’s capital stock over the past century, as highlighted
in figure 1-8. (The capital stock includes physical capital, ranging from
trucks to houses to software to roads.)
Total factor productivity. As discussed above, total factor productivity is the most amorphous input into economic growth. It captures many
aspects, from technological innovation to the quality of institutions that
promote competition and the efficient allocation of scarce resources.
Consequently, historical public and private investment in total factor
productivity is multifaceted and not always straightforward to measure. For
example, one paper estimates that between 20 and 40 percent of growth in
aggregate output per person in the United States between 1960 and 2010 can
be explained by improved talent allocation brought on by reduced discrimination and changing preferences among Black and white women and Black
men (Hsieh et al. 2019). The Civil Rights Act of 1964 likely contributed to
this reduced discrimination and thus, in addition to rectifying long-standing
injustices, it functioned as an investment in the economy. But this type of
investment is difficult to quantify, given that it is concurrent with broader
social change.

Figure 1-8. U.S. Capital Stock, 1925–2021
Trillions of 2021 dollars
90
80
70
60
50
40
30
20
10
0

1925 1931 1937 1943 1949 1955 1961 1967 1973 1979 1985 1991 1997 2003 2009 2015 2021

Sources: Bureau of Economic Analysis; CEA calculations.

32 |

Chapter 1

Other forms of investment are more tangible, such as investments in
research and development. The United States is consistently one of the top
spenders on research and development (OECD 2022a). The majority of this
spending comes from the private sector, but the public sector also plays an
important role, especially in funding basic research (Burke, Okrent, and
Hale 2022).
The information technology revolution exemplifies the complementary roles of public and private investment in the economy. The government
played an essential role in developing groundbreaking technologies, such as
the Internet and the Global Positioning System. These early-stage investments were arguably too risky for any private firm to undertake (Mazzucato
2013). But it was the private sector that transformed these base technologies
into the market-oriented ones that have shaped the way people work and live.
Unlike the number of workers or the stock of physical capital, the return on
these investments cannot be measured directly. However, economists can
infer that total factor productivity is changing when total output changes
more or less than would be expected based on observed changes in the labor
force and the physical capital stock. That is, a greater-than-expected increase
in total output suggests that total factor productivity has increased, whereas
a smaller-than-expected increase suggests that it has decreased.
Figure 1-9 shows total factor productivity growth in the United States
since 1953. In the short term, productivity growth fluctuates considerably,

Figure 1-9. Total Factor Productivity Growth, 1953–2021
Percent annual growth, five-year moving average
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0
–0.5
–1.0
–1.5

1953

1958

1963

1968

1973

1978

1983

1988

1993

1998

2003

2008

2013

2018

Source: Bureau of Labor Statistics.

Pursuing Growth-Enhancing Policies in Today’s Changing World | 33

partly because it can only be inferred from other measurements. Still, there
are some notable trends over time. The postwar decades were marked by
relatively high productivity growth, before a notable slowdown in the 1970s
and through the mid-1990s. Productivity growth picked up again into the
2000s, but it fell during the Great Recession. The decade of slow productivity growth in the 2010s was common to most advanced economies, and it is
still not fully understood (Dieppe 2020).

U.S. Economic Growth in Context
Although economic growth in the United States over the past 200 years
has led to enormous gains in material well-being and life expectancy, and
has given the United States an economic and political leadership role on
the global stage, it cannot be taken for granted. For example, in the 1800s,
the United States’ GDP per capita was about 70 percent larger than that
of Argentina. But faster average U.S. GDP growth starting in the second
half of the 20th century caused the two countries to diverge further, and
today U.S. GDP per capita is about three times as large as Argentina’s
(figure 1-10). Singapore, conversely, experienced slower growth for much
of the 20th century before growing rapidly—more quickly than the United
States—beginning in the 1960s; the country’s GDP per capita is now above
that of the United States.

Figure 1-10. GDP per Capita for the United States, Argentina, and
Singapore, 1800–2018
Log scale, real GDP per capita, 2011 dollars
128,000
64,000
32,000
16,000
8,000
4,000
2,000
1,000
1800

1850
United States

Source: Groningen Growth and Development Centre, n.d.
Note: Missing values are interpolated.

34 |

Chapter 1

1900
Argentina

1950
Singapore

2000

Economies have diverged widely in modern history, and though
there have been various factors in this divergence, both public and private institutions—and economic policies—are a central part of the story.
Indeed, Argentina and Singapore are telling case studies in this regard.
The Argentine economy accelerated in the late 1800s thanks to immigration, exports, and foreign investment. However, productivity and economic
growth in the country stagnated in the 20th century in the context of the
Great Depression and political instability, beginning with the military coup
in 1930 (Spruk 2019). Singapore’s economic growth, conversely, is generally considered an example of successful economic policy. The rapid rise of
Singapore and the other “East Asian Tigers” has been attributed to a set of
common, market-friendly economic policies that targeted macroeconomic
stability, public infrastructure and education, and export orientation (World
Bank 1993; Lee 2019).

Sustaining Economic Growth in Today’s Changing World
The preceding discussion highlighted the importance of past and continued
private and public investments in the U.S. economy. Many of these historical investments remain relevant today. The Nation must continue to make
investments to ensure access to high-quality education, from childhood
through adulthood; to maintain its physical infrastructure; and to ensure that
markets remain fair and competitive.
However, these investments are not being made in a vacuum. They
are influenced by changes in society and the economy that have an impact
on the need for, and value of, different kinds of investments—in human and
physical capital, as well as in total factor productivity. Sometimes the private sector adapts quickly and well to these changes; other times, the public
sector needs to spur private investment and provide the necessary guardrails
to protect individuals and the U.S. economic system.

Investing in Human Capital and the Labor Supply: Implications of
More Women Participating in the Labor Force
Millions of American women entered the labor force in the latter half of the
20th century—with substantial implications for society, economic growth,
and public policy. Between 1970 and 2000, U.S. women’s labor force participation rose from roughly 43 percent to 60 percent (figure 1-11). Although
this overall trend masks important differences in levels of participation by
dimensions such as race, age, income, and family status, virtually all groups
of women saw large participation gains over these decades.
This period has been termed the “Quiet Revolution” by the economist
Claudia Goldin, who has identified turning points in the late 1960s and early

Pursuing Growth-Enhancing Policies in Today’s Changing World | 35

Figure 1-11. Women’s Labor Force Participation Rate, 1970–2022
Percent
65

60

55

50

45

40

35
1970

1980

1990

2000

2010

2020

Source: Bureau of Labor Statistics.

1970s in the marriage age, college graduation rate, and extent of professional school enrollment for women—along with the gradual lifting of some
discriminatory barriers for women, shifts in social norms for women’s family and career decisions, and factors accounting for women’s life satisfaction
(Goldin 2006). Thus, Goldin attributes the increase in women’s labor force
participation to many factors, such as reduced labor market discrimination
against women and women’s increased choice in making reproductive
decisions through the invention, dissemination, and legalization of the birth
control pill.
In addition to women’s labor force participation, women’s educational
attainment increased drastically relative to men’s. Today, women earn the
majority of bachelor’s, master’s, and doctoral degrees (figure 1-12).
The economic consequences of these trends are significant. Using
a methodology similar to the one used in the 2015 Economic Report of
the President, updated CEA calculations indicate that the U.S. economy
was almost 10 percent larger in 2019 than it would have been without the
increase in women’s employment and hours worked from 1970 to 2019
(Council of Economic Advisers 2015).
However, starting in about 2000, women’s labor force participation
plateaued and began to decline. Men for their part have also seen a multidecade decline in participation, although they continue to participate at
a higher rate than women. Removing barriers to women’s and men’s participation and educational attainment would ease labor constraints on firms

36 |

Chapter 1

Figure 1-12. Percentage of Postsecondary Degrees Received by
Women, 1980–2020
Percent
65
60
55
50
45
40
35
30
25
20
1980

1985

1990

1995
Bachelor’s

2000
Master’s

2005

2010

2015

2020

Doctoral

Source: U.S. Department of Education, National Center for Education Statistics.
Note: The dashed line indicates 50 percent. Doctoral degrees include all doctoral degrees, including M.D., J.D., and Ph.D.

that want to open or expand, boost long-run economic growth, and increase
prosperity.
One factor affecting labor force participation, particularly for women,
is household, community, and care responsibilities. Before entering the
labor force in large numbers, women had long provided a large share of
unpaid work in their homes and communities, including household maintenance tasks, raising children, caring for elder family members, and volunteering for community projects—work typically not captured in economic
measures like GDP. Today, women working outside the home continue to
disproportionately undertake these tasks; one recent study found that in heterosexual marriages, even when women’s wages are more than double those
of their spouses, women do 44 percent more household work (Siminski and
Yetsenga 2022).
At the same time, in recent decades, the aging of the so-called baby
boom generation (people born roughly between 1946 and 1964) and reduced
fertility rates have increased the demand for senior care while constraining
the supply of younger workers. The ratio of people age 65 and above to the
number of people age 16 to 64, sometimes called the old-age dependency
ratio, has more than doubled during the past seven decades. This has contributed to increased demand for care from adult children, creating the so-called
sandwich generation of people who have care responsibilities for both older

Pursuing Growth-Enhancing Policies in Today’s Changing World | 37

and younger family members (figure 1-13). In 2017 and 2018, more than
8 million parents of children under 18 also provided senior care, including
nearly 5 million mothers (BLS 2019).
Women’s shift into the labor force and demographic changes were
associated with an increased demand for paid workers to provide what was
previously unpaid labor, particularly caring for young children and older or
disabled adults (figure 1-14). These care workers are often paid very low
wages and are disproportionately women, particularly women of color.
To meet care needs, in recent years, multiple States and cities have
passed legislation to provide paid family and medical leave for workers
(National Partnership 2022). Additionally, private firms have increasingly
provided paid family and medical leave, remote work adjustments, and
other benefits to help workers balance their care and work responsibilities
(figure 1-15). However, the lack of a national paid family and medical leave
program, of adequate affordable child care, and of Federal labor laws to
guarantee flexibilities for workers with care responsibilities has limited the
ability for caregivers, especially women, to remain in the labor force.
The actions of the private sector have not been enough to meet the scale
of the problem. Inequality in who has access to these benefits means that
workers in the top 10 percent of the wage distribution are nearly eight times
as likely as the lowest-paid workers to have access to paid family leave and
more than four times as likely to have access to childcare through their work
(BLS 2022a). Low-wage and hourly workers can particularly struggle to

Figure 1-13. Percentage of Households with a Child under 18 That
Have an Adult over Age 65 Years, 1989–2021
Percent
7
6
5
4
3
2
1
0
1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021
Sources: Current Population Survey; CEA calculations.

38 |

Chapter 1

Figure 1-14. Consumption of Nursing Home and Childcare Services and
Women’s Labor Force Participation, 1960–2022
Percentage of GDP

Percent
65

1.2
1.0

60

0.8

55

0.6

50

0.4

45

0.2

40

0.0
1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

2015

2020

35

Consumption of nursing home and childcare services (left axis)
Women’s labor force participation (right axis)
Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations.

Figure 1-15. Share of Private Industry Workers with Access to Benefits
Percent
30
24

25
20
15
10

10

11

9

5

5
0

9

Paid family leave

Childcare
2010

Flexible workplace

2022

Source: Bureau of Labor Statistics.

manage work and care responsibilities due to limited workplace flexibilities,
such as fair and predictable scheduling. Although data are limited, evidence
suggests that the market and public sector underprovide both childcare and
senior care. For example, the CEA’s analysis of the 2019 National Survey
of Early Care and Education indicates that nearly three-quarters of childcare
centers are experiencing excess demand (i.e., have a waiting list or reject
children due to limited capacity).
Pursuing Growth-Enhancing Policies in Today’s Changing World | 39

There are many reasons that the care industry has not been able to
evolve to meet current needs. Among other issues, even as other service-providing industries have benefited from productivity-enhancing technological
advances, the care industry has not; it still takes roughly as many people to
watch 10 children today as it did 50 years ago, and the cost of childcare has
risen in part due to increases in provider wages (although they are still quite
low).3 Between 1990 and 2021, the price of childcare rose by 225 percent
while the median household income rose by roughly 150 percent. In addition, because families face liquidity constraints, like the inability to borrow
against future income, they are often unable to afford the childcare that best
meets their needs.
These challenges have a negative impact on labor force participation,
particularly for women. In 2022, 14.6 percent of women between the age of
25 and 54 said that they were not in the labor force because they were caring
for their home or family, representing roughly 60 percent of all the primeage women not working. While some women may prefer providing family
care to participating in the labor force, research suggests that for others, the
cost of care limits their choices. Studies indicate that government policies
that reduce the costs of care can strengthen participation, particularly for
women (Morrissey 2017; Shen 2021). But relative to its peers, the United
States provides few policies that could help families meet these care needs,
such as paid family and medical leave and childcare investments—an observation researchers often make when discussing trends in U.S. female labor
force participation (Blau and Kahn 2013). Whereas in 1985, the participation rate among women age 25 to 54 in the United States exceeded the rate
in Canada, the United Kingdom, Japan, Australia, and the European Union,
in recent years, the United States has experienced lower women’s participation than Canada, the United Kingdom, Japan, Australia, and the European
Union (OECD 2022b).
Further, care responsibilities affect men’s participation in the economy
along with women’s, particularly as gender norms evolve (figure 1-16).
In 2022, 2 percent of men between the age of 25 and 54 said they did not
work due to home or family responsibilities, up from 0.9 percent in 1995.
Subsidizing care for families while simultaneously investing in the supply of
childcare would likely increase the overall number of workers who are able
to enter the workforce, and thus facilitate economic growth.

The Baumol-Bowen cost disease is what economists call this tendency for wages and costs to rise
in industries that see smaller productivity gains in response to increases in wages and costs from
industries that have seen larger productivity gains (Maiello 2017).

3

40 |

Chapter 1

Figure 1-16. Nonparticipation in the Labor Force, by Reason
Percentage of prime-age people
25

20

15

10

5

0

Men, 1995
Disability

Men, 2022
Retirements

School enrollment

Women, 1995
Home/family care

Women, 2022
ʿʿSomething else’’

Sources: Current Population Survey; CEA calculations.

Investing in Physical Capital: Adapting to the Increasing Effects of
Climate Change
Physical capital is the next important input for economic growth. The kinds
and quantities of physical capital needed are in a constant state of flux. For
much of human history, infrastructure was designed around animal power,
such as horses. In the 19th century, infrastructure began shifting to railroads,
while the 20th century saw rapid shifts toward infrastructure for automobiles
and airplanes. Although much of the transportation infrastructure built in
the 20th century remains useful today, the 21st century has seen massive
investments in network infrastructure to allow for faster and more reliable
communications and Internet access.
At the same time, the large and growing effects of climate change pose
a significant, broad-based risk for physical capital. Over the past century, the
level of carbon dioxide (CO2) in the air has risen drastically (figure 1-17).
In 2013, atmospheric CO2 concentration surpassed 400 parts per million
for the first time in recorded history (Blunden 2014). In 2021, it averaged
nearly 415 parts per million (Lan, Tans, and Thoning 2022). Climate models
find that the increased level of greenhouse gases in the atmosphere is in
turn responsible for rising sea levels, hotter weather, and more common and
severe extreme weather events—trends that are predicted to continue even
with an ambitious reduction of greenhouse gas emissions.

Pursuing Growth-Enhancing Policies in Today’s Changing World | 41

Figure 1-17. Carbon Dioxide Levels Over Time
Parts per million
440

2022
level

400
360
320
280

1950
level

Highest historical COଶ level

240
200
160
–800

–700

–600

–500

–400

–300

–200

–100

0

Thousands of years relative to 1950 (1950 = 0)
Source: Lüthi et al. 2008.
Note: Data come from reconstructions from ice cores.

The economic damage from climate change has already begun to
accrue and have an impact on communities around the globe. Some of
these types of damage emerge in human capital: beyond the effects on
human health (e.g., Carleton et al. 2022), researchers have documented
the effects of climate change on migration flows (Missirian and Schlenker
2017; Jessoe, Manning, and Taylor 2018), violent crime (Ranson 2014),
labor productivity (Graff Zivin and Neidell 2012), and learning (Park et al.
2020; Park, Behrer, and Goodman 2021). However, this damage has also
been observed in physical capital. In the United States, the damage from
billion-dollar disasters (see figure 1-18) now averages roughly $120 billion a
year (Smith 2023). Costs from rising extreme weather are being driven both
by the changing climate and by rapid development in risky areas (Climate
Central and Zillow 2018; Iglesias et al. 2021). Climate change has been
found to affect crop yields and agricultural productivity, and increasingly
frequent heat waves will likely exacerbate increasing strain on electrical
grids (Woetzel et al. 2020; Auffhammer, Baylis, and Hausman 2017).
Instability due to climate change is expected to cause new systemic risks for
financial markets (Financial Stability Oversight Council 2021; Brunetti et
al. 2021). In insurance markets, extreme weather events are driving higher
payouts, which can raise premiums and reduce insurance availability (Lara
2019; Botzen, van den Bergh, and Bouwer 2010). Large disasters could
cause insurance companies to fail altogether—as seen after Hurricane
Andrew’s $15.5 billion in property damage in 1992, and along the Gulf

42 |

Chapter 1

Figure 1-18. Number of Billion-Dollar Natural Disasters in the United States,
1980–2022
Number of billion-dollar natural disasters
25

20

15

10

5

0

1980

1985
Droughts

1990
Floods

1995
Tropical cyclones

2000
Severe storms

2005

2010
Freezes

Wildfires

2015

2020

Winter storms

Source: NCEI 2022.
Note: Disaster costs are adjusted for inflation using the Consumer Price Index for All Urban Consumers.

Coast after more recent hurricane strikes (Gelzinis and Steele 2019; Elliott
2022). Even if markets continue to adapt to past experiences, rising climate
uncertainty may increase the risks of future failures as unanticipated costs
rise for insurance companies.
These effects of climate change have important consequences for
physical capital and will likely require adaptation by institutions—from
insurance and financial markets to construction firms, energy producers, and
the government. To lessen the economic damage of climate change, these
institutions, and many other actors throughout the global economy, will need
to quickly transition away from fossil fuels and emit fewer greenhouse gases
(known as “mitigation”). They will also need to protect physical capital from
damage (“adaptation”), such as by building resilient infrastructure, employing nature-based solutions to improve resilience in the face of more frequent
extreme weather events, and shifting new investments away from high-risk
areas. The scale and timing of climate change have led many researchers to
conclude that both mitigation and adaptation are necessary (IPCC 2014).
Adaptation measures may range from actions on the individual level, like
raising the foundations of houses to accommodate rising sea levels and
changing agricultural cropping practices, to community-level actions, like
building seawalls and expanding reservoirs’ capacities to deal with more
variable rainfall.
Climate change is also expected to alter the productivity and value
of different forms of capital in complex ways. The existing infrastructure,

Pursuing Growth-Enhancing Policies in Today’s Changing World | 43

which was designed for older climate conditions, may underperform in these
new conditions. For instance, major hydropower dams in the Southwest may
soon cease being able to produce electricity because of the decades-long
drought (Ramirez 2022; Partlow 2022; Kao et al. 2022). In contrast, other
forms of capital may become more valuable. Existing sea-walls and riverine
flood defenses provide greater value in the face of the changing climate
that is increasing coastal and inland flooding risks. Additionally, with the
rise of clean energy technologies, some resources like the Sun and wind
have acquired new value and have become important kinds of capital. For
example, though societies have used windmills for centuries, wind has only
become a widely used source of electric power in the last few decades as
technological advances have met the energy needs in a changing climate.
Climate change is already reducing—and will very likely continue
to reduce—growth in GDP (Burke, Hsiang, and Miguel 2015; Newell,
Prest, and Sexton 2021; Kalkuhl and Wenz 2020); this type of harm could
be reduced with greater investment in adaptation. A recent summary of
the literature from the Council of Economic Advisers and the Office of
Management and Budget (2022) shows substantial variation in the estimates of the impact of global warming on U.S. GDP. For example, the
Congressional Budget Office estimates that climate change will reduce
the average annual GDP growth rate by 0.03 percentage point from 2020
to 2050 (Herrnstadt and Dinan 2020), which implies that the level of U.S.
GDP would be just under 1 percent lower by 2050, whereas a study by the
Bank of England (2021) finds that climate damage could reduce U.S. GDP
by over 11 percent by 2050 in a worse-than-expected scenario. These estimates only capture a fraction of climate change costs, however, since many
effects, such as increasing mortality risk and ecosystem disruption, are not
fully reflected in market transactions or GDP estimates (Rennert et al. 2022;
Bastien-Olvera and Moore 2021). By one estimate, more than half of global
GDP is moderately or highly dependent on nature, which is being lost or
dramatically altered by human activity (World Economic Forum 2020).

Investing in the Economy’s Productivity: The New World of Digital
Markets
Rapid advances in information technology in recent decades have had a
substantial impact on how Americans work and live. Computer and information technology occupations now account for 3 percent of all employment in
the United States, and the Bureau of Labor Statistics (BLS) projects that the
number of these jobs will increase by 15 percent over the next decade (BLS
2021, 2022b). Moreover, other occupations that are not explicitly computerrelated increasingly also rely on strong digital skills (Muro et al. 2017).

44 |

Chapter 1

The spread of computer use is not limited to the workplace. In 1984,
8.2 percent of households had a computer at home; by 2021, this share had
climbed to 95 percent (File 2013; U.S. Census Bureau 2021). More recently,
information technology has been applied to traditionally analog devices—
telephones, cars, watches, and the like—in what is sometimes referred to as
the “Internet of Things” (Armstrong 2022). Figure 1-19 shows how rapidly
Internet use has expanded in recent decades, with almost all adults using the
Internet in 2021 compared with just over half in 2000.
New technology is changing the way people interact with each other,
both in markets and socially. Online sales now account for 14.8 percent
of total retail sales, more than doubling their share over the last decade
(U.S. Census Bureau 2023). Most job seekers now look for jobs online
(Hernandez 2017). And people increasingly connect with each other on
digital social media platforms to exchange ideas and information. The largest such platform, Facebook, counted 2.96 billion monthly active users as of
December 2022 (U.S. Securities and Exchange Commission 2023).
Through the lens of the aggregate production function, the contribution of recent technological advances to economic growth has been twofold.
First, it has increased the physical capital stock of the American economy.
The fall in the cost of computing power and the more recent rise of machine
learning and artificial intelligence have led to a proliferation of computers
in workspaces and robots in factories, which are helping workers specialize

Figure 1-19. Percentage of Adults Who Report Using the Internet
Over Time
Percent
100

90
80
70
60
50
40
30
20
10
0
2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

Source: Pew Research.

Pursuing Growth-Enhancing Policies in Today’s Changing World | 45

in tasks for which they have the greatest comparative advantage, like bigpicture strategizing, designing products, and interacting with consumers.
Second, at least in theory, recent technological advances have
increased total factor productivity by enabling new production processes
and making the allocation of resources more efficient. Indeed, increased
investments in computers and software arguably played a substantial role
in the fast productivity growth of the late 1990s and early 2000s (Weller
2002). The economic benefits of broadband Internet access, for example,
have been widely accepted. One study comparing countries that belonged
to the Organization for Economic Cooperation and Development between
1996 and 2007 found that a 10-percentage-point increase in broadband
penetration increased per capita economic growth by 0.9 to 1.5 percentage
points (Czernich et al. 2011). In the United States specifically, one study of
the expansion of broadband access between 1999 and 2007 estimates that
ubiquitous broadband access within a county would increase that county’s
employment rate by 1.8 percentage points compared with no broadband
access (Atasoy 2013). And when the COVID-19 pandemic prevented many
Americans from participating in in-person work and school, new online
technologies enabled people to continue learning and working.
The practical importance of the productivity effect of more recent
developments in machine learning and artificial intelligence remains a
topic of debate, however, especially because the last decade saw the slowest productivity growth in the post–World War II era (according to CEA
calculations using BLS data). One viewpoint is that recent innovations in
technology have been more incremental and not as groundbreaking as previous technological changes (Gordon 2016). Other scholars, in contrast, have
argued that traditional output measures fail to capture the full value of these
new innovations and that their productivity gains will materialize in time
(Brynjolfsson and Petropoulos 2021).
In addition to the direct effects of those technological advances on output growth, policymakers are paying increased attention to the more indirect
ways that these advances are affecting the structure of the U.S. economy.
For example, blockchain technology has fueled the rise of financially innovative digital assets that have proven to be highly volatile and subject to
fraud (White House 2022). The Internet and other new technologies have
allowed for the provision of digital services, increasing the ability of people
to perform and access services remotely, which affects trade, given that
technological advances make it easier for countries to import and export
services than in the past. Additionally, technological advances have raised
distributional concerns, both in terms of access and their usage. Black,
Hispanic, and lower-income Americans are less likely to have home access
to a computer and broadband and the opportunities that those technologies
provide (Pew Research Center 2021b, 2021c). And artificial intelligence
46 |

Chapter 1

has been argued to deepen racial and economic inequities by perpetuating
discrimination in areas such as housing, the criminal justice system, or
mortgage lending (ACLU 2021).
Addressing these areas of concern often draws on the traditional tools
of policymakers in new contexts. For example, policymakers have focused
on the high level of market concentration in the digital economy. Economic
theory has long seen market power and monopolization as threats to productivity and output growth. The digital economy—broadly capturing the
platforms that facilitate the online exchange of goods and information—is
characterized by high levels of concentration, where markets are often
dominated by a small set of firms (Digital Competition Expert Panel 2019).
This concentration can be the result of the economic fundamentals of these
platforms, whose scale can produce value for participants. Inherently, many
of these markets exhibit some form of network externality. For example,
buyers and sellers on an e-commerce platform are generally better off when
more sellers and buyers are on the same platform.
The economics underlying the susceptibility of digital markets to
concentration is not new. But the scale of digital markets is amplified by the
fact that they typically allow for a virtually unlimited number of participants
without congestion. This implies that the “winners” in digital markets—the
small set of firms that dominate the market—end up being larger and are
of significantly greater importance for the overall economy. Because it is
notoriously hard to define markets and there are many ways to measure
concentration, it is difficult to precisely quantify the degree of concentration
in digital markets. Nevertheless, big tech firms such as Amazon, Alphabet,
and Meta have provided some of the most widely used services in recent
years and generally have few direct competitors that come close to their size.
From a policy perspective, these advances pose new challenges. The
degree of concentration in digital markets raises long-standing concerns
about whether dominant players in these markets leverage their market
power to stifle competition and innovation. But unlike in some traditional
markets, much of the value of digital companies comes from network
effects—so antitrust actions may face greater challenges in preserving value
for consumers while addressing problems associated with concentration. A
world where digital technologies make services increasingly easy to trade
requires adjustments to international trade policy. And digital assets require
updating at least some regulations.
In the future, the Internet and digital markets—and further innovation—will have the potential to drive continued increases in productivity.
However, careful policymaking to address both the new and old challenges
presented by these technologies will be necessary to ensure that productivity
and output gains remain strong.

Pursuing Growth-Enhancing Policies in Today’s Changing World | 47

Conclusion
This year’s Report sheds light on these and other changes in the United
States’ economic and social systems and how they challenge established
economic thinking and policymaking.
Chapter 2 summarizes the Nation’s economy during the past year,
characterizing how the continuing recovery from the COVID-19 pandemic
and the impact of Russia’s invasion of Ukraine have shaped the economy,
and how sustained demand imbalances, supply chain delays, and pandemic
policies have affected growth, inflation, and unemployment. It also presents
the macroeconomic forecast underpinning the Biden-Harris Administration’s
Fiscal Year 2024 Budget.
Chapter 3 describes trends in international trade and investment in
2022 and characterizes how shifts over past decades in global interconnectedness have led to new challenges and opportunities for the United States.
There is a need to balance the considerable benefits of globalization through
economic linkages with the risks for economic and national security that
international economic interconnectedness can entail. Working in concert
with U.S. allies and partners can enable the Nation to effectively address
shared challenges and take advantage of new opportunities in the changing
global environment.
Chapters 4, 5, and 6 point to shortcomings in, respectively, the supply of care, the supply of higher education, and the supply of labor—and
highlight their significance for economic prosperity. Chapter 4 illustrates
the significance of early childhood care and education for economic wellbeing and prosperity, focusing on the effects of childcare on children and
families as well as the broader societal benefits. The chapter characterizes
gaps in access and availability, and it details how challenges in the childcare
industry, including the high cost of providing care, prevent the market from
delivering childcare of an optimal quantity or quality. The chapter explains
how policies that address these challenges by supporting families accessing
care and providers supplying care can have substantial, long-run economic
benefits.
Chapter 5 highlights the importance of higher education in this context, with a particular focus on the role that postsecondary institutions play
in creating the skilled workforce. The chapter notes that various features of
the higher education market suggest that promising institution-focused policies and programs could meaningfully improve student outcomes and ensure
that all students have access to a college degree of value.
The recovery from the COVID-19 global pandemic has highlighted
the importance of the labor supply for the economy. Chapter 6 shows that
current labor supply shortfalls in the United States are not merely a lingering effect of the pandemic but are also due to population aging and long-run
48 |

Chapter 1

declines in labor force participation. Policies to draw more adults into the
labor force will be needed, without which the labor supply is likely to be
constrained for the foreseeable future.
Chapter 7 describes the significance of digital markets in the modern
U.S. economy and the tension for this market environment’s regulators
between promoting competition and enabling economies of scale. Digital
markets have grown rapidly, and high levels of consolidation suggest that
the government has a role in protecting consumers and promoting innovation through antitrust action. The importance of network effects means that
regulatory interventions in the digital economy have nuanced effects.
Chapter 8 explores recent developments in digital assets, along with
their opportunities and risks. Although advocates often claim that digital
assets, particularly crypto assets, are a revolutionary innovation, the design
of these assets frequently reflects an ignorance of basic economic principles
that have been learned in economics and finance over centuries, and this
inadequate design is often detrimental to consumers and investors.
Finally, chapter 9 describes the physical risks that the changing climate
poses for U.S. economic production, the well-being of U.S. communities,
and the fiscal position of the Federal Government, as well as opportunities to
manage and reduce these risks. International and domestic climate policy has
historically focused on policies to reduce greenhouse gas emissions, which
are critical for mitigating the worst effects of climate change. However, the
effects of climate change are already being felt across the United States and,
even with ambitious emission reductions, will continue to increase until net
global emissions fall to zero. Policies that enable households, businesses,
and communities to plan for the changing climate and to manage evolving weather risks are an important complement to emission reductions in
reducing the costs of climate change. Chapter 9 thus describes the economic
foundations of these adaptation policies and outlines four pillars that could
inform the Federal adaptation strategy.

Pursuing Growth-Enhancing Policies in Today’s Changing World | 49

Chapter 2

The Year in Review and the Years Ahead
The U.S. economy in 2022 continued to navigate an unprecedented global
pandemic, and weathered an additional price shock to energy and food
caused by Russia’s unprovoked invasion of Ukraine. Despite these and other
challenges, the economy remained resilient with moderate output growth,
strong employment growth, and inflation that peaked and then started to
moderate late in the year (figure 2-1). In the face of supply constraints
and changes in the composition of demand, the primary goal of fiscal and
monetary policy in 2022 was to restore balance to supply and demand, fight
inflation, and return the economy to a path of stable, steady growth.
Russia’s invasion of Ukraine in February created acute supply constraints
to energy, food, and other commodities that raised inflation globally. In

Figure 2-1. The U.S. Economy, 2018–22
A. Real GDP

B. Unemployment rate

2021 dollars (trillions)

Percent

24.5
24.0
23.5
23.0

1

14

1

1

1

22.0
0

20.5
20.0
2018 2019 2020 2021 2022

0

10
8

1

6
4

4
3
2

0

0

1

0
2018 2019 2020 2021 2022

0

1

0

0

0

0

0

1

0

0

2

1

1

1

1

1

1

5
1

0

0

6

1

1

21.0

4-quarter percent change
1

12
1

22.5

21.5

C. Core PCE inflation

0
2018 2019 2020 2021 2022

0

Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations.
Note: Nominal GDP was converted to 2021 dollars using the GDP Price Index. PCE = Personal Consumption
Expenditures Price Index. Core PCE inflation excludes volatile food and energy inflation. All values are seasonally
adjusted.

51

addition, in the first half of the year, the COVID-19 virus continued to weigh
on economies across the world—in the same ways, if to a different extent,
as it had in 2021 (Chetty et al. 2022)—especially when its Omicron variant
caused cases and fatalities to surge in the United States and abroad. Due
to pandemic-related disruptions, global supply chains were stressed. To
support the U.S. economy, the Federal Reserve kept the target range for the
Federal Funds Rate near zero until March. Although the majority of direct
household relief funds from the CARES Act, the American Rescue Plan,
and related legislation had been dispersed by the end of 2021, many of
these funds had not been spent by households, and Americans entered 2022
with historically elevated savings.
Recessions can leave lasting scars, but thanks to the fiscal and monetary
support provided in 2020 and 2021, the United States’ real gross domestic
product (GDP) in 2022 was close to what it had been forecasted before the
pandemic (CBO 2019) to be in 2022. After muted growth for much of the
previous two years, growth in real consumer spending on services was particularly strong during the four quarters of 2022, as spending patterns started
to return to normal. By most measures, the labor market was extraordinarily
tight in 2022, creating some of the most favorable conditions for job seekers
in decades.
As this chapter shows, the government’s comprehensive response to the
pandemic helped achieve the solid positive outcomes of 2022. At the same
time, the combination and interaction of numerous factors exacerbated
the elevated inflation. Although it is difficult to determine the relative
importance of each factor, the pandemic, and responses to it, had substantial
effects on both the supply and demand sides of the economy. Specific factors
of note include pandemic-induced supply disruptions, shifts in consumer
demand, the accumulation of excess savings, and stimulative fiscal and
monetary support throughout 2020 and 2021.

52 |

Chapter 2

In 2022, monetary policy turned to fighting inflation and fiscal policy
focused on strategies to complement that fight, while also working to guide
the economy to stable and steady growth, in 2022 and in the future. Even
before the year began, government spending and deficits fell closer to prepandemic trends. In March, the Federal Reserve began to reverse its asset
purchase program and started what became a swift series of interest rate
hikes; stock markets and residential investment declined quickly. President
Biden authorized a drawdown of the Strategic Petroleum Reserve to lower
gasoline prices after Russia’s invasion of Ukraine. In July and August,
major pieces of legislation were passed to boost the economy’s long-term
supply side. Some measures of labor market tightness and inflation began to
moderate, with inflation showing an easing at the end of the year. The fight
against inflation is expected to continue into 2023, resulting in a near-term
outlook of below-trend GDP growth, a modestly rising unemployment rate,
and falling inflation.
This chapter begins with a review of the economy in 2022, first examining the recovery of GDP and its subcomponents, and then summarizing
the conditions of labor markets and financial markets. Next, the chapter
describes inflation in 2022, discussing possible causes along with the government’s response. Finally, the chapter presents the forecast underpinning
the President’s Fiscal Year 2024 Budget and summaries of the near-term and
long-term outlooks.

The Year in Review: The Continuing Recovery
This section summarizes the U.S. economy in 2022. By many measures,
the economy had recovered from the recession induced by the COVID-19pandemic by the end of 2022; by a few measures, the economy had not. For
example, real GDP was near the level it would have been if it had continued
to grow at its average 2010–19 pace from its prepandemic peak in 2019:Q4.
The unemployment rate was near its prepandemic low for most of the year,
and other labor market indicators showed more tightness than they had in
2019:Q4. On average, wages adjusted for inflation declined over the year,
though they saw growth in the second half. The stock market started the
The Year in Review and the Years Ahead

| 53

year at a record high, but fell over the year, partly due to rising inflation and
tighter monetary policy. By most measures, and especially compared with
recoveries from previous recessions, the economy in 2022 was healthy.

Output in 2022: A Return to Near Its Trend
Real GDP grew by 0.9 percent during the four quarters of 2022, a deceleration from its 5.7 percent pace during 2021. After a rapid decline in 2020 and
a large bounce-back in 2021, the level of GDP in 2022 was roughly at its
prepandemic trend. But GDP growth in 2022 was uneven, negative in the
first half and positive in the second half. Some components increased and
others contracted, reflecting the ongoing adjustment back to “normal” and
away from the atypical spending and investment patterns seen over the past
three years.
As shown in figure 2-2, real GDP in 2022 had rebounded to a level
that was at or above a log-linear trend extrapolated from preceding years of
GDP growth, an important achievement. In some previous economic cycles,
including the recovery from the Great Recession of 2007–9, the economy
took much longer to return to its extrapolated trend, meaning that workers
and consumers suffered negative consequences for a longer period. (See
figure 2-3, panel H, for a comparison of this recovery with other recoveries.)
The longer-run trend level of GDP is a simple estimate of what is sometimes
called potential GDP, which is a measure of what the economy can produce
at full capacity at a particular point in time. Recessions can cause output to

Figure 2-2. GDP and Trend GDP, 2012–22
2021 dollars (trillions, log scale)
25
24
23
22
21
20
19
2012

2013

2014

2015

2016

GDP trend: 2002:Q1–2019:Q4

2017

2018

2019

2020

GDP trend: 2010:Q1–2019:Q4

2021

2022

Actual GDP

Sources: Bureau of Economic Analysis; CEA calculations.
Note: GDP trend lines were calculated by regressing the log of real GDP on time for the specified intervals, and plotting
predicted values from that regression. Nominal GDP was converted to 2021 dollars using the GDP Price Index. All values
are seasonally adjusted.

54 |

Chapter 2

Figure 2-3. The 2019–22 Period Compared with Previous Business Cycles
Index = 100 at business-cycle peak; 2019–22 cycle peak is 2019:Q4

B. Total spending on services

A. Total spending on goods

120
115
110
105
100
95
90
85
80
–4 –3 –2 –1 0

120

Businesscycle peak

110
100
90

Quarters from businesscycle peak →
1 2 3 4 5 6 7 8 9 10 11 12

80
–4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12

C. Business fixed investment

D. Structures investment

120

120

110

110

100

100

90

90

80

80

70

70

60

–4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12

E. Exports

60

–4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12

F. Imports

140
130
120
110
100
90
80
70
–4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12

140
130
120
110
100
90
80
70

G. State and local purchases

H. Gross domestic product (GDP)

–4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12

130

130

120

120

110

110

100

100

90

90

80
–4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12

80

Minimum of previous cycles

–4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12

Maximum of previous cycles

2019–22 cycle

Sources: Bureau of Economic Analayis; CEA calculations.
Note: Panels A and B include spending on goods and services by consumers, businesses, government, and as part of international
trade, as defined in table 1.2.6 in the “National Income and Product Accounts.” Panel D includes business, residential, and
government structures investment, also from table 1.2.6. All values are seasonally adjusted.

run below its trend, which may be followed by faster growth that returns
the level of output toward its trend. Growth can also be so fast that the level
of output rises above its trend, a situation that may lead to high inflation
as aggregate demand outstrips the capacity of the economy to produce the
desired level of goods and services; this is often referred to as an overheated
economy. Usually, high inflation provokes a policy response—for example,
The Year in Review and the Years Ahead

| 55

an interest rate hike by the Federal Reserve—that cools the economy and
returns output to its trend.1
Estimating the trend of GDP is not straightforward. Figure 2-2 plots
two log-linear trend lines estimated over different intervals. The longer
estimation interval suggests that the United States’ output was above its
trend in 2022, while the shorter one suggests that output was below it. Many
other measures suggest the economy was running above its trend in 2022,
including signals of tight labor markets, the elevated inflation rate and the
growth of consumption without corresponding growth in investment or
imports. Further, given the turmoil associated with the pandemic—lower
labor force participation, demand shifts for specific skilled labor categories,
and population movement—and the elevated inflation rate, there is ample
reason to expect that the productive capacity of the economy was temporarily below its usual position in 2022. The position of the economy matters
for the interpretation of growth in 2022, and has implications for the nearterm economic outlook. If GDP was above trend, the slowdown of growth
in 2022, influenced by the Federal Reserve’s rate hikes, would mean the
economy was returning to its trend, and may also presage continued slow
growth in the near term.
To illustrate the strength of the economic recovery in 2021 and 2022
relative to previous recoveries, figure 2-3 consists of eight “butterfly charts”
that plot the evolution of various components of real GDP before and after
the 12 post–World War II business-cycle peaks in the United States, as
determined by the National Bureau of Economic Research. To construct
these charts, each highlighted component of GDP was normalized to equal
100 in the quarter at the peak of each business cycle. The orange lines in the
figure show the maximum paths of each component during the 11 business
cycles before the current cycle; the light blue lines show the minimum paths;
and the gray areas show the range of historical variation. The dark blue lines
plot the postpandemic recession recovery. If, to the right of the green vertical line, a dark blue line is closer to an orange line than to a light blue line,
this means that, relative to previous recessions, the recovery was stronger
for that component.
As can be seen in panel A of figure 2-3, the cumulative growth of real
spending on all goods since the previous business cycle peak in 2019:Q4
through 2022 was at the top of historical experience. Conversely, in panel B
of figure 2-3, real spending on all services was far below the range of historical experience at the end 2021, and growth through 2022 was only enough
for it to recover to the lower historical bound by the end of 2022. As shown
in panels C and D of figure 2-3, though real business fixed investment
remained at the middle of its historical range, real investment in residential
While higher GDP is generally beneficial, high inflation poses costs to the economy. It is these
costs that the policy responses seek to avoid.

1

56 |

Chapter 2

Table 2-1. Real GDP Growth and Its Components, 2022

Component

Q4/Q4 Growth (percent)
(1)

Contribution to
Q4/Q4 GDP Growth
(percentage points)
(2)

Contribution to the
Deviation of 2022:Q4
GDP from Its Trend
(percentage points)
(3)

Total

0.9

0.9

–1.1

Consumer spending
Goods
Durables
Motor vehicles and parts
Nondurables
Services

1.8
–0.9
0.5
–1.5
–1.7
3.2

1.2
–0.2
0.0
0.0
–0.3
1.4

0.5
0.9
0.3
–0.3
0.5
–0.4

Investment
Business fixed investment
Nonresidential equipment
Nonresidential structures
Intellectual property
Housing investment
Change in private inventories

–4.0
4.3
4.0
–3.3
8.5
–19.0
–

–0.7
0.6
0.2
–0.1
0.4
–0.9
–0.4

–2.3
–1.4
–0.8
–0.9
0.4
–1.1
–

Net exports
Exports
Imports

–
5.2
1.8

0.3
0.6
–0.3

–
–1.1
–0.3

Government
Federal
Defense
Nondefense
State and local

0.8
0.1
–0.2
0.5
1.3

0.1
0.0
0.0
0.0
0.1

1.3
1.2
0.6
0.5
0.2

Sources: Bureau of Economic Analysis; CEA calculations.
Note: Column 2 lists the contribution of each component to the annual rate of growth of real GDP. These may not precisely sum to totals
because of approximations to the formulas used in the National Income and Product Accounts. Column 3 shows that that GDP was 1.1
percent below prepandemic trend in 2022:Q4 and how much each component of GDP contributed, negatively or positively, to this
deviation from trend. It was calculated by regressing the log of each real GDP component on time from 2010 to 2019, calculating the
percent difference of the 2022:Q4 level predicted by that regression from the actual 2022:Q4 level of each component, and multiplying
by the importance of that component to overall GDP (the average of the 2019:Q4 and 2022:Q4 ratios of that nominal component of
GDP to total nominal GDP).

and other structures fell in 2022, and its recovery has remained near the bottom of its historical range.
Table 2-1 breaks down real GDP growth into its subcomponents. The
first column lists the four-quarter growth rate for each component over 2022.
The second column lists the contributions of each category to overall real
GDP growth over those quarters. Contributions can be negative or positive.
For example, because real exports grew 5.2 percent during the four quarters
of the year and constituted about 11.7 percent of GDP, its contribution to
real GDP growth was 0.6 percentage point. The first row of the third column
compares the 2022:Q4 level of real GDP with what it would have been if
it had followed its 2010–19 log-linear trend (the light blue line in figure
2-2); all other rows show the approximate contribution of that real GDP

The Year in Review and the Years Ahead

| 57

component to this deviation. The major sectors that grew noticeably faster
than overall GDP in 2022 include consumer spending on services, equipment investment, intellectual property investment, and exports. Imports
also experienced relatively fast growth, but these reduce GDP. Expenditure
categories that grew slower than overall GDP include consumer spending
on goods, nonresidential and residential investment, Federal Government
purchases, and inventory investment. State and local expenditures grew, but
only slowly.
Consumer spending. The nominal goods-to-services consumer spending ratio—which had been in a long-term decline—increased during 2020
and 2021, reaching its highest level since 2006. Real consumer spending
on services fell sharply when the pandemic hit, as in-person activities such
as dining out and traveling became more difficult. In contrast, real goods
spending, after initially falling during the first two pandemic quarters,
rebounded and spiked above its prepandemic level, as people stuck at home
spent a larger share of their total real consumption on goods like furniture,
appliances, and sporting equipment and a smaller share on services.
During 2022, the goods-to-services spending ratio started to normalize; real goods spending fell 0.9 percent during the four quarters of 2022,
while real consumer services spending grew 3.2 percent. Even so, this ratio
remained well above prepandemic norms. Overall, real consumer spending
grew modestly during the four quarters of 2022, at a 1.8 percent annual rate,
with all of that growth accounted for by services.
Investment. Real business fixed investment increased 4.3 percent
during the four quarters of 2022, continuing its steady recovery from its
pandemic-induced low. Investment growth was particularly strong in intellectual property, as it has been for the last decade. But investment by businesses in structures fell 3.3 percent during the four quarters of the year, with
declines in investment in commercial and health care structures and power
and communication structures. Investment increased in manufacturing and
petroleum and natural gas mining structures.
Increases in business fixed investment were offset by declines in fixed
investment in residential and other structures, as the housing market cooled
due to the rise in mortgage rates associated with the Federal Reserve’s tightening cycle. Both business fixed investment and fixed investment in residential and other structures were below their prepandemic trends. Overall,
spending on structures was near the lower end of the business-cycle range,
as shown in panel D of figure 2-3.
Some of the slowing GDP growth in 2022—which followed strong
growth in 2021—was accounted for by inventory investment. The overall
real inventory-to-sales ratio shrank to the lowest on record in 2021:Q2, as
firms fought supply chain bottlenecks and then began to rapidly recover,
with inventory investment at high levels in 2021:Q4 and 2022:Q1. The
58 |

Chapter 2

stock of real inventories continued to grow strongly in 2022, but because
inventory investment was lower in 2022:Q2 and 2022:Q3 than in 2022:Q1,
inventory investment subtracted from real GDP growth in those quarters and
over the four quarters of the year.
Government spending. The Federal Government’s real purchases
(expenditures and gross investment) edged up slightly, by 0.1 percent, during the four quarters of 2022. Most of the surge in Federal spending that
had supported households, businesses, and State and local governments
in 2020 and 2021 consisted of transfers and subsidies that are not directly
part of GDP; while these transfers and subsidies fell, purchases were little
changed. Defense expenditures and gross investment barely changed during
the four quarters of the year, while nondefense purchases edged up. State
and local government purchases increased slowly, by 1.3 percent, during
the four quarters of the year. Relative to the average cyclical response, State
and local purchases were near the lower end of the business-cycle range, as
shown in panel G of figure 2-3.
Imports and exports. Finally, real exports grew faster than overall GDP
during the four quarters of 2022, growing by 5.2 percent at an annual rate,
reflecting the continued reopening of the world economy. Although real
imports grew more slowly than real exports during the four quarters of the
year, at 1.8 percent, that import pace exceeded the growth of real GDP by
0.9 percentage point. Due to the stronger growth in real exports relative to
imports, real net exports partially recovered from their pandemic-induced
decline in 2022, contributing 0.3 percentage point to overall real GDP
growth. (See chapter 3 of this Report for an in-depth discussion of international trade and investment in 2022.)

The Historic Strength of Labor Markets in 2022
Labor markets were very tight in 2022, as the strong economy led firms to
continue to hire workers after pandemic-induced layoffs and hiring pauses.
At the end of the year, the unemployment rate was 3.5 percent, matching
the lowest rate—tied with September 2019 and prepandemic 2020—since
1969. Other labor market measures also showed a historically high degree of
tightness, including the ratio of job openings per unemployed person, shown
in figure 2-4, and the quit rate, considered by some to be the best measure of
labor market tightness (Furman and Powell 2021), which reached at least a
20-year high at the end of 2021 and remained elevated through 2022.
Figure 2-4 shows the ratio of total job openings divided by the total
number of unemployed people. During recessions, this measure tends to
fall, as firms slow hiring, reduce job openings, and lay off workers, and it
plummeted in 2020. By April 2022, however, the measure had climbed to
the highest level on record, indicating that the labor market was unusually

The Year in Review and the Years Ahead

| 59

Figure 2-4. Job Openings per Unemployed Person, 2000–2022
Ratio
2.5

2.0

1.5

1.0

0.5

0.0
2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

Sources: Bureau of Labor Stastistics; CEA calculations.
Note: All values are seasaonally adjusted.

Figure 2-5. The Beveridge Curve at Two Intervals
Vacancy rate (percent)
7
6

December 2022

5
4
3
2
1

2

4

6

8
10
Unemployment rate (percent)

July 2009–February 2020

12

14

16

April 2020–December 2022

Sources: Bureau of Labor Statistics, CEA calculations.
Note: All values are seasonally adjusted.

tight. In the second half of the year, job openings decreased and the number
of unemployed persons increased slightly.
Figure 2-5 shows another view of the labor market: the Beveridge
curve, the relationship between the unemployment rate and the percentage
of job openings relative to labor demand, known as the “vacancy rate.”2 The
2

Labor demand equals job openings plus employment.

60 |

Chapter 2

Beveridge curve during the pandemic-recession recovery, represented by
the dark blue dots, shifted up and out, possibly due to increased pandemicrelated difficulties in hiring and retaining workers. All the months of 2022
are located in the upper-left-hand corner of the figure, where vacancy rates
are high and unemployment rates are low, indicating that labor markets were
tight and that labor demand was high relative to labor supply.
Economists disagree about how much of this labor market tightness
was due to a shortage in the supply of workers versus an excess demand for
workers. On the demand side, the high aggregate demand described later in
this chapter led to an increased demand for workers by businesses. There
are a range of potential supply-side factors, which are discussed in chapter
6 of this Report.

The Cooling of Financial Markets in 2022
The stock market recovered quickly from large declines during the COVID19 pandemic, reaching a new peak at the end of 2021. In early 2022, as
inflation rose and the Federal Reserve began hiking the Federal Funds Rate
to cool off the economy, stock prices declined. The losses in 2022 reversed
only part of the gains made during the previous two years (figure 2-6).
Along with stock prices, bond prices also fell.3 The price of 10-Year
Treasury Notes, which moves inversely to the yield, began the year near
historical highs but ended the year quite a bit lower, likely due in part to
upward revisions in market expectations for the future path of inflation and
associated revisions in market participants’ expectations for the path of the
Federal Funds Rate.4
From near the beginning of the COVID-19 pandemic to the end of
2022, the correlation between changes in stock prices and long-term bond
prices was reversed from its previous sign. From 2000 until the beginning
of the COVID-19 pandemic in 2020, the correlation between changes in
stock prices and bond prices was generally negative (Rankin and Idil 2014).
During this 20-year period, the Federal Reserve lowered the Federal Funds
Rate, increasing bond prices. These increases were primarily in response to
negative aggregate demand shocks, which drove down stock prices, as during a typical recession.
As shown in figure 2-6, the pandemic-induced recession fit this pattern
in early 2020: stock prices fell and bond prices rose. In contrast, in 2022
inflation led the Federal Reserve to raise the Federal Funds Rate, causing
both stock and bond prices to decline. This relationship can be seen in
Bond prices, rather than bond yields, are discussed here in order to simplify the comparison with
stock prices. The spot price of the 10-Year Treasury Note is calculated from the market yield,
assuming no coupons.
4
A complete description of the drivers of changes in the interest rate on 10-Year Treasury Notes is
beyond the scope of this chapter; see Stigum and Crescenzi (2007).
3

The Year in Review and the Years Ahead

| 61

Figure 2-6. Stock Market and Bond Prices, 2019–22
Index; January 2019 = 100

Index; January 2019 = 100

215

125

195

120
115

175

110

155

105
135

100

115

95

95

90

75
2019

2020

2021

S&P 500 index (left axis)

2022

85

10-year Treasury Note price index (right axis)

Sources: Federal Reserve System; Standard & Poor’s (S&P).
Note: The prices of 0 coupon 10-Year Treasury Notes are shown relative to January 2019.

figure 2-6, starting slightly before the tightening cycle began, possibly due
to markets anticipating monetary actions. The change in the sign of this correlation after the start of the pandemic suggests that negative supply shocks
were important for U.S. financial markets in 2022; these shocks moved the
price level higher and output lower—thus hurting stock prices—and led to
increasing interest rates, thus hurting bond prices.

Inflation in 2022
Beyond the developments summarized above in discussing output growth,
the historically strong U.S. labor market, and financial markets, the rise
of inflation in 2021 and its continued elevation through 2022, exacerbated
by Russia’s invasion of Ukraine, were important aspects of 2022’s overall
economic picture. For most of the 2010–19 period, the rate of inflation was
below the Federal Reserve’s long-term 2 percent target. Then the COVID19 pandemic hit the United States in early 2020. Prices fell briefly in the
spring of 2020, when the pandemic initially struck, interrupting many forms
of economic activity; but prices, and the economy, quickly recovered.
Inflation began to climb in 2021. Although, at the end of 2021, many
forecasters predicted that inflation would quickly fall, inflation instead persisted in 2022.5 The year 2022 was one of historically elevated inflation, but
it was also a year that saw many actions taken to bring that elevated inflation
E.g., the 2022:Q1 annualized CPI inflation rate predicted by the December 2021 Blue Chip
consensus was 3.3 percent, close to the Federal Reserve’s target and much lower than the actual
quarterly inflation rate of 9.2 percent.

5

62 |

Chapter 2

Box 2-1. Measures of Consumer Price Inflation
Inflation can be challenging to precisely define and measure. This box
describes what inflation is not and what it is, how the government measures
inflation, and what information key inflation measures provide.
Defining inflation. Inflation can be tricky to talk about. First, inflation
is the rate of change of the price level, not the level of prices. High inflation means that prices are rising rapidly, not that prices are high. Second,
increases in the prices of specific goods and services do not always reflect
inflation. Due to changes in relative demand and supply, prices for specific
goods and services rise and fall relative to each other all the time. For example, during the COVID-19 pandemic, demand for television sets rose, and
their prices increased. Concurrently, demand for airline tickets fell, along
with their prices. Price indices—such as the Consumer Price Index (CPI)
and the Personal Consumption Expenditures (PCE) Price Index, which are
discussed below—aggregate prices in the economy in an attempt to measure
the price level. Inflation is a positive rate of change in the price level.
Measuring inflation. Measuring the price level, and therefore inflation, is a difficult task. This chapter frequently references two measures that
approximate the level of prices faced by consumers: the CPI, produced by
the Bureau of Labor Statistics (BLS); and the PCE Price Index, produced by
the Bureau of Economic Analysis (BEA).
(The main text refers exclusively to the CPI-U, which follows the
market basket of urban consumers. The description “urban” refers to anyone
not living in extremely rural areas, and covers about 90 percent of the U.S.
population. The BLS also supports several other versions of the CPI. The
CPI-W follows the market basket of wage earners; the CPI-E follows the
market basket of the elderly; and the chain CPI follows the same consumers
as the CPI-U, but it aggregates with a formula that allows for more substitution.)
The CPI measures the prices of a fixed basket of consumer goods and
services (BLS 2020). The basket, which was updated every two years from
2002 to 2022 and will be updated every year in the future, approximates the
average consumption of a household as surveyed in the annual Consumer
Expenditure Survey. The assumption of a fixed consumption basket makes
comparing the prices of the same goods and services across time relatively
easy, but it can misrepresent the rate of price changes households actually
face (or experience) if households change what they consume when prices
change. For instance, if the price of oranges falls relative to the price of
apples, consumers will usually buy more oranges and fewer apples. The
PCE Price Index, in contrast to the CPI, uses a formula that allows for such
substitution. Further, while the CPI focuses on out-of-pocket expenditures,
the PCE Price Index captures a wider range of consumer costs—including,
for example, employer-provided health insurance. Largely because the PCE
Price Index allows for more substitution (but also due to other differences),

The Year in Review and the Years Ahead

| 63

the 12-month change in the PCE Price Index has averaged 35 basis points
less than the corresponding change in the CPI for the last 20 years.
Headline inflation versus core inflation. Economists and policymakers focus on price indices that exclude goods and services with volatile
prices, such as food and energy, in order to get a better sense of persistent
movements in inflation (Gordon 1975). Food and energy prices are erratic
largely because they are influenced by weather and international commodity
markets, and therefore can move independently from the other goods and
services whose prices are determined domestically to a greater extent. The
core CPI and the core PCE Price Index exclude food and energy, whereas
the corresponding headline CPI and headline PCE Price Index include food
and energy. Of course, because consumers buy food and energy, headline
inflation measures better reflect the costs consumers actually face.
Monthly versus yearly inflation. Each month, the BLS and BEA
update the CPI and the PCE Price Index, respectively, and the monthover-month percent change in each price level. They also report 12-month
percent changes, which are substantially less volatile because they accumulate month-over-month percent changes over 12 months. Measures of
annualized 3-month or 6-month inflation—the 3-month or 6-month percent
change mathematically adjusted to be comparable to 12-month, or yearly,
rates—can also be calculated from the raw price indices. These measures
are less volatile than monthly inflation but are more timely than yearly inflation. Figure 2-i plots annualized 6-month inflation for four price indices: the
headline CPI, the core CPI, the headline PCE Price Index, and the core PCE
Price Index. All four inflation indices began to increase in 2021 but turned
downward in the second half of 2022.
Figure 2-i. Types of Consumer Price Inflation, 2011–22
Percentage change, 6-month annualized
12
10
8
6
4
2
0
–2
–4
2011

2012

2013
2014
Headline CPI

2015

2016
2017
Headline PCE

2018
2019
Core CPI

Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations.
Note: All values seasonally adjusted.

64 |

Chapter 2

2020
2021
Core PCE

2022

Figure 2-7. The Expectations-Augmented Phillips Curve at Two Intervals
3-month annualized core CPI inflation (percent), controlling for expected inflation (see note)
7
6
5

2022:Q4

4
3
2
1
0

2020:Q4

3

4

5

6
7
8
Unemployment rate (percent)
2009:Q1–2019:Q4

9

10

11

2020:Q4–2022:Q4

Sources: Bureau of Labor Statistics; Federal Reserve Bank of Philidelphia.
Note: CPI = Consumer Price Index. The y axis shows a measure of the actual rate of inflation minus the difference betwen the
expected rate of inflation and the long-term rate of inflation, or π - (E[π] - π*), where π = core CPI inflation, E[π] = 1-year lagged
median 1-year ahead core CPI inflation expectations from the Federal Reserve Bank of Philidelphia’s Survey of Professional
Forecasters, and π* = the long-term (post-2000) average of core CPI inflation, 2.3 percent. Actual CPI inflation values are seasonally
adjusted.

down. As discussed in box 2-1, there are many ways to measure inflation.
One of the most common, the 12-month rate of change in the headline
Consumer Price Index (CPI), peaked at 9.1 percent in June 2022—a pace
not seen since 1981. The fight against inflation has not been an easy one,
but progress has been made as of December 2022, when the 12-month rate
of change in the headline CPI inflation was 2.6 percentage points lower than
in June.
The unexpected nature of the inflation in 2021 and 2022 is exemplified
by figure 2-7. The figure shows an estimate of the Phillips curve, the relationship between inflation, unemployment, and inflation expectations from
2009 until the last prepandemic quarter in 2019:Q4 (dark blue dots), and
during the economic recovery from 2020:Q4 through 2022:Q4 (light blue
dots). The light blue dots are substantially above the dark blue dots, indicating that inflation moved more strongly with unemployment during the
economic recovery than in the previous economic expansion. Investigating
why inflation responded so strongly, and the fiscal and monetary responses
to it, occupies much of the rest of this chapter. (Also see box 2-2.)
Measures of inflation can be approximately decomposed into contributions from subcategories of goods and services. Figure 2-8 plots the
decomposition of annualized three-month headline CPI inflation into five
categories: food; energy; core goods, which exclude food and energy goods;
shelter, which includes rent and “owners’ equivalent rent,” and core services
The Year in Review and the Years Ahead

| 65

Figure 2-8. Decompostion of Inflation, 2019–22
Contributions to quarterly headline CPI inflation, percentage points, quarterly annualized rate
12
10
8
6
4
2
0

–2
–4
–6

2019:Q1

2019:Q3
Food

2020:Q1
Energy

2020:Q3
Core goods

2021:Q1

2021:Q3

2022:Q1

Core services less shelter

2022:Q3

Shelter

Source: Bureau of Labor Statistics.
Note: All values seasonally adjusted.

less shelter, which also excludes food and energy services. The figure shows
that inflation during 2022 in the United States was broad-based, with each of
the subcategories contributing substantially to overall inflation.
The timing of these contributions differs and tells an interesting story.
In early 2021, the contribution of core goods inflation to overall inflation
rose as consumer purchases rotated from services to goods during the pandemic, when supply chains snarled and productive capacity could not rise
fast enough to match the rise in demand. As consumer behavior and supply
chains both normalized in 2022, monthly core goods inflation declined and
actually turned negative in late 2022. The contribution of food and energy
inflation rose in 2021, and continued in 2022. Russia’s invasion of Ukraine
in February 2022 increased pressure on global oil and agricultural commodity markets. Partly as a consequence, the contribution of food and energy
to inflation rose both domestically and globally. Inflation in core services,
which was the primary contributor to overall inflation in the decade before
the pandemic, was only slightly above its prepandemic pace in 2021 but
increased sharply in 2022.
The decomposition shown in figure 2-8 is informative, but it is only an
accounting exercise: it does not explain the underlying economic factors that
led one category to move relative to another. If one category “contributed”
more than another in a certain quarter, it means that prices in that category
were increasing relative to prices in the other category, not necessarily that
price increases in that category were the underlying cause of inflation. For
example, it is possible for headline CPI inflation to be 0.0 percent, with core
66 |

Chapter 2

Figure 2-9. Global Measures of Consumer Price Inflation
12-month percent change in national consumer price indices
12
10
8
6
4
2
0
–2
2018

2019
U.S.

2020
U.K.

2021
EU

2022
Canada

Japan

Sources: U.S. Bureau of Labor Statistics; U.K. Office for National Statistics; Eurostat; Japanese Ministry of Internal Affairs and
Communications; Ministry of Statistics and Programme Implementation of India; Statistics Canada, CEA calculations.
Note: Measures are of headline consumer price inflation less owner-occupied housing (sometimes called the Harmonized Index of
Consumer Prices).

goods inflation contributing negative 2.0 percent and core services inflation contributing positive 2.0 percent. The difference in goods and services
inflation would mean that services prices were increasing relative to goods
prices, not that either was causing inflation. In the next subsection, possible
causes of U.S. inflation in 2022 are examined in detail.
High inflation in 2022 was not just a U.S. phenomenon, as shown in
figure 2-9. In 2021, after years of relative stability, inflation began to climb
across a number of countries. In the second half of 2022, inflation in the
EU and the United Kingdom was higher than in the United States, partially
reflecting the EU countries’ and the United Kingdom’s greater exposure
to the war in Ukraine, and specifically the war’s effect on energy prices.
Inflation in some other countries, such as Japan, remained relatively low,
though well above its prepandemic norm.

Factors That Had an Impact on Inflation in 2021–22
As discussed in box 2-2, the root causes of inflation are imperfectly understood, and economists use many theoretical frameworks to model and study
it. Because the most common framework used to analyze inflation is aggregate supply and demand, this subsection first discusses what are generally
thought of as “supply” factors and then examines what are generally thought
of as “demand” factors. The role of expectations, a common theme in many
inflation frameworks, is also discussed. Fiscal and monetary actions are both

The Year in Review and the Years Ahead

| 67

Box 2-2. The Phillips Curve and
Other Models of Inflation
Economists have spent much time and effort trying to explain and predict
inflation, using a variety of methods and approaches. This box explains
one common model, the Phillips curve; describes its recent history; and
discusses each of its components—inflation; economic tightness, or
“slack”; inflation expectations; and other factors—before briefly discussing theories of inflation that do not depend on a Phillips curve–type
relationship.
The term “Phillips curve” is used to refer both to the empirical
relationship between forms of inflation and measures of economic tightness or slack, used in the macroeconomic model developed by Klein
and Goldberger (1955) and noted by Phillips (1958) (with regard to
wage inflation and unemployment), and to the theoretical relationship
between the two. Today, policymakers and forecasters often refer to the
“expectations-augmented Phillips curve,” which recognizes that inflation expectations can influence inflation independently from measures
of economic tightness or slack.
As shown in figure 2-7 in the main text, the empirical relationship
between the unemployment rate, one measure of tightness, and Core
CPI inflation can change drastically, even when controlling for inflation
expectations. The Phillips curve appeared to have become “flat” in about
2000, as discussed in the 2016 edition of the Economic Report of the
President (CEA 2016). More precisely, the coefficient on the unemployment rate was near zero (hence, the adjective flat). This flatness during
the 2009–19 business-cycle expansion is shown by the dark blue dots
in figure 2-7 and the accompanying flat dark blue dashed line. Elevated
unemployment rates failed to lower inflation during the first half of this
cycle, while the low unemployment rates during the second half of that
cycle failed to increase inflation.
Viewed from the end of 2022, the Phillips curve has substantially
changed, as the decline in the unemployment rate to near historic lows
in 2022 coincided with the first major increase in U.S. inflation since the
1980s, as shown by the light blue dots in figure 2-7 and accompanying
steeply sloped light blue dashed line. The increase in inflation during
2021 and 2022 was much larger than the consensus economic forecast,
perhaps because most forecasters had come to believe in a flat Phillips
curve anchored by stable inflation expectations (Federal Reserve Bank
of Philadelphia 2020).
One of the important questions facing the economy in 2023
is whether the Phillips curve will remain steeply sloped as inflation
continues to cool. If the Phillips curve remains steep, this implies that
inflation may fall without much of an increase in the unemployment rate.
A Phillips curve that returns to near its prepandemic slope would imply

68 |

Chapter 2

that inflation may fall, but with a larger increase in the unemployment
rate than in the second half of 2022.
Measures of inflation in the Phillips curve. As described in box
2-1, measures of inflation that include food and energy prices are volatile
for reasons that have little to do with the domestic economy. Thus, core
inflation measures, which exclude food and energy, fit better and are
preferred for forecasting applications. Some practitioners use estimates
of a deeper, more persistent, underlying inflation rate—as described or
suggested by Ascari and Sbordone (2014), Yellen (2015), and Rudd
(2020)—in order to enhance the fit and predictive power of the Phillips
curve. Figure 2-7 uses annualized 3-month core CPI inflation.
(Simple estimates of this underlying inflation rate involve a
menagerie of methods and measures, as discussed by Detmeister 2011.
These measures include averaging across months of inflation data, using
the inflation rate on specific categories of spending, such as the median
CPI, from the Federal Reserve Bank of Cleveland 2023; and trimming
categories that see the most and least inflation when calculating the
inflation rate, such as the Trimmed-Mean PCE from the Federal Reserve
Bank of Dallas, n.d., among others.)
Measures of economic tightness or slack in the Phillips curve.
Choosing an appropriate measure of economic tightness or slack is a
difficult conceptual issue. “Slack” refers to the intensity of resource
utilization in the economy (Yellen 2015). Figure 2-2 shows one possible
measure of slack: the difference between real GDP and a longer-run trend
of real GDP. The situation at the end of 2022, when real GDP was higher
than its trend, indicates that resource utilization was higher than normal,
which may have fed through to inflationary pressures via increased costs
to firms to produce a unit of output (Boehm and Pandalai-Nayar 2022).
Another commonly used measure of slack is the deviation of the
unemployment rate from the natural rate of unemployment, the rate of
unemployment that would exist when the economy is stable in the longterm and not disrupted by shocks. Estimating the natural rate of unemployment, which is by nature unobservable, is a difficult task. (Many
practitioners estimate the natural rate of unemployment together with
the Phillips curve. But to have separate measurement power, that natural
rate estimate would need to come from a method external to estimation
of the Phillips curve itself, as was done by Michaillat and Saez 2022.)
For simplicity, figure 2-7 uses the unemployment rate alone, without an
external estimate of the natural rate.
Inflation expectations in the Phillips curve. The expectationsaugmented Phillips Curve includes inflation expectations because many
theories of inflation suggest that expectations may in some cases be
self-fulfilling—in other words, if people believe that inflation will rise,
inflation will rise; and if people believe that inflation will fall, it will

The Year in Review and the Years Ahead

| 69

fall. Empirically, expectations are important to explaining the decline
in inflation since the 1970s, and its stability in the 2010s (Blanchard
et al. 2015). The exact link between inflation expectations and actual
inflation is still debated (Rudd 2021; Bernanke 2007, 2022; Werning
2022). Figure 2-7 uses projections of core CPI inflation from the Survey
of Professional Forecasters.
Given the importance of inflation expectations, managing expectations is an important aspect of managing inflation. Inflation expectations
are said to be “anchored” when they do not change much, even when the
economic environment changes. Though many believe that the Federal
Reserve had an implicit inflation target at which it wanted to anchor
inflation starting in the 1990s or earlier, it was only in 2012 that the
Federal Reserve announced an explicit longer-run target of 2 percent
annual PCE Price Index inflation (Federal Reserve 2012). In 2020,
the Federal Reserve revised its “Statement of Longer-Run Goals and
Monetary Policy Strategy” to indicate that it would conduct policy in a
way that seeks to anchor inflation expectations at 2 percent and results
in inflation that averages 2 percent over time (Federal Reserve 2020). As
can be seen below in the text, even though inflation in 2021 and 2022
rose well above 2 percent, measures of long-run inflation expectations
remained relatively stable, lending support to the idea that the Federal
Reserve had successfully anchored inflation expectations.
Other factors. While Phillips curves are often parsimonious models
of inflation, factors other than expectations and slack may be used to
help empirically estimate the curve and control for other influences.
Yellen (2015) highlights the importance of changes in imported goods
prices, which are an input to many production processes and can proxy
for exchange rate dynamics. In a similar vein, below the text highlights
a measure of supply chain pressures and its relation to a producer-side
measure of inflation. The price of energy may also be included, although
pass-through from energy prices to measures of core or underlying inflation has diminished in recent years (Clark and Terry 2010).
Alternative models of inflation. The Phillips curve is one of most
common frameworks that economists use to understand inflation, but it
is far from the only one. For example, when economists talk about how
supply and demand affect inflation, they are usually referring to the
Keynesian Aggregate Demand and Aggregate Supply (AD-AS) model,
which evolved from attempts by John Hicks to formalize the ideas of
John Maynard Keynes in the 1930s (Hicks 1937; Keynes 1936). The
Phillips curve is often considered to be part of Keynesian theory because,
due to the link between employment and real output, something similar
can be implied from the AD-AS model. Keynesian theory can be understood as one explanation for the connection between inflation and slack
observed in the empirical Phillips curve. New Keynesian theory, which

70 |

Chapter 2

is a modern, mathematically formal development of Keynesian theory,
offers a related explanation (Galí 2015). The standard New Keynesian
Phillips curve relates inflation to the theory’s measure of slack and
features a larger role for expectations than most Keynesian models.
Monetarism is both a theory that describes a group of formal
mathematical models and also a set of less formal ideas. As a theory, it
is most associated with Milton Friedman, who famously said, “Inflation
is always and everywhere a monetary phenomenon, in the sense that it
is and can be produced only by a more rapid increase in the quantity of
money than in output” (Friedman 1970). Monetarist models emphasize
inflation as a consequence of the growth of the quantity of money
compared with the level and growth of output, rather than a connection
between inflation and slack.
Finally, a number of models of inflation emphasize the importance
of government debt. One of the best-known of these models, the Fiscal
Theory of the Price Level (FTPL), argues that increases in government
debt that are not backed by credible promises of repayment via increases
in future tax revenue or reductions in future spending lead to inflation
(Cochrane 2023). Proponents and critics of the FTPL disagree over the
direction of causality in this relationship, and the implicit assumptions
that such causality implies (Bassetto 2008).

usually considered to be demand factors in the near term; because they are
both especially important, they are discussed separately.6
Over the last two years, many hypotheses about the causes of the current inflation situation have been proposed by academics, journalists, and
politicians. The goal of this subsection includes reviewing prevalent propositions, not to argue for a single hypothesis or set of hypotheses. The possible causes discussed here likely played some role in the level and elevated
nature of inflation in 2022—and the pandemic was a large exacerbating
cause to each. Interactions between causes likely worsened inflationary
pressures. Frequently cited hypotheses include the shock to energy, food,
and other commodity prices associated with Russia’s invasion of Ukraine;
pandemic-related supply chain issues; the extension of zero interest rate
monetary policy and accompanying quantitative easing; household transfers
In the medium to long terms, both monetary and fiscal actions can influence supply. For example,
low interest rates can spur long-term investment. Government spending can build infrastructure—
e.g., Donaldson and Hornbeck (2016)—and support research and development—e.g., Gross and
Sampat (2020), as discussed in the paragraphs about legislative and executive actions in the text
below. In general, these supply-side factors take longer to impact the economy than do demand-side
effects of monetary and fiscal actions.

6

The Year in Review and the Years Ahead

| 71

Figure 2-10. Supply Chain Pressures and Producer Inflation, 1990–2022
24-month change, index points

12-month percent change

500

10

400

8

300

6

200

4

100

2

0

0

–100
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022

-2

24-month change in cumulative ISM supplier delivery index (left axis)
12-month PPI core finished goods inflation (right axis)
Sources: Bureau of Labor Statistics; Institute of Supply Management (ISM).
Note: PPI = Producer Price Index. The dark line is equal to Σ(Si - 50) with i = 0,23, where Si = the ISM supplier deliveries index,
which is equal to 50 if the number of manufacturers that report lengthening delivery times is equal to the number of manufacturers
that report shortening delivery times. Longer lags include more information, and the 24-month changes fit with recent data on the
change in PPI inflation.

legislated as part of the CARES Act, the American Rescue Plan, and related
legislation; and households’ accumulation of “excess savings.”
The impact of supply factors on inflation. As described in the 2022
edition of the Economic Report of the President, the COVID-19 pandemic
introduced challenges to the labor force and constraints on the supply of
goods and services (CEA 2022). In mid-2022, these disruptions finally
began to ease.
As shown in figure 2-10, increases in supply chain pressures were
strongly correlated with rises in goods inflation in 2022. The measure of
supply chain pressures in the figure is derived from an Institute for Supply
Management (ISM) survey, in which supply managers are asked whether
delivery times for their raw materials are shorter, the same, or longer than
the preceding month. Because the resulting ISM measure captures monthly
changes in delivery times, these responses must be cumulated over time to
make an index of the level of delivery times.7 In figure 2-10, the change
The ISM supplier deliveries index is calculated by subtracting the percentage of supply managers
saying that delivery times are longer from the percentage of supply managers saying that delivery
times are shorter, dividing by 2, and adding 50. To construct this index of delivery time levels, 50
is subtracted from the ISM and the index is cumulated over 24 months. The ISM delivery index
indicates only the one-month change in delivery lags, so cumulating more months includes more
information. Cumulating over the preceding 24-month period fits the recent data on the change in
PPI inflation.

7

72 |

Chapter 2

Figure 2-11. Commodity Pressures and PCE Inflation, 2006–22
70

12-month percent change
7

50

5

30

3

10

1

–10

–1

–30

–3

Russian invasion begins
–50
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

–5

12-month percent change

Retail gasoline price index (left axis)
PCE Price Index inflation (right axis)

FAO food price index

Sources: Bureau of Economic Analysis; U.S. Energy Information Administration; Bloomberg Agriculture Spot Index.
Note: FAO = Food Agriculture Organization of the United Nations. Data are displayed on two axes because commodity and
gasoline prices are much more volatile than inflation. The PCE Price Index is seasonally adjusted.

in this measure of delivery times, over an appropriate interval, is plotted
against the change in the core Producer Price Index (PPI) for finished goods.
The PPI measure reflects prices charged by manufacturers. The relatively
high correlation between the change in delivery times and core PPI finished
goods inflation since 1990 suggests that supply chain issues have a significant impact on finished goods inflation.
According to the ISM survey, suppliers’ delivery times started lengthening substantially shortly after the start of the COVID-19 pandemic, and
most supply managers were reporting lengthening delivery times until
September 2022. Delivery times shortened during the final three months of
the year, but were still elevated at the end of 2022. Another measure of supply chain stress, the Global Supply Chain Pressure Index, produced by the
Federal Reserve Bank of New York, also increased notably in 2020–21, but
fell for most of 2022. Collectively, these measures indicate that supply chain
delays stopped getting worse and even began to unsnarl toward the end of
the year. Still, overall inflation remained high, indicating that the drivers of
inflation had broadened, including to the service economy (Powell 2022a).
Figure 2-11 shows that commodity prices, as represented by gas and
food price inflation, started rising in 2021. These commodities are traded on
international markets, and their prices influence inflation globally. Then, in
February 2022, Russia invaded Ukraine. The resulting chaos, both directly
and indirectly, led food prices to quickly jump higher, and gasoline and
natural gas prices soon followed. As commodity suppliers adapted to the

The Year in Review and the Years Ahead

| 73

Figure 2-12. Employment Cost Index and Inflation, 2013–22
12-month percent change
6
5
4
3
2
1
0

2013

2014

2015

2016

2017

PCE Price Index

2018

2019

2020

2021

2022

Employment Cost Index (ECI)

Sources: Bureau of Labor Statistics; Bureau of Economic Analysis; CEA calculations.
Note: The PCE Price Index is seasonally adjusted, but the ECI is not.

disruption caused by the war, commodity prices fell. Since commodities are
a basic input to most production processes—and consumers directly purchase
some commodities such as food, gasoline, and natural gas directly—higher
commodity prices can quickly feed into overall inflation. Russia’s status as
a major oil exporter led to a spike in many energy prices, and the price of
regular gasoline in the United States peaked at $5.02 a gallon in June. But by
the end of the year, the price of regular gasoline had fallen to $3.20 a gallon,
partly due to the Biden-Harris Administration’s decision to draw down the
Strategic Petroleum Reserve, which is further discussed below.
As the economy continued to recover from the recession in 2020 and
consumer demand for goods and services increased, demand for workers to
produce these goods and services also increased. Illustrated by the ratio of
vacancies to unemployment shown in figure 2-4, the demand for workers
relative to their supply has been high during much of the recovery from the
pandemic-related recession. If firms are having difficulty hiring workers,
then the relative price of workers—that is, hourly compensation—should
increase. Figure 2-12 displays the Employment Cost Index, a measure of
hourly compensation that adjusts for changes in the composition of the
workforce, showing that inflation in 2022 was accompanied by rising wages.
But rising wages can be both a cause and a consequence of inflation (Jordà et
al. 2022). The BLS’s measure of real average wages, or wages relative to the
overall price level, declined overall in 2022, falling in the first half of 2022
before rising in the second half. Some parts of the labor income distribution
saw better real wage outcomes than others, with outcomes positive in the
lowest quartile (Federal Reserve Bank of Atlanta, n.d.).
74 |

Chapter 2

Although there were fears during 2022 of a “wage-price spiral”—
where workers expecting increased inflation would demand higher wages,
which would lead to higher realized inflation, and then workers would
demand even higher wages, and so on—those fears lessened toward the end
of 2022, as inflation and wage growth showed broad slowdowns. Notably,
as shown below with the University of Michigan’s survey results (see figure
2-19), consumers’ short-term inflation expectations remained well below
actual inflation throughout the year, and longer-term expectations remained
anchored.
Some have pointed to another factor that may have influenced the reaction of prices and thus inflation to the COVID-19 shock: increased market
concentration in U.S. industries. More U.S. industries have become dominated by a few, large firms over the last 20 years. There is some evidence
that these firms increase prices in response to cost increases more than firms
without market power would have done in the past (Bräuning, Fillat, and
Joaquim 2022). However, the link between market power and pricing when
subject to shocks like the pandemic is not clear (Syverson 2019). Measuring
market power is a difficult task, and measuring the prices firms charge
above the cost of their inputs, their “markup,” isolated from the effects of
the increased demand and constrained supply of 2022, is even more fraught.
The impact of monetary factors on inflation. By controlling short-term
interest rates, and through them, longer-term interest rates, the Federal
Reserve is able to influence when consumers and businesses spend money
versus save money, thereby affecting aggregate demand. In both traditional
Keynesian and New Keynesian aggregate supply-and-demand frameworks
(see box 2-2), higher interest rates lead to decreases in real output and
inflation, all else being equal (Miranda-Agrippino and Ricco 2021). Figure
2-13 shows that the Federal Reserve kept the Federal Funds Rate close
to zero from April 2020 until it began to raise the Federal Funds Rate in
response to rising inflation in March 2022. By the end of 2022, the Federal
Reserve had increased the Federal Funds Rate to a range between 4.25 and
4.50 percent. The rapid increase in the Federal Funds Rate was an attempt
to bring demand into better alignment with supply and cool inflation. It is
important to note that the Federal Funds Rate alone is not enough to judge
the stance of monetary policy. The Federal Funds Rate is a nominal rate, so
its effect on the real economy depends on inflation. The real Federal Funds
Rate is approximated in figure 2-13 by subtracting short-term expectations
of consumer inflation.8
Another perspective on the stance of monetary policy is the real rate
relative to r*, the long-term real rate consistent with the economy growing
at its long-term trend. Though it is hard to estimate, there is evidence that
Exactly which measure of inflation is appropriate to use to deflate the nominal Federal Funds Rate
is outside the scope of this chapter.

8

The Year in Review and the Years Ahead

| 75

Figure 2-13. Nominal and Real Measures of the Policy Rate, 2016–22
Percent

5
4
3
2
1
0
–1
–2
–3
–4
2016

2017

2018

Federal Funds Rate

2019

2020

Real Federal Funds Rate

2021

2022

Real Federal Funds Rate – r*

Sources: Survey of Professional Forecasters (SPF); Federal Reserve System; CEA calculations.
Note: The bright blue line subtracts 1-year-ahead expected inflation from the SPF from the dark blue line. The orange line
subtracts the median estimate of the appropriate long-term Federal Funds Rate from the Federal Reserve quarterly Summary
of Economic Projections (SEP) from the bright blue line.

r* declined during recent decades (Powell 2018). Because of this decline in
r*, and depending on inflation expectations, low Federal Funds Rates may
not be as stimulative as they were in the past (Jordà and Taylor 2019). The
Federal Open Market Committee (FOMC), in its December 2022 “Summary
of Economic Projections” (Federal Reserve 2022a), suggested that long-run
r*, calculated by subtracting the longer-run inflation rate (2.0 percent) from
the longer-run Federal Funds Rate (2.5 percent), was 0.5 percent. The difference between the real Federal Funds Rate and r*, shown by the orange line
in figure 2-13, is a plausible measure of the stance of monetary policy. At
the end of 2022, the stance of monetary policy, as measured by both the real
Federal Funds Rate and the real Federal Funds Rate minus r*, was above 0
percent, indicating a restrictive monetary policy.
An additional factor in judging the stance of monetary policy is the
Federal Reserve’s balance sheet. In 2020, following the playbook used during the 2007–8 financial crisis, the Federal Reserve announced additional
measures to support the economy, including emergency lending and asset
purchase programs, sometimes known as “quantitative easing.” Figure
2-14 shows that assets held by the Federal Reserve—the sum of Treasuries,
mortgage-backed securities, and all others—grew to $8.2 trillion by the end
of 2021—more than double their size before the COVID-19 pandemic.
The increase in the size of the Federal Reserve’s balance sheet contributed to a substantial increase in measures of the money supply. As discussed
in box 2-2, in 2020, a monetarist would have predicted that the substantial

76 |

Chapter 2

Figure 2-14. The Composition of the Federal Reserve’s Balance Sheet, 2007–22
2021 dollars (trillions)

12-month percent change
6

9
8

5
7

4

6
5

3
4

2

3
2

1
1

0

2007

2008

2009

2010

2011

2012

Treasuries (left axis)
All other (left axis)

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022

0
2023

Mortgage-backed securities (left axis)
Core PCE inflation (right axis)

Sources: Bureau of Economic Analysis; Federal Reserve Bank of St. Louis; CEA calculations.
Note: Excludes unamortized premiums and discounts on securities held outright. Nominal dollars were converted to 2021 dollars using the PCE
Price Index. The PCE Price Index is seasonally adjusted.

increase in “money” at a time when real output was shrinking would lead
to inflation. In 2021 and 2022, with some lag, they would have been right.
But 10 years ago, they would have been wrong. When the Federal
Reserve more than quadrupled its balance sheet in the five years after
the 2007–9 financial crisis, inflation did not rise by much, and it quickly
returned to a stable rate below 2 percent. There are important differences:
the 2007–9 recession was longer and deeper; households and firms had
worse balance sheets; the unique, pandemic-related supply-side challenges
were not present; and the fiscal response to the crisis was smaller (Guerrieri
et al. 2021). Nevertheless, the drastically different result in 2007–8 makes
it hard to draw a straight line between the Federal Reserve’s balance sheet
actions in 2020–22 and inflation (Crawley and Gagnon 2022).
The impact of fiscal factors on inflation. Extraordinary monetary
policy in 2020 and 2021 was accompanied by expansive fiscal policy. In
2020, the pandemic prompted an increase of slightly more than 10 percentage points in the Federal Government’s outlays relative to GDP, the largest
such increase since the increase of nearly 20 percentage points when the
United States entered World War II. Much of this increased spending was
distributed in economic impact payments made directly to households.
Support was also provided via large temporary expansions of unemployment benefits and funds offered to small businesses to maintain payrolls and
extend operations.

The Year in Review and the Years Ahead

| 77

Figure 2-15. The Fiscal Impulse and Inflation, 2012–24

4-quarter percent change

Percentage-point contribution to real GDP

6

15
5
10

4
3

5

2
0

1
0

–5

2012

2013

2014

2015

2016

2017

2018

Taxes and benefits programs (left axis)
Federal spending on goods and services (left axis)

2019

2020

2021

2022

2023

2024

State and local spending (left axis)
Core PCE inflation (right axis)

Sources: Hutchins Center at Brookings Institution; Bureau of Economic Analysis.
Note: All values are seasonally adjusted.

Aggregate supply-and-demand frameworks predict that, all else being
equal, increases in government outlays will increase output and inflation.
Estimates of the “fiscal multiplier,” or the ratio of the change in total real
output to an expansionary fiscal policy action, vary considerably, with different estimates suggesting that government spending increases total output
by more, or by less, than the government spending itself (Ramey 2019).
Empirical estimates of the impact of government spending on inflation are
mixed; a recent meta-analysis found that increases in government spending,
offset by tighter monetary policy, often tend to be deflationary rather than
inflationary (Jørgensen and Ravn 2022).
Figure 2-15 plots the Hutchins Center’s Fiscal Impact Measure (FIM),
which uses information on the Federal Government’s spending on goods and
services, State and local government spending on goods and services, and
taxes and benefit programs to approximate the contribution of fiscal policy
to total real GDP growth each quarter (Belz, Sheiner, and Campbell 2022).
A positive fiscal impulse means that the contribution of fiscal policy to real
GDP is larger than it was the quarter before. Figure 2-15 shows that the
FIM spiked in 2020:Q2, mainly due to an expansion of transfer programs,
and was positive for two of the next three quarters, but was a significant
drag throughout 2022 and is projected to remain negative in 2023 and 2024,
using projections for fiscal policy by the Congressional Budget Office in its
current services baseline.
Table 2-2 highlights legislative and executive actions that cannot be
easily characterized as “fiscal policy”—and hence are outside the scope of
78 |

Chapter 2

Table 2-2. Selected Legislative and Executive Actions in 2022
Date

Action

Goal

April to
October

Release of 180 million barrels of crude oil
from the Strategic Petroleum Reserve

Increase the supply of gasoline to lower its
price, and the prices of other goods

May

Additional funding for domestic fertilizer
production and technical assistance in
agriculture, and expansion of eligibility for
double-cropping insurance

Encourage farmers to expand production,
lowering and stabilizing food prices

May

Housing Supply Action Plan

Increase the supply of available homes to
lower housing costs

June

Ocean Shipping Reform Act

Lower shipping costs and improve supply
chains by fostering compeition

July

President Biden announces a series of
actions that incentivize solar adoption and
energy efficiency upgrades

Lower demand for fossil fuels and lower
energy prices

August

IRA promotes clean energy adoption,
authorizes Medicare to negotiate drug
prices, and caps annual out-of-pocket
prescription costs at $2,000

Increase the supply of clean energy to lower
the price; reduce prices and lower markups
in the pharmecutical industry

October

Executive Order on Promoting Competition
in the American Economy

Lower fees and hidden costs and increase
consumer and small business bargaining
power

Note: IRA = Inflation Reduction Act. This table only captures some of the many actions taken in 2022.

the FIM—which by most economic definitions is primarily concerned with
the levels of government revenue and spending and the path of deficits. The
actions can be roughly divided into two categories. First, there are measures
to promote competition in 2022 and in the future, such as the Ocean Shipping
Reform Act, President Biden’s Executive Order on Promoting Competition
in the American Economy, and the Inflation Reduction Act (IRA).9 Second,
there are measures meant to either directly or indirectly expand the supply
of particular goods or services, such as the President’s decision to tap into
the Strategic Petroleum Reserve to reduce gasoline prices, and executive
actions in May intended to help increase agricultural production and add to
the stock of affordable housing. The actions listed in table 2-2 have likely
lowered costs for specific goods or services, many of which are key inputs
to other industries, and increased the future supply of many products. The
long-term impact of these plans should be disinflationary.
Figure 2-16 shows the Federal Government’s historic primary deficits,
or total revenues minus total spending not including interest payments
on outstanding debt, and those deficits projected for the next 10 years by
the Office of Management and Budget (OMB), which uses the economic
Procompetitive IRA measures include provisions that granted Medicare greater bargaining power
in prescription drug cost negotiations with pharmaceutical companies. The IRA’s clean energy
provisions will boost supply in targeted industries in the long term.

9

The Year in Review and the Years Ahead

| 79

Figure 2-16. OMB’s Primary Deficit Forecast, 2017–33
Percentage of annual fiscal year GDP
0
–2
–4
–6
–8
–10
–12
–14

2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033
Actual

Projected (OMB)

Sources: Office of Management and Budget (OMB); CEA calculations.

assumptions from the Administration forecast presented in the next section.
The winding down of spending under the CARES Act, the American Rescue
Plan, and related legislation, combined with higher tax revenue due to the
recovery in GDP, led to a smaller deficit in 2022 as a share of GDP than
in 2020 and 2021, or the 3 years after the 2007–8 financial crisis; but the
deficit was higher than the post–World War II prepandemic average. One of
the intentions of the reforms to the tax code made during the Biden-Harris
Administration—including an increase in the corporate minimum tax, an
increase in the Internal Revenue Service’s funding to help it bring in uncollected taxes and close loopholes, and a new excise tax on stock buybacks—
is to reduce future deficits (Gleckman and Holtzblatt 2022; Congressional
Research Service 2022).
In an op-ed on May 30, 2022, President Biden said that he expected the
reduction in the Federal deficit in 2022 to help ease price pressures (Biden
2022). Some theories suggest that lower deficits (or higher surpluses) over
time can ease inflationary pressures (see box 2-2). Empirical estimates of the
impact of government deficits on inflation do not provide consistent answers
(Catão and Terrones 2005; Banerjee et al. 2022). Nevertheless, the global
coincidence of unprecedented, deficit-funded fiscal actions begun in 2020,
and the highest rate of inflation in 40 years has convinced some economists
that the two are related (Bordo and Levy 2021).
In 2020 and 2021, partially due to pandemic-era fiscal measures, and
pandemic-related constraints on in-person spending, consumer income
exceeded consumer spending by substantially more than it usually does,

80 |

Chapter 2

Figure 2-17. Excess Savings and Inflation, 2016–22

3-month annualized percent change

2021 dollars (trillions)
1.4

6

1.2

5

1.0

4

$2.7 trillion in excess
savings accumulated
from 2020:Q1 to 2021:Q4

0.8

$0.6 trillion in excess
savings drawn down
from 2022:Q1 to
2022:Q4

0.6
0.4

2
1

0.2
0.0

3

2016

2017

2018

Actual savings (left axis)
Core PCE inflation (right axis)

2019

2020

2021

2022

0

Savings at 2010–19 average rate (left axis)

Sources: Bureau of Economic Analysis; CEA calculations.
Note: The average saving rate from 2010 to 2019 was 7.3 percent. Nominal dollars were converted to 2021 dollars
using the PCE Price Index. All values are seasonally adjusted.

leading to a surplus of savings beyond what would have occurred if the
saving rate (i.e., saving as a share of disposable income) had remained at
prepandemic levels. The buildup of excess savings was due to the increased
precautionary savings and pandemic-related constraints on spending that
led consumers to spend less and save more than usual (Bilbiie et al. 2021)
paired with the direct payments and income support program expansions
included in the CARES Act, the American Rescue Plan, and related legislation. Figure 2-17 plots one measure of excess savings; the dark blue line
represents the deviation of actual saving from what it would have been under
the average quarterly saving rate from 2010 to 2019 (7.3 percent); and the
green shaded area between the dark blue line and the light blue line is the
excess savings in the quarter. By the end of 2021, the amount of cumulative
excess savings peaked at about $2.7 trillion, or more than two months of
usual prepandemic consumer spending.
Given the excess savings, households had the potential to spend more
than they normally would without incurring debt, even after the withdrawal
of some fiscal recovery programs. In an aggregate supply-and-demand
framework, if households spend their excess savings, the spending will
increase aggregate demand, exacerbating inflation when supply is constrained (Aladangady et al. 2022). Excess savings, as shown in figure 2-17,
were drawn down by about $0.6 trillion in 2022, and consumer spending
rose, counteracting the aggregate demand effect of the negative fiscal
impulse shown in figure 2-15. If the drawdown of excess savings, together
The Year in Review and the Years Ahead

| 81

with current income, boosted aggregate demand, it could have contributed
to high inflation in 2021 and 2022.
Additional demand factors affecting inflation. The pandemic and
recovery, supported by funds provided by the CARES Act, the American
Rescue Plan, and related legislation, also generated large and unusual shifts
in consumer demand—most importantly, away from in-person services and
toward distancing-friendly goods, and then back again, as shown in panels B
and C of figure 2-18. In April 2021, possibly driven by this unusual spending on goods, inflation in the price of goods over the preceding 12 months,
as measured by the PCE Price Index, was higher than inflation in the price of
services for the first time in nearly a decade, as shown in panels D through F
of figure 2-18. In the second half of 2022, goods inflation settled some, but
the consumer demand rotation back to services caused services inflation to
increase. Correspondingly, the ratio of the consumption of real goods to that
of real services also rose, and then fell back somewhat toward prepandemic
levels, but remained elevated.
Because consumer spending makes up nearly 70 percent of GDP, it is
informative to look at consumer spending on its own, as a measure of where
the economy in 2022 was relative to its trend, as shown in figure 2-2 above.
Figure 2-18, panel B, shows that goods consumption remained above its

Figure 2-18. Consumer Goods-Services Rotation, 2018–22
A. Total spending

B. Spending on goods

C. Spending on services

17.0
16.5
16.0
PCE
15.5
15.0
14.5
Trend
14.0
13.5
13.0
12.5
2018 2019 2020 2021 2022

6.0
5.8
5.5
PCE on
5.3
goods
5.0
4.8
Trend
4.5
4.3
4.0
2018 2019 2020 2021 2022

11.5

2021 dollars in trillions

2021 dollars in trillions

2021 dollars in trillions
11.0

Trend

10.5
10.0
9.5

PCE on
services

9.0
8.5

8.0
2018 2019 2020 2021 2022

D. Overall prices

E. Goods prices

F. Services prices

115

115

115

111

111

Index; 2021 = 100

107

Index; 2021 = 100

PCE Price
Index

107
103

103
99

Trend

95
2018 2019 2020 2021 2022

Goods PCE
Price Index
PCE Price Index

Index; 2021 = 100

111
107
103

PCE Price
Index
Services PCE
Price Index

99

99

95
2018 2019 2020 2021 2022

95
2018 2019 2020 2021 2022

Sources: Bureau of Economic Analysis; CEA calculations.
Note: PCE = Personal Consumption Expenditures. Trend lines were calculated by regressing each respective series on
time from 2015 to 2019. All values are seasonally adjusted.

82 |

Chapter 2

trend through 2022. Services consumption—as shown in figure 2-18, panel
C—recovering from the obstacles to in-person services during the pandemic
and seeing a rapid rise in prices, remained below its trend. Overall, as shown
in panel A of figure 2-18, consumer spending was near its trend. Business
fixed investment, as broken out in figure 2-3—which is necessary to add to
domestic productive capacity—did not see the same rapid increase as consumption. This disconnect between above trend goods consumption and the
lack of increased production, whether due to supply constraints on production or slow investment, means that domestic supply was not able to provide
the level of goods and services demanded. As supply chain disruptions made
it challenging to address this imbalance through increased imports, inflation
rose as goods prices increased (Guerrieri et al. 2021).
The impact of inflation expectations. Expectations play an important
role in the major frameworks that economists use to analyze inflation, as
described in box 2-2. Some economists think that higher expectations of
future inflation can be self-fulfilling, making efforts to fight inflation more
difficult or painful. If businesses, consumers, and financial market participants expect inflation to be high, they will behave in ways consistent with
this expectation and that may bring about actual higher inflation. For example, workers with high inflation expectations may demand higher wages,
and businesses with high inflation expectations may price goods higher. The
back and forth between these effects can lead to further increases in inflation. In 2022, long-term inflation expectations stayed near their historical
levels, and short-term expectations moved with actual inflation, pointing

Figure 2-19. Actual and Expected Inflation, 2012–22
12-month percent change
10
9
7
6
5
4
3
1
0
-1

2012

2013

2014

2015

1-year expected inflation

2016

2017

2018

2019

5–10 year expected inflation

2020

2021

2022

12-month headline CPI inflation

Sources: University of Michigan; Bureau of Economic Analysis; CEA calculations.

The Year in Review and the Years Ahead

| 83

to inflation expectations that were dependent on actual inflation rather than
being driven independently in a way that could lead to further inflation.
When inflation began to rise in 2021, long-term inflation expectations
had been steady for decades, and even as inflation started to climb, these
expectations remained low. Figure 2-19 plots two of the most commonly
tracked measures of inflation expectations: the median expected annual
price change over the next 12 months, from the University of Michigan’s
monthly survey of households; and the median expected average annual
price change over the next five to 10 years, from the same survey. Although
both measures increased during 2022, they did not increase by nearly as
much as realized inflation. Long-term inflation expectations (5–10 year
expected inflation, the light blue line) in particular were reassuringly stable,
indicating that although elevated inflation was expected in the short run, it
was not expected to last. As discussed in box 2-2, this stability was taken as
evidence that inflation expectations were anchored. Still, toward the end of
2022, some economists worried that the modest increases in long-run inflation expectations, and the possibility of sustained increases in expectations,
would make it harder to bring inflation down (Powell 2022b).

The Forecast for the Years Ahead
The Biden-Harris Administration finalized the latest version of its official
economic forecast on November 28, 2022. This forecast provides the
Administration’s estimated projections of key economic variables over the
next 11 years, from 2023 to 2033, and also includes its forecast for 2022.
During the interval between when this forecast was finalized and the publication of this Report, more 2022 data have become available, so that the
official forecast discussed in this chapter differs from those published more
recently.
This overall forecast is a critical input to the President’s Fiscal Year
2024 Budget, because it is an input into the budget projections of many
Federal agencies, and to projections of tax revenues. The forecast development also provides insight into what challenges lie ahead and where the
economy might need additional investment and support.
COVID-19 continues to generate forecasting uncertainty. Although
U.S. COVID-19 fatalities surged to 1,700 a day in 2022:Q1 due to the new
Omicron variant, they declined to 500 per day in April and then the fourweek moving average fluctuated in the range of 300 to 500 per day for the
rest of the year—held down by vaccinations, increasing immunity, and new
treatments. Further COVID-19 declines or future surges pose upside and
downside risks for the forecast. The potential for future supply chain disruptions due to COVID-19 surges abroad or wartime disruptions provide further
risks; the Russian invasion of Ukraine is another source of uncertainty.
84 |

Chapter 2

Table 2-3. Economic Projections, 2021–33
Percent Change (Q4 to Q4)
Inflation Measures

Level (percent)
Unemployment Rate

Interest Rates

Real
GDP

GDP Price
Index

CPI

Annual

Q4

3-Month
T-Bills

10-Year
T-Notes

Actual
2021
2022

5.7
0.9

6.1
6.4

6.7
7.1

5.4
3.6

4.2
3.6

0.0
2.0

1.4
3.0

Forecast
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032
2033

0.2
0.4
2.1
2.4
2.0
2.0
2.0
2.1
2.2
2.2
2.2
2.2

6.6
2.8
2.1
2.1
2.1
2.1
2.1
2.1
2.1
2.1
2.1
2.1

7.6
3.0
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3

3.7
4.3
4.6
4.4
4.3
4.2
4.1
4.0
3.9
3.8
3.8
3.8

3.8
4.6
4.5
4.4
4.3
4.2
4.1
4.0
3.8
3.8
3.8
3.8

2.0
4.9
3.8
3.0
2.5
2.3
2.2
2.3
2.4
2.4
2.5
2.5

3.0
3.8
3.6
3.5
3.4
3.4
3.4
3.4
3.4
3.4
3.4
3.4

Year

Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; Department of the Treasury; Office of Management
and Budget; CEA calculations.
Note: These forecasts are based on data available as of November 28, 2022; actual data for 2022 arrived later. The interest
rate on 3-month (91-day) Treasury Bills is measured on a secondary-market discount basis.

Averaging these risks, the Administration presents a central forecast; table
2-3 summarizes its key aspects.

The Near Term
For this Report’s near-term forecast, two questions were paramount. First,
does real GDP currently exceed its short- or long-run potential level? And
second, how soon will inflation return to the Federal Reserve’s 2 percent
target, and how will this return influence output and employment?
The Administration forecast largely followed the consensus of Blue
Chip forecasters by revising its GDP forecast downward. Over the six months
between March and October 2022, the Blue Chip consensus economic forecast was revised to show substantially lower real GDP growth and higher
inflation during the two years 2022 and 2023 (see table 2-4). This combination of revisions suggests that the consensus—implicitly—recognized that
demand had exceeded available supply during 2022; the consensus panel did
not make any offsetting upward revisions during the subsequent two years.
The lack of a bounce-back in the consensus forecast for real GDP growth in

The Year in Review and the Years Ahead

| 85

Table 2-4. Evolution of the Blue Chip Consensus Real GDP Forecast
2022

Percent Growth, Annual Average to Annual Average
2023
2024
2025

2026

Real GDP
March 2022
October 2022
Revision

3.5
1.6
–1.9

2.5
0.2
–2.3

2.1
1.5
–0.6

2.0
2.1
0.1

2.0
2.1
0.1

CPI
March 2022
October 2022
Revision

6.2
8.0
1.8

2.6
3.9
1.3

2.3
2.4
0.1

2.2
2.2
0.0

2.2
2.2
0.0

Source: Blue Chip Economic Indicators.
Note: The Blue Chip panel revises its long-term forecast in March and October, with growth rates that are annual average to
annual average.

2024 and 2025 may reflect that the constraints on supply during 2022 partly
reflected long-term factors. Between October and December 2022, inflation
in 2022 came in lower and real GDP growth during 2022 came in stronger
than the Administration had predicted as of November. In light of the new
data available since the forecast was finalized, a forecast assembled today
would differ from that finalized in November.
The forecast given in table 2-3 predicted slow (0.4 percent) real GDP
growth for the four quarters of 2023 because GDP growth may need to be
less than trend growth to alleviate the current tight labor market. The Blue
Chip consensus panel also predicted that 2023 real GDP growth would be
slow over the four quarters of the year.10
The second question, how soon will inflation return to levels consistent
with the Federal Reserve’s target, depends on the success of monetary and
fiscal policy, and the legislative and executive actions discussed above. As
a consequence of the FOMC’s decision to raise the target Federal Funds
Rate from close to 0 percent in February 2022 to between 4.25 and 4.50
percent in December, other short-term rates also increased, including the
yield on 91-day Treasury Bills, which rose 4.2 percentage points during the
12 months of the year to 4.3 percent by the end of the year. Though nominal
interest rates on long-term securities also rose, they did not increase by as
much as short-term rates, perhaps reflecting market confidence that inflation
will recede over the next 10 years. As of November 2022, the Administration
predicted that interest rates would continue to increase during 2023, but
would then begin to decline in 2024. The Administration further predicted
that inflation would fall quickly in 2023 from its 2022 pace as supply chains
unsnarled, and would return to rates consistent with the Federal Reserve’s
In October, the Blue Chip panel predicted that Q4-to-Q4 real GDP growth would be 0.4 percent,
which was lowered to –0.1 percent in the December survey.
10

86 |

Chapter 2

long-term targets by 2024 (see, e.g., the FOMC’s December 14, 2022, statement: Federal Reserve 2022b).
Consistent with slow GDP growth, in November 2022 the
Administration expected the unemployment rate would edge up in 2023,
averaging 4.3 percent but peaking at 4.6 percent in 2023:Q4. The combination of this rising unemployment, slow GDP growth, a falling vacancy rate,
the effects of expected fiscal policies and executive actions, and continued
confidence in the Federal Reserve’s commitment to its 2 percent target rate
was expected to lower the rate of CPI inflation to 3.0 percent during 2023,
and to 2.3 percent during 2024. As mentioned in box 2-1 above, CPI inflation tends to outpace the PCE Price Index; hence, a 2.3 percent CPI inflation
rate is consistent with the Federal Reserve’s target of a 2 percent PCE Price
Index inflation rate. Another measure of inflation, the price index for GDP,
was expected to fall from a forecasted 6.6 percent rate during 2022 to 2.1
percent during 2024.
Post–World War II history suggests that bringing down inflation, via
monetary policy or otherwise, will likely lower employment growth and output growth. Recognizing this relationship, in November the Administration
expected that unemployment would increase during the four quarters of
2023, before starting to decline in 2024. From its expected 4.6 percent
peak in 2023:Q4, the unemployment rate was expected to edge lower to 4.5
percent by the end of 2024, eventually falling—in 2030—to the long-term
rate of 3.8 percent that the Administration considers to be consistent with
stable inflation.
The Administration’s near-term forecasts for real GDP growth in
2023–24, near-term inflation, the unemployment rate, and interest rates were
roughly consistent with the forecast of the Blue Chip Economic Indicators
(the consensus), and that of the FOMC as of November 2022.11

The Long Term
In contrast to the near-term outlook, the Administration’s long-term forecast
for real GDP growth exceeded the October 2022 Blue Chip consensus longterm forecast by an average of 0.2 percentage point a year during the nine
years 2025–33. The Administration believed that potential real GDP growth
in the long run would be modestly higher because of the expected effect of
the President’s proposed economic policies, assuming that they are enacted,
including a range of programs to enhance human capital formation, provide
childcare, and reform immigration policy. In addition, the Administration
recognized that the downward pressure on labor force participation from the
The Congressional Budget Office’s forecast is absent from this list because its latest 2022 forecast
(during the interval that the Administration forecast was in play) was finalized on March 2, 2022,
before the release of much data on GDP and inflation, and was therefore out of date.
11

The Year in Review and the Years Ahead

| 87

Table 2-5. Supply-Side Components of Forecasted Real Output Growth
Percentage-Point Contribution to Annual Real Output Growth
1953:Q2–
2019:Q4

1990:Q3–
2001:Q1

2001:Q1–
2007:Q4

2007:Q4–
2019:Q4

2019:Q4–
2033:Q4

(1)

(2)

(3)

(4)

(5)

1 Population

1.4

1.2

1.1

1.0

0.7

2 Labor force participation rate

0.1

0.1

–0.3

–0.4

–0.2

Component

3 Employed share of labor force

0.0

0.1

0.1

0.1

0.0

4 Average weekly hours

–0.2

–0.1

–0.2

–0.1

0.0

5 Output per hour

2.0

2.4

2.4

1.4

1.6

6 Output per worker differential

–0.3

–0.3

–0.6

–0.4

–0.2

7 Sum: Real GDO

3.0

3.5

2.4

1.7

1.9

Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; Department of the Treasury; Office of Management and Budget; CEA
calculations.
Note: These forecasts are based on data available as of November 28, 2022. Total may not add up due to rounding. 1953:Q2, 1990:Q3,
2001:Q1, 2007:Q4, and 2019:Q4 are all quarterly business-cycle peaks. Population, labor force, and household employment have been
adjusted for discontinuities in the population series. Detailed row defintions: (1) civilian noninstutional population, 16 + (4) nonfarm
business average weekly hours (5) nonfarm business output per hour; output is measured as the average of income- and product-side
measures (6) difference between output-per-worker growth in the economy as a whole and in the nonfarm business sector (7) gross
domestic output (GDO) is the average of GDP and gross domestic income (GDI).

retirement of baby boom cohorts is likely to wane during the last five years
of the budget window (2028–33), as discussed in box 2-3.
Although the circumstances surrounding this year’s near-term forecast
were unique to 2022, the key issues affecting the long-term forecast are less
tied to recent events. These issues can be described most clearly in terms of
the supply-side components of GDP, which, although erratic in the short run,
have more understandable long-term movements.
The first set of key issues has to do with the long-term labor supply.
As discussed in chapter 6 of this Report, the U.S. population is aging. The
first row of table 2-5 shows that the Administration’s forecast expected that
the civilian, noninstitutional population age 16 years and above would grow
by an average of 0.7 percent at an annual rate from 2019 to 2033, below
the average 1.0 percent annual growth rate from 2007 to 2019.12 Much of
this expected growth will likely come from immigration.13 The labor force
participation rate was projected to continue its decline, reflecting the aging
of the baby boom cohorts into retirement. This downward pressure on the
The civilian, noninstitutional population excludes individuals who are incarcerated or are living
in mental health facilities or homes for seniors, or who are on active duty in the Armed Forces.
Projected growth rates come from demographers at the Social Security Administration. Table 2-5
shows projected growth rates for the 15 years since the business cycle peak in 2019:Q4. The choice
of this long period to discuss these supply-side components is because many of these components
move sharply for business-cycle reasons (workweek and productivity), and others have large erratic
components in the short run (labor force participation rate and the productivity differential).
13
Also see Social Security Administration (2022a).
12

88 |

Chapter 2

Box 2-3. Aging and Growth
The United States, like most advanced countries, is going through a
demographic transition, and this will have a large impact on a variety of
economic variables for years to come. In figure 2-ii, the blue line plots
the age distribution of the United States in 2011, the bars show the current age distribution, and the orange line plots the expected age distribution in 2033. Although the U.S. population is still growing, the center of
mass of the age distribution is shifting to the right—that is, to older ages.
Of particular note is the baby boom cohort, whose members were
between 58 and 76 years of age in 2022. Most baby boomers are now
Figure 2-ii. The Evolution of the U.S. Population’s Age Composition
Millions of people
5.0

Baby boom cohort

4.5
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
0.0

0

5

10

15

20

25

30

35

40

45

50

55

60

65

70

75

80

85

90

95

Years of age
2022

2011

2033

Source: Social Security Administration (2022b).
Note: The U.S. Social Security population differs slightly from the U.S. civilian noninstitutional population.

Figure 2-iii. Age–Labor Force Participation Rate Profiles in 2019
Percent
100
90
80
70
60
50
40
30
20
10
0

16

20

24

28

32

36

40

44

Male Age–LFPR profile

48

52

56

60

64

68

72

76

80

Female Age–LFPR profile

Sources: Bureau of Economic Analysis; CEA calculations.
Note: LFPR = labor force participation rate.

The Year in Review and the Years Ahead

| 89

at or above the age of retirement. As they age, the baby boomers will
continue to push out the right tail of the distribution.
Most people retire when they are between the ages of 62, the
earliest age of eligibility under Social Security, and 70, as can be seen
from the sharp decline in participation for those ages shown in the age–
participation rate profiles given in figure 2-iii. Using the Social Security
Administration’s projections for the age distribution through 2033,
together with these age–participation profiles, overall participation is
projected to drop about 0.4 percent (or about 0.2 percentage point) a year
for the next five years. But during the last five years of the forecasted
interval, this downward pressure on the overall labor force participation
rate will be reduced to about 0.2 percent a year, because most of the baby
boom cohort will have already retired. Using the identity shown in table
2-5, the less negative growth in the participation growth rate is expected
to have a positive impact on GDP growth.

participation rate was projected to wane after 2028, however, as discussed in
box 2-3. The workweek (row 4 of table 2-5) was projected to stabilize after
a long historical period of decline attributable to the entry of women, who,
on average, have shorter workweeks than men, and to the declining share of
manufacturing in total employment.
In the Administration’s forecast, the employed share of the labor
force was projected to remain close to its level at the 2019 business-cycle
peak, and therefore made no net contribution over the forecast interval.
Productivity growth (measured as output per hour) was projected to grow
1.6 percent a year over the 15-year interval, somewhat more slowly than its
2.0 percent long-term average but faster than the 1.4 percent growth rate
during the 2007–19 business cycle. Finally, the output per worker differential, which is the difference between the output per person for the economy
as a whole and the output per person in the nonfarm business sector, was
expected to be negative, because of the national income accounting convention that productivity does not grow in the government or household sectors.
Because productivity growth is assumed to be zero for these sectors of the
economy, while productivity growth was forecasted to be positive in the
nonfarm business sector, the differential was necessarily negative. That said,
this differential was projected to be less negative than the historical average
because of the projected declining share of government in total output.
The long-term forecast of the inflation rate was based on the assumption that the Federal Reserve will succeed in hitting its target of 2 percent for
inflation, as measured by the PCE Price Index. Forecasts for future interest
rates were informed by the FOMC’s near-term forecast of the Federal Funds
90 |

Chapter 2

Rate. Projections for the yield on 10-year Treasury Notes lie between the
Blue Chip consensus forecast and the implicit forecast provided by forward
rates derived from the market prices of U.S. Treasury securities.

Conclusion
The forces that have buffeted the U.S. economy since the beginning of the
COVID-19 pandemic only began to calm in 2022. The United States found
itself in an enviable position among advanced economies, with substantial
growth during 2021 and positive growth in 2022, a low unemployment rate,
and lower inflation than some other countries. Moreover, inflation pressures
abated from their mid-year highs by the end of 2022, both in terms of headline and, more importantly for the future, core inflation. The U.S. economy
has, by some economic measures, such as the record low unemployment rate
and the return of output to—or even above—the trend, fully recovered from
the COVID-19-induced recession.
As discussed in this chapter, the rise in inflation during this period
appears to have been driven partly by the intersection of constrained supply
and strong demand. These dynamics reflected the effects of the pandemic on
consumer demand and supply chains, along with the strong fiscal and monetary support that was necessary to offset the unique and powerful negative
shock caused by COVID-19. Though these fiscal and monetary interventions contributed to the strong demand that played a role in the ensuing inflationary pressures, they also set the stage for the historically strong 2021–22
labor market and supported smoothly functioning financial markets. At the
same time, these interventions helped avoid the deep and lasting hardships
that otherwise would likely have beset millions of American households. In
this uncertain environment, as President Biden said at the time, the risk of
doing too little exceeded the risk of doing too much (White House 2021).
Overall, the recovery from the pandemic-induced recession progressed
far enough in 2022 that the U.S. economy is well situated to weather the
anticipated below-trend growth over the near term. The speed and strength
of the pandemic recovery testifies to the power of fiscal and monetary policy
to fight even the largest negative shocks. The government is united in working toward sustainable growth, low inflation, and inclusive prosperity.

The Year in Review and the Years Ahead

| 91

Chapter 3

Confronting New Global Challenges
with Strong International
Economic Partnerships
In 2022, the global economy continued to face challenges as the economic
shocks associated with the COVID-19 pandemic persisted into their third
year. In addition, Russia’s unprovoked invasion of Ukraine disrupted global
commodity markets and caused businesses and governments to reevaluate
key trade and investment linkages. Nevertheless, in the United States, persistently strong global economic ties contributed to the continuing recovery
of manufacturing output, strong consumption, deepening business investment (BEA 2023a), and resilience to shocks. They also provided strategic
room to counter geopolitical aggression.
The global economic shocks of the past three years have highlighted the
need for policies that balance the benefits of these economic ties with the
risks to economic and national security that they can entail. The policy
response to external challenges, along with the pursuit of greener and more
inclusive economic growth at home, will transform the international economic linkages that manifest through global markets for goods, services, and
data. Strong partnerships between governments are essential to effectively
address these challenges.
This chapter begins by describing how the global economic events of 2022
were reflected in the United States’ robust international trade and investment flows. It then examines how ongoing COVID-19 disruptions, more
recent geopolitical tensions, and the expansion of the digital economy have
affected global economic policymaking priorities. It closes by underscoring

93

the critical role of international partnerships between the United States
and its allies and partners in ensuring the effectiveness of their collective
response to these shared challenges.

The United States’ International
Trade and Investment in 2022
As the headline-grabbing supply chain challenges associated with the persistence of COVID-19 retreated, and despite Russia’s invasion of Ukraine,
U.S. international trade and investment reached record highs in 2022.
Trade in goods and services (exports plus imports) increased by 8 percent
compared with 2021 in real, inflation-adjusted terms, surpassing the record
set in 2019 (figure 3-1) and reflecting robust imports and exports of goods,
despite headwinds from slowing global growth and the strong U.S. dollar
(BEA 2023a).
Record imports were driven by a surge in the first quarter of 2022,
which retreated in the second half of the year. Although they declined
from their first-quarter high, they remained strong in historical terms. In
contrast, exports increased relatively steadily to the third quarter, with a
shallow fourth-quarter decline. These distinct paths are reflected in the
sharp increase and subsequent narrowing of the trade deficit (exports minus
imports) in 2022 (figure 3-2). The trade deficit shot to 4.5 percent of gross
domestic product (GDP) in the first quarter of 2022—the largest since the
Figure 3-1. Real U.S. Trade in Goods and Services, 2012–22

Trillions of chained 2021 dollars, quarterly, seasonally adjusted at annual rates
4
3
2
1
0
–1

–2
2012:Q1 2013:Q1 2014:Q1 2015:Q1 2016:Q1 2017:Q1 2018:Q1 2019:Q1 2020:Q1 2021:Q1 2022:Q1
Balance

Exports

Goods exports

Sources: Bureau of Economic Analysis; CEA calculations.

94 |

Chapter 3

Imports

Goods imports

Figure 3-2. U.S. Trade Balance, 2018–22
Percentage of GDP, quarterly
2
1
0
–1
–2
–3
–4
–5
–6

2018:Q1

2018:Q3

2019:Q1

2019:Q3

2020:Q1
Goods

2020:Q3

2021:Q1

Services

2021:Q3

2022:Q1

2022:Q3

Total

Source: Bureau of Economic Analysis.

Figure 3-3. Real U.S. Services Trade, 2012–22
Billions of chained 2021 dollars, quarterly, seasonally adjusted at annual rates
1,800
1,600
1,400
1,200
1,000
800
600
400
200
0
2012:Q1 2013:Q1 2014:Q1 2015:Q1 2016:Q1 2017:Q1 2018:Q1 2019:Q1 2020:Q1 2021:Q1 2022:Q1
Other services exports
Travel and transportation exports

Other services imports
Travel and transportation imports

Sources: Bureau of Economic Analysis; CEA calculations.

third quarter of 2008. The deficit then declined as imports fell from their
peak, reaching 3.2 percent of GDP in the fourth quarter.
Over the past 20 years, the U.S. goods trade deficit has been partially
offset by a surplus in services trade. That is, U.S. exports of services have
consistently exceeded imports of services. However, services surpluses have
been depressed since the abrupt halt in international movements at the onset
of the COVID-19 pandemic as exports of travel and transportation services

Confronting New Global Challenges with Strong International Economic Partnerships | 95

have recovered more slowly than imports.1 In 2022, real travel and transportation services exports had only reached 67 percent of their 2019 level,
whereas imports were at 89 percent (figure 3-3).
In 2022, stronger growth in travel services imports (spending by U.S.
travelers abroad) compared with exports (spending by foreign visitors to the
United States) was likely driven in part by the dollar’s strength (box 3-1).
For transportation services, the differences in recovery paths were compositional: U.S. transportation services exports are typically dominated by passenger air services, so fewer foreign visitors due to COVID-19 suppressed
these exports. While the plurality of U.S. transportation imports are also
typically passenger air services, a large share are maritime freight services.
Since most shipping companies are foreign-owned, record goods imports
pushed these services imports higher (BEA 2023b).
In official U.S. data on services trade, this category is named “transport” rather than
“transportation.”

1

Box 3-1. Effects of the Strengthening
U.S. Dollar on the U.S. Economy
In 2022, the U.S. dollar strengthened against the currencies of its main
trading partners, particularly other advanced economies. The Federal
Reserve’s broad, real exchange rate index increased by 10.7 percent
between January 2022 and its peak in October 2022, falling back at the
end of 2022 to realize a 5.4 percent year-over-year increase in December
2022 (figure 3-i). The dollar’s rise was driven by strong U.S. growth and
rising interest rate differentials, as well as by the appeal of U.S. assets
as safe haven investments as Russia’s invasion of Ukraine stoked global
uncertainty. The weakening of the dollar at the end of the year reflects
the Federal Reserve’s signal that the pace of rate hikes would slow
and signs of relatively strong economic conditions in other advanced
economies.
Dollar exchange rates have an important influence on trade patterns because they determine the price of U.S. goods and services in the
national currencies of the Nation’s trading partners. When the dollar is
strong, it takes more foreign currency to purchase dollar-denominated
goods and services. At the same time, it reduces the dollar cost that U.S.
buyers pay for imported goods and services denominated in foreign
currency, effectively making them cheaper. All else being equal, these
changes in relative prices encourage U.S. buyers to substitute away from
goods and services produced in the United States and toward foreignproduced goods and services (i.e., imports), deepening the U.S. trade
deficit.

96 |

Chapter 3

Figure 3-i. Federal Reserve Board’s Real Broad Dollar Index, 2016–22

Index: 2021 = 100, through December 2022
120
115
Strengthening U.S. dollar
110
105
100
95

90
Jan-2016 Sep-2016 May-2017 Jan-2018 Sep-2018 May-2019 Jan-2020 Sep-2020 May-2021 Jan-2022 Sep-2022
Broad

Advanced foreign economies

Emerging market economies

Sources: Federal Reserve Board; CEA calculations.
Note: Advanced foreign economies include Australia, Canada, the euro area, Japan, Sweden, Switerland, and the United Kingdom.
Emerging market economies include Argentina, Brazil, Chile, China, Colombia, Hong Kong, India, Indonesia, Israel, South Korea,
Malaysia, Mexico, the Philippines, Russia, Saudi Arabia, Singapore, Taiwan, Thailand, and Vietnam.

In 2022, the dollar’s strength was only one of many strong currents shaping trade patterns. As such, it is difficult to distinguish its
effects from other forces. However, as an example, it is likely that the
strength of the dollar contributed to the comparatively stronger rebound
in imports relative to exports of travel services, as depicted in figure 3-3.
This is because when the dollar is strong, as explained above, it has more
value in foreign currency terms, making travel budgets go further and
thus incentivizing increased spending on hotels, restaurants, and other
goods and services by Americans abroad. The opposite effect makes
travel in the United States more expensive for foreign visitors.
The strong dollar also likely dampened U.S. exports of agricultural
commodities like soybeans, cotton, and corn in 2022 (Jiang et al. 2022).
Indeed, exports in the Bureau of Economic Analysis’ (BEA’s) broad
end-use category of food, feed, and beverages, which includes these
agricultural products, fell to its lowest level in real terms since 2015,
another period of the dollar’s strengthening. (The BEA classifies traded
goods in six broad end-use categories: consumer goods; foods, feed, and
beverages; industrial supplies and materials; capital goods; automotive
vehicles, etc.; and other goods.)
Because agricultural commodities tend to have relatively few
intrinsic differences across countries of origin, it is particularly attractive
for buyers to substitute away from U.S. varieties when a strong dollar
increases their relative prices. Indeed, research suggests that exchange
rates are a particularly relevant factor for buyers of less-differentiated
commodities, and U.S. agricultural exports tend to decline in periods of
real dollar strength (Cooke et al. 2016; Mattoo et al. 2017).

Confronting New Global Challenges with Strong International Economic Partnerships | 97

The strong real exports of manufactured goods in 2022 seemingly
conflict with the deterioration of U.S. currency competitiveness. (These
exports are defined as goods exports under the North American Industry
Classification System, chapters 31–33; U.S. Census Bureau 2023b; BLS
2023.) However, this may be explained in part by two offsetting forces.
First, the dollar’s strength lowers the dollar costs of imported inputs
and capital equipment priced in foreign currencies, thus increasing the
cost-competitiveness of U.S. manufacturers that rely on these imports
(Goldberg and Crockett 1998). Second, in 2022 U.S. manufacturers’
loss of currency competitiveness was likely offset by a deterioration of
cost-competitiveness in other countries that were more exposed to rising
input costs from energy price hikes.
A strong dollar can also lower the dollar price of imported consumer goods, dampening inflationary pressures. In practice, however,
the dollar’s impact on movements in U.S. consumer price inflation
has historically been limited, due to the relatively low pass-through of
exchange rate movements to U.S. import prices (Gopinath and Itskhoki
2021; Gopinath, Itskhoki, and Rigobon 2010). Moreover, imported
goods constitute a relatively small share of the basket of goods used to
calculate common measures of inflation—representing only 12.6 percent
of the Consumer Price Index by one estimate (Borusyak and Jaravel
2021)—so declines in prices of imported goods are unlikely to have a
substantial impact on measured inflation in a given period.

Pandemic-Related and Macroeconomic Trends Have Shaped Record
Goods Imports
Strong demand growth and the unwinding of the pandemic-era supply chain
pressures that mounted throughout 2021 underpinned the dramatic increase
in goods imports in the first quarter of 2022 (for the top U.S. import partners,
see box 3-2). Along with the strengthening dollar, these forces sustained
elevated imports through the rest of the year. To illustrate how this pattern
unfolded in record imports in the broad end-use category of consumer
goods, figure 3-4 splits this category in two. The household goods series
depicts trends in real imports of goods most closely associated with household consumption, such as apparel and footwear, cellphones, furniture and
household appliances. The other consumption goods series reflects trends in
real imports of goods like pharmaceuticals, artwork, and gem diamonds that
are less associated with everyday household expenditures.2
The CEA is grateful to the International Trade Programs team in the Economic Indicators Division
of the U.S. Census Bureau for suggesting this division.

2

98 |

Chapter 3

Box 3-2. The United States’ Top
Goods Trading Partners
Although research suggests that the product composition of goods trade
has shifted in recent years, the United States’ top trading partners have
largely remained the same (Bown 2022a). The top U.S. export destinations and import sources are still China and the European Union—the
two largest economies outside the United States—as well as the United
States’ North American neighbors, Mexico and Canada. Together, these
four economies are responsible for over half of U.S. trade (figures 3-ii
and 3-iii).

Figure 3-ii. Top Sources of U.S. Goods Imports, 2022

Share of nominal imports

European Union,
17.0%
Rest of the
world,
38.8%
China,
16.7%

Mexico,
14.0%

Canada,
13.5%
Source: U.S. Census Bureau.

Figure 3-iii. Top U.S. Goods Export Destinations, 2022

Share of nominal exports

European Union,
16.9%

Rest of the
world,
42.7%

China,
7.4%

Mexico,
15.7%

Canada,
17.2%
Source: U.S. Census Bureau.

Confronting New Global Challenges with Strong International Economic Partnerships | 99

Figure 3-4. Real Imports of Consumer Goods, 2018–22

Billions of 2021 dollars
90
80
70
60
50
40
30
20
10
0
Jan-2018

Jul-2018 Jan-2019 Jul-2019
Household goods

Jan-2020 Jul-2020 Jan-2021
Other consumption goods

Jul-2021 Jan-2022 Jul-2022
All consumer goods

Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations.
Note: Consumer goods exclude automobiles and parts. Household goods include apparel, footwear, and other household goods;
furniture and other household goods; household appliances; cell phones and other household goods; and toys, games, and sporting
goods. Real series have been adjusted with the Bureau of Labor Statistics' import price indices.

Figure 3-4 reveals that the first-quarter import surge was largely driven
by household goods, reflecting the pandemic-induced shift in consumption expenditures to goods and away from services (see chapter 2 of this
Report). This shift disproportionately increased import demand throughout
this period, in part simply because goods are more import-intensive than
services. Compounding this, the persistence of remote work and diminished
leisure spending outside the home increased demand for goods like computers and home improvement products that are particularly import-intensive
in the United States (Chetty et al. 2022; Higgins and Klitgaard 2021; IMF
2022a).
In the first quarter, easing of port congestion—in addition to high
inventory investment by businesses responding to global market uncertainty
after months of COVID-19-related supply chain snarls and the impending
threat of Russia’s invasion of Ukraine—further boosted imports. Imports
of household goods decreased from their first-quarter peak as consumption
expenditures began to shift back to services, supply chain backlogs were
cleared, and inventory rebuilding continued (see chapter 2). However,
they remained well-above prepandemic levels throughout the first half of
the year. In the second half of the year, household goods imports declined
even more significantly as rising interest rates began to dampen consumer
demand.
Real imports of capital goods also set a record in 2022, exceeding the
previous record set in 2021 by 10 percent. Together with robust imports of
industrial supplies and materials—fuels, metals, and other key industrial
100 |

Chapter 3

Figure 3-5. Real Imports of Capital Goods (Excluding Automobiles), 2018–22
Billions of chained 2021 dollars
75

70
65
60
55
50
45
Jan-2018

Jul-2018

Jan-2019

Jul-2019

Jan-2020

Jul-2020

Jan-2021

Jul-2021

Jan-2022

Jul-2022

Sources: Census Bureau; CEA calculations.

inputs—these imports supported a strong rebound in domestic output in
2022 (see chapter 2). Like household goods, capital goods imports surged
in the first quarter with relief from pandemic-era port congestion (figure
3-5). Unlike household goods, capital goods imports remained substantially
above prepandemic levels as imports of various types of electrical equipment, industrial machinery, transportation equipment, and information and
communications technology equipment—including semiconductors—benefited from a combination of easing supply constraints and strong business
demand.

Geopolitical Shocks and Global Demand Have Shaped Record Goods
Exports
Real exports of goods surpassed their prepandemic heights of 2019 by 2.6
percent in 2022 (see box 3-2 for the top U.S. export partners). Increased
demand for U.S. energy exports was a key driver, as many countries—particularly in Europe—looked to replace Russia as a source of crude oil and
natural gas supplies. U.S. exports in the broad end-use category of industrial
supplies and materials—which includes energy goods—hit a record high
in 2022, as did exports of consumer goods. In contrast to consumer goods
imports depicted in figure 3-4, the increase in real consumer goods exports
was driven by pharmaceutical goods (figure 3-6).
Shocks from Russia’s invasion of Ukraine had a significant impact on
global commodity markets in 2022 that echoed in U.S. exports. In contrast to
other traded goods, commodities like oil—as well as many metals, minerals,
and agricultural products are relatively standardized across source countries,

Confronting New Global Challenges with Strong International Economic Partnerships | 101

Figure 3-6. Real Exports of Consumer Goods, 2018–22
Billions of 2021 dollars
25

20

15

10

5

0
Jan-2018

Jul-2018

Jan-2019

Household goods

Jul-2019

Jan-2020

Jul-2020

Other consumption goods

Jan-2021

Jul-2021

Pharmaceutical goods

Jan-2022

Jul-2022

All consumer goods

Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations.
Note: Consumer goods exclude automobiles and parts. Household goods include apparel, footwear, and household goods; furniture and
household goods; household appliances; cell phones and other household goods; and toys, games, and sporting goods. Real series adjusted with
BLS import price indices.

allowing buyers to substitute across source countries fairly easily. Because
of this, their price in any given country is largely determined by global
market movements. As such, although Russia and Ukraine are relatively
small trading partners for the United States—representing only 0.5 percent
of U.S. exports and 1.1 percent of U.S. imports in 2021—because they are
major producers and exporters of key commodities, disruptions of their
exports influence the prices U.S. consumers must pay for food and fuel, and
also overall inflation (see chapter 2). In addition, since the United States
is an exporter of some commodities also exported by Russia and Ukraine,
notably energy and agricultural products, disruptions to supplies or changes
in the pattern of exports from these countries can affect U.S. exports as well
(IEA 2022a).
Initially, Russia’s invasion largely cut off Ukraine—a major exporter
of food commodities, especially wheat, corn, and vegetable oil—from
global markets, threatening global food security. The loss of Ukraine’s
export supply, along with the reluctance of global buyers to engage with
Russian exporters on the exports of grains and oil seeds and Russia’s own
export restrictions on fertilizer and other agricultural products, resulted in
contractions along key supply lines for food staples and agricultural inputs
like fuel and fertilizer, sending prices soaring in the immediate aftermath
of the invasion (Glauber and Laborde 2022). Prices retreated as allied
nations successfully collaborated to mitigate disruptions. Nevertheless, the
uncertainty associated with Russia’s domestic actions and aggression toward
Ukraine—including the destruction of infrastructure used to store and
export food commodities, and the naval blockade of Ukraine’s Black Sea
102 |

Chapter 3

Figure 3-7. U.S. Exports of Liquefied Natural Gas, 2021 and 2022

Millions of cubic meters

4.5
3.5

North America
European Economic
Area (including EU)
and U.K.

47.8

China

117.4

21.9

5.1

Other Asia-Pacific

24.7

Rest of the world

39.1

31.1

52.5
165.9

Total
0

20

40

60

80
2021

100

120

140

160

181.9

180

200

2022

Sources: U.S. Census Bureau, accessed with Trade Data Monitor; CEA calculations.
Note: Liquefied natural gas is covered by HS271111. Other Asia-Pacific includes Australia, Japan, South Korea, New Zealand, and all
10 nations that belong to the Association of Southeast Asian Nations: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the
Philippines, Singapore, Thailand, and Vietnam.

trade route—continued to exacerbate elevated prices. This led U.S. exports
of food, feed, and beverages to exceed their 2021 record by 10 percent in
nominal terms, even as they fell to their lowest level since 2015 in real
terms (Foggo and Mainardi 2022; U.S. Census Bureau 2023b; Yale School
of Public Health 2022). Real exports of these products were ultimately
depressed by the strong dollar, weakening global demand and other productspecific factors, including adverse weather conditions.
Disruptions from Russia’s invasion of Ukraine had a more significant
real impact on U.S. exports of energy goods, notably liquefied natural gas
(LNG) and crude oil. The quantity of U.S. exports of LNG and crude oil
rose substantially over 2021’s already-high levels. For LNG, U.S. exports
also shifted dramatically to European countries as Russia restricted its oncedominant supply of natural gas via pipeline (figure 3-7). Crude oil exports
expanded more broadly across destinations, with the notable exception of a
decrease in exports to China (figure 3-8). Although this figure only captures
a single year rather than a trend, research suggests that reductions in China’s
energy imports from the United States in 2022 likely represented a shift to
imports from other sources, including Russia, along with a drop in demand
due to slower Chinese economic growth (Bown 2022b).

International Trade in Services and Digital Trade Have Been Resilient
Through the end of 2022, U.S. trade in services other than travel and transportation was remarkably stable and resilient amid the continued disruption
of the COVID-19 pandemic and rising geopolitical tensions (see figure 3-3).
In part, this is because digital technology enables adaptations that allow
Confronting New Global Challenges with Strong International Economic Partnerships | 103

Figure 3-8. U.S. Exports of Crude Oil, 2021 and 2022

Millions of barrels

183.4
200.4

North America
European Economic
Area (including EU)
and U.K.

400.0
95.4

China

176.0
231.2

Other Asia-Pacific
Rest of the world

552.5

326.5

264.9

60.3

1,175.0

Total
0

200

400

600
2021

800

1000

1200

1,315.6
1400

2022

Sources: U.S. Census Bureau, accessed with Trade Data Monitor; CEA calculations.
Note: Liquefied natural gas is covered by HS271111. Other Asia-Pacific includes Australia, Japan, South Korea, New Zealand, and all 10 nations
that belong to the Association of Southeast Asian Nations: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore,
Thailand, and Vietnam.

many traded services to be remotely provided. Further, many countries have
made efforts to reduce obstacles to digital trade, including by promoting
access to and efficiency of electronic payments (Klapper and Miller 2021).
Just as remote work minimized pandemic-related disruptions in many
domestic industries that specialize in information, digital technologies
allowed movements of service providers to be converted into movements
of data and thus minimized interruptions of international trade in these
industries (Brynjolfsson et al. 2020; Dingel and Neiman 2020; Espitia et al.
2021; Pei, de Vries, and Zhang 2021). Furthermore, limitations on mobility increased demand for other traded digital services as more household
consumption as well as work moved online.
In fact, the pandemic likely accelerated the trend of rising digital
trade flows. Though there is no standardized definition of digital trade, it
can be conceptualized as including three general types of transactions. The
first is traditional e-commerce, whereby the Internet facilitates a purchase
that is delivered offline. The second is digitally provided services, which
are provided and consumed online. This category includes a wide array of
services that are increasingly part of everyday life, including digital media
like streaming music and videos; digital platforms that connect individuals
to make transactions; the services embedded in the Internet of Things, like
“smart” household appliances and connected medical devices; and the cloud
computing services relied on for business operations. The third category
includes data, which are a basic element of many cross-border transactions but can also be deployed by companies as part of their operations or
sold to other businesses to target advertisements, improve manufacturing

104 |

Chapter 3

Figure 3-9. U.S. Trade in Potentially ICT-Enabled Services, 1999–2021
Billions of 2021 dollars
700
600
500
400
300
200
100
0
1999

2001

2003

2005

2007

2009

Imports of potentially ICT-enabled services
Imports of all other services

2011

2013

2015

2017

2019

2021

Exports of potentially ICT-enabled services
Exports of all other services

Sources: Bureau of Economic Analysis; CEA calculations.
Note: ICT = information and communications technology. Price indices for exports and imports of potentially ICT-enabled
services are calculated as the average of price indices for their components (insurance services; financial services; charges for
the use of intellectual property; telecommunications, computer, and information services; other business services; and
personal, cultural, and recreational services), weighted by the category's nominal share. The nominal series is then converted
with this price index.

operations, and power machine learning for artificial intelligence (AI)
tools, among many other uses (Meltzer 2019; OECD 2023a; Staiger 2021a;
Wharton 2019).
Although digital trade cannot be precisely measured using current data
sources, the evidence suggests that there have been dramatic increases during the last two decades. Cross-border data flows that underpin digital trade
transactions are estimated to have increased by a compound annual growth
rate of 45 percent between 2010 and 2019 and by about 40 percent between
2019 and 2021 (Birshan et al. 2022). In comparison, flows of goods and
services grew at a compound annual growth rate of about 3 and 4 percent,
respectively, between 2010 and 2019 (BEA 2023c). Estimates suggest that
e-commerce transactions grew at an average annual rate of 14.5 percent
between 2010 and 2019 and by 30.3 percent between 2019 and 2021.
E-commerce transactions made up an average of 14.5 percent of retail sales
by value in 2022, up from 4.5 percent in 2010 and 10.5 percent in 2019 (U.S.
Census Bureau 2022).
Likewise, the subset of traded services that the BEA defines as
“potentially ICT-enabled” (i.e., information and communications technology–enabled) has grown dramatically over time (figure 3-9).3 Real exports
Potentially ICT-enabled services trade includes the categories of services trade for which digital
technologies are thought to play the most prominent role. These include ICT services themselves,
as well as insurance services, financial services, and charges for the use of intellectual property,
including royalties and licenses.

3

Confronting New Global Challenges with Strong International Economic Partnerships | 105

and imports of potentially ICT-enabled services grew at an average annual
rate of 7.0 and 8.5 percent, respectively, between 1999 and 2021. This was
much faster than real exports and imports of all other services, which grew
at average annual rates of 0.5 and –1.1 percent, respectively, during the same
period.
Unlike traditional trade in goods and services, for many digital trade
transactions, there is no physical movement of a good or a person across
a border. Rather, the transaction is fully realized by data flows. In great
contrast to physical exchanges, the direct, marginal cost to move data across
borders is nearly zero. Moreover, the cost difference in procuring an identical digitally delivered service from nearby versus from far away is also close
to zero (Goldfarb and Tucker 2019). Absent a sharp increase in regulatory
hurdles, digital trade is thus poised for further dramatic increases as digital
technology continues to improve, as the Internet of Things continues to
spread, and as robotics and artificial information technologies are further
developed (Baldwin 2022).
At present, U.S. trade in potentially ICT-enabled services is concentrated among advanced economies (BEA 2022). However, as digital technology develops, and as the infrastructure that enables Internet use improves,
there will be more opportunities to draw on workers and consumers from
around the world to provide and demand a wide range of digital services.
This is likely to propel substantial increases in digital trade with emerging
markets (Baldwin 2022) and provide benefits to U.S. consumers, workers,
and businesses. However, increased competition from service providers
abroad will also likely have negative effects on some American businesses
and workers (box 3-3).

Continued Growth for Foreign Direct Investment Despite Elevated
Uncertainty
Global foreign direct investment (FDI) flows exhibited strong growth during
the first three quarters of 2022; total real global FDI flows grew by 9 percent
during the first three quarters of 2022 compared with the same period in
2021; global FDI flows in the first quarter of 2022 reached their second
highest level in the past five years, increasing by more than 15 percent yearover-year and by over 40 percent compared with the prior quarter (BEA
2023d; OECD 2023b).4 Global FDI as a share of world GDP reached about 2
percent of GDP in the first half of 2022, a continued recovery from the sharp
contraction in international investment during the onset of the COVID-19
pandemic. Though FDI can sometimes pose risks (e.g., to national security
in limited cases), research has found that inward and outward FDI can be
Real FDI flows are calculated as the average of global FDI inflows and outflows in dollars,
deflated by the U.S. Personal Consumption Expenditures Price Index (chain type).

4

106 |

Chapter 3

Box 3-3. Rising Digital Trade and U.S. Labor Markets
Advances in digital technology that facilitate the remote production and
provision of goods and services will create significant opportunities
and challenges for U.S. workers (Amiti and Wei 2006; Eppinger 2019;
Grossman and Rossi-Hansberg 2008). U.S. workers have a comparative
advantage in many tradable services and some sophisticated goods
industries due to skill level and education. Access to a larger global market will allow these industries to expand, increasing demand for these
skills, which may lift wages and provide opportunities for employment
for a portion of the workforce. However, other workers in services industries that compete directly with digitally enabled imports (e.g., a worker
for a traditional big box retailer competing with a foreign e-commerce
company) may face lower wages and job loss. Importantly, research
suggests that these losses may disproportionately affect individuals who
are more economically vulnerable, exacerbating economic inequality
within the United States (Oldenski 2011). In particular, Baldwin (2022)
argues that an expansion of digital services trade may have particularly
negative effects on U.S. workers providing intermediate services (e.g.,
administrative assistants, graphic designers, travel agents, and information technology help staff), who will face rising competition from lowwage counterparts in developing countries.
Labor provisions are a core feature of the Biden-Harris
Administration’s work with U.S. partners on digital trade and featured
in the United States’ discussions with the EU in the U.S.-EU Trade and
Labor Dialogue under the U.S.-EU Trade and Technology Council (DOL
2022), as well as in Pillar 1 of the Indo-Pacific Economic Framework
for Prosperity (USTR 2022b). These provisions aim to ensure that trade
policy supports fair competition for U.S. workers in the digital economy,
raising the standard for workers abroad rather than facilitating competition on the basis of low labor standards.
Research on previous labor market shocks—notably the so-called
China Shock, whereby increased import competition in certain manufacturing sectors led to concentrated and persistent job losses in some communities—has revealed that the costs for many workers to adjust after a
change in the demand for their labor can be very high (Autor, Dorn, and
Hanson 2013, 2016, 2021; Eriksson et al. 2021). This suggests that there
is an essential role for complementary domestic policies to equip U.S.
workers who are exposed to increased competition through digital trade
with the resources to adapt (CEA 2022, chap. 3; Clausing 2019).

Confronting New Global Challenges with Strong International Economic Partnerships | 107

Figure 3-10. Real U.S. Outward Foreign Direct Investment, by Destination,
2012–22
Billions of 2021 dollars, quarterly
70
60
50
40
30
20
10
0
–10
–20
2012:Q1

2013:Q1

2014:Q1

2015:Q1

2016:Q1

2017:Q1

Group of Seven (excluding United States)

2018:Q1

2019:Q1

2020:Q1

2021:Q1

2022:Q1

Large emerging markets: Brazil, China, India

Sources: Bureau of Economic Analysis; CEA calculations.
Note: Data are net financial transactions (without current cost adjustment) on a directional basis, in this case those that relate to
outward investment (U.S. direct investment abroad). Nominal series converted to 2021 dollars using U.S. Personal Consumption
Expenditures Price Index. Data through 2022:Q3.

the source of significant contributions to economic growth and increased
resilience to shocks (Alfaro 2016; OECD 2020a).
Although FDI flows are not directly subject to the same types of physical disruptions as international trade (i.e., the ability to carry out financial
transactions is not affected by issues like port closures or physical distance),
they are similarly responsive to changes in global economic conditions.
Elevated uncertainty about global economic conditions and changes in
the economic policy environment can reduce or reverse investment flows
(Choi, Furceri, and Yoon 2020; Gulen and Ion 2016; Julio and Yook 2016).
Businesses may decide to delay or suspend investment decisions when
uncertainty is high and when investors find it difficult to determine when
conditions are likely to normalize. Following the strong flows in the first
quarter of 2022, elevated global inflation and tightening global financial
conditions, as well as the compounding effects of Russia’s invasion of
Ukraine, resulted in individuals, companies, and governments moderating
global FDI flows in the second and third quarters of 2022 (although they
still grew 5 percent compared with the second- and third-quarter flows in
2021) (OECD 2023b).
Focusing on the United States, in the first half of 2022, the country was
both the largest recipient and largest source of FDI globally (OECD 2022a).
FDI flows into and out of the United States are largely flowing from or to
advanced economies (e.g., the Group of Seven), especially in comparison

108 |

Chapter 3

Figure 3-11. Real U.S. Inward Foreign Direct Investment, by Source, 2012—
22
Billions of 2021 dollars, quarterly
150
100
50
0
–50
–100
–150
2012:Q1

2013:Q1

2014:Q1

2015:Q1

2016:Q1

Group of Seven (excluding U.S.)

2017:Q1

2018:Q1

2019:Q1

2020:Q1

2021:Q1

2022:Q1

Large emerging markets: Brazil, China, India

Sources: Bureau of Economic Analysis; CEA calculations.
Note: Data are net financial transactions (without current cost adjustment) on a directional basis, in this case those that relate to
inward investment (foreign direct investment in the U.S.). Nominal series converted to 2021 dollars using U.S. Personal
Consumption Expenditures Price Index. Data through 2022:Q3.

with FDI to and from large emerging market countries (figures 3-10 and
3-11).
Along these lines, the United States, its allies, and its partners are taking measures to deepen investments in the critical industries in one another’s
economies as a way of reducing dependencies on other countries that have
had an outsized role in these industries, notably China. For instance, the
United States, its allies, and its partners are coordinating to increase their
collective capacity to produce semiconductors (Shivakumar, Wessner, and
Howell 2022). As part of the United States’ CHIPS and Science Act, the
State Department will manage the International Technology Security and
Innovation Fund, which will promote the development of complementary,
secure supply chain investments in key partners to strengthen and support the
U.S. semiconductor industry (U.S. Department of State 2022a). Similarly,
coordinated efforts to catalyze infrastructure investment in emerging and
developing countries through the Partnership for Global Infrastructure and
Investment—particularly to support the digital economy and the green
energy transition—will help reduce uncertainty, strengthen secure supply
chains, create new opportunities for businesses and workers, and boost
overall economic growth (White House 2022a). The increased policy clarity
resulting from these types of commitments and the shared experience of
supply constraints during the pandemic may further catalyze mutual investment, thereby deepening the United States’ investment relationship with its
key allies and partners.

Confronting New Global Challenges with Strong International Economic Partnerships | 109

Global Economic Relations Are at a Turning Point
Since World War II, a central focus of the international economic policies
of the United States, its allies, and its partners has been reducing barriers to
trade and investment in pursuit of greater economic prosperity (Irwin 2022a;
CRS 2023). These policies have led to an expanded and strengthened web of
integrated economic relationships in the form of global supply chains, and
they have supported flows of goods and services across borders that have
substantially increased national incomes around the world (CEA 2022, chap.
6; Irwin 2022b; World Bank 2020). However, disruptions of these flows
during the global COVID-19 pandemic hit critical nodes of supply chains
and hindered production worldwide, amplifying constraints on the supply of
certain essential goods to businesses and households (Espitia, Rocha, and
Ruta 2022). In addition, Russia’s invasion of Ukraine made it imperative for
the United States, its allies, and its partners to sever economic relations with
Russia that could facilitate its military aggression. The resulting economic
sanctions, the reluctance of some international businesses to maintain even
permitted economic relationships with Russia, and Russia’s retaliatory
export restrictions made the risk of undiversified supply chains even more
apparent. They also underscored the power of economic integration as a tool
of foreign policy (Yellen 2022a; Lagarde 2022).
Alongside these shocks, increased competition from imports over
time has also hurt the employment and earnings outcomes for some groups
of workers (box 3-3). Long-standing concerns about the associated role
of international trade in rising income inequality within the United States
(Autor et al. 2014; Chetverikov, Larsen, and Palmer 2016)—along with
concerns about the climate crisis, through the greenhouse gas emissions
embedded in the consumption of tradable goods and services within the
United States—have led to calls to reassess and update the approach to trade
policy in the United States and elsewhere (CEA 2022, chap. 3; Tai 2021a,
2021b; WTO 2022).
Although market incentives and current trade rules do not always align
production and trade flows with broader social, political, environmental,
or national security objectives, international trade and investment can be
powerful sources of economic gains. Empirical research has demonstrated
that in addition to supporting lower costs for businesses and consumers (de
Loecker et al. 2016; Jaravel and Sager 2019), and jobs and higher wages
for workers in export industries (Feenstra et al. 2019; National Security
Council 2022; Riker 2015; U.S. Department of Commerce 2021), trade
and investment facilitate the flow of knowledge across borders, spurring
productivity gains and innovation (Goldberg et al. 2010; Keller and Yeaple
2009). Beyond the United States’ borders, trade and investment with the
United States provides opportunities for many developing countries to fight
110 |

Chapter 3

potentially destabilizing poverty (Irwin 2022b) and can be a foundation for
closer relationships with the United States in other domains (Chivvis and
Kapstein 2022).
Moreover, as leaders have emphasized, global economic integration is also part of a strategy to promote economic resilience and security
(Georgieva, Gopinath, and Pazarbasioglu 2022; Lagarde 2022; Yellen
2022a). Extensive research has found that under a broad set of conditions,
businesses are more resilient during supply disruptions when they are able
to draw on a geographically diverse set of sources rather than a concentrated
source of supply for inputs. Put simply, geographically distributed supplies can act as a “pressure valve” for supply challenges during periods of
idiosyncratic supply disruptions (Bonadio et al. 2021; Eppinger et al. 2021;
Caselli et al. 2020; D’Aguanno et al. 2021; Espitia, Rocha, and Ruta 2022;
Grossman et al. 2021). Although the opportunity to trade does not automatically deliver geographic diversity in sourcing, it does enable it. Similarly,
global markets can serve as a backstop for demand, providing alternative
markets for businesses when domestic demand is low (Caselli et al. 2020;
Lagarde 2022). As such, it is in the interest of the United States to pursue
approaches to lower trade costs within greener, fairer, and more secure trade
and investment partnerships.
The United States, its allies, and its partners have thus reached a turning point in international economic policy, whereby it is necessary to reckon
with a broad mandate: On one hand, it is desirable to maintain the benefits
associated with international trade and investment and to facilitate the
growth of these benefits in the digital sphere. On the other hand, the focus of
trade policy needs to expand beyond reducing barriers. Decisionmakers need
to ensure that policy supports increased resilience to global supply shocks;
limits the ability of adversarial powers to weaponize economic integration
to the United States’ detriment; preserves fair competition in the presence
of large, nonmarket economies; and minimizes exposure to cybersecurity
and regulatory risks, while facilitating digital trade flows. Trade policy can
also advance other objectives that interact with international markets, such
as fighting climate change, promoting workers’ rights and labor standards
both at home and abroad, and expanding the benefits of trade to underserved
communities (Meltzer and Kerry 2019; USTR 2022e). The mandate to balance these priorities exists both at the level of individual policy measures
and for aggregate U.S. policy, making coordination across agencies within
the U.S. government and between U.S. partners increasingly important.
The approach the United States takes to international economic policy in
this challenging environment sends a signal to businesses, consumers, and
governments around the world about U.S. priorities. As such, it forms a key
element of U.S. foreign policy.

Confronting New Global Challenges with Strong International Economic Partnerships | 111

Imperatives of Economic Partnerships in the Changing Global
Environment
Confronting systemic vulnerabilities that have become more prominent over
the past two years while preserving the benefits of international economic
integration to the maximum extent will require close collaboration between
the United States, its allies, and its partners. This subsection explores three
critical policy objectives where cross-border trade and investment play an
essential role in promoting economic well-being and for which there is a
need to calibrate trade policy to meet current challenges: (1) building more
resilient supply chains, (2) responding to adversarial or unfavorable political and economic policies abroad, and (3) safely advancing digital trade.
Although the scope of this chapter is limited to these three areas, the United
States and its allies and partners also face a broader mandate to update and
strengthen the rules, norms, and institutions that underpin international
business and economic relations in the twenty-first century environment.
This includes facing sociopolitical challenges and combating climate change
(CEA 2022, chap. 3). Existing institutions and frameworks for global
dialogue and collaboration remain important as incubators for solutions to
complex and evolving challenges (Staiger 2021b). Today’s challenges also
provide a critical opportunity for the United States to play a global leadership role, working with its allies and partners to chart a modern course for
a greener, more inclusive, more resilient, and more secure global economy
(box 3-4).

Box 3-4. The United States’ New Approach
to Economic Partnerships
The Biden-Harris Administration is pursuing deeper commercial ties
through economic partnerships that address vulnerabilities to external
shocks while making international trade and investment greener and
fairer. The Indo-Pacific Economic Framework for Prosperity, a flagship
effort, consists of four pillars. The trade pillar seeks to craft high-standard, inclusive, free, fair, and open trade commitments. The supply chain
pillar seeks to establish commitments for supply chain transparency,
diversity, and coordination. The clean economy pillar seeks cooperation on clean energy, decarbonization, and infrastructure. And the fair
economy pillar seeks economic frameworks to enforce tax, antibribery,
and anticorruption systems. Commitments within each of the four pillars
will be designed to enhance the benefits for workers in the United States
and around the world (White House 2022b).

112 |

Chapter 3

Resilience during Global Supply Shocks
In the early days of the COVID-19 pandemic, disruptions in the supply of
manufacturing inputs like semiconductors, consumer products like bicycles,
and medical supplies and equipment made Americans acutely aware of the
importance of “supply chain resilience”—that is, the ability of businesses
and public services to continue to provide goods and services when a source
of supply or distribution is suddenly unavailable. The past three years have
demonstrated how shortfalls of inputs or equipment in one industry can
disrupt production and distribution in linked industries, slowing overall
economic output (Cerdeiro and Komaromi 2020). Furthermore, Americans
have witnessed how supply disruptions can even put public health and safety
at risk. This experience has motivated both firms and governments to take
steps to build resilience.
Falling barriers to trade—induced by both policy and technological
change—have enabled businesses to reach around the world to source the
inputs and equipment that ultimately come together to produce the goods
purchased by consumers and public service providers at lower cost, greater
variety, and higher quality (Baldwin and Freeman 2022; de Loecker et al.
2016; Fan, Li, and Yeaple 2015; Krugman 1980). However, the inputs and
equipment themselves are often also an amalgam of raw materials extracted
in one country, processed in another, and combined with more materials in
a third country. As a consequence of this global production process, firms
and governments often have only a limited visibility of the critical nodes of
supply chains, which limits their ability to evaluate or reduce their exposure
to rising geopolitical tensions, climate-related disasters, and other risks. As
such, researchers have emphasized that government support for initiatives to
increase the visibility of supply chains, or to enhance supply chain transparency, can reduce the information costs of broader steps to increase resilience
(CEA 2022, chap. 6; National Academies 2022).
Because stages of production take place globally, engaging with
partner governments to collect and share information can make efforts to
map and monitor supply chains more complete. Such collaboration can alert
governments to potentially fruitful avenues to mitigate destabilizing supply
dependencies and, because sharing information highlights cross-country
interlinkages, it can catalyze coordinated responses during crises. Indeed,
experts have argued that a sustained commitment by countries to share information and coordinate policies affecting the supply of critical health-related
goods and services will be essential in preparing for future public health
crises (Bown 2022c; National Academies 2022). Partnerships to increase
supply chain transparency can also reduce the costs of gathering information to satisfy climate and other policies, such as those that aim to eliminate
trade in products made with forced labor, like the Uyghur Forced Labor

Confronting New Global Challenges with Strong International Economic Partnerships | 113

Prevention Act in the United States (Baldwin and Freeman 2022). More
generally, greater supply chain transparency gives both firms and consumers
the information they need to “vote with their wallets” by choosing to buy
from producers and vendors whose practices are consistent with their own
values (Mollenkopf, Peinkofer, and Chu 2022). In this way, transparency
can leverage market forces to reward and advance greener, more inclusive,
and more secure business practices.
The Biden-Harris Administration has initiated several ongoing dialogues on supply chains that focus on sharing information, designing early
warning systems for supply chain disruptions, developing technical standards, and facilitating private investment. These discussions have been held
through the U.S.-EU Trade and Technology Council, the Quad Critical and
Emerging Technologies Working Group, the Minerals Security Partnership,
and the Indo-Pacific Economic Framework for Prosperity. The United States
also conducts regular bilateral dialogues on supply chains with a number
of countries, including Canada, Mexico, the United Kingdom, Japan, and
South Korea.
These and other partnerships can further contribute to maximizing the
benefits of government incentives to increase productive capacity for critical
goods and materials—that is, traded goods that are essential building blocks
for economically and intrinsically important goods and services, such as
medical and energy supplies and core technologies (Baldwin and Freeman
2022; Miroudot 2021; IMF 2022a; OECD 2020b; White House 2021a).
Cross-country coordination can reduce the risk that competing government
subsidies lead to unproductive excess capacity or an oversupply that blunts
incentives for further innovation. Likewise, since support from foreign
governments can impose economic distortions on domestic competitors,
frameworks for allies and partners to resolve differences can help to limit
those distortions and avoid costly retaliatory measures (Bown and Hillman
2019; Staiger 2021b; Sykes 2015).
Finally, partnerships to encourage cooperation and communication
about industry standards for traded goods and services can enhance the
ability for trade to contribute to supply chain resilience. Though there are
legitimate reasons for countries to have differing approaches to regulations
and standards affecting product design and distribution, fragmentation of
entire supply chains because of regulatory differences can decrease resilience. For example, divergent industry standards may make digital systems
less interoperable or standard manufacturing inputs less substitutable across
production systems, making it more costly to find alternative sources in the
event of a supply disruption. As such, forums to develop internationally recognized product standards as well as those that facilitate information sharing
on domestic regulatory measures play a critical role in facilitating the ability
of trade and investment to promote resilience.
114 |

Chapter 3

Box 3-5. Coordination Has Been Critical for the
Success of the Sanctions Policy toward Russia
Russia’s invasion of Ukraine on February 24, 2022 started the largest
land conflict in Europe since at least the conflicts in the Balkans in
the 1990s. The scale and brutality of this conflict marked an abrupt
departure from the post–World War II—and in particular the post–
Cold War—rules-based global political and economic order (National
Security Council 2022). The coordinated response by the coalition of
the United States and more than 30 allied and partner nations to impose
costs on Russia and address the associated threats to the global economy
highlights how pooling resources and acting in coordination to achieve
a policy goal is often more effective than a unilateral approach (Aslund
and Snegovaya 2021; Berner, Cecchetti, and Schoenholtz 2022).
To date, the coalition’s sanctions against Russia have targeted key
aspects of the Russian economy. Extensive financial sanctions have
restricted capital flows into Russia, depriving it of revenues necessary to
continue funding its war. For example, the United States has prohibited
U.S. persons from making new investments in Russia, and the United
States and its allies and partners have sanctioned major Russian financial
institutions and taken action to remove major Russian banks from the
SWIFT financial messaging system (CRS 2022a). Beginning in early
2022 and continuing through the year, foreign direct investment into
Russia fell sharply (figure 3-iv), highlighting the scope and strength
of coordinated financial sanctions and the private sector’s responses to
Russian aggression (OECD 2022b).
The coalition’s member countries have also imposed extensive
export controls and have revoked Russia’s normal trade relations status,
Figure 3-iv. Real Russian Foreign Direct Investment Net Inflows, 2017—
22
Billions of 2021 dollars, quarterly
15
10
5
0
–5
–10
–15
–20
2017:Q1 2017:Q3 2018:Q1 2018:Q3 2019:Q1 2019:Q3 2020:Q1 2020:Q3 2021:Q1 2021:Q3 2022:Q1 2022:Q3
Sources: Bureau of Economic Analysis; Organization for Economic Cooperation and Development.
Note: Nominal series converted to 2021 dollars using U.S. Personal Consumption Expenditures Price Index. Data through 2022:Q3.

Confronting New Global Challenges with Strong International Economic Partnerships | 115

thereby increasing tariffs on imports from Russia and thus the cost of
doing business with Russia (U.S. Department of Commerce 2022; Tai
2022). The United States’ coordination with its allies and partners on
export controls has hampered Russia’s ability to backfill its imports of
military or dual-use items (U.S. Department of State 2022b). Sanctions
and export controls contributed to a sharp overall drop in Russia’s
imports and a shift in Russia’s energy exports away from Europe, both of
which researchers have characterized as key factors harming the Russian
economy (Demertzis et al. 2022).
The International Monetary Fund estimates that the Russian
economy contracted by 3.4 percent in 2022 (IMF 2022b). In addition,
some analysts estimate that Russia’s economy will continue to suffer
significantly in the medium to long runs. For example, some predictions
suggest that the Russian economy will not return to its prewar level of
real GDP for five years or more (Economist Intelligence Unit 2022).
Importantly, recognizing the potential for negative spillovers to the
global economy from financial and trade sanctions, the United States and
its allies and partners have coordinated to relieve global market stress,
including by ensuring trade channels remained open in selected commodities exported by Russia and Ukraine (IMF 2022b; OECD 2022c).
This meant going after Russian energy in a measured way, by coordinating with partners and allies to allow energy transactions to continue
while also designing price caps on seaborne Russian oil and petroleum
products to limit Russia’s revenue and ensure a stable global supply of
energy (U.S. Department of the Treasury 2022a, 2022b). (The price cap
on seaborne oil entered into force in December 2022. The price cap on
petroleum products entered into force in February 2023.)
In addition, the United States has carved out agricultural commodities, fertilizer, and medical supplies from sanctions and issued extensive
public guidance to ensure these authorizations are well understood (U.S.
Department of the Treasury 2022c). The United States has also worked
with the United Nations to find a pathway for Ukrainian wheat to reenter
global markets: through the Black Sea Grain Initiative, more than 11.1
million metric tons of grains and other foodstuffs left Ukrainian ports
between July 22, 2022, when the program took effect, and November 17,
2022 (United Nations 2022).
The global market’s spillovers from Russia’s war against Ukraine
illustrate the broader themes of this chapter: the past year has been
marked by profound new and lingering disruptions of global commerce.
Nevertheless, global markets remain relatively robust, and economic
coordination—a key element of the post–World War II era—between
the United States and its allies and its partner countries has been critical.
Without coordination in 2022, there was a nontrivial risk that divergent
sanctions policies could have increased confusion and uncertainty in

116 |

Chapter 3

markets to the detriment of the global economy, notably global price
stability. A lack of coordination also could have lessened the impact
on the Russian economy of these sanctions. In coming years, continued
coordination between the United States and its allies and partners will
remain important for crafting effective policies to respond to these kinds
of disruptions and to mitigate the economic and political uncertainty
that may arise as a result of rising geopolitical tensions (Georgieva,
Gopinath, and Pazarbasioglu 2022).

Responding to Geopolitical Challenges
In today’s environment of rising geopolitical tensions and geostrategic competition, the United States’ economic strength is one of its most profound
sources of global power and influence. This strength is greatly enhanced by
the collective economic strength that it can wield, along with its allies and
partners that share its support for a free, open, prosperous, and secure world
(National Security Council 2022). Coordination between the United States
and its allies and partners can enhance the ability of aligned countries to
provide shared security against and resilience in facing adversarial actions
by, for example, enabling a network of alternative sourcing and market
opportunities.
In a recent example, the United States and its allies and partners have
been able to impose significant economic costs on Russia in response to
its invasion of Ukraine in February 2022 (box 3-5). By coordinating their
actions, U.S. allies and partners have been able to limit Russia’s access to
goods and services necessary to pursue its illegal war. Indeed, research has
shown more broadly how coordinated economic actions more effectively
limit a targeted country’s ability to evade economic consequences than do
unilateral measures (Bapat and Morgan 2009; Drury 1998; Peksen 2019).
Equally, economic partnerships can mitigate the economic consequences of adversarial actions targeted at the United States, its allies, and
its partners. Just as concentrated dependencies on foreign adversaries can
create vulnerabilities, diversified linkages with allies and partners can lessen
them. Strong, diverse, and reliable economic linkages between trusted
partners give businesses alternative markets to which they can shift their
sourcing and sales if necessary, mitigating the impact of adversarial actions
(Harrell, Rosenberg, and Saravalle 2018). For example, Russia has sought to
weaponize Europe’s dependence on its supply of natural gas in an attempt to
weaken Europe’s resolve to support Ukraine and to continue imposing costs
on Russia in retaliation for its aggression. However, trade partnerships with
the United States and other allies and partners have ensured that Europe has
Confronting New Global Challenges with Strong International Economic Partnerships | 117

Box 3-6. The U.S.-EU Energy Partnership
Diminishes Russia’s Leverage
One of Russia’s biggest sources of economic leverage has been its
dominance as a supplier of energy via its natural gas pipelines to Europe.
Historically, Russia supplied Europe with roughly one-third of its gas
(Corbeau 2022). Since the start of its invasion of Ukraine, Russia has cut
pipeline deliveries of natural gas to Europe by more than half, and it may
stop flows entirely in 2023 (IEA 2022b).
However, the EU was able to replace some Russian gas with
imported liquefied natural gas, including from the United States, thus
weakening Russia’s ability to impose economic damage by restricting
supplies of this critical source of energy for European households and
industry. Economists estimate that natural gas shortages in Europe could
have caused a contraction in some European economies of up to 6 percent if the global LNG market had been unable to respond (Flanagan et
al. 2022). The ability of the United States to contribute to easing natural
gas shortfalls has thus been critical.
Since Russia’s invasion of Ukraine, the United States and the
European Union have strengthened their cooperation on energy security.
Through the Joint Task Force on Energy Security, the United States
has made commitments to supply LNG to Europe through 2023 (White
House 2022c). Through this partnership, the United States and the EU
have agreed to address short-term energy supply issues with LNG while
minimizing greenhouse gas emissions from LNG through measures to
increase energy efficiency, reduce demand for gas, and regulate methane
emissions. The task force has also led to additional commitments to
advance renewable energy by expediting renewable energy projects and
accelerating the deployment of clean energy technologies (White House
2022d).
The United States is also strengthening its bilateral partnerships
with European countries to increase energy security, empower global
decarbonization efforts, and achieve net-zero economies in hard-toabate energy sectors through clean nuclear energy technology. In 2022,
the United States announced its support for the Front-End Engineering
Design study to provide the basis for the deployment of a small modular
reactor power plant in Romania (U.S. Department of State 2022c);
support for a pilot of commercial-scale production of clean fuels from
small modular reactors in Ukraine (U.S. Department of State 2022d);
and technical assistance for the inaugural civil nuclear project in Poland
(U.S. Department of Energy 2022). These investments will help reduce
dependence on Russian energy in Eastern Europe in both the medium
and long term.

118 |

Chapter 3

had the ability to shift to alternative energy sources, limiting the damage
of Russia’s coercive behavior on households, businesses, and workers (box
3-6).

Promoting Opportunity and Managing Risks in Digital Trade
As discussed earlier in this chapter, digital trade is poised to expand
dramatically as work and consumption increasingly take place online; as
the Internet of Things digitally connects more everyday objects; and as
frontier technologies, for which masses of data are a fundamental input,
such as AI, continue to develop. Digital trade may also provide solutions
for some of the core challenges to global trade and investment discussed
above. For example, with technologies like 3-D printing or other forms of
so-called additive manufacturing, digital information flows can potentially
facilitate the substitution of entire stages of manufacturing supply chains
that currently involve the physical movement of goods, improving resilience
in the presence of supply disruptions (Freund, Mulabdic, and Ruta 2022).
Likewise, products in the growing “TradeTech” industry use advanced technologies, including AI, to enable supply chain transparency and traceability.
These products could reduce the cost of ensuring that supply chains meet
security, social, and environmental criteria that make trade safer, greener,
and more equitable (Capri and Lehmacher 2021). However, digital trade
also creates vulnerabilities that must be managed, especially given rising
geopolitical tensions.
Digital trade has two fundamental requirements. The first is the infrastructure and equipment that transmit, store, and process data flows, including the network of underwater fiber-optic cables that carry more than 95
percent of international data (Comini, Foster, and Srinivasan 2021; Morcos
and Wall 2021; World Economic Forum 2020). The second is a regulatory
environment that permits the flow of data across borders with appropriate
safeguards. Absent guardrails, digital trade can introduce potentially critical
risks to economic well-being and national security through both of these
gateways (Meltzer 2020).
The risks involved are manifest and nontrivial. Among the most salient
are cybersecurity risks: The constant flow of large volumes of digital information creates an appealing target for the theft of data. This can allow competitors to capture intellectual property, including trade secrets, that threaten
American businesses. It can result in unauthorized access to Americans’
sensitive personal information, violating their privacy and potentially
enabling financial or other crimes. Digital technology can allow goods
and services traders to falsify information, potentially facilitating the evasion of national laws, regulations, and standards. Digital systems can also
be manipulated or disabled remotely, potentially compromising national

Confronting New Global Challenges with Strong International Economic Partnerships | 119

defense and critical infrastructure (Meltzer 2020). Estimates suggest that
the economic cost of security breaches of information and technology assets
during 2020 were as high as 6 percent of global GDP (or about $6 trillion).
Other studies suggest that the costs are disproportionately high for critical
industries like health care, transportation, energy, and financial services
(IBM 2022; UNCDF 2022).
The expansion of the digital economy modifies existing markets and
creates new ones, bringing new challenges to protecting consumers and
workers and promoting competition. For example, the difficulties of verifying identity and quality online can compromise consumer protection laws
and labor market protections (Goldfarb and Tucker 2019). Likewise, the
importance of large userbases and quantities of data, and the ability of digitally enabled companies to attract suppliers of products and consumers from
all over the world creates new market concentration dynamics and poses
new challenges to regulators focused on competition policy (see chapter 8).
Governments employ a variety of measures to address these challenges
by regulating the movement, storage, and processing of data. Regulations
affecting digital trade generally fall into a few categories. First, data flow
restrictions—for example, limits on access to digital media—may be used to
protect intellectual property rights or enhance security, among other objectives. Second, so-called data localization policies—government regulations
that determine where and how data related to their citizens, government,
and businesses are stored—may be used to enhance consumer privacy and
facilitate regulation (Casalini and González 2019; CFR 2022). Such policies
may also reflect domestic economic priorities to try to protect industry from
international competition.
These regulatory measures can mitigate some risks associated with
digital trade, but they can also blunt the very benefits they are put in place
to protect (Meltzer 2020). For example, data flow restrictions can hamper
innovation, which benefits from sharing information and knowledge across
borders (Valero 2016; White House 2022e). These restrictions can be particularly detrimental for the development and use of AI technologies, which
rely on the availability of large data sets and are increasingly prominent
in business and important for national security. The ability to aggregate,
store, process, and transmit data across borders is similarly critical for the
financial services sector and its development (Carr, French, and Lowery
2020). Similarly, data localization requirements can increase vulnerability
to cyberthreats by concentrating data, thus making systems easier to target
(Bauer et al. 2014). These requirements can also make integrated risk management, including monitoring and detecting fraud and cybersecurity risks,
more difficult for global firms and institutions to conduct—particularly
those in the financial services sector. Mismatches in equipment standards

120 |

Chapter 3

and regulations can limit system interoperability and thus the resilience of
digital systems.
International cooperation to define the vulnerabilities associated with
data flows and digital supply chains and the regulatory measures that diminish them can reduce the risks and enhance the economic benefits of digital
trade (Ahmed 2019; Casalini et al. 2019; Huang, Madnick, and Johnson
2019; OECD 2015, 2022d). Efforts to enhance workers’ rights and increase
consumer protections from cybercrime and fraud that crosses borders are
integral to these efforts. Indeed, scholars, policymakers, and business leaders have all emphasized the importance of creating an international digital
architecture that promotes trust in data flows (CFR 2022). To do so, governments must grapple with how to provide a regulatory system that is safe and
secure without unnecessarily restricting the benefits of trade. Best practices
in international trade suggest that regulations should be transparent, should
be nondiscriminatory for like products and services, and should not be more

Box 3-7. U.S. Digital Trade Initiatives
Digital trade is an increasingly prominent element of various international
working groups and agreements, reflecting its importance for inclusive
economic growth and security and the challenges policymakers around
the world face in developing appropriate and consistent regulatory
approaches. Attesting to the focus that the Biden-Harris Administration
has placed on ensuring that digital trade benefits people as workers and
consumers, the United States has led efforts to foster trust in the digital
economy, support innovation and competition, promote a resilient and
secure digital infrastructure, ensure consumer protection and privacy,
and address discrimination. It is pursing these efforts by cooperating in
regional partnerships that include the Indo-Pacific Economic Framework
for Prosperity, the World Trade Organization’s (WTO’s) Joint Statement
Initiative on Electronic Commerce, the Americas Partnership for
Economic Prosperity, and the U.S.-Central Asia Trade and Investment
Framework Agreement, as well as bilateral engagements with the United
Kingdom, Kenya, Taiwan, and other countries (CRS 2022b; USTR 2021,
2022a, 2022b, 2022c, 2022d, 2022e; White House 2022f). The United
States has also actively participated in multilateral forums to exchange
information on best practices and promote standards and frameworks for
tackling the risks associated with digital trade. These include the WTO,
the Asia-Pacific Economic Cooperation forum, the Organization for
Economic Cooperation and Development, and the Group of Twenty and
Group of Seven, which together cover a broad set of countries around
the world (USTR 2022e).

Confronting New Global Challenges with Strong International Economic Partnerships | 121

burdensome or restrictive than is necessary to achieve their goals, including
enhanced security and economic resilience (Casalini and González 2019).
In this regard, the Biden-Harris Administration is engaging with various forums to build this trusted system (box 3-7). These include working
with partners and allies to promote an environment that fosters development
of the global economy and facilitates robust cross-border data flows that are
consistent with both privacy and security needs. However, given the rapid
pace at which the digital economy is evolving and the variety of domestic
regulatory objectives, negotiating every aspect of the digital regulation may
not always be desirable or possible. In this context, frameworks to establish
common principles and provide for regulatory transparency have tremendous value (Staiger 2021a).

Conclusion
The record-setting flows of trade and investment in 2022 demonstrate
that the United States remains deeply connected with the global economy.
However, disruptions such as those experienced during the COVID-19
pandemic and rising geopolitical tensions pose fundamental challenges to
globally connected production systems. Though the shock from Russia’s
invasion of Ukraine reverberated primarily in global commodity markets,
it also increased the level of geopolitical uncertainty, which was already
elevated after two years of pandemic-induced stress. The unprovoked invasion of Ukraine has exposed and intensified geopolitical rifts that, along
with the experience of the pandemic-induced supply shock, have increased
the perceived risk and uncertainty associated with global goods trade and
some types of cross-border investments. These uncertainty effects may
have longer-term effects on trade as governments adjust their international
economic policies and businesses change their global sourcing patterns.
Certainly, the economic links between Russia and the rest of the world, and
global markets for the commodities in which Russia is a key player, will
be transformed. Preserving the benefits from international trade and investment, while protecting national security, addressing the effects of climate
change (Tai 2021a; USTR 2022e; White House 2021b), and promoting
resilience and equity in a revitalized domestic economy demands new policy
approaches to respond to both existing and emerging risks.
Given the global nature of the challenges discussed in this chapter,
the policy decisions that the United States, it allies, and its partners make
now will reverberate in international trade and investment for some time.
The importance of partnerships in the modern global economy cannot
be overstated. Enhanced partnerships that feature commitments to share
information and coordinate actions are essential to sustaining the economic
dynamism and productivity delivered through global economic integration
122 |

Chapter 3

in uncertain times (Yellen 2022b). Institutional arrangements must evolve
to ease tensions between openness on one hand, and security and domestic
imperatives on the other hand (Staiger 2021b). Effective coordination both
across and within governments can help to ensure that the individual policies
that sum to the aggregate of international economic policy reflect a deliberate, coordinated policy direction that responds to today’s challenges.

Confronting New Global Challenges with Strong International Economic Partnerships | 123

Chapter 4

Investing in Young Children’s
Care and Education
Investments in the earliest years of a child’s life can generate substantial
benefits, with returns over the child’s lifetime that often considerably exceed
costs. In particular, a large body of evidence demonstrates that early care and
education (ECE) programs can improve children’s short-term development
and long-term well-being, producing benefits not only for them but for
society as well.
ECE programs also support parents’ employment, which has become
increasingly important with the decline in households where a parent stays
home to provide full-time childcare. Women’s labor market options have
grown considerably over the past 50 years, increasing the opportunity costs
of staying out of or reducing time in the workforce (Yellen 2020). Both
men and women point to caregiving and family responsibilities as a major
obstacle for their career advancement, with mothers particularly likely
to report career interruptions and reduced engagement in the labor force
(Parker 2015; Pew Research Center 2022). The challenge of balancing work
and family looms largest for the parents of young children not yet enrolled
in K-12 schooling, and has ramifications across parents’ careers.
As a result of these trends, a market for nonparental ECE services has developed. Care for young children is wide-ranging—from informal care (paid
or unpaid) by relatives, neighbors, or in-home caregivers to formal care in

125

home-based, center-based, or school settings.1 This decentralized patchwork
of providers caring for children in homes, centers, and schools stands in
contrast to, for example, the more structured system of public K-12 education in the United States. Though the ECE market is one upon which many
families rely (NCES 2018), and despite ample evidence that ECE programs
can both effectively facilitate children’s healthy development and support
parents’ employment, the ECE market often does not function well.
This chapter first presents evidence on the effects of ECE investments for
children, their parents, and society. It then discusses ECE market challenges,
including workforce turnover and low pay, the high costs of providing highquality care, price sensitivity among ECE customers, the fragility of the
childcare business model, and the resulting underprovision of high-quality
ECE relative to what would be socially optimal. The chapter closes with a
discussion of the role of public subsidies in supporting a better-functioning
ECE market.

The Effectiveness of Early Childhood Investments
Ample research documents the benefits that ECE investments can generate—both directly, for children who participate and working parents who
rely on the care, and indirectly, through spillovers to their families and communities. This section summarizes and highlights the relevant evidence on
ECE investments’ benefits for children and society, the role of ECE quality
in improving outcomes, and the benefits of ECE for working parents.

Benefits for Children and Society
ECE investments support children’s healthy development and early learning
starting at birth, which cascades into longer-term and broader benefits for
them, their communities, and the economy. A large body of research points
This chapter employs the term “ECE” to encompass childcare, preschool, and prekindergarten
(pre-K) programming, because there is often a significant overlap in how programs are structured,
funded, and delivered. Childcare typically refers to programs serving children from infancy through
school age, while preschool and pre-K commonly refer to programs aimed at the year or two before
formal school entry. As such, preschool programs typically serve children ages three and four years
old and often operate on a school-day and school-year schedule. The terms “childcare,” “preschool,”
and “pre-K” are used in the chapter when the policy, research, or data in question pertain to a
specific segment of the broader ECE landscape.

1

126 |

Chapter 4

to ECE experiences as influential for children’s short-term outcomes, such
as school readiness and early social-emotional and cognitive skill development, as well as long-term outcomes like educational attainment, executive
function, employment, and earnings (Deming 2009; Duncan and Magnuson
2013; Heckman and Kautz 2014; Weiland and Yoshikawa 2013). These
long-term, positive effects have been demonstrated in studies of childcare,
Head Start, and other model preschool programs (Bailey, Sun, and Timpe
2021; Campbell et al. 2014; Gray-Lobe, Pathak, and Walters 2023; Heckman
et al. 2010; Herbst 2017).2
Some studies of short- and medium-term program effects find that
improvements in test scores fade out over time. However, when these studies also track long-term outcomes, they find substantial improvements in life
chances, despite the short-run evaporation of test score gains (Chetty et al.
2011; Deming 2009). Moreover, there are documented complementarities
between ECE investments and subsequent school investments (Johnson and
Jackson 2019). Recent evidence captures the intra- and intergenerational
spillovers of ECE exposure to siblings and the children of those exposed
to the ECE program (Barr and Gibbs 2022; García et al. 2021; García,
Heckman, and Ronda 2021).
When deployed well, ECE investments can advance both economic
efficiency and equity. The return on these investments manifests not only
as improved individual life chances but also as societal benefits, in the form
of greater productivity and economic growth; less individual reliance on
government transfers; and fewer bad outcomes that are costly for society,
such as poor health, high school dropout, and crime (Heckman and Masterov
2007). Figure 4-1 presents a stylized depiction of the return on investments
at various stages of life, with examples of programs in each period. The
figure shows how the Heckman curve, as it is known, maps the economic
argument that $1 invested earlier in life can yield a greater return than $1
invested later (Heckman 2008). In other words, the efficacy of human capital investments likely declines with age, a conclusion that aligns with the science on a child’s developing brain and its malleability during the infant and
toddler years (Knudsen et al. 2006; Shonkoff and Phillips 2000). According
to this argument, policies and programs targeted at the earliest years of life
have the greatest potential to generate large individual and societal returns,
followed by investments in the preschool years, when children are three to
five years of age.
Research that estimates returns on human capital investments over a
wide range of ages is generally consistent with the Heckman curve. In comprehensive assessments of the long-run benefits of specific early childhood
programs, researchers estimate a $7 to $12 return on every $1 invested in
Head Start is the federally funded preschool program for children from low-income families; it
began in 1965 as part of the War on Poverty (ECLKC 2022).

2

Investing in Young Children’s Care and Education | 127

Figure 4-1. Return on Investment in Human Capital, by Age
Early years programs

Rate of return

Preschool programs

K-12 schooling
Job training

0–3
years

4–5
years

Middle childhood,
early adolescence

Late adolescence,
early adulthood

Age at time of investment
Source: Adapted from Heckman (2008).

Perry Preschool (Heckman et al. 2010), and even higher rates of return for
the Carolina Abecedarian Project and the Carolina Approach to Responsive
Education programs (García et al. 2020).

Defining Quality in ECE
Although there is solid evidence that ECE investments can be effective in
the long run, less empirical evidence speaks directly to the features of ECE
that matter for improving children’s outcomes. This research gap is due
in part to limited data on inputs to, and outputs from, ECE programming,
and in part to ECE’s multifaceted aims. In addition, the quality of the ECE
experience is measured relative to the possible alternative settings where
children could spend their time, and these settings vary widely. That said,
there are some aspects that research suggests are important dimensions of
high-quality ECE.
While parents’ definitions of “good” ECE settings are likely subjective
and include family-specific preferences for location, linguistic and cultural
match, hours of operation, and program type, there have been efforts—across
the United States’ mixed delivery system—to define and measure program
quality objectively.3 Beyond the core safety and security requirements,
systematic efforts to boost ECE quality include the Head Start Program
Performance Standards and States’ quality rating and improvement systems
A mixed delivery system provides care through home-based, community-based, and school-based
settings, and can involve funding and accountability from Federal, State, and local sources in
addition to families paying directly for ECE services.

3

128 |

Chapter 4

(Office of Child Care 2011). These systems rely on various components,
depending on the State—including licensure, lead teachers’ educational
attainment, child–caregiver ratios, and other measures—and most States
directly incentivize providers achieving higher levels of quality, as defined
in the State’s system. States make information on program quality publicly
available. Though research has not definitively established links between
quality rating and improvement systems and measurable child outcomes
(Cannon et al. 2017), the evidence suggests that assignment to a low rating
does lead programs to improve on the measured dimensions and influences
parents’ choices (Bassok, Dee, and Latham 2019).
An important, measurable dimension of ECE quality is the nature of
relationships and interactions between ECE staff and children in the care
setting. Evidence suggests that stable, attached child–caregiver relationships
in children’s earliest years provide a critical foundation for their subsequent
healthy development (Hatfield et al. 2016; Pianta 1997; Sabol and Pianta
2012). Indeed, research points to the importance of the caregiver’s focused
attention, which means that having more early childhood educators, or educators who have been trained in how to productively engage with children,
could generate economy-wide, long-term productivity gains (Blau and
Currie 2006). Relatedly, research suggests that ECE staff turnover is associated with children’s weaker language and social skill development (Caven
et al. 2021). Childcare workers experiencing economic stress have a more
difficult time fully engaging with children and offering a high-quality learning experience (Schlieber and Mclean 2020). Evidence also indicates that
improvements in compensation and working conditions can significantly
reduce turnover and are associated with better care and improved child
outcomes (Bassok et al. 2021b; Grunewald, Nunn, and Palmer 2022; King
et al. 2016).
Some comprehensive, model programs have generated large returns,
but they are made up of a package of components—including home visits,
parenting programs, and health and nutrition offerings—which makes it difficult to isolate the impact of specific features of these programs in that evidence base. Notably, ECE settings provide early academic skill building and
educational inputs alongside other types of support for children’s healthy
development, including play-based and social activities, and physical and
mental health and nutrition services. Box 4-1 explains the role of nutrition
support in young children’s development.

Benefits for Working Parents
In addition to benefits for children, ECE programming can be important for
families because it allows parents to participate in the labor market while
raising their children. In 2021, 62 percent of families with children under six

Investing in Young Children’s Care and Education | 129

Box 4-1. Nutrition Support in Early Childhood
ECE settings often provide services and support beyond the classroom,
including programs for parents, health services, and access to food.
For example, from its inception in 1965, the Head Start program was
designed to be a comprehensive early childhood development program,
with an emphasis on health and nutrition components (Vinovskis
2005). Among young children, the Child and Adult Care Food Program
(CACFP) provides funding for healthy meals and snacks for children
in Head Start and other ECE programs. Research suggests that funding
and standard-setting programs like the CACFP are associated with
improvements in child nutrition offerings and reductions in households’
food insecurity (Heflin, Arteaga, and Gable 2015; Korenman et al. 2013;
Ritchie et al. 2012, 2015).
Researchers have established that increased access to healthier
food—provided to children through nutrition assistance for their families
or through meals while in childcare or at school—lead to improved
health, cognitive functioning, and long-term well-being. Evidence
from the introduction of both the Food Stamp Program—now the
Supplemental Nutrition Assistance Program (SNAP)—and the Special
Supplemental Nutrition Program for Women, Infants, and Children
(WIC) across the United States suggests that both programs have
improved children’s early life health outcomes (Almond, Hoynes, and
Schanzenbach 2011; Hoynes, Page, and Stevens 2011) and—if provided
before age five—long-term economic outcomes (Bailey et al. 2020;
Hoynes, Schanzenbach, and Almond 2016).
Young children can also interact with the National School Lunch
Program, School Breakfast Program, and Special Milk Program offered
in participating childcare, preschool, and pre-K settings. Several studies
indicate that school meals can improve nutrition and health outcomes
(Gundersen et al. 2012; Bhattacharya, Currie, and Haider 2006).
Although there are a few exceptions (e.g., Schanzenbach and Zaki 2014),
many studies conclude that higher participation in these meal programs
leads to increases in academic achievement and educational attainment
(Imberman and Kugler 2014; Frisvold 2015; Hinrichs 2010). These
findings are consistent with clinical evidence that nutrition is important
for cognitive performance (Alaimo, Olson, and Frongillo 2001; Wesnes
et al. 2003).
Food assistance programs are an important tool to reduce food
hardship for many Americans, particularly in times of economic distress.
Though food insecurity had been trending down in the decade preceding
the COVID-19 pandemic, most dramatically for Hispanic and Latino
households (figure 4-i), nonwhite children experienced setbacks in food
security in 2020 and 2021. Additionally, gaps that predate the COVID19 pandemic remain between Black and Hispanic or Latino children and

130 |

Chapter 4

Figure 4-i. Food Insecurity among Households with Young Children,
2000–2021
Percentage of households with children under six reporting low or very low food security
25

20

15

10

5

0
2000

2002

2004

2006

2008

Hispanic or Latino
Non-Hispanic Black

2010

2012

2014

2016

2018

2020

Non-Hispanic Asian American or Pacific Islander
Non-Hispanic white

Sources: Current Population Survey; CEA calculations.

white and Asian American or Pacific Islander children (U.S. Census
Bureau 2021a). As of 2021, the rate of food insecurity was higher for
households with children, for households with children under age six,
and particularly for single-woman-headed households than it was for
households overall (USDA 2022a).
As households with children face food insecurity, ECE settings
and schools will continue to serve as an important source of nutritional
assistance for children. Beginning in 2024, as part of ongoing efforts to
advance families’ food security, children who receive free or reducedprice school meals will have access to a permanent food assistance program to address the summer gap in access to nutrition support (USDA
2022b).

years had all parents in the household working (BLS 2022). In large part,
this stems from a rise, over the past half century, in maternal labor force
participation. From the mid-1970s to the mid-1990s, participation rates for
both prime-age women and prime-age mothers grew by about 20 percentage
points. Since then, both rates have plateaued and have even experienced
periods of decline, although they remain higher today than in the mid-1970s
(figure 4-2).

Investing in Young Children’s Care and Education | 131

Figure 4-2. Labor Force Participation Over Time, by Maternal Status
Percentage of prime-age women in the labor force
80
75
70
65
60
55
50
1976

1980

1984

1988

1992

1996
Overall

2000

2004

2008

2012

2016

2020

Mothers

Sources: Current Population Survey; CEA calculations.

As noted in chapter 1 of this Report, the rise in maternal labor force
participation occurred in tandem with a rise in paid ECE and senior care.
Time use data suggest that, among mothers of young children, more highly
educated mothers in particular have reduced time spent on care of their
children concurrent with the rise in maternal employment (Flood et al.
2022). While increased formal ECE was likely partially the result of maternal participation, research indicates that ECE also enables it (Herbst 2022;
Morrissey 2017). Specifically, research examining ECE availability, expansion, and subsidization finds that ECE has large, positive effects on maternal
employment (Blau and Tekin 2007; Gelbach 2002; Herbst 2017). Several
studies of programs in other countries—specifically Canada, Germany, and
Norway—also confirm the responsiveness of mothers’ employment to ECE
expansions (Baker, Gruber, and Milligan 2008; Bauernschuster and Schlotter
2015; Finseraas, Hardoy, and Schøne 2016; Lefebrve and Merrigan 2008).
Evidence from across ECE contexts, including childcare subsidy
receipt and the introduction of public preschool and kindergarten programs,
suggests that certain mothers’ employment is most affected. Those mothers
who respond to program introduction and availability by working more are
those whose youngest child is eligible for the program, and those who are
relatively disadvantaged (i.e., single mothers and those with lower levels of
education) (Cascio 2009; Cascio and Schanzenbach 2013; Fitzpatrick 2010,
2012; Gelbach 2002; Tekin 2005, 2007). Research on the Head Start program similarly documents that program access improved employment and
earnings outcomes for single mothers (Wikle and Wilson 2022).
132 |

Chapter 4

In addition to increasing parents’ likelihood of working at all, policies
that expand access to ECE can boost their productivity in the workplace
by allowing them to get additional education or job training and increasing
the likelihood they will work full time (Davis et al. 2018; Herbst and Tekin
2011). These effects may have been especially important in the context
of the COVID-19 pandemic, which, according to survey evidence, made
parents, and mothers in particular, likely to reduce their work hours or productivity even while remaining in their jobs (Pew Research Center 2022).
Increased access to ECE, such as through policies to expand availability
and reduce costs, would likely enable more parents to work, which could
bolster long-run economic growth and expand the economy’s productive
capacity. However, as the next section describes, the market for ECE faces
fundamental challenges, hampering families’ ability to secure ECE that
meets their needs.

Challenges in the Market for Early Care and Education
Although, as noted above, investments in children can make a difference not
only for the children themselves but also for their families and communities
in ways that spill over to society, it is not at all clear the ECE market works
for both providers and families. Important questions include: can families
that need care access a well-functioning market to meet their needs? And,
is the supply of ECE inefficiently low from society’s perspective? The
evidence indicates that the care economy faces fundamental challenges in
terms of both supply and demand, and thus there is an important opportunity
for effective policies to improve the functioning of this market.
On the supply side, a core concern is whether care businesses that
invest in higher quality—such as through better staff compensation, professional development and coaching for early educators, and lower child–caregiver ratios—can recoup the increased costs while also charging rates that
families can afford.
On the demand side, families face liquidity constraints, given that they
are more likely to be financially strapped when their children are young and
the parents are in the early, and relatively unstable, years of their earnings
trajectories (Davis and Sojourner 2021). That is, many families simply lack
the resources to invest in high-quality care when it is needed and cannot
borrow against future earnings to do so at competitive interest rates. Highquality care can consume a large fraction of families’ budgets, especially
for low-income families (Landivar, Graf, and Rayo 2023; U.S. Department
of the Treasury 2021). As such, many families are sensitive to the price of
childcare and may respond by forgoing market-based care and instead relying on parental care or informal arrangements (Morrissey 2017).

Investing in Young Children’s Care and Education | 133

Workforce Challenges
Early care and education is a labor-intensive industry, and, as discussed
above, a stable, qualified workforce is an essential ingredient in the provision of high-quality ECE services. According to the 2019 National Survey
of Early Care and Education (NSECE), a nationally representative survey of
childcare providers conducted before the pandemic, the average departure
rate of caregivers in center-based care—the share of staff members who
work directly with young children who left the focal program in the last 12
months—was 17 percent. Though this rate of turnover is comparable to the
public teaching profession (16 percent), half of teaching departures are to
another teaching position, whereas evidence suggests that many childcare
providers leave the industry entirely (NCES 2016). Research in Louisiana
suggests even higher turnover overall—finding that more than one-third of
ECE educators depart annually, and that most turnover is a departure from
the ECE profession (Bassok et al. 2021a). Evidence also demonstrates that
turnover varies considerably across centers, with nearly 10 percent losing
more than half their workforce in each year of the three-year study period
(Doromal et al. 2022), a and higher turnover in centers paying low wages
and those serving infants and toddlers (Caven et al. 2021).
The 2019 NSECE also documents that the average longevity for a
home-based ECE provider was relatively short, with about 46 percent of
home-based providers having operated five or fewer years (NSECE 2019).
Though these survey data predate the pandemic, the evidence suggests that
periods of low unemployment in the broader economy are related to higher
turnover in childcare employment (Brown and Herbst 2022). Thus, the
tight labor market during the pandemic recovery period could exacerbate
high workforce turnover and slow the recovery from pandemic-induced job
losses in childcare (see box 4-2). Churn in the workforce prevents workers
from gaining experience in the field and impedes staff continuity in ECE
settings, potentially reducing the quality of care.
The workforce challenges for ECE largely stem from workers’ low
pay. As described below, this low pay results, in part, from the price sensitivity of consumers and the thin profit margins of care businesses. Childcare
workers, who are described in more detail in box 4-3, make low wages
relative to typical nonsupervisory workers. In the United States in December
2022, the typical production or nonsupervisory worker made, on average,
$28.19 an hour, yet production or nonsupervisory childcare workers earned
considerably less, $17.95 an hour (BLS 2023). According to one analysis,
childcare workers earn 23 percent less on average than workers in other
occupations with similar composition by age, education, and other demographic characteristics (Gould 2015). In particular, comparing the earnings
of childcare, pre-K, kindergarten, and elementary school teachers illustrates

134 |

Chapter 4

Box 4-2. The American Rescue Plan
and Support for Childcare
At the beginning of the COVID-19 pandemic, the childcare industry was
severely affected. Between February 2020 and April 2020, childcare
employment fell more than 35 percent. Recognizing the disruptions of
the care infrastructure wrought by the pandemic, the American Rescue
Plan (ARP) Act allocated funds to stabilize childcare, including $24
billion in funding for the new Child Care Stabilization Program. Data
from the U.S. Department of Health and Human Services (2022; also see
White House 2022) indicate that more than 200,000 childcare programs
in the United States, with total capacity to serve as many as 9.5 million
children, have received funding through these grants, intended to help
the industry recover by providing grants to childcare programs to help
cover operational costs such as wages and benefits, rent and utilities, and
program materials and supplies. As of the fall of 2022, the most common
uses of funds were personnel costs at centers, and rent and utilities at
family childcare homes.
These grants likely have had economic consequences that extend
beyond their effects on childcare workers and providers. As described
earlier in this chapter, access to childcare is an important input for parental employment, particularly for women (e.g., Morrissey 2017). This
relationship between employment and access to childcare likely helps to

Figure 4-ii. Percent Change in Maternal Employment

Percent change in the 12-month rolling average of the employment–population ratio relative
to January 2020
4
2
0

–2
–4
–6
–8
–10
2019

2020

Low ARP

2021

High ARP

2022

Sources: Current Population Survey; CEA calculations.
Note: Data are restricted to mothers who have at least one child under six. “Low ARP” refers to employment among
those living in the half of core-based statistical areas (CBSAs) with the lowest provider capacity covered by American
Rescue Plan (ARP) funding per population. “High ARP” refers to employment among those living in the half of
CBSAs with the highest provider capacity covered by ARP funding per population.

Investing in Young Children’s Care and Education | 135

explain the significant, disproportionate drop that women experienced
in their attachment to the labor force at the beginning of the COVID-19
pandemic. Whereas the overall employment–population ratio (i.e., the
employment rate) fell by 16 percent between February 2020 and April
2020, the employment rate for women fell by 18 percent. Several studies
indicate that during these months, employment for mothers of young
children was particularly hard hit (Boesch et al. 2021; Collins et al. 2021;
Heggeness 2020; Tüzemen 2021).
The CEA’s analysis comparing maternal employment among
those living in areas with relatively more provider capacity (as a share
of population) supported by ARP funding to employment among those
living in areas with less provider capacity supported by funding suggests that maternal employment has recovered more quickly in areas
with greater capacity supported by stabilization grants (figure 4-ii). This
analysis does not rule out other potential explanations of the differences
in maternal employment across low- and high-ARP places, including
underlying differences in community characteristics, but it points to
an area for further research to better understand the effects of ARP
childcare stabilization funds on the childcare industry and the parents
who rely on it.

the extent of low compensation among care workers. On average, childcare
workers earn less than half, and preschool workers earn just over half, the
average annual earnings of kindergarten and elementary school teachers
(BLS 2021a). Childcare workers also rarely receive nonwage employee
benefits; only 15 percent of these workers belong to an employer- or unionsponsored health insurance plan, compared with 58 percent of all workers
(Gould 2015; BLS 2021b).
In their labor-intensive industry, which typically has families’ payments as the sole source of revenue, childcare providers have limited options
to cuts costs or raise revenue in order to pay higher wages. Low pay means
that ECE workers are more likely to have an income below the Federal
poverty line; 1 in 7 childcare workers lives in a family with an income below
this threshold, compared with about 1 in 16 families overall (Gould 2015).
In addition, 53 percent of childcare workers rely on a public assistance program, such as Medicaid or SNAP, compared with 21 percent of the United
States’ workforce as a whole (Whitebook et al. 2018).

The High Costs of High-Quality Care
Given the importance of ensuring safe, secure, and high-quality ECE for
infants and young children, and to make quality more visible to families,

136 |

Chapter 4

Box 4-3. Who Works in ECE?
Most childcare workers are women, and they are disproportionately
women of color (Banerjee, Gould, and Sawo 2021). Figure 4-iii shows
the breakdown of childcare employment by gender and race/ethnicity
compared with the overall workforce. About 14 percent of childcare
workers are Black and about 24 percent are Hispanic, higher than the
share of Black and Hispanic workers in the overall workforce (6 and 8
percent, respectively).
Additionally, historical norms that have devalued care work, typically performed by women, and labor market discrimination affecting
women and people of color may exacerbate low pay. The current composition of the care workforce has legacies in slavery, when Black women
acted as caregivers through coercion and force (Glenn 2012). Since the
end of the Civil War, care workers have been shut out from workforce
protections, such as those enacted under the New Deal (Burnham and
Theodore 2012). Lawmakers continue to exclude many care workers
from labor protections and benefits, including minimum wage laws, paid
leave, retirement benefits, and overtime pay. The historical roots of the
devaluation of care work, and the ongoing barriers to equal treatment
that women and people of color face in the labor market, likely continue
to affect the pay and working conditions for ECE workers today.

Figure 4-iii. Racial and Gender Breakdown of Employment
Percentage of employment
100
90
80
70
60
50
40
30
20
10
0

All workers
Men
Hispanic women

White women
AAPI women

Childcare workers
Black women
Women of other race/ethnicity

Source: Gould, Sawo, and Banerjee 2021.
Note: AAPI = Asian American Pacific Islander.

Investing in Young Children’s Care and Education | 137

there are rules and standards for formal (i.e., licensed and regulated) childcare providers. Some regulations vary by State, and others are Federal. For
example, the Child and Adult Care Food Program’s Child Care Standards,
which some childcare centers must meet to receive certain Federal reimbursements, require childcare centers to have at least one early childhood
educator for every four children under age three (but at least six weeks of
age), and one educator for every six children between age three and six.
These quality regulations are critical for ensuring children’s safety and
well-being, and insofar as they require higher staffing levels and childcare
workers with in-demand skills, they necessarily increase providers’ costs of
doing business.
Additionally, though industries such as manufacturing have seen
large technological advances leading to improvements in quality and labor
productivity, these advances are less applicable to labor-intensive, servicebased industries such as ECE. Like those for many services, 60 to 80 percent
of childcare business expenses are for labor (Workman 2018). Increasing
wages in other industries that have higher labor productivity gains means
that wages for care workers must also increase for care businesses to
compete for workers, thus raising overall prices. As noted above, stable
child–caregiver relationships are a key component of high-quality ECE, and
one documented way to improve continuity in the ECE workforce is through
competitive pay (Bassok et al. 2021b; Grunewald, Nunn, and Palmer 2022).

Figure 4-3. Formal ECE Consumption, by Income Level
Percent
70
60

60

49

50
40
30

37

34
29

20

1

2

21

17

13

11

10
0

37

3

Income quintile

Share of households receiving care

4

5

Share of households paying for care

Sources: 2019 National Survey of Early Care and Education; CEA calculations.
Note: Early care and education (ECE) measures are limited to children under age 6 and to “formal” ECE, which includes
paid individuals (with no prior relationship), center-based care, preschool, community-based care, and other organizational
ECE on a regular and irregular basis.

138 |

Chapter 4

High-quality ECE is fundamentally an expensive service, so it is not
surprising that its use and costs vary considerably across the income distribution. Figure 4-3 shows formal ECE consumption by family income level,
giving the proportions of both households receiving care and those paying
for care. Both measures increase with income, and families across the
income distribution are participating in some subsidized care, though much
more pronouncedly, in the lowest income quintiles. Only about 15 percent of
households with young children in the lowest quintile pay for ECE, while 53
percent of those in the highest quintile pay for ECE (NSECE 2020).
Although low-income families more often qualify for subsidized
services, those that pay for ECE devote a larger fraction of their income to
ECE expenses than middle- and high-income families. Recently released
data from the Department of Labor’s National Database of Childcare Prices
document that prepandemic median childcare prices for one child account
for between 8 and 19 percent of median family income in communities
across the country, with even higher prices for infant care (Landivar,
Graf, and Rayo 2023). For low-income families, that burden is even more
pronounced. Figure 4-4 shows average annual ECE expenses as a share of
income for all households with young children receiving formal ECE and for
households that pay for ECE. For those paying for ECE, the share of income
spent on ECE declines sharply by income level. The lowest-income families
that pay for formal ECE spend one-third of their annual income on ECE,

Figure 4-4. Average Annual Expenses for Formal ECE as a Proportion of
Income, by Income Level
Percent
35

33

30
24

25
20

18

15
10

11
9

8

8

10

8

6

5
0

1

2

3

Income quintile

Among households receiving formal care

4

5

Among households paying for formal care

Sources: 2019 National Survey of Early Care and Education; CEA calculations.
Note: Early care and education (ECE) measures are limited to children under age 6 and to “formal” ECE, which includes paid
individuals (with no prior relationship), center-based care, preschool, community-based care, and other organizational ECE on
a regular and irregular basis.

Investing in Young Children’s Care and Education | 139

compared with the highest-income families, which spend about 10 percent
of their annual income on ECE.

ECE Pricing and Price-Sensitive Consumers
As noted above, businesses supplying care services face a pool of consumers
with financial constraints that may limit their ability to afford the cost of
high-quality care. In particular, low- and moderate-income families tend to
be more likely than higher-income ones to curtail their purchases of these
services if the price rises—by forgoing nonparental care altogether or by
relying on informal, unpaid, or lower-quality ECE services.
The budget constraints that families face in turn affect the supply of
high-quality care. ECE providers serving families that are more sensitive to
prices may be unable to afford costly quality improvements. In supplying
care, providers choose their investment in quality at the point where their
marginal revenues equal their marginal costs—that is, where an additional $1
invested is equal to an additional $1 earned. Providers serving low-income
families have little economic ability to improve quality from a relatively low
level; their clients may not be able to pay more for higher-quality care due
to their budget constraints. In theory, with full information and accessible
credit markets, parents may be willing to borrow against future earnings to
access high-quality ECE that meets their and their young children’s needs.
However, such credit is not generally available. As such, families must pay
for childcare out of their current income, which may be particularly constrained when children are young and parents are in the low-earning stages
of their careers (Davis and Sojourner 2021).
Providers serving high-income families, conversely, can more easily
charge higher prices to recoup the costs of their investments in quality.
When these providers invest in better-quality care, their clients are generally
able to pay higher prices, in large part because their family budget/income
can accommodate it. This aspect of the ECE market gives rise to a stronger
relationship between quality and total revenues among providers serving
high-income families.
ECE pricing is also complicated by parents who stay home full time
and informal care providers. The most recently available evidence suggests
that about one in five parents was a full-time, stay-at-home caregiver in 2016
(Livingston 2018). Further, some people who supply childcare services do
so while also caring for their own children, altering their cost-benefit calculus (Porter et al. 2010). These care providers charge lower rates on average
than larger, licensed providers, exerting downward pressure on prices in the
broader ECE market and attracting families that cannot afford center-based
care (National Women’s Law Center 2018). Parents may also shift to relatives, neighbors, or in-home providers on an ad hoc or more permanent basis

140 |

Chapter 4

when market-based options fail to meet their needs. Evidence suggests that
these informal ECE settings are often of lower quality than parental care and
center-based care (Bassok et al. 2016; Flood et al. 2022).
In 2018, 88 percent of childcare businesses were sole proprietors (i.e.,
with no employees other than themselves), and the average receipts per
establishment were about $16,000 (U.S. Census Bureau 2018). Even under
the unreasonable assumption that providers had no expenses, these receipts
put the average sole proprietor at about the 20th percentile of the earnings
distribution. Indeed, absent other resources, at these revenue levels, it would
be difficult to sustain a family by running a childcare business. These data
suggest that some providers may supply care at below-market rates, perhaps
as supplementary income while providing care for their own children or
family members, with altruistic motives, or because of limited employment
options.

Business Model Fragility
As noted throughout this chapter, the ECE market is fundamentally challenged because it cannot provide high quality at prices families can afford.
The ECE market has other fundamental characteristics that are factors in its
business model, which are vulnerable to economic headwinds. Researchers
have confirmed that childcare responds more strongly to negative economic
shocks than other low-wage industries and takes longer to recover from
recessions than the rest of economy (Brown and Herbst 2022). In sum,
ECE is a highly fragmented industry, populated by small firms, often sole
proprietorships, that pay low wages, have high labor turnover, and face low
profit margins of less than 1 percent for most childcare providers (Carson
and Mattingly 2020; Grunewald and Davies 2011; U.S. Department of the
Treasury 2021).
Liquidity challenges for childcare business owners explain why even
a few weeks without revenue is often untenable for ECE providers, as
became evident during the COVID-19 pandemic. According to the CEA’s
analysis of November 2021 Small Business Pulse data, 82 percent of social
assistance small businesses (which include childcare businesses) reported
large or moderate negative effects of the pandemic on the business, compared with 66 percent of small businesses in general. In the same data,
almost double the number of social assistance businesses (nearly 4 percent)
reported temporarily or permanently closing, compared with all small businesses (2 percent). In 2021, social assistance businesses were also more
likely to report that they anticipated needing financial support or additional
capital in the next six months.
The demographic composition of childcare providers may exacerbate the issue of limited access to capital. The owners of private childcare

Investing in Young Children’s Care and Education | 141

Figure 4-5. Ratio of Young Children to Childcare Capacity in 2018

Ratio of children
under 5 to
childcare slots

Sources: Center for American Progress (2020); CEA calculations.

businesses are disproportionately women and people of color, aligning with
the composition of the ECE workforce (as shown in box 4-2), and these
providers may face more pronounced barriers in capital markets. Almost all
childcare businesses—nearly 97 percent—are owned by women, while half
are minority-owned (National Women’s Business Council 2020; Mueller
2020). Yet women and minorities tend to have fewer assets to get them
through tough times. One study indicates that even after controlling for other
differences, small business owners who are women or people of color have
lower loan approval rates and pay higher interest on loans for their businesses (Asiedu, Freeman, and Nti-Addae 2012).

Participation in and Availability of ECE
Data on ECE participation represent the intersection of the supply of and the
demand for ECE slots; participation requires both the availability of a slot,
referring to its provision, along with take-up of the slot, which incorporates
a family’s care preferences and needs. That is, for a family to access ECE in
the United States, there must be an available slot that also meets the family’s
needs in terms of cost, location, operating hours, and quality, among other
factors. According to data on childcare capacity and population by county,
more than half of Americans live in neighborhoods where the number of
young children outpaces the availability of licensed childcare slots by three
to one or more (Malik et al. 2018). Figure 4-5 maps the ratio of children
under age five to licensed childcare slots across U.S. counties.
The ratios of young children to childcare slots are larger when looking
at infant and toddler care, as shown in figure 4-6 for counties in States with
142 |

Chapter 4

Figure 4-6. Ratio of Infants and Toddlers to Childcare Capacity in 2018

Ratio of children
0–2 years of age
to childcare slots

Sources: Malik et al. (2018); CEA calculations.

available data for 2018. In one analysis, researchers found that 80 percent of
the counties for which they had data had at least three infants and toddlers
for every childcare slot for children under three (Jessen-Howard, Malik,
and Falgout 2020). Rural and low-income communities were more likely to
have high child-to-capacity ratios—which could reflect lower demand for
nonparental ECE in those areas—and Hispanic families were more likely to
live in areas with high ratios (Malik et al. 2018).
An undersupply of ECE slots may exacerbate a lack of participation in
formal ECE. In 2019, 53 percent of children age three to six years who were
not yet enrolled in elementary school were in a formal preschool setting
outside the home (U.S. Census Bureau 2021b).4 Prepandemic data point to
existing gaps by race, ethnicity, and family socioeconomic status. Hispanic
children, in particular, have historically participated in formal care at lower
rates, and Black children more likely to be in the care of relatives than other
children (de Brey et al. 2019). Lower-income and disadvantaged families
have used nonparental care at lower rates, though participation among families at the lowest end of the socioeconomic distribution resembles that of
more advantaged families (de Brey et al. 2019; NCES 2022).
Many argue that the differences by socioeconomic status and region of
the country in child participation in ECE is due to a lack of availability of
suitable slots. However, is it possible that what appears to be a lack of availability—more young children than capacity—could in fact be the result of
lower demand due to parents’ preferences? Data sources, including surveys,
In the October School Enrollment Supplement of the Current Population Survey, respondents are
asked if the focal child attends “preschool” or “nursery school.”

4

Investing in Young Children’s Care and Education | 143

Figure 4-7. Excess Demand by Provider Type
Percentage of providers experiencing excess demand
85
80

79

81
75

75
70

70

73

75

71
69

65

65

Overall
73

60

Child age group

Community’s poverty density

Rural

Moderatedensity urban

High-density
urban

High
poverty

Moderate
poverty

Low
poverty

Age 3–5

Age 0–3

50

Age 0–5

55

Community’s urbanicity

Sources: 2019 National Survey of Early Care and Education; CEA calculations.
Note: Excess demand is defined by whether providers have turned families away due to lack of capacity or had a waiting list in
the past year. Line denotes the overall percentage of providers experiencing excess demand (73.4 percent).

can help to identify whether observed participation rates fall short of families’ demand for ECE slots for their children. For example, to conduct the
National Head Start Impact Study, researchers constructed a nationally
representative sample of Head Start grantees. Because excess demand was
a critical feature of the study design, grantees could only participate if they
expected to be oversubscribed in the fall of 2002; 89 percent of Head Start
grantees in the nationally representative sample were not serving all eligible
children in the community who wanted Head Start (U.S. Department of
Health and Human Services 2005). Analysis of prepandemic data found that
there were 63 Head Start slots for every 100 income-eligible, preschool-age
children who lived within 5 miles of a Head Start center (Ghertner and
Schreier 2022).
In addition to the Head Start program, other data sources similarly
document excess demand for ECE providers’ limited slots. National Survey
of Early Care and Education data for 2019 suggest that 73 percent of centerbased care providers experienced excess demand for childcare slots, in
that they either rejected families because they were too full or maintained
waiting lists. As presented in figure 4-7, excess demand varied by provider
type. Providers serving only infants and toddlers and those serving all young
children were more likely to report excess demand for their services (81
percent and 79 percent, respectively) than those serving only preschool-age
children (65 percent). Though excess demand did not vary linearly with
community poverty, the level of urbanicity was important, with providers in
rural areas less likely to report excess demand. There is also a limited supply
144 |

Chapter 4

Figure 4-8. Reasons Households Face Difficulty Finding Care

Percentage of all households that face difficulty finding care reporting primary reason for difficulty

100
90
80
70
60
50
40
30
20

Mother’s employment
Cost

Child age group
Lack of available slots

Income relative to poverty
threshold
Quality

Rural

Urban

Below

Above

Age 3–5

Age 0–2

Not in labor force

Unemployed

Employed

0

All

10

Location

Location

Sources: National Center for Education Statistics (2019); CEA calculations.
Note: Households included are those that reported some or much difficulty finding the type of childcare or early childhood program
they wanted for their child, or reported that they did not find the childcare program they wanted. Households are grouped by their
response to the question “What was the main reason for the difficulty finding childcare or early childhood programs?” The four
most common reasons are displayed, so the bars do not sum to 100.

of childcare subsidies funded by the Federal Government and States; only
16 percent of the children who were eligible for oversubscribed subsidies in
2019 received them (Chien 2022).
On the consumer side, households also report difficulty accessing
care that meets their needs. In 2019, 76 percent of households that searched
for care for their young children had difficulty finding care that met their
needs (National Household Education Surveys Program 2019). Among this
group, when respondents were asked the main reason for difficulty, the most
common barrier was cost, followed by a lack of open slots (figure 4-8).
Other significant barriers in the search for care included locational challenges and insufficient quality. Cost was a particularly pronounced concern
among urban households and households with an income below the poverty
threshold. A lack of available slots at the ECE providers they contacted was
a more salient difficulty for households with working mothers, those above
the poverty threshold, and those in rural areas. The disconnect between
families’ reports of difficulty finding ECE and providers’ reports of a lower
incidence of excess demand in rural areas perhaps suggests that the types of
available care, ages served, or other program features offered in rural areas

Investing in Young Children’s Care and Education | 145

do not meet families’ needs. Previous research also documents more pronounced search difficulty for Black and Hispanic households (NCES 2018).
The undersupply of ECE warrants attention because of the documented
effectiveness of investments in facilitating parents’ labor force attachment
as well as in improving children’s short- and long-term outcomes, both of
which are important for individual well-being and strong economic growth.

The Role of Subsidies in the Market for Care
The sizable social benefits of high-quality ECE and the challenges in the
ECE market create an opportunity for policy innovation. Hendren and
Sprung-Keyser (2020) document the returns on various investments across
the life cycle using a metric called the marginal value of public funds, which
includes any increased revenue and cost savings for the government, and
find that investments in childhood health and education yield the largest
returns. Though other public and private entities also spend money on ECE
in the United States, increased Federal funding could help move the quality
of care for young children closer to the socially optimal level (Davis and
Sojourner 2021). Research indicates that improving ECE—and reaping the
social and economic benefits of investing in children—requires (1) broadening access, and in particular, addressing disparities by race, ethnicity, and
family socioeconomic status; (2) incentivizing supply building, including
workforce support; and (3) ensuring quality.

International Comparisons
Many countries around the world subsidize ECE (Olivetti and Petrongolo
2017). Whereas among all countries that belong to the Organization for
Economic Cooperation and Development (OECD), national governments
spend an average of 0.74 percent of their gross domestic product on ECE,
the United States spends only 0.33 percent (OECD 2021). As discussed in
chapter 6 of this Report, women’s labor force participation in the United
States has stagnated and fallen behind participation rates in many other
comparable countries. Researchers have advanced the relative lack of
family-friendly policies in the United States as one potential explanation
for why U.S. women’s labor force participation has failed to increase at the
same rates as its peer countries (Blau and Kahn 2013).
Among OECD countries, the United States has one of the lowest
ECE participation rates among children age three to five years, at 66 percent (OECD, n.d.). This rate was essentially unchanged between 2015 and
2020, measured before the COVID-19 pandemic. Notably, several OECD
countries have universal or near-universal ECE participation rates among
children age three to five. This group includes Ireland, which experienced

146 |

Chapter 4

a large increase, from 79 percent in 2015 to universal participation in 2020
(OECD, n.d.), concurrent with major reforms of and national investment
in ECE, including improved compensation for early educators (Moloney
2021).
Though the United States stands out among advanced economies for
its relatively low amount of spending on preschool-age children (age three to
five), as measured by spending per child served or as a proportion of gross
domestic product, public spending on ECE for the youngest children from
infancy to age two is particularly low (OECD 2021). Many other countries,
particularly the Nordic ones, spend the most on ECE for infants and toddlers
and continue to invest heavily in the years before school entry.
The United States’ ECE landscape is different from those of many
other OECD countries, and it is also importantly embedded in a different
policy context that has implications for the functioning of ECE programs.
Of the OECD countries with available data, more than 70 percent have a
centralized authority for ECE, with oversight for the system that serves
children from birth or age one through primary school entry, unlike the
United States; many also have established a right to at least one year of
ECE enrollment before age five (OECD 2019). In addition, parental leave
policies in many other countries alleviate pressure on the ECE infrastructure
for providing infant care, which is the costliest to provide and the least agile
in accommodating fluctuations in enrollment, due in part to smaller group
sizes and child-to-adult ratios (Landivar, Graf, and Rayo 2023; OECD
2011; Office of Child Care, n.d.). All OECD countries, with the exception
of the United States, offer nationwide paid maternity leave (OECD 2016).
Many also offer paid paternity leave after the birth of a child, and 23 OECD
countries provide paid parental leave that allows parents to share caregiving
responsibilities in that time period (OECD 2016).

Subsidies in the United States’ ECE Market
Subsidizing the United States’ ECE infrastructure more robustly could make
it possible for care providers to invest in high-quality services, including
adequately compensating workers, at a price that families can afford. Box
4-4 outlines the major Federal investments in ECE.
Two recent working papers find that a combination of subsidies targeting low-income families coupled with provider-side investments is the most
effective means to expand enrollment in high-quality ECE (Bodéré 2023;
Borowsky et al. 2022). Subsidies tied to the cost of providing high-quality
care allow providers to invest in costly quality improvements, and adjusting
the price childcare consumers pay based on their income makes it easier for
families to fit high-quality care into their budgets.

Investing in Young Children’s Care and Education | 147

Box 4-4. Federal ECE Investments
Currently, the Federal Government invests in ECE through several
channels, some of which direct funding toward private and public
organizations to provide free or subsidized services, while others provide
financial resources directly to families for spending on ECE services.
Head Start is the federally funded program, operated by public
agencies, private nonprofit and for-profit organizations, Tribal governments, and school systems, providing free ECE for preschool-aged
children from low-income families (ECLKC 2022). The Early Head
Start program serves pregnant women and infants and toddlers from
low-income households through home visitation and center-based services (ECLKC 2019).
The Preschool Development Grant–Birth through Five also invests
in ECE, with the goal of supporting systemic enhancements in strategic
planning, family engagement, workforce development, and quality
improvement across all ECE programs, including but not limited to
States’ preschool programs (OESE 2023; Office of Early Childhood
Development 2022).
The Child Care and Development Fund (CCDF), authorized by
the Child Care and Development Block Grant (CCDBG) Act, provides
funding to States, territories, and Tribal governments to invest in ECE
programs as well as directly to low-income families pursing work, education, or training opportunities to spend on childcare (Office of Child
Care 2022).
Some ECE benefits operate through the tax code: the now fully
refundable Child and Dependent Care Credit, a tax credit that supports
working families with childcare expenses (IRS 2022), and the EmployerProvided Child Care Credit, which provides tax credits to employers
with qualified childcare expenditures, including operating on-site childcare facilities or contracting with childcare providers to offer services to
their employees (Smith, McHenry, and Mullaly 2021).

Recent childcare policy proposals would encourage States to build
the supply of high-quality ECE and expand access to it through, in part,
incentives for providers to increase investments in quality. In addition, these
proposals include subsidies targeting low- and middle-income households.
Both these features would allow providers to recoup the cost of additional
quality investments, counteracting market frictions that lead to underinvesting in quality, as discussed above.
Investing in quality will require both process improvements and better
job quality for care workers to attract and retain people with the appropriate

148 |

Chapter 4

Box 4-5. New Data and New Methods to
Inform Investments in Children
Understanding the current lay of the land in ECE and how to effectively
invest in children requires continued innovation in data infrastructure and
research methods. There are no systematically collected measures of ECE
programming, inputs, and outcomes across the mixed delivery system,
and information on ECE enrollment from household surveys lags real
time considerably. Misalignment in the timing and incidence of costs and
benefits creates challenges for public investments in children; the time
horizon over which such investments realize their returns is long, and most
budget scoring calculations fail to account for long-term benefits.
Timely, responsive data collection. Issues with the limited data on
ECE provision and participation, and the timeliness of its availability
existed before the COVID-19 pandemic, but real-time data collection
became increasingly important during the pandemic for assessing which
programs or elements of programs were achieving their intended goals
(Cajner et al. 2022). Two surveys that emerged in response have been
widely used in analysis: the Household Pulse Survey and the School
Pulse Panel. In theory, these ongoing surveys have much potential to
both inform policy and support research, but the Household Pulse Survey
has issues with representativeness and low response rates (Bradley et al.
2021). Redesign and incentives could address these problems, and gathering data on households and schools over time holds promise for use in
future research and policymaking.
Unlocking and expanding the potential of existing data sources is
likely to be more cost-effective than collecting new data. Administrative
data, for example, often contain rich information on children’s and families’ interactions with services. The ability to link administrative data over
time and across sources could facilitate many fruitful research pursuits to
inform policy and practice (Bigelow et al. 2021).
Measuring long-run effects. Several new methods capitalize on the
documented relationships between short-term metrics and longer-term
outcomes of interest to project or estimate the long-run and broader
impact of interventions. One recent paper documents this evolution in
economic research on the effects of the U.S. social safety net on children,
as causal methods have evolved, sufficient time has elapsed, and data
availability has improved (Aizer, Hoynes, and Lleras-Muney 2022). New
and reinvigorated approaches to capturing long-term effects include lifecycle benefit forecasts (García et al. 2020), surrogate indices (Athey et al.
2019), and the framework of the marginal value of public funds (Hendren
and Sprung-Keyser 2020). Ongoing innovation in this space demonstrates
that there is interest in, and urgency to, more quickly measuring the broad
and full impact of programs. This need is particularly pressing when
assessing programs and policies that affect children.

Investing in Young Children’s Care and Education | 149

skills. Evidence indicates that labor supply in ECE settings responds to
higher wages, which suggests that as ECE jobs become higher quality,
more qualified people will remain in care jobs and seek to be hired by care
businesses (Blau 1993; Borowsky et al. 2022; Mocan 2007). Therefore, the
supply and quality of ECE would increase, helping to counteract the longstanding undersupply of high-quality care.
As discussed in box 4-4, the Federal Government currently invests
in ECE programming through multiple avenues, and many States have
proceeded with efforts to increase availability and lower costs, often
using Federal funding from the ARP. A number of States—including
Connecticut, Delaware, Georgia, Maine, Maryland, Oregon, Pennsylvania,
and Vermont—are offering one-time bonuses for care workers or are permanently subsidizing pay increases (Child Care Aware 2022). In Texas, for
example, lawmakers increased reimbursements to providers serving infants
and toddlers from low-income households, and have required childcare
programs receiving public subsidies to participate in the Texas Rising Star
quality rating and improvement system (Goldstein 2022).
The Federal Government could also play a significant role in improving data infrastructure that supports effective policymaking for ECE through
better real-time information on availability and participation, and by building evidence. Box 4-5 discusses some of the new developments on this front,
and avenues for improvement.

Conclusion
Early care and education programs play an important dual role for families:
(1) contributing to young children’s development of cognitive and socialemotional skills; and (2) supporting parents’ engagement with the labor
market. Both these channels also generate substantial benefits for society.
Ensuring that all children have access to high-quality ECE requires investing
both in families’ ability to access programs and in the provision of these programs—including supporting workforce improvements and smart capacity
expansion. Such investments in ECE can yield significant long-run benefits
not only for the affected children themselves but also for society at large.
Although the COVID-19 pandemic exacerbated many gaps in the
Nation’s ECE infrastructure, many of these challenges—and particularly disparities by race/ethnicity and family income—existed before the
pandemic. There are critical issues in the market for ECE programs and
services, and the Nation’s economy often fails to support care businesses,
care workers, and the families in need of their services. These challenges
ultimately lead to low pay for workers and exacerbate the undersupply of
high-quality, affordable, and accessible care for families. However, these
problems could be mitigated with policy improvements.
150 |

Chapter 4

Carefully designed government policies would address frictions in the
ECE market—including workforce challenges and low pay, the high costs
of high-quality care provision, families’ price sensitivity, and the fragility
of the ECE business model—thereby making childcare more affordable
while improving pay for workers and ensuring investments in quality. The
government could thus foster a better-functioning ECE market by funding
subsidies for childcare providers, including incentives to improve the quality
of care and higher caregiver pay, alongside subsidies and publicly provided
ECE programming for families. Together, these policies would address both
the supply and demand sides of the ECE market, ensuring that providers
are willing and able to provide the high-quality, affordable care needed by
families and society.

Investing in Young Children’s Care and Education | 151

Chapter 5

Building Stronger
Postsecondary Institutions
The United States’ postsecondary education system is, in many ways,
the envy of the world. Relative to most other international systems, U.S.
postsecondary institutions are more numerous, diverse, and decentralized,
as well as more likely to offer opportunities for exploration, transfer, and
reentry. Students are likely to benefit from having flexibility to find the
program that fits them best. The high number of institutions relative to those
of peer countries may help spur innovation among competing institutions to
be responsive to the needs of students. These features help explain why the
United States is the top destination for international college students: over 1
million international students were enrolled in U.S. colleges in 2020—more
than triple the number in 1980—and now account for one-fifth of all crosscountry student migration in postsecondary education (Bound et al. 2021;
Institute of International Education 2020).
Nonetheless, as the demand for highly educated workers has increased
over the past half century, earning a valuable postsecondary credential has
remained a challenge for many Americans. The United States no longer
leads the world in postsecondary attainment, and large gaps by income and
race have widened over the past several decades. This has consequences
both for individuals, who miss out on the personal benefits of postsecondary
education, and for society, which forgoes the increased civic participation,
lower reliance on public benefits, increased tax revenues, higher economic
growth, and other benefits that such education brings. Though college continues to be a good investment on average, increased student debt burdens
relative to a generation ago mean this investment includes risks that some
153

students can be left worse off if their education does not yield the labor
market benefits they expect.
Federal and State support for postsecondary education has long included
direct funding for institutions, but in recent decades the primary form of
support has shifted to financial aid for students. These efforts have been
essential to help offset rising tuition and fees, which before accounting
for financial aid, have roughly tripled in real terms since 1980 and have
increased even more at public four-year institutions (Ma and Pender
2022a). Yet policies aimed at institutions and the programs they offer—to
build capacity, to support colleges in serving students well, and to hold
them accountable when they do not—are a critical complement to policies
lowering financial barriers to attendance. Federal policy can influence the
quality of postsecondary options with both carrots, such as Federal support
to help institutions improve student outcomes, and sticks, such as policies
to hold institutions accountable for the economic value they provide. Where
there are geographic barriers to access, institution-oriented policies can
help facilitate the equitable expansion of high-value programs and deter the
expansion of lower-value ones.
Before considering institution-oriented policies, this chapter first describes
the landscape of U.S. postsecondary education, documenting the extraordinary variation across such institutions, and summarizing evidence that institutions and their programs are themselves a critical determinant of student
success. The chapter next explains the rationale for Federal investment in
postsecondary education, and places the U.S. model of postsecondary education finance in historical and international contexts. The decentralized “high
tuition, high aid” model currently used in the United States has some advantages but also generates economic risks for students who fail to graduate or
whose education does not pay off in the labor market. Imperfections in the
market for postsecondary education limit the potential of the market alone
to drive improvements. Such imperfections include geographic constraints,

154 |

Chapter 5

informational and behavioral constraints, and production constraints that
limit institutions’ ability to react quickly to fluctuating demand. The chapter
documents one source of production constraints: State appropriations for
public postsecondary institutions tend to fall during economic recessions,
precisely when demand for enrollment in such institutions tends to rise.
The rest of the chapter considers how Federal policy can help support
postsecondary institutions, reviewing a range of options to improve or
maintain the quality of such institutions, to hold institutions accountable
for student outcomes, and to reduce geographic barriers to access. The
institution-oriented policy efforts described in this chapter have the potential
to improve the landscape of postsecondary options. Throughout the discussion, the chapter highlights actions that the Biden-Harris Administration has
already undertaken to improve the postsecondary institutional landscape,
with the ultimate goal of ensuring that all students have access to a college
education of value.

The U.S. Postsecondary Institutional Landscape
The degree of heterogeneity in the institutional landscape is a distinctive
feature of the U.S. postsecondary system. Colleges in the public, private
nonprofit, and for-profit sectors offer a different mix of programs, enroll
a different composition of students, and are financed in different ways.
Four-year institutions offer bachelor’s degrees in fields that can vary substantially in their connection to specific occupations. Community colleges
offer a range of credentials, including academic associate degrees (e.g.,
for students who intend to transfer to a four-year program); occupational
associate degrees; short-term certificates intended to help students access
the labor market quickly; and, increasingly, bachelor’s degrees. Historically
Black Colleges and Universities and Tribal Colleges and Universities have
an additional mission: to serve communities that have historically been
excluded from postsecondary education. In addition, institutions vary in the
extent to which their students graduate and succeed in the labor market. This
institutional landscape is both a driver and consequence of how the United
States supports higher education and has implications for how its quality can
be improved.

Building Stronger Postsecondary Institutions | 155

Figure 5-1. Distribution of Enrollment Across Institution Types, by Student Characteristics

Percentage enrolled in each institution
Total enrollment
Age 24 and under
(66.2%)
Age 25 and over
(33.8%)
White
(48.1%)
Hispanic or Latino
(18.9%)
Black or African American
(12.0%)
Asian
(6.9%)
American Indian or
Alaskan Native (0.6%)
Native Hawaiian or Other
Pacific Islander (0.2%)

0

10

Private not–for–profit, 4–year or above

20

30

Public, 4–year or above

40

50

Public, 2–year

60

70

Private for–profit, any level

80

90

Other institutions

Source: National Center for Education Statistics, Integrated Postsecondary Education Data System Fall Enrollment component, 2021 provisional data.
Note: The category “other institutions” encompasses public institutions and private not-for-profits of less than 2 years, as well as private not-for-profits of 2 years. Reported in parentheses under each category is the
percentage of total fall enrollment in all institutions captured by the given population. Percentages do not add up to 100 due to rounding and the omission of students with two or more races, students of unknown
race/ethnicity, and students who are nonresident aliens.

Institutions Serve a Diverse Student Population
U.S. college students vary in age and residential status. Only about 13
percent of undergraduates both started college before age 20 and live on
the campus of a residential four-year college (NCES 2022a, 2022b, 2022c).
Nearly 30 percent of enrolled students started their programs at age 20 or
above (NCES 2022a). Among enrolled students younger than 20, about
40 percent attend two-year (or less) institutions, and only about half the
remaining students attending four-year institutions live on campus (NCES
2022d, 2022e). Over one-third of enrolled undergraduates are 25 or older,
a proportion that rises to nearly 44 percent for community colleges and to
nearly 62 percent for the for-profit sector (NCES 2022f).
Undergraduates are, on average, fairly diverse with respect to income
and race, and institutions vary substantially in the extent to which they enroll
different types of students. In any given year, nearly one-third of undergraduates receive Pell Grants, a proxy for a low family income (NCES 2020a).
Institutions vary substantially in the extent to which they serve low-income
students, with about 16 percent of campuses having fewer than one-fourth
of students receiving Pell Grants and about 22 percent having more than
three-fourths receiving them.1 Overall, low-income students are relatively
similarly represented across the two- and four-year sectors and are overrepresented in the for-profit sector, where about 53 percent of undergraduate
students receive Pell Grants (NCES 2020b, 2020c).
CEA calculations, using data from the College Scorecard. These College Scorecard data were the
most recent available publicly, as of September 2022, which for most measures reflect the 2020–21
academic year.

1

156 |

Chapter 5

100

Nearly two-fifths of undergraduates self-identify as Black, Hispanic,
Asian, American Indian or Alaskan Native, or Native Hawaiian or Pacific
Islander. Most such student groups are more heavily represented in community colleges and the for-profit sector. As figure 5-1 shows, students
who self-identify as Black, Hispanic, American Indian or Alaskan Native,
or Native Hawaiian or Pacific Islander are substantially more likely than
white students to attend for-profit institutions. For-profit institutions also
disproportionately attract those age 25 or above.

Institutions Vary in Their Prices and Spending on Students
Postsecondary institutions vary not only in the students they serve but also in
the prices they charge and the amount they spend on student instruction. As
table 5-1 shows, the average institution has an undergraduate sticker price
of roughly $13,000 in tuition and fees per year; has a total cost of attendance of roughly $25,000 after housing, food, books, and other expenses
are included; and has a net cost of nearly $15,000 after grant aid has been
accounted for. Such prices vary tremendously by sector. Private nonprofit
and for-profit colleges have undergraduate net costs of over $20,000 a
year, while public four- and two-year institutions cost roughly $14,000 and
$7,000 for undergraduates, respectively, after accounting for grants.
Institutions also vary widely in the amount of resources they have
available and spend on student instruction—the clearest measure available

Building Stronger Postsecondary Institutions | 157

Table 5-1. College Prices and Expenditures by Sector
All Institutions

Private
Not-for-Profit,
4-Year

Public,
4-Year

Public,
2-Year

Private
For-Profit,
Any Level

Tuition and fees

$12,602

$34,235

$9,149

$3,338

$14,913

Total cost of attendance

$25,235

$49,401

$22,529

$13,170

$26,204

Net cost of attendance

$14,762

$26,045

$13,812

$7,101

$20,400

Instructional expenditures per
FTE student

$9,633

$14,071

$10,617

$6,292

$3,691

Measure

Sources: College Scorecard; CEA calculations.
Note: FTE = full-time equivalent. College prices and expenditures are per academic year, for full-time enrollment.

of an institution’s financial investment in learning.2 Figure 5-2 shows that
this resource distribution is highly skewed, with 70 percent of institutions
spending less than $10,000 per full-time-equivalent student each year and 9
percent spending more than $50,000. Such spending can buy smaller class
sizes, higher-quality instructors, better academic support services, and other
resources that may contribute to student success. As table 5-1 shows, there
is clear variation across sectors in these spending patterns. Across most sectors, higher prices tend to translate into higher spending on students. Private,
nonprofit, four-year colleges spend about $14,100 a year on instruction;
public four-year colleges spend about $10,600; and public two-year colleges
spend about $6,300. The exception to the pattern are for-profit colleges,
where students pay relatively high net prices but receive the lowest instructional spending of any sector (about $3,700).

Institutions Vary in Their Student Outcomes
Student outcomes, such as degree completion rates, also vary substantially
by postsecondary institution. A relatively high fraction of U.S. undergraduate students who start college do not complete their degrees (Bound,
Lovenheim, and Turner 2010; Bowen, Chingos, and McPherson 2009).
Though recent research suggests that graduation rates have increased
somewhat since 1990, fewer than 60 percent of undergraduates seeking a
bachelor’s degree complete such a degree within six years of entry (Denning
et al. 2022). As panel A of figure 5-3 shows, the average college student
attends an institution where about 55 percent of undergraduate students
complete their degree within 150 percent of the time expected (i.e., three
years for two-year colleges and six years for four-year colleges). This
average masks substantial variation, as nearly one-tenth of colleges have
undergraduate completion rates under 25 percent and over one-third have
completion rates above 75 percent.
The instructional spending measure, available in the Integrated Postsecondary Data System (https://
nces.ed.gov/ipeds/), measures total spending across both undergraduates and graduate students, so
care should be taken when comparing such figures with undergraduate costs of attendance.

2

158 |

Chapter 5

Figure 5-3. Variation in Undergraduate Student Outcomes
A. Variation in Completion Rate

B. Variation in Students’ Earnings

Sources: College Scorecard; CEA calculations.
Note: The orange line in Panel A denotes the average fraction of students completing their
undergraduate education within 150% of the expected time. The orange line in Panel B denotes the
average fraction of former students earning more than a high school graduate.

Not all noncompletion is problematic. The U.S. postsecondary system
allows many students to explore college, even when they are uncertain about
the experience. A recent pre-COVID-19-pandemic, large-scale survey of
Americans who had attended college but had not completed their degree
reveals a range of self-reported reasons for this noncompletion (Gallup
2019). Some students’ reasons for leaving suggest that better institutional
practices or financial aid policies might have helped them complete their
degrees, while other reported reasons indicate that some students leave after
learning that college was not a good fit for them. Such exploration can be
Building Stronger Postsecondary Institutions | 159

Table 5-2. Student Outcomes by Sector (percent)
Measure
Degree completion rate
Proportion of students out-earning
typical high school graduate

All Institutions

Private
Not-for-Profit,
4-Year

Public,
4-Year

Public,
2-Year

Private
For-Profit,
Any Level

50

63

56

29

47

70

77

75

59

59

Sources: College Scorecard; CEA calculations.

costly for students attending high-priced institutions. This suggests a policy
role for balancing the benefits of exploration with the need to protect students from making poor investments of time and money.
Variation in postcollege earnings by institution is also striking. One
such measure, available in the College Scorecard, is the percentage of a
given college’s Federal-aid-receiving undergraduate students who, 10 years
after entry, earn at least as much as a typical worker whose highest level
of education completed is high school.3 Comparing earnings with those of
workers who are high school graduates provides a rough proxy for whether
a college’s enrollees have better economic outcomes than if they had not
enrolled in college at all.
The average college student attends an institution where about 60
percent of undergraduate Federal aid recipients out-earn a typical high
school graduate. Yet, as shown in panel B of figure 5-3, at 19 percent of
colleges, fewer than half of such students out-earn high school graduates 10
years later. Relatedly, colleges also vary widely in the extent to which their
students experience upward economic mobility, measured by the fraction of
students entering from the bottom quintile of the income distribution who
later reach the top quintile (Chetty et al. 2017). Degree completion rates
and postcollege earnings indicate striking variation across college sectors,
as shown in table 5-2. Four-year institutions tend to have higher completion
rates and earnings than two-year institutions. Students at community colleges have similar earnings outcomes to students at for-profit colleges, even
though community colleges are substantially less expensive for students to
attend.4

Institutions Matter for Student Outcomes
The extent to which variation in student outcomes is driven by institutions
themselves is challenging to measure. Some outcome differences are due
The College Scorecard (https://collegescorecard.ed.gov/) is a website created by the U.S. Department
of Education that gives students, families, and other interested parties information about the cost and
value of nearly all higher education institutions. The earnings measure discussed here comes from a
national average of the earnings of all those age 25 to 34 who indicated that high school completion
was their highest level of education, were working, and were not enrolled in school during the
measurement year. This threshold is about $31,000 in 2020 dollars.
4
The for-profit figures cited here cover all for-profit institutions.
3

160 |

Chapter 5

to differences in students across institutions. A large and growing body of
literature documents that some portion of undergraduate student outcome
differences across institutions is causally attributable to the institutions
themselves: colleges appear to vary widely in their effects on students. A
given institution’s effects do not appear to be inherent, but depend in part
upon available resources and how those resources are spent; better-resourced
institutions and those that spend more per student on instruction generally
produce better outcomes, including higher graduation rates and labor market
earnings (Lovenheim and Smith 2022). As the evidence suggests, students
of all kinds appear to benefit from attending the college with the best track
record available to them, with the worst colleges leaving the typical student
worse off than they would have been if they had not attended college at all.
Within the four-year college sector, researchers have found that
students are more likely to graduate and have higher earnings when they
attend colleges with more resources, academically stronger peers, and better historical student outcomes such as graduation rates and earnings. Such
patterns hold even when comparing otherwise similar students who enroll in
different colleges (Long 2008; Smith 2013; Mountjoy and Hickman 2021;
Cohodes and Goodman 2014). Evidence from States including Texas and
California suggests that gaining access to well-resourced flagship institutions increases graduation rates and earnings, including for those whose
access comes as a result of “Top Percent” guaranteed admissions policies
(Hoekstra 2009; Andrews, Li, and Lovenheim 2016; Bleemer 2021; Black,
Denning, and Rothstein 2020).
Public four-year colleges have been documented to have substantially
positive effects on students. For example, research in a variety of States
shows that students whose academic background gives them access to
less selective public four-year institutions are more likely to graduate and
have higher earnings than those lacking such access (Zimmerman 2014;
Goodman, Hurwitz, and Smith 2017; Smith, Goodman, and Hurwitz 2020;
Kozakowski 2023). Consistent with observable resource differences across
sectors, enrollment in four-year colleges generally improves student outcomes even more for those choosing between two- and four-year options.
Comparing otherwise similar students who differ only in their proximity
to two- and four-year options suggests that four-year college enrollment
increases the rate of degree completion and may increase earnings (Rouse
1995; Mountjoy 2022).
On average, community colleges have been shown to generate positive effects on students and substantially better outcomes than the for-profit
colleges that enroll similar student populations (Cellini and Turner 2019;
Armona and Cao 2022). Researchers have found that enrolling and completing an associate degree at a two-year college generally improves outcomes
relative to not enrolling or completing one at all (Belfield and Bailey 2017;
Building Stronger Postsecondary Institutions | 161

Mountjoy 2022). Further, they document that, relative to those who start but
do not complete their two-year degrees, graduates of community colleges see
substantial increases in their annual income five to nine years after college
entry (Jepsen, Troske, and Coomes 2014; Bahr et al. 2015; Liu, Belfield,
and Trimble 2015; Bahr 2016; Bettinger and Soliz 2016; Xu, Jaggars, and
Fletcher 2016; Dadgar and Trimble 2015; Belfield 2015). The return from
a two-year degree is even higher in subsequent years after entry and during
economic recessions (Minaya and Scott-Clayton 2022). Some high-demand
community college programs, such as nursing, raise students’ earnings so
much that expanding the number of available slots in such programs would
more than pay for itself via increased tax revenues returned to State and local
governments (Grosz 2020).
For-profit colleges have been found to generate particularly poor outcomes for their enrollees. Advocates of for-profit colleges have argued that
such poor outcomes are due to the disadvantages with which their students
start (Cellini and Koedel 2017). Differences in student composition have
not, however, been enough to explain the large differences in outcomes
between for-profits and other institution types (Deming, Goldin, and Katz
2012; Scott-Clayton 2018a). Community colleges appear to improve earnings more than for-profit colleges, even when accounting for variations in
student characteristics (Cellini and Turner 2019; Armona and Cao 2022).
Numerous studies comparing the earnings of the same for-profit students
before and after enrollment find that such students see little or no earnings
increase relative to those who do not attend college or to those enrolled
in public colleges (Cellini and Turner 2019; Cellini and Koedel 2017).
Résumé audit studies similarly suggest that for-profit degree holders receive
employer callbacks less often than otherwise-identical degree holders from
public colleges and no more often than those with no college education at all
(Darolia et al. 2015; Deming et al. 2016). Enrollment in for-profit colleges
increases debt and worsens labor market outcomes relative to other two- and
four-year options (Armona, Chakrabarti, and Lovenheim 2022). Nearly
two-fifths of for-profit college chains have negative returns for Federal aid
recipients compared with returns from simply gaining experience in the
labor market (Armona and Cao 2022).

The Rationale for and Delivery of Public
Postsecondary Investment
To assess strategies to promote access and improve quality in the postsecondary sector, it is useful to understand the economic rationale for public
sector involvement, and to consider the possible forms such involvement
can take.

162 |

Chapter 5

The Economic Rationale for Public Sector Investment
A key motivation for promoting college is its value as an economic investment for both students and society. For example, though students express
many reasons for pursuing postsecondary education, including personal
exploration and growth, getting a better job tops the list (Fishman 2015;
Stolzenberg et al. 2020). From a societal perspective, expanding educational
access has been associated with economic growth, much like when the
United States in the 20th century led the world in the transition to mass
secondary—high school—education. This expansion also helped to dampen
inequality (Goldin and Katz 2008).
However, in recent decades, as the demand for highly educated
workers has continued to increase, the United States has faced significant
challenges in the transition to broad-based postsecondary education and
training (Goldin and Katz 2008; Neelakantan and Romero 2017). Although
postsecondary enrollment has increased substantially since 1980, the pace
has slowed since 2000 (Ma, Pender, and Welch 2019). The United States is
no longer a global leader in college degree attainment for adults age 25 to
34 years, and the 43 percent completion rate among those entering associate
degree programs in the United States is among the lowest of all countries
belonging to the Organization for Economic Cooperation and Development
(OECD) that reported their equivalent of this statistic (NCES 2021; OECD
2022). Failing to navigate the transition to broader postsecondary education
would represent a missed opportunity, given the substantial private and
societal benefits of college (described in box 5-1). Of particular concern is
the large and growing gap in bachelor’s degree attainment between highand low-income families, which is wider for cohorts born in the 1980s than
for those born in the 1960s (Chetty et al. 2020; Bailey and Dynarski 2011).
Racial disparities in college attainment have also grown over a similar time
span, particularly among women (Emmons and Ricketts 2017; Ma, Pender,
and Welch 2019).
Finally, despite the substantial private and public benefits, many students cannot afford to attend a postsecondary institution without financial
assistance. The problem is that the benefits of college accrue over the course
of a lifetime, while the bill must be paid in advance. Typically, individuals
solve this problem by borrowing to pay for an investment up front, such as
when purchasing a car or home. However, private lenders typically do not
provide loans unsecured by collateral (e.g., those for a car or a house that
can be repossessed) because a college education cannot be returned or resold
if the individual fails to make interest payments and defaults on the loan
(Barr 2004). The existence of such “credit constraints” provides an important rationale for the public sector providing loans to students at subsidized
interest rates.

Building Stronger Postsecondary Institutions | 163

Box 5-1. The Private and Public Benefits of College
The earnings premium for those with a college education is well
documented (Barrow and Malamud 2015). Less well known is that the
benefits of college accrue to a broad range of students at a broad range
of schools. Students with relatively low grades and test scores who
enroll in four-year institutions derive significant earnings benefits from
college attendance (Zimmerman 2014; Ost, Pan, and Webber 2018;
Smith, Goodman, and Hurwitz 2020), as do the 35 percent of students
who enroll in open-access community colleges rather than not enrolling
at all (Kane and Rouse 1995; Mountjoy 2022; NCES 2022g). Associate
degrees—and even some shorter-course credentials in high-demand
occupational fields—yield substantial returns in many fields, including for older students and displaced workers (Grosz 2020; Jacobson,
LaLonde, and Sullivan 2005; Jepsen, Troske, and Coomes 2014).
Postsecondary education also serves the public good. College
attainment leads to increased civic participation, lower rates of involvement in the criminal justice system and reliance on public benefits,
increased tax revenues, higher economic growth, and improved health
in the next generation (Dee 2003; Lochner and Moretti 2004; Lochner
2011; Oreopoulos and Salvanes 2011; Hout 2012; Ma, Pender, and
Welch 2019; Aghion et al. 2009; Currie and Moretti 2003). Reducing
racial disparities in college attainment is particularly urgent, given that
underrepresentation in highly credentialed professions can adversely
affect the treatment and outcomes of historically excluded groups. For
example, recent evidence indicates that students benefit from exposure
to instructors of the same race (Fairlie, Hoffman, and Oreopoulos 2014;
Gershenson, Hansen, and Lindsay 2021; Gershenson et al. 2022; Lusher,
Campbell, and Carrell 2018), and that Black patients benefit from access
to Black physicians (Alsan, Garrick, and Graziani 2019; Greenwood et
al. 2020). Finally, postsecondary education and training serve as a form
of social insurance, increasing workers’ resilience during economic
shifts and mitigating the negative consequences of recessions (Hyman
2018; Barr and Turner 2015; Minaya and Scott-Clayton 2022; Barnes
et al. 2021).

How Public Funds Are Delivered: Student Aid and Institutional
Support
Public funding to promote college access and completion can be delivered
directly to institutions, to support programming and keep prices below
cost, or directly to students, who then use financial aid to help pay tuition
and other costs at the institution of their choice. In primary and secondary

164 |

Chapter 5

education, government support is delivered primarily through institutions,
in the public provision of free schools, and with supplemental supports such
as free meals delivered through those schools. Many countries follow this
model, not only for primary and secondary education but also for postsecondary education, with direct institutional support for predominantly public
institutions helping to keep tuition prices low (Marcucci 2013). Along these
lines, at least 20 States currently offer statewide free community college programs (Mishory 2018; Education Commission of the States 2022), and the
Biden-Harris Administration has developed a framework for a nationwide
free community college program (White House 2021).
The Federal Government’s earliest investments in postsecondary education also focused primarily on expanding capacity and keeping prices low
at public institutions, which even today enroll over three-quarters of U.S.
undergraduates (IPEDS 2020). The foundations of many of today’s State
colleges and universities can be traced to direct institutional support—such
as the Federal Morrill Land Grant Act of 1862, which granted each State
30,000 acres of public land to establish public postsecondary institutions;
the Second Morrill Act of 1890, which directed Federal funds to newly
designated Historically Black Colleges and Universities; and the subsequent
significant push by States to establish and expand two-year colleges in the
1960s (Cohen, Brawer, and Kisker 2013). State and local direct appropriations for public institutions, which help keep tuition prices below the full
cost of provision, remained the largest source of government support for
postsecondary education in the United States through the end of the 20th
century (Dynarski, Page, and Scott-Clayton 2022).
The Higher Education Act of 1965, which established the foundations
of today’s Federal student aid programs (including the precursor to Pell
Grants), marked a significant shift from institution-focused to studentfocused assistance (Fountain 2017; Leslie and Johnson 1974). Delivering
support via student aid may conserve public resources by targeting subsidies to those students who are most in need, support institutional quality
by bringing in additional resources from those who can afford to pay, and
promote competition and choice by enabling students to use their aid at the
institutions they judge as highest value (Barr 2004). State and local direct
appropriations for institutions have fallen from nearly two-thirds of support
for undergraduates in 1990–91 to just under one-third in the academic year
2018–19 (Dynarski, Page, and Scott-Clayton 2022). Student aid is now the
primary mode of support in the United States, providing $174 billion in
grants, loans, and other direct support for undergraduates in 2021–22, with
Federal sources accounting for about half this total, and loans accounting
for about half of Federal student aid (Dynarski, Page, and Scott-Clayton
2022; Ma and Pender 2022b). The result, as shown in figure 5-4, is that only
England exceeds the United States in the level of both tuition and student
Building Stronger Postsecondary Institutions | 165

Figure 5-4. Average Public Tuition and Fees and Percentage of Students
Receiving Public Financial Aid—Bachelor’s Degree Programs, 2019–20
Average public tuition and fees (listed prices in U.S. dollars converted using purchasing power parity)
14,000

England

12,000
10,000

United States

Chile

8,000
6,000

Australia

4,000
2,000
0

Austria

0

10

New Zealand

Italy Spain
France

Germany
20

30

40

50

60

70

80

90

100

Percentage of students receiving public financial aid
Source: Organization for Economic Cooperation and Development (2021, tables C5.1 and C5.2).
Note: Data refer to the academic year 2019-20 and are based on a special survey administered by the OECD in 2021.

aid (OECD 2021). The contemporary “high-tuition, high-aid” model of U.S.
postsecondary finance is thus distinctive in both international and historical
contexts.
In line with this high-tuition, high-aid model, inflation-adjusted published tuition and fees before accounting for financial aid (“sticker prices”)
have since 1980 nearly tripled in the public two-year sector, more than
tripled in the private not-for-profit four-year sector, and nearly quadrupled
in the public four-year sector, though such prices have stabilized over the
past decade (Ma and Pender 2022a, 2022b). At the same time, net prices in
the United States—tuition and fees minus grants and scholarships—have
increased much more slowly than published prices, and have actually
remained flat or declined over the past decade (Ma and Pender 2022b).
The Biden-Harris Administration has taken a number of steps to continue
to improve college affordability and help student loan borrowers, including
providing a $900 increase to grant aid for low-income students through the
Pell Grant program over the last two years, streamlining and improving
student loan repayment, and pursuing debt relief through the HEROES Act.
The model of financing students rather than institutions has helped
fuel a system of postsecondary education that is diverse and decentralized,
with more opportunities for exploration, transfer, and reentry (Labaree 2017;
Goldin and Katz 2008), because students decide where their subsidized dollars will be spent. In contrast, many other countries deliver funding primarily
to public institutions that students can attend free (Marcucci 2013) but that

166 |

Chapter 5

often offer fewer spots in a more constrained set of programs. The diversity
and flexibility of the U.S. system may help explain why the United States
is the top destination for international students (Institute of International
Education 2020). In recent decades, some countries that formerly provided
fully free public institutions have shifted to the U.S. model as a way to maintain affordability while expanding postsecondary capacity and improving
quality (Marcucci 2013; Murphy, Scott-Clayton, and Wyness 2019).
Still, the U.S. model of postsecondary education finance is not without
challenges. Over the longer term, the dramatic increases in sticker prices
have made it more difficult for today’s students and families to pay for
college relative to a generation ago. In 2022–23, the maximum Pell Grant,
the largest source of grant aid in the United States, only covered 30 percent
of published tuition, fees, room, and board at the typical public, four-year
institution, down from 50 percent in 1988–89 and nearly 80 percent in 1975
(Ma and Pender 2022b; Baum, Payea, and Steele 2009; Protopsaltis and
Parrott 2017).
Despite the availability of aid, research shows that tuition prices still
influence students’ persistence and degree completion, even after initial
enrollment (Acton 2021). Prospective students—particularly those who
would be first in their families to attend college—may not even know that
financial aid exists and thus may be dissuaded by the sticker price alone
(Levine, Ma, and Russell 2022). Research has shown that the process of
applying for financial aid is itself a barrier to access (Bettinger et al. 2012),
and that students are more likely to apply when aid is guaranteed in advance
(Dynarski et al. 2021). Further, research indicates many students are reluctant to borrow (Boatman, Evans, and Soliz 2017). This may reduce the
effectiveness of loans relative to grants of the same size.
Further, the decision to invest in college entails risks—in particular,
the risk that the earnings students gain will be less than the cost they pay for
their education. The breadth, flexibility, and multiple entry and exit points
in the U.S. system also mean more risks of making mistakes and falling off
track (Labaree 2017; Goldin and Katz 2008; Scott-Clayton 2012). Fewer
than two-thirds of students who enroll in college finish any degree within
six years (National Student Clearinghouse Research Center 2022a). Even
among those who graduate with at least a four-year degree, roughly one in
five male college graduates and about one in seven female college graduates has earnings no higher than the typical worker with only a high school
diploma (Ma, Pender, and Welch 2019).
Because many students rely on debt to finance a portion of their
education, some students who attend college may end up worse off, even
though the expected return is high on average. Nearly one-third of students
who take on debt do not receive a degree (Miller 2017). More than one in
four borrowers experience a student loan default within 12 years of college
Building Stronger Postsecondary Institutions | 167

Box 5-2. International Comparison of IncomeDriven Student Loan Repayment
Like the United States, postsecondary education systems in Australia
and England also combine high tuition with high financial support
for students. In contrast to the United States, students in England and
Australia can fully defer tuition payments until after college and then
repay via income-driven repayment (IDR). Under IDR, student loan
repayments are capped at a fixed percentage of income, mitigating the
risk that college enrollment leads to incomes too low to repay such debts.
Research from the United States finds that IDR enrollment reduces
borrowers’ risk of delinquency and default (Mueller and Yannelis 2019;
Herbst 2023).
IDR plans vary substantially across these countries in two
important ways. First, U.S. undergraduate loans, though capped below
most students’ cost of attendance, are available for a wider variety of
programs, including short-term credentials and those at thousands of
for-profit institutions (U.S. Department of Education 2023a; Ma and
Pender 2022a). Both England and Australia allow undergraduates to
borrow the full amount of public tuition but restrict the institutions
eligible for IDR. England directs IDR primarily to public university
students, and Australia originally restricted IDR to four-year colleges,
only in 2009 expanding eligibility to vocational programs (Barr et al.
2019; Student Loans Company 2022). Second, IDR is the only loan
repayment option in England and Australia, with automated enrollment
and payments income-adjusted and collected automatically through the
tax authority. In contrast, borrowers in the United States need to opt
into IDR and annually update their own income (Barr et al. 2019). Only
about one-third of U.S. student borrowers in 2022 were enrolled in such
a plan (CEA calculations, based on Federal student loan portfolio data
by repayment plan, from the U.S. Department of Education 2022a). The
Biden-Harris Administration has proposed reforms of IDR to reduce
monthly and lifetime payments, especially for low- and middle-income
borrowers, and to eliminate barriers that prevent borrowers from accessing IDR.

entry, including nearly half of Black student loan borrowers (Scott-Clayton
2018b). One tool for mitigating repayment risk in a high-tuition, high-aid
model are income-driven repayment plans—but as box 5-2 discusses, the
implementation and use of these plans differ substantially by country.
Finally, the global experience suggests that countries expanding
student aid to for-profit institutions face challenges in regulating quality
to address poor student outcomes in this sector (Usher 2019; Salto 2019).
168 |

Chapter 5

Although for-profit higher education is not unique to the United States, it is
unusual in terms of both its size in the United States and its integration into
the student aid system, including access not only to student loan dollars but
also to nonrepayable grant aid (Kinser 2016; Levy 2019). For-profit colleges
in the United States account for 12 percent of Federal student aid dollars and
30 percent of student loan defaults, even though they enroll only 8 percent
of students (Century Foundation 2021).

The Imperfect Market for Postsecondary Institutions
In an idealized market, the United States’ approach of providing portable
financial aid to support consumer choice might be sufficient to ensure that
high-quality choices actually exist. At least in theory, this approach should
promote institutional quality by weeding out low-quality institutions or
prompting them to improve and encouraging better ones to expand (Barr
2004; Fountain 2017). The postsecondary education market, however,
is too imperfect for institutional improvements to emerge simply from
students voting with their feet (Leslie and Johnson 1974). Institutions may,
for example, be able to attract students regardless of their program quality.
Three main types of constraints—geographic constraints, informational
and behavioral constraints, and constraints on colleges’ ability to expand
quickly—diminish the power of market forces to promote productive innovation, improve quality, and drive down prices through student choice and

Figure 5-5. Distance Between Home and College, 2004–16

Percentage of undergraduates
50
45
40
35
30
25
20
15
10
5
0

2004
Within 10 miles

2008

2012

Between 11 and 20 miles

Between 21 and 50 miles

2016
Over 50 miles

Source: National Center for Education Statistics (National Postsecondary Student Aid Study, 2016 undergraduates).

Building Stronger Postsecondary Institutions | 169

competition alone. The resulting institutional landscape offers variety, but it
is not always clear which aspects of this variety benefit students.

Geographic Constraints
The first main types of constraints, again, are geographic. First, as figure
5-5 shows, most students attend college close to home, limiting the scope
for choice and competition. About 60 percent of U.S. undergraduates attend
a college within 20 miles of their home, and the fraction attending within
just 10 miles of home grew from nearly 39 percent in 2004 to 44 percent in
2016 (NCES 2022h). These proportions are substantially higher for students
of color and low-income students (NCES 2022i, 2022j).
These geographic constraints make college markets “thin,” as many
students do not have a substantial number of options to choose from if they
want or need to stay close to home (Hillman 2016; Blagg and Chingos
2016). The median commuting zone has just two colleges of any type or
level. About 23 percent of those age 18 to 44 years live in a commuting
zone with at most one public four-year college; about 27 percent live in a
commuting zone with at most one public two-year college. Students with no
options nearby must incur the high cost of relocating or lengthy commuting
if they wish to attend college. Those with limited choices nearby may enroll
in a program that is a poor fit for their goals (Klasik, Blagg, and Pekor
2018). Online programs provide an alternative but many generate poor
student outcomes, as further discussed below.

Informational and Behavioral Constraints
Even for students with options close to home, informational and behavioral
constraints can complicate decisionmaking. The United States’ college
landscape is particularly complex, with 63 percent more bachelor’s-degreefocused institutions per capita than Canada, 71 percent more than the United
Kingdom, and 67 percent more than Australia (World Higher Education
Database, n.d.). The American community college, serving multiple missions including both transfer and terminal associate degrees, is a distinctive
type of institution that only recently has begun to develop in other countries
(Redden 2010). The United States also has a large for-profit college sector,
adding to an already large and varied set of options students face.
College is not a simple consumer good, but an “experience good”
for which students may not have well-formed preferences in advance. The
decision to enroll in college is made infrequently in one’s lifetime, limiting the opportunity to learn from previous decisions. Colleges differ along
numerous dimensions, including both content and quality, which may be
difficult to observe in advance. Benefits are uncertain and accrue over long
time horizons, making it difficult for students to compare options. Even the

170 |

Chapter 5

best-prepared students may oversimplify or avoid decisions when choices
are complex, information is limited, and preferences are not well established (Hoxby and Avery 2013; Beshears et al. 2008; Lavecchia, Liu, and
Oreopoulos 2016; Ross et al. 2013).
Comparing financial aid offers can be particularly opaque. A recent
report from the Government Accountability Office (GAO) found that 41
percent of colleges in a nationally representative survey did not provide
information on net prices in their financial aid offers, and an additional 50
percent understated net prices by omitting some costs or including loans
that must be repaid (GAO 2022). This complexity also affects students after
college as they attempt to navigate student loan repayments (Turner 2021).
Finally, many prospective college students are relatively young and
inexperienced financial decisionmakers, which increases both their susceptibility to marketing campaigns and the likelihood of decision mistakes
(Beshears et al. 2008; Agarwal et al. 2009). Indeed, reports have found that
some for-profit colleges take advantage of this by outspending their public
counterparts 20-to-1 on advertising (Cellini and Chaudhary 2020) and
using dubious claims about future employment prospects to recruit students
(McMillan-Cottom 2017; GAO 2010).

College Expansion Constraints
In a simpler market, increased demand for the best products can induce successful producers to expand and new producers to enter the market. The substantial fixed costs and labor-intensive model of traditional postsecondary

Figure 5-6. Per-Student State and Local Funding for Public Higher
Education, 1989–2019
2019 dollars
10,000
9,500
9,000
8,500
8,000
7,500
7,000
6,500
6,000
5,500
5,000

1989

1994

1999

2004

2009

2014

2019

Source: College Board Trends in College Pricing 2021b, as compiled by Ma and Pender (2021).
Note: Shaded areas indicate recessions.

Building Stronger Postsecondary Institutions | 171

education, however, constrain institutions’ ability to quickly respond to
increased demand without diluting students’ experience, as discussed below.
As figure 5-6 shows, per-student State and local funding is procyclical,
falling during times of economic contraction. Demand for postsecondary
education is, however, countercyclical, as students tend to seek skill training when employment opportunities are worse since the opportunity cost
of enrollment is lower when jobs are scarce (Barr and Turner 2015). The
combination of public funding’s procyclicality and demand’s countercyclicality means that per-student funding shrinks precisely when enrolling in
a postsecondary program makes the most economic sense (Ma and Pender
2022b; Kane et al. 2005). This pattern leads to both higher tuition and lower
resources provided per student during recessions, which has been documented to harm students’ outcomes (Chakrabarti, Gorton, and Lovenheim
2020; Bound, Lovenheim, and Turner 2010; Bound and Turner 2007;
Deming and Walters 2017).
Community college enrollments are particularly sensitive to economic conditions, partly because they are open-access institutions to which
unemployed or underemployed adults often turn for midcareer training.
Community college enrollments rise by about 1 to 3 percent overall for
every increase of 1 percentage point in the local unemployment rate, with
greater responsiveness among those age 25 and above (Hillman and Orians
2013; Betts and McFarland 1995). The only exception to this pattern has
been the weak labor market early in the COVID-19 pandemic, when community college enrollment declined in part because instruction, particularly
in fields requiring hands-on training, was disrupted by pandemic conditions
(Schanzenbach and Turner 2022).
At the same time, students are more likely to enroll in for-profit
institutions when funding for local public institutions decreases (Cellini
2009; Goodman and Henriques Volz 2020). Although causal research does
not establish the mechanisms underlying this result, when resources per
student fall, four-year colleges may not be able to expand enrollment to
meet demand. Community colleges do not typically have enrollment caps,
but when public institutions have fewer resources per student, students may
have more difficulty registering for the courses they want at the times they
want, or they may be discouraged by staffing constraints that affect their
ability to navigate registration, financial aid, and other aspects of enrollment. In contrast, for-profit institutions can cut costs and expand more
quickly than traditional institutions by offering more highly standardized
curricula, a more limited range of programs, fewer in-person courses, and
lower-paid instructors (Deming, Goldin, and Katz 2012). A large portion of
for-profit programs are fully online, making them particularly attractive to
students lacking alternatives close to home (NCES 2019). The heavy concentration of online programs in the for-profit sector may partly explain why
172 |

Chapter 5

for-profit enrollment declined much less than community college enrollment
in the first year of the pandemic, when demand for remote learning increased
substantially (National Student Clearinghouse 2022b).

Institution-Focused Policies That Promote
Access to Postsecondary Value
Research has shown that the quality of institutions matters for student
outcomes. Thus, policies aimed at institutions—to build capacity, to support
colleges in serving students well, and to hold them accountable when they
do not—are critical to ensuring that all students have access to an education
of value. Federal policy can influence the quality of postsecondary options
by supporting evidence-informed strategies to expand supply and improve
outcomes at public institutions, while holding all institutions accountable
for the value they provide and protecting students from the worst options.

Supporting the Quality of Existing Colleges and Programs
As more attention has been given to increasing college completion—not just
enrollment—the base of evidence has grown for promising programs and
policies (e.g., see the recent review by Dynarski et al. 2022). This subsection
considers the potential benefits of expanding specific institutional programs
with a track record of success, as well as the potential benefits of more flexible institutional support.
Enhanced guidance and advising. Personalized guidance, coaching,
and/or mentoring have been shown to help college students overcome
both academic and nonacademic challenges. Several randomized-control
studies find that such services can help students persist and complete their
degrees at higher rates (Dynarski et al. 2022). Bettinger and Baker (2014)
find that four-year college students randomly assigned to receive access to
individualized student coaching from outside professionals were more likely
to persist and graduate. Oreopoulos and Petronijevic (2018) find that oneto-one coaching by upper-year undergraduate mentors improved students’
academic performance, while less intense text and email “nudge” interventions did not. Randomized studies have found positive effects of related
interventions for community college students as well (Linkow et al. 2017,
2019; Evans et al. 2020).
Comprehensive programs. Comprehensive programs that provide
multifaceted financial, academic, and nonacademic supports have shown
particularly dramatic effects. Of these, the best known is the City University
of New York’s (CUNY’s) Accelerated Study in Associate Program (ASAP).
In addition to waiving tuition and fees, ASAP provides textbook vouchers,
free transportation, a dedicated one-to-one adviser, and enhanced tutoring

Building Stronger Postsecondary Institutions | 173

and career counseling. Students are required to enroll full time. A randomized evaluation found that the program nearly doubled associate degree
completion rates three years after entry (40 vs. 22 percent), with large effects
on completion persisting after six years (Scrivener et al. 2015; Weiss et al.
2019). ASAP has since been successfully replicated in Ohio (Sommo et al.
2018; Miller et al. 2020), and CUNY is piloting a version of the program at
several of its four-year campuses (CUNY, n.d.). The version implemented
in Ohio, though less expensive per student than the original CUNY model,
cost 42 percent more per student than business as usual—but because the
program dramatically increased completion rates, it lowered the average
cost per graduate (Miller et al. 2020). CUNY’s ASAP was estimated to
raise degree completion rates enough to more than cover its costs, so that
enrolling 1,000 more students was estimated to provide taxpayers with fiscal
benefits of $46 million in 2010 dollars (Levin and Garcia 2013).
Direct institutional support. All the programs described above require
resources. Research indicates that per-student institutional resources are an
important driver of college persistence and completion (Bound and Turner
2007; Bound, Lovenheim, and Turner 2012; Webber and Ehrenberg 2010;
Cohodes and Goodman 2014; Deming and Walters 2017). This echoes
findings that resources matter for student outcomes in the K-12 context,
particularly for low-income students (Jackson, Johnson, and Persico 2015;
Hyman 2017). How resources are spent matters but, because the optimal use
of funds may vary from context to context, general funding support—with
appropriate guardrails—may give institutions the flexibility they need to
optimize. Box 5-3 describes some of the Biden-Harris Administration’s
efforts on this front.
Various scholars have offered proposals for what a more regular program of Federal institutional support for postsecondary education could look
like. Existing Federal support for K-12 schools provides one model: Federal
grants have long been provided to districts, schools, and States through Title
I of the Elementary and Secondary Education Act (Skinner and Cooper
2020). Hiler and Whistle (2018), for example, propose a version of Title I
funding for postsecondary education that could be based on the number and
proportion of Pell Grant recipients enrolled. Federal grants that match State
spending—which have proven effective in increasing State spending on
other programs, such as Medicaid (Kane et al. 2005)—could reduce the risk
that Federal dollars simply crowd out State investment in public colleges
(Deming 2017). Some scholars have suggested that aid be targeted to community colleges, the sector with the greatest need and potential (Goolsbee,
Hubbard, and Ganz 2019).

174 |

Chapter 5

Box 5-3. Policies Focused on Direct Institutional Support
The Biden-Harris Administration has made direct institutional support
a priority. The College Completion Fund for Postsecondary Student
Success (funded by the Consolidated Appropriations Act of 2022,
and following similar proposals in the American Families Plan and
the President’s Budget Request) in 2022 provided $5 million in competitive grants to postsecondary institutions to support “data-driven
and evidence-based reforms that encourage postsecondary retention,
transfer, and completion” (U.S. Department of Education 2022b). These
funds were targeted to institutions that disproportionately serve students
of color and low-income students, with priority given to community colleges. Congress provided an additional $45 million for the program for
Fiscal Year 2023 (U.S. Department of Education 2023b).
During the COVID-19 pandemic, direct support for institutions
was a key aspect of Federal support for postsecondary education. The
American Rescue Plan Act of 2021 provided nearly $40 billion in
institutional support via the Higher Education Emergency Relief Fund
(HEERF). HEERF, initially established by the Coronavirus Aid, Relief,
and Economic Security (CARES) Act of 2020, required institutions to
spend half the funds on emergency student aid and the other half on “any
costs associated with significant changes to the delivery of instruction
due to the coronavirus” (U.S. Department of Education 2020, 2022c).
About 90 percent of HEERF-participating institutions reported the
program helped them keep students enrolled who might have otherwise
dropped out (U.S. Department of Education 2023c). Evidence on similar
programs during the Great Recession suggests that they help public
research institutions maintain or increase their expenditures on both
research and instruction (Dinerstein et al. 2014). Many states have used
funding from the American Rescue Plan to expand or strengthen colleges and job training programs, seeing these as core strategies to build
back from the pandemic (U.S. Department of the Treasury 2022).

Institutional Accountability
Accountability policies are wide-ranging, and they include (1) strict
accountability policies that cut off institutions from Federal or State aid
completely if they fail to meet certain minimum standards; (2) performancebased funding, whereby financial assistance is at least partly conditioned
on institutional performance; and (3) policies that increase transparency
and rely on the market to self-regulate. Evidence regarding each option is
discussed in turn.

Building Stronger Postsecondary Institutions | 175

Strict accountability. A variety of past and current Federal regulations
suggest potentially promising results from holding postsecondary institutions accountable for program-level student outcomes as a condition of
eligibility for Federal financial aid for students. For-profit colleges are most
affected by such regulations, in part because of the legal authority granted
to regulators under Federal law and partly because of their poor observed
student outcomes (Cellini and Koedel 2017). Cutting off aid from programs
that leave students unable to repay their loan debt is at least partly effective in discouraging enrollment in such programs (Darolia 2013). Cellini,
Darolia, and Turner (2020) further show that when for-profit colleges
experience large drops in annual enrollment due to policy sanctions, most
such students shift to community colleges, whose loan default outcomes are
substantially better. Kelchen and Liu (2022) demonstrate that having debtto-earnings ratios in excess of prescribed limits made poor-performing colleges and programs more likely to close, even though the regulations were
rescinded by the Trump Administration before any sanctions were actually
applied. Box 5-4 describes these regulations further.
Performance-based funding. Currently, roughly 30 States have implemented policies that partly tie higher education appropriations to outcomes
such as graduation rates. Known as “performance-based funding,” this
strategy is an attempt to improve institutional accountability. A review of the
evidence shows, however, little sign of such measures inducing institutional
improvements in student outcomes such as degree completion (Ortagus et
al. 2020). Researchers have also found that performance-based funding can
incentivize behavior counterproductive to increasing the available quality of
college opportunities, with some public, four-year institutions boosting their
outcomes by decreasing admission rates and reducing enrollment of underrepresented students of color (Ortagus et al. 2020; Birdsall 2018). Some
States have modified their plans to include additional incentives for improving measures related to equity, with potentially promising evidence that
such equity-focused modifications can improve enrollments of low-income
students and students of color (Gándara and Rutherford 2018).
Increasing the transparency of student outcomes. Increasing the
transparency of student outcomes can potentially make college quality more
salient, both to prospective students and to institutions themselves, increasing the competitive pressure to improve. Research indicates, for example,
that improving students’ information on labor market outcomes can influence their major choice (Baker et al. 2018; Wiswall and Zafar 2015). In
addition to the College Scorecard, several States have their own databases
of earnings data, organized by institution or industry. For example, the
Salary Surfer (salarysurfer.cccco.edu), a collection of earnings data from the
California Community College System, provides average salary information
two years before, two years after, and five years after graduation, by industry
176 |

Chapter 5

Box 5-4. Gainful Employment and
Other Accountability Regulations
Since its 1965 enactment, the Higher Education Act (HEA) has defined
the types of institutions and programs that are eligible to participate in
Federal financial aid programs. Under current law, educational programs
must lead to a degree at a nonprofit or public institution or must prepare
students for “gainful employment in a recognized occupation” in order to
be eligible for financial aid under Title IV of the HEA (U.S. Department
of Education 2022d). The “gainful employment” requirement was not,
however, defined in regulations for the first few decades of the HEA.
In 2014, the Obama-Biden Administration finalized regulations defining gainful employment as requiring aid-eligible certificate and degree
programs to meet a specific debt-to-earnings ratio for graduates. The
Department of Education estimated that 840,000 students’ programs
would not meet this standard, nearly all at for-profit institutions (U.S.
Department of Education 2014). The gainful employment regulation
was rescinded under the Trump Administration. The Biden-Harris
Administration is in the process of reinstating a new such rule to ensure
that Federal funds are not directed to programs that do not lead to gainful
employment (U.S. Department of Education 2022e).
The Administration has already taken other actions designed to
increase the accountability of postsecondary institutions and programs
to students and taxpayers. The Department of Education has solicited
public comment on the development of an annual watch list identifying
programs with the lowest financial value and announced plans to request
improvement plans from the institutions that offer such programs. The
Department of Education also reestablished the enforcement unit in the
Office of Federal Student Aid to hold institutions accountable, and withdrew authorization from the accreditor ACICS, which oversaw for-profit
institutions involved in some of the worst outcomes for students. The
Administration also closed a long-standing loophole that encouraged
for-profit institutions to aggressively target and recruit veterans and
their families. Research indicates that such institutions lower veterans’
earnings (Barr et al. 2021) and use this additional revenue stream to raise
tuition rather than improve quality (Baird et al. 2022). Recent regulations enacted by the Biden-Harris Administration will ensure that private
for-profit colleges derive at least 10 percent of their revenue from nonFederal sources, including veterans’ benefits, as required under changes
made by Congress to the 90/10 rule in the American Rescue Plan Act.

Building Stronger Postsecondary Institutions | 177

and subfield. The U.S. Census Bureau has released the Post-Secondary
Employment Outcomes data product since 2017, providing earnings data by
institution and degree program up to 10 years after graduation, and showing
the flows of graduates from various degree programs into employment in
various industries.
Evidence from the release of the College Scorecard and earlier efforts
at transparency suggests, however, that transparency on its own may have
a limited short-term impact on student application and behavior. Publishing
annual lists of institutions with the highest levels or changes in costs for
students did not appear to affect those institutions’ prices or enrollments, at
least in the short run (Baker 2020). For the first time, in 2015, the Scorecard
made widely available average graduation rates and earnings of students
enrolling at thousands of colleges nationwide. Research indicates that this
release had limited effects on college search and application behavior, with
effects concentrated among more advantaged students (Huntington-Klein
2016; Hurwitz and Smith 2017; Meyer and Rosinger 2019). The longerterm effects of the College Scorecard and other transparency efforts may,
however, be more meaningful than the short-term effects, as information
takes time to reach students, families, school counselors, and other decisionmakers. More research is needed to isolate such longer-term effects of data
transparency on college quality.

Addressing Geographic Barriers to Access
Additional policy efforts may be needed to more directly address geographic
constraints on access. Though the COVID-19 pandemic has led to increased
awareness of the feasibility of remote learning at scale, it has also shown
its limitations. This subsection discusses the evidence on the effectiveness
of online education as well as other, more promising alternatives to provide
more students with access to high-quality college experiences on the campus
of their local high school. For older returning students, box 5-5 provides
additional information on local workforce training interventions that have
demonstrated promise in improving outcomes.
Online programs. Some have suggested expanding online options to
reduce geographic barriers to access, but research findings suggest caution
about this approach. In some settings, such as four-year colleges, there are
examples of students doing equally well across both online and in-person
formats (Figlio, Rush, and Yin 2013; Bowen et al. 2014), as well as in
blended learning approaches combining online and in-person components
(Bowen et al. 2014; Alpert, Couch, and Harmon 2016). Other research
finds, however, that courses taught through online formats often lead to
worse learning outcomes than their in-person counterparts (Joyce et al.
2015; Alpert, Couch, and Harmon 2016; Krieg and Henson 2016). Research

178 |

Chapter 5

Box 5-5. Supporting Workforce Training Quality
Community colleges are the primary providers of education and training
targeted at labor market needs, with Pell Grants now the largest source
of funding for workforce training for low-income Americans (Ma and
Pender 2022b; Holzer 2008). Postsecondary institutions that provide
such training may, however, be slow to respond to sectoral shifts and
changes in employers’ specific skill needs (Katz et al. 2022). The traditional academic schedule and program offerings may not always fit the
needs of nontraditional students, such as workers displaced midcareer.
Though displaced workers are eligible for Federal student aid, they may
not be aware of their eligibility or how to use it (Barr and Turner 2018).
Federal training resources specifically developed to serve such workers
are funded at much lower levels—through the Workforce Investment
and Opportunity Act—and with generally positive but somewhat mixed
evidence of effectiveness (Rothstein et al. 2022; Holzer 2021).
In this context, sectoral employment and training programs have
shown promise. These programs typically involve partnerships between
employers or industry associations, training providers (often community
colleges), workforce boards, and intermediary organizations such as
unions and local nonprofits (Holzer 2015). These programs may improve
alignment between training programs and workforce needs, and the
wraparound services they often provide may increase students’ likelihood of completion. Research on sectoral employment programs, many
located at community colleges, concluded that such programs lead to
substantial earnings gains for participants (Katz et al. 2022). Additional
positive evidence comes from the Trade Adjustment Act Community
College and Career Training (TAACCCT) Initiative, which provided
$1.9 billion in grants to postsecondary institutions to enhance their
workforce training capacity in the wake of the Great Recession (U.S.
Department of Labor 2022a). A meta-analysis of quasi-experimental
studies of TAACCCT programs concluded that the investments had
overall positive effects on program completion as well as labor market
outcomes (Blume et al. 2019).
Registered Apprenticeships provide another promising pathway
to well-paying jobs. Registered Apprenticeships are formally approved
by the U.S. Department of Labor or a State Apprenticeship Agency and
are vetted to ensure that they align with industry needs in high-demand
fields (U.S. Department of Labor 2022b). In this “earn and learn” model,
workers get paid while training under the supervision of a mentor, typically for two to four years, and simultaneously receive supplementary
classroom instruction. One study found that, for every $1.00 invested,
employers receive $1.44 in direct and indirect benefits in the years during and after training an apprentice (Kuehn et al. 2022). Research on the
causal impact of such programs is limited, though descriptive research

Building Stronger Postsecondary Institutions | 179

indicates workers experience strong wage growth in the year after
finishing the program (Walton, Gardiner, and Barnow 2022). The BidenHarris Administration has expanded Registered Apprenticeships through
additional funding and efforts like the launch of the Apprenticeship
Ambassador Initiative (White House 2022).

during the COVID-19 pandemic found that college students performed
worse in courses that shifted or later remained online (Bird, Castleman, and
Lohner 2022; Kofoed et al. 2021). Online coursework appears to work least
well for less academically prepared students, in both the for-profit sector
(Bettinger et al. 2017; Bird, Castleman, and Lohner 2022) and the community college sector (Xu and Jaggars 2013). Many existing postsecondary
options that are fully online do not appear to improve students’ employment
opportunities or earnings relative to no postsecondary education (Deming et
al. 2016; Hoxby 2018). Online delivery of coursework may not even be less
costly than in-person education (Hemelt and Stange 2020). This evidence
suggests that online education is unlikely to fully address geographic barriers to high-quality programs.
Leveraging local institutions. New, high-value college opportunities
could also be created closer to home and earlier in students’ lives. Local
community colleges are increasingly offering bachelor’s degrees, which
may reduce the distance some students need to travel for such programs. As
of 2022, about 15 percent of community colleges offered at least one bachelor’s degree program (Love 2022). Evidence is not yet available regarding
the impact of such programs. “Dual enrollment” similarly brings college
coursework closer by allowing high school students to take college-level
courses, often delivered at the high school itself to minimize travel (Marken,
Gray, and Lewis 2013). Enrollment in such coursework has grown rapidly
in the last two decades (An and Taylor 2019). The limited evidence suggests
that dual enrollment improves postsecondary trajectories. For instance, early
exposure to dual-credit advanced algebra coursework increases the rigor
of high school mathematics coursework taken and raises four-year college
enrollment rates (Hemelt, Schwartz, and Dynarski 2020).
Early college high schools are a more intensive version of dual enrollment, in which high schools form partnerships with local colleges to offer
students the opportunity to earn an associate degree or equivalent amounts
of college credit at little cost to their families (Webb 2014). Students admitted into early college high schools are more likely to earn college degrees
and earn them faster than similar students denied admission (Edmunds et
al. 2020; Song et al. 2021). Expansion of dual enrollment and early college
180 |

Chapter 5

opportunities should be done with equity considerations featured prominently. Dual enrollment students are more likely to be white, high income,
and high achieving than the typical student, likely because higher-income
schools are more likely to offer dual enrollment and higher-income students
at a given high school are more likely to enroll in such coursework (An and
Taylor 2019). Promoting equitable access will require proactive planning
and outreach by policymakers and educators.

Conclusion
The diversity and flexibility of the United States’ system of postsecondary
education are among its greatest assets, and part of what makes it unique
globally. These features also introduce complexity and risks for prospective students. In a simpler marketplace, choice and competition might be
sufficient to promote quality improvements and to drive bad options out
of business. But investing in postsecondary education is not like buying
groceries or even a car. Most students are limited geographically, leaving
them with a narrow set of options, and the choices they do have may be
hard to fully evaluate and compare in advance. Further, public institutions
are often constrained in their ability to meet demand, while less-constrained
private, for-profit institutions have poor track records with respect to student
outcomes. Students today rely more heavily on student loans to pay for
college than did their counterparts a generation ago, increasing the risk of
financial hardship for those who attend college but leave without gaining
valuable skills.
An examination of institution-oriented policy options reveals three
major themes. First, a variety of institutional programs—many of them
pioneered by community colleges—have demonstrated great potential for
improving student outcomes. Many of these promising programs require
additional resources to expand, and the Federal Government can both invest
directly in postsecondary institutions and encourage States to increase their
own investments. Second, discouraging the proliferation of low-quality
postsecondary options is important in limiting the potential for students to
make enrollment choices with low or negative returns. Finally, policymakers
should continue exploring ways to address geographic barriers to college
access through programs such as dual enrollment, early college, and community college baccalaureate degrees.
Robust Federal and State efforts to improve the affordability of college have made progress in recent decades in expanding access to college.
Yet, as this chapter has documented, making a good educational investment
requires attention to both price and quality. Institution-oriented policies can
help the U.S. postsecondary system build on its strengths, and ensure that all

Building Stronger Postsecondary Institutions | 181

students who aspire to college have access to options that are both affordable
and of high quality.

182 |

Chapter 5

Chapter 6

Supply Challenges in U.S. Labor Markets
Despite the enormous disruption of the COVID-19 pandemic, U.S. labor
markets remained tight throughout 2022. For much of the last year, there
were two job openings for every unemployed person—an unprecedented
gap between labor demand and supply that has shifted the balance of power
between workers and businesses. The resulting surge in hiring empowered
many workers to change jobs and careers, with many job switchers experiencing substantial wage gains. Workplace organizing increased, with union
representation petitions rising sharply in 2022, as workers sought to use their
increased leverage to negotiate better working conditions.
It is tempting to assume that today’s hiring challenges are primarily due to
the lingering effects of the pandemic. However, tight labor markets predated
the pandemic, and demographic trends indicate that labor supply challenges are likely to remain, even as the pandemic recedes. The baby boom
generation is aging out of the labor force, and there are not enough younger
workers to replace them. This tightening of the supply of labor due to population aging is a principal cause of current hiring challenges, constraining
the economy’s capacity for growth by slowing the rate at which businesses
can expand hiring. Unless efforts are undertaken to mitigate the impact of
demographic change—by drawing more adults into the labor market and/or
increasing immigration flows—the labor supply is likely to be constrained
for the foreseeable future.
This chapter examines both short- and long-run challenges for the U.S. labor
supply. In the short run, some lingering effects of the pandemic remain,
principally in the form of heightened labor market exits of older workers.

183

Immigration inflows, which were falling before the pandemic began, fell
steeply when the U.S. borders closed and are just starting to recover. Chief
among the long-run challenges are demographic trends, particularly population aging, but also falling labor force participation among prime-age adults.
The chapter concludes with a description of several options to boost the U.S.
labor supply; it also takes a closer look at labor markets experiencing especially acute labor shortfalls to see how macroeconomic forces are affecting
specific industries.

Labor Supply Fundamentals
What determines the supply of labor? Simply put, individuals who are
able to work decide whether to join the labor force, and if they do, how
many hours they will work. Individuals can also decide to leave the labor
force—to retire, to seek further schooling, or to care for small children. The
aggregate labor supply of a nation is the sum of these individual choices.
The aggregate labor supply is also a function of the size of the working-age
population, which depends on fertility choices of earlier generations as well
as immigration flows.
In the simple framework used in economics textbooks to model the
labor supply decision, employment is a choice between earning a wage and
the alternative uses of that time (e.g., household tasks, childcare, leisure).
Households also require a steady source of income to pay for necessary
goods and services. This very simple model ignores many important variables that go into the labor supply decision, such as the psychological and
social rewards of work and the nonmonetary aspects of particular jobs. Yet
this simple model does allow economists to make useful predictions of how
individual decisions are affected by more easily observed factors, such as
changing wages and the availability of other household income.
Chief among these inferences is that individuals who cannot meet
essential consumption needs without working are highly likely to participate
in the labor market. This suggests that individuals without a source of preexisting wealth or nonwage income are more likely to seek formal employment. A second implication is that individuals are more likely to enter the
labor market when wages are high, when nonparticipation becomes more
costly in lost earnings. This model also predicts that participation will fall
when wages decline, for example, due to negative labor demand shocks.
As discussed later in the chapter, many economists believe that declining

184 |

Chapter 6

relative wages for non-college-educated workers are chiefly responsible for
men’s declining participation in recent decades.

Trends in U.S. Labor Market Participation
The labor force participation rate (LFPR) is defined as the share of the population age 16 years and above who are working or who are actively seeking
employment (BLS 2022). The labor force participation rate is an important
measure of labor market potential and health (box 6-1). Nonparticipation in
paid activity is not necessarily a source of concern—many nonparticipants
are retirees, students, or parents with young children, many of whom do not
desire formal employment. However, low participation rates can indicate an
untapped potential labor supply, which includes individuals on the sidelines
who would enter the labor market if attractive opportunities were available
or obstacles to formal employment were removed.
As shown in figure 6-1, labor force participation rose markedly in the
second half of the last century, from 59.6 to 67.1 percent between 1968 and
2000. This growth in participation was due to the increased labor market
activity of women—facilitated by changing societal norms, access to birth

Box 6-1. Labor Supply Terminology
Discussions about the labor supply can be confusing, given that the term
“labor supply” is sometimes used to indicate the aggregate labor supply
and at other times refers to labor force participation. In this chapter, we
distinguish which term we are using as follows:
• Labor supply typically refers to the aggregate labor supply, which
is a function of the adult population (age 16 years and above), as
well as the share of the adult population that participates in the labor
market. However, analyses of the labor supply often take population
trends as given and focus on the labor supply decisions of individuals, which are reflected in the labor force participation rate and the
employment rate, as defined next here.
• The labor force participation rate (LFPR) is the share of the noninstitutionalized adult population participating in the labor market;
this includes people who are currently working or who are seeking
employment.
• The employment-to-population ratio, or the employment rate, is the
share of the noninstitutionalized adult population that is employed.
It is a participation measure similar to the LFPR but does not include
unemployed people in the numerator.

Supply Challenges in U.S. Labor Markets | 185

Figure 6-1. U.S. Labor Force Participation Rate, 1948–2022
Participation rate, in percent, age 16 years and above
90

80

70

60

50

40

30
1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 2020
All

Female

Male

Sources: Bureau of Labor Statistics; CEA calculations.
Note: Data are seasonally adjusted. Rate is for age 16 years and above.

control, and improved education and labor market opportunities—with large
gains especially among married women (e.g., Blau and Kahn 2007; Goldin
and Katz 2002; Black and Juhn 2000). The increase in women’s participation was more than enough to offset the decline in participation among men
since the 1950s. As a result of more women entering the labor market and
favorable demographic trends (the baby boom generation swelled the ranks
of the workforce during this period), the U.S. labor supply grew steadily
until 2000.
Labor force participation in the United States began to decline after
2000 due to both supply and demand factors. The U.S. economy experienced
two demand shocks during this period: the dot-com crash, which ended the
economic expansion of the 1990s; and the global financial crisis, which
began in 2007. Women’s participation growth also leveled off and began to
decline after 2000. But the most significant factor pushing down participation in recent years has been the aging of the workforce, with the oldest
baby boomers entering their retirement years at the beginning of the global
financial crisis.

Why Worry About Slower Labor Supply Growth?
Declining labor force participation and slowing U.S. population growth
mean that there is a dwindling supply of workers. A principal reason to
be concerned about slower labor supply growth is that it implies slower
economic growth (for further discussion, see chapter 1 of this Report). The
186 |

Chapter 6

growth of economic output is determined by labor supply growth, capital
investments, and productivity growth—all else being equal, if the labor
supply’s growth slows, so too does economic growth. As labor market
participation declines with age, an aging population also reduces the fraction
of active workers in the population, thereby putting downward pressure on
output per capita if not accompanied by capital investments or increases in
productivity. Strong per capita economic growth was a primary driver of
rising living standards over the last century; the aging population could have
a negative impact on improvements in U.S. living standards in the future.
Although demographic change is relatively easy to forecast, it is more
difficult to account for changes in technology and productivity that may
dampen the impact of the aging population on future economic growth.
Cutler and others (1990) posit that labor scarcity could spur labor-saving
technological innovation that would offset the impact of demographic change
on output growth. Most studies have concluded that the relationship between

Box 6-2. Work and Leisure in the United States and Europe
John Maynard Keynes famously predicted that within two generations
workers would work only 15 hours a week. For today’s Americans,
that is simply not the case, despite living in a vastly richer country than
Keynes experienced in the early 1900s. After decades of decline, the
length of the typical American workweek began increasing in the 1970s,
despite widespread expectations that productivity growth would lead to
more leisure time for workers. Schor (1993) highlighted the plight of
the “overworked American” in an influential book on work and leisure
in the United States. She determined that a weakened labor movement
and the erosion of workers’ power were largely responsible for setbacks
in converting productivity gains into a shorter workweek for workers.
Americans in the labor market now work longer hours, have less
sick leave, and take fewer vacations than those in other wealthy nations.
In 1960, hours of work and labor market participation rates were similar
in the United States and Europe. But by 2000, there was a large gap in
the work effort of the typical person in the United States compared with
their counterparts in Europe. While the typical American works as many
hours a year as they did in the 1970s, Europeans generally work much
less, working fewer hours and weeks throughout the year. As suggested
by Schor, differences in labor regulation and unionization appear to be
the dominant factors explaining differences in annual hours worked
per year between the United States and Europe (Alesina, Glaeser, and
Sacerdote 2005).

Supply Challenges in U.S. Labor Markets | 187

Box 6-3. Deaths of Despair in the United States
For white Americans between age 45 and 54, average life expectancy is
no longer increasing; in fact, it even declined for several years, a pattern
not previously seen outside pandemics or wars. This occurred during a
period when Black Americans saw gains in life expectancy, narrowing
the enduring gap in outcomes between these two groups. Increases in
mortality rates among whites are largely accounted for by higher rates
of suicide, opioid overdoses, and alcohol abuse (Case and Deaton 2015).
The term “deaths of despair” was coined by Case and Deaton (2020)
in their influential book, which documents the impact of declining
economic opportunity on the health and well-being of working-class
society in the United States. They explain that these deaths of despair
primarily affect white Americans without a four-year college degree,
living in areas of the country that have a very low share of the workingage population employed.
While economists usually frame employment as a choice between
paid work and alternative uses of time, Case and Deaton’s (2020) work
highlights the importance of good jobs in providing meaning, structure,
and purpose to a community. They write: “Destroy work and, in the end,
working-class life cannot survive. It is the loss of meaning, of dignity,
of pride, and of self-respect that comes with the loss of marriage and of
community that brings on despair, not just or even primarily the loss of
money.” Their research shows how the diminished economic prospects
of white working-class Americans constitute not only an economic crisis
but also a public health crisis.
Recent papers have challenged the notion that growing deaths
of despair are a phenomenon unique to economically disadvantaged
whites; deaths from suicide, drug use, and alcohol use have risen even
more among Indigenous persons living in the United States (Friedman et
al. 2023). Among Indigenous persons, local economic conditions have
a heterogenous effect on deaths by suicide and drug use, suggesting
that improvements in economic conditions alone may not be enough to
reduce deaths of despair (Akee et al. 2022).

an aging population and output is negative (e.g., Gagnon, Johannsen, and
Lopez-Salido 2021; Maestas, Mullen, and Powell 2022; and Sheiner 2014).
However, Acemoglu and Restrepo (2020) find that the impact on technological change dominates, and that an older workforce increases economic
growth. Eggertsson, Lancastre, and Summers (2019) similarly find that
population aging can increase growth due to increased national savings that
accrue as populations age, driving down interest rates. However, as interest
rates approach zero and cannot fall further, this mechanism is disrupted, and
188 |

Chapter 6

they find that the impact of aging on growth becomes negative. Overall, the
evidence suggests that aging populations slow per capita economic growth,
but the economy’s potential to adapt to the tightening of labor supply in
ways that spur productivity cannot be discounted.
From an individual perspective, the fact that fewer people are engaged
in paid labor market activity is not necessarily negative and could simply reflect personal choices. By way of example, John Maynard Keynes
famously predicted in 1930 that technological progress would increase
living standards so much that his grandchildren would work just 15 hours
a week, devoting the rest of their time to leisure (Keynes 2010; orig. pub.
1930). As noted in box 6-2, this prediction did not come to pass for workers
in the United States. But evidence suggests that many nonparticipants, if not
otherwise engaged in schooling or caring for young children, are not happier
than those who work. Prime-age men who are out of the labor force report
low levels of emotional well-being, and derive little meaning from how they
spend their time (Krueger 2017). They are also less likely to be married and
much more likely to be living in poverty. Localities where employment rates
have fallen the most have also seen a sharper rise in opioid deaths and suicides (see box 6-3), suggesting significant community stress in these areas.

Causes of U.S. Labor Supply Challenges
Slowing population growth and declining labor force participation are
significant headwinds for U.S. labor supply. If these trends persist, and
offsetting increases in productivity or capital intensity fail to materialize,
there will not be enough workers to meet long-run demand. Because understanding the causes of current labor supply challenges is necessary to craft
effective policy solutions, this section provides an overview of the dominant
factors driving slower growth of U.S. labor supply since 2000.

Demographic Trends
Demographic trends are the principal cause of near-term U.S. labor supply
challenges. Over the next decade, the share of the population in their prime
working years (between age 25 and 54) will decline in the United States
and in many other countries, as shown in figure 6-2. These demographic
trends are the result of a sharp decline in fertility rates between 1960 and
the late 1970s, with fertility remaining at or below replacement rates for the
last several decades. Additionally, life expectancy in the United States has
also not kept pace with that of other wealthy nations, and was decreasing
for some groups even before the COVID-19 pandemic (see box 6-3). Due
to low fertility rates, the vast majority of near-term working-age population

Supply Challenges in U.S. Labor Markets | 189

Figure 6-2. Percentage of Total Projected Population That Is Prime Age, 2026–50
Percent
43
42
41
40
39
38
37
36
35
34
33
2026

2030
Canada

2034
China

2038
France

2042

Germany

2046

United States

2050

United Kingdom

Sources: World Bank; United Nations Population Projections.
Note: ‟Prime age” is 25–54 years.

Figure 6-3. Prime-Age versus Overall Labor Force Participation, 1990–2022

Prime-age participation rate, age 25–54 (percent)

Overall participation rate, age 16+ (percent)

85

68

84

67

83

66

82

65

81

64

80

63

79

62

78

61

77
1990

1994

1998

2002

Prime age (left axis)

2006

2010

2014

2018

2022

60

Overall (age 16+) (right axis)

Source: Current Population Survey data.
Note: ‟Prime age” is 25–54 years.

growth will be accounted for by immigrants and their descendants born in
the United States (Blau and Mackie 2017).
The tightening of labor supply conditions due to these demographic
trends was well under way before the pandemic, as can been seen in figure
6-3. Between 1990 and 2008, prime-age and overall labor force participation moved more or less in tandem. Starting in 2009, however, the two
categories began to diverge, as baby boomers began to enter their early
190 |

Chapter 6

retirement years. Increases in the participation rate among older workers
and slack labor demand conditions in the years after the financial crisis
dampened but did not completely offset the initial effect of retirements on
the labor supply (Aaronson et al. 2014; Abraham and Kearney 2020). As
labor demand recovered and labor markets tightened in the years preceding
the COVID-19 pandemic, many workers left the sidelines and entered the
labor market in response, easing concerns about the labor supply. Then the
pandemic arrived, and COVID-19—a virus that is particularly dangerous
for older members of the workforce—sped up many of the forces reshaping
the labor market. In December 2022, the prime-age LFPR was just shy of its
prepandemic peak, but overall participation had fallen by a full percentage
point, due primarily to population aging and the increase in the propensity
of older workers to retire during the pandemic.

Declining Labor Market Participation Among Men
In addition to population aging, another cause of slowing labor supply
growth is the decline in participation among men, particularly those without
a college degree. The participation rate for U.S. men in their prime-age
working years peaked in the 1950s and has been falling in earnest since
the mid-1960s. This decline is sharper than in other advanced economies.
Prime-age men in the United States and the United Kingdom had similar
rates of participation in the 1980s and 1990s, but the participation of men in
the United States continued to fall after 2000, while the United Kingdom’s
participation rates remained relatively flat. This trend has particularly significant implications for economic growth, because individuals are at their
most productive in their prime-age working years.
The underlying causes of declining male participation have been the
subject of much scholarly interest but still remain an open debate. This very
large body of research is summarized here to explain the potential causes of
declining male participation and to illuminate potential policies to counteract the ongoing decline.
Spousal income for heterosexual men. Increases in married women’s
labor supply could reduce male labor force participation by reducing the
cost of nonparticipation and increasing responsibilities at home, such as
childrearing and senior care. However, the evidence suggests that this is
not a significant factor driving down male participation. Men with working
wives and men with children have had the smallest declines in participation
among all men (Juhn and Potter 2006; CEA 2016). As discussed in more
detail in box 6-4, nonparticipating men are more likely than other groups to
rely on income from other family members, usually parents. It is plausible
that changing trends in household formation, such as more adults living with
their parents, will reduce male participation. However, the causality may go

Supply Challenges in U.S. Labor Markets | 191

Box 6-4. On What Income Do Jobless Men Live?
How do men between the age of 25 and 54 who do not work fund food,
shelter, and other necessities? Figure 6-i documents sources of income
for prime-age men who were not in the labor force in 2022. For comparison purposes, we also provide the composition of household income for
men in the labor force and for women. A primary source of income for
nonparticipating men is other household members, principally parents.
In contrast, income provided by other household members accounts for
only a small share of income for other groups. Spousal income available to nonparticipants is smaller than it is for men in the labor force,
in part reflecting lower marriage rates. Government transfer income,
particularly disability insurance, is a key source of income for some
nonparticipating men, although, as can be seen in figure 6-i, it accounts
for a relatively small share of income for nonparticipants as a group.

Figure 6-i. Sources of Annual Income for Prime-Age Workers by Sex and
Labor Force Status, 2022
Average annual income (thousands of dollars)
160
140
120
100
80
60
40
20
0

Male NILF
Wage and salary

Male labor force
Spouse

Female NILF

Social insurance and government transfers

Female labor force
Other

Other householder

Sources: Bureau of Labor Statistics; CEA calculations.
Note: NILF = not in the labor force.

in the other direction: these trends themselves may be an outcome of higher
housing costs and fewer labor market opportunities for young workers (Fry,
Passel, and Cohn 2020; Matsudaira 2015).
Disability insurance. Social Security Disability Insurance (SSDI) is
another candidate for a supply-side explanation of declines in male labor
force participation rates. SSDI receipts increased for several decades before
peaking in 2010, after which their incidence fell (CBPP 2021). A substantial body of research indicates that the availability of SSDI benefits lowers
participation for workers who are on the margin of eligibility (e.g., Bound
1989; Autor and Duggan 2003; Maestas, Mullen, and Strand 2013; and
192 |

Chapter 6

Gelber, Moore, and Strand 2017). However, SSDI receipts do not appear
to be an important determinant of declining male participation rates. From
1967 until 2014, prime-age male participation fell 7.5 percentage points,
while the share of prime-age men who received SSDI benefits increased by
2 percentage points (CEA 2016). Moreover, this 2-percentage-point rise in
the SSDI take-up rate among prime-age men should not be interpreted as
having caused lower participation, given that many of the men receiving
SSDI benefits would likely not have participated due to their disabilities.
An analysis conducted by the CEA finds that, under reasonable assumptions,
holding SSDI receipts constant for prime-age men during the 1967–2014
period would only have eliminated between 0.3 and 0.5 percentage point of
the observed reduction in prime-age men’s participation (CEA 2016).
Rising incarceration rates. As shown in figure 6-4, Black men have
a lower labor force participation rate in the United States than Hispanic or
white men, and participation among Black men has been falling more steeply
than that of other groups. A steep rise in incarceration rates beginning in the
1980s is a potential culprit in the declining employment prospects of Black
men, who face a much higher risk of incarceration than white men. Because
standard labor market statistics exclude institutionalized populations, they
understate the impact of rising incarceration rates on employment among
Black men. Doleac (2016) shows that accounting for the incarcerated population lowered the employment rate (i.e., the percentage of the population

Figure 6-4. Prime-Age Male Labor Force Participation, by Race, 1976–2022
Participation rate, age 25–54 (percent)
100

95

90

85

80

75

70

1976

1981

1986

1991
White

1996

2001
Black

2006

2011

2016

2021

Hispanic

Sources: Bureau of Labor Statistics; CEA calculations.
Note: ‟Prime age” is 25–54 years. Data are annual averages.

Supply Challenges in U.S. Labor Markets | 193

that is employed) of Black men in 2014 by about 4 percentage points, with
only a minimal impact on white men’s employment.
Incarceration is also likely to have a negative impact on employment
after release—an effect that would be reflected in official statistics. Formerly
incarcerated people face a number of barriers to formal employment: limited
labor market experiences while incarcerated, laws preventing them from
being employed in certain jobs, and employer practices that discourage hiring those with criminal records. Résumé-audit studies have found that an
applicant’s criminal record is a significant barrier to finding employment
(Pager 2003). Mueller-Smith (2015) finds that for individuals with a previous formal labor market attachment, incarceration decreases the probability
of subsequent employment, especially for those serving longer terms. Some
recent papers using administrative data have not found strong evidence of
significant scarring effects on postincarceration earnings and employment
(Garin et al. 2022; Looney and Turner 2018). However, this empirical result
is a consequence of poor labor market opportunities preceding incarceration;
formerly incarcerated persons are disproportionately drawn from neighborhoods in extreme economic distress. A reasonable interpretation of these
findings is that while incarceration likely does have an impact on future
employment and earnings, many of the challenges that formerly incarcerated
persons face in the labor market start long before their incarceration begins.
Abraham and Kearney (2020) use estimates of the formerly incarcerated population and Mueller-Smith’s (2015) estimates of scarring effects to
roughly calculate the impact of rising incarceration rates on declines in the
overall employment rate. They estimate that rising incarceration accounted
for a decline of 0.12 percentage point in the employment-to-population ratio
between 1999 and 2018, a period when the ratio declined 3.8 percentage
points. Though admittedly a rough estimate, their calculations suggest that
rising incarceration accounts for a small part of observed declines in overall
employment. However, rising incarceration disproportionally affects Black
communities, so incarceration’s role in driving down participation among
Black men is likely much larger.
Geographic mismatches. Labor force participation varies dramatically
across the United States, with striking gaps even among prime-age adults
(Nunn, Parsons, and Shambaugh 2019). Migration flows within the United
States were quite high in the mid-20th century. Those picking up stakes generally moved to where they could find work, and migration flows redirected
population on net from low-income to high-income regions (Blanchard
and Katz 1992). However, since 1980, internal migration has declined, and
moves have become less likely to reallocate population to more prosperous
parts of the country (Ganong and Shoag 2017). Reduced internal migration
may be a result of increased housing costs and/or increased licensing costs

194 |

Chapter 6

for certain occupations and industries (Hsieh and Moretti 2019; Johnson and
Kleiner 2020).
Declines in labor migration may have exacerbated declining labor
force participation, with workers increasingly exiting the labor market
instead of migrating in response to regional shocks (Dao, Furceri, and
Loungani 2017). This change has been evident in manufacturing employment declines since 2000; unlike previous downturns, those who lost jobs
have been less likely to migrate to other regions and more likely to exit the
labor force (Autor, Dorn, and Hanson 2021; Charles, Hurst, and Schwartz
2018). However, some economists have argued that declining mobility is an
appropriate response to improved information about distant labor markets
reflecting rational expectations about potential employment success in those
locations (Kaplan and Schulhofer-Wohl 2017) and to declines in urban
premiums for non-college-educated workers (Autor 2019).
The extent to which declining geographic mobility has exacerbated
declining labor force participation remains an open question. The fact that
out-migration responses to regional downturns have declined suggests that it
does play a role, but the importance of this role cannot be fully ascertained
with the evidence. However, the reduction in domestic migration to areas
with better economic opportunities is well established in the literature.
This indicates that policies designed to pull workers into the labor market
will have limited success unless they can also improve job opportunities in
regions where participation is currently low.
Demand factors: import competition and technological change. The
labor supply model outlined earlier in the chapter posits another theory for
declining participation: wages. In the model, adverse demand shocks can
have a negative impact on an individual’s decision to participate through
wages alone. The steepest declines in participation since the 1970s have
been among men without a four-year degree; these men have also experienced declines in wages throughout much of this period. It is reasonable to
wonder, therefore, if declines in labor demand can account for declines in
participation, particularly among men without a four-year degree.
Why would demand for labor have fallen disproportionately for men
without a college degree? Possible causes include globalization and technological change. These dual forces are thought to be key drivers of “job
polarization”—a term used to describe the relative growth of high- and lowskill jobs and the disappearance of middle-skill opportunities (Acemoglu
and Autor 2010; Autor and Dorn 2013). Middle-skill jobs generally include
tasks that are most vulnerable to automation and offshoring—making it difficult to distinguish the relative impact of each factor on employment. Job
losses during the two recessions preceding the COVID-19 pandemic were
concentrated among middle-skill jobs, and workers who lost those jobs

Supply Challenges in U.S. Labor Markets | 195

tended to exit the labor market rather than take lower-paying work (Foote
and Ryan 2015).
A number of papers have linked declines in U.S. manufacturing
employment to increased import competition from China (e.g., Autor,
Dorn, and Hanson 2013; Autor et al. 2014; Pierce and Schott 2016; and
Acemoglu et al. 2015). Increased imports from China reduced demand
for domestically produced manufacturing goods, which reduced demand
for U.S. manufacturing workers, who are disproportionately less-educated
men. More recent research by Bloom and others (2019) suggests that the
negative impact of the “China shock” on U.S. manufacturing employment
was sizable between 2000 and 2007 but has not exacerbated manufacturing
declines in more recent years. However, increased import competition is not
the only cause of manufacturing employment declines; automation has also
increased. Looking specifically at the role of robots in employment declines,
Acemoglu and Restrepo (2020) estimate that each robot displaces about 5.6
workers. Using this estimate, Abraham and Kearney (2020) tentatively conclude that increases in the stock of robots between 1999 and 2018 resulted
in the loss of 1.1 million jobs during this period.
Many economists believe that demand factors are the principal cause
of declining male labor force participation. In their comprehensive review
and meta-analysis of recent research on declining overall employment rates,
Abraham and Kearney (2020, 636) state that “our review of the evidence
leads us to conclude that, among the factors whose effects we are able to
quantify, labor demand factors are the most important drivers of the secular decline in employment over the 1999 to 2018 period.” However, their
estimates indicate that almost half the fall in employment rates during this
period is unexplained after accounting for changes in demand. The CEA
(2016) also concluded that low wages were the primary driver of male participation declines, with smaller roles for supply factors.
A seeming puzzle with regard to the view that declining wages are driving participation declines is that real wages for lower-skilled men rebounded
in the 1990s and the 2010s, periods when the participation of men continued
to fall. One possibility, suggested by Wu (2022), is that it is not absolute
but relative declines in wages that reduce participation. Widening inequality
means that relative wages for men without a four-year degree have declined
steadily for many decades, reducing their status, marriage prospects, and job
satisfaction. Wu finds that changes in relative wages account for almost half
the growth in labor force exits among noncollege men between 1980 and
2019. In a related paper, Binder and Bound (2019) argue that supply and
demand factors are likely not additive but interactive, with negative demand
shocks for noncollege men leading to less stable employment and lowering
marriage rates, in turn leading to changes in household formation that reduce
the male labor supply. Together, these papers suggest that demand factors
196 |

Chapter 6

and rising inequality may together have a negative impact on participation
by lowering the relative gain in status attainable through work.
Summary of the evidence on the declining male LFPR. Despite significant scholarly interest in the decline of male labor force participation,
questions remain. The evidence suggests that demand factors have played
an important role, with globalization and automation reducing employment
and wages, particularly for men without college degrees. However, a sizable
participation gap remains unexplained by changes in labor demand. Supply
factors, such as increasing incarceration and disability insurance, have also
exacerbated the decline, although the impact of these factors has been small.
An open question for research is how changes in gender roles and household
formation—particularly declining marriage rates and more adults living with
their parents—interact with demand factors and have exacerbated declines
in participation.
Despite the incomplete nature of the evidence on participation declines,
the extensive research literature does point to policy measures that could
boost participation among men. In particular—given the importance of
demand factors and rising inequality in driving down participation—efforts
to improve wages and working conditions for men without a four-year
degree would likely draw more of them into the labor market. Several policy
options to boost participation by addressing these factors are discussed in
greater detail later in the chapter.

Female Labor Force Participation: The United States Falls Behind
Despite declining labor force participation among men, the U.S. labor supply
grew for much of the last century, largely due to the growing participation of
women. The many social and economic factors driving the growth of female
participation in the last century are the subject of a large body of literature, a
review of which is beyond the scope of this chapter. What is more relevant
for current labor supply challenges is that the growth in women’s participation in the United States leveled off in the 1990s and began to decline. This
stagnation did not occur in other advanced economies, where female participation continued to grow. As shown in figure 6-5, female participation
levels and trends in the United States were similar to those in Canada and the
United Kingdom until about 1995. Female participation continued to grow
in these two countries after 1995, unlike in the United States. By 2015, U.S.
female participation rates were below those for women in Japan, who were
far less likely to work than women in the United States as recently as 2005.
Most of literature on declining female participation since 2000 has
focused on factors affecting the maternal labor supply. When discussing
why U.S. female participation has fallen behind that of other countries, a frequent observation is that the United States lacks publicly provided childcare

Supply Challenges in U.S. Labor Markets | 197

Figure 6-5. Prime-Age Female Labor Force Participation, 1984–2021
Participation rate, age 25–54, selected OECD countries (percent)
90

85
80
75
70
65
60
55
50

1984

1989

1994
Canada
United Kingdom

1999

2004
France
United States

2009

2014

2019

Japan
OECD average

Source: Organization for Economic Cooperation and Development.
Note: ‟Prime age” is 25 to 54 years.

and paid family and medical leave policies, which are common in most
advanced economies. The United States spent only $2,600 on care and early
education per child under 6 years of age in 2017, compared with the EU
average of $5,500 (OECD 2019). In consequence, childcare in the United
States consumes a significant portion of family budgets, with care costing up
to one-third of the average earnings of a single mother (Ziliak 2014). Most
of the empirical evidence indicates that publicly provided care options for
young children boost the maternal labor supply (e.g., Gelbach 2002; Baker,
Gruber, and Milligan 2008; and Haeck, Lefebvre, and Merrigan 2015), as
discussed in the next section on policy options. Chapter 4 of this Report also
discusses more broadly the social and economic benefits of greater public
support for early care and education.
However, there is no strong evidence tying childcare costs facing
families to declines in female participation since 2000. Declines in participation among women are broad-based and are actually steepest among
single women without children, whose participation declined by 7 percentage points between 1989 and 2016 (see figure 6-6). In many ways, female
participation trends after 2000 resemble those of men in the United States,
who also had declining participation relative to other advanced economies
during this period. While the factors driving declining participation among
men have been the subject of much research, much less attention has focused
on declines in women’s participation—with a few notable exceptions, such
as Black, Schanzenbach, and Breitwieser (2017) and Abraham and Kearney
(2020). Although research has been understandably focused on the role of
198 |

Chapter 6

Figure 6-6. Prime-Age Female Labor Force Participation Rate, 1976–2022
Participation rate, age 25–54 (percent)
90
85
80
75
70
65
60
55
50
45

1976

1981

1986

Single, no children

1991

1996

Married, no children

2001

2006

Single with children

2011

2016

2021

Married with children

Sources: Bureau of Labor Statistics; CEA calculations.
Note: ‟Prime age” is 25–54 years. Data are annual averages.

care in boosting the maternal labor supply, the factors driving declining
participation among women without children are also worthy of further
investigation.

The COVID-19 Pandemic’s Lingering Effects on the Labor Supply
The forces constraining the supply of workers at the onset of the COVID-19
pandemic were amplified as countries attempted to mitigate the spread of
the virus. Retirements among baby boomers, already fueling hiring challenges in many industries, spiked as older workers faced new and potentially
severe health risks at work. Some employers encouraged workers to take
early retirement in order to slash workforces in the face of reduced demand.
Immigration bans and closed borders halted the flow of foreign-born workers who were critical for many industries, particularly food services and
agriculture. The pandemic also disrupted the availability of childcare and
in-person school, making it difficult for many parents to return to work.
Prime-age participation has mostly rebounded from the pandemic
(see box 6-5), and recent efforts to clear the backlog of applicants have
returned immigration flows to prepandemic levels. But the lingering effects
on the labor supply remain, principally lower participation rates among
older workers. While retirements have increased in previous downturns
(Gorodnichenko, Song, and Stolyarov 2013; Coile and Levine 2011), the
pandemic-induced recession had a particularly severe impact. Quarterly
retirement rates increased by 5 percentage points at the start of the pandemic,
Supply Challenges in U.S. Labor Markets | 199

Box 6-5. The Missing Prime-Age Workers
Early during the COVID-19 pandemic, declines in prime-age labor
market participation were primarily due to pandemic-related disruptions.
Layoffs, illness, and care responsibilities pushed many participants out
of the labor market (Garcia and Cowan 2022; Goda and Soltas 2022;
Cajner and others 2020). As businesses and schools reopened, and
vaccines were rolled out, prime-age participation rebounded quickly
(Forsythe, Kahn, Lange, and Wiczer 2022; Hansen, Sabia, and Schaller
2022). Individuals with disabilities actually increased their participation
relative to prepandemic levels, likely due to increased telework and
remote work practices adopted during the pandemic (Ne’eman and
Maestas 2022). Yet despite tight labor markets and steadily rising wages,
growth in prime-age participation slowed markedly in the latter half of
2022; at the end of the year, it remained 0.6 percentage point below
the level in February 2020. As policymakers had hoped that continued
prime-age participation growth would ease ongoing inflationary pressures, concern about these missing workers grew. But why prime-age
participation remained below prepandemic levels three years after the
start of the pandemic cannot be easily ascertained.
One possibility is that the level of participation at the end of the
last expansion was an exception and not the rule. The last expansion
pulled many workers into the labor market, and the continued growth in
participation between 2016 and 2019 surprised many economists. It may
be that participation rates were well above the trend at the end of the long
expansion that preceded the pandemic (Barkin 2022).
Another possibility is that participation will keep growing as
long as the current expansion continues. That participation decisions
can respond to favorable economic conditions with a lag is suggested
by Cajner, Coglianese, and Montes (2021), who find that participation
declines after a negative shock can last up to four years, with nonparticipants who return to school or shift to care responsibilities during the
downturn accounting for much of the lagged response. If households
adapted to the pandemic in ways that can take a while to unwind, it may
be that more prime-age workers will return to the labor market over time.

a much larger spike in the retirement rate than occurred during the financial
crisis (McEntarfer 2022). Unlike previous downturns, the pandemic-induced
growth in retirements appears largely unrelated to local economic conditions, suggesting that the retirement surge may have been primarily driven
by COVID-19 health concerns (Coile and Zhang 2022). A recent paper
suggests that the increase in housing wealth during the pandemic may have

200 |

Chapter 6

also played an important role, with more retirements among older workers in
housing markets with stronger price growth (Favilukis and Li 2023).
As of the end of 2022, the growth in the retired share of the U.S.
population accounted for nearly all the shortfall in labor force participation
relative to prepandemic levels. In a recent paper looking at the surge in
retirements during the pandemic, Montes, Smith, and Dajon (2022) estimate
that almost half the retirements since the start of the pandemic have been
“excess” retirements that would likely have not occurred in absence of the
pandemic. They find particularly sharp increases in retirements among
workers over 65, among whites, and among those with a four-year degree.
Given the advanced age of most excess retirees, it seems unlikely that a large
proportion will return to the labor force. The authors also find that flows
into retirement remained elevated almost three years after the start of the
pandemic. They conclude that it may take some time for retirement behavior
to return to prepandemic norms.

Options to Boost the U.S. Labor Supply
As detailed in this chapter, the United States faces long-run headwinds
for its labor supply that may have an impact on future economic growth.
This section discusses policy options for boosting the U.S. labor supply.
Although the focus here is on broad-based measures, labor supply challenges are often unique to specific markets, and policy solutions to increase
the labor supply in general may be insufficient to remedy supply issues in
specific occupations and industries. To highlight this fact, this section also
discusses the supply challenges facing two particular labor markets, medical
care (box 6-6) and local public education (box 6-7) in greater detail.

Increasing Immigration
Increasing immigration to the United States is frequently cited as a way to
mitigate the consequences of the aging population. Immigration increases
potential output by increasing the size of the labor force; because new
immigrants are typically working age, it also lessens the effect of the
aging population on per capita economic growth. Immigrants also make
other important contributions to the U.S. economy. For example, given
that they often have fewer long-standing family and social ties, they are
more mobile than workers born in the country and are more responsive
to local economic conditions (Basso and Peri 2020). Skilled immigrants
have been found to boost innovation and technological change, which is
an added contribution to overall economic growth (Bernstein et al. 2022;
Hunt and Gauthier-Loiselle 2010). Overall, research also suggests that the
effects of newly arrived immigrants on the wages and employment of the

Supply Challenges in U.S. Labor Markets | 201

Box 6-6. A Critical Shortfall of Nurses and Physicians
Demographic shifts that began before the COVID-19 pandemic had
already started to manifest in a noticeable reduction in the supply of
health care providers. Researchers have documented the effect of the
aging of the U.S. population on the supply of nurses and physicians
in certain specialties such as primary care and psychiatry, along with
geographic misallocation affecting rural areas (Buerhous, Auerbach, and
Staiger 2017; Petterson et al. 2012; Satiani et al. 2018; Ricketts 2005).
Given that the youngest members of the baby boom generation are just
reaching retirement age, the aging of the population will continue to
affect the availability of health care providers for the foreseeable future
by simultaneously reducing the supply of providers and increasing
demand for health care.
Compounding these predictable shifts, the pandemic exerted a
historic shock on the health care workforce, exacerbating existing challenges. Unprecedented rises in the demand for health care overwhelmed
providers. Many left their jobs to protect themselves and their families
from catching the virus, to care for their young children and elderly
parents, and to focus on their physical and emotional health and lessen
burnout (Galvin 2021). These actions increased the burden for providers
who remained in the system.
Swift, short-term solutions were implemented to stabilize the
supply of critical health care workers. Hospitals utilized travel nurses to
fill short-term increases in demand (Gottlieb and Zenilman 2020). The
wages of these traveling nurse positions were set significantly higher
than those of other nurses, which resulted in many nurses being willing to move to areas of the country with the greatest short-term need.
Through provider payment incentive modifications, access to telehealth
services was expanded for many Americans. Many States also relaxed
their scope-of-practice restrictions, allowing for greater utilization of
nurse practitioners and physicians’ assistants (Volk et al. 2021).
Although these short-term solutions helped during the crisis,
improvements in the education and training of health care providers will
be required to maintain an adequate supply and distribution of providers
in the long run. There are currently too few nurse educators to meet the
demand. There are also too few nursing clinical placement spots to provide clinical experience for nursing students. Poor working conditions
in many hospitals led to high turnover in the nursing profession well
before the pandemic; improvements in patient-to-nurse staffing ratios
and management practices would reduce turnover and improve patient
outcomes (Vahey et al. 2004; Aiken et al. 2022). For physicians, there is
an insufficient number of residency slots. Increasing funding for medical
residency programs will also likely be needed to boost the supply of
physicians (GAO 2021).

202 |

Chapter 6

Box 6-7. Staffing Challenges in K-12 Education
Between February 2020 and October 2022, employment in local education fell by almost 300,000 workers, about 3.5 percent of this workforce.
According to survey results from a nationally representative sample of
public schools, 53 percent indicated that they were entering the 2022–23
academic year understaffed. Among those schools, respondents reported
that their highest need areas were special education teachers (65 percent)
and transportation staff (59 percent) (U.S. Department of Education
2022). Declines in education employment since the COVID-19 pandemic have not been broad-based but in fact have been concentrated in
lower-income communities, as shown in figure 6-ii.
Understanding the cause of these shortfalls is complicated, as
there is no single education labor market (Goldhaber et al. 2015).
Additionally, the lack of adequate data makes it difficult to identify
areas experiencing difficulties in hiring (Nguyen, Lam, and Bruno 2022).
Staffing challenges have long plagued schools that serve high proportions of students living in poverty and who belong to minority groups,
and hiring challenges are more pronounced in specialty teaching areas
such as special education, English language learning, and high school
science, technology, engineering, and mathematics (Boyd et al. 2005;
Cowan et al. 2016; Murnane and Steele 2007). Qualified teachers are
not evenly distributed across schools and students, with poor, Black, and
Hispanic students being much more likely to experience novice teachers
(James and Wyckoff 2022).
Figure 6-ii. Percent Change in Teacher Employment, 2019–22

Change in the 12-month rolling average of employment relative to January 2020
2
0
–2
–4
–6
–8
–10
Jan-2019

Jul-2019

Jan-2020

Jul-2020

Lower income

Jan-2021

Jul-2021

Jan-2022

Jul-2022

Higher income

Sources: Current Population Survey; CEA calculations.
Note: Teacher employment is limited to government employees and includes preschool and kindergarten teachers, elementary
and middle school teachers, secondary school teachers, special education teachers, tutors, and other teachers and instructors.
“Lower income” refers to employment among those living in the half of core-based statistical areas (CBSAs) with the highest
share of households with total household income under $50,000. “Higher income” refers to employment among those living in
the half of CBSAs with the lowest share of households with total income under $50,000.

Supply Challenges in U.S. Labor Markets | 203

When properly diagnosed, policy remedies emerge for areas with
particular teacher and school staffing needs. Addressing these localized challenges requires targeted efforts, such as incentives to serve in
hard-to-staff schools and high-need areas, and innovation and flexibility
about alternative pathways into the profession and licensure reciprocity
(Dee and Goldhaber 2017). The keys to addressing teacher and staffing
challenges are facilitating mobility into these local labor markets and
encouraging retention in high-need positions once they are filled. Some
of these measures, such as relaxing licensing requirements, have also
contributed to meeting long-term goals such as creating a more diverse
teacher workforce (Bacher-Hicks, Chi, and Orellana 2021).

domestic population are quantitatively very small, and that the fiscal effects
of immigration are generally positive. For example, as new immigrants tend
to be working age, they pay taxes without incurring the fiscal costs of youth
and early education (for a comprehensive review of the fiscal and economic
impact of immigration, see Blau and Mackie 2017).
There is also a potential pool of laborers already residing in the United
States without legal authorization to work and/or a path to citizenship. Legal
permanent residence would expand the employment opportunities for a
significant portion of this population. As such, immigration reform that provides a path to citizenship for the estimated 11 million undocumented individuals would help to increase the labor supply (Migration Policy Institute
2022). Additional immigration reforms could include removing per-country
caps on employment, expanding diversity lottery visas, and expanding the
J-1 exchange visa program, which would bring additional faculty, scientists,
and students to the United States for training and sharing knowledge and
methods.

Drawing More Adults into the Labor Market
Labor force participation among working-age adults in the United States is
falling, and it is now lower than that in other developed nations. A likely culprit is the lack of public sector support for workers and families in the United
States relative to other wealthy countries. Policies directed at improving the
labor market prospects of nonparticipants and removing obstacles to their
employment could increase participation among prime-age adults. This
subsection outlines several policy options for drawing more adults into the
labor market.
Improving care options. Public spending on childcare and senior care
in the United States is very low relative to that in other advanced economies.

204 |

Chapter 6

The United States is also one of the few countries that has no guaranteed
paid family and medical leave. In the absence of public support, the economic burden of caring for family members falls chiefly on women, whose
labor market participation and lifetime earnings fall in consequence. Given
women’s low participation rates relative to men, policies that reduce their
care burden are a promising avenue for increasing participation and reducing
gender disparities.
The preponderance of empirical evidence suggests that childcare
and preschool programs have a positive impact on maternal labor force
participation (e.g., Bauernschuster and Schlotter 2015; Morrissey 2016;
and Wikle and Wilson 2022). Some of the evidence arises from research on
a policy change in Quebec, which introduced highly subsidized universal
childcare in the late 1990s. These subsidies led to a very large increase in
the use of care and a sizable and long-lasting impact on mothers’ participation (Baker, Gruber, and Milligan 2008; Haeck, Lefebvre, and Merrigan
2015). Morrissey (2017) evaluates a range of studies and concludes that a
10 percent reduction in the cost of childcare would likely increase maternal
participation in the range of 0.5 to 2.5 percent. Paid family and medical
leave can also help workers stay connected to their jobs while addressing
family-related needs, maintaining good job matches, and boosting long-term
attachment to the labor market (Baum and Ruhm 2016; Anand, Dague, and
Wagner 2021). Blau and Kahn (2013) estimate that nearly one-third of the
gap in U.S. women’s participation relative to that in other developed countries can be explained by the relative lack of such family-friendly policies.
Criminal justice reform and removing barriers to reentry. The United
States incarcerates people at a higher rate than any other country in the
world (World Prison Brief 2021). Reducing the punitiveness of the criminal
justice system in the United States would reduce the large fiscal burden of
its current system and the collateral damage of incarceration on affected
communities. It would also increase the labor supply by reducing both the
incarcerated population and the scarring effects of incarceration on employment. In recent decades, several States have experimented with criminal
justice reforms aimed at reducing incarceration rates, with noteworthy success. For example, California introduced changes to sentencing for less serious offenses and lessened punitive measures for technical parole violations,
changes that caused a sharp decline in incarceration (Lofstrom and Raphael
2013). A series of reforms in New York to divert drug offenders to treatment facilities and to relax mandatory minimum sentencing practices have
also sharply reduced incarceration rates in that State (Greene and Mauer
2010). In both States, these periods of reduction in incarceration generally
coincided with declining crime rates.
High incarceration rates have also created a large population of
formerly incarcerated individuals who face significant barriers to reentry.
Supply Challenges in U.S. Labor Markets | 205

Removing barriers to employment for the formerly incarcerated would
likely improve their employment prospects after release. One such reform,
undertaken by several States in recent years, is to remove occupational
licensing barriers for people with arrest and conviction records. In many
States, a prior arrest or conviction, no matter how long ago, can prevent
one from becoming a licensed barber or cosmetologist, a drug counselor,
or a firefighter (Rodriguez and Avery 2016). Noting that information about
prior arrests or incarceration decades after the event in question is of limited
information value to employers, Piehl (2016) advocates reforms that would
set a time limit on information about past convictions for employment background checks.
Expanding the Earned Income Tax Credit. The Earned Income Tax
Credit (EITC) is a large government program that raises the after-tax return
to work for low- and moderate-income households, particularly those with
dependent children. As one factor depressing labor market participation is
stagnating wages for non–college educated workers, the EITC can create
additional incentives for participation by increasing the returns to work. A
large body of research has shown that the EITC increased the labor supply
of low-income mothers, a group it principally targeted (e.g., Bastian 2020;
Eissa and Liebman 1996; and Meyer and Rosenbaum 2001). However,
the maximum credit for families with two or fewer children has remained
flat in real terms for many decades (Hoynes, Rothstein, and Ruffini 2017).
Increasing the generosity of the current credits and/or expanding the EITC
to provide more incentives to low-wage workers without dependent children
would likely boost participation.
Regional economic development. Efforts to improve the economic
performance of a particular region usually (but not always) target areas that
have experienced downturns, with the intent of helping its residents. While
not explicitly intended to increase participation, improving economic opportunities in declining areas would likely improve participation in areas with
low employment rates by boosting local labor demand.
Regional economic development strategies can take many forms. A
common form is that of enterprise zones, which provide tax incentives and
sometimes exemptions from regulations, with the intent of spurring business
investment and growth. Evidence on the effectiveness of enterprise zones
in improving employment opportunities is mixed, and uncertainty remains
about which policies work, how they work, and for whom they work (e.g.,
Neumark and Kolko 2010; Neumark and Simpson 2015; and Ham et al.
2011). However, evidence on the effectiveness of regional economic development programs involving infrastructure expenditures and investments in
higher education and research is more promising. Kline and Moretti (2014)
find positive long-run effects of the Tennessee Valley Authority, which

206 |

Chapter 6

administers an ambitious regional development plan, on manufacturing
employment and income in the targeted region.
The Economic Development Administration (EDA) also encourages
economic development in regional clusters, with notable successes, including Milwaukee’s water cluster and St. Louis’s agricultural technology
initiatives (Feldman 2022). An expansion of these efforts was included in
the 2021 American Rescue Plan, which funded EDA’s Build Back Better
Regional Challenge, ultimately awarding 21 coalitions the resources to
develop emerging regional industry clusters. The recent CHIPS and Science
Act also has an explicitly place-based approach to boosting innovation and
commercial activity, and it authorizes the establishment of the new Regional
Technology and Innovation Hub Program at EDA to reduce geographic disparities and promote the growth of technology clusters in underrepresented
and promising regions.
Other regional economic development strategies include workforce
development programs that aim to train people in new and emerging industries and occupations. These programs provide skill upgrading and certification programs for displaced workers and others who would otherwise be
unable to access (at reasonable costs) the training required for employment
in these industries or occupations.
Nonmonetary incentives and job quality. As noted previously in this
chapter, the classic labor supply model frames work as an exchange of
effort for monetary compensation. Implicit in this framing is the notion that
money alone motivates participation in the labor market. But the evidence
suggests that workers care about nonpecuniary aspects of work—such as the
meaningfulness of the employer mission, social interaction, greater scheduling flexibilities, and self-direction (Cassar and Meier 2018; Nikolova and
Cnossen 2020; Clark 2015). Preferences for nonpecuniary amenities vary
across workers, with women and nonwhites tending to value job quality
attributes more than white men (Katz, Congdon, and Shakesprere 2022).
These preferences can shape lifetime earnings in significant ways. For
example, Wiswall and Zafar (2017) find that a quarter of the early career
wage gaps for women can be explained by a higher preference among
women for jobs with greater flexibility and stability.
Job attributes vary widely across the working population. A national
survey of working conditions in the American workplace found that men
without a college degree, women, and younger workers generally experience substantially worse working conditions. Specifically, they tend to
have less control over their work schedule, experience more verbal abuse
and harassment, and have greater exposure to safety hazards (Maestas et
al. 2017). Data on preferences indicate that, unsurprisingly, workers prefer
work to have more “good job” aspects and fewer “worse job” aspects. This
evidence has led many to speculate that improvements in job quality that
Supply Challenges in U.S. Labor Markets | 207

would improve worker welfare could also potentially increase labor market
participation. Some aspects of job quality can be improved through policies
such as mandated sick leave or changes to scheduling practices. But many
job quality attributes will remain the outcome of business decisions made
by employers.
Improving workers’ bargaining power. As indicated previously in this
chapter, stagnating wages and rising inequality are key drivers of declines in
labor force participation. Though many of the factors responsible for demand
declines are global, inequality has risen more in the United States than in
other advanced economies. This is at least partially due to declining worker
power, particularly declines in unionization (Grossman and Oberfield 2022;
Stansbury and Summers 2020). Worker power enables workers to negotiate
with employers for better pay, safe conditions, predictable working hours,
and other aspects of the work environment. Unions have historically been an
important force in increasing workers’ leverage.
Despite the recent upswing in petitions for union elections, union
density in the United States continues to decline, from about one-third of
the private sector workforce in 1950 to just over 6 percent today. The consequences of unions’ decline for workers include lower wages (e.g., Card
1996), including for nonunionized workers in the same sector (Farber 2005).
Union density may also be tied to trends in income inequality, with U.S.
inequality rising as union density has fallen (Farber et al. 2021). In short, as
unionization has fallen, workers’ incomes have stagnated relative to output
growth.
Globalization, technological change, and employer concentration are
commonly cited as key factors driving declining unionization. However,
many economists have pointed out that these factors do not fully explain
why unionization in nontradable goods sectors has fallen at a similar rate, or
why unionization is lower in the United States than in other Western countries (Levy and Temin 2007; Schmitt and Mitukiewicz 2012). More likely
reasons for declining worker power are institutional changes within the
United States—particularly the expansion of right-to-work States, greater
employer opposition to organizing efforts, and decreased enforcement of
labor laws.
The Biden-Harris Administration supports the Protecting the Right to
Organize Act, or PRO Act, which would help restore that stated policy of the
National Labor Relations Act, to “encourag[e] the practice and procedure of
collective bargaining and [protect] the exercise by workers of full freedom
of association, self-organization, and designation of representatives of their
own choosing, for the purpose of negotiating the terms and conditions of
their employment or other mutual aid or protection” by making it easier
for workers to unionize by preventing companies from holding mandatory
antiunion meetings and by imposing penalties on employers that retaliate
208 |

Chapter 6

against organizers (White House 2021). The Administration has also taken
significant steps to improve workers’ leverage, appointing former union
officials to the National Labor Relations Board, increasing funding to allow
the Board to pursue its statutory remit, establishing a task force to promote
labor organizing, and adding prevailing wage and apprenticeship requirements to the recent CHIPS and Science Act and Inflation Reduction Act.

Conclusion
The United States faces a large shortfall in its labor supply as it continues to
recover from the COVID-19 pandemic. This shortfall is not merely a lingering effect of the pandemic but is also due to long-run demographic trends
and declines in labor market participation by adults. Without increased
immigration and/or efforts to draw more adults into the labor market, the
labor supply is likely to be constrained for the foreseeable future. The
shrinking share of adults in the workforce and the Nation’s aging population
may have a negative impact on its living standards through slower economic
growth. More proactive policies to increase the labor supply—such as higher
public spending on childcare, increasing immigration, and improving workers’ bargaining power—are needed to counteract these demographic trends.

Supply Challenges in U.S. Labor Markets | 209

Chapter 7

Competition in the Digital Economy:
New Technologies, Old Economics
Digital markets have become an integral part of Americans’ daily lives. Over
14 percent of retail shopping now happens digitally (U.S. Census Bureau
2022), and digital markets now account for more than $2 trillion in value
(over 10 percent of gross domestic product) and employ 8 million workers
in the American economy (Highfill and Surfield 2022). The economic forces
operating in digital markets are not particularly new; however, when combined with the scale afforded by digital settings, the low costs of connecting
with others, and the large amounts of data being collected, the economics
of these markets lead to new implications for how these markets look, how
they operate, how they make an impact on the economy and society, and
how they should be regulated.
Nearly all digital markets feature positive “network effects”—meaning that
the value of a product or service increases as the number of users grows (i.e.,
as the “network” gets bigger)—so having fewer, larger service providers can
benefit users. A social media website, for example, is of little value to its
users if it has very few users; it is, in fact, more convenient to have all your
friends accessible via the same website. In addition to network effects, digital settings enable a global scale and the unprecedented collection of data,
which can all favor the rise of dominant firms. These forces can also act as
barriers to entry, preventing new firms from challenging dominant ones.
Healthy competition among many firms pushes companies to produce goods
at their lowest possible cost, offer products and services at the best prices,
provide better wages and working conditions, create new technologies,

211

and develop and sell new products that people want to buy. This, in turn,
ensures that economic agents make the best use of society’s resources. In
contrast, dominant firms with significant market power may use this power
to increase prices, reduce quality, and lower output, making consumers and
other market participants worse off. This is why regulations are necessary
to ensure that the competitive process is protected and to maintain a level
playing field for all market participants.
This chapter reviews some of the potential economic benefits delivered by
digital markets, such as lower search costs and increased variety. The chapter also explores other characteristics of digital markets that differentiate
them from their offline counterparts, including the ability of firms to gather
a huge variety and volume of data on users, potentially without their knowledge, either by running experiments or simply monitoring users’ behavior,
and rapidly process these data to derive significant value. These data can
be used to improve firms’ product offerings, which can benefit users, or for
other purposes, such as personalized pricing, which may benefit firms but
harm users.
The chapter closes with a discussion of the regulation of digital markets.
Regulators’ challenge is to deliver all the benefits of competition—such as
innovation, privacy, and low prices—in a setting where economic factors
may drive markets toward fewer competitors. As a result, regulators should
seek to lower barriers to entry and also prevent a dominant firm from
exploiting its power either in the same or a related market, or to engage in
practices that harm consumers or other market participants in other ways.
For regulators overseeing digital markets relative to offline ones, new areas
of concern include the misuse of consumer data and collusion by pricing
algorithms. Overall, digital markets present significant opportunities to
benefit society if regulators, enforcers, and courts can adapt to the new
digital landscape.

212 |

Chapter 7

The Benefits of Digital Markets
In this chapter, the term “digital markets” encompasses the interfaces that
electronically bring together various agents for economic or social purposes.
Although there is no unanimously accepted definition of digital markets or
what goods and services they include, the chapter refers to these diverse
interfaces—including app stores, operating systems, search engines, social
media platforms, web browsers, and online marketplaces. Unlike many
offline settings, where buyers and sellers typically transact directly with one
another, most digital markets involve an intermediary that brings together
different agents and facilitates their interactions. In addition, “marketplaces”
include not only traditional marketplaces—where buyers sell tangible items
to consumers, as would occur in offline markets—but also markets where
different economic agents are being matched. For example, an online job
search website would be classified as a “market,” as would a ride-sharing
application on a mobile phone that connects drivers with riders.
In many cases, users may value the additional convenience of having
interactions facilitated digitally (Goldfarb and Tucker 2019). Digital markets have also provided other benefits to consumers by creating new forms
of price competition and saving time from travel or searches for goods and
services, among others. For example, one early study (Brynjolfsson and
Smith 2000) found that Internet retailers’ prices were 9 to 16 percent lower
and that they changed their prices by increments up to 100 times smaller as
compared with traditional retailers, suggesting that they have lower costs
for instituting price changes and that these savings are partially passed on
to customers. However, other studies have produced more nuanced results,
such as a more recent study (Cavallo 2017), which finds that online and
offline prices are often identical among the largest firms. In e-commerce,
digital markets allow for greatly increased product variety because there
is much less of a physical inventory constraint when products are shipped
directly to consumers. Digital markets also have benefits for businesses.
They can potentially compete in markets that would otherwise be too costly
to enter. The next sections further explore the value of these aspects of
digital markets.

Reducing Search Costs
The seminal work of Stigler (1961) explores the value of lowering search
costs. Digital markets theoretically enable perfect price comparisons across
the universe of retailers of the same good at low cost, and also lower the
acquisition costs of information. For example, digital marketplaces like
eBay and Etsy are able to reduce search costs—such as the costs incurred
to find a particular product or service, including the cost of the time spent
looking—by bringing together and matching large numbers of buyers and
Competition in the Digital Economy: New Technologies, Old Economics | 213

sellers that would otherwise spend a great deal of time searching for one
another to transact a unique item. An early study in the digital era (Brown
and Goolsbee 2002) found that the Internet led to lower prices for term life
insurance. Other studies from the same period found that digital markets
reduced prices for consumers, such as estimates of an average of 2 percent
saved by customers of online car-buying referral services (Scott Morton,
Zettelmeyer, and Silva-Risso 2001) and an average of 16 percent saved
by consumers shopping for electronic products using an online price comparison tool (Baye, Morgan, and Scholten 2003). More recently, researchers
have investigated the potential trade-offs between reducing search costs
and increasing the potential for collusion; issues related to collusion are
addressed later in the chapter.
In theory, digital markets should be inherently more competitive,
thanks to the low search costs and increased price transparency, all else
being equal. However, one natural response by firms to combat this is to
introduce obfuscation. Ellison and Ellison (2009) document that firms face
a very high price sensitivity of consumers in online marketplaces that make
price comparisons easy. As a result, sellers undertake price obfuscation
behaviors, such as making product descriptions complicated so that comparisons are difficult, creating multiple versions of the same product, and
attempting to “upsell” consumers who were drawn to an initial low price.
Such behaviors have been documented in multiple government sources and
findings, or engage in so-called drip pricing strategies (Blake et al. 2021;
FTC 2017; CFPB 2022; White House 2016).

Increased Variety
Consumers have also benefited from increased access to variety in both
products and services that has been enabled by digital markets. Brynjolfsson,
Hu, and Smith (2003) estimate that the benefits to consumers attributable to
increased product variety among online booksellers may be 7 and 10 times
larger than those from increased competition and lower prices. Quan and
Williams (2018) estimate that the value of the online footwear market is 5.8
percent greater than the traditional local retail market due to the increased
variety available, and Gentzkow (2007) finds that a free online version of
a newspaper in Washington was worth $0.35 per reader per day, or a total
gain of about $52 million per year in 2021 dollars. One study also found the
availability of online services meant that consumers in smaller, less densely
populated places could be better connected to national markets, increasing
their access to a larger variety of goods and services (Sinai and Waldfogel
2004). It is worth noting, however, that if a particular firm achieves dominance in a market, the variety offered becomes something that this firm can
control.

214 |

Chapter 7

“Free” Products and Services
The set of products and services available in digital markets that appear to
be “free” for consumers is large (e.g., Internet search engines, email, digital
maps, music streaming, video streaming, price comparison tools, and online
games). Research has shown that consumers value online tools like search
engines and email services in thousands of dollars per year (Brynjolfsson
et al. 2019). This phenomenon is not unique to digital markets; broadcast
television and radio are free for those with a television set or radio, and some
newspapers are offered for free. This apparently free access is often made
possible by business models that depend on advertising revenue and collection of user data to subsidize consumer products and services. For example,
figure 7-1 shows the exponential growth in advertising revenue for Google
and Facebook, which enables them to offer a number of ad-supported
products and services. A counterpoint to many “free” goods and services is
that they could have negative externalities, meaning that there are external
costs to society beyond the prices being paid for them. In other words, these
products may not be free after all; instead, users are paying for them—for
instance, by indirectly “selling” their data. The chapter elaborates on this
dynamic in the next section.
Given that so many products and services have zero monetary costs for
consumers in digital markets and that these markets have become so large
and pervasive, it is possible that U.S. current national accounts are missing
Figure 7-1. Growth in Advertising Revenue by Digital Platform, 2002–21
Millions of 2021 dollars
250,000

200,000

150,000

100,000

50,000

-

2002

2004

2006

2008

2010
Google

2012

2014

2016

2018

2020

Facebook/Meta

Sources: U.S. Securities and Exchange Commission, Bureau of Economic Analysis, and CEA calculations.
Note: Google revenue includes advertising revenue across Google Search, YouTube, and Google Network members. Facebook/Meta
revenue includes all advertising revenue. Nominal values are adjusted by the U.S. Personal Consumption Expenditures Price Index (chain).

Competition in the Digital Economy: New Technologies, Old Economics | 215

much of the value that is created in these markets. One paper proposes a way
to account for this with a new measure of gross domestic product, called
“GDP-B” (Brynjolfsson et al. 2019).
Many have also argued that some of the innovations in digital markets
have had unintended or negative side effects on society more generally. Box
7-1 explores research on the broader societal implications of digital markets.

Box 7-1. The Societal Implications of Digital Markets
Many digital services serve not only economic purposes but also important social and political ones. As Americans spend more time online,
these services are becoming an important conduit for learning and sharing information about contemporary events and social movements, both
domestically (Suh, Vasi, and Chang 2017; DeLuca, Lawson, and Sun
2012; Carney 2016; Mundt, Ross, and Burnett 2018) and internationally
(Gorodnichenko, Pham, and Talavera 2021; Aday et al. 2013). Online
services, including social media platforms, also play an increasingly
large role in political campaigns and advertising, as evidenced by the
growing amount that politicians spend on digital advertising (Williams
and Gulati 2017; Barrett 2021).
This increase in the political information circulating online has
influenced how Americans engage in politics. For instance, being
exposed to online political information like social media advertisements
has changed how people express their beliefs, including through their
voting behavior (Beknazar-Yuzkashev and Stalinski 2022; DiGrazia
et al. 2013). In addition, these effects often extend across networks of
friends and social contacts (Bond et al. 2012; Jones et al. 2017).
Social media platforms may exacerbate political polarization
(Allcott et al. 2020). One study found that exposure to Twitter bots
disseminating opposing views reinforced preexisting political positions
(Bail et al. 2018). Levy (2021) conducted an experiment showing that
social media algorithms limited exposure to news outlets with opposing
views, increasing polarization. Conversely, other studies have suggested
that the role of social media platforms in spurring political polarization
is limited (Prior 2013; Fiorina and Abrams 2008; Boxell, Gentzkow, and
Shapiro 2017).
Racism, sexism, and discrimination also exist online, and in some
cases, this can escalate to more hateful content and conduct. In an
experiment conducted on eBay, Ayres, Banaji, and Jolls (2015) found
evidence of racial discrimination, with Black sellers making less than
white sellers, despite selling the same product: baseball cards. Similar
results were found by Doleac and Stein (2013). Expanded broadband
Internet access has also been associated with a rise in hate crimes (Chan,
Ghose, and Seamans 2016), as has reliance on social media and support

216 |

Chapter 7

for Islamophobic policies (Lajevardi, Oskooii, and Walker 2022). One
particularly salient example involved Microsoft, which launched an artificial-intelligence-powered Twitter bot (automated online social media
accounts are known as “bots”) named “Tay” in 2016 that was intended
to learn as it interacted with users. The bot lasted one day before it was
taken down for tweeting racist, misogynistic, and transphobic content
(Victor 2016). A similar fate befell a South Korean chatbot after it began
using homophobic slurs (McCurry 2021).
Another concern involving online services is their ability to easily
spread misleading or factually incorrect information. For example, one
study found that fake news stories were widely circulated during the
2016 presidential election, with inaccurate stories favoring at least one
of the two candidates being shared roughly 38 million times (Allcott
and Gentzkow 2017). Bots were also found to play a role in spreading and amplifying misinformation during the COVID-19 pandemic
(Himelein-Wachowiak et al. 2021; Xu and Sasahara 2022; Ayers et al.
2021), which became factors in COVID-19 vaccine hesitancy (Garett
and Young 2021; Neely et al. 2022; Pierri et al. 2022).
Finally, as social media plays a more central role in society,
significant concerns have been raised about their effect on mental
health, particularly among younger users. In 2021, the Surgeon General
released a report titled “Protecting Youth Mental Health” (U.S. Surgeon
General’s Advisory 2021) that specifically cited the dangers that arise
when social media companies “[focus] on maximizing time spent, not
time well spent.” The report called for additional research on the specific
risks and harms presented by social media platforms.

How Is Competition Different in Digital Markets?
Economists are interested in encouraging competition because competition typically results in markets that deliver consumers and other market
participants the best choices, highest quality, and lowest prices, among
other benefits. When many firms are offering similar products to consumers, consumers will choose to buy at the lowest prices, which gives firms an
incentive to lower their prices. It also gives an incentive for firms to improve
the quality of the product they offer by innovating, as this may be a means
to attract consumers. If instead there is a single firm offering a product, that
firm is likely able to increase its price or diminish its quality without losing
many of its customers, as their customers do not have any good alternatives.
This is why economists typically view a market dominated by a few large
firms as unlikely to be good for consumers or other market participants.

Competition in the Digital Economy: New Technologies, Old Economics | 217

This section introduces the main characteristics of digital markets and
discusses how they can lead to markets becoming dominated by only a few
large firms. None of these characteristics are unique to digital markets; but,
as argued later in this chapter, network effects in combination with vast
amounts of data and the unlimited scale possible in digital settings can result
in concentrated markets.

Big Data
In digital markets, huge amounts of data are generated as a by-product of
activity. While a traditional retailer can observe what products you decided
to purchase, digital retailers observe what you searched for, what you were
shown, and what you ultimately decided to buy. Further, given that online
retailers control search results and site layout for each individual separately,
they are able to use these data to personalize your experience in a way that
traditional retailers could never do. Because of this, users’ data can have
increasing returns to scale and scope (Bergemann and Bonatti 2019) especially at smaller initial scales. The result is that data can serve as a barrier to
entry for new firms that reduces competition.
In addition, the flexibility of the digital setting makes the process
of conducting experiments much easier by greatly lowering the cost and
increasing the scale at which firms can run experiments (e.g., Dubé and
Misra 2023). The data gathered from experiments can be used to further
improve product quality and the user experience but may also be used to set
prices, manipulate behavior, or to pursue price discrimination strategies that
ultimately lead to consumers being worse off. This research raises important
questions about how consumer data are gathered and used, how technology
may lead to consumer harm in some settings, and whether this suggests a
role for regulation.
Related to the previous discussion of “free” products, users are often
paying for services with their data as the “price” is the associated loss of privacy without further compensation. In fact, some products and services exist
solely for the purpose of collecting valuable and sensitive user data. These
data may be used in ways of which users are unaware; they may be used for
targeted behavioral advertising, personalized pricing, or sold to firms known
as “data brokers,” which aggregate user data from multiple sources to sell
as a product. Box 7-2 explores the types of information collected and sold
by data brokers. The existence of data brokers could be negative for consumers, if their data are used in inappropriate ways, or possibly positive for
consumers, if data are a barrier to entry and data brokers enable more firms
to enter the market. The Federal Trade Commission (FTC) called attention
to the data broker industry as early as 2014 with a report calling for greater
transparency (FTC 2014).

218 |

Chapter 7

Box 7-2. Consumer Data as a Business Model
At the nexus of big data are companies known as data brokers, which
serve two primary functions: acquiring data and monetizing data (Crain
2018; Gu, Madio, and Reggiani 2021). These firms compile data from
a variety of sources, including through public government records or
through cooperative agreements, whereby a data broker and another
entity like a retailer mutually share their records. Alternatively, brokers
can purchase or license consumer data from retailers, banks, brokerages,
and other data brokers (U.S. Senate 2013; FTC 2014). Using a combination of information gathered and inferences made based on these data,
brokers assemble profiles and segments of consumers to predict how
they might behave; for instance, their propensity to purchase certain
products or services (FTC 2014; Mishra 2021).
Although Americans may be aware that their data are being collected to be resold, theoretical and empirical studies have suggested that
users might be unaware of the scale or degree to which they are being
monitored (Crain 2018; Choi, Jeon, and Kim 2019; Acquisti, Taylor, and
Wagman 2016). In fact, almost every American has had their data collected by one, and likely many, of the major brokers, given that multiple
brokers have information on nearly every American. For example, by
2014, one broker, Acxiom, had more than 3,000 data points on nearly
every U.S. consumer and information on 700 million people globally
(FTC 2014). Others had information on 99.99 percent of all U.S.
properties or payroll data from 1.4 million businesses (Sherman 2021).
One data set used for marketing purposes had over 75,000 elements,

Figure 7-i. How Data Brokers Aggregate Data from Government, Commercial, and Publicly Available
Sources to Build In-Depth Profiles of Consumers
Identifiers

Sensitive information

Demographics

Name, address, geolocation, contact
information

Social Security number, driver’s license
number, birthdate, birthdates of family
members

Religion, language, marital status,
education level, veteran in
household, foreign language
household, single parent status

Court and public record

Financials

Bankruptcies, criminal offenses,
judgments, voter registration, party
identification

Owns stocks and bonds, investment
interests, credit history, life
insurance, net worth indicator,
types of credit cards, tax return
transcripts, holder of gold or
platinum card

General interests
Apparel preferences, gambling, life
events (e.g., expecting parent),
magazine subscriptions, political
leanings, preferred music/movie
genres, membership club, activism

Social media

Purchase behavior

Health

Buying channel, holiday gifts, type
of entertainment purchased,
average days between orders,
dollars spent, self-help book
purchases, Internet shopper

Over-the-counter drug purchases,
tobacco usage, propensity to order
prescriptions online, health/disability
insurance, brand name preferences,
allergy sufferer, corrective lenses users

Previous purchases, “heavy
Facebook user,” uploaded pictures,
social media accounts, usage
Home and neighborhood
Type of housing, home equity,
home loan amount and interest rate,
move-in date, neighborhood crime
rates, presence of home pool

Sources: Data from FTC (2014); CEA compilation.

Competition in the Digital Economy: New Technologies, Old Economics | 219

2/7/2023

including markers for whether someone was a whiskey drinker, had life
insurance, enjoyed romance novels, or used yeast infection products
(U.S. Senate 2013). In some cases, these data sets can also identify
individuals as financially vulnerable. For example, some tags that might
be associated with a profile include “rural and barely making it,” “tough
start: young single parents,” and “zero mobility” (U.S. Senate 2013).
Figure 7-i provides examples of the different types of data that a broker
might collect on (or infer about) a single individual to build out a profile
that it may sell to its clients.
In August 2022, the FTC filed a lawsuit against one of these data
brokers, Kochava Inc., for selling individuals’ precise geolocation and
movement data, including “to and from sensitive locations . . . associated with medical care, reproductive health, religious worship, mental
health,” and shelters for at-risk populations (FTC 2022). According to
the lawsuit, Kochava claimed that on average, it was “observing more
than 90 daily transactions per device.” The FTC alleged that Kochava’s
clients who purchased the data would be able to identify or infer an individual’s identity (based on their nighttime location) as well as whether
they visited sensitive locations, such as a reproductive health clinic, a
place of worship, or a domestic violence shelter.

Network Effects
Network effects refer to any situation where the value of a product or
service to an economic agent depends on the number of users (i.e., the size
of the network) engaging with it. For example, the value of a messaging
app depends on the number of users it has. Or the value of an e-commerce
website for buyers depends on the number of sellers on the website, and vice
versa. In many markets with network effects, the principal economic benefit
comes from interactions between different types of participants (Rochet and
Tirole 2003). Research has demonstrated the importance of network effects
in many digital and traditional markets (Gandal 1994, 1995; Saloner and
Shepard 1995; Rysman 2004); and with the proliferation of digital markets,
network effects have become increasingly salient. A central feature of digital
markets for determining competitive outcomes is the strength of network
effects.
Network effects can be categorized in two ways: direct and indirect.
Direct network effects are benefits or costs derived from the total number of
users that belong to the network, and the benefit or cost to a user increases
with the number of other users. Take, for instance, a video-conferencing
service. There is little incentive for users to join if there are few other users;
but as the user base grows, the service becomes more and more appealing to
220 |

Chapter 7

consumers. This is an example of a positive network effect, which is common to social media and instant messaging, among others. In contrast, congestion is a common form of negative network effect in telecommunications
networks. Cellular data networks suffer from reduced speeds when a large
number of users are accessing the network simultaneously, for instance.
Indirect network effects occur when groups of different users interact and a given user benefits (or suffers) from having more users on the
service from the other group(s). This situation exists for services such as
e-commerce marketplaces, app stores, job-matching services, and food
delivery services. For example, if a certain job-posting website has the most
applicants looking for jobs, employers will find that site most appealing for
posting jobs. Similarly, applicants will be more likely to look for openings
on the website that has the most employers posting jobs on it. This creates a
reinforcing cycle of more job applicants looking for jobs and more employers posting job openings. Another example would be marketplaces—either
digital or brick-and-mortar—where more sellers attract more buyers, and
vice versa. This dynamic is illustrated using a neighborhood farmers’ market
in figure 7-2. A farmers’ market exhibits indirect network effects because
the benefits for buyers and sellers increase with the number of agents of
the other type present. As the farmers’ market attracts more sellers offering
more varieties, the value of going to the market increases among potential
buyers. And because more buyers are circulating in the market, the value of
going to the market for potential sellers of additional goods increases. Of
course, if the farmers’ market became too crowded, additional buyers and
sellers would start to create negative congestion effects. Digital markets do
not face this physical space constraint and therefore can continue to grow as
more buyers and sellers enter the market.
Figure 7-2. Network Effects Are Present in Many Markets—Not Just Online
Sellers

More buyers
attract more
sellers

Farmers’ Market

More sellers
attract more
buyers

Buyers

Sources: Eggs, honey, bread, and basket icons from Freepik via flaticon.com; face icons from Adobe Stock images.

Competition in the Digital Economy: New Technologies, Old Economics | 221

2/7

Network effects have been considered a potential source of market
power—the ability to raise prices without losing many customers—since
before the rise of digital markets. In general, the presence of network effects
constitutes a barrier to entry that raises the costs for new competitors to
enter the market. If a new firm wanted to start a rival food delivery app to
compete with an established firm, the new firm would be at a tremendous
disadvantage because consumers and restaurants would likely see more
value in the established firm’s network than in a start-up with a small
network. Caillaud and Jullien (2003) describe how network effects create a
chicken-and-egg problem that can hinder competition. In order for start-up
competitors to attract buyers to a new e-commerce service and away from a

Box 7-3. Glossary for Describing Digital Markets
two-sided market

network effects

multi-homing

tipping-point market

222 |

Chapter 7

A two-sided market is a market where
a firm enables interactions (i.e., acts as
an intermediary or platform), bringing
together two sets of parties (e.g., buyers
and sellers) to transact and operate. For
example, a ride-sharing service operates
in a two-sided market by connecting riders
and drivers.
Network effects refer to phenomena where
the value of a product or service increases or
decreases as the number of users increases
or decreases. For example, as more people
sign up for a messaging service, it becomes
a “better” service compared with a messaging service that has few users.
Using more than one competing service
provider is referred to as multi-homing.
For example, users may switch between
two different ride-sharing services to take
advantage of different prices or a shorter
waiting time.
A tipping-point market is a “winner-takeall” market, where consumers flock to one
or a few firms as opposed to patronizing
many firms. For example, the market of
social media platforms often “tips” in
favor of dominant social media platforms
with many users (as opposed to numerous
platforms with few users).

more established one, the competitor needs many sellers; but to attract sellers, they need many buyers. This dynamic can inhibit competition and can
make a market susceptible to the phenomenon known as tipping.
A tipping point is generally defined as a critical juncture beyond which
a significant and potentially unstoppable change takes place. The application
of tipping points to the economics of firms that bring together two different
types of economic agents to intermediate their interaction—these markets
are referred to as “two-sided markets”—goes back to Fudenberg and Ellison
(2003), who identified the role of what we now label as network effects in
creating the conditions for dominant firms to emerge. These markets often
“tip” in favor of the leading firms, meaning that one or two firms drive out
their competitors and dominate the market. Box 7-3 is a glossary of the
terms used to describe digital markets.

Multi-Homing
Another pivotal factor of digital markets for determining competitive outcomes is the degree to which one type of user elects to use only one service
among a group of competitors, which is referred to as “single-homing.” In
other cases, users may be willing to use multiple competing services, or
“multi-home,” such as when a consumer pulls up two different ride-sharing
applications on their phone to compare prices. All else being equal, if users
are willing to use multiple, competing services, then these services are less
able to raise prices or set terms that are unfavorable to users because they
are more willing to take their business elsewhere (Teh et al., forthcoming).
When one side of the market multi-homes and the other single-homes,
competition between services for users that only use one will be fierce
(Armstrong 2006), because the service is the exclusive means by which the
multi-homing side can reach those single-homing users, allowing higher
prices to be charged on the multi-homing side (Jullien, Pavan, and Rysman
2021). Hence, users’ willingness to use multiple, competing services can
limit market power, giving the service an incentive to hinder users from
multi-homing (Scott Morton et al. 2021). This can be accomplished through
the use of switching costs—that is, costs that users would incur if they tried
to transfer their business to a competitor (Scott Morton et al. 2019). Firms
can impose switching costs through exclusive contracts or agreements, loyalty programs, termination fees, or a lack of data portability.

When Do Markets Tip?
Tipping occurs more easily in digital markets than offline markets due to
their combination of positive network effects, valuable data, and a potentially massive scale. Whether a market will tip depends, however, on the
willingness of users to switch between different services for the provision

Competition in the Digital Economy: New Technologies, Old Economics | 223

of goods and services (i.e., whether they multi-home). When positive
network effects exist and consumers have a high propensity to use a single
service, digital firms can often leverage network effects to entrench their
market power. For instance, a social media platform may be incentivized to
limit the ability of nonusers to connect and share content with users. For a
consumer, this means that if he or she quits the platform, it would essentially
sever the connections the user has made with other users of the application.
This can keep the consumer locked in to a service, even if they have other
concerns—for instance, regarding their privacy. Ultimately, as users are
incentivized to join the largest network(s), the market can tip in favor of one
or more dominant firms (Kades and Scott Morton 2020).
Once a market has tipped in favor of a dominant firm, potential
entrants that might want to offer innovative new features or charge lower
fees would face a very uphill climb in establishing themselves. That is, the
benefits of competition we would normally expect will not be realized.
A dominant firm also has an incentive to acquire any potential entrant to
prevent competition in the market. Dominant firms may further exploit their
dominance in a market to give themselves an advantage in other markets,
harming competition. Four factors are credited with preventing tipping in a
two-sided market: product differentiation, multi-homing, interoperability,
and congestion (Jullien, Pavan, and Rysman 2021).
Product differentiation. If a competitor offers a higher-quality experience or other differentiated features beyond its role as an intermediary, it can
draw enough customers who find these services valuable to enable it to survive. One example of how firms attempt to differentiate is to have superior
recommendation algorithms so that they are better able to match consumers
with products. Another is how firms make the process of transacting as
simple as possible, thus requiring less effort on behalf of buyers and sellers.
Multi-homing. When users of a service are willing to also use competing services, neither service has much market power over those users.
Therefore, neither is likely to achieve dominance. Firms know this, and thus
they actively engage in behavior that makes it more difficult for users to also
use a competing service. Their tactics include things like having exclusive
content, for example, among competing streaming services. If all videostreaming services offered the same content, consumers would likely choose
the one with the lowest price; but once a streaming service has exclusive
content that consumers demand, consumers will not be as willing to switch
to other services. Another approach might be to have a loyalty program that
makes users less willing to use other services.
Interoperability. Making services “interoperable”—able to exchange
data between themselves—weakens the network effects of either individual
service. With interoperability, network effects no longer exist at the firm
level; rather, they would aggregate at the market level (Kades and Scott
224 |

Chapter 7

Morton 2020). Take the example of short-message/messaging service
(SMS) text messaging. This clearly has a positive network effect, given
that the value of SMS text messaging increases as more people have mobile
phones that can send and receive these messages. This network effect is
not firm-specific because the SMS text network is interoperable between
cellular carriers and telephone operating systems. In contrast, an app like
iMessage by Apple is only available on Apple devices and has no interoperability with Android messaging apps, so the network effect is firm-specific
to Apple. By broadening network effects from only accruing at the firm
level to covering the entire market, interoperability directly challenges
the mechanism that can entrench the market power of dominant firms and
spurs competition in the market. Open standards that allow interoperability
between different firms’ products—for example, the universal serial bus
standard—are one way to achieve network effects at the market level and
encourage robust competition.
Congestion. Finally, congestion—a negative network effect—tends to
make the growth of some services beyond a certain size untenable due to
the degradation of services as users are added to the network. In most digital markets, this is of less concern as the scale of most services is limitless
before encountering congestion; however, as a social network grows, it may
be subject to greater problems of fraud, cybersecurity attacks, and content
moderation.
Of these factors, firms operating in digital markets have the ability to
control their degree of product differentiation and interoperability as well
as to influence the tendency toward multi-homing (Athey and Scott Morton
2022). Regulators of these digital markets want to bring the benefits of competition to the economy and protect consumers either by acting to prevent
markets from tipping in the first place or taking action in markets that have
tipped.

The Role of Law and Regulation in the Digital Market
Economists often evaluate the benefits and costs of an action or innovation
in terms of its value to society as a whole. When represented mathematically, this is called the “social welfare function.” This function includes the
benefits and costs for consumers, producers, and the government as well as
any benefits or costs for society stemming from inefficiency or externalities. These benefits and costs are not only measured in terms of prices and
quantities for the economy’s goods and services but can also include effects
on less tangible things like innovation, inequality, and well-being. All these
concerns may inform the priorities of regulators and law enforcement in
digital markets; this section focuses on the direct implications of the economic model underlying competition in digital markets.
Competition in the Digital Economy: New Technologies, Old Economics | 225

U.S. antitrust laws seek to promote competition and protect market participants, including workers, consumers, sellers, and buyers from
anticompetitive mergers and business practices. The enforcement of these
laws is conducted by the U.S. Department of Justice (DOJ) and the FTC as
well as by other Federal and State agencies. In addition, agencies such as
the Federal Communications Commission and the FTC also have relevant
regulatory (i.e., rulemaking) authority. The Biden-Harris Administration’s
competition policy is overseen by the White House Competition Council,
which was established by the President’s “Executive Order to Promote
Competition in the American Economy,” which was issued on July 9, 2021
(White House 2021).
The antitrust agencies monitor the conduct of firms, with a specific
focus on mergers, monopolization, unfair methods of competition, and collusion. Before the 1980s, the antitrust agencies focused heavily on mergers
and monopolization activity because firms that control a significant share
of the market (or potentially all of it, in the case of a monopoly) generally
have a greater ability to raise prices and reduce quantities or engage in other
anticompetitive practices in an effort to maximize their profits. Though the
focus of antitrust agencies shifted away from monopolization activity for a
time, enforcement against monopolies has seen renewed attention in the past
several years. The FTC also has authority to deter unfair or deceptive acts
and privacy and data security degradations, which can intersect with competition oversight. A recent example of such practices is the $5 billion fine
imposed on Facebook in 2019 for misleading consumers about their privacy
on the platform (FTC 2019).
The DOJ and FTC are also guided in their enforcement activities by
a body of case law that has been developed over the last century. Much
of this case law has focused on regulating mergers, particularly mergers
between competitors selling the same or very similar products (“horizontal
mergers”), with the aim of balancing the potential efficiency gains from
the combination passed on to consumers against the risks posed by the
loss of competition between the merging firms, such as higher prices or
reduced innovation. As discussed above, digital markets, in combination
with network effects, are predisposed to become highly concentrated and
be controlled by a few large firms. Though concentration alone is neither
procompetitive nor anticompetitive, highly concentrated markets are more
susceptible to anticompetitive practices. Existing competition laws and
regulations written before the emergence of digital markets may not have
fully anticipated how these markets would function and may therefore be
insufficient to ensure robust competition and protect consumers and other
market participants.

226 |

Chapter 7

Network Effects Create a Competitive Moat
If network effects at the firm level are sufficiently strong, having larger
firms may be better for customers. For example, as noted above, messaging services may be more useful when they have more users. Competition
among many small, incompatible messaging services is unlikely to benefit
consumers, given the fixed costs and returns to scale. And yet, left to its
own devices, a dominant messaging service would likely raise prices above
a competitive level, provide lower quality, potentially innovate less, or do
all of the above. This would be seen as a market failure, which should be
addressed via regulation, nationalization, or antitrust enforcement (Joskow
and Rose 1989; Joskow 2007; Smiley and Greene 1983).
Further, network effects have long been recognized as potentially
becoming an “economic moat”—a protective barrier that guards a profitable
business (the “castle”)—in that they lead to customers being locked in to
certain products, making mass migration to a new product unlikely unless
accompanied by a simultaneous technological advance somewhere else in
the ecosystem (Bresnahan 2002). New entrants are less likely to be successful when facing an entrenched firm with network effects or the benefits of
scale, eliminating some benefits of competition.
The messaging service example is illustrative, in that a potential solution to bringing back the benefits of competition in the presence of network
effects may be interoperability, although interoperability alone may not
suffice to fully restore competition. Interoperability expands the benefits
of network effects from the firm level to the market level. Requiring that
competing services interoperate is one remedy that can dissolve some of the
anticompetitive outcomes of network effects because all competitors would
share the same network effect. Thus, interoperability would mean that both
old and new services would need to compete on other dimensions like quality to keep users on their services.
Another related tool is data portability, the idea that consumers can
take, or “port,” their data to a different service. This reduces the switching
cost created by network effects. For example, imagine that a user wants to
switch from one music streaming service to another. One barrier for the
consumer would be having to give up their playlists and liked songs. Data
portability would allow the user to download and port these playlists to
another streaming service, thereby reducing the barrier to switching. Both
data portability and interoperability can make it more appealing for a potential entrant to introduce a competing service and increase the likelihood of
new innovations being able to succeed.

Competition in the Digital Economy: New Technologies, Old Economics | 227

The Challenge of Preserving Competition in Digital Markets
Traditional competition policy analysis often focuses on estimating changes
in prices to assess effects on consumers. However, this approach faces new
challenges in digital markets arising from several sources—notably, the
provision of free goods and services, and the cross-subsidization in markets
with indirect network effects. For “free” goods with no monetary price, in a
more competitive market, the true price could be negative (e.g., consumers
could be paid to watch ads or fill out surveys with their personal data), or
service could be better. As a result, demonstrating anticompetitive harm may
require alternative measures rather than prices.
Research into the effects of mergers in digital markets demonstrates
heightened complexity in the expression of competitive effects. Chandra
and Collard-Wexler (2009) empirically show that mergers of firms in twosided markets may not lead to higher prices on either side of the market in
an application to the Canadian newspaper industry; and Song (2021) shows
that mergers between firms in two-sided markets can lead to either higher
or lower prices after the merger, but that even agents that experience higher
prices may be better off due to increased network effects. Another study, of
the merger of two platforms for pet-sitting services (Farronato, Fong, and
Fradkin, forthcoming), found that on average consumers were not substantially better off with one platform than two competing ones because the
network effects were not large enough to balance the losses due to higher
prices and reduced variety after the acquired platform was shut down. In
markets with indirect network effects, policies intended to increase competition may need to account for how an intervention on one side will affect
the well-being and behavior on both sides of the market because pricing is
linked to the costs and price sensitivity of users on both sides (Evans 2003;
Wright 2004).
These challenges are exacerbated by the scale of the task of protecting competition in digital markets. For example, large tech companies are
highly acquisitive. Figure 7-3 shows that the volume and value of mergers
and acquisitions among tech firms is large, a trend that has drawn the attention of antitrust authorities. Reviewing these acquisitions for anticompetitive
harm requires significant resources due to the complexity of the markets, the
sophistication of the firms, and the need to look beyond the impact on retail
prices alone.
Finally, digital markets can be highly dynamic, appearing and evolving
rapidly. This can limit the ability of regulators to use current and historical
data to analyze market behavior. In addition, it can be quite difficult for
regulators to identify nascent competitors and potential entrants in assessing
proposed mergers. Further, when antitrust authorities do identify such anticompetitive mergers (DOJ 2020), the lack of prices for the potential entrant

228 |

Chapter 7

Figure 7-3. Completed Acquisitions by Large Tech Firms
LinkedIn
Microsoft

Motorola
Alphabet

Beats
Apple
WhatsApp
Meta
Whole Foods
Amazon

2009

2011

2013

2015

2017

2019

2021

2023

Sources: Bloomberg; Crunchbase; Mergr; Alphabet; Meta; Amazon; Google; Microsoft; TechCrunch.
Note: The dark blue circle radius is proportional to the deal size, and the small black dots indicate acquisitions with no publicly available price. Deals include completed mergers -and-acquisitions deals
by the specified firm as of the fall of 2022.

or lack of significant market share for the nascent competitor are again
problematic for traditional competition analysis, since anticompetitive harm
has often been demonstrated using economic models showing that mergers
would lead to higher prices. These challenges underscore the need for further research and approaches to evaluating competitive effects in complex
digital environments. This is work the antitrust agencies are well positioned
to do, in concert with academics and other stakeholders.

Preventing the Extension of Dominance into Adjacent Markets
Digital markets with network effects, big data, and a global scale have
tended to coalesce on a small number of dominant firms. An obvious concern is that firms could exploit their dominance in one market to gain market
power in or dominate adjacent markets. This type of conduct could be illegal
under Section 2 of the Sherman Antitrust Act.
Today, there are many examples of digital markets where a dominant
firm also competes in an adjacent market: Google and Apple operate app
stores, in which their own apps compete with other apps; Amazon operates
an e-commerce marketplace, where its Amazon Basics brand competes
directly with those from other firms; and Microsoft operates a video game
marketplace, where they also compete as a video game developer. In these
situations, one concern is that the dominant firm could have an unfair
advantage for its competitive products, known as “self-preferencing.” For
example, Apple was alleged to give its own apps higher priority when a
person searched its app store (Mickle 2019).

Competition in the Digital Economy: New Technologies, Old Economics | 229

If dominant firms exploit their dominance to give their own offerings
an advantage, consumers may not get the full benefits of competition. One
approach a regulator or legislature might take to improving the functioning
of certain markets is to prohibit self-preferencing and similar practices.
However, such a ban could be challenging to enforce, as a regulator would
need to show that self-preferencing is intentionally built into a service
instead of just occurring organically because, for example, the owner’s
products have received better reviews.
A related concern about marketplace operators that compete on
their own marketplaces is the issue of how competitors’ data are used.
Marketplace operators are able to gather extensive data on competitors’
products and customers, and they may have an incentive to use those data
strategically, either in the design of their own competing products or in their
pricing or promotional strategies. They could also intentionally limit what
data from the site are available to competitors. Any of these actions would
further put competing firms at a competitive disadvantage. A regulator may
want to prohibit the use of competitors’ data or insist on the fair treatment
of marketplace data for all firms in order to reset the competitive landscape,
although enforcement of such a regulation could be a challenge requiring
significant monitoring and oversight.
The ability of a dominant firm to extend its dominance into adjacent
markets is a threat to competition. Society may miss out on certain innovative products if entrepreneurs realize that their product may just get copied
by a dominant marketplace operator and, therefore, decide against investing
in developing it. In addition, the better product may not “win” on an uneven
playing field. Regulators can address this market failure by clarifying who
owns what rights to the data collected and leveling the playing field for all
firms in online markets. An overview of some of the approaches that regulators are taking, both internationally and in the U.S., is presented in box 7-4.

Preventing the Misuse of Consumer Data
Assessing the competitive effects of data usage and policies can be difficult.
Research suggests that when data can be used to reduce a firm’s exposure to
risk, it can lead to increased innovation or efficiencies, potentially driving
down prices (Eeckhout and Veldkamp 2022; Kirpalani and Philippon 2020;
Competition Bureau Canada 2017). However, data can also become a barrier to entry that insulates firms from competition. Prüfer and Schottmüller
(2022) show that under certain conditions, a data advantage can lead to
market tipping. In addition, the ability of firms to collect massive amounts
of data about individuals raises clear concerns about privacy and also about
data protection, as leaks of massive data sets could expose individuals to

230 |

Chapter 7

Box 7-4. International and Subnational
Efforts at Regulatory Reform
Numerous antitrust and consumer protection efforts are occurring both
internationally and in the United States at the State level. For instance,
the European Commission has proposed a pair of new laws focused on
regulating digital markets—the Digital Markets Act (DMA) and the
Digital Services Act (DSA) (Council of the European Union 2022).
The DMA aims to promote competition by establishing rules about
the types of conduct in which large “gatekeeper” firms can engage
(European Parliament and European Commission 2022). In order to be
designated a “gatekeeper,” in each of the last three financial years, a firm
must have had at least 10,000 annual business users established in the
European Union, 45 million monthly users established or located in the
European Union, and €7.5 billion (about $7.4 billion in 2021 dollars)
in annual revenue across the EU or a €75 billion market capitalization
(about $74.4 billion in 2021 dollars). It must also provide the same
“core platform” services—for example, web browsing, messaging, and
social media—in at least three EU member states. To foster competition
between firms and reduce barriers to entry, the DMA lays out requirements by which gatekeepers must abide. For example, gatekeepers must
allow for data portability and must make messaging services interoperable. They must also be more transparent about their mergers and
acquisitions and must allow users to uninstall predownloaded software
on the gatekeeper’s operating system. At the same time, the DMA also
restricts gatekeepers from engaging in certain business practices, like
preferencing their own products over those of competitors on their platform (“self-preferencing”) or combining users’ personal data across the
gatekeeper’s different core platform services. The DMA also prohibits
gatekeeper firms from engaging in certain price-setting practices and
creating operating terms that discriminate against certain businesses and
app developers. For instance, the DMA makes it illegal for gatekeepers
to make business users sign agreements to not offer better terms on other
platforms (known as most-favored-nation clauses). These agreements
have the potential to dampen competition, raise prices and fees, and
reduce entry by competitors offering lower-priced alternatives (Boik and
Corts 2016; Baker and Chevalier 2013; Wang and Wright, forthcoming).
While the DMA primarily focuses on regulating the conduct
of a few very large firms in an effort to promote competition, the
DSA addresses the wider societal implications associated with digital
markets and establishes regulations focused on filtering illegal content
and protecting the fundamental rights of consumers online (European
Parliament and Council of the European Union 2022). For example, the
DSA requires that firms inform users about how and why advertisements
are being targeted to them. It also bans firms from using personal data

Competition in the Digital Economy: New Technologies, Old Economics | 231

to target advertisements if the firm is reasonably aware that the user is
a minor. In addition, the DSA includes numerous other provisions, such
as requiring online intermediaries to moderate illegal content (including
hate speech), while giving regulators wide-ranging powers to request
access to very large online platforms’ business practices and algorithms.
In addition to new laws being passed abroad, certain States of the
United States are also passing new regulations targeting digital markets,
with a specific focus on consumers’ data rights. As of late 2022, five
States—California, Colorado, Connecticut, Utah, and Virginia—had
passed comprehensive State-level regulations on consumer data and
privacy rights in digital markets (NCSL 2022; Connecticut 2022).
For example, Connecticut passed a law in 2022 that gave consumers
more control over how their data could be collected, used, or accessed
(Connecticut 2022). Once the law takes effect, in July 2023, consumers
will have the right to access, correct, and delete records of their personal
data. Connecticut residents will also be able to opt out of having their
personal data sold or used for targeted advertising.

identity theft or other financial harm (Ichihashi 2020; Chapman and Bodoni
2022; O’Sullivan 2021).
For all these concerns about the misuse of data and protection of
privacy, a practical intervention is to regulate how data can be collected,
used, shared, and stored. The authors of one study explore mediated data
sharing to reduce the correlation between users’ data and thus to mitigate
externalities that create excessive data sharing (Acemoglu et al. 2022). They
propose sharing data with a third party that would transform their data to
remove correlation with other users before sharing it with services requested
by the user. Other policies that might impose fewer costs include “right-tobe-forgotten” provisions, which create time limits on data retention (Chiou
and Tucker 2017).

Monitoring Pricing Algorithms and Collusion
Concerns have been raised that pricing algorithms could facilitate explicit
price collusion by reducing uncertainty about consumer demand. O’Connor
and Wilson (2021) suggest that this improved forecasting could either lead
to lower prices and increased consumer benefits or enhance the ability of
firms to support collusive arrangements. Other studies of retail gasoline
markets have raised concerns about online price disclosure and experimentation facilitating the coordination of prices across firms (Luco 2019; Byrne
and de Roos 2019). A simple example would be the use of posted prices
232 |

Chapter 7

Box 7-5. Artificial Intelligence and Digital Markets
A fundamental aspect of the operation of digital markets is using
artificial intelligence (AI) to translate the data available to firms into
actionable predictions, recommendations, and decisions (OECD 2019).
Many of the features that make digital markets so appealing to users are
powered by machine learning and other algorithmic tools (Brown 2021).
Indeed, many of the key features of digital markets—efficient matching,
low search costs, an unmatched variety of products, and personalization
of prices—are made possible by a combination of data availability and
the application of AI techniques like neural networks, natural language
processing, or other forms of machine learning. Though the use of these
algorithms can improve the experience of users and increase firms’
profitability, there are ongoing concerns that they will displace workers;
introduce racial or other sorts of bias into these systems; make digital
marketplaces even harder to regulate; and meaningfully impact individuals’ or communities’ rights, opportunities, or access to critical resources
or services.
For ride-sharing companies like Uber and Lyft, machine learning
is the key to their ability to set prices to assure that there are enough
drivers on the road to meet customer demand (Liu et al. 2022). AI also
allows social media platforms to optimize their content. TikTok relies
on its algorithm’s ability to use its wealth of data to select content that
will keep users engaged longer (Smith 2021; Wall Street Journal 2021).
Further, the ability of firms like Amazon to have the products that a
customer is looking for in stock without having to maintain a surplus
inventory is driven by AI-based predictions about demand at any given
point in the future (Amazon 2021). All these features of digital markets
are made possible because of the combination of data and algorithms.
However, the reach of AI in digital markets raises concerns that
there could be a wave of automation of jobs (Sisson 2022). Even in
cases where AI augments existing labor, as with Uber’s algorithmic
management of its drivers or Amazon’s of its warehouse workers, some
workers report deep levels of frustration and resentment due to such
concerns as the degree of surveillance and the lack of transparency about
AI decisionmaking (Möhlmann and Henfridsson 2019).
AI also has been shown to perpetuate and potentially exacerbate
biases already present in society. There is a robust literature on this
relationship, with findings of discrimination based on race alone found in
algorithmic risk assessments in the health care space, facial recognition
systems, and natural language processing (Obermeyer et al. 2019; Furl,
Phillips, and O’Toole 2002; Caliskan 2021). Major players in the digital
market have long struggled with these issues; for example, Amazon’s
attempt to build an AI-based hiring program resulted in a system that
taught itself to prioritize male candidates and penalize résumés that

Competition in the Digital Economy: New Technologies, Old Economics | 233

mentioned women’s colleges and made other references to women
(Dastin 2018). These biases can be both intentional, as when Facebook’s
AI-based advertising made it possible for advertisers to exclude specific
users based on their race, and unintentional, as when women were
shown fewer career ads because the cost to advertise to women was
higher online (Zang 2021; Lambrecht and Tucker 2019). Even when an
algorithm itself does not increase bias, differential rates of utilization of
the algorithm can deepen racial and gender disparities, as in the case of
Airbnb’s Smart Pricing tool (Zhang et al. 2021).
As governments around the world consider how best to regulate
digital markets, they are confronting the fact that AI’s role in this market
introduces levels of opacity and complexity that can hinder reasonable
efforts at oversight (European Parliament 2022; Kroll 2021). Further,
complexities emerge in assessing the intent of firms, which can be an
important part of many regulatory systems (Chin 2019). Processes like
algorithmic audits have been proposed as tools to overcome the “black
box” features of AI that can create substantial information asymmetries
between firms and regulators (Guszcza et al. 2018). These audits have
received attention in areas related to hiring, and they are being actively
considered both internationally and within the United States (Lee and
Lai 2021; Engler 2021; Digital Regulation Cooperation Forum 2022).
In 2022, the Biden-Harris Administration released the “Blueprint for an
AI Bill of Rights” (White House 2022), which outlines five principles to
guide AI system design that will protect the American public.

online to institute price matching, enabling firms to potentially achieve a
higher price than they could achieve if their rivals’ price was uncertain,
as price-matching policies remove the incentive for competitors to lower
prices. There is evidence that artificial-intelligence-based algorithms can
potentially adapt to raise prices in a coordinated fashion, even if they have
not been explicitly programmed to do so (Harrington 2018). This form of
tacit collusion may be difficult to detect. In addition to the possibility of collusion through the use of algorithmic pricing, the use of automated software
can support prices above competitive levels. This can intensify merger price
effects in ways that are not accounted for in a traditional merger analysis and
also generate greater price dispersion in the market (Brown and MacKay,
forthcoming). In order to guard against the threats of tacit collusion and
explicit price fixing enabled by pricing algorithms, antitrust authorities
may require additional resources (i.e., computing, personnel, and financial
resources). Box 7-5 explores other ways in which artificial intelligence
affects the functioning of digital markets.

234 |

Chapter 7

Conclusion
Although the basic economics of digital markets are well understood, when
combined with the effects of scale and the data collection potential of the
digital world, they raise new concerns. Many digital markets have become
dominated by a few firms or even one firm, and these dominant firms have
incentives to protect their existing position, to extend their market power
into other markets, and to exploit the huge amounts of data being gathered
on their users.
Governments must ensure that the benefits of competition—such as
innovation, privacy, choice, and low prices—are realized while protecting market participants and promoting a fair and contestable playing field.
Competition regulation and enforcement must adapt to the changes brought
on by the digital revolution, given that harm to competition, market participants, workers, and consumers is now being manifested in novel ways.
Creating digital markets that work for everyone would allow their full
potential to be shared by all Americans.

Competition in the Digital Economy: New Technologies, Old Economics | 235

Chapter 8

Digital Assets: Relearning
Economic Principles
Multiple financial crises have struck the United States during the last
two centuries. Many of these crises have been caused by institutions that
function like banks but are not registered or regulated as banks, so-called
shadow banks. For example, the 1907 crisis—then called a “panic”—was
mainly caused by trust companies, which were State-chartered entities that
competed with banks for deposits. Because these trusts were not part of
the central payments system, and thus processed only a small amount of
payments, they did not hold a large amount of cash relative to deposits. To
earn profits, they made as many loans as possible. After a series of events
in October 1907 set off a rush for withdrawals, several trusts faced a run
and were forced to suspend credit and liquidate assets, acting as a catalyst
for a larger fire sale in financial markets. To save the financial system, J. P.
Morgan, owner of the eponymous bank, and a small number of other financial leaders individually chose which banks to bail out (Moen and Tallman
2015). This helped government policymakers realize that when faced with
a crisis, the financial system, as then constituted, would rely on a privileged
group of individuals seeking to maximize their own profits rather than on
institutions that had an obligation to protect the public’s interest. This realization helped lead to the creation of the Federal Reserve—the centralized
entity that first aimed to serve as the lender of last resort and, over time,
also obtained the exclusive power to issue U.S. dollar notes and manage the
Nation’s monetary policy.
Fast forward 100 years, and digital asset proponents are now aspiring to
create a decentralized financial system without relying on governments
237

and their regulatory frameworks, which were shaped by important lessons
learned from multiple previous crises, including the 1907 panic. Digital
assets are electronic representations of value and operate as part of a complex
and interconnected digital ecosystem. Crypto assets are a subset of digital
assets that use cryptographic techniques and distributed ledger technology
(DLT) but exclude central bank digital currencies (U.S. Department of the
Treasury 2022a). DLTs rely on networks to store and process transactions.
This chapter primarily examines crypto assets, whose proponents have been
relearning the lessons from previous financial crises the hard way. In addition to the decentralized custody and control of money, it has been argued
that crypto assets may provide other benefits, such as improving payment
systems, increasing financial inclusion, and creating mechanisms for the distribution of intellectual property and financial value that bypass intermediaries that extract value from both the provider and recipient. Looking under
the hood at these arguments, however, shows a more complicated picture.
So far, crypto assets have brought none of these benefits. Meanwhile, the
costs generated by several of their aspects—such as those for consumers,
the physical environment, and the financial system—are not only substantial
but are also being accrued in the present. Indeed, crypto assets to date do not
appear to offer investments with any fundamental value, nor do they act as
an effective alternative to fiat money, improve financial inclusion, or make
payments more efficient; instead, their innovation has been mostly about
creating artificial scarcity in order to support crypto assets’ prices—and
many of them have no fundamental value. This raises the question of the role
of regulation in protecting consumers, investors, and the rest of the financial
system from panics, crashes, and fraud related to crypto assets. Even so, as
companies and governments experiment with DLT, it is conceivable that
some of their potential benefits may be realized in the future.

238 |

Chapter 8

The Perceived Appeal of Crypto Assets
This section reviews the potential benefits that crypto assets may offer, as
often touted by their proponents, while the next section evaluates what they
have actually achieved. To introduce the digital asset landscape, figure 8-1
illustrates certain types of digital assets. The label “cryptocurrency” is used
in the industry to connote a crypto asset that is promoted to be an alternative
payment instrument. “Stablecoin” is also an industry label for a form of
crypto asset that is purportedly backed by a portfolio of underlying assets
and claimed to have a stable exchange value with these assets. While some
stablecoins mainly aim to become payment instruments, other stablecoins
mainly aim to provide returns from investments. Regardless of the label
used, a crypto asset may be, among other things, a security, a commodity,
a derivative, or another type of financial product, depending on the facts
and circumstances. Nonfungible tokens are the other primary type of crypto
asset; they use DLT to track ownership of digital goods but are not a main
focus of this chapter.
The term “crypto asset” excludes digital currencies that may be issued
by a central bank. Though central bank digital currencies might be designed
to operate using DLT, there is no requirement for them to be on DLT, and a
central bank digital currency does not necessarily involve using DLT (White
House 2022a).
Figure 8-1. A Taxonomy of Digital Assets and Central Bank Money

Central bank
money

Digital assets

Crypto assets
CBDCs
NFTs

Cash

Cryptocurrencies

Stablecoins
Sources: CEA analysis; Hoffman (2022).
Note: NFTs = nonfungible tokens. Not drawn to scale. Cash represents currency as well as reserves. Regardless of the label used, a
crypto asset may be, among other things, a security, a commodity, a derivative, or other financial product, depending on the facts and
circumstances.

Digital Assets: Relearning Economic Principles | 239

Crypto assets have gained substantial popularity in recent years—
particularly since the beginning of the COVID-19 pandemic in 2020. As
shown in figure 8-2, the estimated market values of selected crypto assets
have increased significantly in recent years and reached a collective peak
of nearly $3 trillion in November 2021. As of the end of December 2022,
crypto assets collectively had a reported market value of a little under $1
trillion, due to a large downturn in prices over the year, and largely reflecting
the failures of certain prominent crypto asset projects and firms.
The development of crypto assets and their underlying distributed ledger technology have the potential to transform industries and business models. Recognizing both the potential opportunities and actual risks of crypto
assets, in March 2022, President Biden signed Executive Order 14067,
“Ensuring Responsible Development of Digital Assets” (White House
2022b), which tasked the Administration to study the effects of these novel
assets. As a result, departments and agencies of the Federal Government
have produced nine reports examining the implications of crypto assets for
consumers, businesses, financial stability, national security, and the physical
environment (White House 2022c).
The first crypto asset, Bitcoin, was launched in 2009, shortly after
the global financial crisis, as something of a repudiation of the existing
financial intermediaries that caused the crisis (Nakamoto 2008). Bitcoin was

Figure 8-2. Market Capitalization of Selected Crypto Assets, 2020–22
Trillions of dollars (nominal)
3.0

2.5

2.0

1.5

1.0

0.5

0.0
Jan-2020

Jun-2020

Nov-2020
Bitcoin

Apr-2021
Ether

BNB

Sep-2021
Cardano

Feb-2022
XRP

Source: Coin Metrics, Inc; Federal Reserve Board of Governors Financial Stability Report.
Note: Total market cap figures are subject to revision from Coin Metrics.

240 |

Chapter 8

Other

Jul-2022

Dec-2022

Box 8-1. What Are the Functions of Money?
In early history, bartering was a common way for people to exchange
goods and services. Bartering, however, takes time, because individuals
need to find another person who is willing to trade one physical good
or service for another. A workaround for this was the invention of
money; some of the earliest forms of money appeared in about 1200
BCE (Tikkanen, n.d.). Money’s key innovation was to facilitate trade
between individuals by using an item that had a common representation
of value that was widely agreed upon by members of society. That is,
instead of having to take a goat everywhere and hoping to find someone
who wanted the goat, money enabled individuals to carry something that
everyone valued, such as polished beads, which could be exchanged for
a wide variety of goods and services (Jordan 1997).
The first money was in the form of things like seashells, beaver
pelts, and even large stones (Tikkanen, n.d.; Hudson’s Bay Company
History Foundation 2016; Goldstein and Kestenbaum 2010). Eventually,
money took the form of “specie,” or coins such as gold and silver, which
could be produced to a specific standard of weight (Velde 2012). While
money like specie money was decidedly more convenient than carrying
around a goat, it was still cumbersome to transport. To get around this,
paper money was created, which was substantially easier to transport.
To ensure that paper money still had financial value, it was “backed”
by specie (Tikkanen, n.d.). That is, the paper money essentially served
as a promissory note for specie sitting in a bank, and it could be freely
redeemed.
This system worked well, but it had a key vulnerability that became
a common theme of many crises: banks could earn higher profits by
issuing more paper currency than the amount of specie they held in their
vault. For example, a bank could hold 50 gold coins, but could issue 100
units of a paper currency, each giving the holder the right to 1 gold coin.
Then, if all holders of the currency demanded their money back at the
same time, the bank would not have enough gold coins to meet the holders’ redemptions (Diamond and Dybvig 1983). This dynamic—referred
to as a bank run—also has a long history, dating back to as early as the
fourth century BCE (Flood 2012).
Eventually, institutions and faith in currencies—particularly the
U.S. dollar—became strong enough that specie was not needed to
assuage investors’ concerns about what was “backing” the currency. This
led to the creation and adoption of “fiat” currency, or currency issued by
the government that is not redeemable for specie. Fiat currency’s value
is largely a function of (1) the currency being the only instrument with
which individuals can pay taxes; (2) the strength of the government’s
institutions, such as the legal system and military; and (3) a shared social
trust in the value of the money itself (Bank of England 2020).

Digital Assets: Relearning Economic Principles | 241

Money, as defined in the Uniform Commercial Code and certain
other specialized sources, is a medium of exchange currently authorized
or adopted by a domestic or foreign government (U.S. Commercial
Code, n.d.). In contrast, here the economic functions and common
understanding of money are considered. For a type of money to actually
be useful in the economic sense, there must be wide agreement about
its value—either derived from assets backing it (e.g., the gold standard)
or from things like institutions and social trust. Money serves three core
functions: as a medium of exchange, as a unit of account, and as a store
of value (U.S. Department of the Treasury 2022b).
First, money can serve as a medium of exchange if it can be used
widely to trade for goods and services. For example, the U.S. dollar can
be used for purchasing anywhere in the country, and even in many places
abroad. In contrast, for example, while cigarettes are often used inside
prisons to trade for goods and services, they cannot be used to purchase
groceries or buy plane tickets (Lankenau 2007).
Second, money can be considered a unit of account if it acts as a
benchmark upon which the values of different goods and services can
be compared. For example, instead of estimating how many chickens
it would take to trade for one cow, a person can instead simply express
the value of chickens relative to cows through their respective monetary
values—so if 1 chicken costs $10 and 1 cow costs $2,000, then a person
can simply use their relative dollar values to conclude that 200 chickens
are worth the same as 1 cow.
Finally, money can be a store of value if its purchasing power does
not fluctuate dramatically over short intervals of time. For example, the
number of apples a $10 bill can buy does not vary much from one day
to the next. This is one reason why very high levels of inflation—socalled hyperinflation—can create uncertainty in the purchasing power
of money.
“Sovereign money” is money issued by the governing authority
of an independent country. Sovereign money can easily satisfy money’s
functions to serve as a medium of exchange and as a store of value over
time. This is because sovereign money is an information-insensitive
asset; it is unlikely that one side of a transaction is acting based on
private information about the value of sovereign money (Gorton and
Zhang 2022). The more information-sensitive an asset is, the less likely
it is to be a medium of exchange. For example, if there is a high possibility that someone is buying gold to protect themselves against losses
from holding another asset, the gold seller may decide that it is better
not to exchange gold for that asset. Sovereign money is also a liability
of the central bank, meaning that its value is backed by the bank. The
U.S. dollar is widely accepted as a medium of exchange, and it is also a
store of value. Indeed, roughly half of all international trade is invoiced

242 |

Chapter 8

in dollars (CRS 2022). This does not mean that all sovereign currencies
have the features of money. For example, Zimbabwe’s currency lost its
role as a store of value in 2007, when its annual inflation rate rose to over
66,000 percent (Siegel 2008). In Zimbabwe’s case, consumers and firms
shifted toward the widespread use of other sovereign currencies, which
effectively replaced Zimbabwe’s currency (Noko 2011).
Bank deposits can also act as money. Banks offer deposit accounts
to their customers, and these deposits are pegged one-for-one against
sovereign currencies. The value of this private form of money is generally supported by a nexus of regulatory and supervisory requirements,
such as capital and liquidity requirements, designed to protect the
customer against a possible bank run. This account-based private money
is linked to an individual person or entity. In contrast to sovereign
currencies, there are limits on account-based money to circulate. For
example, if Jeff writes a check to Greta to pay rent, Greta’s check from
Jeff represents money that belongs to Jeff (i.e., the money is linked to
his deposit account), and she can redeem it in exchange for circulating
currency (cash). Although Greta is legally allowed to exchange Jeff’s
check for gasoline, third-party checks are not widely accepted as a payment method. Hence, in reality, Greta first needs to cash the check and
then purchase gasoline.

designed as a purported peer-to-peer payment system that does not rely on
intermediation by a “trusted authority” to keep track of transactions. Instead,
Bitcoin uses cryptography to record transactions across an open (“permissionless”) network of computers.1 These transactions are recorded digitally
on a “blockchain,” which uses cryptographic techniques to link transactions
to each other in a manner that makes it challenging to edit or tamper with
previous transactions. Because the Bitcoin blockchain is a public ledger,
network participants can view and validate transactions as they happen in
real time.2 The supply of bitcoins is capped to ensure that each unit retains
value, since digital assets otherwise could be reproduced perfectly forever,
and they would have no value if there were an unlimited supply. This “artificial scarcity” was one important feature of Bitcoin, and has been replicated
by many new crypto assets introduced since Bitcoin.
There are also “permissioned” DLTs, where all nodes have to be given permission to participate in
the network. However, if the trust in the network is established by authentication, that runs counter
to the purpose of the trustless system.
2
Formally, the network tracks the “unspent transaction output” from transactions for each account,
which represents the transfer of specific units (e.g., like coins being transferred between individuals),
or by how much available funds exceed withdrawals.
1

Digital Assets: Relearning Economic Principles | 243

Both the number of crypto assets and their combined market value
have risen over time, reflecting their increasing popularity around the world.
There are several possible benefits that proponents claim for this popularity
of crypto assets. These claims are reviewed in the next subsections.

Claim: Crypto Assets Could Be Investment Vehicles
People invest in assets with the hope of making returns on their investments
by accepting a certain level of risk of loss. For example, traditional investments such as equities and bonds offer a certain level of expected returns for
their risk exposure. Similar to these traditional types of assets, it has been
argued that crypto assets are also investment vehicles that offer an expected
return for a given risk exposure. Hence, depending on the risk appetite of
investors, one might invest in crypto assets with the hope of quickly making
a large profit. Moreover, some have argued that crypto assets can serve as a
hedge against inflation, hoping their value will keep pace with or rise more
than the rate of inflation.

Claim: Cryptocurrencies Could Offer Money-like Functions without
Relying on a Single Authority
One stated goal of cryptocurrencies has been to create a financial system
that is “censorship resistant” and unable to be controlled by a government,
instead distributing control among pseudonymous global actors that do not
rely upon any trust in existing financial institutions. In particular, some
cryptocurrencies aim to replace central authorities that issue money by
instead relying on a distributed network, with benefits spread across the
network that issues representation of value that can be minted and transacted
without central authorities. For example, when implementing monetary
policy, governments can profit from issuing money because the value of
money is generally higher than the cost of issuing it (this is called “seigniorage”). In contrast, many cryptocurrencies aim to distribute the profit from
issuing a cryptocurrency by rewarding participants that can verify a transaction through a consensus mechanism (Acemoglu 2021). In this process,
participants can be rewarded with the new issuance of a cryptocurrency as
well as transaction fees, earning them a profit for supporting the distributed
network that maintains the cryptocurrency. This could be seen as a novel
way to distribute the profits from issuing new assets. Box 8-1 discusses the
functions of money.

Claim: Crypto Assets Could Enable Fast Digital Payments
In recent years, the usage of cash has declined dramatically as the usage of
digital payments has increased substantially. Figure 8-3 demonstrates the
trends in cash and check transactions against those in debit/credit payments,
244 |

Chapter 8

Figure 8-3. Payment Types Used in the United States Over Time
Percentage of payments in a typical month
70
60
50
40
30
20
10
0
2009

2010

2011

2012

2013

Cash and check

2014

2015

2016

Debit/credit card

2017

2018

2019

2020

Other

Sources: Federal Reserve Bank of Atlanta; CEA calculations.

which are forms of digital transactions. In the last decade, payments in cash
and checks have declined dramatically, while digital payments have notably
increased.
As the demand for digital payments increases, it has been argued that
stablecoins could be used as near-instant 24/7 payment instruments (Liao
and Caramichael 2022). As of December 22, 2022, there were about 200
stablecoins, with an estimated market size of roughly $140 billion. The two
crypto assets Tether and USD Coin alone accounted for roughly 80 percent
of the total market of stablecoins.3 Since stablecoins try to be pegged to a
reference asset such as the U.S. dollar (or another currency, or a basket of
currencies), proponents argue that stablecoins could eliminate exchange
risk when used as a settlement method. That is, if one stablecoin is always
worth $1, then an individual using a stablecoin to buy or sell goods has the
expectation that its nominal purchasing power will not change dramatically
after their transaction. Stablecoins have been suggested as a possible way to
simplify cross-border transactions and remittances.

Claim: Crypto Assets Could Increase Financial Inclusion
Some segments of the U.S. population are unbanked, meaning they do
not own a bank account. Others are underbanked—that is, they own
bank accounts but often use expensive nonbank financial services. Black
households have disproportionately higher rates of being unbanked and
underbanked (FDIC 2022). Crypto assets often are promoted as a tool for
3

Market capitalizations exhibit volatility. See, e.g., CoinMarketCap (2023).

Digital Assets: Relearning Economic Principles | 245

reaching these populations to improve their access to financial services and
build wealth to achieve upward mobility. For example, many crypto assets
do not impose minimum account requirements or charge overdraft fees, in
contrast to some traditional banking institutions. Unbanked individuals cite
such attributes as primary reasons they do not have bank accounts (FDIC
2022). A recent report found that minority households are more likely to
have invested in crypto assets than other households (Faverio and Massarat
2022).

Claim: Crypto Assets Could Improve the United States’ Current
Financial Technology Infrastructure
The distributed ledger technology that underlies many crypto assets is based
on a number of technological advances. It addresses the problem in certain
circumstances of establishing trust and a consensus on the true history of
transactions among a group of “mutually suspicious” parties. It is effectively
a shared database whose contents can generally be trusted, even though
it is operated by entities that generally do not have a reason to trust one
another. For crypto assets, the database stores the set of transactions that
have occurred among network participants. In addition, more recent developments in DLT have enabled new features and improved efficiency, such
as “smart contracts,” which automatically trigger particular actions without
the need for ongoing oversight. Box 8-2 further describes how Bitcoin and
distributed ledgers work.

The Reality of Crypto Assets
This section investigates the claimed benefits reviewed earlier in the chapter
and presents the risks and costs of crypto assets.

Crypto Assets Are Mostly Speculative Investment Vehicles
As shown in figure 8-4, compared with many other asset types, crypto assets
are very volatile, and, hence, highly risky. Because they are very volatile,
crypto assets can be used for speculation, an investment strategy that seeks
to make a profit from short-run trading. One reason many crypto assets are
highly volatile is that many of them do not have a fundamental value. For
example, stocks are claims on the future profits of firms and debt is a claim
on interest and principal payments. Even commodities such as gold and
silver have fundamental values, because they can be used in jewelry and
for special manufacturing purposes (Nogrady 2016). Conversely, unbacked
crypto assets are traded without fundamental anchors, suggesting that their
market prices only reflect speculative demand, or market sentiment, not
claims on cash flow. Relatedly, the U.S. Department of Labor (2022) issued
246 |

Chapter 8

Box 8-2. How Does Bitcoin Work?
This box explains how Bitcoin functions, as it was the first crypto asset.
Subsequent crypto assets have often incorporated key features from this
design. Bitcoin relies on several innovations, including the novel use of
a hash function, a well-established cryptographic technique.
What is a hash function? A hash function, which is sometimes
called a “one-way” algorithm or “trap-door” algorithm, uses a mathematical algorithm to take an input (e.g., a number, a string of letters)
and produce an output that satisfies three requirements: (1) reproducibility—or running the algorithm on the same input always produces
the same output; (2) irreversibility—or even knowing the algorithm, it
is not possible to easily invert the output to recover what the input was;
and (3) collision avoidance—or any unique input string must produce
exactly one unique output. This is a “one-way” function, in that there is
no efficient way to recover the input from just the output; the only way
would be to hash every possible input to see if it matches the output.
Figure 8-i gives examples of hashed output.
The hash function is usually quick and has many applications.
For example, most websites do not store a person’s actual password on
their servers; instead, they store a hash of the password. That way, if
there were ever a hack of their systems, the hackers would only have
the hashed versions, which would not work as passwords and could not
easily be used to determine passwords. When you log onto a website,
its server hashes the password you enter and compares that with what
is stored in its database and only lets you in if they match. Note that
a change of the input as seemingly small as from “hello” to “Hello”
usually creates a drastically different hash, and that a vastly different
phrase produces a hash that is equally random. Two key participants in
the Bitcoin space are users and miners.
Users. Crypto assets generally require a user to have a “wallet.”
A digital wallet is a software application, piece of hardware, or other
device or service that stores a user’s public and private cryptographic

Figure 8-i. Examples of Hashed Output
Input Text

Hashed Output (in hexadecimal using the SHA-256 algorithm)

hello

2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824

Hello

185f8db32271fe25f561a6fc938b2e264306ec304eda518007d1764826381969

The quick brown
fox jumps over the d7a8fbb307d7809469ca9abcb0082e4f8d5651e46d3cdb762d02d0bf37c9e592
lazy dog
Source: CEA analysis.

Digital Assets: Relearning Economic Principles | 247

keys, which allow users to interact with one or more blockchains and
send and receive crypto assets. Users can have custodial wallets, which
are provided and maintained by an intermediary or third-party provider,
or non-custodial wallets, also known as unhosted wallets, for which
users are responsible for their own wallets and private keys. For Bitcoin,
wallets have an associated “private key,” typically a randomly generated string of digits, which can be hashed to derive a “public key.” The
public key similarly can be used to generate the wallet’s address using a
different, known hash function. Anyone can initiate a transfer to a wallet
if they know its address. This is used as either the source or destination
of transfers on the Bitcoin blockchain. However, to send crypto assets,
one needs to know the private key for the wallet that is sending (Outten
2021). In particular, someone wanting to send crypto assets can construct
the transaction, create a hash of it, and combine that with a private key
to create a digital signature of the transaction. A useful analogy is that
the public key is akin to your home address, while the private key is the
physical key to your home. It is the difference between letting someone
know where you live versus giving them access to your house. Any node
of the network can then compare the hash of the digital signature with
the public key, and with the hash of the transaction data, and determine
if the transaction is valid. Nodes will reject any invalid transactions, so
private keys are required to transfer crypto assets.
From the perspective of the user, who typically uses a wallet app to
manage this process, all that is needed is the knowledge of the addresses
of the sending and receiving accounts, the private key if sending, the
amount, and a fee. The fee incentivizes miners to include the user’s
transaction in an upcoming block. A transaction with a high fee is more
likely to be included in upcoming blocks than one with a low (or zero)
fee. This means that transactions with low fees may takes days to be
processed or may not be processed at all.
Miners. The key part of the Bitcoin ecosystem that is different
from physical currency is that there are no central, trusted arbiters of
truth. Instead, the system operates by consensus among nodes of the
network about what the truth is (i.e., the distribution of bitcoins across all
wallets). This means, in theory, that governance of the cryptocurrency is
arbitrated by network participants, not a central authority, although control in some blockchains is more centralized as there may be a significant
concentration among network participants that effectively consolidates
governance between a few parties.
The Bitcoin blockchain uses what is called the SHA-256 algorithm
(developed by the National Security Agency and the National Institute of
Standards and Technology), which, for any text input, always produces a
64-digit (256-bit) hexadecimal output string (Brown 2002). The Bitcoin

248 |

Chapter 8

blockchain and many other cryptocurrencies use a “proof-of-work”
method to achieve a consensus among all the nodes of the network.
Miners monitor the network and maintain a pool of transactions
that are yet to be validated. In a proof-of-work network, the network’s
miners are competing to be the ones to successfully mine the next block
of transactions in the chain. The actual way this is accomplished is that
the miner puts together a candidate block of transactions to include as
well as a “block header,” or some metadata for the block (Rybarczyk
2020). These metadata include the hash of the last successfully mined
block of the chain, the version of software used, and some technical
parameters that are explained just below: the target difficulty, a digital
signature unique to the block of transactions they are including (the
“Merkle root”), and the “nonce.” They then take all the information in
the block header, combine it into one string, and push it through the
SHA-256 algorithm to get the hash of that information.
Here is the competition aspect: the nonce field is a number that
miners can choose arbitrarily. Their goal is to pick a nonce such that the
resulting hash—a hexadecimal number—is less than the target—also a
hexadecimal number—currently set by the blockchain. Given how the
hashing process works, there is no way to do this efficiently; a miner
must continue trying different numbers until they are successful. Since
the nonce must be an 8-digit hexadecimal number, a little over 4 billion
nonces can be tried. If no possibility is successful, the miner needs to get
creative in how to try new hashes against the target, such as changing
the set of transactions that are included in the block, which changes the
Merkle root in the header, thus changing the proposed block’s hash.
While finding a valid nonce and set of transactions requires a large
amount of brute-force computing power, verifying that a proposed block
is valid is trivial—nodes just need to compute the hash of the proposed
block and compare it with the target—and this means that once a block
is found to be valid and is broadcast across the network, a consensus
can be quickly reached that it is a valid block. At that point, it is added
to the chain, and competition commences on adding the next block of
transactions, the next element of truth in the system.
Miners receive two types of compensation for the work that they
do: the fees that are included in the transactions they choose to put in
a block; and the “miner reward,” defined by the blockchain’s protocol.
For Bitcoin, the mining reward was initially 50 bitcoin for every mined
block, but this has diminished due to a “halving rule.” This rule limits
the total supply of bitcoins to 21 million over the lifetime of the coin and
means that every four years, the payout for mining a new block falls in
half. The reward was 6.25 bitcoin, as of December 31 2022; but, given
prevailing prices, this was worth over $100,000 (Coindesk 2022). The
“target” difficulty parameter is adjusted every two weeks to ensure that a

Digital Assets: Relearning Economic Principles | 249

new coin is mined roughly every 10 minutes. As the number of resources
dedicated to mining has increased, higher levels of difficulty have been
required to keep pace. In the five years before October 2022, the number
of attempts to mine a typical block of the Bitcoin chain increased by
a factor of 19 (BTC 2022). Once the maximum supply of 21 million
bitcoins is reached (which is projected to occur in about 2140), miners
will only benefit from transaction fees (Timón 2016).
Why does the blockchain mechanism “work”? Once the blockchain
is running, suppose a bad actor wanted to modify the history of the
blockchain by, for example, inserting a fraudulent transaction in an
earlier block. In theory, this would not work, since any other node of
the network could immediately verify that this block did not previously
belong in the chain because no subsequent block would point to its
(changed) hash as being its predecessor. So, a bad actor would need to
recompute the entire chain, from the fraudulent block to the current one, 2/21/2023
with new hashes, which would require an inordinate amount of computing power. This highlights the origins of blockchain technology in
ensuring trust among mutually suspicious groups (Chaum 1982). Figure
8-ii demonstrates how a blockchain is formed.
Many other blockchains have a design similar to that of Bitcoin,
although with different parameters and features, such as smart contracts.
Ethereum, for example, allows more daily transactions than Bitcoin, is
calibrated to have blocks added every 12 seconds, and recently switched
its consensus protocol to be less energy-intensive (Etherscan 2022). An
important criticism of crypto assets is their energy intensity. A more
complete discussion of the technological options of blockchain design is
beyond the scope of this chapter.

Figure 8-ii. Blockchain Blocks Linked by Hashed Values of Their Contents

Transactions:
…
…
…

Block #203

Bitcoin version:
Previous block hash:
Merkle root hash:
Time
Bits:
Nonce:
Transactions:
…
…
…

Hash of this block

Block #202
Hash of this block

Block #201
Bitcoin version:
Previous block hash:
Merkle root hash:
Time
Bits:
Nonce:

Bitcoin version:
Previous block hash:
Merkle root hash:
Time
Bits:
Nonce:
Transactions:
…
…
…

Source: CEA compilation.
Note: “Bits” refer to the current difficulty level of mining a new coin. It is stored in an encoded manner, but a lower target implies a higher difficulty. As
of October 2022, it required testing approximately 31 trillion nonces to mine a block of Bitcoin.

250 |

Chapter 8

Figure 8-4. Volatility of Crypto Assets versus Certain Traditional Assets, 2017–22
Thirty-day rolling standard deviation of daily returns
0.12

0.10

0.08

0.06

0.04

Crypto assets
High-yield bonds

Real estate
Foreign exchange

U.S. equities
Investment-grade bonds

12-01-2022

09-01-2022

06-01-2022

03-01-2022

12-01-2021

09-01-2021

06-01-2021

03-01-2021

12-01-2020

09-01-2020

06-01-2020

03-01-2020

12-01-2019

09-01-2019

06-01-2019

03-01-2019

12-01-2018

09-01-2018

06-01-2018

03-01-2018

12-01-2017

0.00

09-01-2017

0.02

Emerging market equities
Commodities

Sources: Bloomberg L.P.; CEA calculations.

guidance to protect investors’ retirement plans with respect to this asset type.
Recall that one of the purported benefits of crypto assets like Bitcoin was to
hedge against inflation, meaning that their value does not erode as inflation
increases. But as inflation increased globally in the second half of 2021 and
in 2022, the prices of crypto assets collapsed, proving them to be, at best, an
ineffective inflation hedge.

Cryptocurrencies Generally Do Not Perform All the Functions of
Money as Effectively as Sovereign Money, such as the U.S. Dollar
As discussed in box 8-1, money serves three functions: as a unit of account,
which means that it acts as a benchmark upon which the values of different goods and services can be compared; as a medium of exchange, which
means that it can be used to trade goods and services; and as a store of
value, which means that the amount of goods and services that a unit of the
money can buy does not fluctuate dramatically over short intervals of time.
Although cryptocurrencies currently serve each of these functions, they only
do so in limited ways in the United States, so they do not serve, from an
economic perspective, as an effective alternative to the U.S. dollar.
For the first monetary function question, cryptocurrencies can serve as
a unit of account, given that the relative values of goods and services can
be expressed in cryptocurrency (e.g., a single chicken in commerce is worth
roughly 0.0001 bitcoin). However, individuals would likely need to first
convert bitcoins or other cryptocurrencies to dollars to understand relative
values as cryptocurrencies are not as effective as the U.S. dollar as a medium

Digital Assets: Relearning Economic Principles | 251

of exchange (discussed below). Thus, cryptocurrencies currently do not fully
serve as units of account.
The second question is whether cryptocurrencies can serve as a
medium of exchange. The answer is that in the United States, they are not as
effective a medium of exchange as the U.S. dollar. This is because they can
be used to purchase other cryptocurrencies and to buy goods and services at
a smaller number of firms relative to the U.S. dollar (Modderman 2022). The
strength of the U.S. dollar is derived from several important factors, such as
faith in government institutions and the legal system, but cryptocurrencies
lack these factors.
Third, cryptocurrencies currently experience substantial amounts of
volatility, and thus are not stable stores of value. For example, the value of
a bitcoin (relative to the U.S. dollar) increased by over 1,000 percent from
March 2019 to March 2021, and then decreased by over 70 percent from
November 2021 to October 2022. This volatility means that anyone who is
using bitcoins to store their savings is subject to high-volatility risk in their
purchasing power. As figure 8-4 shows, the volatility of cryptocurrencies
outpaces those of many other financial asset types. Cryptocurrencies regularly exhibit a similar amount of volatility as U.S. equities experienced at the
onset of the COVID-19 pandemic.
There is also tension in an asset being promoted as both money and an
investment vehicle. As money, the instrument should have a stable value,
suggesting limited price volatility. But as a risky asset, it should experience
price volatility, for which an investor would be compensated with a high
expected return. Holding everything else constant, the riskier an asset is, the
less likely it can effectively serve as money.
In sum, in addition to generally being speculative assets, cryptocurrencies currently are not effective alternatives to sovereign money such as the
U.S. dollar. As mentioned above, most cryptocurrencies do not have fundamental value, but that is not a requirement for them to function as money.
In fact, sovereign money does not have a fundamental or intrinsic value
(Berentsen and Schär 2018). Even so, sovereign money can easily satisfy
money’s requirements, as discussed in box 8-2. The main reason for this
is that the value of sovereign money is backed by a trusted institution—the
central bank. One important feature of many cryptocurrencies is validating
transactions through consensus mechanisms, which are a way to distribute
profits from new issuance among participants such as cryptocurrency miners that verify the cryptocurrency transactions. (See box 8-3 for the impact
of cryptocurrency mining on the physical environment.) Hence, the supply
of cryptocurrency generally increases with the number of verified cryptocurrency transactions. In the case of a new issuance of sovereign money,
monetary policy reasons play a major role, and the resulting profits from
the new issuance of sovereign money accrue to governments. In advanced
252 |

Chapter 8

Box 8-3. Crypto Asset Mining as a
Risk to the Environment
The growth of trading in crypto assets has necessitated a corresponding
increase in the mining of crypto assets. As discussed in box 8-2, crypto
asset “mining” (cryptomining for short) is a process by which highpowered computers perform calculations to verify transactions using
distributed ledger technology for some kinds of crypto assets (White
House 2022d).
Cryptomining can be lucrative for successful miners, which are
compensated with the crypto assets they are mining but which also
consume large amounts of energy. According to recent estimates by
Goldman Sachs, cryptomining accounted for more than 2 percent of U.S.
power consumption as of early 2022. The amount of electricity used to
mine bitcoins in the United States is similar to what is used to power
all the country’s home computers or residential lighting (White House
2022d). A recent inquiry by Congress into the electricity consumption
of cryptominers found that just seven of the largest cryptomining operations in the United States had a combined capacity of 1,045.3 megawatts
as of February 2022, with plans to expand capacity significantly in the
coming months and years. For comparison, these miners alone could use
roughly as much power as all residential units in Houston, the Nation’s
fourth-largest city (Tabuchi 2022).
While comparing usage across different types of activities is difficult because not all activity is recorded on-chain, some have estimated
that in 2021 mining a single bitcoin used roughly the same amount of
electricity as nine years’ worth of the average American household’s
consumption (Huang, O’Neill, and Tabuchi 2021). Bitcoin additionally uses more energy than several entire countries, such as Finland,
Belgium, and Chile (University of Cambridge 2022). Globally, Bitcoin
accounts for 0.42 percent of all electricity usage. This effectively means
that Bitcoin is using the same amount of electricity as a medium-sized
advanced economy.
Not all cryptomining operations consume the same amounts of
power. Energy-intensive consensus mechanisms, such as a proof-ofwork, use substantial amounts of power by encouraging machines in
a network to race against each other to solve a mathematical puzzle.
Bitcoin, which accounted for over of a third of all crypto assets’ value as
of December 2022, is the most notable crypto asset that is mined using
proof-of-work. Ethereum, conversely, switched in September 2022 from
a proof-of-work consensus mechanism to a proof-of-stake consensus
mechanism that selects specific miners to validate a transaction at a
given point in time, thereby reducing electricity usage in exchange
for reducing the security of the network and increasing the power of
individual actors vis-à-vis the network’s intensity. There are benefits

Digital Assets: Relearning Economic Principles | 253

and drawbacks from different consensus mechanisms, and they have different energy, transparency, and security attributes. Despite Ethereum’s
switch to proof-of-stake, Bitcoin has not announced plans to make a
similar change.
Evidence suggests that cryptomining has substantial costs for local
communities and has few, if any, attendant benefits. Cryptomining facilities produce substantial noise pollution, which has been compared to a
“jet-like roar” (Williams 2022). Cryptomining facilities can also lead to
increases in local air and water pollution (White House 2022d).
Local cryptomining operations also push up community electricity
prices, as increased electricity consumption forces generators to rely
on more expensive energy sources and, in the case of communities
with hydropower where cryptomining operations are often located,
reduces electricity surpluses. For example, in the Mid-Columbia Basin
of Washington State, an energy surplus produced by hydroelectric
dams originally pushed down electricity prices for both residents and
businesses. But after cryptomining facilities began placing additional
demand on the energy grid, exports of energy surpluses decreased,
substantially raising residential electricity prices (Samford and Domingo
2019).
Continuously running an electricity grid at maximum capacity can
cause grid equipment that was not designed for such high-intensity usage
to degrade over time, increasing the risk of fire in vulnerable communities. In places like Texas, which expects to add 27 gigawatts of additional
cryptomining demand in the next four years—equal to roughly 30 percent
of the generation capacity of the entire Texas grid—cryptomining could
increase the likelihood of power crises, where demand overwhelms the
grid’s ability to provide sufficient generation (Calma 2022).
Furthermore, the intensive nature of mining bitcoins requires frequently replacing machines, and as the old equipment becomes nonfunctional, it can become “e-waste,” which often contains toxic chemicals
and heavy metals that can leach into soils if not properly disposed of
(de Vries and Stoll 2021). Just as mining energy-usage comparisons are
difficult, comparing e-waste across activities is imprecise, especially
because old machines used to mine bitcoins may be temporarily retired
but then used again if the price increases enough (White House 2022d).
With that being said, some have estimated that it would take as much as
114,000 Visa transactions to generate the same amount of e-waste as a
single bitcoin transaction. Alternatively, a single bitcoin transaction may
generate more e-waste than 2.7 iPhones (Digiconomist 2022).

254 |

Chapter 8

economies such as the United States, the profits from the issuance of sovereign currency benefit taxpayers by lowering tax needs, as central banks
effectively return these profits as government revenue.

Stablecoins Can Be Subject to Run Risk
Some cryptocurrencies, specifically stablecoins, are promoted to have the
potential to be fast digital payment instruments. A fundamental problem
with stablecoins is one that has been known in the traditional banking sector for centuries: run risk (Humphrey 1975). If stablecoin holders wish to
redeem their stablecoins for $1 each, this will require the stablecoin issuer
to liquidate some of its reserves (Adams and Ibert 2022). Depending on
how liquid these reserves are, and the state of broader financial conditions,
this liquidation may lead to disruptions in the markets for the reserve assets
and reduce the market value of the issuer’s remaining reserves because the
sales of the reserve assets put further downward pressure on the prices of
remaining reserves. If reserves are falling in value at the same time holders
are seeking redemptions, then the issuer may receive less than $1 for each
$1 placed in stablecoins, thereby causing the stablecoin issuer to become
insolvent. In fact, money market funds, which have balance sheet characteristics that a number of stablecoins purport to have, faced runs during the
2008 financial crisis and at the onset of the COVID-19 pandemic in 2020
(Schmidt, Timmermann, and Wermers 2016; Anadu et al. 2021).
Deposits in bank accounts can be used to make payments, and banks
aim to maintain parity between deposits and dollars; that is, $1 deposited in
a bank account can be withdrawn for $1 at a later point in time. One important distinction between stablecoins and bank deposits is that in the United
States, bank deposits are subject to a comprehensive set of regulatory and
supervisory requirements. In contrast, stablecoins are not subject to requirements designed to maintain this exchange rate.
A different approach to maintaining a stablecoin that does not fully rely
on holding reserves is the so-called algorithmic stablecoin of TerraUSD (and
the closely linked Luna token), which had the stated objective to maintain
its exchange rate peg with the U.S. dollar using an algorithm (Baughman et
al 2022). The idea behind the Terra/Luna coins was that Terra (known as
UST) was a stablecoin pegged to $1 and was maintained through arbitrage
(Wong 2022). Theoretically, 1 UST could always be traded for $1 worth of
Luna. If the value of Terra ever fell below $1, arbitrageurs could exchange
1 Terra for $1 worth of Luna, a different coin. In theory, this would allow
the arbitrageur to make a gain, decrease the supply of Terra (the exchanged
token was “burned”), and raise the value of Terra. If the value of Terra rose
above $1, arbitrageurs could buy (“mint”) 1 UST in exchange for $1 of
Luna, making a small gain but increasing the supply of Terra and pushing

Digital Assets: Relearning Economic Principles | 255

down its value. This was meant to be the mechanism to keep the value of
Terra at $1, although there was also a reserve of other cryptocurrencies kept
to support the peg, but not enough to fully cover the market value of Terra.
At one point, Terra was the world’s fourth-largest stablecoin, in part due to
the fact that people who were willing to deposit UST on Anchor, a smart
contract-lending protocol, which promised investors an annual interest rate
of 19.5 percent on their investments (Briola et al. 2023). Eventually, a run
occurred as a few major withdrawals in May 2022 knocked UST off its $1
peg, leading to a stampede out of Terra into Luna, depressing Luna’s value,
and ultimately causing the total crash of the two cryptocurrencies.
Another key risk of stablecoins for U.S. retail users is that redemption
may be a secondary concern for liquidity on crypto asset trading platforms.
As noted in the Financial Stability Oversight Council’s “Digital Asset
Financial Stability Risks and Regulation Report,” U.S. retail customers
cannot directly redeem the two largest stablecoins (Tether and USD Coin)
for dollars (U.S. Department of the Treasury 2022a). Stablecoin holders that
lack redemption rights may be unable to find willing counterparties to exit
their stablecoin positions.
Gorton and Zhang (2021) evaluate a number of solutions to the run risk
of stablecoins. For example, they assert that if stablecoins are required to be
fully backed by safe assets, they would risk attracting funds that would ordinarily go to banks, which make loans. This would have the potential to hurt
credit availability for individuals and firms. In subsequent research, Gorton
and Zhang (2022) argue that stablecoins could challenge the government’s
monetary authority to have an exclusive monopoly on currency issuance and
disrupt financial stability.
Stablecoins currently have a few major impediments against becoming
fast payment instruments. For one, stablecoins are too risky to satisfy this
need at present. Additionally, as discussed below, general concerns about
consumer and investor protections in the crypto asset space also apply to
stablecoins (U.S. Department of the Treasury 2022a). Nevertheless, there
is continuing experimentation in using distributed ledger technology for
digital payment systems. While crypto assets are currently not payment or
settlement technologies for the rest of the financial system, it is still possible
that in the future, their underlying DLT could be adapted into a payment or
settlement system for the broader financial system.

Crypto Assets Can Be Harmful to Consumers and Investors
For consumers and investors to use crypto assets to access financial services,
the crypto asset industry must have sound consumer, investor, and market
protections. However, many participants in the crypto asset industry are
not acting in compliance with existing laws and regulations, and some

256 |

Chapter 8

of the most common unlawful activities in the crypto asset industry are
scams especially aimed at retail investors (U.S. Department of the Treasury
2022a). One of the principal areas where there is mass noncompliance
is disclosure surrounding crypto assets that are securities. This lack of
disclosure prevents investors from recognizing that most crypto assets have
no fundamental value. For example, many fraudsters develop intricate and
professional-looking websites that purport to offer investors an exciting,
high-return investment opportunity. When a victim gives crypto assets to
the criminal to invest, the criminal can simply abscond with the funds.
Examples of this includes a matter in September 2021, when the U.S.
Securities and Exchange Commission (SEC) filed an action against the
platform BitConnect for allegedly committing $2 billion worth of fraud
(SEC 2021a). In its action, the SEC alleged that BitConnect purported to
offer investors a “lending” program using a “proprietary volatility software
trading bot,” but instead simply took investors’ crypto assets and transferred
them into digital wallets controlled by the criminals. To date, the SEC has
filed charges alleging a number of fraudulent offerings and other types of
misconduct involving crypto assets (SEC 2022).
In May 2021, the Federal Trade Commission (FTC) released a post
detailing the increase in scams involving crypto assets since October 2020
(Fletcher 2021). Between October 2020 and May 2021, more than 7,000
people reported losses from these scams, which totaled more than $80 million, with a median loss of $1,900. One particular type of scam identified by
the FTC is “giveaway scams,” where promoters claim to instantly multiply a
given number of crypto assets but instead appropriate the crypto assets upon
receipt. According to the FTC, young people were most susceptible to this
type of fraud; those between 20 and 39 years of age lose far more money to
investment fraud than any other type, more than half of which was attributable to crypto assets.
In November 2022, the Consumer Financial Protection Bureau (CFPB)
released a bulletin summarizing the consumer complaints it had received
about crypto assets (CFPB 2022). In a period of less than four years, from
October 2018 to September 2022, the CFPB received more than 8,300 complaints related to crypto assets, with the majority received since 2020. In
this period, roughly 40 percent of crypto asset complaints handled were primarily frauds and scams. Transactional issues with crypto assets and issues
with assets not being available when promised made up about another 40
percent of complaints. Other risks identified in the CFPB’s bulletin included
romance scams and “pig butchering,” difficulty obtaining restitution, and
fraudulent transactions.4
Pig butchering refers to a practice where scammers develop close personal relationships with a
victim in order to convince them to set up crypto asset accounts from which the scammers can steal.

4

Digital Assets: Relearning Economic Principles | 257

Furthermore, there can be conflicts of interest at crypto asset platforms. For example, some crypto asset platforms combine exchange, brokerage, market making, and clearing agency functions. This vertical integration
of products and services has long been prohibited in traditional markets and
leads to risks to customers. For instance, a platform that combines exchange
and market making functions would have an incentive to trade ahead of its
own customers, and would have less incentive to seek out best executions
for its customers. FTX, one of the largest crypto asset platforms until 2022,
reportedly transferred billions of dollars in customer accounts to its affiliated trading firm, Alameda Research (Goldstein et al. 2022). By borrowing
against FTT, the native token of FTX, Alameda Research reportedly made
risky bets and lost a large fraction of FTX customers’ funds (Tortorelli and
Rooney 2022). In November 2022, FTX and its affiliates declared bankruptcy and the price of FTT posted massive losses; at this time it is unclear
whether FTX customers and creditors will get their funds back (Ge Huang,
Osipovich, and Kowsmann 2022).

There Have Been Limited Economic Benefits from DLT Technology
The ability of DLT to solve the difficult problem of ensuring that two parties that do not have a reason to trust each other can nonetheless transact
securely is a notable achievement of computer science. This solution has led
to excitement about DLT, with even some enthusiasm that this technology
will change the way business is done (Iansiti and Lakhani 2017). DLT and
blockchain technology are not necessarily suitable for all applications; some
considerations have been proposed for successful blockchain technology
applications (Yaga et al. 2018). See box 8-4 for the proposed DLT use cases.
However, at its core, DLT is simply a database, and many proposed DLTbased projects do not actually employ decentralization (as discussed below).
Some have sought to profit from the hyperbole of blockchain—it has
become a common tactic for non-crypto-related businesses to announce a
“pivot to blockchain” to generate interest in a product or enterprise (Griffith
2018). For example, in December 2017, a beverage maker named “Long
Island Iced Tea” added “Blockchain” to its name—though changing nothing
substantive about its business—and its stock shares tripled in value (Cheng
2017). Ultimately, three persons involved with the firm were charged with
insider trading by the SEC, which alleged that these insiders used the “pivot
to blockchain” tactic to increase the firm’s share prices before they sold their
stakes in the firm (SEC 2021b).
In addition, many prominent technologists have noted that distributed
ledgers are either not particularly novel or useful or they are being used in
applications where existing alternatives are far superior. For example, Bruce
Schneier (2019), a cybersecurity expert, has called crypto assets “useless”

258 |

Chapter 8

Box 8-4. Proposed Uses of Distributed Ledger Technology
The excitement generated about DLT has drawn substantial investment
capital and has prompted governments and firms outside the crypto
asset industry to experiment with its underlying technological processes.
In some cases, this excitement has led to large writedowns or failed
projects. Here, we review three current cases and give examples of
experimentation.
Walmart Canada and supply chains. A commonly touted use for
distributed ledger technology is supply chains, where a single, distributed ledger could improve traceability throughout a supply chain and
reconcile records between a firm and its multiple suppliers (Laaper,
n.d.). In 2021, Walmart Canada launched a blockchain that attempted
to handle payment disputes between 70 third-party freight carriers. An
article in the Harvard Business Review dubbed the experiment “a tremendous success,” noting that before the blockchain system, 70 percent
of invoices were disputed, but after the rollout, that share dropped to
less than 1 percent (Vitasek et al. 2022). Though seemingly impressive,
the firm that partnered with Walmart Canada to develop the blockchain
platform stated in a report describing the project that the platform ran
on “more than 600 virtual machines (VMs) to securely store and manage data points from thousands of transactions per day” (Hyperledger
Foundation, n.d.). This implies that each VM is, at a maximum, handling
17 transactions per day. For reference, a minimally configured AWS
(Amazon Web Services) RDS (relational data store) database with two
VMs configured with best practices could process thousands, if not tens
of thousands, of transactions per second (Amazon 2017). Furthermore,
a prominent technologist stated that it was not even obvious what functional role blockchain was playing in the system, and that the program
was more akin to using an existing technology in an inefficient way
(Orosz 2022).
Helium and the decentralized Internet. Helium is a company
that is attempting to build a peer-to-peer wireless network by allowing
users to buy “hotspots”—small devices that can send data over long
distances—that can, together, create a Wi-Fi network. When the company was founded, it did not intend to have crypto assets as a central
part of its business model (Roose 2022). Instead, it attempted to use
traditional economic incentives for those helping build the network by
simply sharing some of the fees from network users to hotspot owners.
In 2019, however, the company pivoted and attempted to make crypto
assets central to its business model by creating an incentive system
where users that purchased hotspots that cost roughly $500 (and thus
contributed to the network) were rewarded with Helium crypto asset
tokens. If the prices of tokens rose, then so, too, would the reward for

Digital Assets: Relearning Economic Principles | 259

owning a hotspot, thus encouraging more users to build out the necessary
network infrastructure.
After this pivot, large venture capital firms like Andreessen
Horowitz (also known as a16z) helped Helium raise hundreds of millions of dollars in equity (Seward 2021). Alameda Research (the failed
hedge fund affiliated with FTX) was also a large investor in Helium.
Despite the sizable funding and widespread interest, Helium came under
scrutiny in July 2022, when its cofounder tweeted that the company had
generated $2 million a month in fees from new users joining (buying
hotspots), but only $6,500 (0.3 percent) of that was from users actually using the Internet service (Levine 2022). Furthermore, a Forbes
investigation in September 2022 found that the executives of the firm
gave themselves and their families a windfall in Helium tokens early in
the company’s history that was not publicly disclosed (Emerson, Jeans,
and Liu 2022). Also, in September 2022, Helium ended the use of its
own blockchain, which purportedly incentivized broader provision of
Internet access as a core feature (“proof of coverage”) and shifted its
operations and coins to the Solana blockchain, the same technology
on which many other speculative crypto assets are traded, calling into
question whether this use could be distinguished from any other type of
crypto asset (Yaffe-Bellany 2022). Although these pieces of news may
present a significant headwind for Helium’s future, the Helium token
nonetheless has a market value (as of December 22, 2022) of over $253
million (CoinMarketCap 2022).
Nonfungible tokens and virtual real estate. Nonfungible tokens
(NFTs) are digital assets that are not interchangeable. Each NFT is
unique, with its ownership recorded on a distributed ledger. Ownership
of an NFT can pass between two users by recording the transaction and
transferring it on a blockchain. NFTs often contain a pointer to a digital
object, such as an image file. As a famous example, in March 2021 Jack
Dorsey, the cofounder and former CEO of Twitter, auctioned off an NFT
of an image of his first tweet on Twitter from 2006, with the winning bid
coming in at more than $2.9 million (Locke 2021). While anyone could
create (“mint”) a new NFT of the same digital image (and the digital
image can be easily reproduced), the original transaction is maintained
on a blockchain, so it would not truly be the same (OpenSea 2022). This
highlights the “artificial scarcity” view of crypto assets.
Borri, Liu, and Tsyvnski (2022) studied the market for NFTs from
2018 to 2021 and created an index of NFT value based on the repeat
sales method. They found the average NFT market return was 2.5 percent a week in this period, although with a weekly standard deviation of
19 percent. This highlights the volatility and variability of NFT returns.
The market for NFTs cooled in 2022; the owner of Dorsey’s tweet listed

260 |

Chapter 8

it for sale in April 2022 for $48 million, but the highest bid as of January
4, 2023, was about $82,000 (OpenSea 2022).
NFTs can be a natural way to track ownership of virtual real estate.
Several different “metaverses” have begun offering “land” in virtual
worlds. Ownership of land translates into the title of a virtual property
being recorded on a distributed ledger. What one does with their land
depends on the platform—on Decentraland, a large metaverse platform,
owners are free to develop their land as they see fit: they could open a
store selling virtual goods, create a game app for visitors, build a gallery
for their virtual art collection, or build a virtual “house” (Kamin 2021).
Dowling (2022) studied the value of land in Decentraland and found
that the daily values of the virtual land tokens between 2019 and 2021
changed with extreme volatility. As in the physical world, location matters—while the average transaction value for a property in the data set
is $1,311, a firm paid $2.5 million for land in Decentraland’s Fashion
District (Putzier 2021).
Experimentation. The current uses discussed above have demonstrated only limited, if any, economic benefits so far. Even so,
proponents still claim that this technology could find productive uses
in the future as companies and governments continue experimenting
with potential uses; however, they often use “permissioned” networks
of machines that have been authenticated as a trusted member of the
network (Oracle 2022). For example, it is possible that distributed
ledger technologies can be used to improve the settlement and clearing
processes of banks (Bech et al. 2020). In fact, as mentioned above, banks
are experimenting with distributed ledger technology to improve the
efficiency of trading, clearing, settlement, and custody (Yang 2022). In
addition, the New York Innovation Center of the Federal Reserve Bank
of New York (2022) is participating in an experiment with the notion of
a regulated liability network, a conceptual financial market infrastructure
that could enable transactions between regulated financial institutions
potentially using DLT.

and has noted that despite claims of being decentralized and trustless,
blockchain-based applications are in practice neither; often, users access
their crypto assets by going to a limited set of crypto asset platforms, and a
small group of miners perform the majority of mining in most crypto assets,
an activity that has costly implications for the physical environment, as
discussed in box 8-3. When it comes to the “trustlessness” of blockchains,
Schneier notes that a blockchain does not eliminate the need for trust but
simply shifts trust away from individuals and institutions to a technology—
along with all its features and bugs.
Digital Assets: Relearning Economic Principles | 261

James Mickens, a leading computer scientist who studies distributed
systems, has stated that in addition to not actually being decentralized and
trustless, blockchains are often a very poor fit for their purported uses
(Mickens 2018). This is primarily because the instant that the identity of a
person or firm is needed (as is the case for supply chains, medical records,
and land deeds), existing technologies can solve the same problem in a
much more efficient way. For example, many of the cybersecurity benefits
of an immutable, distributed blockchain can be replicated through existing
features like tamper resistance (the ability to not change digital signatures at
a later point in time) and nonrepudiation (a receipt of a sender of information’s identity that is delivered to both the sender and receiver of information, thus guaranteeing that both parties have processed the information)
(World Bank, n.d.; NIST, n.d.).
Proponents of blockchain technology claim that it will not only
improve firms’ performance but also be the backbone of an entirely new
Internet. Web3—the so-called new Internet—purports to retain all the privacy/networking benefits of the earliest versions of the Internet that existed
roughly before 2000 (often called “web1,” which featured decentralized,
community-governed open protocols), while keeping the high functionality
of various features of web2 (the current version of the Internet) without the
existing dependencies on large centralized firms like Google and Apple
(Dixon 2021). However, Moxie Marlinspike (2022), the cryptographer and
founder of the messaging app Signal, argues that the reason the current
Internet features so much centralization is because it makes things easier,
for two specific reasons. First, he argues that a decentralized Internet would
require individuals and firms to host their own servers. However, centralized
hosting of servers can be done much more cheaply and reliably by large
entities and therefore benefits from economies of scale. Second, he notes
that protocols—or the rules that Internet systems run on—are much more
difficult to change than platforms. That is, centralized, non-open-source
protocols can be managed by a single entity (as opposed to many), facilitating a wider variety of features that can change with much greater speed
than if they were decentralized. Marlinspike also notes that web3 is already
trending toward a centralized structure because of the ease and convenience
that centralization brings, but in a much clunkier way than if traditional
technology were being used. He specifically notes that “once a distributed
ecosystem centralizes around a platform for convenience, it becomes the
worst of both worlds: centralized control, but still distributed enough to
become mired in time.”

262 |

Chapter 8

Table 8-1. Top Ten Crypto Derivative Platforms by Open
Interest
Rank
(1)
1
2
3
4
5
6
7
8
9
10

Exchange
(2)
BTCEX
Binance
BTCC Futures
Deepcoin
BingX
Bitget Futures
OKX
Bybit
MEXC Global
Bitmart Futures

24-Hour Open
Interest (Nominal $)
(3)
$8,314,364,513
$7,714,660,817
$5,103,831,418
$4,781,751,226
$4,334,560,170
$4,331,916,947
$3,586,501,924
$3,397,272,483
$3,228,041,626
$2,707,627,218

24-Hour Volume
(Nominal $)
(4)

$7,180,531,116
$32,741,616,672
$7,968,963,153
$9,854,658,307
$5,165,147,675
$5,414,169,494
$8,449,781,644
$8,090,497,597
$2,263,323,835
$4,283,383,129

Source: CoinGecko. Data were collected on January 19, 2023.

The Risks of Financial Innovation
While the crypto assets ecosystem and its underlying technology introduce
the potential for newfound efficiencies, efforts to challenge basic economic
principles have frequently resulted in financial calamities. The economist
Hyman Minsky hypothesized that financial crises often follow a similar
cycle, whereby initially strong investments turn increasingly more speculative until a bubble bursts (Minsky 1992). Further, Minsky stated that this
repeatedly happens because regulators are initially vigilant in the immediate
aftermath of a crisis; but as time goes on, and the instrument of speculation
changes, regulators take a less proscriptive approach to not harm “innovation” (Minsky 2008). According to Minsky, this relaxed regulatory environment invariably leads to another crisis. Indeed, other economists have argued
that the most effective financial regulation has been introduced only after a
crisis has occurred (Gorton 2012). Minsky’s theories became popular in the
aftermath of the global financial crisis, when complicated financial products
involving mortgages that exacerbated the crisis were initially hailed as
innovative, and individuals discussing their risks were labeled “Luddites”
by prominent commentators (Cassidy 2008; Wheatley 2013).
Minsky’s writings, as they apply to past financial crises, may prove
instructive for policymakers today. Fortunately, there has not yet been a
systemic crisis caused by crypto assets, in part because they are not yet fully
integrated with the rest of the financial system, giving policymakers time to

Digital Assets: Relearning Economic Principles | 263

act appropriately. The risks presented by crypto assets stem from excessive
speculation, high leverage, run risk, environmental harm from crypto asset
mining, and fraudulent activities that harm retail investors and corporations. Because crypto assets appear to be here to stay, policymakers should
consider these risks to avoid a “Minsky moment” caused by crypto assets.

Other Risks from Crypto Assets
Some risks that apply to crypto assets require further examination. Many
of these risks are not unique to crypto assets; combined with innovative
technology, they pose challenges for policymakers and regulators trying to
minimize risks while encouraging responsible innovation.
Leverage risks. Crypto asset derivative platforms—where investors
can buy and sell financial derivatives linked to crypto assets—have seen
substantial growth in the past two years (Damalas et al. 2022). Table 8-1
shows that the top 10 platforms for crypto asset derivatives, which account
for roughly 76 percent of all volume in these derivatives, have over $47 billion in open interest and roughly $91 billion in daily trading as of January
18, 2023. According to one international regulator, one of the largest
platforms, Binance, refuses to provide adequate and reliable information in
response to regulatory requests (FCA 2021).
Exchanges frequently tout the high amount of leverage they offer clients, stating that investors can take up to 100-to-1 leverage (debt-to-equity
ratios) (Pechman 2021). These derivative platforms can create financial
instability because positions with high leverage (debt-to-equity ratios) can
amplify a shock to prices of crypto assets and lead to large losses and even
defaults (U.S. Department of the Treasury 2022c). In particular, leverage
leaves little room for prices to fall in a short amount of time, as steep price
declines could induce brokers to issue large margin calls, thus forcing
broader liquidation (Carapella et al. 2022).
A relatively new application of DLT in financial markets where
there is a relatively unknown amount of leverage is so-called decentralized
finance (DeFi). DeFi attempts to offer financial products, such as loans, on
the blockchain through the use of “smart contracts” (Carapella et al. 2022).
The basic promise behind DeFi is to replace financial intermediaries, instead
linking savers directly with borrowers (or buyers with sellers), allowing
them to save on the spread that traditional intermediaries charge for creating
the match with software. Though DeFi applications claim to help broaden
access to credit by decreasing intermediation fees, they create serious risks
to investors and cause at least two risks for the broader financial system:
the use of significant leverage, and the performance of regulated functions
without compliance with appropriate regulations. DeFi platforms acting as
unregulated banks, broker-dealers, exchanges and other entities subject to

264 |

Chapter 8

regulation should be operating in compliance with existing regulations and
rules. DeFi lending platforms effectively receive funds from investors and
use them to generate loans, promising interest to investors. This dynamic
inherently causes run risks, where more investors try to redeem more of
their funds than the platform can accommodate at a given time, thus causing
the platform to either suspend convertibility or fail outright (Carapella et al.
2022). Furthermore, DeFi presents the opportunity for “synthetic leverage,”
whereby investors can mask the true amount of leverage they are undertaking from the party from which they are borrowing (Tian 2021). If DeFi were
limited to small, retail investors, the failure of a DeFi platform could still
hurt these investors, but the shock could be relatively contained. Banking
agencies issued a statement that expressed concerns with business models
that are concentrated in crypto-asset-related activities or have concentrated
exposures to the crypto asset sector (Federal Reserve Board 2023).
Price volatility. Most crypto assets experience substantial price volatility. Holding such volatile assets could present challenges for large financial
institutions if they were permitted to hold crypto assets, as the volatility
would lead to constant changes on the asset side of their balance sheets. This
volatility, in turn, could increase funding costs for banks and other financial
institutions, thereby requiring banks—which fundamentally borrow so as
to be able to lend—to increase the funding costs (interest rates) that they
charge, leading to tighter credit conditions.
Currently, this contagion risk is relatively muted, given that banks are
limited in their ability to conduct crypto-related activities, such as acting
as custodians of crypto assets (i.e., holding crypto assets for clients, not
on their own balance sheets) (OCC 2020). Indeed, banking regulators such
as the Federal Reserve have issued guidance requiring regulated financial
institutions to inform their regulator before engaging in crypto-asset-related
activity (Gibson and Belsky 2022). But other, less-regulated financial institutions, such as hedge funds, are increasingly investing in crypto assets.
Such activity of lightly regulated or nonregulated entities can lead to “liquidity spirals,” as described by Brunnermeier and Pederson (2007). These spirals occur when a dramatic crash in the price of an asset—such as a crypto
asset—leads a hedge fund to be margin-called, requiring the fund to sell off
other positions to meet the margin call. If enough funds are exposed to the
asset or assets with declining prices, then sell-offs could be broad enough to
cause a deterioration in market liquidity.
Illicit finance risks. Crypto assets are the standard form of payment
extorted from victims of “ransomware,” whereby a malicious actor hacks
an organization and demands payment to release control of the victim’s network and often to purportedly forgo leaking the victim’s stolen data. Crypto
assets remove a critical friction in performing a ransomware hack. Because
the attacker can demand that crypto assets be sent to a pseudonymous wallet
Digital Assets: Relearning Economic Principles | 265

Figure 8-5. Nominal Cyber Insurance Prices Over Time
Nominal index value: June 2014 =100

Annual change, in percent

450

135%

400

115%

350

95%

300
250

75%

200

55%

150

35%

100

15%

50
0

Jun-2014

Jun-2015

Jun-2016

Jun-2017

Jun-2018

Year-on-year % change (right axis)

Jun-2019

Jun-2020

Jun-2021

Jun-2022

-5%

Cyber insurance pricing index (left axis)

Source: Howden Nova Analytics platform.

instead of a bank account linked to a specific person, attackers can more
easily launder or obfuscate payments made to them, in comparison with
fiat currency (U.S. Department of Justice 2022). Importantly, like other
financial assets, crypto assets can be misused for a range of illicit activities,
including ransomware payments. Crypto assets have also been misused by
human traffickers, by individuals exploiting children for sexual abuse, and
by drug traffickers and scammers; to fund the activities of rogue regimes,
such as the recent thefts by the Lazarus Group, which is affiliated with North
Korea; and to finance terrorist activities (GAO 2021; U.S. Department of the
Treasury 2022d). The other key illicit financing risks associated with crypto
assets come from gaps in implementation of the international Anti-MoneyLaundering/Combating-the-Financing-of-Terrorism (AML/CFT) standards
abroad; the use of anonymity‑enhancing technologies; in some cases the lack
of covered financial institutions as intermediaries—and thus the absence of
AML/CFT controls—in some crypto asset transactions; and service providers that are noncompliant with AML/CFT and other regulatory obligations,
including compliance with sanctions obligations. With regard to the last,
when crypto asset firms fail to register with the appropriate regulator, fail
to establish sufficient AML/CFT controls, or do not comply with sanctions
obligations, criminals are more likely to exploit their services successfully,
including to circumvent U.S. and United Nations sanctions.
Ransomware uses. As hacking to receive crypto assets becomes more
widespread, more firms will attempt to insure themselves against these
attacks by purchasing cyber insurance. However, the existence of such
insurance may not eliminate the underlying problem, and instead may even

266 |

Chapter 8

Table 8-2. Ransomware and Downtime Costs by Country, 2020
Country
(1)

Total Submissions
(2)

Minimum Cost ($,
Nominal)
(3)

Estimated Costs ($,
Nominal)
(4)

United States
France
Spain
Italy
Germany
Canada
United Kingdom
Australia
Austria
New Zealand
Total

15,672
4,476
4,088
3,835
3,747
3,236
2,718
2,072
819
265
40,928

5,123,606,318
1,452,222,393
1,332,008,900
1,255,260,122
1,214,481,832
1,058,505,964
878,155,444
678,541,158
268,888,310
86,448,688
13,348,119,130

20,494,425,272
5,808,889,571
5,328,035,599
5,021,040,489
4,857,927,329
4,234,023,855
3,512,621,775
2,714,164,633
1,075,553,242
345,794,755
53,392,476,519

Source: Emsisoft Malware Lab.

create an incentive for hackers to attack insured firms and get paid by insurance. In fact, in an interview with The Record, a member of the Russian
hacking group REvil was explicitly asked if they targeted organizations that
have cyber insurance. The member responded: “Yes, this is one of the tastiest morsels. Especially to hack the insurers first—to get their customer base
and work in a targeted way from there. And after you go through the list,
then hit the insurer themselves” (Smilyanets 2021).
One can observe evidence consistent with this vicious cycle from
cyber insurance prices. The insurance brokerage Howden compiles a
“Global Cyber Insurance Pricing Index,” which broadly measures premiums
for cyber insurance (Howden 2023). As shown in figure 8-5, the cost of
cyber insurance has increased more than 300 percent since July 2014.
In addition to paying for ransom costs, companies affected by ransomware attacks typically are unable to maintain their business activity until
they have made the payment. In its annual “State of Ransomware” report,
the cybersecurity firm Emsisoft estimated the combined cost of ransom
payments and business downtime to be $19.6 billion in the United States
in 2020, and roughly $51 billion in total across the United States, France,
Spain, Italy, Germany, Canada, the United Kingdom, Australia, Austria, and
New Zealand (as shown in table 8-2) (Emsisoft Malware Lab 2021).
It is crucial to note that the costs described here are direct costs. The
indirect costs are likely higher. Instead of engaging in productive activities
where firms have comparative advantages, they must divert resources to
activities and products that help fend off attackers, such as buying cyber
insurance and adding more personnel for information technology security.
Thus, both welfare and economy-wide production decrease by a multiple of

Digital Assets: Relearning Economic Principles | 267

the direct dollar costs of resources that firms are using to stop ransomware
attacks.

Investing in the Nation’s Digital Financial Infrastructure
The growth of crypto assets has revealed a demand for a faster and more
inclusive financial system with a real-time payment system and circulating
digital money. Some have hoped that crypto assets could act as a form of
decentralized money, making the U.S. payment systems faster, cheaper,
safe, and more inclusive. This vision has not been realized. That said, there
are still other ways near-term progress can be made on at least some of these
goals. As a regulator of and participant in the Nation’s payment systems, the
Federal Reserve has a historical role in maintaining these systems’ integrity
(Federal Reserve Board, n.d.). For example, in the past decentralized payment systems were costly, in part, because some banks did not pay the
full amount of a check from other banks—so-called nonpar collection or
nonpar banking (Federal Reserve Board 1988). In some cases, this was done
by levying a fee on checks deposited from other banks. Shortly after the
establishment of the Federal Reserve System, it started providing payment
services to banks, and over time it helped eliminate nonpar banking (Federal
Reserve Bank of Minneapolis 1988).
This section first discusses an upcoming improvement to U.S. payments, which will help many consumers and businesses make cheap, instant
payments. It then discusses the possibility of introducing a central bank
digital currency (CBDC), which is a digital form of money. While operating
under the supervision of a trusted authority, both these mechanisms have the
potential to realize many of the benefits that crypto asset developers have
promised.

The FedNow Instant Payment System
In terms of overall value as of 2020, the largest retail payment system in the
United States was the Automated Clearinghouse (ACH) (Federal Reserve
Board 2022a). ACH provides an electronic means to exchange funds
between banks and other depository institutions (Federal Reserve Bank of
San Francisco, n.d.). Typical ACH payments include salaries, consumer and
corporate bills, interest payments, dividends, and Social Security payments.
Peer-to-peer payment platforms such as Venmo complete transfers that are
in and out of their platforms by accessing ACH network services through
a participant bank (Venmo, n.d.). The regional Federal Reserve banks and
the Electronic Payment Network are the country’s two national ACH operators (Federal Reserve Board 2020). The prevalence of ACH offers many
benefits; but a larger, more fast-paced economy is starting to arise. ACH

268 |

Chapter 8

payments can be processed in same-day batches between banks, throughout
the day, but a standard ACH transfer can take up to three business days for
funds to be settled and available to end users. In addition, ACH settlements
occur only on business days (Nacha 2021). Businesses and individuals alike
are increasingly in need of faster payment systems.
Advances in technology have created an opportunity for significant
improvements in the way individuals and businesses make payments in
today’s economy. In recent years, members of Congress, staff members of
the Department of the Treasury, and other experts have called for the Federal
Reserve to offer a faster payment system for both businesses and retail users
(Warren 2019; Mnuchin and Phillips 2018; Klein 2019). As a result of the
COVID-19 pandemic and increased consumer demand for e-commerce
options, many businesses have also increased their efforts to offer quicker
payment options (Rathjen 2022).
In response, the Federal Reserve has prioritized designing and developing a faster payment system (Federal Register 2019).5 The Federal Reserve
plans to launch this new system, which is called the FedNow Service, later
in 2023 (Federal Reserve Board 2022b). Through financial institutions
participating in FedNow, businesses and individuals will be able to send
and receive payments conveniently, and recipients will have nearly instant
access to funds, giving them greater flexibility to manage their money and
make time-sensitive payments. This service will be operational 24 hours a
day and 7 days a week. This uninterrupted processing of fund transfers is
an important improvement over existing payment systems (Federal Reserve
Board 2022b, 2022c, 2022d). This service is different from peer-to-peer
services such as Venmo in many ways. For example, funds transferred via
FedNow will be available more quickly than those that must first exit a
peer-to-peer payment service and then enter the ACH bank transfer process,
which can take time to settle.
Beyond speed and convenience, near instant payments can yield real
economic benefits for both individuals and businesses by allowing them
to make time-sensitive payments whenever needed and providing them
with more flexibility in managing their money. In particular, near instant
payments under FedNow could bring significant benefits to vulnerable segments of the population. Slow payment systems can cost Americans billions
of dollars. In addition to incurring bank overdraft fees, consumers can be
forced to use high-cost alternatives like check cashers and payday lenders
(Klein 2019). In 2019, it was estimated that a fast payment system such as
FedNow could reduce these kinds of fees, generating savings of more than
$7 billion a year for American households (Klein 2019). Because lowerincome individuals are more likely to be hurt by slow payment systems,
Note that there is a private faster payment system, RTP, whose adoption has been low (Clearing
House 2022).

5

Digital Assets: Relearning Economic Principles | 269

they could especially gain from these savings if FedNow is adopted widely.
Using innovation productively and responsibly in this way could make
banking services more inclusive.
FedNow requires commitment and active engagement by the private
sector to make it interoperable, which means connecting and communicating with other payment services (Federal Reserve Board 2022c). According
to the Federal Reserve, interoperability is crucial for “payment messages
[to be] routed or exchanged and settled such that the sender may initiate a
payment that will seamlessly reach the receiver. With interoperability, an
individual or business with a bank account would be able to send a payment
to another individual or business without having to choose, understand, or
even be aware of the path taken by the payment.” While noting that interoperability can take different forms, the Federal Reserve has maintained that it
alone cannot fully establish the interoperability of FedNow; achieving this
will require active partnership and collaboration with the financial industry
(Federal Reserve Board 2022c).
Some have suggested that near instant digital payment systems like
FedNow may reduce the need for circulating digital money (NAFCU
2022). In this case, the benefits of circulating digital money after FedNow
is launched may be minimal. In fact, Federal Reserve governor Michelle
Bowman commented in August 2022 that “my expectation is that FedNow
addresses the issues that some have raised about the need for a CBDC”
(Bowman 2022). Conversely, FedNow is intended to mainly focus on
domestic payments and may bring limited improvements to the cross-border
payment system, at least initially. In addition, FedNow is not a digital asset,
which can be used in settlements or provide transaction programmability,
roles that circulating digital money could play in the globally integrated
financial system.

Central Bank Digital Currencies
It is important to note that money can come both in a physical format (e.g.,
cash) and in a digital format (e.g., electronic bank accounts). Thus, a central
bank’s digital currency is a liability of a central bank similar to cash, but
it exists on a digital platform, where it can be exchanged and settled in
real time. A CBDC system is made up of the CBDC itself, the public and
private sector components that work alongside the CBDC, and the laws
and regulations that apply to these digital assets (White House 2022a). A
CBDC system can be set up in numerous different ways, such as a wholesale
CBDC, which allows for access only by financial institutions (e.g., banks);
and a retail CBDC, which allows for access by individuals. “That said,
certain design features and questions related to the underlying infrastructure

270 |

Chapter 8

of CBDC may blur these distinctions to some degree” (U.S. Department of
the Treasury 2022e).
As of January 5, 2023, 11 countries have launched CBDCs (Atlantic
Council 2022). In addition, a number of foreign central banks, including
the European Central Bank and the Bank of Japan, are exploring CBDCs;
and some central banks, such as the People’s Bank of China, are piloting a retail CBDC (Gorton and Zhang 2022). While some countries have
considered using DLT for their CBDC, it is worth noting that many of the
pilot programs for CBDC systems are not built on DLT; instead, they rely
on a trusted central authority—a country’s central bank—to operate key
aspects of the CBDC system. This seems likely to be the case if a U.S.
CBDC is introduced. A White House assessment of a potential U.S. CBDC
system recently noted that “while a U.S. CBDC system could, in theory, be
mostly ‘permissionless’ from a governance standpoint, this design choice
introduces a large number of technical complexities and practical limitations
that strongly suggest a permissionless approach does not make sense for a
system that has at least one trusted entity (i.e., the central bank)” (White
House 2022a). This is somewhat ironic, given that this is different from an
oft-cited founding principle of crypto assets like Bitcoin, whose purported
aim was to create decentralized money without any trusted central authority.
A U.S. CBDC—a digital form of the U.S. dollar—would have the
potential to offer significant benefits. It could enable a payment system that
is more efficient, provide a foundation for further technological innovation,
facilitate faster cross-border transactions, and be environmentally sustainable (White House 2022a). It could also promote financial inclusion and
equity by enabling access for a broad range of consumers (Maniff 2020).
A potential U.S. CBDC could also help support other policy goals. For
example, a potential U.S. CBDC could help ensure that such payment systems are aligned with the principles of human rights, democratic values, and
privacy (U.S. Department of the Treasury 2022e).
There are also some risks from having a CBDC in the financial system.
Similar to one-to-one backed stablecoins, CBDCs may also pose credit
availability risks (U.S. Department of the Treasury 2022b). That is, a widely
available CBDC could serve as a substitute for commercial bank deposits.
Just as in the case of stablecoins that are fully backed by safe assets, this
substitution effect could reduce the aggregate amount of deposits in the
banking system, which could in turn increase bank funding expenses, and
thus could reduce credit availability or raise credit costs for households and
businesses. In addition, because central bank money is the safest form of
money, a widely accessible CBDC would be particularly attractive to riskaverse users (and likely more so than a stablecoin), especially during times
of stress in the financial system. The ability to quickly convert bank deposits
into a CBDC could make systemic bank runs more likely or more severe
Digital Assets: Relearning Economic Principles | 271

(Bank of Canada et al. 2021). In addition, CBDCs could cause operational
risks. If the CBDC platform could not function due to a system failure or a
cyberattack, it could erode investors’ confidence.
Recognizing the potential benefits and risks from a U.S. CBDC, the
Biden-Harris Administration has developed “Policy Objectives for a U.S.
CBDC System,” which reflect the Federal Government’s priorities for a
potential U.S. CBDC (White House 2022e). These objectives flesh out the
goals outlined for a CBDC in the Executive Order. According to these objectives, the “U.S. CBDC system, if implemented, should protect consumers,
promote economic growth, improve payment systems, provide interoperability with other platforms, advance financial inclusion, protect national
security, respect human rights, and align with democratic values.”

Conclusion
Innovation in financial services brings both risks and opportunities for the
broader economy. It can challenge business models and existing industries,
but it cannot challenge basic economic principles, such as what makes
an asset effective as money and the incentives that give rise to run risk.
Although the underlying technologies are a clever solution for the problem
of how to execute transactions without a trusted authority, crypto assets currently do not offer widespread economic benefits. They are largely speculative investment vehicles and are not an effective alternative to fiat currency.
Also, they are too risky at present to function as payment instruments or to
expand financial inclusion. Even so, it is possible that their underlying technology may still find productive uses in the future as companies and governments continue to experiment with DLT. In the meantime, some crypto
assets appear to be here to stay, and they continue to cause risks for financial
markets, investors, and consumers. Much of the activity in the crypto asset
space is covered by existing regulations and regulators are expanding their
capabilities to bring a large number of new entities under compliance (SEC
2022). Other parts of the crypto asset space require coordination by various
agencies and deliberations about how to address the risks they pose (U.S.
Department of the Treasury 2022a).
Certain innovations, such as FedNow and a potential U.S. CBDC,
could help bring the U.S. financial infrastructure into the digital era in a clear
and simple way, without the risks or irrational exuberance brought by crypto
assets. Hence, continued investments in the Nation’s financial infrastructure
have the potential to offer significant benefits to consumers and businesses,
but regulators must apply the lessons that civilization has learned, and thus
rely on economic principles, in regulating crypto assets.

272 |

Chapter 8

Chapter 9

Opportunities for Better Managing
Weather Risk in the Changing Climate
Global temperatures as high as those in recent years are unprecedented in
the time span of human civilization and have likely not been seen in at least
the last 125,000 years of Earth’s history (Gulev et al. 2021). Many nations,
including the United States, are working ambitiously to limit the impact of
climate change by reining in greenhouse gas emissions and harnessing the
opportunities of the clean energy transition. However, given the time it takes
to transform the global energy system and for the climate to respond, the
climate will continue changing at least until global greenhouse gas emissions fall to zero. In the coming decades, more intense and frequent weather
extremes and the uncertainty of the changing climate will present a range
of economic and financial risks to the U.S. economy and will confront the
Federal Government with related fiscal challenges. Physical climate risks
can be managed by anticipating and planning for coming changes in climate,
a process known as adaptation. Adaptation presents opportunities to lower
climate change costs over the long-term while also building resilience to
natural hazards and weather risks today.
The design of climate adaptation policies must recognize that actors across
the United States, including individuals and businesses and all levels of
government, already face incentives to adapt to climate change. But they
also face informational, financial, and legal constraints that may limit their
ability to adapt. Targeting adaptation policies to alleviate these constraints
and address related market failures should be most effective in supporting
private action.

273

The Federal role in climate change adaptation is complicated by the complex, nested governance structure of relevant policy areas. Many important
areas that are relevant to adaptation, from land use planning and zoning
to the regulation of insurance markets, are governed at the State or local
level. The Federal Government, however, has a strong interest in advancing reforms in these areas to address climate change because of its role in
managing risks across the United States, from credit and insurance provision
to disaster response and recovery to social safety net programs.
The risks that climate change poses are multidimensional, regionally
specific, and vary based on underlying socioeconomic vulnerabilities.
Adaptation policies need to be targeted to particular settings and therefore
will need to be both varied and complex. This chapter proposes four overarching objectives for structuring further Federal adaptation efforts:
•

Producing and disseminating knowledge about climate risk

•

Long-term planning for the climate transition

•

Ensuring accurate pricing of climate risks

•

Protecting the vulnerable.

T

he United States has joined nations around the world in acting to
reduce greenhouse gas emissions. If fully implemented, national
pledges may limit global warming to 3.6°F (2°C; Meinshausen et
al. 2022), achieving a primary goal of the Paris Agreement (United Nations
Climate Change, n.d.). The steeply falling costs of low-carbon technologies
and the increasingly ambitious climate policies of many countries are bending the global emissions curve, rendering worst-case outcomes of 8–10°F
of warming by the end of the century increasingly unlikely (Hausfather and
Moore 2022). The United States has implemented major domestic legislation to achieve its own goals to reduce emissions to 50–52 percent below
2005 levels by 2030 and to reach net-zero emissions by 2050. In particular,
the August 2022 Inflation Reduction Act and the November 2021 Bipartisan
Infrastructure Law together make $430 billion in investments in American
infrastructure, with a focus on the climate challenges facing our Nation (U.S.
Department of Energy 2022).
However, even given the ambitious action to rein in emissions that
will be required to meet these national commitments, the climate will

274 |

Chapter 9

continue changing for the foreseeable future, for two main reasons. First, it
will take decades to completely transform the global energy system, which
currently relies heavily on greenhouse-gas-generating fossil fuels. Second,
the climate system will take years to respond to changes in emissions. At
present, temperatures across the lower 48 States are about 1.8°F above their
level in 1900 (Vose et al. 2017, 185); even if all nations meet their emission
reduction goals, global warming will at least double by 2100. (Meinshausen
et al. 2022).
The effects of global warming are already apparent across the United
States, in the form of more extreme heat and longer heat waves (National
Oceanic and Atmospheric Administration, n.d.; Lipton et al. 2018; Gutiérrez
et al. 2021, p. 2004); more extreme rainfall events and associated flooding (U.S. Environmental Protection Agency 2021; Davenport, Burke,
and Diffenbaugh 2021); more frequent and intense droughts driving huge
wildfires (Williams, Cook, and Smerdon 2022; Borunda 2021; Burke et
al. 2021); and higher sea levels driving coastal flooding and worse storm
surges (Hino et al. 2019; Marder 2020). Among the events that have been
formally linked to climate change in recent years are the ongoing drought
in California (Diffenbaugh, Swain, and Touma 2015); exceptionally dry
conditions in the Southwest (Park Williams, Cook, and Smerdon 2022);
extreme heat in the Pacific Northwest (Bercos-Hickey et al. 2022); flooding
in the mid-Atlantic (Winter et al. 2020); major western wildfires (Yu et al.
2021); the damage caused by Hurricane Harvey, which inundated Houston
in 2017 (Frame et al. 2020; World Weather Attribution 2017); and severe,
rain-induced flooding in Louisiana in 2016 (van der Wiel et al. 2017).
Fluctuating weather conditions have always presented challenges. The
inherent variability of the atmospheric system means that specific weather
conditions cannot be predicted—even in principle—beyond a 7- to 10-day
predictability horizon. In a stable climate, however, the probability of different weather conditions can be accurately estimated. A stable climate allows
actors to understand the risks of weather-dependent outcomes and to plan
accordingly, in designing infrastructure, allocating investments, and adjusting daily routines and habits. For example, statistical methods currently
used to design infrastructure assume a stable and unchanging climate, where
the probability of extreme weather over the lifetime of the infrastructure
remains constant at historical values (Milly et al. 2018).
With the human-induced climate change of today, however, it is
no longer possible to assume that the future will be like the past and to
use unadjusted past experience as a guide for the future. Decisions made
using only historical weather records will become increasingly inaccurate
and costly as weather patterns change (Milly et al. 2018; Electric Power
Research Institute 2022). Weather variability occurring in the context of the

Opportunities for Better Managing Weather Risk in the Changing Climate | 275

changing climate will result in repeated experiences of historically unprecedented weather conditions (Fisher, Sippel, and Knutti 2021).
Even small changes in average climate conditions can produce large
changes in the probability of previously rare weather events. Social, financial, and infrastructural systems that manage these risks typically have
certain tolerances, with steeply increasing costs when these thresholds are
exceeded. For instance, construction is often designed to standards based
on historical weather conditions, such as a 1-in-100-years rain event, and
conditions exceeding these design thresholds can produce dangerous conditions that have high economic and social costs (American Society of Civil
Engineers 2018, 239). A shifting climate can quickly render these standards
obsolete. In the example shown in figure 9-1 using an illustrative climate
distribution, the mean changes by just under 20 percent, but the probability
of an extreme event (the area of the distribution to the right of the green line)
increases by about 80 percent.
Modern societies have been and continue to be ordered for a climate
that no longer exists; therefore, the projected rapid changes in the climate
system will pose major, evolving risks for economic, social, infrastructural,
and governance systems in coming decades. Recognizing and planning for
these risks—a process that is often called adaptation—can reduce costs,
improve stability, and protect the most vulnerable people and communities.
Because climate change touches many aspects of economic production and
societal well-being, adaptation policies need to be equally broad, ranging
Figure 9-1. Small Changes in Climate Can Greatly Increase the Probability of
Extreme Weather Events
Probability of occurrence

Past climate

System design standard

New climate

Weather metric (e.g., five-day cumulative rainfall)
Source: CEA calculations.

276 |

Chapter 9

from the provision of decision-relevant climate information to the regulation of insurance markets to improved building codes and zoning. Box 9-1
describes some of the ongoing investments being made by the Biden-Harris
Administration to build resilience while reducing and managing the costs of
a changing climate.

Economic Principles of Adaptation Policy and Planning
The economic principles that support adaptation planning for climate change
are both different from, and more varied than, those underpinning the
reduction of greenhouse gas emissions, typically referred to as mitigation.
Mitigation is a classic example of a public good. The costs of emissions
accrue to people all around the world and will last far into the future.
Because the market prices of fossil fuels do not incorporate these large
social costs, climate change can be understood as a global externality—what
Nicholas Stern has termed “the greatest market failure the world has ever
seen” (Stern 2006). Private markets have little incentive to provide emissions reduction in the absence of government action. Moreover, because the
benefits of reducing emissions accrue globally and are not captured within
the borders of a single country, nations must cooperate to address the climate
challenge (Nordhaus 2019). Aligning the incentives of private actors and
nations throughout the world to account for the climatic effects of fossil
fuels requires coordinated action.
In contrast, many adaptation decisions are private goods, in that
both the costs and benefits are largely internalized by the decisionmaker
(Mendelsohn 2000; Kahn 2021; Kolstad and Moore 2020). For adaptation,
communities, households, and businesses all have their own motivations for
responding to and planning for climate risks. Examples include a homeowner
investing in reinforced windows to defend against stronger storms, a farmer
choosing what crops to grow in response to changing drought conditions,
and a business adjusting suppliers to reduce weather-related disruptions that
are shifting due to climate change. Though there are public goods problems
and other market failures related to adaptation (discussed in detail later in
the chapter), they are varied, specific, localized, and very different from the
global externality problem that characterizes mitigation.
Indeed, there is already evidence that private actors are starting to consider emerging climate risks in their decisions. For example, recent research
suggests that both the risks of sea-level rise and the productivity effects of
extreme heat are reflected in property and agricultural land prices (Keys and
Mulder 2020; Bernstein, Gustafson, and Lewis 2019; Baldauf, Garlappi,
and Yannelis 2020; Severen, Costello, and Deschênes 2018). Climate risk
premiums are also showing up in longer-term corporate and municipal
bonds (Painter 2020; Acharya et al. 2022; Goldsmith-Pinkham, Gustafson,
Opportunities for Better Managing Weather Risk in the Changing Climate | 277

Box 9-1. Adaptation and Resilience Investments
of the Biden-Harris Administration
The 2021 Bipartisan Infrastructure Law (BIL) and the 2022 Inflation
Reduction Act (IRA) both contain a number of provisions to build
resilience to natural disasters and adapt social and economic systems to
reduce the costs of climate change. The Biden-Harris Administration is
in the process of implementing these laws, making historic investments
in climate change resilience. The BIL in particular provides $50 billion
for adaptive investments such as support for energy-efficiency improvements in low-income households, grants to states and territories for
resiliency projects and flood mitigation, dedicated funding to improve
resiliency of transportation systems, and funding for wildfire defense
and coastal adaptation (White House 2022g, 2022h; U.S. Department of
Transportation 2022; U.S. Department of Commerce 2022). Resiliency
provisions within the IRA include tax credits and rebates for improving home energy efficiency and funding for addressing droughts and
improving water infrastructure (White House 2022i).
Beyond the investments provided by the BIL and IRA, the United
States is pursuing a multipronged adaptation strategy that includes:
inter-agency coordination on climate extremes, building codes and
climate-related financial risks; the provision of climate data tools such
as the Climate Mapping for Resilience and Adaptation portal; and building resilience into Federal procurement (UN Framework Convention on
Climate Change 2021; Climate Mapping for Resilience and Adaptation,
n.d.). Principal agencies are now required to develop and implement
Climate Adaptation and Resilience plans and report annually on implementation and progress under the plans (White House 2022j). Several
programs target assistance to groups that may be particularly vulnerable
such as low-income and Tribal communities (White House 2022g,
2022h).
These historic investments are laying the foundation that will
be required for building adaption and resilience in the United States.
This chapter describes both the broad economic principles that underlie
adaptation policy generally and that could support future work building
on this foundation. For instance, spending on adaptation and resilience
projects will be most effective when coupled with reforms to zoning,
building codes and infrastructure standards (mostly governed at the
State and local level; see figure 9-3), as well as clear communication of
strategic priorities for the management of climate risks at all levels of
government.

278 |

Chapter 9

and Lewis 2021). There is also evidence that property market adjustments
are concentrated in regions where more people report believing in climate
change, which suggests continuing pricing frictions in areas where there is
greater skepticism about climate change (Barrage and Furst 2019; Bernstein,
Gustafson, and Lewis 2019; Baldauf, Garlappi, and Yannelis 2020; Severen,
Costello, and Deschênes 2018).
The importance of private incentives in shaping climate action has
implications for the design of adaptation policy. First, effective adaptation policy will recognize that individuals, households, businesses, and
communities will act in response to the shifting climate. But these actions
will be defined by legal, informational, and financial constraints, along
with the sometimes-distorted or perverse incentives that these actors face.
Government action that targets constraints and market failures that impede
adaptation should be most effective in supporting and enabling private
action rather than crowding out actions that would have occurred anyway.
Examples of these constraints and market failures, as well as policies that
can address them, are discussed in detail later in the chapter.
Second, government policies and programs already play a role in
determining how the costs of weather-related hazards are distributed
through society, a role that will likely grow in importance as climate change
effects worsen. Individuals with more wealth and a higher income are better able to avoid, prepare for, and recover from weather-related shocks.
This means that, in the absence of counteracting policies, climate change
costs will likely be disproportionately borne by the poor and marginalized.
Programs of public lending, insurance, grants, and welfare can be designed
to reallocate some of these risks and thus reduce the regressive nature of
climate change costs. However, the existing disaster response and social
support system is composed of legacy programs that were designed in a
pre–climate change era and thus may not be adequate for addressing climate
change. Programs are distributed across multiple agencies and levels of
government, often with burdensome applications or complex requirements,
making their net distributive effect difficult to determine (Mach et al. 2019;
Howell and Elliott 2019). In fact, there is evidence that postdisaster aid can
exacerbate rather than mitigate preexisting inequalities (Billings, Gallagher,
and Ricketts 2022). Comprehensive reevaluation and reform of the system
composed of these interacting support programs, building on the ongoing
adaptation work within the Biden-Harris Administration (box 9-1), will be
required to ensure that the system is able to protect the vulnerable as climate
change damage grows.
The next sections first review new evidence on the economic costs and
financial risks that climate change poses to the United States and to Federal
finances and then outline the role that a Federal adaptation strategy could
play in managing and reducing physical climate risk.
Opportunities for Better Managing Weather Risk in the Changing Climate | 279

The Economic Costs and Financial Risks of
Climate Change in the United States
Shifting climate patterns are producing a wide array of risks affecting the
well-being of American communities. Earlier research on climate change
economics assumed that higher-income countries like the United States
would be able to manage the effects of changing weather conditions relatively easily because of the small share of clearly exposed sectors of the
economy, such as agriculture and forestry, and the assumption that adjusting
to climate change would be straightforward (Mendelsohn, Nordhaus, and
Shaw 1994; Nordhaus 1991, 930). However, this assessment needs to be
reconsidered in light of new economic evidence and the increasing intensity
and frequency of extreme weather events across the United States that can
be formally attributed to climate change (Seneviratne et al. 2021).

The Costs of Climate Change for the United States’ Well-Being and
Prosperity
Weather variability has a range of effects within the United States. For
example, studies have shown that very hot temperatures have adverse
effects—including increasing premature mortality and worsening of the
health of newborn babies (Deschênes and Greenstone 2011; Deschênes,
Greenstone, and Guryan 2009; Barreca and Schaller 2020); decreasing crop
yields (Schlenker and Roberts 2009); adversely affecting mental health
(Burke et al. 2018); lowering the labor supply in exposed industries (Graff
Zivin and Neidell 2014); increasing violence (Mukherjee and Sanders 2021);
and reducing students’ ability to learn (Park et al. 2020). These effects are
not borne equally across geographic regions or economic sectors within the
United States, and they are felt most acutely among disadvantaged groups
with a high vulnerability to natural hazards (box 9-2).
Climate models predict that extreme heat will become more frequent
and intense as climate change continues (IPCC 2021). Today, in many parts
of the United States, the heat wave season is nearly three times longer than
in the 1960s; in the summer of 2022 alone, 400 U.S. locations broke their
monthly temperature records (Lipton et al. 2018; Stevens and Samenow
2022; U.S. Global Change Research Program 2018, figure 1.2b). There is
some evidence that people, businesses, and communities can adjust to hotter
temperatures over time—for instance, by changing the timing of outdoor
activities (Graff Zivin and Neidell 2014; Dundas and von Haefen 2020)
or by using more energy for cooling (Auffhammer 2022; Deschênes and
Greenstone 2011). But these adaptations are costly, do not eliminate climate
change costs, and may reduce the quality of life (Deschênes 2022).

280 |

Chapter 9

Box 9-2. Climate Change Will Most Likely Interact
with and Exacerbate Existing Inequalities
The effects of climate change are not evenly distributed across the U.S.
population by income, race, or ethnicity. Lower-income communities
have fewer resources to prepare for or respond to extreme weather
events, leaving them more vulnerable to their effects. For instance,
residents of lower-income communities are less likely to evacuate away
from the path of hurricanes and tend to live in more vulnerable structures, leaving them at a higher risk of mortality or injury (Deng et al.
2021; Fothergill and Peek 2022). Low-income Americans are less able to
adjust their activities to avoid exposure to wildfire-induced air pollution
or to adjust air-conditioning in response to extreme heat (Burke et al.
2022; Cong et al. 2022). They are more likely to work in industries such
as agriculture and construction that are highly exposed to dangerously
high temperatures (U.S. Environmental Protection Agency 2021). Major
natural disasters are more likely to lead to financial hardships, such as
debt defaults and bankruptcies in low-income and minority communities
(Billings, Gallagher, and Ricketts 2022; Jerch, Kahn, and Lin 2022).
Injustices throughout U.S. history mean that these effects are also
strongly differentiated by race. For instance, because of forced relocation
away from their tribal homelands, many Native American people live on
marginal lands that are highly susceptible to wildfires, extreme heat, and
droughts (Farrell et al. 2021). Minority and low-income areas within cities, including formerly redlined neighborhoods, are substantially hotter
than wealthier, whiter, and nonredlined areas (Hoffman, Shandas, and
Pendleton 2020; Benz and Burney 2021); Black and Hispanic students
are more likely to attend schools without air-conditioning (Park et al.
2020); and minority communities are more likely to be affected by
expected increases in extreme heat and coastal flooding due to climate
change (U.S. Environmental Protection Agency 2021; Wing et al. 2022).
Wealth and assets allow households to avoid, prepare for, respond
to, and recover from weather-related shocks, leaving minority populations
that lack these assets more exposed to intensifying weather extremes.
Minority populations, particularly African Americans, were barred from
accessing avenues for wealth accumulation for centuries—for instance,
through discriminatory home-lending practices that persisted through
much of the 20th century—leading to stark disparities today in wealth
and assets by race and ethnic group; the median wealth of white households is almost eight times that of Black households (Rothstein 2017;
Derenoncourt et al. 2022; Cook 2014; Bhutta et al. 2020).
Moreover, disadvantaged and racial minority communities have
generally received less financial assistance after disasters than affluent
white communities (National Advisory Council 2020), partly because
some of this aid is tied to property ownership, from which minorities have

Opportunities for Better Managing Weather Risk in the Changing Climate | 281

been historically excluded. The Biden-Harris Administration is working to address these inequities. The Federal Emergency Management
Agency (FEMA) has committed to establishing an equitable and fair
distribution of Federal aid and assistance and to increase access for
underserved populations (U.S. Department of Homeland Security 2022).
In response to past inequities, the U.S. Environmental Protection Agency
recently created the Office of Environmental Justice and External Civil
Rights, which seeks to coordinate and prioritize environmental justice
within the agency and in its partnerships with other Federal agencies
(U.S. Environmental Protection Agency, n.d.). More generally, the
Biden-Harris Administration is working to direct 40 percent of the
benefits of climate and clean energy investments to disadvantaged communities. Several programs in the IRA and BIL, most notably the $27
billion Greenhouse Gas Reduction Fund, also target those communities
(U.S. Environmental Protection Agency 2022; White House, n.d.).

Beyond extreme heat, climate change is associated with a host of
other costly events. About one-third of the cost of major flood events since
1988, totaling $79 billion, has been attributed to climate change (Davenport,
Burke, and Diffenbaugh 2021). The western United States is currently having the worst drought in at least 1,200 years, requiring costly cutbacks in
water use and endangering the functioning of the Lake Meade and Lake
Powell reservoirs (Wheeler et al. 2022; Park Williams, Cook, and Smerdon
2022; Borunda 2021). California and the Pacific Northwest have suffered
devastating wildfires that blanketed cities under thick smoke, undermining decades of air quality gains driven by the Clean Air Act and forcing
preemptive blackouts to avoid igniting wildfires, temporarily cutting off
power to millions of customers (Burke et al. 2022; Childs et al. 2022; Goss
et al. 2020; Chediak 2019). Hurricane Ian struck Florida in September 2022,
causing a coastal storm surge of up to 18 feet and widespread inland flooding; it will end up being one of the costliest storms on record, with losses
to residential and commercial property estimated at between $36 billion and
$62 billion (CoreLogic 2022; Paquette and Kornfield 2022).
Unprecedented extreme events are exposing the weaknesses of aging
U.S. infrastructure, which was designed to operate in different climate conditions. Multiple infrastructural systems gradually built over many decades—
including electricity grids, dams and irrigation systems, coastal and riverine
defenses, roads and railways, and ports—will need to be quickly redesigned,
retrofitted, or rebuilt to maintain their functionality in the changing climate.
And this investment in climate resilience will need to be made while also
addressing the estimated $2.6 trillion in deferred infrastructure investments
282 |

Chapter 9

required over the next 10 years just to restore existing infrastructure to working order. These costs will likely be borne by cities, States, and the Federal
government, along with specific infrastructure user groups such as electric
utility customers or irrigation district members (American Society of Civil
Engineers 2021).
The complexity of global supply chains means that extreme weather
events around the world can ripple through international trade networks, to
affect American producers and consumers (Woetzel et al. 2020a). Pankratz
and Schiller (2022) show how disruption from extreme heat and flooding in
supplier locations is transmitted through supply chains to affect the revenues
of customer firms. As climate change intensifies, the chances of major
disruptive weather events occurring simultaneously in multiple regions will
increase, causing larger and more systemic threats to supply chains. The
summer of 2022 saw major heat and drought events in the United States,
Europe, and China disrupt global production, which prevented the transporting of agricultural products along rivers and shut down electricity generation
for car and electronics factories, and thus exacerbated supply chain challenges (Ahmedzade et al. 2022; Bradsher and Dong 2022; Plume 2022).
Crop failures and other effects of climate change could also exacerbate
volatile conditions in fragile nation-states, leading to instability and conflict—with spillover effects to the United States via migration and escalation
of local conflicts into national security concerns (Missirian and Schlenker
2017; Benveniste, Oppenheimer, and Fleurbaey 2020; Mach et al. 2019;
White House 2015a). Studies have suggested that both the conflict in Syria
and the flows of migrants from Central America have been exacerbated by
climate-related stressors (Ash and Obradovich 2019; Kelley et al. 2015;
Lustgarten 2020). Recent massive flooding in Pakistan led to the inundation
of one-third of the country by floodwaters, internal displacement of 33 million people, massive disruption of the agricultural sector, and a sovereign
debt downgrade (Lu 2022; Fitch Ratings 2022). By shifting where nonhuman species live and how they interact with humans, climate change could
even increase the risks of zoonotic disease spillovers (Carlson et al. 2022).
There is some evidence that climate change could also affect macroeconomic growth. Empirical investigations of the causal relationship
between temperature fluctuations and gross domestic product (GDP) generally find negative effects from hotter temperatures, particularly in poor and
hot countries, with some indications that this heat depresses growth rates
(Dell, Jones, and Olken 2012; Burke, Hsiang, and Miguel 2015; BastienOlvera, Granella, and Moore 2022). According to modeling and simulation
studies of the macroeconomy, negative effects of climate change on growth
greatly increase both the magnitude and uncertainty of aggregate climate
costs (Moore and Diaz 2015; Newell, Prest, and Sexton 2021), but little is
currently understood about the mechanisms connecting weather shocks and
Opportunities for Better Managing Weather Risk in the Changing Climate | 283

long-term climate trends to macroeconomic variables. Plausible mechanisms include faster depreciation of the capital stock resulting from more
intense extremes (Hallegatte, Hourcade, and Dumas 2007; Otto et al. 2023),
effects to productivity growth (Ortiz-Bobea et al. 2021), or effects on the
labor force that either depress the supply of labor or slow the accumulation
of human capital via effects on learning (Graff Zivin and Neidell 2014; Park
et al. 2020). Improving empirical evidence and modeling capabilities in this
area should be a high priority for future research.
Because climate change affects many aspects of well-being, including
those not traded in traditional markets, the costs of climate change and losses
of other natural capital are mismeasured by GDP (Coyle 2015; Brunetti et
al. 2021; Svartzman et al. 2021; NGFS-INSPIRE 2022). For example, production that emits climate-change-causing greenhouse gases adds to GDP,
and so too do expenditures to adapt to climate damage or reduce emissions.
Meanwhile, many important services that nature provides, including reducing risks to health and protection from climate-driven extreme weather
events, are not reflected in GDP or are misattributed. Examples include the
role urban trees play in providing shade and lowering heat extremes or the
value of intact wetlands in reducing coastal storm damage. A more complete
accounting system than GDP that tracks national wealth—the stock of multiple forms of capital that produce flows of both market and nonmarket benefits—could provide clearer macroeconomic information on climate change
than exclusive reliance on GDP (Agarwala and Coyle 2021; Dasgupta
2021). Including natural capital in measures of wealth would help track
climate change costs and nature loss in ways that complement GDP and fill
in important blind spots. This is why the Biden-Harris Administration has
begun the process of rigorously measuring natural capital in a way that could
inform a more complete picture of economic progress and climate change
costs (White House 2022c).

Climate Change and Financial Stability
Climate change risks have historically been unpriced in private markets,
but as these risks become increasingly apparent and investors become more
cognizant of the threat, price adjustments for exposed assets would be
expected. Property and long-lived physical infrastructure are particularly
exposed, and risk becoming stranded as climate conditions shift beyond
their design standard, thus causing investments to underperform or fail
altogether. There is substantial evidence that current natural hazard risks are
undercapitalized in property markets, a setting that could produce sudden
price shocks in response to new information that reveals underlying risks
to buyers (Bakkensen and Barrage 2022; Baldauf, Garlappi, and Yannelis
2020; Hino and Burke 2021; Gibson and Mullins 2020).

284 |

Chapter 9

Certain financial instruments—such as insurance contracts, catastrophe bonds, and mortgages—that directly or indirectly price weather-related
risks are also highly exposed to climate change. Rapid changes in asset
prices or reassessments of the risks in response to a shifting climate could
produce volatility and cascading instability in financial markets if not anticipated by regulators. Because of the interaction of long-lived investments and
direct exposure to weather extremes, property insurance against catastrophic
natural hazards is at the forefront of climate change risk exposure and is
already showing signs of strain in several states (box 9-3).
Governments have typically stepped in to provide coverage when private insurers pull out from particularly risky areas or hazards. More than 95
2/6/2023
percent of flood policies in the United States are insured through the Federal
National Flood Insurance Program (NFIP), and the number of policies in
State-run insurance plans has more than doubled since 1990 (Kousky et al.
2018). Figure 9-2 shows the long-run growth in State-run disaster insurance plans combined with a sharp rise following major hurricane strikes in
2004 and 2005, after which the largest insurance companies pulled out of
Florida (Leefeldt 2022). Although the State was able to move policies off
its rolls and back into the private market in the 2012–15 period, Florida’s
public insurer, Florida Citizens, is once again the largest property insurer in

Figure 9-2. Count of Policies under U.S. Residual Property Insurance
Market, 1990–2021, with Geographic Breakdown for 2021
Policies in force, millions
3.5

Other, 12.0%

3.0

Massachusetts, 9.1%
California, 10.4%

2.5

Texas, 12.4%

2.0

North Carolina, 19.5%

1.5
1.0

Florida, 36.6%

0.5
0.0
1990

1995

2000

2005

2010

2015

2020

Sources: Insurance Information Institute, n.d.; Citizens Property Insurance Corporation of Florida, n.d.
Note: Data are linearly interpolated for the years 1991–94, 1996–98, and 2001–2. Policies from North Carolina are included
only for 2011 (2.2 percent of total policies in that year) and later.

Opportunities for Better Managing Weather Risk in the Changing Climate | 285

Box 9-3. Disaster Insurance in the Changing Climate:
Challenges and Opportunities for Reform
Even in the absence of climate change, weather-related extremes—such
as flooding, hurricanes, and wildfires—pose particular challenges
to the insurance industry. Losses due to natural disasters are highly
concentrated in space and time, leaving insurers financially vulnerable
to a major weather event (Wagner 2020). Moreover, the distribution of
losses from these events is “thick-tailed,” a statistical property meaning
that expected losses are heavily influenced by extremely rare events, the
risks of which are difficult to quantify and price (Conte and Kelly 2018;
Kousky 2022, 38–42). In the face of these challenges, insurers must limit
exposure by either exiting from or limiting activity in certain markets or
by purchasing reinsurance, which raises costs.
For these reasons, even without climate change, natural disasters
hover on the edge of insurability. Without reforms to improve the
functioning of insurance markets and reduce the costs of extremes (for
instance through zoning changes and building code improvements),
climate change could well make many more hazards uninsurable as the
frequency and intensity of extremes increase. Climate change increases
uncertainty, particularly in the tails of the distribution that drive expected
losses, and raises the risk of completely unprecedented events for which
there is no historical analog (figure 9-1). In the absence of high-quality,
trusted information on quickly evolving climate risks, ambiguity in how
to price extreme weather risks could lead insurers to leave certain markets altogether. Major insurers have already stopped offering hurricane
wind coverage along the Gulf Coast and are increasingly exiting high
fire-risk areas in California (Sadasivam 2020; Elliott 2022; Schuppe
2022; Querolo and Sullivan 2019). Moreover, the increasing likelihood
that multiple catastrophic events could occur concurrently could raise
costs or limit the availability of reinsurance.
A lack of access to insurance makes extreme events more costly
because it slows economic recovery in affected communities and raises
the probability of cascading financial hardships, such as mortgage
defaults and debt delinquencies (Billings, Gallagher, and Ricketts
2022; Kousky 2019; Kousky, Palim, and Pan 2020; Otto et al. 2023).
Therefore, reforms to address challenges in hazard insurance markets
should be a high priority for adaptation policy. A major issue in the
U.S. disaster insurance system is that although climate change presents a systemic threat that simultaneously raises the risk of multiple
perils (wind, fires, and floods) across the United States, hazards are
insured on a peril-by-peril basis and are regulated at the State level.
Alternative models—such as those used in France, Spain, and New
Zealand—instead create broad, diversified risk pools by mandating
comprehensive, multiperil catastrophe insurance while also providing an

286 |

Chapter 9

explicit public reinsurance backstop that limits the exposure of private
insurers (Kousky 2022, 53–55). These kinds of reforms will likely be
increasingly important to stabilize insurance markets and expand access
in the face of climate change.

the State after seeing a 48 percent growth in policies during 2022 (Florida
Citizens 2023; Insurance Journal 2022). Other sharp growth in State insurance rolls in recent years has come from California, where wildfire risk has
led private insurers to pull out and the number of policies on the State’s
FAIR plan to increase by over 80 percent since 2018 (State of California
2018; Insurance Information Institute, n.d.–a). These programs strain
government finances because premiums generally do not cover payouts,
meaning that losses are covered with general tax revenue or debt issuance
(Hartwig and Wilkinson 2016).
Even with these implicit public subsidies that effectively transfer risks
to general State and Federal taxpayers, the penetration of natural hazard
insurance is low; for instance, only one-third of homes within FEMAdefined 100-year flood zones have flood insurance while fewer than 3 percent outside these flood zones have it, despite still being at risk of flooding
(Evan et al. 2020). Increased risks of delinquencies and defaults after disasters can have subsequent implications for property and mortgage markets,
particularly in the absence of insurance (Kousky 2019; Billings, Gallagher,
and Ricketts 2022; Kousky, Palim, and Pan 2020). Sastry (2022) suggests
that mortgage lenders respond to the availability of federally backed flood
insurance by requiring higher down payments when public insurance is
either limited or not required, changing the demographics of eligible home
buyers.
Climate-change-driven weather extremes can have subsequent effects
on State and municipal finances through several pathways beyond public
insurance plans. First, responding to climate change places additional
burdens on local budgets, which could cause serious financial hardships,
particularly in communities with smaller budgets. The need to either rebuild
damaged infrastructure after disasters or upgrade existing infrastructure
to prepare for climate change raises the cost burden for cities, States, and
Tribes. Second, repeated occurrences of climate extremes can threaten
the property tax base and cause a decrease in revenues. For instance, the
McKinsey Global Institute has estimated that an extreme storm surge event
in 2050 would cause damage equivalent to 10 percent of the total market
value of properties in Miami–Dade County and as much as 30 percent
in Lee County, which was recently inundated by 18 feet of storm surge
Opportunities for Better Managing Weather Risk in the Changing Climate | 287

from Hurricane Ian (Woetzel et al. 2020b; Paquette and Kornfield 2022).
Residential real estate construction has been an important driver of growth
in some U.S. coastal communities, and declines in response to emerging
climate risks could have serious implications for local economies, employment, and tax revenue (Brunetti et al. 2022).
Several researchers have found evidence that the municipal bond
market is beginning to account for these risks in its pricing of loans to
municipalities. Bonds for cities and towns in areas exposed to sea-level rise
carry a premium, with significantly larger effects on longer-maturing bonds,
implying that investors expect either a decline in cash flow or increasing
volatility in exposed cities (Painter 2020; Goldsmith-Pinkham, Gustafson,
and Lewis 2021). Acharya and others (2022) find evidence for the pricing of
extreme heat in municipal and corporate bonds, beginning in about 2013–15,
and also larger effects on longer-term bonds. Higher borrowing costs strain
municipal finances and make it even more challenging for cities to finance
disaster reconstruction or adaptive infrastructure investments without either
raising taxes or diverting resources from other public services. In areas with
local declines in tax revenue and rising climate change costs, municipal
bankruptcies may be increasingly likely. Jerch, Kahn, and Lin (2023) find
evidence that hurricane strikes decrease tax revenues and raise the risk of
municipal default over the following decade, with the largest effects being
felt in disadvantaged communities. In addition to losses to creditors, bankruptcy costs are borne by current and future residents in the form of higher
taxes and service fees (Chapman, Lu, and Timmerhoff 2020).

The Federal Fiscal Implications of Physical Climate Risk
Climate change affects the Federal fiscal outlook via numerous pathways.
On the revenue side, it threatens economic output, leading to a lower tax
base. One estimate by the White House’s Office of Management and Budget
(White House 2022b) suggests that the Federal Government could see 7.1
percent lower annual tax revenue by 2100 as a result of the adverse effect
of climate change on macroeconomic growth. Though some of this could
be offset by increasing taxes on income or capital, Barrage (2020) points
out that the distortionary effects of these revenue-raising mechanisms can
be substantial, increasing climate change costs by up to about 30 percent.
Ongoing research within the Biden-Harris Administration is expanding the
capacity of the Federal Government to integrate the modeling of both the
physical and transition risks of climate change into macroeconomic forecasting in order to better explain and plan for these effects (White House 2022f).
On the expenditure side, many Federal operations are being affected by
the changing climate. Though these effects are too extensive to enumerate
in detail here, this section briefly reviews four primary pathways by which
288 |

Chapter 9

the Federal Government is exposed to physical climate risk: risk assumption, the operation and financing of climate-exposed assets, the provision of
national public goods, and social safety net programs.

Risk Assumption
By fully or partially assuming certain types of risk, the Federal Government
is able to attract private investment and support production across broad
sections of the economy. One of the most significant examples of this is
the Federal role in housing: Through the government-sponsored enterprises
(GSEs)—Fannie Mae and Freddie Mac, which are privately owned but federally chartered and are currently in conservatorship—and Federal agencies
(e.g., the Department of Housing and Urban Development, Ginnie Mae, and
the Department of Veterans Affairs), the Federal Government guarantees
mortgages and securities backed by mortgages. Together, the GSEs and
Ginnie Mae accounted for more than 65 percent ($7.7 trillion) of total outstanding mortgage debt in 2022 (Urban Institute 2022). The growing damage
from hurricanes, storm surges, and wildfires has implications for defaults,
recoveries, and other key cost drivers—and, by extension, for Federal loss
exposure (Kousky, Palim, and Pan 2020; Rossi 2020; Woetzel et al. 2020b).
There is evidence that private lenders are shifting climate-exposed loans
into the GSEs, which may bear a substantial share of the increasing climate
risk in the absence of policies that manage Federal exposure (Ouazad and
Kahn 2022).
In addition to its support for the housing finance system, the Federal
Government also directly assumes risk through various insurance programs.
Flooding is the most frequent and most costly natural disaster in the United
States, and the Federal Government underwrites essentially all home flood
insurance policies via the NFIP (Federal Emergency Management Agency
2010; Kousky et al. 2018; Federal Insurance and Mitigation Administration
2022). Climate change will increase costs from flooding due to both more
intense rainfall and higher sea levels, which worsen flooding from storm
surges and slow drainage in low-lying coastal areas. The NFIP is already
at risk of financial insolvency; it has a debt to the Treasury of $18.1 billion, despite the fact that Congress canceled $17 billion of its debt in
2017 (Federal Emergency Management Agency, n.d.; Environmental Law
Institute 2022, 702). Without fundamental reforms of the U.S. disaster insurance system and of the Federal Government’s role in managing these risks
(box 9-3), these losses will continue to grow (White House 2022b).

Climate-Exposed Assets
The Federal Government owns and operates critical climate-sensitive
infrastructure, most significantly dams, irrigation systems, and major flood

Opportunities for Better Managing Weather Risk in the Changing Climate | 289

defenses such as river and coastal levees, along with buildings, military
installations, and other physical assets that may be at risk from climate
change (White House 2022b; U.S. Department of Defense 2021a). These
assets were built over many decades at substantial cost and are now critical
foundations for communities and regional economies across the United
States.
The Bureau of Reclamation (2022) is the Nation’s largest wholesale
water supplier, operating 338 reservoirs and maintaining 487 dams that
supply water to about 10 percent of U.S. residents, as well as supplying
hydropower and water for agricultural irrigation. The functioning of some
of these systems will be challenged by the changing hydroclimate, which is
already bringing both more intense rainfall and more droughts, including the
persistent megadrought currently hitting western States (Kao et al. 2022).
The Central Valley Project, which supplies water to cities and farmers in
California, has slashed its water deliveries to cities and has completely cut
water to many farmers in 4 of the last 10 years (James 2022). The project’s
long-term operation is further threatened by sea-level rise, which will
increase the salinity of the California Delta and eventually render its water
unfit for drinking and irrigation (State of California 2018; Fleenor et al.
2008). Water levels in Lake Meade and Lake Powell reservoirs are close
to the critical threshold, below which they will cease to produce electricity (Wheeler et al. 2022). The costs of either maintaining water and power
services from Federal facilities in the changing climate or decommissioning
projects and finding alternative solutions for dependent communities has not
been fully estimated.
The U.S. Army Corps of Engineers (n.d.) is tasked with building certain public infrastructure projects that manage the risks of flooding, including riverine and coastal levees and flood control dams throughout the country. More intense rainfall events, higher sea levels, and stronger storms are
expected to increase the costs of maintaining existing flood protection and
expanding it to newly at-risk areas. The costs resulting from climate change
for Federal flood control could be extremely high. Future expenditures will
depend on high-level strategic decisions that have yet to be made regarding
what role flood protection infrastructure will play in managing growing
coastal and inland flood risks. For example, the Corps of Engineers has
released a feasibility study for a plan to protect the New York metropolitan
area from coastal storms. This plan—which includes storm surge barriers,
floodwalls, levees, seawalls, and other measures—would cost upward of
$52 billion, with 65 percent borne by the Federal Government (New York
District 2022).

290 |

Chapter 9

The Provision of National Public Goods
A central function of the Federal Government is to provide national public
goods, most critically national defense, which accounted for about 45
percent of Federal discretionary spending in 2021 (CBO 2022a). Climate
change poses threats to U.S. national security, which raises questions
about its implications for defense spending and the Federal budget (U.S.
Government Accountability Office 2022; National Intelligence Council
2021). The Department of Defense has identified climate change as a factor
in national security planning and as an urgent and growing threat to U.S.
security (White House 2015b; Department of Defense 2021b). Climate
change effects are expected to increase global tensions, as nations compete
for scarcer resources—threatening health and human rights and triggering
conflict and mass migration (White House 2022e).
A second aspect of Federal public goods provision that is substantively
affected by climate change is the stewardship of natural resources, public
lands, and biodiversity. By altering the climatic environment to which
ecosystems are adapted, climate change threatens to degrade ecosystem
functioning and species survival (U.S. Global Change Research Program
2018). The additional costs of managing public lands in this rapidly shifting
environment are not fully known. An example of costs that have been partially quantified are annual Federal wildland fire suppression expenditures,
which, on average, have more than tripled since 1989, partly driven by
intense, climate-change-driven droughts in the West (CBO 2022b). Moore
and others (2022) estimate that direct spending on biodiversity conservation
via the Endangered Species Act could increase by 75 percent (roughly $34
billion) by 2100, as unmitigated climate change pushes an estimated one in
six species toward extinction (Urban 2015).

The Programs of the Social Safety Net
The various Federal programs known as the social safety net provide benefits and assistance to maintain a minimum level of well-being for the U.S.
population. Climate change could increase the burden on these programs
through a number of pathways, most notably health-related expenditures and
assistance for disaster response and recovery.
Federal health programs—namely, Medicare and Medicaid—represented 38 percent of total national health expenditures in 2021, or about
$1.6 trillion (Centers for Medicare & Medicaid Services 2022). Several
studies have estimated that health-related risks constitute the largest fraction
of climate-change-related damage, with particularly severe effects on those
over 65 years of age, who are much more likely to be treated through government programs (Rennert et al. 2022; Hsiang et al. 2017; Carleton et al.
2022). A White House study estimated annual Federal health care costs from
Opportunities for Better Managing Weather Risk in the Changing Climate | 291

the effects of climate change on air quality, Valley Fever, southwestern dust,
and wildfires by 2100 at between $835 million and $22 billion (White House
2022b). An additional unquantified, but likely much higher, cost would
arise from hospitalizations resulting from extreme heat conditions, such as
dehydration, renal failure, and stroke (Green et al. 2010; Wondmagegn et
al. 2021).
Federal programs play a critical role in supporting communities
affected by natural disasters. The Congressional Budget Office (CBO)
estimates that the expected annual loss from hurricane winds and stormrelated flooding is $56 billion, or 0.3 percent of 2019 GDP, with annual
costs to the Federal budget of $18 billion through disaster assistance and
NFIP claims (CBO 2019). However, Deryugina (2017) shows that disasters
have far larger fiscal implications due to increases in social insurance payments—such as Medicaid, disability insurance, and income maintenance
programs—which persist for 10 years after a storm hits. The Federal
Government supports postdisaster response and recovery, not just through
the immediate FEMA response but also through low-interest loans from the
Small Business Administration and Community Development Block Grant
Disaster Recovery funding from the Department of Housing and Urban
Development to aid rebuilding disaster-affected communities (Howe et al.
2022, 700–704; U.S. Small Business Administration, n.d.).

Market Failures and Distortions That Slow
Adaptive Adjustments and Policy Responses
As described above, shifting weather risks present incentives to private
actors to adjust so as to reduce the negative effects of climate change and
take advantage of any opportunities it offers. Indeed, there is evidence that
these adjustments are already happening, in prices that seem to be starting
to account for climate change risks (Keys and Mulder 2020; Bernstein,
Gustafson, and Lewis 2019; Baldauf, Garlappi, and Yannelis 2020; Severen,
Costello, and Deschênes 2018), as well as other evidence that households
and local governments are altering practices in response to or in anticipation
of the changing climate (Berrang-Ford et al. 2021).
Private adaptive adjustments occurring in the United States are subject
to market imperfections—along with informational, institutional, legal, and
financial constraints—that could limit or slow adaptation. Public adaptation
policy should target these barriers to enable faster and more effective private action. This section reviews major market failures, imperfections, and
distortions that are relevant to managing physical climate risks and describes
the policy tools that can address them.

292 |

Chapter 9

Imperfect Information on Physical Climate Risks
Adaptation to the changing climate requires incorporating information
about how shifting weather patterns will alter the distribution of future
weather risks. This is particularly important for decisions that are highly
sensitive to climate change through long-lived investments or exposure to
low-probability, high-consequence tail risks. A substantial body of literature
indicates that individuals consistently underestimate or discount the probabilities of catastrophic events, a phenomenon that can lead to low rates of
disaster insurance coverage and underinvestment in risk reduction (Wagner
2022; Bakkensen and Barrage 2022; Royal and Walls 2018). Information
on climate risks that is of high quality and is trusted, decision-relevant, and
widely disseminated is foundational for adaptation planning and is urgently
needed. Yet it is now largely missing. Modeling tools used to understand the
global climate system, termed general circulation models, are most accurate
over large spatial scales and long time frames. Different, more fine-tuned
tools are needed to make information on specific risks in particular places
over short to medium time frames widely available to stakeholders making
adaptation decisions (Fiedler et al. 2021; Pitman et al. 2022; American
Society of Civil Engineers 2018, 7).
Governments already support a global network of satellites, in situ
observing systems, weather stations, modeling facilities, and technical
workforces that produce public weather forecasts. Similarly, as climate
change information becomes an essential complement of weather data, governments will need to play a role in funding the production of and access to
this essential public good to support climate-informed decisionmaking by
many actors. This includes not just developing the ability of climate science
to provide better information at the spatial and temporal scale at which decisions are made but also supporting the training of highly skilled workers
who can translate and disseminate this information to the public and private
actors seeking to use it (Fiedler et al. 2021; Kopp 2021). The Biden-Harris
Administration has begun this work through development of the Climate
Mapping for Resilience and Adaptation tool (CMRA, n.d.).

Information Asymmetries
Information asymmetries occur when one party in a transaction has more
information than another, which can lead to price distortions and market
failure (Akerlof 1970). In the climate risk context, information asymmetries
could arise when buyers and sellers have varying knowledge about an
asset’s climate change exposure, such as a property’s propensity to flood
or wildfire risk. In this regard, Keenan and Bradt (2020) and Ouazad and
Kahn (2022) find evidence of information asymmetries operating in coastal
mortgage markets, with lenders shifting risks of flooding-exposed properties
Opportunities for Better Managing Weather Risk in the Changing Climate | 293

onto Fannie Mae and Freddie Mac or to other lenders that have less knowledge of local risk exposure.
Mandatory disclosure laws are one tool governments can use to correct information asymmetries. These require parties to share particular types
of information relevant to asset valuation. A proposed rule by the U.S.
Securities and Exchange Commission (2022) would add climate-related
risks to the required disclosures of publicly traded companies. Disclosure
laws related to property transactions are governed at the State level and
vary widely in the degree to which they require sellers to disclose climaterelated risks (particularly flooding) to buyers (Hino and Burke 2021; Natural
Resources Defense Council, n.d.). Only a handful of States—notably,
Louisiana and Texas—have strong flood disclosure regulations to protect
buyers, while 16 have no disclosure requirements at all (Federal Emergency
Management Agency 2022).
Building codes and standards can be another way to protect buyers
from information asymmetries in property markets. Details of building
construction that determine how vulnerable a structure is to water, wind,
and wildfire hazards can be highly technical and not easily understood by
home buyers. This creates a market failure, if more resilient but also more
expensive construction cannot command a premium because these qualities
are not easily observable. By setting common minimum standards for all
construction, governments can prevent a race to the bottom in building quality (White House 2022f).
Adverse selection in insurance markets—where buyers know more
about their risk than providers, leading more risky individuals to opt into
insurance at a given price—is another form of information asymmetry.
There is some evidence of adverse selection operating in U.S. disaster insurance markets, where those with a lower flood risk (e.g., from an elevated
house) are less likely to purchase coverage (Wagner 2022; Bradt, Kousky,
and Wing 2021). In the absence of corrective policies, adverse selection
can lead to an unraveling of insurance markets as insurers raise rates to
cover the higher risk, driving lower-risk individuals from the marketplace
and further concentrating insured risks. This market failure historically has
been addressed through purchase mandates that solve the adverse selection
problem by requiring everyone to participate in insurance markets thereby
pooling the risk. Insurance mandates can also improve welfare in settings
without adverse selection but where individuals systematically underestimate their exposure to a catastrophic loss (Wagner 2022).

Externalities and Public Goods
Although many adaptation actions are private goods that result from individual households and firms weighing their own costs and benefits, important

294 |

Chapter 9

public adaptation goods will also be underprovided without government
action (Mendelsohn 2000). Examples include building infrastructure for
coastal protection; expanding the tree cover for urban cooling and other
nature-based forms of climate adaptation; basic research relevant to adaptation, such as developing improved crop varieties; and the protection of
species or ecosystems threatened by the changing climate. But unlike greenhouse gas mitigation, which is a global public good, many public adaptation
benefits are more local and therefore may be best provided by State, Tribal,
and local governments. The Federal Government may still have a role to
play in coordinating, supplying information, and reducing transaction costs.
In addition to pure public goods, some private adaptations may involve
externalities that require collective action by communities or higher levels
of government. This is particularly the case for environmental goods and
natural resource management, where preexisting inefficiencies related to the
lack of established property rights can be exacerbated by climate change.
Examples include the spillover effects of coastal protection onto neighbors,
and the increasing drawing down of open-access aquifers to meet higher
crop water requirements resulting from warmer temperatures (Beasley and
Dundas 2021; Gopalakrishnan et al. 2017; Rosa et al. 2020). Threats to
biodiversity are similarly characterized by market failures and problems of
public good provision. Though habitat conversion, pollution, and invasive
species are primary drivers of species endangerment, climate change further
stresses threatened species and exacerbates these drivers (Tilman et al. 2017;
Moore et al. 2022; Hashida et al. 2020). In these settings, reforms to more
sustainably manage natural resources will have ancillary benefits in reducing climate damage and can be thought of as an important tool for managing
climate risks.

Credit Constraints
A number of adaptive actions require upfront capital investments that
will pay off gradually. Examples range from homeowners installing airconditioners to farmers adopting irrigation to manage increasing heat and
droughts to major community projects to slow or prevent coastal erosion.
If actors are unable to finance these investments at competitive interest
rates, they will be underprovided relative to an optimal level, meaning that
governments may play an important role in alleviating credit constraints
to enable adaptation. Examples of relevant government programs include
targeted subsidies for home efficiency upgrades such as certain provisions
in the Bipartisan Infrastructure Law and the Inflation Reduction Act (box
9-1). This will be particularly compelling for populations that have been historically underserved by financial institutions and for financing adaptations

Opportunities for Better Managing Weather Risk in the Changing Climate | 295

where there is a strong social interest in ensuring adaptation to alleviate
climate-related risks.
Credit constraints do not just apply to individuals and business but
may also limit the ability of States and municipalities to fund major public
adaptation projects. Most States require voter approval for general obligation bonds backed by future tax revenues or have other constitutional limits
on long-term debt issuance, constraints that do not apply to the Federal
Government (Kiewiet and Szakaty 1996). The costs of major infrastructure
projects to manage growing climate risks may be beyond the fiscal reach
of many local governments, particularly if climate risks are simultaneously
jeopardizing future tax revenue. Low-cost loans or grants from the Federal
Government may therefore be important sources of financing for municipalities and States seeking to make major investments to reduce climate
risks, a recent example being pilot grants for voluntary relocation of Tribal
communities made by the U.S. Department of the Interior (2022).

Moral Hazard
“Moral hazard” refers to a phenomenon in insurance markets whereby
access to insurance lowers incentives for risk-reducing or risk-avoiding
behavior, increasing overall hazard costs. Settings with pervasive moral hazard can see higher insurance premiums or an unraveling of private insurance
markets. Other programs that shift the costs of hazards—whether through
subsidized insurance, publicly provided protection, or loan guarantees—can,
unless carefully structured, also create moral hazard distortions. Annan and
Schlenker (2015) estimate that the moral hazard associated with subsidized
crop insurance has reduced the incentives of farmers to adapt to extreme
heat and has led to a higher sensitivity to heat in insured crops. Baylis and
Boomhower (2023) find that the implicit subsidy from public wildland fire
fighting can reach 20 percent of home values in low-density, wildfire-prone
areas. Similar indirect subsidies for building in flood-prone areas have likely
led to more people and property in these risky areas (Panjwani 2022).
The moral hazard problem does not only apply to individuals; it can
also apply to State and local governments. Many decisions relevant to reducing the costs of weather-related disasters—including zoning, building codes,
and land-use management—are made at the State or local level (figure
9-3). State and local governments making these decisions see benefits in
growth and tax revenues, but they are shielded from the full costs of risky
development because of the Federal Government’s assumption of disaster
risk through the NFIP and disaster relief programs. Several States have seen
rapid development in areas exposed to coastal flooding by sea-level rise,
with local governments permitting two or three times more construction in
these risky areas than in safer regions (Climate Central and Zillow 2018).

296 |

Chapter 9

Reforming Federal programs and private insurance contracts to incentivize
or require risk-reducing activities or to place a higher share of costs on those
undertaking the risk (e.g., through higher deductibles) can help mitigate
issues of moral hazard (Kousky 2022, 38).

Four Potential Pillars of the Federal Adaptation
Strategy and Major Policy Opportunities
Adaptation to climate change is characterized by complex governance across
multiple scales, with relevant decisionmaking operating at the national,
State, Tribal, and local levels (figure 9-3). Given the complex regulatory and
planning processes relevant to managing climate risks and the local nature
of many adaptation benefits, this nested governance structure may be appropriate (Dietz, Ostrom, and Stern 2003). Federal adaptation policy needs to
be developed with an appreciation of the multilayered, complex regulatory
systems that characterize adaptation-relevant policy areas. This final sec2/6/2023
tion outlines four broad, cross-cutting roles for further Federal adaptation
efforts to support specific policymaking across these many issue areas, and
it highlights major opportunities for action in each area.

Producing and Disseminating Knowledge about Climate Risk
As firms, local governments, and individuals increasingly seek to account
for climate change in their planning and investments, actionable information
will be a necessary input. From home buyers deciding where to move to
local governments planning new stormwater drainage to businesses looking to disclose their climate risk exposure, actors across the economy will

Local

Trade

Defense

Immigration

Public lands and
natural resource
management

Health and safety

Disaster aid and
recovery

Regulation of insurance
markets

Real estate disclosure

Building codes

State

Land use planning and
zoning

Federal

Infrastructure planning
and regulation

Figure 9-3. Governance of Climate Risk Is Complex and Multiscale

Sources: Howe et al. (2002); CEA calculations.

Opportunities for Better Managing Weather Risk in the Changing Climate | 297

require high-quality, trusted, and accessible climate impact information.
This is essential “informational infrastructure”—technical information
produced at high fixed costs but with broad applicability and value. Thanks
to strong Federal support, the United States is a global leader in climate science. But the modeling tools used to understand the global climate system
are not yet designed to deliver the decision-specific information needed by
most stakeholders to manage climate risk (Fiedler et al. 2021; Pitman et al.
2022; ASCE 2018, 7).
Major opportunity: invest in the Federal capacity for catastrophic
climate risk modeling. The U.S. government has an opportunity to lead the
world in developing a high-performance public capability for catastrophic
climate risk modeling. Managing evolving climate risks will require new
scientific approaches that combine insights from climate models with other
tools, such as statistical modeling and detailed engineering data to produce
decision-relevant climate information tailored to the needs of stakeholders
across the United States (Pitman et al. 2022). Catastrophe modeling is used
in the insurance industry to understand the risks of extreme events, but it is
done by just a few companies, is expensive to access, and can be difficult
to evaluate (ModEx, n.d.). Given both the growing role of the public sector
in absorbing climate risk and the need for many actors—ranging from State
and local governments to homeowners to general businesses—to understand
their exposure, information that is publicly available, credible, and from
trusted sources is urgently needed. The U.S. government has the opportunity
to build on its existing foundation of excellence in climate modeling and
Earth system sciences to develop this critical capacity.

Long-Term Planning for the Climate Transition
Long-term, forward-looking planning that anticipates coming climate
change is necessary to avoid unnecessary losses and destabilizing effects.
Neumann and others (2021) estimate that proactive adaptations that anticipate future climate can reduce the costs of climate change for the United
States’ road, rail, and coastal infrastructure by a factor of between 3 and
6 by 2090, compared with purely reactive adaptation; Diaz (2016) finds
a similar magnitude of savings for forward-looking adaptation for global
coastal defenses.
The Federal Government, in a number of its capacities, from the
Social Security Administration to the management of National Parks, has a
particular role in the long-term stewardship of U.S. assets. It regularly makes
decisions that will have consequences for decades if not centuries into the
future. Planning across all affected Federal agencies should recognize the
effects of climate change that are already apparent and are expected to intensify well into the future. Exposure to climate hazards should be incorporated

298 |

Chapter 9

into an agency’s enterprise risk management process. For example, the U.S.
Department of Defense (2021b) is planning to integrate climate risk into all
relevant hazard threat assessments.
In addition, high-level strategic planning will be essential to identify
critical risks that climate change poses for agencies’ missions, high-priority
opportunities to address these risks, and the additional resources or legislative changes that may be required to realize these opportunities. Current and
future climate change effects may necessitate difficult trade-offs involving many stakeholders with conflicting interests. Timely work to identify
principles and priorities that will guide agencies will improve coordination,
identify necessary reforms that may take a long time to implement, and
ultimately lower costs and improve effectiveness, a motivation behind the
ongoing agency climate adaptation planning process (box 9-1).
Clarity at the Federal level regarding priorities for funding public
adaptation efforts is important for driving action by State and local governments. Estimated costs just for protection from sea-level rise are large,
and are likely far beyond the means of many coastal communities (Diaz
2016). Other costs of responding to droughts, wildfires, and inland floods
will further strain government budgets. As described above, Federal loans
and grants will be essential to alleviate credit constraints for State and local
governments, but these resources are necessarily limited. Establishing clear
funding priorities and resolving uncertainty over which protection costs the
Federal Government will finance can assist State and local actors in their
own planning for the climate transition.
Major opportunity: use access to Federal funds to incentivize subnational adaptive reforms. Many policies critical to building long-term
resilience to natural disasters are controlled at the State or local level (figure
9-3). However, Federal funds flowing through States both directly and indirectly finance long-lived, climate-exposed infrastructure and development
projects (CBO 2018). If physical climate risks are not fully integrated into
agency enterprise risk management, these investments risk underperforming and becoming stranded. Similarly, decisions to convert land subject to
wildfires or flooding to developed uses implicates the Federal Government
via its various risk-absorbing functions, such as mortgage guarantees, flood
insurance, and disaster management and response. Reforms to zoning, building codes, insurance markets, and residential disclosures can all have major
benefits in reducing the costs of disasters.
Climate-relevant Federal investments can be tied to the enactment
of adaptive reforms that will protect both affected communities and the
Federal budget. For example, FEMA has proposed reforms of the NFIP
to Congress that would, if enacted, condition participation in the program
on the development of community-level flood disclosure requirements for
property transactions (U.S. Department of Homeland Security 2022), while
Opportunities for Better Managing Weather Risk in the Changing Climate | 299

provisions of the Bipartisan Infrastructure Law lower the non-Federal share
of certain grants for transportation projects prioritized in a State’s Resilience
Improvement Plan (U.S. Department of Transportation 2022).

Ensuring the Accurate Pricing of Climate Risk
Provided that actionable and credible information on climate risk is available, prices would be expected to adjust, sending accurate signals to actors
to reallocate investment and production in response to and in anticipation
of the changing climate. However, market failures arising from information
asymmetries or misaligned incentives can distort these signals and require
a policy response. One role of the Federal adaptation strategy should be to
identify and correct these market failures to enable stronger market signals
that would guide adaptive decisions over the longer term. Specific market
failures relevant to adaptation and policy tools to address them were discussed in the previous section; they include information provision, disclosure requirements, building standards, and insurance purchase mandates. An
important example is the recent reform of pricing in the NFIP, termed Risk
Rating 2.0, which prices policies based on individualized flood risk assessments while continuing to provide discounts for investments by individuals
or communities that lower flood costs (CRS 2022).
Market mechanisms can play an important role in allocating resources
efficiently and sending price signals to market actors on the scarcity of
resources. In places where markets are missing or incomplete, climatechange-induced scarcity could exacerbate existing distortions—meaning
that reforms to expand market access or establish property rights over
common-pool resources could lower total costs, one example being the
allocation of water use in California (Arellano-Gonzalez et al. 2021). Even
in the absence of markets, using mechanisms such as auctions to allocate
resources cost-effectively could be a useful strategy to manage scarcity
under climate change (Hagerty and Leonard 2022).
Major opportunity: develop quality and transparency standards for
climate data and the modeling used in market transactions. An important
part of supporting the integration of physical climate risks into market prices
will be oversight of the quality of climate information being used. Pricing
climate risk requires the use of specialized modeling tools, and evaluating
the quality of this information is a technical and highly specialized skill.
Use of proprietary models of natural hazard risk that cannot be evaluated
by the expert community as an input into significant regulatory decisions
has caused tensions in the past, particularly in the insurance industry (Xu,
Webb, and Evans 2019). Developing minimum standards and reporting
requirements for the climate data used to inform significant investment
decisions could help build trust, ensure quality, and enable broader adoption.

300 |

Chapter 9

This is particularly important when insurance contracts are leveraged into
more complex financial instruments, such as catastrophe bonds and other
insurance-linked securities, or when used as input into scenario exercises
central banks are beginning to use to evaluate climate risks to the financial
system (Insurance Information Institute, n.d.–b; Braun and Kousky 2021;
U.S. Federal Reserve 2023; Financial Stability Board 2022). Oversight and
evaluation of the Earth system models used for significant economic or
regulatory decisionmaking could be important in preventing natural disasters from triggering more systemic failures across the financial system.

Protecting the Vulnerable
Climate change is expected to increase weather-related hazards for many
Americans, but its effects will not likely be felt equally (see box 9-2 above).
Low-income and disadvantaged communities are both more exposed to
climate effects (e.g., through working in industries exposed to extreme heat,
such as agriculture and construction) and lack assets that can be drawn on
to smooth the costs of weather-related disasters. In the absence of policies
addressing the needs of low-income and marginalized communities, preexisting vulnerabilities—such as inadequate health care, poor-quality or
overcrowded housing, and food insecurity—will likely interact with climate
change effects to worsen inequalities. Addressing these underlying vulnerabilities and developing policies targeted at disadvantaged populations
should be a critical part of an effective U.S. adaptation strategy, building on
the robust set of existing programs within the Biden-Harris Administration
targeted at low-income and disadvantaged communities (boxes 9-1 and 9-2).
As prices adjust to reflect changing climate risks, this could present challenges for low-income communities, for whom higher prices are
particularly burdensome. For instance, low-income households are already
less likely to have flood insurance and almost 9 percent of policyholders in FEMA-defined 100 year floodplains pay more than 5 percent of
their income for flood insurance premiums and fees (U.S. Department of
Homeland Security 2018). To the extent possible, rather than restricting
price adjustments, which would blunt the incentive for private risk reduction
and increase risks over the long term, policies should seek to address these
adverse distributional effects via targeted lump sum transfers. More generally, policies that seek to accelerate income growth and increase access to
wealth-building opportunities for the poorest Americans should be thought
of as broadly adaptive. Although by no means the main goal of programs
that may be primarily focused on educational opportunities, housing affordability, or poverty alleviation, by increasing resources available to the most
vulnerable that can be drawn on to manage the effects of climate shocks,

Opportunities for Better Managing Weather Risk in the Changing Climate | 301

they should lower overall vulnerability and decrease climate costs over the
long term.
Major opportunity: develop criteria for public adaptation funding that
reflect the social value of investments. Criteria for the prioritization and
evaluation of public adaptation projects that accurately reflect the social
benefits of these investments should be developed. Historically, public
investments in flood defenses or community risk reductions have used the
value of protected property as a key metric in evaluating project benefits
(McGee 2021). However, inequality in property values and ownership in the
United States reflects decades of exclusion of racial minorities from homeownership and public investment (Rothstein 2017). Evaluating the benefits
of climate protections solely using property values is unlikely to capture the
full, multidimensional benefits of these projects and risks perpetuating these
historical injustices and exacerbating differences in vulnerability (Martinich
et al. 2013). Additional criteria that capture differential vulnerability and
variations in the extent to which communities are able to self-insure and
recover from damage could be developed to assess project outcomes.
Major opportunity: reenvisioning social insurance under climate
change. The increasing frequency and intensity of climate-change-related
disasters, along with the disruptions and dislocations required to adjust to
changing conditions, will likely challenge the policies and programs that
spread risks and protect the vulnerable as never before. Understanding the
burden this will place on traditional social insurance programs in the United
States and identifying reforms to strengthen the social safety net is essential
in this era of climate change. In addition, a process for reimagining the
public role in catastrophe management and social solidarity in the face of
rising risks is also urgently needed. The United States’ current approach—
managing catastrophic perils in a piecemeal way with fiscally unstable
public insurance programs—will only become more problematic as climate
change continues. U.S. policymakers should seriously consider models from
other countries, where governments act as backstop reinsurers, capping
catastrophic losses in the private sector to crowd in private financing while
also spreading the risk broadly through mandated natural disaster coverage
(Kousky 2022, 53–56).

Conclusion
The United States is making historic investments that will transform the
American energy system to address the challenge of climate change and
meet President Biden’s goal of halving U.S. greenhouse gas emissions by
2030. These investments are central to the global effort to rein in emissions
and limit the effects of climate change.

302 |

Chapter 9

Even with the massive shift already under way in global energy production, models show the climate will continue changing for decades (IPCC
2021; Meinshausen et al. 2022). Shifting weather patterns will likely subject
communities to unprecedented extremes making the management of weather
risks and natural disasters increasingly difficult. A large body of literature,
as well as the mounting costs of climate-change-associated extreme events,
shows that the U.S. economy is sensitive to the effects of climate change.
Without forward-looking adaptive planning that anticipates changing conditions, climate costs will very likely keep growing, compounding risks to
infrastructural, social, economic, and financial systems across the United
States.
Managing the risks of climate change is a complex challenge across
multiple policy areas, characterized by varying market failures and nested
governance structures. However, the particular capacities, authorities, and
interests of the Federal Government mean that it should play an essential role
in leading adaptation policy development and structuring the responses of
subnational and private actors to emerging climate risk. An effective Federal
adaptation strategy includes producing and disseminating knowledge about
climate risk, long-term planning for the climate transition, ensuring accurate
pricing of climate risks, and protecting the vulnerable. Managing the risks
and consequences of the warming planet, along with continued efforts to
reduce greenhouse gas emissions, will allow the Nation to face the climate
challenges of the 21st century.

Opportunities for Better Managing Weather Risk in the Changing Climate | 303

References
Chapter 1
Acemoglu, D., D. Autor, and D. Lyle. 2004. “Women, War, and Wages: The Effect of
Female Labor Supply on the Wage Structure at Midcentury.” Journal of
Political Economy 112, no. 3: 497–551. https://doi.org/10.1086/383100.
ACLU (American Civil Liberties Union). 2021. “How Artificial Intelligence Can Deepen
Racial and Economic Inequities.” By Olga Akselrod. https://www.aclu.org/
news/privacy-technology/how-artificial-intelligence-can-deepen-racial-andeconomic-inequities.
Armstrong, M. 2022. “The Market for Smart Home Devices Is Expected to Boom Over
the Next 5 Years.” World Economic Forum. https://www.weforum.org/
agenda/2022/04/homes-smart-tech-market.
Atasoy, H. 2013. “The Effects of Broadband Internet Expansion on Labor Market
Outcomes.” ILR Review 66, no. 2. https://doi.org/10.1177/001979391306600202.
Auffhammer, M., P. Baylis, and C. Hausman. 2017. “Climate Change Is Projected to
Have Severe Impacts on the Frequency and Intensity of Peak Electricity
Demand Across the United States.” Proceedings of the National Academy of
Sciences 114, no. 8. https://doi.org/10.1073/pnas.1613193114.
Bank of England. 2021. “Key Elements of the 2021 Biennial Exploratory Scenario:
Financial Risks from Climate Change.” https://www.bankofengland.co.uk/
stress-testing/2021/key-elements-2021-biennial-exploratory-scenariofinancial-risks-climate-change.
Bastien-Olvera, B., and F. Moore. 2021. “Use and Non-Use Value of Nature and the
Social Cost of Carbon.” Nature Sustainability 4, no. 1: 101–8. https://doi.
org/10.1038/s41893-020-00615-0.
Blau, F., and L. Kahn. 2013. “Female Labor Supply: Why Is the United States Falling
Behind?” American Economic Review 103, no. 3: 251–56. https://doi.
org/10.1257/aer.103.3.251.
BLS (U.S. Bureau of Labor Statistics). 2019. “Number and Percent of Eldercare
Providers Who Were Parents of Household Children Under Age 18 by Sex and
Selected Characteristics, Averages for the Combined Years 2017–2018.” https://
www.bls.gov/news.release/elcare.t09.htm.
———. 2021. “Employment by Detailed Occupation.” https://www.bls.gov/emp/tables/
emp-by-detailed-occupation.htm.

305

———. 2022a. “Employee Benefits in the United States.” https://www.bls.gov/ncs/ebs/
benefits/2022/home.htm.
———. 2022b. “Computer and Information Technology Occupations.” https://www.bls.
gov/ooh/computer-and-information-technology/home.htm.
Blunden, J. 2014. “2013 State of the Climate: Carbon Dioxide Tops 400 PPM.” National
Oceanic and Atmospheric Administration. https://www.climate.gov/newsfeatures/understanding-climate/2013-state-climate-carbon-dioxide-tops400-ppm.
Bolt, J., and J. van Zanden. 2020. “Maddison Style Estimates of the Evolution of the
World Economy.” Groningen Growth and Development Centre. http://reparti.
free.fr/maddi2020.pdf.
Botzen, W., J. van den Bergh, and L. Bouwer. 2010. “Climate Change and Increased Risk
for the Insurance Sector: A Global Perspective and an Assessment for the
Netherlands.” Natural Hazards 52: 577–98. https://doi.org/10.1007/
s11069-009-9404-1.
Brunetti, C., B. Dennis, D. Gates, D. Hancock, D. Ignell, E. Kiser, G. Kotta, A. Kovner,
R. Rosen, and N. Tabor. 2021. “Climate Change and Financial Stability.” Board
of Governors of the Federal Reserve System. https://www.federalreserve.gov/
econres/notes/feds-notes/climate-change-and-financial-stability-20210319.html.
Brynjolfsson, E., and G. Petropoulos. 2021. “The Coming Productivity Boom.” MIT
Technology Review. https://www.technologyreview.com/2021/06/10/1026008/
the-coming-productivity-boom/.
Burke, A., A. Okrent, and K. Hale. 2022. “The State of U.S. Science and Engineering
2022.” National Science Foundation. https://ncses.nsf.gov/pubs/
nsb20221/u-s-and-global-research-and-development.
Burke, M., S. Hsiang, and E. Miguel. 2015. “Global Non-Linear Effect of Temperature
on Economic Production.” Nature 527: 235–39. https://doi.org/10.1038/
nature15725.
Capka, J. 2006. “Celebrating 50 Years: The Eisenhower Interstate Highway System.”
U.S. Department of Transportation. https://www7.transportation.gov/testimony/
celebrating-50-years-eisenhower-interstate-highway-system.
Carleton, T., A. Jina, M. Delgado, M. Greenstone, T. Houser, S. Hsiang, A. Hultgren, R.
Kopp, K. McCusker, I. Nath, J. Rising, A. Rode, H. Kwon Seo, A. Viaene, J.
Yuan, and A. Tianbo Zhang. 2022. “Valuing the Global Mortality Consequences
of Climate Change Accounting for Adaptation Costs and Benefits.” Quarterly
Journal of Economics 137, no. 4: 2037–105. https://doi.org/10.1093/qje/
qjac020.
Carter, S., S. Gartner, M. Haines, A. Olmstead, R. Sutch, and G. Wright, eds. 2006.
Historical Statistics of the United States. Cambridge: Cambridge University
Press. https://hsus.cambridge.org/HSUSWeb/HSUSEntryServlet.

306 |

References

Climate Central and Zillow. 2018. “Ocean at the Door: New Homes and the Rising Sea.”
http://assets.climatecentral.org/pdfs/Nov2018_Report_OceanAtTheDoor.
pdf?pdf=OceanAtTheDoor-Report.
Council of Economic Advisers. 2015. Economic Report of the President. https://www.
whitehouse.gov/wp-content/uploads/2021/07/2015-ERP.pdf.
Council of Economic Advisers and Office of Management and Budget. 2022. “ClimateRelated Macroeconomic Risks and Opportunities.” https://www.whitehouse.
gov/wp-content/uploads/2022/04/CEA_OMB_Climate_Macro_WP_2022430pm.pdf.
Czernich, N., O. Falck, T. Kretschmer, and L. Woessmann. 2011. “Broadband
Infrastructure and Economic Growth.” Economic Journal 121, no. 552: 505–32.
https://doi.org/10.1111/j.1468-0297.2011.02420.x.
DeLong, B. 2022. Slouching Towards Utopia: An Economic History of the Twentieth
Century. New York: Basic Books. https://www.basicbooks.com/titles/jbradford-delong/slouching-towards-utopia/9780465019595/.
Dieppe, A. 2020. “The Broad-Based Productivity Slowdown, in Seven Charts.” World
Bank Blogs. https://blogs.worldbank.org/developmenttalk/
broad-based-productivity-slowdown-seven-charts.
Digital Competition Expert Panel. 2019. “Unlocking Digital Competition.” https://assets.
publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/
file/785547/unlocking_digital_competition_furman_review_web.pdf.
Elliott, D. 2022. “Insurances Woes in Coastal Louisiana Make Hurricane Recovery
Difficult.” National Public Radio, July 27. https://www.npr.
org/2022/07/27/1113639292/
insurances-woes-in-coastal-louisiana-make-hurricane-recovery-difficult.
File, T. 2013. “Computer and Internet Use in the United States: Population
Characteristics.” U.S. Census Bureau. https://www.census.gov/content/dam/
Census/library/publications/2013/demo/p20-569.pdf.
Financial Stability Oversight Council. 2021. “Report on Climate-Related Financial Risk.”
U.S. Department of the Treasury. https://home.treasury.gov/system/files/261/
FSOC-Climate-Report.pdf.
FRED (Federal Reserve Economic Data). 2022. “Gross Private Domestic Investment
(GPDI).” Federal Reserve Bank of St. Louis. https://fred.stlouisfed.org/series/
GPDI.
Gelzinis, G., and G. Steele. 2019. “Climate Change Threatens the Stability of the
Financial System.” Center for American Progress. https://www.
americanprogress.org/article/
climate-change-threatens-stability-financial-system/.
Goldin, C. 2006. “The Quiet Revolution That Transformed Women’s Employment,
Education, and Family.” AEA Papers and Proceedings, 1–21. https://scholar.
harvard.edu/files/goldin/files/the_quiet_revolution_that_transformed_womens_
employment_education_and_family.pdf.
References | 307

Goldin, C., and L. Katz. 2009. “Why the United States Led in Education: Lessons from
Secondary School Expansion, 1910 to 1940.” In Human Capital and
Institutions, edited by D. Eltis, F. Lewis, and K. Sokoloff. Cambridge:
Cambridge University Press. https://scholar.harvard.edu/lkatz/publications/
why-united-states-led-education-lessons-secondary-schoolexpansion-1910-1940.
Goodwin, D. 2001. “The Way We Won: America’s Economic Breakthrough During World
War II.” American Prospect. https://prospect.org/health/
way-won-america-s-economic-breakthrough-world-war-ii/.
Gordon, R. 2016. The Rise and Fall of American Growth. Princeton, NJ: Princeton
University Press. https://press.princeton.edu/books/hardcover/9780691147727/
the-rise-and-fall-of-american-growth.
Graff Zivin, J., and M. Neidell. 2012. “The Impact of Pollution on Worker Productivity.”
American Economic Review 102, no. 7: 3652–73. https://doi.org/10.1257/
aer.102.7.3652.
Groningen Growth and Development Centre. No date. “Maddison Database 2010.”
https://www.rug.nl/ggdc/historicaldevelopment/maddison/releases/
maddison-database-2010.
Guiso, L., Sapienza, P., and L. Zingales. 2006. “Does Culture Affect Economic
Outcomes?” Journal of Economic Perspectives 20, no. 2: 23–48. https://pubs.
aeaweb.org/doi/pdfplus/10.1257/jep.20.2.23.
Hernandez, R. 2017. “Online Job Search: The New Normal.” U.S. Bureau of Labor
Statistics. https://www.bls.gov/opub/mlr/2017/beyond-bls/online-job-search-thenew-normal.htm.
Herrnstadt, E., and T. Dinan. 2020. “CBO’s Projection of the Effect of Climate Change
on U.S. Economic Output.” Working paper, Congressional Budget Office.
https://www.cbo.gov/system/files/2020-09/56505-Climate-Change.pdf.
Hsieh, C., E. Hurst, C. Jones, and P. Klenow. 2019. “The Allocation of Talent and U.S.
Economic Growth.” Econometrica 87, no. 5: 1439–74. https://doi.org/10.3982/
ECTA11427.
Iglesias, V., A. Braswell, M. Rossi, M. Joseph, C. McShane, M. Cattau, M. Koontz, J.
McGlinchy, R. Nagy, J. Balch, S. Leyk, and W. Travis. 2021. “Risky
Development: Increasing Exposure to Natural Hazards in the United States.”
Earth’s Future 9, no. 7. https://doi.org/10.1029/2020EF001795.
IPCC (Intergovernmental Panel on Climate Change). 2014. Climate Change 2014
(Assessment Review 5): Synthesis Report, edited by R. Pachauri, L. Mayer, and
Core Writing Team. Geneva: IPCC. https://www.ipcc.ch/site/assets/
uploads/2018/02/SYR_AR5_FINAL_full.pdf.
Jessoe, K., D. Manning, and J. Taylor. 2018. “Climate Change and Labour Allocation in
Rural Mexico: Evidence from Annual Fluctuations in Weather.” Economic
Journal 128, no. 608: 230–61. https://doi.org/10.1111/ecoj.12448.

308 |

References

Kalkuhl, M., and L. Wenz. 2020. “The Impact of Climate Conditions on Economic
Production. Evidence from a Global Region of Panels.” Journal of
Environmental Economics and Management 103. https://doi.org/10.1016/j.
jeem.2020.102360.
Kao, S., M. Ashfaq, D. Rastogi, S. Gangrade, R. Uría Martínez, A. Fernandez, G.
Konapala, N. Voisin, T. Zhou, W. Xu, H. Gao, B. Zhao, and G. Zhao. 2022.
“The Third Assessment of the Effects of Climate Change on Federal
Hydropower.” Oak Ridge National Laboratory. https://info.ornl.gov/sites/
publications/Files/Pub168510.pdf.
Kober, N., and D. Rentner. 2020. “History and Evolution of Public Education in the U.S.”
Center on Education Policy. https://files.eric.ed.gov/fulltext/ED606970.pdf.
Lan, X., P. Tans, and K. Thoning. 2022. “Trends in Globally-Averaged CO2 Determined
from NOAA Global Monitoring Laboratory Measurements.” Global Monitoring
Laboratory. https://gml.noaa.gov/ccgg/trends/gl_data.html.
Lara, R. 2019. “Fact Sheet: Impact of Wildfires on Insurance Non-Renewals and
Availability.” California Department of Insurance. http://www.insurance.
ca.gov/0400-news/0100-press-releases/2019/upload/nr063_factsheetwildfire.pdf.
Lebergott, S. 1966. “Labor Force and Employment, 1800–1960.” In Output, Employment,
and Productivity in the United States after 1800, edited by D. Brady, 117–204.
Cambridge, MA: National Bureau of Economic Research. https://www.nber.org/
system/files/chapters/c1567/c1567.pdf.
Lee, J. 2019. “Lessons of East-Asia’s Human Capital Development.” Project Syndicate.
https://www.project-syndicate.org/commentary/human-capital-east-asiadevelopment-strategy-education-by-lee-jong-wha-2019-01
Lüthi, D., M. Le Floch, B. Bereiter, T. Blunier, J. Barnola, U. Siegenthaler, D. Raynaud,
J. Jouzel, H. Fischer, K. Kawamura, and T. Stocker. 2008. “High-Resolution
Carbon Dioxide Concentration Record 650,000–800,000 Years Before Present.”
Nature 453: 379–82. https://doi.org/10.1038/nature06949.
Maiello, M. 2017. “Diagnosing William Baumol’s Cost Disease.” Chicago Booth Review.
https://www.chicagobooth.edu/review/diagnosing-william-baumolscost-disease.
Mankiw, N. 2010. Macroeconomics, 7th ed. New York: Worth. https://jollygreengeneral.
typepad.com/files/n.-gregory-mankiw-macroeconomics-7th-edition-2009.pdf.
Mauro, P. 1995. “Corruption and Growth.” Quarterly Journal of Economics 110, no. 3:
681–712. https://www.jstor.org/stable/pdf/2946696.pdf.
Mazzucato, M. 2013. The Entrepreneurial State: Debunking Public vs. Private Sector
Myths. New York: Anthem Press. https://marianamazzucato.com/books/
the-entrepreneurial-state.
Migration Policy Institute. No date. “U.S. Immigrant Population and Share over Time,
1850–Present.” https://www.migrationpolicy.org/programs/data-hub/charts/
immigrant-population-over-time.

References | 309

Missirian, A., and W. Schlenker. 2017. “Asylum Applications Respond to Temperature
Fluctuations.” Science 358, no. 6370: 1610–14. https://www.science.org/
doi/10.1126/science.aao0432.
Morrissey, T. 2017. “Child Care and Parent Labor Force Participation: A Review of the
Research Literature.” Review of Economics of the Household 15, no. 1: 1–24.
https://doi.org/10.1007/s11150-016-9331-3.
Muro, M., S. Liu, J. Whiton, and S. Kulkarni. 2017. “Digitalization and the American
Workforce.” Brookings Institution. https://www.brookings.edu/research/
digitalization-and-the-american-workforce/.
National Archives. No date. “Executive Order 8802: Prohibition of Discrimination in the
Defense Industry (1941).” https://www.archives.gov/milestone-documents/
executive-order-8802.
National Center for Education Statistics. 1993. “120 Years of American Education: A
Statistical Portrait.” https://nces.ed.gov/pubs93/93442.pdf.
National Centers for Environmental Education. 2022. “United States Billion Dollar
Disaster Events 1980–2022 (CPI-Adjusted).” https://www.ncei.noaa.gov/access/
billions/time-series.
National Partnership for Women and Families. 2022. “State Paid Family and Medical
Leave Insurance Laws.” https://www.nationalpartnership.org/our-work/
resources/economic-justice/paid-leave/state-paid-family-leave-laws.pdf.
National World War II Museum. No date. “Research Starters: U.S. Military by the
Numbers.” https://www.nationalww2museum.org/students-teachers/studentresources/research-starters/research-starters-us-military-numbers.
Newell, R., B. Prest, and S. Sexton. 2021. “The GDP–Temperature Relationship:
Implications for Climate Change Damages.” Journal of Environmental
Economics and Management 108. https://doi.org/10.1016/j.jeem.2021.102445.
OECD (Organization for Economic Cooperation and Development). 2022a. “Gross
Domestic Spending on R&D.” https://data.oecd.org/rd/gross-domesticspending-on-r-d.htm.
———. 2022b. “LFS by Sex and Age: Indicators.” https://stats.oecd.org/index.
aspx?r=967539#.
Park, R., A. Behrer, and J. Goodman. 2021. “Learning Is Inhibited by Heat Exposure,
Both Internationally and Within the United States.” Nature Human Behavior 5:
19–27. https://doi.org/10.1038/s41562-020-00959-9.
Park, R., J. Goodman, M. Hurwitz, and J. Smith. 2020. “Heat and Learning.” American
Economic Journal: Economic Policy 12, no. 2: 306–39. https://doi.org/10.1257/
pol.20180612.
Partlow, J. 2022. “Disaster Scenarios Raise the Stakes for Colorado River Negotiations.”
Washington Post, December 17. https://www.washingtonpost.com/climateenvironment/2022/12/17/colorado-river-crisis-conference/.

310 |

References

Pew Research Center. 2021a. “Internet/Broadband Fact Sheet.” https://www.pewresearch.
org/internet/fact-sheet/internet-broadband/.
———. 2021b. “Digital Divide Persists Even as Americans with Lower Incomes Make
Gains in Tech Adoption.” https://www.pewresearch.org/fact-tank/2021/06/22/
digital-divide-persists-even-as-americans-with-lower-incomes-make-gains-intech-adoption/.
———. 2021c. “Home Broadband Adoption, Computer Ownership Vary by Race,
Ethnicity in the U.S.” https://www.pewresearch.org/fact-tank/2021/07/16/
home-broadband-adoption-computer-ownership-vary-by-race-ethnicity-inthe-u-s/.
Pfeiffer, D. 1995. “Ike’s Interstates at 50: Anniversary of the Highway System Recalls
Eisenhower’s Role as Catalyst.” Prologue Magazine 38, no. 2. https://www.
archives.gov/publications/prologue/2006/summer/interstates.html.
Ramirez, R. 2022. “The West’s Historic Drought Is Threatening Hydropower at Hoover
Dam.” CNN. https://www.cnn.com/2022/08/16/us/hoover-dam-hydropowerdrought-climate/index.html.
Ranson, M. 2014. “Crime, Weather, and Climate Change.” Journal of Environmental
Economics and Management 67, no. 3: 274–302. https://doi.org/10.1016/j.
jeem.2013.11.008.
Rennert, K., F. Errickson, B. Prest, L. Rennels, R. Newell, W. Pizer, C. Kingdon, J.
Wingenroth, R. Cooke, B. Parthum, D. Smith, K. Cromar, D. Diaz, F. Moore,
U. Müller, R. Plevin, A. Raftery, H. Sevcikova, H. Sheets, J. Stock, T. Tan, M.
Watson, T. Wong, and D. Anthoff. 2022. “Comprehensive Evidence Implies a
Higher Social Cost of CO2.” Nature 610, no. 1: 687–92. https://doi.
org/10.1038/s41586-022-05224-9.
Sablik, T. 2020. “Electrifying Rural America.” Federal Reserve Bank of Richmond.
https://www.richmondfed.org/publications/research/econ_focus/2020/q1/
economic_history.
Shen, K. 2021. “Who Benefits from Public Financing of Home Care for Low-Income
Seniors?” Working paper, Harvard University. https://scholar.harvard.edu/files/
kshen/files/caregivers.pdf.
Siminski, P., and R. Yetsenga. 2022. “Specialization, Comparative Advantage, and the
Sexual Division of Labor.” Journal of Labor Economics 40, no. 4. https://doi.
org/10.1086/718430.
Smith, A. 2023. “2022 U.S. Billion-Dollar Weather and Climate Disasters in Historical
Context.” U.S. Department of Commerce. https://www.climate.gov/newsfeatures/blogs/2022-us-billion-dollar-weather-and-climate-disastershistorical-context.
Spruk, R. 2019. “The Rise and Fall of Argentina.” Latin American Economic Review 28.
https://doi.org/10.1186/s40503-019-0076-2.
U.S. Census Bureau. 1975. “Energy.” In Historical Statistics of the United States,
Colonial Times to 1970, 811–33. Washington: U.S. Government Publishing
References | 311

Office. https://www2.census.gov/library/publications/1975/compendia/hist_
stats_colonial-1970/hist_stats_colonial-1970p2-chS.pdf.
———. 2021. “Selected Social Characteristics in the United States.” https://data.census.
gov/table?q=DP02:+SELECTED+SOCIAL+CHARACTERISTICS+IN+THE+
UNITED+STATES&g=0100000US&tid=ACSDP1Y2021.DP02.
———. 2023. “Quarterly Retail E-Commerce Sales, 3rd Quarter 2022.” https://www.
census.gov/retail/mrts/www/data/pdf/ec_current.pdf.
U.S. Securities and Exchange Commission. 2023. “Meta Platforms, Inc.: Form 10-K for
Fiscal Year Ended December 31, 2022.” EDGAR Database. https://www.sec.
gov/ix?doc=/Archives/edgar/data/1326801/000132680123000013/meta20221231.htm#i6df229dad1864210ab76200083e26819_79.
Weiss, T. 1999. “Estimates of White and Nonwhite Gainful Workers in the United States
by Age Group, Race, and Sex Decennial Census Years, 1800–1900.” Historical
Methods: A Journal of Quantitative and Interdisciplinary History 32, no. 1:
21–36. https://doi.org/10.1080/01615449909598924.
Weller, C. 2002. “Learning Lessons from the 1990s.” Economic Policy Institute. https://
www.epi.org/publication/webfeatures_viewpoints_l-t_growth_lessons/.
White House. 2022. “Fact Sheet: White House Releases First-Ever Comprehensive
Framework for Responsible Development of Digital Assets.” https://www.
whitehouse.gov/briefing-room/statements-releases/2022/09/16/
fact-sheet-white-house-releases-first-ever-comprehensive-framework-forresponsible-development-of-digital-assets/.
———. 2023. “Fact Sheet: Biden-Harris Administration Releases National Strategy to
Put Nature on the Nation’s Balance Sheet.” https://www.whitehouse.gov/ostp/
news-updates/2023/01/19/fact-sheet-biden-harris-administration-releases-nationalstrategy-to-put-nature-on-the-nations-balance-sheet/.
Woetzel, J., D. Pinner, H. Samandari, H. Engel, M. Krishnan, B. Boland, and C. Powis.
2020. “Climate Risk and Response: Physical Hazards and Socioeconomic
Impacts.” McKinsey Global Institute. https://www.mckinsey.com/~/media/
mckinsey/business%20functions/sustainability/our%20insights/climate%20
risk%20and%20response%20physical%20hazards%20and%20
socioeconomic%20impacts/mgi-climate-risk-and-response-full-report-vf.pdf.
World Bank. 1993. The East Asian Miracle: Economic Growth and Public Policy. New
York: Oxford University Press. https://documents1.worldbank.org/curated/
en/975081468244550798/pdf/multi-page.pdf.
World Economic Forum. 2020. “Nature Risk Rising: Why the Crisis Engulfing Nature
Matters for Business and the Economy.” https://www3.weforum.org/docs/
WEF_New_Nature_Economy_Report_2020.pdf.

312 |

References

Chapter 2
Aladangady, A., D. Cho, L. Feiveson, and E. Pinto. 2022. “Excess Savings during the
COVID-19 Pandemic.” Board of Governors of the Federal Reserve System.
https://www.federalreserve.gov/econres/notes/feds-notes/excess-savings-duringthe-covid-19-pandemic-20221021.html.
Ascari, G., and A. Sbordone. 2014. “The Macroeconomics of Trend Inflation.” Journal of
Economic Literature 52: 679–739. https://www.aeaweb.org/articles?id=10.1257/
jel.52.3.679.
Banerjee R., V. Boctor, A. Mehrotra, and F, Zampolli. 2022. “Fiscal Deficits and Inflation
Risks: The Role of Fiscal and Monetary Regimes.” Working paper, Bank for
International Settlements. https://www.bis.org/publ/work1028.pdf.
Bassetto, M. 2008. “Fiscal Theory of Price Level.” In The New Palgrave Dictionary of
Economics, edited by S. Durlauf and L. Blume. London: Palgrave. http://users.
nber.org/~bassetto/research/palgrave/ftheorypost.pdf.
Belz, S., L. Scheiner, and S. Campbell. 2022. “A Guide to the Hutchins Center Fiscal
Impact Measure.” Brookings Institution. https://www.brookings.
edu/2022/02/17/a-guide-to-the-hutchins-center-fiscal-impact-measure/.
Bernanke, B. 2007. “Inflation Expectations and Inflation Forecasting.” Transcript of
speech delivered at Monetary Economics Workshop of the National Bureau of
Economic Research Summer Institute, Cambridge, MA, July 10. https://www.
federalreserve.gov/newsevents/speech/Bernanke20070710a.htm.
———. 2022. “Inflation Expectations and Monetary Policy.” Speech delivered at
Inflation Expectations: Determinants and Consequences Conference, May 19.
https://www.nber.org/lecture/2022-inflation-expectations-determinants-andconsequence-keynote-ben-bernanke-inflation-expectations.
Biden, J. 2022. “Joe Biden: My Plan for Fighting Inflation.” Wall Street Journal, May 30.
https://www.wsj.com/articles/my-plan-for-fighting-inflation-joe-biden-gas-priceseconomy-unemployment-jobs-covid-11653940654?mod=opinion_major_pos5.
Bilbiie, F., G. Eggertsson, G. Primiceri, and A. Tambalotti. 2021. “‘Excess Savings’ Are
Not Excessive.” Liberty Street Economics, Federal Reserve Bank of New York.
https://libertystreeteconomics.newyorkfed.org/2021/04/
excess-savings-are-not-excessive/.
Blanchard, O., E. Cerutti, and L. Summers. 2015. Inflation and Activity: Two
Explorations and Their Monetary Policy Implications. NBER Working Paper
21726. Cambridge, MA: National Bureau of Economic Research. https://www.
nber.org/papers/w21726.
Boehm, C., and N. Pandalai-Nayar. 2022. “Convex Supply Curves.” American Economic
Review 112, no. 12: 3941–69. https://www.aeaweb.org/articles/pdf/doi/10.1257/
aer.20210811.

References | 313

Bordo, M., and M. Levy. 2021. “Do Enlarged Fiscal Deficits Cause Inflation? The
Historical Record.” Economic Affairs 41, no. 1: 59–83. https://onlinelibrary.
wiley.com/doi/abs/10.1111/ecaf.12446.
Bräuning, F., J. Fillat, and G. Joaquim. 2022. “Cost–Price Relationships in a Concentrated
Economy.” Federal Reserve Bank of Boston. https://www.bostonfed.org/
publications/current-policy-perspectives/2022/cost-price-relationships-in-aconcentrated-economy.aspx.
Catão, L., and M. Terrones. 2005. “Fiscal Deficits and Inflation.” Journal of Monetary
Economics 52, no. 3: 529–54. https://www.sciencedirect.com/science/article/
abs/pii/S0304393205000139.
CBO (Congressional Budget Office). 2019. “The Budget and Economic Outlook: 2019 to
2029.” https://www.cbo.gov/system/files/2019-03/54918-Outlook-3.pdf.
———. 2022. “Chapter 2: The Economic Outlook.” https://www.cbo.gov/system/
files?file=2022-05/57950-Chapter-2.pdf.
CEA (Council of Economic Advisers). 2016. “The Year in Review and the Years Ahead.”
https://www.govinfo.gov/content/pkg/ERP-2016/pdf/ERP-2016-chapter2.pdf.
———. 2022. Economic Report of the President. https://www.whitehouse.gov/
wp-content/uploads/2022/04/ERP-2022.pdf.
Chetty, R., J. Friedman, M. Stepner, and Opportunity Insights Team. 2022. The Economic
Impacts of COVID-19: Evidence from a New Public Database Built Using
Private Sector Data. NBER Working Paper 27431. Cambridge, MA: National
Bureau of Economic Research. https://www.nber.org/system/files/working_
papers/w27431/w27431.pdf.
Clark, T., and S. Terry. 2010. “Time Variation in the Inflation Passthrough of Energy
Prices.” Journal of Money, Credit and Banking 42, no. 7: 1419–33. http://www.
jstor.org/stable/40925694.
Cochrane, J. 2023. The Fiscal Theory of the Price Level. Princeton, NJ: Princeton
University Press. https://press.princeton.edu/books/hardcover/9780691242248/
the-fiscal-theory-of-the-price-level.
Congressional Research Service. 2022. “An Excise Tax on Stock Repurchases and Tax
Advantages of Buybacks over Dividends.” https://crsreports.congress.gov/
product/pdf/IF/IF11960.
Crawley, E., and E. Gagnon. 2022. “Substitutability between Balance Sheet Reductions
and Policy Rate Hikes: Some Illustrations and a Discussion.” Board of
Governors of the Federal Reserve System. https://doi.
org/10.17016/2380-7172.3147.
Detmeister, A. 2011. “The Usefulness of Core PCE Inflation Measures.” Finance and
Economics Discussion Series, Federal Reserve Board. https://www.
federalreserve.gov/pubs/feds/2011/201156/201156abs.html.

314 |

References

Donaldson, D., and R. Hornbeck. 2016. “Railroads and American Economic Growth: A
‘Market Access’ Approach.” Quarterly Journal of Economics 131, no. 2:
799–858. https://doi.org/10.1093/qje/qjw002.
Federal Reserve (Board of Governors of the Federal Reserve System). 2012. “Federal Reserve
Issues FOMC Statement of Longer-Run Goals and Policy Strategy.” https://www.
federalreserve.gov/newsevents/pressreleases/monetary20120125c.htm.
———. 2020. “2020 Statement on Longer-Run Goals and Monetary Policy Strategy.”
https://www.federalreserve.gov/monetarypolicy/review-of-monetary-policystrategy-tools-and-communications-statement-on-longer-run-goals-monetarypolicy-strategy.htm.
———. 2022a. “Summary of Economic Projections.” https://www.federalreserve.gov/
monetarypolicy/files/fomcprojtabl20221214.pdf.
———. 2022b. “Federal Reserve Issues FOMC Statement.” https://www.federalreserve.
gov/monetarypolicy/files/monetary20221214a1.pdf.
Federal Reserve Bank of Atlanta, Center for Human Capital Studies. No date. “Wage
Growth Tracker.” https://www.atlantafed.org/chcs/wage-growth-tracker.
Federal Reserve Bank of Cleveland. 2023. “Median CPI.” https://www.clevelandfed.org/
indicators-and-data/median-cpi.
Federal Reserve Bank of Dallas. No date. “Trimmed Mean MCE Inflation Rate.” https://
www.dallasfed.org/research/pce.
Federal Reserve Bank of Philadelphia. 2020. “Fourth Quarter 2020 Survey of
Professional Forecasters.” https://www.philadelphiafed.org/surveys-and-data/
real-time-data-research/spf-q4-2020.
Friedman, M. 1970. “The Counter-Revolution in Monetary Theory.” Institute of
Economic Affairs, Occasional Paper 33. https://onlinelibrary.wiley.com/doi/
pdf/10.1002/9781119205814.app2.
Furman, J., and W. Powell. 2021. “What Is the Best Measure of Labor Market
Tightness?” Peterson Institute for International Economics. https://www.piie.
com/blogs/realtime-economic-issues-watch/
what-best-measure-labor-market-tightness.
Galí, J. 2015. Monetary Policy, Inflation, and the Business Cycle: An Introduction to the
New Keynesian Framework and Its Applications—Second Edition. Princeton,
NJ: Princeton University Press. https://press.princeton.edu/books/
hardcover/9780691164786/monetary-policy-inflation-and-the-business-cycle.
Gleckman, H., and J. Holtzblatt. 2022. “Cutting Through the Misinformation About the
IRS’s Plan to Spend $80 Billion.” Tax Policy Center. https://www.
taxpolicycenter.org/taxvox/cutting-through-misinformation-about-irss-planspend-80-billion.
Gordon, R. 1975. “Alternative Reponses of Policy to External Supply Shocks.” Brookings
Papers on Economic Activity, no 1: 183–206. https://www.brookings.edu/
wp-content/uploads/1975/01/1975a_bpea_gordon.pdf.

References | 315

Gross, D., and B. Sampat. 2020. America, Jump-Started: World War II R&D and the
Takeoff of the U.S. Innovation System. NBER Working Paper 27375.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w27375.
Guerrieri, V., G. Lorenzoni, L. Straub, and I. Werning. 2021. “Monetary Policy in Times
of Structural Reallocation.” Working paper, Jackson Hole Economic Policy
Symposium. https://www.kansascityfed.org/documents/8322/JH_Guerrieri.pdf.
Hicks, J. 1937. “Mr. Keynes and the ‘Classics’: A Suggested Interpretation.”
Econometrica 5, no. 2: 147–59. https://doi.org/10.2307%2F1907242.
Jordà, Ò., C. Liu, F. Nechio, and F. Rivera-Reyes. 2022. “Wage Growth When Inflation Is
High.” Federal Reserve Bank of San Francisco. https://www.frbsf.org/
economic-research/publications/economic-letter/2022/september/
wage-growth-when-inflation-is-high/.
Jordà, Ò., and A. Taylor. 2019. “Riders on the Storm.” Working paper, Federal Reserve
Bank of San Francisco. https://doi.org/10.24148/wp2019-20.
Jørgensen, P., and S. Ravn. 2022. “The Inflation Response to Government Spending
Shocks: A Fiscal Price Puzzle?” European Economic Review 141. https://doi.
org/10.1016/j.euroecorev.2021.103982.
Keynes, J. 1936. The General Theory of Employment, Interest, and Money. Boston:
Houghton Mifflin. https://archive.org/details/generaltheoryofe00keyn_0/page/
n9/mode/2u.
Klein, L., and A. Goldberger. 1955. An Econometric Model of the United States, 1929–
1952. Amsterdam: North Holland. https://doi.org/10.2307/2227976.
Michaillat, P., and E. Saez. 2022. u* = √uv. NBER Working Paper 30211. Cambridge,
MA: National Bureau of Economic Research. https://doi.org/10.3386/w30211.
Miranda-Agrippino, S., and G. Ricco. 2021. “The Transmission of Monetary Policy
Shocks.” American Economic Journal: Macroeconomics 5, no. 3: 74–107.
https://doi.org/10.1257/mac.20180124.
Phillips, A. 1958. “The Relation Between Unemployment and the Rate of Change of
Money Wage Rates in the United Kingdom, 1861–1957.” Economica 25, no.
100: 289–99. https://doi.org/10.2307/2550759.
Powell, J. 2018. “Monetary Policy in a Changing Economy.” Transcript of speech at
Changing Market Structure and Implications for Monetary Policy
Symposium,Jackson Hole, WY, August 24. https://www.federalreserve.gov/
newsevents/speech/powell20180824a.htm.
———. 2022a. “Inflation and the Labor Market.” Transcript of a speech at the Hutchins
Center of Fiscal and Monetary Policy, Brookings Institution, Washington,
November 30. https://www.federalreserve.gov/newsevents/speech/
powell20221130a.htm.
———. 2022b. “Monetary Policy and Price Stability.” Transcript of speech at
Reassessing Constraints on the Economy and Policy Symposium, Jackson Hole,

316 |

References

WY, August 26. https://www.federalreserve.gov/newsevents/speech/
powell20220826a.htm.
Ramey, V. 2019. “Ten Years After the Financial Crisis: What Have We Learned from the
Renaissance in Fiscal Research?” Journal of Economic Perspectives 33, no. 2:
89–144. https://doi.org/10.1257/jep.33.2.89.
Rankin, E., and M. Idil. 2014. “A Century of Stock-Bond Correlations.” Reserve Bank of
Australia. https://www.rba.gov.au/publications/bulletin/2014/sep/pdf/bu-0914-8.
pdf.
Rudd, J. 2020. “Underlying Inflation: Its Measurement and Significance.” FEDS Notes.
Washington: Board of Governors of the Federal Reserve System, September 18.
https://doi.org/10.17016/2380-7172.2624.
———. 2021. “Why Do We Think That Inflation Expectations Matter for Inflation? (And
Should We?)” Finance and Economics Discussion Series. https://doi.
org/10.17016/FEDS.2021.062.
Social Security Administration. 2022a. “Table V.A2: Immigration Assumptions.” https://
www.ssa.gov/oact/TR/2022/lr5a2.html.
———. 2022b. “The 2022 Annual Report of The Board of Trustees of The Federal
Old-Age and Survivors Insurance and Federal Disability Insurance Trust
Funds.” https://www.ssa.gov/oact/tr/2022/tr2022.pdf.
Stigum, M., and A. Crescenzi. 2007. “Stigum’s Money Market, 4E.” https://www.
mhprofessional.com/stigum-s-money-market-4e-9780071448451-usa.
Syverson, C. 2019. “Macroeconomics and Market Power: Context, Implications, and
Open Questions.” Journal of Economic Perspectives 33, no. 3: 23–43. https://
doi.org/10.1257/jep.33.3.23.
U.S. Bureau of Labor Statistics. 2020. “Consumer Price Index: Overview.” https://www.
bls.gov/opub/hom/cpi/home.htm.
Werning, I. 2022. Expectations and the Rate of Inflation. NBER Working Paper 30260.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w30260.
White House. 2021. “Remarks by President Biden on the State of the Economy and the
Need for the American Rescue Plan.” https://www.whitehouse.gov/briefingroom/speeches-remarks/2021/02/05/
remarks-by-president-biden-on-the-state-of-the-economy-and-the-need-for-theamerican-rescue-plan/.
Yellen, J. 2015. “Inflation Dynamics and Monetary Policy.” Transcript of Philip Gamble
Memorial Lecture, University of Massachusetts, Amherst, September 24.
https://www.federalreserve.gov/newsevents/speech/yellen20150924a.htm.

References | 317

Chapter 3
Ahmed, U. 2019. “The Importance of Cross-Border Regulatory Cooperation in an Era of
Digital Trade.” World Trade Review 18, no. S1: S99–S120. https://doi.
org/10.1017/S1474745618000514.
Alfaro, L. 2016. “Gains from Foreign Direct Investment: Macro and Micro Approaches.”
World Bank Economic Review 30, no. S1: S2–S15. https://academic.oup.com/
wber/article/30/Supplement_1/S2/2897332.
Amiti, M., and S. Wei. 2006. Service Offshoring, Productivity, and Employment:
Evidence from the United States. IMF Working Paper 05/238. Washington, DC:
International Monetary Fund. https://papers.ssrn.com/sol3/papers.
cfm?abstract_id=888107.
Aslund, A., and M. Snegovaya. 2021. “The Impact of Western Sanctions on Russia and
How They Can Be Made Even More Effective.” Atlantic Council. https://www.
atlanticcouncil.org/wp-content/uploads/2021/05/The-impact-of-Western-sanctions-onRussia-and-how-they-can-be-made-even-more-effective-5.2.pdf.
Autor, D., D. Dorn, and G. Hanson. 2013. “The Geography of Trade and Technology
Shocks in the United States.” American Economic Review 103, no. 3: 220–25.
https://doi.org/10.1257/aer.103.3.220.
———. 2016. “The China Shock: Learning from Labor-Market Adjustment to Large
Changes in Trade.” Annual Review of Economics 8: 205–40. https://doi.
org/10.1146/annurev-economics-080315-015041.
———. 2021. On the Persistence of the China Shock. NBER Working Paper 29401.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w29401.
Autor, D., D. Dorn, G. Hanson, and J. Song. 2014. “Trade Adjustment: Worker-Level
Evidence.” Quarterly Journal of Economics 129, no. 4: 1799–1806. https://doi.
org/10.1093/qje/qju026.
Baldwin, R. 2022. “The Peak Globalisation Myth: Part 4—Services Trade Did Not Peak.”
Center for Economic and Policy Research. https://cepr.org/voxeu/columns/
peak-globalisation-myth-part-4-services-trade-did-not-peak.
Baldwin, R., and R. Freeman. 2022. “Risks and Global Supply Chains: What We Know
and What We Need to Know.” Annual Review of Economics 14: 153–80.
https://doi.org/10.1146/annurev-economics-051420-113737.
Bapat, N., and T. Morgan. 2009. “Multilateral Versus Unilateral Sanctions Reconsidered:
A Test Using New Data.” International Studies Quarterly 53, no. 4: 1075–94.
https://doi.org/10.1111/j.1468-2478.2009.00569.x.
Bauer, M., H. Lee-Makiyama, E. van der Marel, and B. Verschelde. 2014. “The Costs of
Data Localisation: Friendly Fire on Economic Recovery.” European Centre for
International Political Economy, Occasional Paper 3/2014. https://ecipe.org/
wp-content/uploads/2014/12/OCC32014__1.pdf.

318 |

References

BEA (U.S. Bureau of Economic Analysis). 2022. “International Data.” https://www.bea.
gov/itable/international-transactions-services-and-investment-position.
———. 2023a. “Real Gross Domestic Product, Chained Dollars.” https://apps.bea.gov/
iTable/?reqid=19&step=3&isuri=1&select_all_years=0&nipa_table_list=6&
series=q&first_year=2018&last_year=2020&scale=-9&categories=survey.
———. 2023b. “Real Exports and Imports of Goods and Services by Type of Product,
Chained Dollars.” https://apps.bea.gov/iTable/?reqid=19&step=4&isuri=1&1921=flatfiles#eyJhcHBpZCI6MTksInN0ZXBzIjpbMSwyLDNdLCJkYXRhIjpbW
yJDYXRlZ29yaWVzIiwiU3VydmV5Il0sWyJOSVBBX1RhYmxlX0xpc3QiLCIxMzQiXV19.
———. 2023c. “International Trade in Goods and Services.” https://www.bea.gov/data/
intl-trade-investment/international-trade-goods-and-services.
———. 2023d. “Price Indexes for Gross Domestic Product.” https://apps.bea.gov/iTable/?reqid=19&step=2&isuri=1&categories=survey#eyJhcHBpZCI6MTksInN0Z
XBzIjpbMSwyLDNdLCJkYXRhIjpbWyJjYXRlZ29yaWVzIiwiU3VydmV5Il0sWyJOSVBBX1RhYmxlX0xpc3QiLCI0Il1dfQ.
Berner, R., S. Cecchetti, and K. Schoenholtz. 2022. “Russian Sanctions: Some Questions
and Answers.” Center for Economic and Policy Research. https://cepr.org/
voxeu/columns/russian-sanctions-some-questions-and-answers.
Birshan, M., T. Devesa, H. Samandari, J. Seong, S. Smit, O. White, and J. Woetzel. 2022.
“Global Flows: The Ties That Bind an Interconnected World.” MicKinsey
Global Institute. https://www.mckinsey.com/capabilities/strategy-and-corporatefinance/our-insights/global-flows-the-ties-that-bind-in-an-interconnected-world.
BLS (U.S. Bureau of Labor Statistics). 2023. “Import/Export Price Indexes (MXP).”
https://www.bls.gov/mxp/.
Bonadio, B., Z. Huo, A. Levchenko, and N. Pandalai-Nayar. 2021. “Global Supply
Chains in the Pandemic.” Journal of International Economics 133. https://doi.
org/10.1016/j.jinteco.2021.103534.
Borusyak, K., and X. Jaravel. 2021. The Distributional Effects of Trade: Theory and
Evidence from the United States. NBER Working Paper 28957. Cambridge,
MA: National Bureau of Economic Research. https://www.nber.org/system/
files/working_papers/w28957/w28957.pdf.
Bown, C. 2022a. “Four Years into the Trade War, Are the US and China Decoupling?”
Peterson Institute for International Economics. https://www.piie.com/blogs/realtime-economics/four-years-trade-war-are-us-and-china-decoupling.
———. 2022b. “First Trade War, Now Russia’s Real War. Why U.S. Exports to China
Continue to Suffer.” Peterson Institute for International Economics. https://
www.piie.com/blogs/realtime-economics/
first-trade-war-now-russias-real-war-why-us-exports-china-continue-suffer.
———. 2022c. “The WTO and Vaccine Supply Chain Resilience during a Pandemic.”
Peterson Institute for International Economics, Working Paper 22-15. https://

References | 319

www.piie.com/publications/working-papers/wto-and-vaccine-supplychain-resilience-during-pandemic.
Bown, C., and J. Hillman. 2019. “WTO’ing a Resolution to the China Subsidy Problem.”
Journal of International Economic Law 22, no. 4: 557–78. https://doi.
org/10.1093/jiel/jgz035.
Brynjolfsson, E., J. Horton, A. Ozimek, D. Rock, G. Sharma, and H. TuYe. 2020.
COVID-19 and Remote Work: An Early Look at U.S. Data. NBER Working
Paper 27344. Cambridge, MA: National Bureau of Economic Research. https://
doi.org/10.3386/w27344.
Capri, A., and W. Lehmacher. 2021. “How COVID-19 Accelerated the Shift Towards
TradeTech.” World Economic Forum. https://www.weforum.org/
agenda/2021/01/how-covid-19-has-accelerated-the-shift-towards-tradetech/.
Carr, B., C. French, and C. Lowery. 2020. “Data Localization: Costs, Tradeoffs, and
Impacts Across the Economy.” Institute of International Finance. https://www.
iif.com/portals/0/Files/content/Innovation/12_22_2020_data_localization.pdf.
Caselli, F., M. Koren, M. Lisicky, and S. Tenreyro. 2020. “Diversification Through
Trade.” Quarterly Journal of Economics 135, no. 1: 449–502. https://doi.
org/10.1093/qje/qjz028.
Casalini, F., and J. González. 2019. Trade and Cross-Border Data Flows. OECD Trade
Policy Paper 220. Paris: OECD Publishing. https://doi.org/10.1787/
b2023a47-en.
Casalini, F., J. González, and E. Moïsé. 2019. Approaches to Market Openness in the
Digital Age. OECD Trade Policy Paper 219. Paris: OECD Publishing. https://
doi.org/10.1787/818a7498-en.
CEA (Council of Economic Advisers). 2022. Economic Report of the President.
Washington: U.S. Government Publishing Office. https://www.whitehouse.gov/
wp-content/uploads/2022/04/ERP-2022.pdf.
Cerdeiro, D., and A. Komaromi. 2020. Supply Spillovers During the Pandemic: Evidence
from High-Frequency Shipping Data. IMF Working Paper 20/284. https://www.
imf.org/en/Publications/WP/Issues/2020/12/18/Supply-Spillovers-Duringthe-Pandemic-Evidence-from-High-Frequency-Shipping-Data-49966.
CFR (Council on Foreign Relations). 2022. “Confronting Reality in Cyberspace: Foreign
Policy for a Fragmented Internet.” Independent Task Force Report 80. https://
www.cfr.org/report/confronting-reality-in-cyberspace.
Chetty, R., J. Friedman, N. Hendren, M. Stepner, and Opportunity Insights Team. 2022.
The Economic Impacts of COVID-19: Evidence from a New Public Database
Built Using Private Sector Data. NBER Working Paper 27431. Cambridge,
MA: National Bureau of Economic Research. https://doi.org/10.3386/w27431.
Chetverikov, D., B. Larsen, and C. Palmer. 2016. “IV Quantile Regression for GroupLevel Treatments, with an Application to the Distributional Effects of Trade.”
Econometrica 84, no. 2: 809–33. https://doi.org/10.3982/ECTA12121.

320 |

References

Chivvis, C., and E. Kapstein. 2022. “U.S. Strategy and Economic Statecraft:
Understanding the Trade-Offs.” Carnegie Endowment for International Peace.
https://carnegieendowment.org/2022/04/28/u.s.-strategy-and-economic-statecraftunderstanding-tradeoffs-pub-86995.
Choi, S., D. Furceri, and C. Yoon. 2020. “Policy Uncertainty and Foreign Direct
Investment.” Review of International Economics 29, no. 2: 195–227. https://doi.
org/10.1111/roie.12495.
Clausing, K. 2019. Open: The Progressive Case for Free Trade, Immigration, and Global
Capital. Cambridge, MA: Harvard University Press.
Comini, N., V. Foster, and S. Srinivasan. 2021. “Improving Data Infrastructure Helps
Ensure Equitable Access for Poor People in Poor Countries.” World Bank Data
Blog. https://blogs.worldbank.org/opendata/improving-data-infrastructurehelps-ensure-equitable-access-poor-people-poor-countries.
Cooke, B., G. Nigatu, K. Heerman, M. Landes, and R. Seely. 2016. “Global
Macroeconomic Developments Drive Downturn in U.S. Agricultural Exports.”
U.S. Department of Agriculture. https://www.ers.usda.gov/webdocs/
outlooks/35809/59822_aes-94.pdf?v=203.4.
Corbeau, A. 2022. “How Deep Is Europe’s Dependence on Russian Oil?” Columbia
Climate School, State of the Planet. https://news.climate.columbia.
edu/2022/03/14/qa-how-deep-is-europes-dependence-on-russian-oil/.
CRS (Congressional Research Service). 2022a. “Russia’s War on Ukraine: Financial and
Trade Sanctions.” https://crsreports.congress.gov/product/pdf/IF/IF12062.
———. 2022b. “Biden Administration Plans for an Indo-Pacific Economic Framework.”
https://crsreports.congress.gov/product/pdf/IN/IN11814.
———. 2023. “U.S. Trade Policy: Background and Current Issues.” https://crsreports.
congress.gov/product/pdf/IF/IF10156.
D’Aguanno, L., O. Davies, A. Dogan, R. Freeman, S. Lloyd, D. Reinhardt, R. Sajedi, and
R. Zymek. 2021. “Global Value Chains, Volatility and Safe Openness: Is Trade
a Double-Edged Sword?” Bank of England Financial Stability Paper 46. https://
www.bankofengland.co.uk/financial-stability-paper/2021/global-value-chainsvolatility-and-safe-openness-is-trade-a-double-edged-sword.
De Loecker, J., P. Goldberg, A. Khandelwal, and N. Pavcnik. 2016. “Prices, Markups, and
Trade Reform.” Econometrica 84, no 2: 445–510. https://doi.org/10.3982/
ECTA11042.
Demertzis, M., B. Hilgenstock, B. McWilliams, E. Ribakova, and S. Tagliapietra. 2022.
“How Have Sanctions Impacted Russia?” Bruegel Policy Contribution 18/22.
https://www.bruegel.org/sites/default/files/2022-10/PC%2018%202022_1.pdf.
Dingel, J., and B. Neiman. 2020. “How Many Jobs Can Be Done at Home?” Journal of
Public Economics 189. https://doi.org/10.1016/j.jpubeco.2020.104235.
DOE (U.S. Department of Energy). 2022. “Poland and U.S. Announce Strategic
Partnership to Launch Poland’s Civil Nuclear Program.” https://www.energy.

References | 321

gov/articles/poland-and-us-announce-strategic-partnership-launch-polandscivil-nuclear-program.
DOL (U.S. Department of Labor). 2022. “U.S. Department of Labor Joins U.S. Trade
Representative, European Commission to Host First Principals’ Meeting of the
U.S.-EU Trade and Labor Dialogue.” https://www.dol.gov/newsroom/releases/
ilab/ilab20221205.
Drury, A. 1998. “Revisiting Economic Sanctions Reconsidered.” Journal of Peace
Research 35, no. 4: 497–509. https://doi.org/10.1177/0022343398035004006.
Economist Intelligence Unit. 2022. “Russia: Forecast Summary.” http://country.eiu.com/
article.aspx?articleid=1802639163&Country=Russia&topic=Economy&subtopi
c=Forecast&subsubtopic=Forecast+summary.
Eppinger, P. 2019. “Service Offshoring and Firm Employment.” Journal of International
Economics 117: 209–28. https://doi.org/10.1016/j.jinteco.2019.01.007.
Eppinger, P., G. Felbermayr, O. Krebs, and B. Kukharskyy. 2021. “Decoupling Global
Value Chains.” Center for Economic Studies and Ifo Institute, Working Paper
6079. https://www.econstor.eu/bitstream/10419/235449/1/cesifo1_wp9079.pdf.
Eriksson, K., K. Russ, J. Shambaugh, and M. Xu. 2021. “Reprint: Trade Shocks and the
Shifting Landscape of U.S. Manufacturing.” Journal of International Money
and Finance 114. https://www.sciencedirect.com/science/article/pii/
S0261560621000589.
Espitia, A., A. Mattoo, N. Rocha, M. Ruta, and D. Winkler. 2021. “Pandemic Trade:
COVID-19, Remote Work, and Global Value Chains.” World Bank Working
Paper 9508. https://documents1.worldbank.org/curated/
en/843301610630752625/pdf/Pandemic-Trade-Covid-19-Remote-Work-andGlobal-Value-Chains.pdf.
Espitia, A., N. Rocha, and M. Ruta. 2022. “How Export Restrictions Are Impacting
Global Food Prices.” World Bank Private Sector Development Blog. https://
blogs.worldbank.org/psd/how-export-restrictions-are-impacting-globalfood-prices.
Fan, H., Y. Li, and S. Yeaple. 2015. “Trade Liberalization, Quality, and Export Prices.”
Review of Economics and Statistics 95, no. 5: 1033–51. https://doi.org/10.1162/
REST_a_00524.
Feenstra, R., H. Ma, and Y. Xu. 2019. “U.S. Exports and Employment.” Journal of
International Economics 120: 46–58. https://doi.org/10.1016/j.
jinteco.2019.05.002.
Flanagan, M., A. Kammer, A. Pescatori, and M. Stuermer. 2022. “How a Russian Natural
Gas Cutoff Could Weigh on Europe’s Economies.” International Monetary
Fund Blog. https://www.imf.org/en/Blogs/Articles/2022/07/19/
blog-how-a-russias-natural-gas-cutoff-could-weigh-on-european-economies.
Foggo, J., and B. Mainardi. 2022. “Boiling the Frog: Russia’s Black Sea Aggression Part
II, the War.” Center for European Policy Analysis. https://cepa.org/article/
boiling-the-frog-russias-black-sea-aggression-part-ii-the-war/.
322 |

References

Freund, C., A. Mulabdic, and M. Ruta. 2022. “Is 3D Printing a Threat to Global Trade?
The Trade Effects You Didn’t Hear About.” Journal of International Economics
138. https://doi.org/10.1016/j.jinteco.2022.103646.
Georgieva, K., G. Gopinath, and C. Pazarbasioglu. 2022. “Why We Must Resist
Geoeconomic Fragmentation—and How.” International Monetary Fund Blog.
https://www.imf.org/en/Blogs/Articles/2022/05/22/
blog-why-we-must-resist-geoeconomic-fragmentation.
Glauber, J., and D. Laborde. 2022. “How Sanctions on Russia and Belarus Are Impacting
Exports of Agricultural Products and Fertilizer.” International Food Policy
Research Institute Blog. https://www.ifpri.org/blog/how-sanctionsrussia-and-belarus-are-impacting-exports-agricultural-products-and-fertilizer..
Goldberg, L., and K. Crockett. 1998. “The Dollar and U.S. Manufacturing.” Current
Issues in Economics and Finance (Federal Reserve Bank of New York) 4, no.
12. https://www.newyorkfed.org/research/current_issues/ci4-12.html.
Goldberg, P., A. Khandelwal, N. Pavcnik, and P. Topalova. 2010. “Imported Intermediate
Inputs and Domestic Product Growth: Evidence from India.” Quarterly Journal
of Economics 125, no. 4: 1727–67. https://doi.org/10.1162/
qjec.2010.125.4.1727.
Goldfarb, A., and C. Tucker. 2019. “Digital Economics.” Journal of Economic Literature
57, no. 1: 3–43. https://doi.org/10.1257/jel.20171452.
Gopinath, G., and O. Itskhoki. 2021. Dominant Currency Paradigm: A Review. NBER
Working Paper 29556. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w29556.
Gopinath, G., O. Itskhoki, and R. Rigobon. 2010. “Currency Choice and Exchange Rate
Pass-Through.” American Economic Review 100, no. 1: 304–36. https://doi.
org/10.1257/aer.100.1.304.
Grossman, G., E. Helpman, and H. Lhuillier. 2021. Supply Chain Resilience: Should
Policy Promote Diversification or Reshoring? NBER Working Paper 29330.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w29330.
Grossman, G., and E. Rossi-Hansberg. 2008. “Trading Tasks: A Simple Theory of
Offshoring.” American Economic Review 98, no. 5: 1978–97. https://doi.
org/10.1257/aer.98.5.1978.
Gulen, H., and M. Ion. 2016. “Policy Uncertainty and Corporate Investment.” Review of
Financial Studies 29, no. 3: 523–64. https://doi.org/10.1093/rfs/hhv050.
Harrell, P., E. Rosenberg, and E. Saravelle. 2018. “China’s Use of Coercive Economic
Measures.” Center for a New American Security. https://www.cnas.org/
publications/reports/chinas-use-of-coercive-economic-measures.
Higgins, M., and T. Klitgaard. 2021. “How Much Have Consumers Spent on Imports
during the Pandemic?” Liberty Street Economics, Federal Reserve Bank of New
York. https://libertystreeteconomics.newyorkfed.org/2021/10/
how-much-have-consumers-spent-on-imports-during-the-pandemic/.
References | 323

Huang, K., S. Madnick, and S. Johnson. 2019. “Framework for Understanding
Cybersecurity Impacts on International Trade.” Working Paper 2019-23,
Cybersecurity Interdisciplinary Systems Laboratory, Sloan School of
Management, Massachusetts Institute of Technology. https://papers.ssrn.com/
sol3/papers.cfm?abstract_id=3555341.
IBM. 2022. “Cost of a Data Breach 2022.” https://www.ibm.com/reports/data-breach.
IDC (International Data Corporation). 2021. “Digital Transformation Investments to
Represent More Than Half of All ICT Investment by 2024, According to IDC
FutureScape.” https://www.idc.com/getdoc.jsp?containerId=prUS48333121.
IEA (International Energy Agency). 2022a. “Frequently Asked Questions on Energy
Security.” https://www.iea.org/articles/frequently-asked-questions-onenergy-security.
———. 2022b. “How to Avoid Gas Shortages in the European Union in 2023: The Need
for Action.” https://www.iea.org/reports/how-to-avoid-gas-shortages-in-theeuropean-union-in-2023/the-need-for-action#abstract.
IMF (International Monetary Fund). 2022a. “World Economic Outlook, April 2022: War
Sets Back the Global Recovery.” https://www.imf.org/en/Publications/WEO/
Issues/2022/04/19/world-economic-outlook-april-2022#Global-Trade-and-ValueChains-in-the-Pandemic.
———. 2022b. “World Economic Outlook, October 2022: Countering the Cost-of-Living
Crisis.” https://www.imf.org/en/Publications/WEO/Issues/2022/10/11/
world-economic-outlook-october-2022.
Irwin, D. 2022a. The Trade Reform Wave of 1985–1995. NBER Working Paper 29973.
Cambridge, MA: National Bureau for Economic Research. https://doi.
org/10.3386/w29973.
———. 2022b. “Globalization Enabled Nearly All Countries to Grow Richer in Recent
Decades.” Peterson Institute for International Economics. https://www.piie.com/
blogs/realtime-economic-issues-watch/
globalization-enabled-nearly-all-countries-grow-richer-recent.
Jaravel, X., and E. Sager. 2019. “What Are the Price Effects of Trade? Evidence from the
U.S. and Implications for Quantitative Trade Models.” Board of Governors of
the Federal Reserve System, Finance and Economics Discussion Series,
Working Paper 2019-68. https://papers.ssrn.com/sol3/papers.
cfm?abstract_id=3473054.
Jiang, H., B. Kenner, D. Russell, and J. Kaufman. 2022. “Outlook for U.S. Agricultural
Trade: November 2022.” U.S. Department of Agriculture. https://www.ers.usda.
gov/webdocs/outlooks/105356/aes-122.pdf?v=5558.3.
Julio, B., and Y. Yook. 2016. “Policy Uncertainty, Irreversibility, and Cross-Border Flows
of Capital.” Journal of International Economics 103: 13–26. https://www.
sciencedirect.com/science/article/abs/pii/S0022199616300915.
Keller, W., and S. Yeaple. 2009. “Multinational Enterprises, International Trade, and
Productivity Growth: Firm-Level Evidence from the United States.” Review of
324 |

References

Economics and Statistics 91, no. 4: 821–31. https://direct.mit.edu/rest/
article/91/4/821/57821/Multinational-Enterprises-International-Trade-and.
Klapper, L., and M. Miller. 2021. “The Impact of COVID-19 on Digital Financial
Inclusion.” World Bank. https://www.gpfi.org/sites/gpfi/files/sites/default/
files/5_WB%20Report_The%20impact%20of%20COVID-19%20on%20
digital%20financial%20inclusion.pdf.
Krugman, P. 1980. “Scale Economies, Product Differentiation, and the Pattern of Trade.”
American Economic Review 70, no 5: 950–59. https://www.jstor.org/
stable/1805774#metadata_info_tab_contents.
Lagarde, C. 2022. “A New Global Map: European Resilience in a Changing World.”
Transcript of speech at Peterson Institute for International Economics Event,
Washington, April 22. https://www.ecb.europa.eu/press/key/date/2022/html/ecb.
sp220422~c43af3db20.en.html.
Mattoo, A., P. Mishra, and A. Subramanian. 2017. “Beggar-Thy-Neighbor Effects of
Exchange Rates: A Study of the Renminbi.” American Economic Journal:
Economic Policy 9, no. 4: 344–66. https://doi.org/10.1257/pol.20150293.
Meltzer, J. 2019. “A WTO Reform Agenda: Data Flows and International Regulatory
Cooperation.” Brookings Global Economy and Development, Working Paper
130. https://www.brookings.edu/wp-content/uploads/2019/09/WTO-ReformAgenda_final.pdf.
———. 2020. “Cybersecurity, Digital Trade, and Data Flows: Rethinking a Role for
International Trade Rules.” Brookings Global Economy and Development,
Working Paper 123. https://papers.ssrn.com/sol3/papers.cfm?abstract_
id=3595175.
Meltzer, J., and C. Kerry. 2019. “Cybersecurity and Digital Trade: Getting It Right.”
Brookings Institution. https://www.brookings.edu/research/cybersecurityand-digital-trade-getting-it-right/.
Miroudot, S. 2021. “Lessons from the Pandemic for Trade Cooperation on Cross-Border
Supply Chains.” In Revitalizing Multilateralism: Pragmatic Ideas for the New
WTO Director-General, edited by S. Evenett and R. Baldwin, 141–54.
Washington: Center for Economic and Policy Research. http://ciwto.uibe.edu.
cn/docs/2021-07/492701afda8648478dc08aca4231d0bb.pdf#page=140.
Mollenkopf, D., S. Peinkofer, and Y. Chu. 2022. “Supply Chain Transparency: Consumer
Reactions to Incongruent Signals.” Journal of Operations Management 68, no.
4: 306–27. https://doi.org/10.1002/joom.1180.
Morcos, P., and C. Wall. 2021. “Invisible and Vital: Undersea Cables and Transatlantic
Security.” Center for Strategic and International Studies. https://www.csis.org/
analysis/invisible-and-vital-undersea-cables-and-transatlantic-security.
National Academies of Sciences, Engineering, and Medicine. 2022. Building Resilience
into the Nation’s Medical Product Supply Chains. Washington: National
Academies Press. https://doi.org/10.17226/26420.

References | 325

National Security Council. 2022. “National Security Strategy.” https://www.whitehouse.
gov/wp-content/uploads/2022/10/Biden-Harris-Administrations-NationalSecurity-Strategy-10.2022.pdf.
OECD (Organization for Economic Cooperation and Development). 2015. Digital
Security Risk Management for Economic and Social Prosperity: OECD
Recommendation and Companion Document. Paris: OECD Publishing. https://
doi.org/10.1787/9789264245471-en.
———. 2020a. “Foreign Direct Investment Flows in the Time of COVID-19.” https://
read.oecd-ilibrary.org/view/?ref=132_132646-g8as4msdp9&title=Foreigndirect-investment-flows-in-the-time-of-COVID-19.
———. 2020b. “COVID-19 and Global Value Chains: Policy Options to Build More
Resilient Production Networks.” https://www.oecd.org/coronavirus/policyresponses/covid-19-and-global-value-chains-policy-options-to-build-moreresilient-production-networks-04934ef4/.
———. 2022a. “FDI in Figures.” https://www.oecd.org/investment/investment-policy/
FDI-in-Figures-October-2022.pdf.
———. 2022b. “International Investment Implications of Russia’s War Against Ukraine
(Abridged Version).” https://www.oecd.org/ukraine-hub/policy-responses/
international-investment-implications-of-russia-s-war-against-ukraine-abridgedversion-6224dc77/.
———. 2022c. “Paying the Price of War: OECD Economic Outlook, Interim Report,
September 2022.” https://www.oecd.org/economic-outlook/september-2022/.
———. 2022d. Cross-Border Data Flows: Taking Stock of Key Policies and Initiatives.
Paris: OECD Publishing. https://doi.org/10.1787/5031dd97-en.
———. 2023a. “The Impact of Digitalisation on Trade.” https://www.oecd.org/trade/
topics/digital-trade/.
———. 2023b. “FDI Flows.” https://data.oecd.org/fdi/fdi-flows.htm.
Oldenski, L. 2011. “The Task Composition of Offshoring by U.S. Multinationals.” Forum
for Research in Empirical International Trade Working Paper. https://www.freit.
org/WorkingPapers/Papers/ForeignInvestment/FREIT262.pdf.
Pei, J., G. de Vries, and M. Zhang. 2021 “International Trade and COVID-19: City-Level
Evidence from China’s Lockdown Policy.” Journal of Regional Science 62, no.
3: 670–95. https://doi.org/10.1111/jors.12559.
Peksen, D. 2019. “When Do Imposed Economic Sanctions Work? A Critical Review of
the Sanctions Effectiveness Literature.” Defence and Peace Economics 30, no.
6: 635–47. https://doi.org/10.1080/10242694.2019.1625250.
Riker, D. 2015. “Export-Intensive Industry Pay More on Average: An Update.” U.S.
International Trade Commission, Office of Economics, Research Note 201504A. https://www.usitc.gov/publications/332/ec201504a.pdf.
Shivakumar, S., C. Wessner, and T. Howell. 2022. “Opportunities and Pitfalls for U.S.-EU
Collaboration on Semiconductor Value Chain Resilience.” Center for Strategic

326 |

References

and International Studies. https://www.csis.org/analysis/opportunities-andpitfalls-us-eu-collaboration-semiconductor-value-chain-resilience.
Staiger, R. 2021a. Does Digital Trade Change the Purpose of a Trade Agreement? NBER
Working Paper 29578. Cambridge, MA: National Bureau for Economic
Research. https://doi.org/10.3386/w29578.
———. 2021b. A World Trading System for the Twenty-First Century. NBER Working
Paper 28947. Cambridge, MA: National Bureau for Economic Research. https://
doi.org/10.3386/w28947.
Sykes, A. 2015. “The Limited Economic Case for Subsidies Regulation.” Stanford Law
and Economics, Olin Working Paper 472. https://papers.ssrn.com/sol3/papers.
cfm?abstract_id=2531051.
Tai, K. 2021a. “Remarks from Ambassador Katherine Tai on Trade, Policy, the
Environment and Climate Change.” https://ustr.gov/about-us/policy-offices/
press-office/speeches-and-remarks/2021/april/remarks-ambassadaorkatherine-tai-trade-policy-environment-and-climate-change.
———. 2021b. “Remarks of Ambassador Katherine Tai Outlining the Biden-Harris
Administration’s ‘Worker-Centered’ Trade Policy.” https://ustr.gov/about-us/
policy-offices/press-office/speeches-and-remarks/2021/june/
remarks-ambassador-katherine-tai-outlining-biden-harris-administrationsworker-centered-trade-policy.
———. 2022. “Statement from Ambassador Katherine Tai on the Announcement of
Additional Economic Actions Against Russia.” https://ustr.gov/about-us/policyoffices/press-office/press-releases/2022/march/
statement-ambassador-katherine-tai-announcement-additional-economicactions-against-russia.
UNCDF (United Nations Capital Development Fund). 2022. “The Role of Cybersecurity
and Data Security in the Digital Economy.” https://policyaccelerator.uncdf.org/
policy-tools/brief-cybersecurity-digital-economy.
United Nations. 2022. “The Black Sea Grain Initiative: What It Is, and Why It’s
Important for the World.” UN News, September 16. https://news.un.org/en/
story/2022/09/1126811.
U.S. Census Bureau. 2022. “Quarterly Retail E-Commerce Sales.” https://www.census.
gov/retail/ecommerce.html.
———. 2023a. “Country and Product Trade Data.” https://www.census.gov/foreign-trade/
statistics/country/index.html.
———. 2023b. “U.S. International Trade in Goods and Services, December and Annual
2022.” https://www.census.gov/foreign-trade/Press-Release/ft900/ft900_2212.pdf.
U.S. Department of Commerce. 2021. “Jobs Supported by U.S. Exports.” https://www.
trade.gov/data-visualization/jobs-supported-us-exports.
———. 2022. “Commerce Department Expands Restrictions on Exports to Russia and
Belarus in Response to Ongoing Aggression in Ukraine.” https://www.

References | 327

commerce.gov/news/press-releases/2022/04/
commerce-department-expands-restrictions-exports-russia-and-belarus.
U.S. Department of State. 2022a. “The Passage of the CHIPS and Science Act of 2022.”
https://www.state.gov/the-passage-of-the-chips-and-science-act-of-2022/.
———. 2022b. “Fact Sheet: The Impact of Sanctions and Export Controls on the Russian
Federation.” https://www.state.gov/
the-impact-of-sanctions-and-export-controls-on-the-russian-federation/.
———. 2022c. “United States Takes Next Step in Supporting Innovative Clean Nuclear
Technology in Europe.” https://www.state.gov/united-states-takes-nextstep-in-supporting-innovative-clean-nuclear-technology-in-europe/.
———. 2022d. “Special Presidential Envoy for Climate Kerry and Ukraine Minster of
Energy Galushchenko Announce Cooperation on a Clean Fuels from Small
Modular Reactors Pilot, COP27 Climate Conference.” https://www.state.gov/
special-presidential-envoy-for-climate-kerry-and-ukraine-minister-of-energygalushchenko-announce-cooperation-on-a-clean-fuels-from-small-modular-reactors-pilot-cop27-climate-conference/.
U.S. Department of the Treasury. 2022a. “Updates Related to Price Cap on Russian Oil.”
https://home.treasury.gov/policy-issues/financial-sanctions/
recent-actions/20220902_33.
———. 2022b. “Fact Sheet: Limiting Kremlin Revenues and Stabilizing Global Energy
Supply with a Price Cap on Russian Oil.” https://home.treasury.gov/news/pressreleases/jy1141.
———. 2022c. “OFAC Food Security Fact Sheet: Russia Sanctions and Agricultural Trade.”
https://home.treasury.gov/system/files/126/russia_fact_sheet_20220714.pdf.
USTR (Office of the U.S. Trade Representative). 2021. “Joint Statement on the Results of
the Council Meeting of the U.S.–Central Asia Trade and Investment Framework
Agreement (TIFA).” https://ustr.gov/about-us/policy-offices/press-office/pressreleases/2021/march/joint-statement-results-council-meeting-us-central-asiatrade-and-investment-framework-agreement.
———. 2022a. “Joint Statement on the U.S./U.K. Dialogues on the Future of Atlantic
Trade.” https://ustr.gov/about-us/policy-offices/press-office/press-releases/2022/
march/joint-statement-usuk-dialogues-future-atlantic-trade.
———. 2022b. “Ministerial Text for the Trade Pillar of the Indo-Pacific Economic
Framework for Prosperity.” https://ustr.gov/sites/default/files/2022-09/IPEF%20
Pillar%201%20Ministerial%20Text%20(Trade%20Pillar)_FOR%20
PUBLIC%20RELEASE%20(1).pdf.
———. 2022c. “United States and Kenya Announce the Launch of the U.S.–Kenya
Strategic Trade and Investment Partnership.” https://ustr.gov/about-us/policyoffices/press-office/press-releases/2022/july/united-states-and-kenya-announcelaunch-us-kenya-strategic-trade-and-investment-partnership.
———. 2022d. “United States and Taiwan Commence Formal Negotiations on U.S.–
Taiwan Initiative on 21st Century Trade.” https://ustr.gov/about-us/
328 |

References

policy-offices/press-office/press-releases/2022/august/
united-states-and-taiwan-commence-formal-negotiations-us-taiwan-initiative21st-century-trade.
———. 2022e. “2022 Trade Policy Agenda and 2021 Annual Report of the President of
the United States on the Trade Agreements Program.” https://ustr.gov/sites/
default/files/2022%20Trade%20Policy%20Agenda%20and%202021%20
Annual%20Report%20(1).pdf.
Valero, P. 2016. “This Is Why We Should Care About the Free Flow of Data Between
Countries.” World Economic Forum. https://www.weforum.org/agenda/2016/07/
free-flow-of-data-between-countries/.
Wharton School. 2019. “Your Data Is Shared and Sold; . . . What’s Being Done About
It?” Knowledge at Wharton, October 28. https://knowledge.wharton.upenn.edu/
article/data-shared-sold-whats-done/.
White House. 2021a. “Executive Order on America’s Supply Chains.” https://www.
whitehouse.gov/briefing-room/presidential-actions/2021/02/24/
executive-order-on-americas-supply-chains/.
———. 2021b. “Fact Sheet: Prioritizing Climate in Foreign Policy and National
Security.” https://www.whitehouse.gov/briefing-room/statementsreleases/2021/10/21/fact-sheet-prioritizing-climate-in-foreign-policy-andnational-security/.
———. 2022a. “Fact Sheet: President Biden and G7 Leaders Formally Launch the
Partnership for Global Infrastructure and Investment.” https://www.whitehouse.
gov/briefing-room/statements-releases/2022/06/26/fact-sheet-president-biden-andg7-leaders-formally-launch-the-partnership-for-global-infrastructure-andinvestment/.
———. 2022b. “Fact Sheet: In Asia, President Biden and a Dozen Indo-Pacific Partners
Launch the Indo-Pacific Economic Framework for Prosperity.” https://www.
whitehouse.gov/briefing-room/statements-releases/2022/05/23/
fact-sheet-in-asia-president-biden-and-a-dozen-indo-pacific-partners-launch-theindo-pacific-economic-framework-for-prosperity/.
———. 2022c. “Joint Readout of U.S.-EU Task Force Meeting on Energy Security.”
https://www.whitehouse.gov/briefing-room/statements-releases/2022/11/07/
joint-readout-of-u-s-eu-task-force-meeting-on-energy-security/.
———. 2022d. “Joint Statement between the United States and the European
Commission on European Energy Security.” https://www.whitehouse.gov/
briefing-room/statements-releases/2022/03/25/joint-statement-between-theunited-states-and-the-european-commission-on-european-energy-security/.
———. 2022e. “A Declaration for the Future of the Internet.” https://www.whitehouse.
gov/wp-content/uploads/2022/04/Declaration-for-the-Future-for-the-Internet_
Launch-Event-Signing-Version_FINAL.pdf
———. 2022f. “Fact Sheet: President Biden Announces the Americas Partnership for
Economic Prosperity.” https://www.whitehouse.gov/briefing-room/statements-

References | 329

releases/2022/06/08/fact-sheet-president-biden-announces-the-americaspartnership-for-economic-prosperity/.
World Bank. 2020. World Development Report 2020: Trading for Development in the Age
of Global Value Chains. Washington: World Bank. https://doi.org/10.1596/9781-4648-1457-0.
World Economic Forum. 2020. “A Roadmap for Cross-Border Data Flows: Future-Proofing
Readiness and Cooperation in the New Data Economy.” https://www3.weforum.
org/docs/WEF_A_Roadmap_for_Cross_Border_Data_Flows_2020.pdf.
WTO (World Trade Organization). 2022. “The Carbon Content of International Trade.”
Trade and Climate Change, Information Brief 4. https://www.wto.org/english/
news_e/news21_e/clim_03nov21-4_e.pdf.
Yale School of Public Health, Humanitarian Research Lab. 2022. “Ukraine’s Crop
Storage Infrastructure: Post-Invasion Impact Assessment.” Conflict
Observatory, September 15. https://hub.conflictobservatory.org/portal/apps/
sites/#/home/pages/grain-1.
Yellen, J. 2022a. “Remarks by Secretary of the Treasury Janet L. Yellen on Way Forward
for the Global Economy.” Transcript of speech delivered to Atlantic Council,
Washington, April 13. https://home.treasury.gov/news/press-releases/jy0714.
———. 2022b. “Remarks by Secretary of the Treasury Janet L. Yellen at LG
Sciencepark.” Transcript of speech delivered at LG Sciencepark, Seoul, July 19.
https://home.treasury.gov/news/press-releases/jy0880.

Chapter 4
Aizer, A., H. Hoynes, and A. Lleras-Muney. 2022. “Children and the U.S. Social Safety
Net: Balancing Disincentives for Adults and Benefits for Children.” Journal of
Economic Perspectives 36, no. 2: 149–74. https://www.aeaweb.org/
articles?id=10.1257/jep.36.2.149.
Alaimo, K., C. Olson, and E. Frongillo Jr. 2001. “Food Insufficiency and American
School-Aged Children’s Cognitive, Academic, and Psychosocial Development.”
Pediatrics 108, no. 1: 44–53. https://doi.org/10.1542/peds.108.1.44.
Almond, D., H. Hoynes, and D. Schanzenbach. 2011. “Inside the War on Poverty: The
Impact of Food Stamps on Birth Outcomes.” Review of Economics and
Statistics 93, no. 2: 387–403. https://doi.org/10.1162/REST_a_00089.
Asiedu, E., J. Freeman, and A. Nti-Addae. 2012. “Access to Credit by Small Businesses:
How Relevant Are Race, Ethnicity, and Gender.” American Economic Review
102, no. 3: 532–37. https://doi.org/10.1257/aer.102.3.532.
Athey, S., R. Chetty, G. Imbens, and H. Kang. 2019. The Surrogate Index: Combining
Short- Term Proxies to Estimate Long-Term Treatment Effects More Rapidly
and Precisely. NBER Working Paper 26463. Cambridge, MA: National Bureau
of Economic Research. https://www.nber.org/papers/w26463.

330 |

References

Bailey, M., H. Hoynes, M. Rossin-Slater, and R. Walker. 2020. Is the Social Safety Net a
Long-Term Investment? Large-Scale Evidence from the Food Stamps Program.
NBER Working Paper 26942. Cambridge, MA: National Bureau of Economic
Research. https://www.nber.org/papers/w26942.
Bailey, M., S. Sun, and B. Timpe. 2021. “Prep School for Poor Kids: The Long-Run
Impacts of Head Start on Human Capital and Economic Self-Sufficiency.”
American Economic Review 111, no. 12: 3963–4001. https://doi.org/10.1257/
aer.20181801.
Baker, M., J. Gruber, and K. Milligan. 2008. “Universal Child Care, Maternal Labor
Supply, and Family Well Being.” Journal of Political Economy 116, no. 4:
709–45. https://www.journals.uchicago.edu/doi/abs/10.1086/591908.
Banerjee, A., E. Gould, and M. Sawo. 2021. “Setting Higher Wages for Child Care and
Home Health Care Workers Is Long Overdue.” Economic Policy Institute.
https://files.epi.org/uploads/237703.pdf.
Barr, A., and C. Gibbs. 2022. “Breaking the Cycle? Intergenerational Effects of an
Antipoverty Program in Early Childhood.” Journal of Political Economy 130,
no. 12: 3253–85. https://doi.org/10.1086/720764.
Bassok, D., T. Dee, and S. Latham. 2019. “The Effects of Accountability Incentives in
Early Childhood Education.” Journal of Policy Analysis and Management 38,
no. 4: 838–66. https://onlinelibrary.wiley.com/doi/10.1002/pam.22149.
Bassok, D., J. Doromal, M. Michie, and V. Wong, 2021b. “The Effects of Financial
Incentives on Teacher Turnover in Early Childhood Settings: Experimental
Evidence from Virginia.” University of Virginia EdPolicyWorks. https://files.
elfsightcdn.com/022b8cb9-839c-4bc2-992e-cefccb8e877e/6de6fd54-e921-4c88a452-ad7cabccc362.pdf.
Bassok, D., M. Fitzpatrick, E. Greenberg, and S. Loeb. 2016. “Within- and BetweenSector Quality Differences in Early Childhood Education and Care.” Child
Development 87, no. 5. https://doi.org/10.1111/cdev.12551.
Bassok, D., A. Markowitz, L. Bellows, and K. Sadowski. 2021a. “New Evidence on
Teacher Turnover in Early Childhood.” Educational Evaluation and Policy
Analysis 43, no. 1: 172–80. https://doi.org/10.3102/0162373720985340.
Bauernschuster, S., and M. Schlotter. 2015. “Public Child Care and Mothers’ Labor
Supply: Evidence from Two Quasi-Expirements.” Journal of Public Economics
123: 1–16. https://doi.org/10.1016/j.jpubeco.2014.12.013.
Bhattacharya, J., J. Currie, and S. Haider. 2006. “Breakfast of Champions? The School
Breakfast Program and the Nutrition of Children and Families.” Journal of
Human Resources 41, no. 3: 445–66. http://jhr.uwpress.org/content/XLI/3/445.
short.
Bigelow, J., A. Pennington, K. Schaberg, and D. Jones. 2021. “A Guide for Using
Administrative Data to Examine Long-Term Outcomes in Program Evaluation.”
OPRE Report 145. https://www.acf.hhs.gov/sites/default/files/documents/opre/
T2P%20Guide_sept-2021.pdf.

References | 331

Blau, D. 1993. “The Supply of Child Care Labor.” Journal of Labor Economics 11, no. 2:
324–47. https://www.journals.uchicago.edu/doi/abs/10.1086/298299.
Blau, D., and J. Currie. 2006. “Pre-School, Day Care, and After-School Care: Who’s
Minding the Kids?” Handbook of the Economics of Education 2: 1163–1278.
https://doi.org/10.1016/S1574-0692(06)02020-4.
Blau, D., and E. Tekin. 2007. “The Determinants and Consequences of Child Care
Subsidies for Single Mothers in the USA.” Journal of Population Economics
20: 719–41. https://doi.org/10.1007/s00148-005-0022-2.
Blau, F., and L. Kahn. 2013. “Female Labor Supply: Why Is the United States Falling
Behind?” American Economic Review 103, no. 3: 251–56. https://www.aeaweb.
org/articles?id=10.1257/aer.103.3.251.
BLS (U.S. Bureau of Labor Statistics). 2021a. “Occupational Employment and Wage
Statistics.” https://www.bls.gov/oes/current/oes_stru.htm.
———. 2021b. “National Compensation Survey: Employee Benefits in the United
States.” https://www.bls.gov/ebs/home.htm.
———. 2022. “Table 4: Families with Own Children: Employment Status of Parents by
Age of Youngest Child and Family Type, 2020–2021 Annual Averages.” https://
www.bls.gov/news.release/famee.t04.htm.
———. 2023. “Table B-8: Average Hourly and Weekly Earnings of Production and
Nonsupervisory Employees on Private Nonfarm Payrolls by Industry Sector,
Seasonally Adjusted.” https://www.bls.gov/news.release/empsit.t24.htm.
Bodéré, P. 2023. “Dynamic Spatial Competition in Early Education: An Equilibrium
Analysis of the Preschool Market in Pennsylvania.” Working paper, New York
University, Department of Economics. https://pierrebodere.github.io/content/
bodere_jmp.pdf.
Boesch, T., R. Grunewald, R. Nunn, and V. Palmer. 2021. “Pandemic Pushes Mother of
Young Children Out of the Labor Force.” Federal Reserve Bank of
Minneapolis. https://www.minneapolisfed.org/article/2021/pandemic-pushesmothers-of-young-children-out-of-the-labor-force .
Borowsky, J., J. Brown, E. Davis, C. Gibbs, C. Herbst, A. Sojourner, E. Tekin, and M.
Wiswall. 2022. An Equilibrium Model of the Impact of Increased Public
Investment in Early Childhood Education. NBER Working Paper 30140.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w30140.
Bradley, V., S. Kuriwaki, M. Isakov, D. Sejdinovic, X. Meng, and S. Flaxman. 2021.
“Unrepresentative Big Surveys Significantly Overestimated U.S. Vaccine
Uptake.” Nature 600: 695–700. https://www.nature.com/articles/
s41586-021-04198-4?s=03.
Brown, J., and C. Herbst. 2022. “Childcare over the Business Cycle.” Journal of Labor
Economics 40, no. 1: 429–68. https://www.journals.uchicago.edu/doi/
full/10.1086/718189 .

332 |

References

Burnham, L., and N. Theodore. 2012. “Home Economics: The Invisible and Unregulated
World of Domestic Work.” National Domestic Workers Alliance. https://www.
domesticworkers.org/reports-and-publications/
home-economics-the-invisible-and-unregulated-world-of-domestic-work/.
Cajner, T., L. Feiveson, C. Kurz, and S. Tevlin. 2022. “Lessons Learned from the Use of
Nontraditional Data during COVID-19.” Hamilton Project and Hutchins Center
on Fiscal & Monetary Policy at Brookings. https://www.hamiltonproject.org/
assets/files/Lessons_Learned_from_the_Use_of_Nontraditional_Data_during_
COVID-19.pdf.
Campbell, F., G. Conti, J. Heckman, S. Moon, R. Pinto, E. Pungello, and Y. Pan. 2014.
“Early Childhood Investments Substantially Boost Adult Health.” Science 343,
no. 6178: 4178–85. https://doi.org/10.1126/science.1248429.
Cannon, J., G. Zellman, L. Karoly, and H. Schwartz. 2017. Quality Rating and
Improvement Systems for Early Care and Education Programs: Making the
Second Generation Better. Santa Monica, CA: RAND Corporation. https://doi.
org/10.7249/PE235.
Carson, J., and M. Mattingly. 2020. “COVID-19 Didn’t Create a Child Care Crisis, but
Hastened and Inflamed It.” Federal Reserve Bank of Boston and Carsey School
of Public Policy. https://carsey.unh.edu/publication/child-care-crisis-COVID-19.
Cascio, E. 2009. “Maternal Labor Supply and the Introduction of Kindergartens into
American Public Schools.” Journal of Human Resources 44, no. 1: 140–70.
https://doi.org/10.3368/jhr.44.1.140.
Cascio, E., and D. Schanzenbach. 2013. “The Impacts of Expanding Access to HighQuality Preschool Education.” Brookings Institution. https://www.brookings.
edu/bpea-articles/the-impacts-of-expanding-access-to-high-qualitypreschool-education/.
Caven, M., N. Khanani, X. Zhang, and C. Parker. 2021. “Center- and Program-Level
Factors Associated with Turnover in the Early Childhood Education
Workforce.” U.S. Department of Education, Institute of Education Sciences,
National Center for Education Evaluation and Regional Assistance. https://ies.
ed.gov/ncee/edlabs/regions/northeast/pdf/REL_2021069.pdf.
Center for American Progress. 2020. “U.S. Child Care Deserts.” https://childcaredeserts.org/.
Chetty, R., J. Friedman, N. Hilger, E. Saez, D. Schanzenbach, and D. Yagan. 2011. “How
Does Your Kindergarten Classroom Affect Your Earnings? Evidence from
Project Star.” Quarterly Journal of Economics 126, no. 4: 1593–1660. https://
doi.org/10.1093/qje/qjr041.
Chien, N. 2022. “Factsheet: Estimates of Child Care Eligibility & Receipt for Fiscal Year
2019.” Office of the Assistant Secretary for Planning and Evaluation, U.S.
Department of Health and Human Services. https://aspe.hhs.gov/sites/default/
files/documents/1d276a590ac166214a5415bee430d5e9/cy2019-child-caresubsidy-eligibility.pdf.

References | 333

Child Care Aware of America. 2022. “State Session Round Up, Summer 2022.” https://
info.childcareaware.org/blog/state-session-round-up-summer-2022.
Collins, C., L. Landivar, L. Ruppanner, and W. Scarborough. 2021. “COVID-19 and the
Gender Gap in Work Hours.” Gender Work Organ 28: 549–60. https://
onlinelibrary.wiley.com/doi/full/10.1111/gwao.12506.
Davis, E., C. Carlin, C. Krafft, and N. Forry. 2018. “Do Child Care Subsidies Increase
Employment Among Low-Income Parents?” Journal of Family and Economic
Issues 39: 662–82. https://doi.org/10.1007/s10834-018-9582-7.
Davis, E., and A. Sojourner. 2021. “Increasing Federal Investment in Children’s Early
Care and Education to Raise Quality, Access, and Affordability.” Brookings
Institution. https://www.brookings.edu/research/increasing-federal-investmentin-childrens-early-care-and-education-to-raise-quality-access-and-affordability/.
de Brey, C., L. Musu, J. McFarland, S. Wilkinson-Flicker, M. Diliberti, A. Zhang, C.
Branstetter, and X. Wang. 2019. “Status and Trends in the Education of Racial
and Ethnic Groups 2018.” U.S. Department of Education, NCES 2019-038.
https://nces.ed.gov/pubs2019/2019038.pdf.
Deming, D. 2009. “Early Childhood Intervention and Life-Cycle Skill Development:
Evidence from Head Start.” American Economic Journal: Applied Economics
1, no. 3: 111–34. https://doi.org/10.1257/app.1.3.111.
Doromal, J., D. Bassok, L. Bellows, and A. Markowitz. 2022. “Hard-to-Staff Centers:
Exploring Center-Level Variation in the Persistence of Child Care Teacher
Turnover.” Early Childhood Research Quarterly 61: 170–78. https://doi.
org/10.1016/j.ecresq.2022.07.007.
Duncan, G., and K. Magnuson. 2013. “Investing in Preschool Programs.” Journal of
Economic Perspectives 27, no. 2: 109–32. http://dx.doi.org/10.1257/
jep.27.2.109.
ECLKC (Early Childhood Learning & Knowledge Center). 2022. “Head Start Program
Facts: Fiscal Year 2021.” https://eclkc.ohs.acf.hhs.gov/about-us/article/
head-start-program-facts-fiscal-year-2021.
———. 2019. “Head Start Timeline.” https://eclkc.ohs.acf.hhs.gov/about-us/article/
head-start-timeline.
Finseraas, H., I. Hardoy, and P. Schøne. 2016. “School Enrolment and Mothers’ Labor
Supply: Evidence from a Regression Discontinuity Approach.” Review of
Economics of the Household 15: 621–38. https://doi.org/10.1007/
s11150-016-9350-0.
Fitzpatrick, M. 2010. “Preschoolers Enrolled and Mothers at Work? The Effects of
Universal Prekindergarten.” Journal of Labor Economics 28, no. 1: 51–85.
https://www.journals.uchicago.edu/doi/full/10.1086/648666#_i2.
———. 2012. “Revising Our Thinking About the Relationship Between Maternal Labor
Supply and Preschool.” Journal of Human Resources 47, no. 3: 583–612.
https://doi.org/10.3368/jhr.47.3.583.

334 |

References

Flood, S., J. McMurry, A. Sojourner, and M. Wiswall. 2022. “Inequality in Early Care
Experienced by U.S. Children.” Journal of Economic Perspectives 36, no. 2:
199–222. https://doi.org/10.1257/jep.36.2.199.
Frisvold, D. 2015. “Nutrition and Cognitive Achievement: An Evaluation of the School
Breakfast Program.” Journal of Public Economics 124: 91–204. https://doi.
org/10.1016/j.jpubeco.2014.12.003.
García, J., F. Bennhoff, D. Leaf, and J. Heckman. 2021. The Dynastic Benefits of Early
Childhood Education. NBER Working Paper 29004. Cambridge, MA: National
Bureau of Economic Research. https://doi.org/10.3386/w29004.
García, J., J. Heckman, D. Leaf, and M. Prados. 2020. “Quantifying the Life-Cycle
Benefits of an Influential Early-Childhood Program.” Journal of Political
Economy 128, no. 7: 2502–41. https://doi.org/10.1086/705718.
García, J., J. Heckman, and V. Ronda. 2021. The Lasting Effects of Early Childhood
Education on Promoting the Skills and Social Mobility of Disadvantaged
African Americans. NBER Working Paper 29057. Cambridge, MA: National
Bureau of Economic Research. https://doi.org/10.3386/w29057.
Gelbach, J. 2002. “Public Schooling for Young Children and Maternal Labor Supply.”
American Economic Review 92, no. 1: 307–22. https://www.jstor.org/
stable/3083335.
Ghertner, R., and A. Schreier. 2022. “Young Children’s Geographic Access to Head Start
Preschool, 2022.” Office of the Assistant Secretary for Planning and
Evaluation, U.S. Department of Health and Human Services. https://aspe.hhs.
gov/sites/default/files/documents/73453cd79dfe28592a0396ca2a0c75ee/
Geographic-Access-Head-Start-Brief.pdf.
Glenn, E. 2012. Forced to Care: Coercion and Caregiving in America. Cambridge, MA:
Harvard University Press. https://www.hup.harvard.edu/catalog.
php?isbn=9780674064157.
Goldstein, D. 2022. “With Child Care Scarce, States Try to Fix ‘a Broken Market.’” New
York Times, June 18. https://www.nytimes.com/2022/06/18/us/child-care-stateregulations.html.
Gould, E. 2015. “Child Care Workers Aren’t Paid Enough to Make Ends Meet.”
Economic Policy Institute. https://www.epi.org/publication/child-careworkers-arent-paid-enough-to-make-ends-meet/.
Gould, E., M. Sawo, and A. Banerjee. 2021. “Care Workers Are Deeply Undervalued and
Underpaid.” Economic Policy Institute, Working Economics Blog. https://www.
epi.org/blog/care-workers-are-deeply-undervalued-and-underpaid-estimatingfair-and-equitable-wages-in-the-care-sectors/.
Gray-Lobe, G., P. Pathak, and C. Walters. 2023. “The Long-Term Effects of Universal
Preschool in Boston.” Quarterly Journal of Economics 138, no. 1: 363–411.
https://doi.org/10.1093/qje/qjac036.

References | 335

Grunewald, R., and P. Davies. 2011. “Hardly Child’s Play: Times Have Been Even
Tougher than Usual for District Childcare Providers.” Federal Reserve Bank of
Minneapolis. https://www.minneapolisfed.org/article/2011/hardly-childs-play.
Grunewald, R., R. Nunn, and V. Palmer. 2022. “Examining Teacher Turnover in Early
Care and Education.” Federal Reserve Bank of Minneapolis. https://www.
minneapolisfed.org/article/2022/
examining-teacher-turnover-in-early-care-and-education.
Gundersen, C., B. Kreider, and J. Pepper. 2012. “The Impact of the National School
Lunch Program on Child Health: A Nonparametric Bounds Analysis.” Journal
of Econometrics 166, no. 1: 79–91. https://doi.org/10.1016/j.jeconom.
2011.06.007.
Hatfield, B., M. Burchinal, R. Pianta, and J. Sideris. 2016. “Thresholds in the Association
Between Quality of Teacher–Child Interactions and Preschool Children’s
School Readiness Skills.” Early Childhood Research Quarterly 36: 561–71.
https://www.sciencedirect.com/science/article/abs/pii/S0885200615300053.
Heckman, J. 2008. “Schools, Skills, and Synapses.” Economic Inquiry 46, no. 3:
289–324. https://doi.org/10.1111/j.1465-7295.2008.00163.x.
Heckman, J., and T. Kautz. 2014. “Fostering and Measuring Skills Interventions that
Improve Character and Cognition.” In The Myth of Achievement Tests: The
GED and the Role of Character in American Life, edited by J. Heckman, J.
Humphries, and T. Kautz. Chicago: University of Chicago Press. https://doi.
org/10.7208/chicago/9780226100128.003.0009.
Heckman, J., and D. Masterov. 2007. “The Productivity Argument for Investing in Young
Children.” Review of Agricultural Economics 29, no. 3: 446–93. https://doi.
org/10.1111/j.1467-9353.2007.00359.x.
Heckman, J., S. Moon, R. Pinto, P. Savelyev, and A. Yavitz. 2010. “The Rate of Return to
the HighScope Perry Preschool Program.” Journal of Public Economics 94, no.
1: 114–28. https://doi.org/10.1016/j.jpubeco.2009.11.001.
Heflin, C., I. Arteaga, and S. Gable. 2015. “The Child and Adult Care Food Program and
Food Insecurity.” Social Service Review 89, no. 1: 77–98. https://www.journals.
uchicago.edu/doi/abs/10.1086/679760.
Heggeness, M. 2020. “Estimating the Immediate Impact of the COVID-19 Shock on
Parental Attachment to the Labor Market and the Double Bind of Mothers.”
Review of Economics of the Household 18: 1053–78. https://link.springer.com/
article/10.1007/s11150-020-09514-x.
Hendren, N., and B. Sprung-Keyser. 2020. “A Unified Welfare Analysis of Government
Policies.” Quarterly Journal of Economics 135, no. 3: 1209–1318. https://doi.
org/10.1093/qje/qjaa006.
Herbst, C. 2017. “Universal Child Care, Maternal Employment, and Children’s Long-Run
Outcomes: Evidence from the U.S. Lanham Act of 1940.” Journal of Labor
Economics 35, no. 2. https://doi.org/10.1086/689478.

336 |

References

———. 2022. “Child Care in the United States: Markets, Policy, and Evidence.” Journal
of Policy Analysis and Management. https://doi.org/10.1002/pam.22436.
Herbst, C., and E. Tekin, 2011. “Do Child Care Subsidies Influence Single Mothers’
Decision to Invest in Human Capital?” Economics of Education Review 30, no.
5: 901–12. https://doi.org/10.1016/j.econedurev.2011.03.006.
Hinrichs, P. 2010. “The Effects of the National School Lunch Program on Education and
Health.” Journal of Policy Analysis and Management 29, no. 3: 479–505.
https://doi.org/10.1002/pam.20506.
Hoynes, H., M. Page, and A. Stevens. 2011. “Can Targeted Transfers Improve Birth
Outcomes? Evidence from the Introduction of the WIC Program.” Journal of
Public Economics 95, nos. 7–8: 813–27. https://doi.org/10.1016/j.
jpubeco.2010.12.006.
Hoynes, H., D. Schanzenbach, and D. Almond. 2016. “Long-Run Impacts of Childhood
Access to the Safety Net.” American Economic Review 106, no. 4: 903–34.
https://www.aeaweb.org/articles?id=10.1257/aer.20130375.
Imberman, S., and A. Kugler. 2014. “The Effect of Providing Breakfast in Class on
Student Performance.” Journal of Policy Analysis and Management 33, no. 3:
669–99. https://doi.org/10.1002/pam.21759.
IRS (Internal Revenue Service). 2022. “Child and Dependent Care Credit FAQs.” https://
www.irs.gov/newsroom/child-and-dependent-care-credit-faqs.
Jessen-Howard, S., R. Malik, and M. Falgout. 2020. “Costly and Unavailable: America
Lacks Sufficient Child Care Supply for Infants and Toddlers.” Center for
American Progress. https://www.americanprogress.org/article/
costly-unavailable-america-lacks-sufficient-child-care-supply-infants-toddlers/.
Johnson, R., and C. Jackson. 2019. “Reducing Inequality through Dynamic
Complementarity: Evidence from Head Start and Public School Spending.”
American Economic Journal: Economic Policy 11, no. 4: 310–49. https://doi.
org/10.1257/pol.20180510.
King, E., A. Johnson, D. Cassidy, Y. Wang, J. Lower, and V. Kintner-Duffy. 2016.
“Preschool Teachers’ Financial Well-Being and Work Time Supports:
Associations with Children’s Emotional Expressions and Behaviors in
Classrooms.” Early Childhood Education Journal 44: 545–53. https://link.
springer.com/article/10.1007/s10643-015-0744-z.
Knudsen, E., J. Heckman, J. Cameron, and J. Shonkoff. 2006. “Economic,
Neurobiological, and Behavioral Perspectives on Building America’s Future
Workforce.” Proceedings of the National Academy of Sciences 103, no. 27:
10155–62. https://doi.org/10.1073/pnas.0600888103.
Korenman, S., K. Abner, R. Kaestner, and R. Gordon. 2013. “The Child and Adult Care
Food Program and the Nutrition of Preschoolers.” Early Childhood Research
Quarterly 28, no. 2: 325–36. https://doi.org/10.1016/j.ecresq.2012.07.007.
Landivar, L., N. Graf, and G. Rayo. 2023. “Childcare Prices in Local Areas: Initial
Findings from the National Database of Childcare Prices.” U.S. Department of
References | 337

Labor, Women’s Bureau Issue Brief. https://www.dol.gov/sites/dolgov/files/
WB/NDCP/WB_IssueBrief-NDCP-final.pdf.
Lefebvre, P., and P. Merrigan. 2008. “Child Care Policy and the Labor Supply of Mothers
with Young Children: A Natural Experiment from Canada.” Journal of Labor
Economics 26, no. 3: 519–48. https://doi.org/10.1086/587760.
Livingston, G. 2018. “Stay-at-Home Moms and Dads Account for About One-in-Five
U.S. Parents.” Pew Research Center. https://www.pewresearch.org/facttank/2018/09/24/stay-at-home-moms-and-dads-account-for-about-onein-five-u-s-parents/.
Malik, R., K. Hamm, L. Schochet, C. Novoa, S. Workman, and S. Jessen-Howard. 2018.
“America’s Child Care Deserts in 2018.” Center for American Progress. https://
www.americanprogress.org/article/americas-child-care-deserts-2018/.
Mocan, N. 2007. “Can Consumers Detect Lemons? An Empirical Analysis of Information
Asymmetry in the Market for Child Care.” Journal of Population Economics
20: 743–80. http://www.jstor.org/stable/40344407.
Moloney, M. 2021. “Ireland’s Reform Agenda: Transforming the Early Childhood
Education and Care Sector into One of the Best in the World.” International
Perspectives on Early Childhood Teacher Education in the 21st Century,
93–109. https://doi.org/10.1007/978-981-16-5739-9_7.
Morrissey, T. 2017. “Child Care and Parent Labor Force Participation: A Review of the
Research Literature.” Review of Economics of the Household 15: 1–24. https://
doi.org/10.1007/s11150-016-9331-3.
Mueller, E. 2020. “‘Crashing Down’: How the Child Care Crisis Is Magnifying Racial
Disparities.” Politico. https://www.politico.com/news/2020/07/22/
coronavirus-child-care-racial-disparities-377058.
National Household Education Surveys Program. 2019. “Young Children’s Care and
Education Before Kindergarten.” National Center for Education Statistics.
https://nces.ed.gov/nhes/young_children.asp.
National Women’s Business Council. 2020. “Annual Report 2020.” https://cdn.www.
nwbc.gov/wp-content/uploads/2020/12/21113833/pdf/NWBC-2020-AnnualReport-Final.pdf.
National Women’s Law Center. 2018. “Family, Friend, and Neighbor Care: Facts and
Figures.” https://d3n8a8pro7vhmx.cloudfront.net/rrnetwork/pages/103/
attachments/original/1522790259/family_friend___neighbor_care_fact_sheetfinal.pdf?1522790259.
NCES (National Center for Education Statistics). 2016. “Teacher Turnover: Stayers,
Movers, and Leavers.” Condition of Education, U.S. Department of Education,
Institute of Education Sciences. https://nces.ed.gov/programs/coe/pdf/coe_slc.pdf.
———. 2018. “Early Childhood Care Arrangements: Choices and Costs.” Condition of
Education, U.S. Department of Education, Institute of Education Sciences.
https://nces.ed.gov/programs/coe/indicator/tca.

338 |

References

———. 2022. “Enrollment Rates of Young Children.” Condition of Education, U.S.
Department of Education, Institute of Education Sciences. https://nces.ed.gov/
programs/coe/indicator/cfa.
NSECE (National Survey of Early Care and Education). 2019. “Home-Based Public-Use
Data File.” https://www.childandfamilydataarchive.org/cfda/archives/cfda/
studies/37941/datadocumentation.
———. 2020. “National Survey of Early Care and Education 2019: Household
Questionnaire.” https://www.acf.hhs.gov/opre/report/
national-survey-early-care-and-education-2019-household-questionnaire.
OECD (Organization for Economic Cooperation and Development). 2011. “Encouraging
Quality in Early Childhood Education and Care.” OECD Network on Early
Childhood Education and Care, Survey for the Quality Toolbox and ECE
Portal. https://www.oecd.org/education/school/48483436.pdf.
———. 2016. “Parental Leave: Where Are the Fathers?” https://www.oecd.org/policybriefs/parental-leave-where-are-the-fathers.pdf.
———. 2019. “Education at a Glance 2019: OECD Indicators.” https://www.oecdilibrary.org/sites/f8d7880d-en/1/2/3/2/index.html?itemId=/content/publication/
f8d7880d-en&mimeType=text/html&_csp_=b2d87f13821f45339443c7ca94aafe
46&itemIGO=oecd&itemContentType=book.
———. 2021. “Public Spending on Childcare and Early Education?” https://www.oecd.
org/els/soc/PF3_1_Public_spending_on_childcare_and_early_education.pdf.
———. No date. “OECD.stat Enrollment Rates by Age.” https://stats.oecd.org/Index.
aspx?datasetcode=EAG_ENRL_RATE_AGE.
Office of Child Care. 2011. “A Foundation for Quality Improvement Systems: State
Licensing, Preschool, and QRIS Program Quality Standards.” U.S. Department
of Health and Human Services, Administration for Children and Families.
https://childcareta.acf.hhs.gov/sites/default/files/public/a_foundation_for_
quality.pdf.
———. 2022. “Child Care and Development Fund Reauthorization.” U.S. Department of
Health and Human Services, Administration for Children and Families. https://
www.acf.hhs.gov/occ/ccdf-reauthorization.
———. No date. “Ratios and Group Sizes.” U.S. Department of Health and Human
Services, Administration for Children and Families. https://childcare.gov/
consumer-education/ratios-and-group-sizes.
Office of Early Childhood Development. 2022. “Preschool Development Birth through
Five (PDG B–5).” https://www.acf.hhs.gov/ecd/early-learning/
preschool-development-grants.
OESE (Office of Elementary and Secondary Education). 2023. “Preschool Development
Grant–Birth through Five.” https://oese.ed.gov/offices/office-of-discretionarygrants-support-services/innovation-early-learning/
preschool-development-grants/.

References | 339

Olivetti, C., and B. Petrongolo. 2017. “The Economic Consequences of Family Policies:
Lessons from a Century of Legislation in High-Income Countries.” Journal of
Economic Perspectives 31, no. 1: 205–50. https://pubs.aeaweb.org/doi/
pdfplus/10.1257/jep.31.1.205.
Parker, M. 2015. “Women More Than Men Adjust Their Careers for Family Life.” Pew
Research Center. https://www.pewresearch.org/fact-tank/2015/10/01/
women-more-than-men-adjust-their-careers-for-family-life/.
Pew Research Center. 2022. “Working Mothers Are More Likely Than Working Fathers
to Say They Felt Like They Could Not Give 100% At Work, Needed to Reduce
Their Hours During the Pandemic.” https://www.pewresearch.org/facttank/2022/05/06/working-moms-in-the-u-s-have-faced-challenges-on-multiple
-fronts-during-the-pandemic/ft_2021-01-26_workingparents_04-2/.
Pianta, R. 1997. “Adult–Child Relationship Processes and Early Schooling.” Early
Education and Development 8, no. 1: 11–26. https://doi.org/10.1207/
s15566935eed0801_2.
Porter, T., D. Paulsell, P. Del Grosso, S. Avellar, R. Hass, and L. Vuong. 2010. A Review
of the Literature on Home-Based Child Care: Implications for Future
Directions. Princeton, NJ: Mathematica Policy Research. https://mathematica.
org/publications/a-review-of-the-literature-on-homebased-child-careimplications-for-future-directions.
Ritchie, L., M. Boyle, K. Chandran, P. Spector, S. Whaley, P. James, S. Samuels, K.
Hecht, and P. Crawford. 2012. “Participation in the Child and Adult Care Food
Program Is Associated with More Nutritious Foods and Beverages in Child
Care.” Childhood Obesity 8, no. 3. https://doi.org/10.1089/chi.2011.0061.
Ritchie, L., S. Sharma, G. Gildengorin, S. Yoshida, E. Braff-Guajardo, and P. Crawford.
2015. “Policy Improves What Beverages Are Served to Young Children in
Child Care.” Journal of the Academy of Nutrition and Dietetics 115, no. 5:
724–30. https://doi.org/10.1016/j.jand.2014.07.019.
Sabol, T., and R. Pianta. 2012. “Recent Trends in Research on Teacher–Child
Relationships.” Attachment & Human Development 14, no. 3: 213–31. https://
doi.org/10.1080/14616734.2012.672262.
Schanzenbach, D., and M. Zaki. 2014. Expanding the School Breakfast Program: Impacts
on Children’s Consumption, Nutrition, and Health. NBER Working Paper
20308. Cambridge, MA: National Bureau of Economic Research. https://www.
nber.org/system/files/working_papers/w20308/w20308.pdf.
Schlieber, M., and C. McLean. 2020. “Educator Work Environments Are Children’s
Learning Environments: How and Why They Should be Improved.” Center for
the Study of Child Care Employment. https://cscce.berkeley.edu/blog/
educator-work-environments-are-childrens-learning-environments-how-andwhy-they-should-be-improved/.

340 |

References

Shonkoff, J., and D. Phillips. 2000. From Neurons to Neighborhoods: The Science of
Early Childhood Development. Washington: National Academies Press. https://
doi.org/10.17226/9824
Smith, L., K. McHenry, and H. Mullaly. 2021. “What Is the Employer-Provided Child
Care Credit (45F)?” Bipartisan Policy Center. https://bipartisanpolicy.org/blog/
what-is-employer-provided-child-care-credit-45f/.
Tekin, E. 2005. “Child Care Subsidy Receipt, Employment, and Child Care Choices of
Single Mothers.” Economics Letters 89, no. 1: 1–6. https://doi.org/10.1016/j.
econlet.2005.03.005.
———. 2007. “Childcare Subsidies, Wages, and Employment of Single Mothers.”
Journal of Human Resources 42, no. 2: 453–87. https://doi.org/10.3368/jhr.
XLII.2.453.
Tüzemen, D. 2021. “Women Without a College Degree, Especially Minority Mothers,
Face a Steeper Road to Recovery.” Federal Reserve Bank of Kansas City.
https://www.kansascityfed.org/Economic%20Review/documents/8243/
EconomicReviewV106N3Tuzemen.pdf.
U.S. Census Bureau. 2018. “CBP and NES Combined Report.” https://www.census.gov/
data/tables/2018/econ/cbp/2018-combined-report.html.
———. 2021a. “Food Security.” https://www.census.gov/data/datasets/time-series/demo/
cps/cps-supp_cps-repwgt/cps-food-security.html.
———. 2021b. “School Enrollment in the United States: October 2019: Detailed Tables.”
https://www.census.gov/data/tables/2019/demo/school-enrollment/2019-cps.
html.
USDA (U.S. Department of Agriculture). 2022a. “Key Statistics & Graphics.” https://
www.ers.usda.gov/topics/food-nutrition-assistance/food-security-in-the-u-s/
key-statistics-graphics/#children.
———. 2022b. “Summer Electronic Benefit Transfer for Children (SEBTC).” https://
www.fns.usda.gov/ops/summer-electronic-benefit-transfer-children-sebtc.
U.S. Department of Health and Human Services. 2005. “Head Start Impact Study: First
Year Findings.” Administration for Children and Families. https://files.eric.
ed.gov/fulltext/ED543015.pdf.
———. 2020. “National Survey of Early Care and Education 2019: Household
Questionnaire.” Administration for Children and Families. https://www.acf.hhs.
gov/opre/report/national-survey-early-care-and-education-2019-householdquestionnaire.
———. 2022. “ARP Child Care Stabilization Funding State Fact Sheets.” https://www.
acf.hhs.gov/occ/map/arp-act-stabilization-funding-state-fact-sheets.
U.S. Department of the Treasury. 2021. “The Economics of Child Care Supply in the
United States.” https://home.treasury.gov/system/files/136/The-Economics-ofChildcare-Supply-09-14-final.pdf.

References | 341

Vinovskis, M. 2005. The Birth of Head Start: Preschool Education Policies in the
Kennedy and Johnson Administrations. Chicago: University Chicago Press.
https://press.uchicago.edu/ucp/books/book/chicago/B/bo3533726.html.
Weiland, C., and H. Yoshikawa. 2013. “Impacts of a Prekindergarten Program on
Children’s Mathematics, Language, Literacy, Executive Function, and
Emotional Skills.” Child Development 84, no. 6: 2112–30. https://doi.
org/10.1111/cdev.12099.
Wesnes, K., C. Pincock, D. Richardson, G. Helm, and S. Hails. 2003. “Breakfast Reduces
Declines in Attention and Memory Over the Morning in Schoolchildren.”
Appetite 41, no. 3: 329–31. https://doi.org/10.1016/j.appet.2003.08.009.
Whitebook, M., C. McLean, L. Austin, and B. Edwards. 2018. “Early Childhood
Workforce Index 2018.” Center for the Study of Child Care Employment.
https://cscce.berkeley.edu/wp-content/uploads/2022/04/Early-ChildhoodWorkforce-Index-2018.pdf.
White House. 2022. “Fact Sheet: American Rescue Plan Funds Provided a Critical
Lifeline to 200,000 Child Care Providers—Helping Millions of Families to
Work.” https://www.whitehouse.gov/briefing-room/statementsreleases/2022/10/21/fact-sheet-american-rescue-plan-funds-provided-a-criticallifeline-to-200000-child-care-providers-helping-millions-of-families-to-work/.
Wikle, J., and R. Wilson. 2022. “Access to Head Start and Maternal Labor Supply:
Experimental and Quasi-Experimental Evidence.” Journal of Labor Economics.
https://doi.org/10.1086/720980.
Workman, S. 2018. “Where Does Your Child Care Dollar Go?” Center for American
Progress. https://www.americanprogress.org/article/child-care-dollar-go/.
Yellen, J. 2020. “The History of Women’s Work and Wages and How It Has Created
Success for Us All.” Brookings Institution. https://www.brookings.edu/essay/
the-history-of-womens-work-and-wages-and-how-it-has-created-success-forus-all/.

Chapter 5
Acton, R. 2021. “Effects of Reduced Community College Tuition on College Choices and
Degree Completion.” Education Finance and Policy 16, no. 3: 388–417. https://
doi.org/10.1162/edfp_a_00313.
Agarwal, S., J. Driscoll, X. Gabaix, and D. Laibson. 2009. “The Age of Reason: Financial
Decisions over the Life Cycle and Implications for Regulation.” Brookings
Papers on Economic Activity, no. 2. https://www.brookings.edu/wp-content/
uploads/2016/07/2009b_bpea_agarwal-1.pdf.
Aghion, P., L. Boustan, C. Hoxby, and J. Vandenbussche. 2009. “The Causal Impact of
Education on Economic Growth: Evidence from the U.S.” Brookings Papers on
Economic Activity, no. 1: 1−73. https://scholar.harvard.edu/files/aghion/files/
causal_impact_of_education.pdf.

342 |

References

Alsan, M., O. Garrick, and G. Graziani. 2019. “Does Diversity Matter for Health?
Experimental Evidence from Oakland.” American Economic Review 109, no.
12: 4071−111. https://doi.org/10.1257/aer.20181446.
Alpert, W., K. Couch, and O. Harmon. 2016. “A Randomized Assessment of Online
Learning.” American Economic Review 106: 378–82. https://doi.org/10.1257/
aer.p20161057.
An, B., and J. Taylor. 2019. “A Review of Empirical Studies on Dual Enrollment:
Assessing Educational Outcomes.” Higher Education: Handbook of Theory and
Research 34: 99–151. https://nacep.org/docs/briefs/An%20&%20Taylor%20
(2019).pdf.
Andrews, R., J. Li, and M. Lovenheim. 2016. “Quantile Treatment Effects of College
Quality on Earnings.” Journal of Human Resources 51, no. 1: 200–238. https://
doi.org/10.3368/jhr.51.1.200.
Armona, L., and S. Cao. 2022. “Redesigning Federal Student Aid in Sub-Baccalaureate
Education.” Social Science Research Network. https://ssrn.com/
abstract=4300755.
Armona, L., R. Chakrabarti, and M. Lovenheim. 2022. “Student Debt and Default: The
Role of For-Profit Colleges.” Journal of Financial Economics 144, no. 1:
67–92. https://doi.org/10.1016/j.jfineco.2021.12.008.
Bahr, P. 2016. “The Earnings of Community College Graduates in California.” Working
paper, Center for Analysis of Postsecondary Education and Employment.
https://capseecenter.org/wp-content/uploads/2016/12/the-earnings-ofcommunity-college-graduates-in-california.pdf.
Bahr, P., S. Dynarski, B. Jacob, D. Kreisman, A. Sosa, and M. Wiederspan. 2015. “Labor
Market Returns to Community College Awards: Evidence from Michigan.”
Working paper, Center for Analysis of Postsecondary Education and
Employment. https://capseecenter.org/labor-market-returns-michigan/.
Bailey, M., and S. Dynarski. 2011. Gains and Gaps: Changing Inequality in U.S. College
Entry and Completion. NBER Working Paper 17633. Cambridge, MA: National
Bureau of Economic Research. https://doi.org/10.3386/w17633.
Baird, M., M. Kofoed, T. Miller, and J. Wenger. 2022. “Veteran Educators or
For-Profiteers? Tuition Responses to Changes in the Post-9/11 GI Bill.” Journal
of Policy Analysis and Management 41, no. 4: 1012−39. https://doi.
org/10.1002/pam.22408.
Baker, D. 2020. “‘Name and Shame’: An Effective Strategy for College Tuition
Accountability?” Educational Evaluation and Policy Analysis 42, no. 3:
393–416. https://doi.org/10.3102/0162373720937672.
Baker, R., E. Bettinger, B. Jacob, and I. Marinescu. 2018. “The Effect of Labor Market
Information on Community College Students’ Major Choice.” Economics of
Education Review 65: 18–30. https://doi.org/10.1016/j.econedurev.2018.05.005.
Barnes, M., L. Bauer, W. Edelberg, S. Estep, R. Greenstein, and M. Macklin. 2021. “The
Social Insurance System in the U.S.: Policies to Protect Workers and Families.”
References | 343

Brookings Institution. https://www.brookings.edu/wp-content/uploads/2021/06/
Social-Insurance-FP_v4.5.pdf.
Barr, A., L. Kawano, B. Sacerdote, W. Skimmyhorn, and M. Stevens. 2021. You Can’t
Handle the Truth: The Effects of the Post-9/11 G.I. Bill on Higher Education
and Earnings. NBER Working Paper 29024. Cambridge, MA: National Bureau
of Economic Research. https://doi.org/10.3386/w29024.
Barr, A., and S. Turner. 2015. “Out of Work and Into School: Labor Market Policies and
College Enrollment during the Great Recession.” Journal of Public Economics
124: 63–73. https://doi.org/10.1016/j.jpubeco.2014.12.009.
———. 2018. “A Letter and Encouragement: Does Information Increase Postsecondary
Enrollment of UI Recipients?” American Economic Journal: Economic Policy
10, no. 3: 42–68. https://www.jstor.org/stable/26529036.
Barr, N. 2004. “Higher Education Funding.” Oxford Review of Economic Policy 20, no.
2: 264–83. https://doi.org/10.1093/oxrep/grh015.
Barr, N., B. Chapman, L. Dearden, and S. Dynarski. 2019. “The U.S. College Loans
System: Lessons from Australia and England.” Economics of Education Review
71: 32–48. https://doi.org/10.1016/j.econedurev.2018.07.007.
Barrow, L., and O. Malamud. 2015. “Is College a Worthwhile Investment?” Annual
Review of Economics 7: 519–55. https://doi.org/10.1146/
annurev-economics-080614-115510.
Baum, S., K. Payea, and P. Steele. 2009. “Trends in Student Aid.” College Board. https://
research.collegeboard.org/media/pdf/trends-student-aid-2009-full-report.pdf.
Belfield, C. 2015. “Weathering the Great Recession with Human Capital? Evidence on
Labor Market Returns to Education from Arkansas.” Working paper, Center for
Analysis of Postsecondary Education and Employment. https://files.eric.ed.gov/
fulltext/ED562519.pdf.
Belfield, C., and T. Bailey. 2017. “The Labor Market Returns to Sub-Baccalaureate
College: A Review.” Working paper, Center for Analysis of Postsecondary
Education and Employment. https://capseecenter.org/wp-content/
uploads/2017/04/labor-market-returns-sub-baccalaureate-college-review.pdf.
Beshears, J., J. Choi, D. Laibson, and B. Madrian. 2008. “How Are Preferences
Revealed?” Journal of Public Economics 92, nos. 8−9: 1787−94. https://doi.
org/10.1016/j.jpubeco.2008.04.010.
Bettinger, E., and R. Baker. 2014. “The Effects of Student Coaching: An Evaluation of a
Randomized Experiment in Student Advising.” Educational Evaluation and
Policy Analysis 36, no. 1. https://doi.org/10.3102/0162373713500523.
Bettinger, E., L. Fox, S. Loeb, and E. Taylor. 2017. “Virtual Classrooms: How Online
College Courses Affect Student Success.” American Economic Review 107, no.
9: 2855–75. https://www.aeaweb.org/articles?id=10.1257/aer.20151193.
Bettinger, E., B. Long, P. Oreopoulos, and L. Sanbonmatsu. 2012. “The Role of
Application Assistance and Information in College Decisions: Results from the

344 |

References

H&R Block Fafsa Experiment.” Quarterly Journal of Economics 127, no. 3:
1205–42. https://doi.org/10.1093/qje/qjs017.
Bettinger, E., and A. Soliz. 2016. “Returns to Vocational Credentials: Evidence from Ohio’s
Community and Technical Colleges.” Working paper, Center for Analysis of
Postsecondary Education and Employment. https://www.capseecenter.org/
wp-content/uploads/2016/10/returns-to-vocational-credentials.pdf.
Betts, J., and L. McFarland. 1995. “Safe Port in a Storm: The Impact of Labor Market
Conditions on Community College Enrollments.” Journal of Human Resources
30, no. 4: 741–65. https://doi.org/10.2307/146230.
Bird, K., B. Castleman, and G. Lohner. 2022. “Negative Impacts from the Shift to Online
Learning during the COVID-19 Crisis: Evidence from a Statewide Community
College System.” Annenberg Institute, EdWorkingPaper 20-299. https://www.
edworkingpapers.com/sites/default/files/ai20-299.pdf.
Birdsall, C. 2018. “Performance Management in Public Higher Education: Unintended
Consequences and Implications of Organizational Diversity.” Public
Performance & Management Review 41, no. 4: 669–95. https://doi.org/10.1080/
15309576.2018.1481116.
Black, S., J. Denning, and J. Rothstein. 2020. Winners and Losers? The Effect of Gaining
and Losing Access to Selective Colleges on Education and Labor Market
Outcomes. NBER Working Paper 26821. Cambridge, MA: National Bureau of
Economic Research. https://doi.org/10.3386/w26821.
Blagg, K., and M. Chingos. 2016. “Choice Deserts: How Geography Limits the Potential
Impact of Earnings Data on Higher Education.” Urban Institute. https://www.
urban.org/sites/default/files/publication/86581/choice_deserts_3.pdf.
Bleemer, Z. 2021. “Top Percent Policies and the Return to Postsecondary Selectivity.”
Working paper, University of California. https://zacharybleemer.com/
wp-content/uploads/2020/10/ELC_Paper.pdf.
Blume, G., E. Meza, D. Bragg, and I. Love. 2019. “Estimating the Impact of Nation’s
Largest Single Investment in Community Colleges.” Center on Education and
Labor Education Policy. https://www.newamerica.org/education-policy/reports/
estimating-impact-taaccct/.
Boatman, A., B. Evans, and A. Soliz. 2017. “Understanding Loan Aversion in Education:
Evidence from High School Seniors, Community College Students, and
Adults.” Aera Open 3, no. 1: 1–16. https://journals.sagepub.com/doi/
pdf/10.1177/2332858416683649.
Bound, J., B. Braga, G. Khanna, and S. Turner. 2021. The Globalization of Postsecondary
Education: The Role of International Students in the U.S. Higher Education
System. NBER Working Paper 28342. Cambridge, MA: National Bureau of
Economic Research. https://www.nber.org/papers/w28342.
Bound, J., M. Lovenheim, and S. Turner. 2010. “Why Have College Completion Rates
Declined? An Analysis of Changing Student Preparation and Collegiate

References | 345

Resources.” American Economic Journal: Applied Economics 2, no. 3: 129–57.
https://doi.org/10.1257/app.2.3.129.
———. 2012. “Increasing Time to Baccalaureate Degree in the United States.” Education
Finance and Policy 7, no. 4: 375–424. https://doi.org/10.1162/EDFP_a_00074.
Bound, J., and S. Turner. 2007. “Cohort Crowding: How Resources Affect Collegiate
Attainment.” Journal of Public Economics 91, no. 5: 877–99. https://doi.
org/10.1016/j.jpubeco.2006.07.006.
Bowen, W., M. Chingos, K. Lack, and T. Nygren. 2014. “Interactive Learning Online at
Public Universities: Evidence from a Six-Campus Randomized Trial.” Journal
of Policy Analysis and Management 33: 94–111. https://doi.org/10.1002/
pam.21728.
Bowen, W., M. Chingos, and M. McPherson. 2009. Crossing the Finish Line: Completing
College at America’s Public Universities. Princeton, NJ: Princeton University
Press. https://press.princeton.edu/books/paperback/9780691149905/
crossing-the-finish-line.
Cellini, R. 2009. “Crowded Colleges and College Crowd-Out: The Impact of Public
Subsidies on the Two-Year College Market.” American Economic Journal:
Economic Policy, no. 2: 1–30. https://www.aeaweb.org/articles?id=10.1257/
pol.1.2.1.
Cellini, S., and L. Chaudhary. 2020. “Commercials for College? Advertising in Higher
Education.” Brookings Institution. https://www.brookings.edu/research/
commercials-for-college-advertising-in-higher-education/.
Cellini, S., R. Darolia, and L. Turner. 2020. “Where Do Students Go When For-Profit
Colleges Lose Federal Aid?” American Economic Journal: Economic Policy
12, no. 2: 46−83. https://doi.org/10.1257/pol.20180265.
Cellini, S., and C. Koedel. 2017. “The Case for Limiting Federal Student Aid to
For-Profit Colleges.” Journal of Policy Analysis and Management 36, no. 4:
934–42. https://doi.org/10.1002/pam.22008.
Cellini, S., and N. Turner. 2019. “Gainfully Employed? Assessing the Employment and
Earnings of For-Profit College Students Using Administrative Data.” Journal of
Human Resources 54, no. 2: 342–70. https://doi.org/10.3368/
jhr.54.2.1016.8302R1.
Century Foundation. 2021. “Voters Overwhelmingly Support Guardrails on For-Profit
Colleges, Finds TCF and Data for Progress Poll.” https://tcf.org/content/
about-tcf/voters-overwhelmingly-support-guardrails-profit-collegesfinds-tcf-data-progress-poll/.
Chakrabarti, R., N. Gorton, and M. Lovenheim. 2020. State Investment in Higher
Education: Effects on Human Capital Formation, Student Debt, and Long-Term
Financial Outcomes of Students. NBER Working Paper 27885. Cambridge,
MA: National Bureau of Economic Research. https://www.nber.org/papers/
w27885.

346 |

References

Chetty, R., J. Friedman, E. Saez, N. Turner, and D. Yagan. 2017. Mobility Report Cards:
The Role of Colleges in Intergenerational Mobility. NBER Working Paper
23618. Cambridge, MA: National Bureau of Economic Research. https://www.
nber.org/papers/w23618.
Chetty, R., J. Friedman, E. Saez, N. Turner, and D. Yagan. 2020. “Income Segregation
and Intergenerational Mobility Across Colleges in the United States.” Quarterly
Journal of Economics 135, no. 3: 1567−633. https://doi.org/10.1093/qje/
qjaa005.
Cohen, A., F. Brawer, and C. Kisker. 2013. The American Community College. Hoboken,
NJ: John Wiley & Sons. https://www.wiley.com/en-us/The+American+
Community+College,+6th+Edition-p-9781118449813.
Cohodes, S., and J. Goodman. 2014. “Merit Aid, College Quality, and College
Completion: Massachusetts’ Adams Scholarship as an In-Kind Subsidy.”
American Economic Journal: Applied Economics 6, no. 4: 251–85. https://doi.
org/10.1257/app.6.4.251.
College Scorecard. 2022. U.S. Department of Education. https://collegescorecard.ed.gov/.
CUNY (City University of New York). No date. “ASAP [Accelerated Study in Associated
Programs].” https://www1.cuny.edu/sites/asap/about/.
Currie, J., and E. Moretti. 2003. “Mother’s Education and the Intergenerational
Transmission of Human Capital: Evidence from College Openings.” Quarterly
Journal of Economics 118, no. 4: 1495–532. https://doi.org/10.1162/003355
303322552856.
Dadgar, M., and M. Trimble. 2015. “Labor Market Returns to Sub-Baccalaureate
Credentials: How Much Does a Community College Degree or Certificate
Pay?” Educational Evaluation and Policy 37, no. 4. https://files.eric.ed.gov/
fulltext/ED533520.pdf.
Darolia, R. 2013. “Integrity Versus Access? The Effect of Federal Financial Aid
Availability on Postsecondary Enrollment.” Journal of Public Economics 106:
101−14. https://doi.org/10.1016/j.jpubeco.2013.08.001.
Darolia, R., C. Koedel, P. Martorell, K. Wilson, and F. Perez-Arce. 2015. “Do Employers
Prefer Workers Who Attend For-Profit Colleges? Evidence from a Field
Experiment.” Journal of Policy Analysis and Management 34, no. 4: 881–903.
https://doi.org/10.1002/pam.21863.
Dee, T. 2003. Are There Civic Returns to Education? NBER Working Paper 9588.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w9588.
Deming, D. 2017. “Increasing College Completion with a Federal Higher Education
Matching Grant.” Hamilton Project. https://www.hamiltonproject.org/assets/
files/increasing_college_completion_with_federal_higher_education_
matching_grant_pp.pdf.

References | 347

Deming, D., C. Goldin, and L. Katz. 2012. “The For-Profit Postsecondary School Sector:
Nimble Critters or Agile Predators?” Journal of Economic Perspectives 26, no.
1: 139–64. https://www.aeaweb.org/articles?id=10.1257/jep.26.1.139.
Deming, D., and C. Walters. 2017. The Impact of Price Caps and Spending Cuts on U.S.
Postsecondary Attainment. NBER Working Paper 23736. Cambridge, MA:
National Bureau of Economic Research. https://doi.org/10.3386/w23736.
Deming, D., N. Yuchtman, A. Abulafi, C. Goldin, and L. Katz. 2016. “The Value of
Postsecondary Credentials in the Labor Market: An Experimental Study.”
American Economic Review 106, no. 3: 778–806. https://doi.org/10.1257/
aer.20141757.
Denning, J., E. Eide, K. Mumford, R. Patterson, and M. Warnick. 2022. “Why Have
College Completion Rates Increased?” American Economic Journal: Applied
Economics 14, no. 3: 1–29. https://pubs.aeaweb.org/doi/pdfplus/10.1257/
app.20200525.
Dinerstein, M., C. Hoxby, J. Meer, and P. Villanueva. 2014. “Did the Fiscal Stimulus
Work for Universities?” In How the Financial Crisis and Great Recession
Affected Higher Education, 263–320. Chicago: University of Chicago Press.
https://www.nber.org/system/files/chapters/c12864/c12864.pdf.
Dynarski, S., C. Libassi, K. Michelmore, and S. Owen. 2021. “Closing the Gap: The
Effect of Reducing Complexity and Uncertainty in College Pricing on the
Choices of Low-Income Students.” American Economic Review 111, no.6:
1721–56. https://www.aeaweb.org/articles?id=10.1257/aer.20200451.
Dynarski, S., A. Nurshatayeva, L. Page, and J. Scott-Clayton. 2022. Addressing
Non-Financial Barriers to College Access and Success: Evidence and Policy
Implications. NBER Working Paper 30054. Cambridge, MA: National Bureau
of Economic Research. https://doi.org/10.3386/w30054.
Dynarski, S., L. Page, and J. Scott-Clayton. 2022. College Costs, Financial Aid, and
Student Decisions. NBER Working Paper 30275. Cambridge, MA: National
Bureau of Economic Research. https://doi.org/10.3386/w30275.
Education Commission of the States. 2022. “State Information Request: College Promise
Programs.” https://www.ecs.org/wp-content/uploads/State-InformationRequest_College-Promise-Programs.pdf.
Edmunds, J., F. Unlu, J. Furey, E. Glennie, and N. Arshavsky. 2020. “What Happens
When You Combine High School and College? The Impact of the Early College
Model on Postsecondary Performance and Completion.” Sage Journals 42, no.
2. https://doi.org/10.3102/0162373720912249.
Emmons, W., and L. Ricketts. 2017. “College Is Not Enough: Higher Education Does Not
Eliminate Racial Wealth Gaps.” Federal Reserve Bank of St. Louis Review 99,
no. 1: 7–39. https://doi.org/10.20955/r.2017.7-39.
Evans, W., M. Kearney, B. Perry, and J. Sullivan. 2020. “Increasing Community College
Completion Rates among Low-Income Students: Evidence from a Randomized
Controlled Trial Evaluation of a Case-Management Intervention.” Journal of

348 |

References

Policy Analysis and Management 39, no. 4: 930–65. https://doi.org/10.1002/
pam.22256.
Fairlie, R., F. Hoffman, and P. Oreopoulous. 2014. “A Community College Instructor
Like Me: Race and Ethnicity Interactions in the Classroom.” American
Economic Review 104, no. 8: 2567−91. https://doi.org/10.1257/aer.104.8.2567.
Figlio, D., M. Rush, and L. Yin. 2013. “Is It Live or Is It Internet? Experimental
Estimates of the Effects of Online Instruction on Student Learning.” Journal of
Labor Economics 31, no. 4: 763–84. https://www.journals.uchicago.edu/doi/
epdf/10.1086/669930.
Fishman, R. 2015. “2015 College Decisions Survey: Part I: Deciding to Go to College.”
New America. https://static.newamerica.org/attachments/3248-deciding-to-goto-college/CollegeDecisions_PartI.148dcab30a0e414ea2a52f0d8fb04e7b.pdf.
Fountain, J. 2017. “The Campus-Based Financial Aid Programs: Background and Issues.”
Congressional Research Service, Report R45024. https://sgp.fas.org/crs/misc/
R45024.pdf.
Gándara, D., and A. Rutherford. 2018. “Mitigating Unintended Impacts? The Effects of
Premiums for Underserved Populations in Performance Funding Policies for
Higher Education.” Research in Higher Education 59, no. 6, 681–703. https://
doi.org/10.1007/s11162-017-9483-x.
Gallup, Lumina Foundation, and Strada Education Network. 2019. “Some College and
No Degree: How Individuals Who Attend and Don’t Graduate Feel About
Education.” https://www.luminafoundation.org/wp-content/uploads/2019/12/
some-college-no-degree.pdf.
GAO (U.S. Government Accountability Office). 2010. “For-Profit Colleges: Undercover
Testing Finds Colleges Encouraged Fraud and Engaged in Deceptive and
Questionable Marketing Practices.” https://www.gao.gov/assets/gao-10-948t.pdf.
———. 2022. “Financial Aid Offers: Action Needed to Improve Information on College
Costs and Student Aid.” https://www.gao.gov/assets/gao-23-104708.pdf.
Gershenson, S., M. Hansen, and C. Lindsay. 2021. Teacher Diversity and Student
Success: Why Racial Representation Matters in the Classroom. Cambridge,
MA: Harvard Education Press. https://eric.ed.gov/?id=ED611696.
Gershenson, S., C. Hart, J. Hyman, C. Lindsay, and N. Papageorge. 2022. “The Long-Run
Impacts of Same-Race Teachers.” American Economic Journal: Economic
Policy 14, no. 4: 300–342. https://www.aeaweb.org/articles?id=10.1257/
pol.20190573.
Goldin, C., and L. Katz. 2008. The Race Between Education and Technology. Cambridge,
MA: Harvard University Press. https://www.hup.harvard.edu/catalog.
php?isbn=9780674035300.
Goodman, J., M. Hurwitz, and J. Smith. 2017. “Access to 4-Year Public Colleges and
Degree Completion.” Journal of Labor Economics 35, no. 3: 829–67. https://
doi.org/10.1086/690818.

References | 349

Goodman, S., and A. Henriques Volz. 2020. “Attendance Spillovers between Public and
For-Profit Colleges: Evidence from Statewide Variation in Appropriations for
Higher Education.” Education Finance and Policy, no. 15: 428–56. https://doi.
org/10.1162/edfp_a_00281.
Goolsbee, A., G. Hubbard, and A. Ganz. 2019. “A Policy Agenda to Develop Human
Capital for the Modern Economy.” Aspen Institute. https://www.aspeninstitute.
org/wp-content/uploads/2019/01/1.1-Pgs-16-39-A-Policy-Agenda-to-DevelopHuman-Capital-for-the-Modern-Economy.pdf.
Greenwood, B., R. Hardeman, L. Huang, and A. Sojourner. 2020. “Physician-Patient
Racial Concordance and Disparities in Birthing Mortality for Newborns.”
Proceedings of the National Academy of Sciences 117, no. 35: 21194−200.
https://www.pnas.org/doi/10.1073/pnas.1913405117.
Grosz, M. 2020. “The Returns to a Large Community College Program: Evidence from
Admissions Lotteries.” American Economic Journal: Economic Policy 12, no.
1: 226–53. https://www.aeaweb.org/articles?id=10.1257/pol.20170506.
Hemelt, S., N. Schwartz, and S. Dynarski. 2020. “Dual Credit Courses and the Road to
College: Experimental Evidence from Tennessee.” Journal of Policy Analysis
and Management 39, no. 3: 686–719. https://doi.org/10.1002/pam.22180.
Hemelt, S., and K. Stange. 2020. “Why the Move to Online Instruction Won’t Reduce
College Costs.” Brookings Institution. https://www.brookings.edu/blog/browncenter-chalkboard/2020/07/28/
why-the-move-to-online-instruction-wont-reduce-college-costs/.
Herbst, D. 2023. “The Impact of Income-Driven Repayment on Student Borrower
Outcomes.” American Economic Journal: Applied Economics 15, no. 1: 1–25.
https://www.aeaweb.org/articles?id=10.1257/app.20200362.
Hiler, T., and W. Whistle. 2018. “Creating a ‘Title I’ for Higher Ed.” Third Way. https://
www.thirdway.org/memo/creating-a-title-i-for-higher-ed.
Hillman, N. 2016. “Geography of College Opportunity: The Case of Education Deserts.”
American Educational Research Journal 53, no. 4. https://doi.org/10.3102/0002
831216653204.
Hillman, N., and E. Orians. 2013. “Community Colleges and Labor Market Conditions:
How Does Enrollment Demand Change Relative to Local Unemployment
Rates?” Research in Higher Education 54, no. 7: 765–80. https://www.jstor.org/
stable/24571744.
Hoekstra, M. 2009. “The Effect of Attending the Flagship State University on Earnings:
A Discontinuity-Based Approach.” Review of Economics and Statistics 91, no.
4: 717–24. https://doi.org/10.1162/rest.91.4.717.
Holzer, H. 2008. “Workforce Training: What Works? Who Benefits?” Wisconsin Family
Impact Seminars. https://www.purdue.edu/hhs/hdfs/fii/wp-content/
uploads/2015/07/s_wifis28c02.pdf.
———. 2015. “Sector-Based Training Strategies: The Challenges of Matching Workers
and Their Skills to Well-Paying Jobs.” U.S. Department of Labor, Future of
350 |

References

Work Paper Series. https://www.dol.gov/sites/dolgov/files/OASP/legacy/files/
Future_of_work_sector_based_training_strategies.pdf.
——— 2021. “After COVID-19: Building a More Coherent and Effective Workforce
Development System in the United States.” Hamilton Project. https://tacc.org/
sites/default/files/2021-02/holzer_coherent_effective_workforce.pdf.
Hout, M. 2012. “Social and Economic Returns to College Education in the United
States.” Annual Review of Sociology 38: 379−400. https://doi.org/10.1146/
annurev.soc.012809.102503.
Hoxby, C. 2018. “Online Postsecondary Education and Labor Productivity.” In
Education, Skills, and Technical Change: Implications for Future U.S. GDP
Growth, edited by C. Hulten and V. Ramey, 401–60. Chicago: University of
Chicago Press. https://www.nber.org/system/files/chapters/c13709/c13709.pdf.
Hoxby, C., and C. Avery. 2013. “The Missing ‘One-Offs’: The Hidden Supply of HighAchieving, Low-Income Students.” Brookings Papers on Economic Activity,
no. 1: 1–65. https://www.brookings.edu/wp-content/uploads/2016/07/2013a_
hoxby.pdf.
Huntington-Klein, N. 2016. “The Search: The Effect of the College Scorecard on Interest
in Colleges.” Unpublished manuscript. https://nickchk.com/HuntingtonKlein_2017_The_Search.pdf.
Hurwitz, M., and J. Smith. 2017. “Student Responsiveness to Earnings Data in the
College Scorecard.” Economic Inquiry 56, no. 2: 1220−43. https://doi.
org/10.1111/ecin.12530.
Hyman, B. 2018. “Can Displaced Labor Be Retrained? Evidence from Quasi-Random
Assignment to Trade Adjustment Assistance.” Proceedings, Annual Conference
on Taxation and Minutes of the Annual Meeting of the National Tax Association
111: 1–70. https://www.jstor.org/stable/26939524.
Hyman, J. 2017. “Does Money Matter in the Long Run? Effects of School Spending on
Educational Attainment.” American Economic Journal: Economic Policy 9, no.
4: 256–80. https://doi.org/10.1257/pol.20150249.
Institute of International Education. 2020. “Project Atlas 2020 Release: A Quick Look at
Global Mobility Trends.” https://iie.widen.net/s/g2bqxwkwqv/project-atlasinfographics-2020.
IPEDS (Integrated Postsecondary Education Data System). 2020. “Table 1. Number and
Percentage Distribution of Students Enrolled at Title IV Institutions, by Control
of Institution, Student Level, Level of Institution, Enrollment Status, and Other
Selected Characteristics: United States, Fall 2020.” National Center for
Education Statistics. https://nces.ed.gov/ipeds/search/ViewTable?tableId=29448.
Jackson, C., R. Johnson, and C. Persico. 2015. The Effects of School Spending on
Educational and Economic Outcomes: Evidence from School Finance Reforms.
NBER Working Paper 20847. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w20847.

References | 351

Jacobson, L., R. LaLonde, and D. Sullivan. 2005. “Estimating the Returns to Community
College Schooling for Displaced Workers.” Journal of Econometrics 125, nos.
1–2: 271–304. https://doi.org/10.1016/j.jeconom.2004.04.010.
Jepsen, C., K. Troske, and P. Coomes. 2014. “The Labor-Market Returns to Community
College Degrees, Diplomas, and Certificates.” Journal of Labor Economics 32,
no. 1: 95–121. https://doi.org/10.1086/671809.
Joyce, T., S. Crockett, D. Jaeger, O. Altindag, and S. O’Connell. 2015. “Does Classroom
Time Matter?” Economics of Education Review 46: 64–77. https://www.
sciencedirect.com/science/article/abs/pii/S0272775715000254.
Kane, T., P. Orszag, E. Apostolov, R. Inman, and A. Reschovsky. 2005. “Higher
Education Appropriations and Public Universities: Role of Medicaid and the
Business Cycle.” Brookings-Wharton Papers on Urban Affairs, 99–146. http://
www.jstor.org/stable/25067418.
Kane, T., and C. Rouse. 1995. “Labor-Market Returns to Two- and Four-Year College.”
American Economic Review 85, no 3: 600−614. https://www.jstor.org/
stable/2118190.
Katz, L., J. Roth, R. Hendra, and K. Schaberg. 2022. “Why Do Sectoral Employment
Programs Work? Lessons from WorkAdvance.” Journal of Labor Economics
40, no. S1: 249–91. https://doi.org/10.1086/717932.
Kelchen, R., and Z. Liu. 2022. “Did Gainful Employment Regulations Result in College
and Program Closures?” Education Finance and Policy 17, no. 3: 454−78.
https://doi.org/10.1162/edfp_a_00340.
Klasik, D., K. Blagg, and Z. Pekor. 2018. “Out of the Education Desert: How Limited
Local College Options Are Associated with Inequity in Postsecondary
Opportunities.” Social Sciences 7, no. 9: 165. https://www.mdpi.
com/2076-0760/7/9/165.
Kinser, K. 2016. “Paying for For-Profit Higher Education: Implications of the United
States Case.” In Student Financing of Higher Education: A Comparative
Perspective, edited by D. Heller and C. Callender, 98–114. London: Routledge.
https://www.routledge.com/Student-Financing-of-Higher-Education-AComparative-Perspective/Heller-Callender/p/book/9781138645417.
Kofoed, M., L. Gebhart, D. Gilmore, and R. Moschitto. 2021. “Zooming to Class?
Experimental Evidence on College Students’ Online Learning during COVID19.” Institute of Labor Economics. https://docs.iza.org/dp14356.pdf.
Kozakowski, W. 2023. “Are Four-Year Public Colleges Engines for Economic Mobility?
Evidence from Statewide Admissions Thresholds.” Annenberg Institute,
EdWorkingPaper 23-727. https://doi.org/10.26300/vapt-4e25.
Krieg, J., and S. Henson. 2016. “The Educational Impact of Online Learning: How Do
University Students Perform in Subsequent Courses?” Education Finance and
Policy 11, no. 4: 426–48. https://doi.org/10.1162/EDFP_a_00196.
Kuehn, D., S. Mills De La Rosa, R. Lerman, and K. Hollenbeck. 2022. Do Employers
Earn Positive Returns to Investments in Apprenticeship? Evidence from
352 |

References

Registered Programs under the American Apprenticeship Initiative. Report
prepared for U.S. Department of Labor, Employment and Training
Administration. Rockville, MD: Abt Associates. https://www.abtassociates.com/
files/insights/reports/2022/aai-roi-final-report-508c_9-16-22.pdf.
Labaree, D. 2017. A Perfect Mess: The Unlikely Ascendancy of American Higher
Education. Chicago: University of Chicago Press. https://press.uchicago.edu/
ucp/books/book/chicago/P/bo19995111.html.
Lavecchia, A., H. Liu, and P. Oreopoulos. 2016. “Behavioral Economics of Education:
Progress and Possibilities.” Handbook of the Economics of Education 5: 1–74.
https://doi.org/10.1016/B978-0-444-63459-7.00001-4.
Leslie, L., and G. Johnson. 1974. “The Market Model and Higher Education.” Journal of
Higher Education 45, no. 1: 1−20. https://doi.org/10.1080/00221546.1974.1177
6918.
Levin, H., and E. Garcia. 2013. “Benefit-Cost Analysis of Accelerated Study in
Associated Programs (ASAP) of the City University of New York (CUNY).”
Center for Benefit-Cost Studies in Education. https://www1.nyc.gov/assets/
opportunity/pdf/Levin_ASAP_Benefit_Cost_Report_FINAL_05212013.pdf.
Levine, P., J. Ma, and L. Russell. 2022. “Do College Applicants Respond to Changes in
Sticker Prices Even When They Don’t Matter?” Education Finance and Policy,
1–30. https://doi.org/10.1162/edfp_a_00372.
Levy, D. 2019. “Juxtaposing Global and U.S. Private Higher Education: What Is to Be
Learned?” In International Perspectives in Higher Education: Balancing
Access, Equity, and Cost, edited by J. Delisle and A. Usher, 49–66. https://
www.routledge.com/Student-Financing-of-Higher-Education-A-ComparativePerspective/Heller-Callender/p/book/9781138645417?utm_source=cjaffiliates
&utm_medium=affiliates&cjevent=5a91649a9cc411ed82b6abb90a82b832.
Linkow, T., E. Bumgarner, H. Didriksen, K. Lack, A. Nichols, E. Dastrup, S. Dastrup, and
B. Gamse. 2019. “The Story of Scaling Up: Interim Report on the Impact of
Success Boston’s Coaching for Completion.” Abt Associates. https://files.eric.
ed.gov/fulltext/ED602748.pdf.
Linkow, T., B. Gamse, F. Unlu, E. Bumgarner, H. Didriksen, J. Furey, M. Meneses, M.
Sami, and A. Nichols. 2017. “The Power of Coaching: Highlights from the
Interim Report on the Impact of Success Boston’s Transition Coaching on
College Success.” Abt Associates. https://files.eric.ed.gov/fulltext/ED582090.pdf.
Liu, V., C. Belfield, and M. Trimble. 2015. “The Medium-Term Labor Market Returns to
Community College Awards: Evidence from North Carolina.” Economics of
Education Review 44: 42–55. https://doi.org/10.1016/j.econedurev.2014.10.009.
Lochner, L. 2011. Non-Production Benefits of Education: Crime, Health, and Good
Citizenship. NBER Working Paper 16722. Cambridge, MA: National Bureau of
Economic Research. https://doi.org/10.3386/w16722.

References | 353

Lochner, L., and E. Moretti. 2004. “The Effect of Education on Crime: Evidence from
Prison Inmates, Arrests, and Self-Reports.” American Economic Review 94, no.
1: 155−89. https://doi.org/10.1257/000282804322970751.
Long, M. 2008. “College Quality and Early Adult Outcomes.” Economics of Education
Review 27, no. 5: 588–602. https://doi.org/10.1016/j.econedurev.2007.04.004.
Love, I. 2022. “Why Community College Bachelor’s Degrees? Similarities and
Differences Across Urban and Rural Settings.” New America. https://www.
newamerica.org/education-policy/briefs/
why-community-college-bachelors-degrees/.
Lovenheim, M., and J. Smith. 2022. Returns to Different Postsecondary Investments:
Institution Type, Academic Programs, and Credentials. NBER Working Paper
29933. Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w29933.
Lusher, L., D. Campbell, and S. Carrell. 2018. “TAs Like Me: Racial Interactions
between Graduate Teaching Assistants and Undergraduates.” Journal of Public
Economics 159: 203–24. https://doi.org/10.1016/j.jpubeco.2018.02.005.
Ma, J., and M. Pender. 2021. “Trends in College Pricing and Student Aid 2021.” College
Board. https://research.collegeboard.org/media/pdf/trends-college-pricingstudent-aid-2021.pdf.
———. 2022a. “Trends in College Pricing 2022: Data in Excel.” College Board. https://
research.collegeboard.org/media/xlsx/trends-college-pricing-excel-data-2022.xlsx.
———.2022b. “Trends in College Pricing and Student Aid 2022.” College Board. https://
research.collegeboard.org/media/pdf/trends-in-college-pricing-student-aid-2022.
pdf.
Ma, J., M. Pender, and M. Welch. 2019. “Education Pays 2019.” College Board. https://
research.collegeboard.org/media/pdf/education-pays-2019-full-report.pdf.
Marcucci, P. 2013. “The Politics of Student Funding Policies from a Comparative
Perspective.” In Student Financing of Higher Education: A Comparative
Perspective, edited by D. Heller and C. Callender, 9–31. London: Routledge.
https://www.routledge.com/Student-Financing-of-Higher-Education-AComparative-Perspective/Heller-Callender/p/book/9781138645417.
Marken, S., L. Gray, and L. Lewis. 2013. “Dual Enrollment Programs and Courses for
High School Students at Postsecondary Institutions: 2010–11.” National Center
for Education Statistics. http://nces.ed.gov/pubs2013/2013002.pdf.
McMillan-Cottom, T. 2017. Lower Ed: The Troubling Rise of For-Profits. New York:
New Press. https://thenewpress.com/books/lower-ed.
Meyer, K., and K. Rosinger. 2019. “Applying Behavioral Insights to Improve
Postsecondary Education Outcomes: A Review of Obama Administration
Efforts and Next Steps Under the Trump Administration.” Journal of Policy
Analysis and Management 38, no. 2: 481−99. https://doi.org/10.1002/
pam.22123.

354 |

References

Miller, B. 2017. “Who Are Student Loan Defaulters?” Center for American Progress.
https://www.americanprogress.org/article/student-loan-defaulters/.
Miller, C., C. Headlam, M. Manno, and D. Cullinan. 2020. “Increasing Community
College Graduation Rates with a Proven Model: Three-Year Results from the
Accelerated Study in Associate Programs (ASAP) Ohio Demonstration.”
MDRC. https://www.mdrc.org/publication/increasing-community-collegegraduation-rates-proven-model.
Minaya, V., and J. Scott-Clayton. 2022. “Labor Market Trajectories for Community
College Graduates: How Returns to Certificates and Associate’s Degrees
Evolve over Time.” Education Finance and Policy 17, no. 1: 53–80. https://doi.
org/10.1162/edfp_a_00325.
Mishory, J. 2018. “The Future of Statewide College Promise Programs.” Century
Foundation. https://tcf.org/content/report/
future-statewide-college-promise-programs/.
Mountjoy, J. 2022. “Community Colleges and Upward Mobility.” American Economic
Review 112, no. 8: 2580–2630. https://www.aeaweb.org/articles?id=10.1257/
aer.20181756.
Mountjoy, J., and B. Hickman. 2021. The Returns to College(s): Relative Value-Added
and Match Effects in Higher Education. NBER Working Paper 29276.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w29276.
Mueller, H., and C. Yannelis. 2019. “The Rise in Student Loan Defaults.” Journal of
Financial Economics 131, no. 1: 1–19. https://doi.org/10.1016/j.
jfineco.2018.07.013.
Murphy, R., J. Scott-Clayton, and G. Wyness. 2019. “The End of Free College in
England: Implications for Enrolments, Equity, and Quality.” Economics of
Education Review 71: 7–22. https://www.sciencedirect.com/science/article/abs/
pii/S0272775717306404.
National Student Clearinghouse Research Center. 2022a. “Completing College: National
and State Reports.” https://nscresearchcenter.org/completing-college/.
_______. 2022b. “Overview: Spring 2022 Enrollment Estimates.” https://
nscresearchcenter.org/wp-content/uploads/CTEE_Report_Spring_2022.pdf.
NCES (National Center for Education Statistics). 2019. “Number and Percentage of
Students Enrolled in Degree-Granting Postsecondary Institutions, by Distance
Education Participation, Location of Student, Level of Enrollment, and Control
and Level of Institution: Fall 2017 and Fall 2018.” https://nces.ed.gov/
programs/digest/d19/tables/dt19_311.15.asp.
———.2020a. “Percent of Undergraduate Students Receiving Pell Grants.” https://nces.
ed.gov/ipeds/TrendGenerator/app/answer/8/35.
———.2020b. “Percent of Undergraduate Students Receiving Pell Grants, by Level of
Institution: 2020–21.” https://nces.ed.gov/ipeds/TrendGenerator/app/
build-table/8/35?cid=5.
References | 355

———.2020c. “Percent of Undergraduate Students Receiving Pell Grants (Limited by
Control of Institution).” https://nces.ed.gov/ipeds/TrendGenerator/app/
answer/8/35?f=4%3D3.
———. 2021. “Percentage of the Population 25 to 64 Years Old Who Attained a
Postsecondary Degree, by Highest Degree Attained, Age Group, and Country:
2020.” U.S. Department of Education. https://nces.ed.gov/programs/digest/d21/
tables/dt21_603.30.asp?current=yes.
———. 2022a. “Age at Start of Postsecondary Education by NPSAS Institution Sector (4
with Multiple).” U.S. Department of Education, Quick Launch qquayy. https://
nces.ed.gov/datalab/powerstats/121-national-postsecondary-student-aid-study2016-undergraduates/percentage-distribution.
———. 2022b. “Residence While Enrolled by Age at Start of Postsecondary Education.”
U.S. Department of Education, Quick Launch hgtstb. https://nces.ed.gov/
datalab/powerstats/121-national-postsecondary-student-aid-study-2016undergraduates/percentage-distribution.
———. 2022c. “NPSAS Institution Level by Age at Start of Postsecondary Education
and Residence While Enrolled.” U.S. Department of Education, Quick Launch
rgcflp. https://nces.ed.gov/datalab/powerstats/121-national-postsecondarystudent-aid-study-2016-undergraduates/percentage-distribution.
———. 2022d. “NPSAS Institution Level by Age as of 12/31/2015.” U.S. Department of
Education, Quick Launch ljkiep. https://nces.ed.gov/datalab/powerstats/121national-postsecondary-student-aid-study-2016-undergraduates/
percentage-distribution.
———. 2022e. “Residence While Enrolled by Age as of 12/31/2015. Filtered by NPSAS
Institution Level (4-year).” U.S. Department of Education, Quick Launch
krdscg. https://nces.ed.gov/datalab/powerstats/121-national-postsecondarystudent-aid-study-2016-undergraduates/percentage-distribution.
———. 2022f. “Age as of 12/31/2015 by NPSAS Institution Sector (4 with Multiple).”
U.S. Department of Education, Quick Launch vppist. https://nces.ed.gov/
datalab/powerstats/121-national-postsecondary-student-aid-study-2016undergraduates/percentage-distribution.
———. 2022g. “Institution Sector (4 with Multiple) by Enrollment Pattern. Year 2016.”
U.S. Department of Education, Quick Launch: heqmyc. https://nces.ed.gov/
datalab/powerstats/121-national-postsecondary-student-aid-study-2016undergraduates/percentage-distribution/trend.
———. 2022h. “Distance from Student’s Home (in Miles) to NPSAS Institution by
Institution Sector (4 with Multiple); Years 2004, 2008, 2012 and 2016.” U.S.
Department of Education, Quick Launch: lqtmop. https://nces.ed.gov/datalab/
powerstats/121-national-postsecondary-student-aid-study-2016-undergraduates/
percentage-distribution/trend.
———. 2022i. “Distance from Student’s Home (in Miles) to NPSAS Institution by Race/
Ethnicity (with Multiple) without Foreign Students; Years 1996, 2008, 2012 and

356 |

References

2016.” U.S. Department of Education, Quick Launch: ubmvzr. https://nces.
ed.gov/datalab/powerstats/121-national-postsecondary-student-aid-study-2016undergraduates/percentage-distribution/trend.
———. 2022j. “Distance from Student’s Home (in Miles) to NPSAS Institution by
Income Percentile Rank for All Students. Years 2004, 2008, 2012 and 2016.”
U.S. Department of Education, Quick Launch: svljuv. https://nces.ed.gov/
datalab/powerstats/121-national-postsecondary-student-aid-study-2016undergraduates/percentage-distribution/trend.
Neelakantan, U., and J. Romero. 2017. “Falling Short: Why Isn’t the U.S. Producing
More College Graduates?” Federal Reserve Bank of Richmond. https://www.
richmondfed.org/-/media/RichmondFedOrg/publications/research/annual_
report/2017/article.pdf.
OECD (Organization for Economic Cooperation and Development). 2021. Education at a
Glance 2021: OECD Indicators. Paris: OECD Publishing. https://doi.
org/10.1787/b35a14e5-en.
———. 2022. Education at a Glance 2022: OECD Indicators. Paris: OECD Publishing.
https://doi.org/10.1787/3197152b-en.
Oreopoulos, P., and U. Petronijevic. 2018. “Student Coaching: How Far Can Technology
Go?” Journal of Human Resources 53, no. 2: 299–329. https://doi.org/10.3368/
jhr.53.2.1216-8439R.
Oreopoulos, P., and K. Salvanes. 2011. “Priceless: The Nonpecuniary Benefits of
Schooling.” Journal of Economic Perspectives 25, no. 1: 159−84. https://doi.
org/10.1257/jep.25.1.159.
Ortagus, J., R. Kelchen, K. Rosinger, and N. Voorhees. 2020. “Performance-Based
Funding in American Higher Education: A Systematic Synthesis of the Intended
and Unintended Consequences.” Educational Evaluation and Policy Analysis
42, no. 4. https://doi.org/10.3102/0162373720953128.
Ost, B., W. Pan, and D. Webber. 2018. “The Returns to College Persistence for Marginal
Students: Regression Discontinuity Evidence from University Dismissal
Policies.” Journal of Labor Economics 36, no. 3: 779–805. http://doi.
org/10.1086/696204.
Protopsaltis, S., and S. Parrott. 2017. “Pell Grants: A Key Tool for Expanding College
Access and Economic Opportunity—Need Strengthening, Not Cuts.” Center on
Budget and Policy Priorities. https://www.cbpp.org/research/federal-budget/
pell-grants-a-key-tool-for-expanding-college-access-and-economic.
Redden, E. 2010. “The ‘Community College’ Internationally.” Inside Higher Ed. https://
www.insidehighered.com/news/2010/06/16/community-college-internationally.
Ross, R., S. White, J. Wright, and L. Knapp. 2013. “Using Behavioral Economics for
Postsecondary Success.” ideas42. http://www.ideas42.org/wp-content/
uploads/2015/05/Using-Behavioral-Economics-for-Postsecondary-Success_
ideas42_2013.pdf.

References | 357

Rothstein, J., R. Santillano, T. von Wachter, W. Khan, and M. Yang. 2022. “Identifying
the Impacts of Job Training Programs in California.” California Policy Lab.
https://www.capolicylab.org/
identifying-the-impacts-of-job-training-programs-in-california/.
Rouse, C. 1995. “Democratization or Diversion? The Effect of Community Colleges on
Educational Attainment.” Journal of Business and Economic Statistics 13, no. 2:
217–24. https://www.tandfonline.com/doi/abs/10.1080/07350015.1995.10524596.
Salto, D. 2019. “Brazil: Expanding Access Through Private Institutions.” In International
Perspectives in Higher Education: Balancing Access, Equity, and Cost, edited
by J. Delisle and A. Usher, 149–68. https://www.aei.org/research-products/
book/international-perspectives-in-higher-education-balancing-accessequity-and-cost/.
Schanzenbach, D., and S. Turner. 2022. “Limited Supply and Lagging Enrollment:
Production Technologies and Enrollment Changes at Community Colleges
during the Pandemic.” Journal of Public Economics 212. https://doi.
org/10.1016/j.jpubeco.2022.104703.
Scott-Clayton, J. 2012. Information Constraints and Financial Aid Policy. NBER
Working Paper 17811. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w17811.
———. 2018a. “What Accounts for Gaps in Student Loan Default, and What Happens
After.” Brookings Institution. https://www.brookings.edu/wp-content/
uploads/2018/06/Report_Final.pdf.
———. 2018b. “The Looming Student Loan Default Crisis Is Worse Than We Thought.”
Brookings Institution. https://www.brookings.edu/wp-content/uploads/2018/01/
scott-clayton-report.pdf.
Scrivener S., M. Weiss, A. Ratledge, T. Rudd, C. Sommo, and H. Fresques. 2015.
“Doubling Graduation Rates: Three-Year Effects of CUNY’s Accelerated Study
in Associate Programs (ASAP) for Developmental Education Students.”
MDRC. https://www.mdrc.org/sites/default/files/
doubling_graduation_rates_fr.pdf.
Skinner, R., and C. Cooper. 2020. “FY2019 State Grants Under Title I-A of the
Elementary and Secondary Education Act (ESEA).” Congressional Research
Service. https://www.everycrsreport.com/files/20200311_R46269_
eb6aebd236181eef9c73e8ec2f3b932a3e244798.pdf.
Smith, J. 2013. “Ova and Out: Using Twins to Estimate the Educational Returns to
Attending a Selective College.” Economics of Education Review 36: 166–80.
https://doi.org/10.1016/j.econedurev.2013.06.008.
Smith, J., J. Goodman, and M. Hurwitz. 2020. The Economic Impact of Access to Public
Four-Year Colleges. NBER Working Paper 27177. Cambridge, MA: National
Bureau of Economic Research. https://doi.org/10.3386/w27177.

358 |

References

Sommo, C., D. Cullinan, M. Manno, S. Blake, and E. Alonzo. 2018. “Doubling
Graduation Rates in a New State: Two-Year Findings from the ASAP Ohio
Demonstration.” MDRC. https://files.eric.ed.gov/fulltext/ED592008.pdf.
Song, M., K. Zeiser, D. Atchison, and I. Brodziak de los Reyes. 2021. “Early College,
Continued Success: Longer-Term Impact of Early College High Schools.”
Journal of Research on Educational Effectiveness 14, no.1: 116–42. https://doi.
org/10.1080/19345747.2020.1862374.
Stolzenberg, E., M. Aragon, E. Romo, V. Couch, D. McLennan, M. Eagan, and N. Kang.
2020. The American Freshman: National Norms Fall 2019. Los Angeles: Higher
Education Research Institute of the University of California, Los Angeles. https://
www.heri.ucla.edu/monographs/TheAmericanFreshman2019.pdf.
Student Loans Company. 2022. Student Loans in England: Financial Year 2021–22.
Statistical Publication SP01/2022. Glasgow: Student Loans Company. https://
assets.publishing.service.gov.uk/government/uploads/system/uploads/
attachment_data/file/1092922/slcsp012022.pdf.
Turner, L. 2021. “The Importance of ‘Choice Architecture’ for Student Loan Repayment
Decisions and Outcomes.” Postsecondary Equity and Economics Research
Project. https://www.peerresearchproject.org/peer/research/body/Turner-PaperChoice-Architecture.pdf.
U.S. Department of Education. 2014. “Fact Sheet on Final Gainful Employment
Regulations.” https://www2.ed.gov/policy/highered/reg/hearulemaking/2012/
gainful-employment-fact-sheet-10302014.pdf.
———. 2020. “Cover Letter.” https://www2.ed.gov/about/offices/list/ope/
heerfinstitutionalcoverletter.pdf.
———. 2022a. “Federal Student Loan Portfolio.” https://studentaid.gov/data-center/
student/portfolio.
———. 2022b. “During ‘Raise the B.A.R.’ Summit, Education Department Announces
College Completion Fund Competition to Support Postsecondary Student
Success.” https://www.ed.gov/news/press-releases/
during-%E2%80%98raise-bar%E2%80%99-summit-education-departmentannounces-college-completion-fund-competition-support-postsecondarystudent-success.
———. 2022c. “ARP: American Rescue Plan (HEERF III).” https://www2.ed.gov/about/
offices/list/ope/arp.html.
———. 2022d. “Gainful Employment Information.” https://studentaid.gov/data-center/
school/ge.
———. 2022e. “Issue Paper 3: Gainful Employment.” https://d1y8sb8igg2f8e.cloudfront.
net/documents/Issue_Paper_3_Gainful_Employment_FINAL_3-8-22.pdf.
———. 2023a. “The U.S. Department of Education Offers Low-Interest Loans to
Eligible Students to Help Cover the Cost of College or Career School.” https://
studentaid.gov/understand-aid/types/loans/subsidized-unsubsidized.

References | 359

———. 2023b. “Department Awards Grants to Improve Opportunities and Outcomes for
Nation’s Postsecondary Students.” https://www.ed.gov/news/press-releases/
department-awards-grants-improve-opportunities-and-outcomes-nationspostsecondary-students.
———. 2023c. “Higher Education Emergency Relief Fund: 2021 Annual Performance
Report.” https://www2.ed.gov/about/offices/list/ope/heerf-2021-annualperformance-report.pdf.
U.S. Department of Labor. 2022a. “Trade Adjustment Assistance Community College and
Career Training (TAACCCT) Grant Program Evaluation.” https://www.dol.gov/
agencies/oasp/evaluation/currentstudies/14.
———. 2022b. “ApprenticeshipUSA: A Proven Solution for Employers.” https://www.
apprenticeship.gov/sites/default/files/dol-industry-factsheet-employer-v10.pdf.
U.S. Department of the Treasury. 2022. “American Rescue Plan State and Local Fiscal
Recovery Funds: Project Highlights.” https://home.treasury.gov/system/
files/136/American-Rescue-Plan-Anniversary-SLFRF-Examples.pdf.
Usher, A. 2019. “The Architecture of Student Loan Systems.” In International
Perspectives in Higher Education: Balancing Access, Equity, and Cost, edited
by J. Delisle and A. Usher, 87–106. https://www.aei.org/research-products/
book/international-perspectives-in-higher-education-balancing-accessequity-and-cost/.
Walton, D., K. Gardiner, and B. Barnow. 2022. Expanding Apprenticeship to New Sectors
and Populations: The Experiences and Outcomes of Apprentices in the
American Apprenticeship Initiative. Prepared for the U.S. Department of Labor,
Employment and Training Administration. Rockville, MD: Abt Associates.
https://www.abtassociates.com/files/insights/reports/2022/aai-outcomes-studyfinal-report-508.pdf.
Webb, M. 2014. Early College Expansion: Propelling Students to Postsecondary Success,
at a School Near You. Washington: Jobs for the Future. https://eric.
ed.gov/?id=ED559689.
Webber, D., and R. Ehrenberg. 2010. “Do Expenditures Other Than Instructional
Expenditures Affect Graduation and Persistence Rates in American Higher
Education?” Economics of Education Review 29, no. 6: 947–58. https://doi.
org/10.1016/j.econedurev.2010.04.006.
Weiss, M., A. Ratledge, C. Sommo, and H. Gupta. 2019. “Supporting Community
College Students from Start to Degree Completion: Long-Term Evidence from
a Randomized Trial of CUNY’s ASAP.” American Economic Journal: Applied
Economics 11, no. 3: 253–97. https://doi.org/10.1257/app.20170430.
White House. 2021. “Fact Sheet: The American Families Plan.” https://www.whitehouse.
gov/briefing-room/statements-releases/2021/04/28/fact-sheet-the-americanfamilies-plan/.
———. 2022. “Fact Sheet: Biden-Harris Administration Launches the Apprenticeship
Ambassador Initiative to Create Equitable, Debt-Free Pathways to High-Paying

360 |

References

Jobs.” https://www.whitehouse.gov/briefing-room/statements-releases
/2022/09/01/
fact-sheet-biden-harris-administration-launches-the-apprenticeship-ambassadorinitiative-to-create-equitable-debt-free-pathways-to-high-paying-jobs/
Wiswall, M., and B. Zafar. 2015. “How Do College Students Respond to Public
Information About Earnings?” Journal of Human Capital 9, no. 2: 117–69.
https://doi.org/10.1086/681542.
World Higher Education Database. No date. “The World of Higher Education at Your
Fingertips.” https://www.whed.net/home.php.
Xu, D., and S. Jaggars. 2013. “The Impact of Online Learning on Students’ Course
Outcomes: Evidence from a Large Community and Technical College System.”
Economics of Education Review 37: 46–57. https://doi.org/10.1016/j.
econedurev.2013.08.001.
Xu, D., S. Jaggars, and J. Fletcher. 2016. “How and Why Does Two-Year College Entry
Influence Baccalaureate Aspirants’ Academic and Labor Market Outcomes.”
Working paper, Center for Analysis of Postsecondary Education and
Employment. https://ccrc.tc.columbia.edu/media/k2/attachments/CAPSEE-howand-why-two-year-college-entry-influence-outcomes.pdf.
Zimmerman, S. 2014. “The Returns to College Admission for Academically Marginal
Students.” Journal of Labor Economics 32, no. 4: 711–54. https://doi.
org/10.1086/676661.

Chapter 6
Aaronson, S., T. Cajner, B. Fallick, F. Galbis-Reig, C. Smith, and W. Wascher. 2014.
“Labor Force Participation: Recent Developments and Future Prospects.”
Brookings Papers on Economic Activity 2: 197–275. https://doi.org/10.1353/
eca.2014.0015.
Abraham, K., and M. Kearney. 2020. “Examining the Decline in the U.S. Employmentto-Population Ratio: A Review of the Evidence.” Journal of Economic
Literature 58, no. 3: 585–643. https://doi.org/10.1257/jel.20191480.
Acemoglu, D., and D. Autor. 2010. Skills, Tasks and Technologies: Implications for
Employment and Earnings. NBER Working Paper 16982. Cambridge, MA:
National Bureau of Economic Research. https://doi.org/10.3386/w16082.
Acemoglu, D., D. Autor, D. Dorn, G. Hanson, and B. Price. 2015. “Import Competition
and the Great U.S. Employment Sag of the 2000s.” Journal of Labor
Economics 34, no. S1: 141–98. https://doi.org/10.1086/682384.
Acemoglu, D., and P. Restrepo. 2017. “Secular Stagnation? The Effect of Aging on
Economic Growth in the Age of Automation.” American Economic Review 107,
no. 5: 174–79. https://doi.org/10.1257/aer.p20171101.
Aiken, L., D. Sloane, M. McHugh, C. Pogue, and K. Lasater. 2022. “A Repeated CrossSectional Study of Nurses Immediately Before and During the COVID-19

References | 361

Pandemic: Implications for Action.” Nursing Outlook 71, no. 2: 1–10. https://
doi.org/10.1016/j.outlook.2022.11.007.
Akee, R., D. Feir, M. Gorzig, and S. Myers Jr. 2022. “Native American ‘Deaths of
Despair’ and Economic Conditions.” IZA Discussion Paper 15546. https://www.
econstor.eu/bitstream/10419/265767/1/dp15546.pdf.
Alesina, A., E. Glaeser, and B. Sacerdote. 2005. Work and Leisure in the U.S. and
Europe: Why So Different? NBER Working Paper 11278. Cambridge, MA:
National Bureau of Economic Research. https://doi.org/10.3386/w11278.
Anand, P., L. Dague, and K. Wagner. 2021. The Role of Paid Family Leave in Labor
Supply Responses to a Spouse’s Disability or Health Shock. NBER Working
Paper 28808. Cambridge, MA: National Bureau of Economic Research. https://
doi.org/10.3386/w28808.
Autor, D. 2019. Work of the Past, Work of the Future. NBER Working Paper 25588.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w25588.
Autor, D., and D. Dorn. 2013. “The Growth of Low-Skill Service Jobs and the
Polarization of the U.S. Labor Market.” American Economic Review 103, no. 5:
1553–97. https://doi.org/10.1257/aer.103.5.1553.
Autor, D., D. Dorn, and G. Hanson. 2013. “The China Syndrome: Local Labor Market
Effects of Import Competition in the United States.” American Economic
Review 103, no. 6: 2121–68. https://doi.org/10.1257/aer.103.6.2121.
———. 2021. On the Persistence of the China Shock. NBER Working Paper 29401.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w29401.
Autor, D., D. Dorn., G. Hanson, and J. Song. 2014. “Trade Adjustment: Worker-Level
Evidence.” Quarterly Journal of Economics 129, no. 4: 1799–860. https://doi.
org/10.1093/qje/qju026.
Autor, D., and M. Duggan. 2003. “The Rise in the Disability Rolls and the Decline in
Unemployment.” Quarterly Journal of Economics 118, no. 1: 157–206. https://
doi.org/10.1162/00335530360535171.
Bacher-Hicks, A., O. Chi, and A. Orellana. 2021. “COVID-19 and the Composition of the
Massachusetts Teacher Workforce.” Boston University, Wheelock College of
Education and Human Development. https://wheelockpolicycenter.org/
wp-content/uploads/2021/10/TeacherWorkforce_PolicyBrief_Final.pdf.
Baker, M., J. Gruber, and K. Milligan. 2008. “Universal Child Care, Maternal Labor
Supply, and Family Well-Being.” Journal of Political Economy 116, no. 4:
709–45. https://doi.org/10.1086/591908.
Barkin, T. 2022. “Is a Labor Challenge Coming?” Speech to Virginia Economic Summit
and Forum on International Trade, December 2. https://www.richmondfed.org/
press_room/speeches/thomas_i_barkin/2022/barkin_speech_20221202

362 |

References

Basso, G., and G. Peri. 2020. “Internal Mobility: The Greater Responsiveness of ForeignBorn to Economic Conditions.” Journal of Economic Perspectives 34, no. 3:
77–98. https://doi.org/10.1257/jep.34.3.77.
Bastian, J. 2020. “The Rise of Working Mothers and the 1975 Earned Income Tax
Credit.” American Economic Journal: Economic Policy 12, no. 3: 44–75.
https://doi.org/10.1257/pol.20180039.
Bauernschuster, S., and M. Schlotter. 2015. “Public Child Care and Mothers’ Labor
Supply—Evidence from Two Quasi-Experiments.” Journal of Public
Economics 123: 1–16. https://doi.org/10.1016/j.jpubeco.2014.12.013.
Baum, C., and C. Ruhm. 2016. “The Effects of Paid Family Leave in California on Labor
Market Outcomes.” Journal of Policy Analysis and Management 35, no. 2:
333–56. https://doi.org/10.1002/pam.21894.
Bernstein, S., R. Diamond, A. Jiranaphawiboon, T. McQuade, and B. Pousada. 2022. The
Contribution of High-Skilled Immigrants to Innovation in the United States.
NBER Working Paper 30797. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w30797.
Binder, A., and J. Bound. 2019. “The Declining Labor Market Prospects of LessEducated Men.” Journal of Economic Perspectives 33, no. 2: 163–90. https://
doi.org/10.1257/jep.33.2.163.
Black, S., and C. Juhn. 2000. “The Rise of Female Professionals: Are Women
Responding to Skill Demand?” American Economic Review 90, no. 2; 450–55.
http://www.jstor.org/stable/117267.
Black, S., D. Schanzenbach, and A. Breitwieser. 2017. “The Recent Decline in Women’s
Labor Force Participation.” Hamilton Project. https://www.brookings.edu/
wp-content/uploads/2017/10/es_10192017_decline_womens_labor_force_
participation_blackschanzenbach.pdf.
Blanchard, O., and L. Katz. 1992. “Regional Evolutions.” Brookings Papers on Economic
Activity, no. 1. https://www.brookings.edu/bpea-articles/regional-evolutions/.
Blau, F., and L. Kahn. 2007. “Changes in the Labor Supply Behavior of Married Women:
1980–2000.” Journal of Labor Economics 25 no. 3: 393–438. https://www.
journals.uchicago.edu/doi/full/10.1086/513416.
———. 2013. “Female Labor Supply: Why Is the United States Falling Behind?”
American Economic Review 103, no. 3: 251–56. https://doi.org/10.1257/
aer.103.3.251.
Blau, F., and C. Mackie, eds. 2017. The Economic and Fiscal Consequences of
Immigration. National Academies of Sciences, Engineering, and Medicine.
Washington: National Academies Press. https://doi.org/10.17226/23550.
Bloom, N., A. Kurmann, K. Handley, and P. Luck. 2019. “The Impact of Chinese Trade
on U.S. Employment: The Good, The Bad, and The Apocryphal.” Society for
Economic Dynamics. https://www.gsb.stanford.edu/faculty-research/
publications/impact-chinese-trade-us-employment-good-bad-apocryphal.

References | 363

BLS (U.S Bureau of Labor Statistics). 2022. “Current Population Survey: Concepts and
Definitions.” https://www.bls.gov/cps/definitions.htm.
Bound, J. 1989. “The Health and Earnings of Rejected Disability Insurance Applicants.”
American Economic Review 79, no. 3: 482–503. https://doi.org/10.3386/w2816.
Boyd, D., H. Lankford, S. Loeb, and J. Wyckoff. 2005. “Explaining the Short Careers of
High-Achieving Teachers in Schools with Low-Performing Students.”
American Economic Review 95, no. 2: 166–71. https://doi.org/10.1257/0002828
05774669628.
Buerhous, P., D. Auerbach, and D. Staiger. 2017. “How Should We Prepare for the Wave
of Retiring Baby Boomer Nurses?” Health Affairs. https://doi.org/10.1377/
forefront.20170503.059894.
Cajner, T., J. Coglianese, and J. Montes. 2021. “The Long-Lived Cyclicality of the Labor
Force Participation Rate.” Working paper, Federal Reserve Board of Governors.
https://www.johncoglianese.com/publication/lfpr-cyclicality/lfpr-cyclicality.pdf.
Cajner, T., L. Crane, R. Decker, J. Grigsby, A. Hamins-Puertolas, E. Hurst, C. Kurz, and
A. Yildirmaz. The U.S. Labor Market during the Beginning of the Pandemic
Recession. NBER Working Paper 27159. Cambridge, MA: National Bureau of
Economic Research. https://doi.org/10.3386/w27159.
Card, D. 1996. “The Effect of Unions on the Structure of Wages: A Longitudinal
Analysis.” Econometrica 64, no. 4: 957–79. https://doi.org/10.2307/2171852.
Case, A., and A. Deaton. 2015. “Rising Morbidity and Mortality in Midlife among White
Non-Hispanic Americans in the 21st Century.” Proceedings of the National
Academy of Sciences 112, no. 49: 15078–83. https://doi.org/10.1073/
pnas.1518393112.
———. 2020. Deaths of Despair and the Future of Capitalism. Princeton, NJ: Princeton
University Press. https://press.princeton.edu/books/hardcover/9780691190785/
deaths-of-despair-and-the-future-of-capitalism.
———. 2021. “Life Expectancy in Adulthood Is Falling for Those Without a B.A.
Degree, but as Educational Gaps Have Widened, Racial Gaps Have Narrowed.”
Proceedings of the National Academy of Sciences 118, no. 11: e2024777118.
https://www.pnas.org/content/118/11/e2024777118.
Cassar, L., and S. Meier. 2018. “Nonmonetary Incentives and the Implications of Work as
a Source of Meaning.” Journal of Economic Perspectives 32, no. 3: 215–38.
https://doi.org/10.1257/jep.32.3.215.
CEA (Council of Economic Advisers). 2016. “The Long-Term Decline in Prime-Age
Male Labor Force Participation.” https://obamawhitehouse.archives.gov/sites/
default/files/page/files/20160620_cea_primeage_male_lfp.pdf.
Center on Budget and Policy Priorities. 2021. “Social Security Disability Insurance.”
https://www.cbpp.org/research/social-security/
social-security-disability-insurance-0.

364 |

References

Charles, K., E. Hurst, and M. Schwartz. 2018. The Transformation of Manufacturing and
the Decline in U.S. Employment. NBER Working Paper 24468. Cambridge,
MA: National Bureau of Economic Research. https://doi.org/10.3386/w24468.
Clark, A. 2015. “What Makes a Good Job? Job Quality and Job Satisfaction.” IZA World
of Labor. https://wol.iza.org/articles/what-makes-good-job-job-quality-andjob-satisfaction.
Clemens, M., and E. Lewis. 2022. The Effect of Low-Skill Immigration Restrictions on
U.S. Firms and Workers: Evidence from a Randomized Lottery. NBER Working
Paper 30589. Cambridge, MA: National Bureau of Economic Research. https://
doi.org/10.3386/w30589.
Coglianese, J. 2018. “The Rise of In-and-Outs: Declining Labor Force Participation of
Prime Age Men.” Working paper, Harvard University. https://www.
johncoglianese.com/publication/in-and-outs/in-and-outs.pdf.
Coile, C., and P. Levine. 2011. “Recessions, Retirement, and Social Security.” American
Economic Review 101, no. 2: 23–28. https://doi.org/10.1257/aer.101.3.23.
Coile, C., and H. Zhang. 2022. Recessions and Retirement: New Evidence from the
COVID-19 Pandemic. PRC Working Paper 20. Philadelphia: Pension Research
Council, Wharton School, University of Pennsylvania. http://dx.doi.
org/10.2139/ssrn.4199666.
Cowan, J., D. Goldhaber, K. Hayes, and R. Theobald. 2016. “Missing Elements in the
Discussion of Teacher Shortages.” Educational Researcher 45, no. 8: 460–62.
https://doi.org/10.3102/0013189X16679145.
Cutler, D., J. Poterba, L. Sheiner, and L. Summers. 1990. “An Aging Society:
Opportunity or Challenge?” Brookings Papers on Economic Activity 1: 1–73.
https://doi.org/10.2307/2534525.
Dao, M., D. Furceri, and P. Loungani. 2017. “Regional Labor Market Adjustment in the
United States: Trend and Cycle.” Review of Economics and Statistics 99, no. 2:
243–57. https://doi.org/10.1162/REST_a_00642.
Dee, T., and D. Goldhaber. 2017. “Understanding and Addressing Teacher Shortages in
the United States.” Hamilton Project. https://www.hamiltonproject.org/assets/
files/understanding_and_addressing_teacher_shortages_in_us_pp.pdf.
Doleac, J. 2016. “Increasing Employment for Individuals with Criminal Records.”
Hamilton Project. https://www.brookings.edu/wp-content/uploads/2016/10/
es_20161021_prisoner_reentry_doleac.pdf.
Eggertsson, G., M. Lancastre, and L. Summers. 2019. “Aging, Output Per Capita, and
Secular Stagnation.” American Economic Review: Insights 1, no. 3: 325–42.
https://doi.org/10.1257/aeri.20180383.
Eissa, N., and J. Liebman. 1996. “Labor Supply Response to the Earned Income Tax
Credit.” Quarterly Journal of Economics 111, no. 2: 605–37. https://doi.
org/10.2307/2946689.

References | 365

Farber, H. 2005. “Nonunion Wage Rates and the Threat of Unionization.” Industrial
Labor and Relations Review 58, no. 3: 335–52. https://journals.sagepub.com/
doi/abs/10.1177/001979390505800302?journalCode=ilra.
Farber, H., D. Herbst, I. Kuziemko, and S. Naidu. 2021. “Unions and Inequality over the
Twentieth Century: New Evidence from Survey Data.” Quarterly Journal of
Economics 136, no. 3: 1325–85. https://doi.org/10.1093/qje/qjab012.
Favilukis, J., and G. Li. 2023. “The Great Resignation Was Caused by the COVID-19
Housing Boom.” Social Science Research Network. https://ssrn.com/
abstract=4335860.
Feldman, M. 2022. “Place-Based Economic Development.” Issues in Science and
Technology 39, no. 1: 44–46. https://issues.org/
place-based-economic-development-feldman/.
Foote, C., and R. Ryan. 2015. Labor Market Polarization Over the Business Cycle.
NBER Working Paper 21030. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w21030.
Forsythe, E., L. Kahn, F. Lange, and D. Wiczer 2022. Where Have All the Workers Gone?
Recalls, Retirements, and Reallocation in the COVID Recovery. NBER
Working Paper 30387. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w30387.
Fry, R., J. Passel, and D. Cohn. 2020. “A Majority of Young Adults in the U.S. Live with
Their Parents for the First Time since the Great Depression.” Pew Research
Center. https://www.pewresearch.org/
fact-tank/2020/09/04/a-majority-of-young-adults-in-the-u-s-live-with-theirparents-for-the-first-time-since-the-great-depression/.
Gagnon, E., B. Johannsen, and D. López-Salido. 2021. “Understanding the New Normal:
The Role of Demographics.” IMF Economic Review 69: 357–90. https://doi.
org/10.1057/s41308-021-00138-4.
Ganong, P., and D. Shoag. 2017. “Why Has Regional Income Convergence in the U.S.
Declined?” Journal of Urban Economics 102: 76–90. https://doi.org/10.1016/j.
jue.2017.07.002.
GAO (U.S. Government Accountability Office). 2021. “Physician Workforce: Caps on
Medicare-Funded Graduate Medical Education at Teaching Hospitals.” https://
www.gao.gov/products/gao-21-391.
Garcia, K., and B. Cowan. 2022. The Impact of U.S. School Closures on Labor Market
Outcomes during the COVID-19 Pandemic. NBER Working Paper 29641.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w29641.
Galvin, G. 2021. “Nearly 1 in 5 Health Care Workers Have Quit Their Jobs during the
Pandemic.” Morning Consult. https://morningconsult.com/2021/10/04/
health-care-workers-series-part-2-workforce/.
Garin, A., D. Koustas, C. McPherson, S. Norris, M. Pecenco, E. Rose, Y. Shem-Tov, and
J. Weaver. 2022. “The Impact of Incarceration on Employment, Earnings, and
366 |

References

Tax Filing.” Working paper, Joint Statistical Research Program of the Statistics
of Income Division, U.S. Internal Revenue Service. https://www.irs.gov/pub/
irs-soi/07-2022-impact-incarceration-employment-earnings-tax-filing.pdf.
Gelbach, J. 2002. “Public Schooling for Young Children and Maternal Labor Supply.”
American Economic Review 92, no. 1: 307–22. https://doi.org/10.1257/0002828
02760015748.
Gelber, A., T. Moore, and A. Strand. 2017. “The Effect of Disability Insurance Payments
on Beneficiaries’ Earnings.” American Economic Journal: Economic Policy 9,
no. 3: 229–61. https://doi.org/10.1257/pol.20160014.
Goda, G., and E. Soltas. 2022. The Impacts of COVID-19 Illnesses on Workers. NBER
Working Paper 30435. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w30435.
Goldhaber, D., C. Grout, K. Holden, and N. Brown. 2015. “Crossing the Border?
Exploring the Cross-State Mobility of the Teacher Workforce.” Educational
Researcher 44, no. 8: 421–31. https://doi.org/10.3102/0013189X15613981.
Goldin, C., and L. Katz. 2002. “The Power of the Pill: Oral Contraceptives and Women’s
Career and Marriage Decisions.” Journal of Political Economy 110, no. 4:
730–70. https://doi.org/10.1086/340778.
Gorodnichenko Y., J. Song, and D. Stolyarov. 2013. Macroeconomic Determinants of
Retirement Timing. NBER Working Paper 19638. Cambridge, MA: National
Bureau of Economic Research. https://doi.org/10.3386/w19638.
Gottlieb, J., and A. Zenilman. 2020. When Workers Travel: Nursing Supply During
COVID-19 Surges. NBER Working Paper 28240. Cambridge, MA: National
Bureau of Economic Research. https://doi.org/10.3386/w28240.
Greene, J., and M. Mauer. 2010. “Downscaling Prisons.” Sentencing Project. https://
www.justicestrategies.org/sites/default/files/publications/inc_
DownscalingPrisons2010.pdf.
Grossman, G., and E. Oberfield. 2022. “The Elusive Explanation for the Declining Labor
Share.” Annual Review of Economics 14: 93–124. https://dx.doi.org/10.1146/
annurev-economics-080921-103046.
Haeck, C., P. Lefebvre, and P. Merrigan. 2015. “Canadian Evidence on Ten Years of
Universal Preschool Policies: The Good and the Bad.” Labour Economics 36:
137–57. https://doi.org/10.1016/j.labeco.2015.05.002.
Ham, J., C. Swenson, A. İmrohoroğlu, and H. Song. 2011. “Government Programs Can
Improve Local Labor Markets: Evidence from State Enterprise Zones, Federal
Empowerment Zones and Federal Enterprise Community.” Journal of Public
Economics 95, nos. 7–8: 779–97. https://doi.org/10.1016/j.jpubeco.2010.11.027.
Hansen, B., J. Sabia, and J. Schaller 2022. Schools, Job Flexibility, and Married Women’s
Labor Supply. NBER Working Paper 29660. Cambridge, MA: National Bureau
of Economic Research. https://doi.org/10.3386/w29660.

References | 367

Hoynes, H., J. Rothstein, and K. Ruffini. 2017. “Making Work Pay Better Through an
Expanded Earned Income Tax Credit.” Hamilton Project. https://www.
hamiltonproject.org/assets/files/making_work_pay_expanded_eitc_Hoynes_
Rothstein_Ruffini.pdf.
Hsieh, C., and E. Moretti. 2019. “Housing Constraints and Spatial Misallocation.”
American Economic Journal: Macroeconomics 11, no. 2: 1–39. https://doi.
org/10.1257/mac.20170388.
Hunt, J., and M. Gauthier-Loiselle. 2010. “How Much Does Immigration Boost
Innovation?” American Economic Journal: Macroeconomics 2: 31–56. https://
doi.org/10.1257/mac.2.2.31.
James, J., and J. Wyckoff. 2022. “School Segregation, Teacher Sorting, and the
Distribution of Teachers.” Working paper, Annenberg Institute at Brown
University. https://doi.org/10.26300/tv5x-6t21.
Johnson, J., and M. Kleiner. 2020. “Is Occupational Licensing a Barrier to Interstate
Migration?” American Economic Journal: Economic Policy 12, no. 3: 347–73.
https://pubs.aeaweb.org/doi/pdfplus/10.1257/pol.20170704.
Juhn, C., and S. Potter. 2006. “Changes in Labor Force Participation in the United
States.” Journal of Economic Perspectives 20, no. 3: 27–46. https://doi.
org/10.1257/jep.20.3.27.
Kaplan, G., and S. Schulhofer-Wohl. 2017. “Understanding the Long-Run Decline in
Interstate Migration.” International Economic Review 58, no. 1: 57–94. https://
doi.org/10.1111/iere.12209.
Katz, B., W. Congdon, and J. Shakesprere. 2022. “Measuring Job Quality: Current
Measures, Gaps, and New Approaches.” Urban Institute Research Report.
https://www.urban.org/sites/default/files/2022-04/Measuring%20Job%20
Quality.pdf.
Keynes, J. 2010. “Economic Possibilities for Our Grandchildren.” Essays in Persuasion,
321–32. Orig. pub. 1930. https://doi.org/10.1007/978-1-349-59072-8_25.
Kline, P., and E. Moretti. 2014. “Local Economic Development, Agglomeration
Economies, and the Big Push: 100 Years of Evidence from the Tennessee Valley
Authority.” Quarterly Journal of Economics 129, no. 1: 275–331. https://doi.
org/10.1093/qje/qjt034.
Krueger, A. 2017. “Where Have All the Workers Gone? An Inquiry into the Decline of
the U.S. Labor Force Participation Rate.” Brookings Papers on Economic
Activity, no. 2. https://www.brookings.edu/wp-content/uploads/2018/02/
kruegertextfa17bpea.pdf.
Levy, F., and P. Temin. 2007. Inequality and Institutions in 20th-Century America. NBER
Working Paper 13106. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w13106.
Lofstrom, M., and S. Raphael. 2013. “Impact of Realignment on County Jail
Populations.” Public Policy Institute of California. https://gspp.berkeley.edu/
assets/uploads/research/pdf/p73.pdf.
368 |

References

Looney, A., and N. Turner. 2018. Work and Opportunity Before and After Incarceration.
Economic Studies at Brookings. Washington: Brookings Institution. https://
www.brookings.edu/wp-content/uploads/2018/03/es_20180314_
looneyincarceration_final.pdf.
Maestas, N., K. Mullen, and D. Powell. 2022. The Effect of Population Aging on
Economic Growth, the Labor Force, and Productivity. NBER Working Paper
22452. Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w22452.
Maestas, N., K. Mullen, D. Powell, R. von Wachter, and J. Wenger. 2017. “Working
Conditions in the United States: Results of the 2015 American Working
Conditions Survey.” RAND Corporation Research Report. https://doi.
org/10.7249/RR2014.
Maestas, N., K. Mullen, and A. Strand. 2013. “Does Disability Insurance Receipt
Discourage Work? Using Examiner Assignment to Estimate Causal Effects of
SSDI Receipt.” American Economic Review 103, no. 5: 1797–829. https://doi.
org/10.1257/aer.103.5.1797.
Matsudaira, J. 2015. “Economic Conditions and the Living Arrangements of Young
Adults: 1960 to 2011.” Journal of Population Economics 29: 167–95. https://
doi.org/10.1007/s00148-015-0555-y.
McEntarfer, E. 2022. Older Workers, Retirement, and Macroeconomic Shocks. PRC
Working Paper 2022-13. Philadelphia: Pension Research Council, Wharton
School, University of Pennsylvania. http://dx.doi.org/10.2139/ssrn.4169082.
Meyer, B., and D. Rosenbaum. 2001. “Welfare, the Earned Income Tax Credit, and the
Labor Supply of Single Mothers.” Quarterly Journal of Economics 116, no. 3:
1063–1114. https://doi.org/10.1162/00335530152466313.
Migration Policy Institute. 2022. “What Has Been the Impact of the Pandemic on
Immigrant Communities and U.S. Immigration Trends and Policies Two Years
On? New Report Assesses.” https://www.migrationpolicy.org/news/
pandemic-impact-immigrants-us-immigration-two-years-on.
Montes, J., C. Smith, and J. Dajon. 2022. “The Great Retirement Boom”: The
Pandemic-Era Surge in Retirements and Implications for Future Labor Force
Participation. Finance and Economics Discussion Series, Working Paper 081.
Washington: Federal Reserve Board. https://doi.org/10.17016/FEDS.2022.081.
Morrissey, T. 2017. “Child Care and Parent Labor Force Participation: A Review of the
Research Literature.” Review of Economics of the Household 15: 1–24. https://
doi.org/10.1007/s11150-016-9331-3.
Mueller-Smith, M. 2015. “The Criminal and Labor Market Impacts of Incarceration.”
Working paper, University of Michigan. https://www.irp.wisc.edu/newsevents/
workshops/2015/participants/papers/10-Mueller-Smith-IRP-draft.pdf.
Murnane, R., and J. Steele. 2007. “What Is the Problem? The Challenge of Providing
Effective Teachers for All Children.” Future of Children 17, no. 1: 15–43.
https://doi.org/10.1353/foc.2007.0010.

References | 369

Ne’eman, A., and N. Maestas 2022. How Has COVID-19 Impacted Disability
Employment? NBER Working Paper 30640. Cambridge, MA: National Bureau
of Economic Research. https://doi.org/10.3386/w30640.
Neumark, D., and J. Kolko. 2010. “Do Enterprise Zones Create Jobs? Evidence from
California’s Enterprise Zone Program.” Journal of Urban Economics 68, no. 1:
1–19. https://doi.org/10.1016/j.jue.2010.01.002.
Neumark, D., and H. Simpson. 2015. “Place-Based Policies.” Handbook of Regional and
Urban Economics 5: 1197–287. http://dx.doi.org/10.1016/B978-0-444-595317.00018-1.
Nguyen, T., C. Lam, and P. Bruno. 2022. “Is There a National Teacher Shortage? A
Systematic Examination of Reports of Teacher Shortages in the United States.”
Working paper, Annenberg Institute at Brown University. https://doi.
org/10.26300/76eq-hj32.
Nikolova, M., and F. Cnossen. 2020. “What Makes Work Meaningful and Why
Economists Should Care About It.” Labour Economics 65: 101847. https://doi.
org/10.1016/j.labeco.2020.101847.
Nunn, R., J. Parsons, and J. Shambaugh. 2019. “Labor Force Nonparticipation: Trends,
Causes, and Policy Solutions.” Hamilton Project. https://www.brookings.edu/
wp-content/uploads/2019/10/ES_THP_labor-force-nonparticipation_final.pdf.
OECD (Organization for Economic Cooperation and Development). 2019. “Public
Spending on Childcare and Early Education.” OECD Family Database. https://
www.oecd.org/els/soc/PF3_1_Public_spending_on_childcare_and_early_
education.pdf.
Pager, D. 2003. “The Mark of Criminal Record.” American Journal of Sociology 108, no.
5: 937–75. https://www.jstor.org/stable/pdf/10.1086/374403.
Petterson, S., W. Liaw, R. Phillips, D. Rabin, D. Meyers, and A. Bazemore. 2012.
“Projecting U.S. Primary Care Physician Workforce Needs: 2010–25.” Annals
of Family Medicine 10, no. 6: 503–9. https://doi.org/10.1370/afm.1431.
Piehl, A. 2016. “Putting Time Limits on the Punitiveness of the Criminal Justice System.”
Hamilton Project. https://www.hamiltonproject.org/assets/files/reducing_
punitiveness_piehl_policymemo.pdf.
Pierce, J., and P. Schott. 2016. “The Surprisingly Swift Decline of U.S. Manufacturing
Employment.” American Economic Review 106, no. 7: 1632–62. https://doi.
org/10.1257/aer.20131578.
Ricketts, T. 2005. “Workforce Issues in Rural Areas: A Focus on Policy Equity.”
American Journal of Public Health 95, no. 1: 42–48. https://doi.org10.2105/
AJPH.2004.047597.
Rodriguez, M., and B. Avery. 2016. “Unlicensed & Untapped: Removing Barriers to State
Occupational Licenses for People with Records.” National Employment Law
Project. https://s27147.pcdn.co/wp-content/uploads/Unlicensed-UntappedRemoving-Barriers-State-Occupational-Licenses.pdf.

370 |

References

Satiani, A., J. Niedermier, B. Satiani, and D. Svendsen. 2018. “Projected Workforce of
Psychiatrists in the United States: A Population Analysis.” Psychiatric Services
69, no. 6: 710–13. https://doi.org/10.1176/appi.ps.201700344.
Schmitt, J., and A. Mitukiewicz. 2012. “Politics Matter: Changes in Unionisation Rates in
Rich Countries, 1960–2010.” Industrial Relations Journal 43, no. 3: 260–80.
https://doi.org/10.1111/j.1468-2338.2012.00675.x.
Schor, J. 1993. The Overworked American: The Unexpected Decline of Leisure. New
York: Basic Books. https://www.basicbooks.com/titles/juliet-b-schor/
the-overworked-american/9780465054343/.
Sheiner, L. 2014. “The Determinants of the Macroeconomic Implications of Aging.”
American Economic Review 104, no. 5: 218–23. https://doi.org/10.1257/
aer.104.5.218.
Stansbury, A., and L. Summers. 2020. The Declining Worker Power Hypothesis: An
Explanation for the Recent Evolution of the American Economy. NBER
Working Paper 27193. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w27193.
U.S. Department of Education. 2022. “2022 School Pulse Panel.” Institute of Education
Sciences. https://ies.ed.gov/schoolsurvey/spp/.
Vahey, D., L. Aiken, D. Sloane, S. Clarke, and D. Vargas. 2004. “Nurse Burnout and
Patient Satisfaction.” Med Care 42 (2 Suppl.): 1157–66. https://www.ncbi.nlm.
nih.gov/pmc/articles/PMC2904602/.
Volk, J., D. Palanker, M. O’Brien, and C. Goe. 2021. “States’ Actions to Expand
Telemedicine Access During COVID-19 and Future Policy Considerations.”
Commonwealth Fund. https://www.commonwealthfund.org/publications/issuebriefs/2021/jun/states-actions-expand-telemedicine-access-covid-19.
White House. 2021. “Statement by President Joe Biden on the House Taking up the PRO
Act.” https://www.whitehouse.gov/briefing-room/statements-releases/2021
/03/09/statement-by-president-joe-biden-on-the-house-taking-up-the-pro-act/.
Wikle, J., and R. Wilson. 2022. “Access to Head Start and Maternal Labor Supply:
Experimental and Quasi-Experimental Evidence.” Working paper, Brigham
Young University. https://economics.byu.edu/00000173-9aea-d2bd-a9f7fbeb30dd0000/hs-laborsupply-wikle-wilson-july2020-pdf.
World Prison Brief. 2021. “World Prison Population List.” Institute for Crime & Justice
Policy Research. https://www.prisonstudies.org/sites/default/files/resources/
downloads/world_prison_population_list_13th_edition.pdf.
Wu, P. 2022. Wage Inequality and the Rise in Labor Force Exit: The Case of U.S.
Prime-Age Men. Research Department Working Paper 22-16. Boston: Federal
Reserve Bank of Boston. https://doi.org/10.29412/res.wp.2022.16.
Ziliak, J. 2014. “Supporting Low-Income Workers through Refundable Child-Care
Credits.” Brookings Institution. https://www.brookings.edu/research/
supporting-low-income-workers-through-refundable-child-care-credits/.

References | 371

Chapter 7
Acemoglu, D., A. Makhdoumi, A. Malekian, and A. Ozdaglar. 2022. “Too Much Data:
Prices and Inefficiencies in Data Markets.” American Economic Journal:
Microeconomics 14, no. 4: 218–56. https://doi.org/10.1257/mic.20200200.
Acquisti, A., C. Taylor, and L. Wagman. 2016. “The Economics of Privacy.” Journal of
Economic Literature 54, no. 2: 442–92. http://doi.org/10.1257/jel.54.2.442.
Aday, S., H. Farrell, D. Freelon, M. Lynch, J. Sides, and M. Dewar. 2013. “Watching
from Afar: Media Consumption Patterns Around the Arab Spring.” Sage
Journals 57, no. 7: 899–919. https://doi.org/10.1177/0002764213479373.
Allcott, H., L. Braghieri, S. Eichmeyer, and M. Gentzkow. 2020. “The Welfare Effects of
Social Media.” American Economic Review 110, no. 3: 629–76. https://doi.
org/10.1257/aer.20190658.
Allcott, H., and M. Gentzkow. 2017. “Social Media and Fake News in the 2016
Election.” Journal of Economic Perspectives 31, no. 2: 211–36. https://doi.
org/10.1257/jep.31.2.211.
Amazon. 2021. “The Evolution of Amazon’s Inventory Planning System.” https://www.
amazon.science/latest-news/the-evolution-of-amazons-inventory-planning-system.
Armstrong, M. 2006. “Competition in Two-Sided Markets.” RAND Journal of Economics
37, no. 3: 668–91. https://www.jstor.org/stable/pdf/25046266.pdf.
Athey, S., and F. Scott Morton. 2022. “Platform Annexation.” Antitrust Law Journal 84,
no. 3: 677–703. https://siepr.stanford.edu/publications/working-paper/
platform-annexation.
Ayres, I., M. Banaji, and C. Jolls. 2015. “Race Effects on eBay.” RAND Journal of
Economics 46, no. 4: 891–917. https://doi.org/10.1111/1756-2171.12115.
Ayers, J., B. Chu, Z. Zhu, E. Leas, D. Smith, M. Dredze, and D. Broniatowski. 2021.
“Spread of Misinformation About Face Masks and COVID-19 by Automated
Software on Facebook.” JAMA Internal Medicine 181, no. 9: 1251–53. https://
doi.org/10.1001/jamainternmed.2021.2498.
Bail, C., L. Argyle, T. Brown, J. Bumpus, H. Chen, M. Hunzaker, J. Lee, M. Mann, F.
Merhout, and A. Volfovsky. 2018. “Exposure to Opposing Views on Social
Media Can Increase Political Polarization.” PNAS 115, no. 37: 9216–21. https://
doi.org/10.1073/pnas.1804840115.
Baker, J., and J. Chevalier. 2013. “The Competitive Consequences of Most-FavoredNation Provisions.” Antitrust Magazine 27, no. 2. https://papers.ssrn.com/sol3/
papers.cfm?abstract_id=2251165.
Baye, M., J. Morgan, and P. Scholten. 2003. “The Value of Information in an Online
Consumer Electronics Market.” Journal of Public Policy and Marketing 22, no.
1: 17–25. https://www.jstor.org/stable/30000838.

372 |

References

Barrett, B. 2021. “Commercial Companies in Party Networks: Digital Advertising Firms
in U.S. Elections from 2006–2016.” Political Communication 39, no. 2:
147–65. https://doi.org/10.1080/10584609.2021.1978021.
Beknazar-Yuzbashev, G., and M. Stalinski. 2022. “Do Social Media Ads Matter for
Political Behavior? A Field Experiment.” Journal of Public Economics 214.
https://doi.org/10.1016/j.jpubeco.2022.104735.
Bergemann, D., and A. Bonatti. 2019. “Markets for Information: An Introduction.”
Annual Review of Economics 11: 85–107. https://doi.org/10.1146/
annurev-economics-080315-015439.
Berkshire Hathaway Inc. 2007. “Letter to Shareholders of Berkshire Hathaway Inc.”
https://www.berkshirehathaway.com/letters/2007ltr.pdf.
Blake, T., S. Moshary, K. Sweeney, and S. Tadelis. 2021. “Price Salience and Product
Choice.” Marketing Science 40, no. 4: 619–36. https://pubsonline.informs.org/
doi/10.1287/mksc.2020.1261.
Boik, A., and K. Corts. 2016. “The Effects of Platform Most-Favored-Nation Clauses on
Competition and Entry.” Journal of Law and Economics 59, no. 1: 105–34.
https://doi.org/10.1086/686971.
Bonbright, J. 1961. Principles of Public Utility Rates. New York: Columbia University
Press.
Bond, R., C. Fariss, J. Jones, A. Kramer, C. Marlow, J. Settle, and J. Fowler. 2012. “A
61-Million-Person Experiment in Social Influence and Political Mobilization.”
Nature 489: 295–98. https://doi.org/10.1038/nature11421.
Boxell, L., M. Gentzkow, and J. Shapiro. 2017. “Greater Internet Use Is Not Associated
with Faster Growth in Political Polarization among U.S. Demographic Groups.”
PNAS 114, no. 40: 10612–17. https://doi.org/10.1073/pnas.1706588114.
Bresnahan, T. 2002. “The Economics of the Microsoft Case.” Working Paper 232,
Stanford Law School. http://dx.doi.org/10.2139/ssrn.304701.
Brown, J., and A. Goolsbee. 2002. “Does the Internet Make Markets More Competitive?
Evidence from the Life Insurance Industry.” Journal of Political Economy 110,
no. 3: 481–507. https://doi.org/10.1086/339714.
Brown, S. 2021. “Machine Learning, Explained.” Massachusetts Institute of Technology,
Sloan School of Management. https://mitsloan.mit.edu/ideas-made-to-matter/
machine-learning-explained.
Brown, Z., and A. MacKay. Forthcoming. “Competition in Pricing Algorithms.”
American Economic Journal: Microeconomics. https://www.aeaweb.org/
articles?id=10.1257/mic.20210158.
Brynjolfsson, E., A. Collis, E. Diewert, F. Eggers, and K. Fox. 2019. GDP-B: Accounting
for the Value of New and Free Goods in the Digital Economy. NBER Working
Paper 25695. Cambridge, MA: National Bureau of Economic Research. https://
doi.org/10.3386/w25695.

References | 373

Brynjolfsson, E., Y. Hu, and M. Smith. 2003. “Consumer Surplus in the Digital Economy:
Estimating the Value of Increased Product Variety at Online Booksellers.”
Management Science 49, no. 11: 1580–96. https://www.jstor.org/
stable/4134002.
Brynjolfsson, E., and M. Smith. 2000. “Frictionless Commerce? A Comparison of
Internet and Conventional Retailers.” Management Science 46, no. 4: 563–85.
https://www.jstor.org/stable/2661602.
Byrne, D., and N. de Roos. 2019. “Learning to Coordinate: A Study in Retail Gasoline.”
American Economic Review 109, no. 2: 591–619. https://pubs.aeaweb.org/doi/
pdfplus/10.1257/aer.20170116.
Caillaud, B., and B. Jullien. 2003. “Chicken & Egg: Competition among Intermediation
Service Providers.” RAND Journal of Economics 34, no. 2: 309–28. https://
www.jstor.org/stable/pdf/1593720.pdf.
Caliskan, A. 2021. “Detecting and Mitigating Bias in Natural Language Processing.”
Brookings Institution. https://www.brookings.edu/research/
detecting-and-mitigating-bias-in-natural-language-processing/.
Carney, N. 2016. “All Lives Matter, but So Does Race: Black Lives Matter and the
Evolving Role of Social Media.” Humanity & Society 40, no. 2: 180–99. https://
journals.sagepub.com/doi/abs/10.1177/0160597616643868.
Cavallo, C. 2017. “Are Online and Offline Prices Similar? Evidence from Large MultiChannel Retailers.” American Economic Review 107, no. 1: 283–303. https://
www.aeaweb.org/articles?id=10.1257/aer.20160542.
CFPB (Consumer Financial Protection Bureau). 2022. “Credit Card Late Fees.” https://
files.consumerfinance.gov/f/documents/cfpb_credit-card-late-fees_
report_2022-03.pdf.
Chan, J., A. Ghose, and R. Seamans. 2016. “The Internet and Racial Hate Crimes: Offline
Spillovers from Online Access.” MIS Quarterly 40, no. 2: 381–403. http://
dx.doi.org/10.2139/ssrn.2335637.
Chandra, A., and A. Collard-Wexler. 2009. “Mergers in Two-Sided Markets: An
Application to the Canadian Newspaper Industry.” Journal of Economics &
Management Strategy 18, no. 4: 1045–70. https://doi.
org/10.1111/j.1530-9134.2009.00237.x.
Chapman, P., and S. Bodoni. 2022. “Twitter Probed in EU for Pre-Musk Data Leak of 5.4
Million Users.” Bloomberg. https://www.bloomberg.com/news/articles/202212-23/twitter-probed-by-top-eu-privacy-watchdog-for-pre-musk-eraleak?leadSource=uverify%20wall.
Chin, C. 2019. “Assessing Employer Intent When AI Hiring Tools Are Biased.”
Brookings Institution. https://www.brookings.edu/research/assessingemployer-intent-when-ai-hiring-tools-are-biased/.
Chiou, L., and C. Tucker. 2017. Search Engines and Data Retention: Implications for
Privacy and Antitrust. NBER Working Paper 23815. Cambridge, MA: National
Bureau for Economic Research. https://doi.org/10.3386/w23815.
374 |

References

Choi, J., D. Jeon, and B. Kim. 2019. “Privacy and Personal Data Collection with
Information Externalities.” Journal of Public Economics 173: 113–24. https://
doi.org/10.1016/j.jpubeco.2019.02.001.
Competition Bureau Canada. 2017. “Big Data and Innovation: Implications for
Competition Policy in Canada.” https://www.competitionbureau.gc.ca/eic/site/
cb-bc.nsf/eng/04304.html.
Connecticut, State of. 2022. “An Act Concerning Personal Data Privacy and Online
Monitoring, Senate Bill No. 6, February Session, Connecticut General
Assembly, 2022.” https://www.cga.ct.gov/2022/amd/S/pdf/2022SB-00006R00SA-AMD.pdf.
Council of the European Union. 2022. “Digital Markets Act (DMA): Agreement Between
the Council and the European Parliament.” https://www.consilium.europa.eu/en/
press/press-releases/2022/03/25/
council-and-european-parliament-reach-agreement-on-the-digital-markets-act/.
Crain, M. 2018. “The Limits of Transparency: Data Brokers and Commodification.” New
Media and Society 20, no. 1: 88–104. https://journals.sagepub.com/doi/
abs/10.1177/1461444816657096.
Dastin, J. 2018. “Amazon Scraps Secret AI Recruiting Tool That Showed Bias Against
Women.” Reuters. https://www.reuters.com/article/us-amazon-com-jobsautomation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-againstwomen-idUSKCN1MK08G.
DeLuca, K., S. Lawson, and Y. Sun. 2012. “Occupy Wall Street on the Public Screens of
Social Media: The Many Framings of the Birth of a Protest Movement.”
Communication, Culture and Critique 5, no. 4: 483–509. https://doi.
org/10.1111/j.1753-9137.2012.01141.x.
Digital Regulation Cooperation Forum. 2022. “Auditing Algorithms: The Existing
Landscape, Role of Regulators and Future Outlook.” https://www.gov.uk/
government/publications/findings-from-the-drcf-algorithmic-processingworkstream-spring-2022/auditing-algorithms-the-existing-landscape-role-of-regulatorsand-future-outlook.
DiGrazia, J., K. McKelvey, J. Bollen, and F. Rojas. 2013. “More Tweets, More Votes:
Social Media as a Quantitative Indicator of Political Behavior.” PLoS ONE 8,
no. 11. https://doi.org/10.1371/journal.pone.0079449.
DOJ (U.S. Department of Justice). 2020. “Justice Department Sues to Block Visa’s
Proposed Acquisition of Plaid.” https://www.justice.gov/opa/pr/
justice-department-sues-block-visas-proposed-acquisition-plaid.
Doleac, J., and L. Stein. 2013. “The Visible Hand: Race and Online Market Outcomes.”
Economic Journal 123, no. 572: 469–92. https://doi.org/10.1111/ecoj.12082.
Dubé, J., and S. Misra. 2023. “Personalized Pricing and Consumer Welfare.” Journal of
Political Economy 131, no. 1: 131–89. https://doi.org/10.1086/720793.

References | 375

Eeckhout, J., and L. Veldkamp. 2022. Data and Market Power. NBER Working Paper
30022. Cambridge, MA: National Bureau of Economic Research. https://www.
nber.org/system/files/working_papers/w30022/w30022.pdf.
Ellison, G., and S. Ellison. 2009. “Search, Obfuscation, and Price Elasticities on the
Internet.” Econometrica 77, no. 2: 427–52. https://doi.org/10.3982/ECTA5708.
Ellison, G., and D. Fudenberg. 2003. Knife Edge of Plateau: When Do Market Models
Tip? NBER Working Paper 9528. Cambridge, MA: National Bureau of
Economic Research. https://www.nber.org/papers/w9528.
Engler, A. 2021. “Auditing Employment Algorithms for Discrimination.” Brookings
Institution. https://www.brookings.edu/research/auditing-employmentalgorithms-for-discrimination/.
European Parliament. 2022. “European Parliament Legislative Resolution of 5 July 2022
on the Proposal for a Regulation of the European Parliament and of the Council
on a Single Market for Digital Services (Digital Services Act) and Amending
Directive 2000/31/EC.” https://www.europarl.europa.eu/doceo/document/
TA-9-2022-0269_EN.html#title2.
European Parliament and Council of the European Union. 2022. “Regulation (EU)
2022/2065 of the European Parliament and of the Council of 19 October 2022
on a Single Market for Digital Services and Amending Directive 2000/31/EC
(Digital Services Act).” https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?u
ri=CELEX:32022R2065&from=EN.
European Parliament and European Commission. 2022. “On Contestable and Fair
Markets in the Digital Sector and Amending Directives (EU) 2019/1937 and
(EU) 2020/1828 (Digital Markets Act).” https://eur-lex.europa.eu/legal-content/
EN/TXT/PDF/?uri=CELEX:32022R1925&from=EN.
Evans, D. 2003. “The Antitrust Economics of Multi-Sided Platform Markets.” Yale
Journal on Regulation 20, no. 2. https://www.semanticscholar.org/paper/
The-Antitrust-Economics-of-Multi-Sided-Platform-Evans/
aef4ba3a170b9863f305c8562ad0ce1726ae2f98.
Farronato, C., J. Fong, and A. Fradkin. Forthcoming. “Dog Eat Dog: Balancing Network
Effects and Differentiation in a Digital Platform Merger.” Management Science.
https://www.hbs.edu/faculty/Pages/item.aspx?num=62569.
FTC (U.S. Federal Trade Commission). 2014. “Data Brokers: A Call for Transparency
and Accountability.” https://www.ftc.gov/system/files/documents/reports/databrokers-call-transparency-accountability-report-federal-trade-commission-may2014/140527databrokerreport.pdf/.
———. 2017. “Economic Analysis of Hotel Resort Fees.” https://www.ftc.gov/system/
files/documents/reports/economic-analysis-hotel-resort-fees/p115503_hotel_
resort_fees_economic_issues_paper.pdf.
———. 2019. “FTC Imposes $5 Billion Penalty and Sweeping New Privacy Restrictions
on Facebook.” https://www.ftc.gov/news-events/news/press-releases/2019/07/
ftc-imposes-5-billion-penalty-sweeping-new-privacy-restrictions-facebook.

376 |

References

———. 2022. “Case No. 2:22-cv-377: Complaint for Permanent Injunction and Other
Relief.” https://www.ftc.gov/system/files/ftc_gov/pdf/1.%20Complaint.pdf.
Fiorina, M., and S. Abrams. 2008. “Political Polarization in the American Public.” Annual
Review of Political Science 11: 563–88. https://doi.org/10.1146/annurev.
polisci.11.053106.153836.
Furl, N., P. Phillips, and A. O’Toole. 2002. “Face Recognition Algorithms and the OtherRace Effect: Computational Mechanisms for a Developmental Contact
Hypothesis.” Cognitive Science 26, no. 6: 797–815. https://doi.org/10.1016/
S0364-0213(02)00084-8.
Gandal, N. 1994. “Hedonic Price Indexes for Spreadsheets and an Empirical Test for
Network Externalities.” RAND Journal of Economics 25, no. 1: 160–70. https://
www.jstor.org/stable/2555859#metadata_info_tab_contents.
———. 1995. “Competing Compatibility Standards and Network Externalities in the PC
Software Market.” Review of Economics and Statistics 77, no. 4: 599–608.
https://www.jstor.org/stable/2109809#metadata_info_tab_contents.
Garett, R., and S. Young. 2021. “Online Misinformation and Vaccine Hesitancy.”
Translational Behavioral Medicine 11, no. 12: 2194–99. https://doi.
org/10.1093/tbm/ibab128.
Gentzkow, M. 2007. “Valuing New Goods in a Model with Complementarity: Online
Newspapers.” American Economic Review 97, no. 3: 713–44. https://www.jstor.
org/stable/30035018.
Goldfarb, A., and C. Tucker. 2019. “Digital Economics.” Journal of Economic Literature
57, no. 1: 3–43. https://doi.org/10.1257/jel.20171452.
Gorodnichenko, Y., T. Pham, and O. Talavera. 2021. “Social Media, Sentiment and Public
Opinions: Evidence from #Brexit and #USElection.” European Economic
Review 136. https://doi.org/10.1016/j.euroecorev.2021.103772.
Gu, Y., L. Madio, and C. Reggiani. 2022. “Data Brokers Co-Opetition.” Oxford Economic
Papers 74, no. 3: 820–39. https://doi.org/10.1093/oep/gpab042.
Gu, Y., L. Madio, and C. Reggiani. 2022. “Data Brokers Co-Opetition.” Oxford Economic
Papers 74, no. 3: 820–39. https://doi.org/10.1093/oep/gpab042.
Guszcza, J., I. Rahwan, W. Bible, M. Cebrian, and V. Katyal. 2018. “Why We Need to
Audit Algorithms.” Harvard Business Review. https://hbr.org/2018/11/
why-we-need-to-audit-algorithms.
Harrington, J. 2018. “Developing Competition Law for Collusion by Autonomous
Artificial Agents.” Journal of Competition Law and Economics 14, no. 3:
331–63. https://doi.org/10.1093/joclec/nhy016.
Highfill, T., and C. Surfield. 2022. “New and Revised Statistics of the U.S. Digital
Economy, 2005–2021.” U.S. Bureau of Economic Analysis. https://www.bea.
gov/system/files/2022-11/new-and-revised-statistics-of-the-us-digitaleconomy-2005-2021.pdf.

References | 377

Himelein-Wachowiak, M., S. Giorgi, A. Devoto, M. Rahman, L. Ungar, A. Schwartz, D.
Epstein, L. Leggio, and B. Curtis. 2021. “Bots and Misinformation Spread on
Social Media: Implications for COVID-19.” JMIR Publications 23, no. 5.
https://doi.org/10.2196/26933.
Ichihashi, S. 2020. “Online Privacy and Information Disclosure by Consumers.”
American Economic Review 110, no. 2: 569–95. https://doi.org/10.1257/
aer.20181052.
Jones, J., R. Bond, E. Bakshy, D. Eckles, and J. Fowler. 2017. “Social Influence and
Political Mobilization: Further Evidence from a Randomized Experiment in the
2012 U.S. Presidential Election.” PLoS ONE 12, no. 4. https://pubmed.ncbi.
nlm.nih.gov/28445476/.
Joskow, P. 2007. “Chapter 16 Regulation of Natural Monopoly.” Handbook of Law and
Economics 2: 1227–1348. https://doi.org/10.1016/S1574-0730(07)02016-6.
Joskow, P., and N. Rose. 1989. “The Effects of Economic Regulation.” Handbook of
Industrial Organization 2: 1449–1506. https://doi.org/10.1016/
S1573-448X(89)02013-3.
Jullien, B., A. Pavan, and M. Rysman. 2021. “Chapter 7: Two-Sided Markets, Pricing,
and Network Effects.” Handbook of Industrial Organization 4, no. 1: 485–592.
https://www.sciencedirect.com/science/article/abs/pii/S1573448X21000078?via
%3Dihub.
Kades, M., and F. Scott Morton. 2020. “Interoperability as a Competition Remedy for
Digital Networks.” Working paper, Washington Center for Equitable Growth.
https://equitablegrowth.org/working-papers/
interoperability-as-a-competition-remedy-for-digital-networks/.
Kahn, A. 1988. The Economics of Regulation. Cambridge, MA: MIT Press. https://
mitpress.mit.edu/9780262610520/the-economics-of-regulation/.
Kirpalani, R., and T. Philippon. 2020. Data Sharing and Market Power with Two-Sided
Platforms. NBER Working Paper 28023. Cambridge, MA: National Bureau for
Economic Research. https://doi.org/10.3386/w28023.
Kroll, J. 2021. “Why AI Is Just Automation.” Brookings Institution. https://www.
brookings.edu/research/why-ai-is-just-automation/.
Lajevardi, N., K. Oskooii, and H. Walker. 2022. “Hate, Amplified? Social Media News
Consumption and Support for Anti-Muslim Policies.” Journal of Public Policy,
1–28. https://doi.org/10.1017/S0143814X22000083.
Lambrecht, A., and C. Tucker. 2019. “Algorithmic Bias? An Empirical Study of Apparent
Gender-Based Discrimination in the Display of STEM Career Ads.”
Management Science 65, no. 7: 2966–81. https://doi.org/10.1287/
mnsc.2018.3093.
Lee, N., and S. Lai. 2021. “Why New York City Is Cracking Down on AI in Hiring.”
Brookings Institution. https://www.brookings.edu/blog/techtank/2021/12/20/
why-new-york-city-is-cracking-down-on-ai-in-hiring/.

378 |

References

Levy, R. 2021. “Social Media, News Consumption, and Polarization: Evidence from a
Field Experiment.” American Economic Review 113, no. 3: 831–70. https://doi.
org/10.1257/aer.20191777.
Liu, Y., R. Jia, J. Ye, and X. Qu. 2022. “How Machine Learning Informs Ride-Hailing
Services: A Survey.” Communications in Transportation Research 2. https://doi.
org/10.1016/j.commtr.2022.100075.
Luco, F. 2019. “Who Benefits from Information Disclosure? The Case of Retail
Gasoline.” American Economic Journal: Microeconomics 11, no. 2: 277–305.
https://www.jstor.org/stable/pdf/26641423.pdf.
McCurry, J. 2021. “South Korean AI Chatbot Pulled from Facebook After Hate Speech
Towards Minorities.” Guardian, January 13. https://www.theguardian.com/
world/2021/jan/14/time-to-properly-socialise-hate-speech-ai-chatbot-pulledfrom-facebook.
Mickle, T. 2019. “Apple Dominates App Store Search Results, Thwarting Competitors.”
Wall Street Journal, July 23. https://www.wsj.com/articles/
apple-dominates-app-store-search-results-thwarting-competitors-11563897221.
Mishra, S. 2021. “The Dark Industry of Data Brokers: Need for Regulation?”
International Journal of Law and Information Technology 29, no. 4: 395–410.
https://doi.org/10.1093/ijlit/eaab012.
Möhlmann, M., and O. Henfridsson. 2019. “What People Hate About Being Managed by
Algorithms, According to a Study of Uber Drivers.” Harvard Business Review.
https://hbr.org/2019/08/what-people-hate-about-being-managed-by-algorithmsaccording-to-a-study-of-uber-drivers.
Mundt, M., K. Ross, and C. Burnett. 2018. “Scaling Social Movements Through Social
Media: The Case of Black Lives Matter.” Social Media + Society 4, no. 4.
https://journals.sagepub.com/doi/full/10.1177/2056305118807911.
NCSL (National Conference of State Legislatures). 2022. “State Laws Related to Digital
Privacy.” https://www.ncsl.org/research/telecommunications-and-informationtechnology/state-laws-related-to-internet-privacy.aspx.
Neely, S., C. Eldredge, R. Ersing, and C. Remington. 2022. “Vaccine Hesitancy and
Exposure to Misinformation: A Survey Analysis.” Journal of General Internal
Medicine 37, no. 1: 179–87. https://doi.org/10.1007/s11606-021-07171-z.
Obermeyer, Z., B. Powers, C. Vogeli, and S. Mullainathan. 2019. “Dissecting Racial Bias
in an Algorithm Used to Manage the Health of Populations.” Science 366, no.
6464: 447–53. https://doi.org/10.1126/science.aax2342.
O’Connor, J., and N. Wilson. 2021. “Reduced Demand Uncertainty and the Sustainability
of Collusion: How AI Could Affect Competition.” Information Economics and
Policy 54. https://doi.org/10.1016/j.infoecopol.2020.100882.
OECD (Organization for Economic Cooperation and Development). 2019. “OECD AI
Principles Overview.” https://oecd.ai/en/ai-principles.

References | 379

O’Sullivan, D. “Half a Billion Facebook Users’ Information Posted on Hacking Website,
Cyber Experts Say.” CNN Business. https://www.cnn.com/2021/04/04/tech/
facebook-user-info-leaked/index.html.
Pierri, F., B. Perry, M. DeVerna, K. Yang, A. Flammini, F. Menczer, and J. Bryden. 2022.
“Online Misinformation Is Linked to Early COVID-19 Vaccination Hesitancy
and Refusal.” Scientific Reports 12, no.1. https://doi.org/10.1038/
s41598-022-10070-w.
Prior, M. 2013. “Media and Polarization.” Annual Review of Political Science 16:
101–27. https://doi.org/10.1146/annurev-polisci-100711-135242.
Prüfer, J., and C. Schottmüller. 2022. “Competing with Big Data.” Journal of Industrial
Economics 69, no. 4: 967–1008. https://doi.org/10.1111/joie.12259.
Quan, T., and K. Williams. 2018. “Product Variety, Across-Market Demand
Heterogeneity, and the Value of Online Retail.” RAND Journal of Economics
49, no. 4: 877–913. https://www.jstor.org/stable/45147416.
Rochet, J., and J. Tirole. 2003. “Platform Competition in Two-Sided Markets.” Journal of
the European Economic Association 1, no. 4: 990–1029. https://doi.
org/10.1162/154247603322493212.
Rysman, M. 2004. “Competition between Networks: A Study of the Market for Yellow
Pages.” Review of Economic Studies 71, no. 2: 483–512. https://doi.
org/10.1111/0034-6527.00512.
Saloner, G., and A. Shepard. 1995. “Adoption of Technologies with Network Effects: An
Empirical Examination of the Adoption of Automated Teller Machines.” RAND
Journal of Economics 26, no. 3: 479–501. https://www.jstor.org/stable/2555999.
Schmalensee, R. 1978. “A Note on Economies of Scale and Natural Monopoly in the
Distribution of Public Utility Services.” Bell Journal of Economics 9, no. 1:
207–76. https://doi.org/10.2307/3003626.
Scott Morton, F., P. Bouvier, A. Ezrachi, B. Jullien, R. Katz, G. Kimmelman, A.
Melamed, and J. Morgenstern. 2019. “Committee for the Study of Digital
Platforms Market Structure and Antitrust Subcommittee.” Chicago Booth
Stigler Center for the Study of the Economy and the State. https://research.
chicagobooth.edu/-/media/research/stigler/pdfs/market-structure-report.pdf.
Scott Morton, F., G. Crawford, J. Crémer, D. Dinielli, A. Fletcher, P. Heidhues, M.
Schnitzer, and K. Seim. 2021. “Equitable Interoperability: The ‘Super Tool’ of
Digital Platform Governance.” Yale Tobin Center for Economic Policy, Digital
Regulation Policy. https://tobin.yale.edu/sites/default/files/Equitable%20
Interoperability.pdf.
Scott Morton, F., F. Zettelmeyer, and J. Silva-Risso. 2001. “Internet Car Retailing.”
Journal of Industrial Economics 49, no. 4: 501–19. https://www.jstor.org/
stable/3569793.
Sherman, J. 2021. “Data Brokers and Sensitive Data on U.S. Individuals: Threats to
American Civil Rights, National Security, and Democracy.” Duke University,
Sanford School of Public Policy. https://techpolicy.sanford.duke.edu/
380 |

References

wp-content/uploads/sites/4/2021/08/Data-Brokers-and-Sensitive-Data-on-USIndividuals-Sherman-2021.pdf.
Sinai, T., and J. Waldfogel. 2004. “Geography and the Internet: Is the Internet a Substitute
or a Complement for Cities?” Journal of Urban Economics 56, no. 1: 1–24.
https://doi.org/10.1016/j.jue.2004.04.001.
Sisson, P. 2022. “Robots Aren’t Done Reshaping Warehouses.” New York Times, July 12.
https://www.nytimes.com/2022/07/12/business/warehouse-technology-robotics.
html.
Smiley, R., and W. Greene. 1983. “Determinants of the Effectiveness of Electric Utility
Regulation.” Resources and Energy 5, no. 1: 65–81. https://doi.
org/10.1016/0165-0572(83)90018-X.
Smith, B. 2021. “How TikTok Reads Your Mind.” New York Times, December 5. https://
www.nytimes.com/2021/12/05/business/media/tiktok-algorithm.html.
Song, M. 2021. “Estimating Platform Market Power in Two-Sided Markets with an
Application to Magazine Advertising.” American Economic Journal:
Microeconomics 13, no. 2: 35–67. https://doi.org/10.1257/mic.20160052.
Spence, A. 1975. “Monopoly, Quality, and Regulation.” Bell Journal of Economics 6, no.
2: 417–29. https://doi.org/10.2307/3003237.
State of New York et al. v. Facebook, Inc. 2020. New York State, Attorney General’s
Office. https://ag.ny.gov/sites/default/files/state_of_new_york_et_al._v._
facebook_inc._-_filed_public_complaint_12.11.2020.pdf.
Stigler, G. 1961. “The Economics of Information.” Journal of Political Economy 69, no.
3: 213–25. https://www.jstor.org/stable/1829263.
Suh, C., I. Vasi, and P. Chang. 2017. “How Social Media Matter: Repression and the
Diffusion of the Occupy Wall Street Movement.” Social Science Research 65:
282–93. https://doi.org/10.1016/j.ssresearch.2017.01.004.
Teh, T., C. Liu, J. Wright, and J. Zhou. Forthcoming. “Multi-Homing and Oligopolistic
Platform Competition.” American Economic Journal: Microeconomics. https://
www.aeaweb.org/articles?id=10.1257/mic.20210324.
U.S. Census Bureau. 2022. “Quarterly Retail E-Commerce Sales, 2nd Quarter 2022.”
https://www.census.gov/retail/mrts/www/data/pdf/ec_current.pdf.
U.S. Senate. 2013. “What Information Do Data Brokers Have on Consumers, and How
Do They Use It?” https://www.govinfo.gov/content/pkg/CHRG-113shrg95838/
pdf/CHRG-113shrg95838.pdf.
U.S. Surgeon General’s Advisory. 2021. “Protecting Youth Mental Health.” U.S.
Department of Health and Human Services. https://www.hhs.gov/sites/default/
files/surgeon-general-youth-mental-health-advisory.pdf.
Victor, D. 2016. “Microsoft Created a Twitter Bot to Learn from Users; It Quickly
Became a Racist Jerk.” New York Times, March 24. https://www.nytimes.
com/2016/03/25/technology/microsoft-created-a-twitter-bot-to-learn-fromusers-it-quickly-became-a-racist-jerk.html.

References | 381

Wall Street Journal. 2021. “Inside TikTok’s Algorithm: A WSJ Video Investigation.” July 21.
https://www.wsj.com/articles/tiktok-algorithm-video-investigation-11626877477.
Wang, C., and J. Wright. Forthcoming. “Platform Investment and Price Parity Clauses.”
Journal of Industrial Economics. https://app.scholarsite.io/julian-wright/articles/
platform-investment-and-price-parity-clauses-3.
Weyl, E. 2010. “A Price Theory of Multi-Sided Platforms.” American Economic Review
100, no. 4: 1642–72. https://doi.org/10.1257/aer.20180478.
White House. 2021. “Executive Order on Promoting Competition in the American
Economy.” https://www.whitehouse.gov/briefing-room/presidentialactions/2021/07/09/executive-order-on-promoting-competition-in-theamerican-economy/.
———. 2022. “Blueprint for an AI Bill of Rights: Making Automated Systems Work for
the American People.” https://www.whitehouse.gov/wp-content/
uploads/2022/10/Blueprint-for-an-AI-Bill-of-Rights.pdf.
White House, National Economic Council. 2016. “The Competition Initiative and Hidden
Fees.” https://obamawhitehouse.archives.gov/sites/whitehouse.gov/files/
documents/hiddenfeesreport_12282016.pdf.
Williams, C., and G. Gulati. 2017. “Digital Advertising Expenditures in the 2016
Presidential Election.” Sage Journals 36, no. 4: 406–21. https://doi.
org/10.1177/0894439317726751.
Wright, J. 2004. “One-Sided Logic in Two-Sided Markets.” Review of Network
Economics 3, no. 1: 1–21. https://doi.org/10.2202/1446-9022.1042.
Xu, W., and K. Sasahara. 2022. “Characterizing the Roles of Bots on Twitter during the
COVID-19 Infodemic.” Journal of Computational Social Science 5: 591–609.
https://doi.org/10.1007/s42001-021-00139-3.
Zang, J. 2021. “How Facebook’s Advertising Algorithms Can Discriminate by Race and
Ethnicity.” Tech Science. https://techscience.org/a/2021101901/.
Zhang, S., N. Mehta, P. Singh, and K. Srinivasan. 2021. “Frontiers: Can an Artificial
Intelligence Algorithm Mitigate Racial Economic Inequality? An Analysis in
the Context of Airbnb.” Management Science 40: no. 5: 813–20. https://doi.
org/10.1287/mksc.2021.1295.

Chapter 8
Acemoglu, D. 2021. “The Bitcoin Fountainhead.” https://www.project-syndicate.org/
commentary/bitcoin-an-appealing-distraction-by-daron-acemoglu-2021-10.
Adams, A., and M. Ibert. 2022. “Runs on Algorithmic Stablecoins: Evidence from Iron,
Titan, and Steel.” https://www.federalreserve.gov/econres/notes/feds-notes/runson-algorithmic-stablecoins-evidence-from-iron-titan-and-steel-20220602.html.

382 |

References

Amazon. 2017. “Amazon Aurora PostgreSQL-Compatible Edition Benchmarking Guide.”
https://d1.awsstatic.com/product-marketing/Aurora/RDS_Aurora_PostgreSQL_
Performance_Assessment_Benchmarking_V1-0.pdf.
Anadu, K., M. Cipriani, R. Craver, and G. La Spada. 2021. “COVID Response: The
Money Market Mutual Fund Liquidity Facility.” Federal Reserve Bank of New
York, Staff Report 980. https://www.newyorkfed.org/medialibrary/media/
research/staff_reports/sr980.pdf.
Atlantic Council. 2022. “Central Bank Digital Currency Tracker.” https://www.
atlanticcouncil.org/cbdctracker/.
Bank of Canada, European Central Bank, Bank of Japan, Sveriges Riksbank, Swiss
National Bank, Bank of England, Board of Governors the Federal Reserve
System, and Bank for International Settlements. 2021. “Central Bank Digital
Currencies: Financial Stability Implications.” https://www.bis.org/publ/othp42_
fin_stab.pdf.
Bank of England. 2020. “Why Does Money Depend on Trust?” https://www.
bankofengland.co.uk/knowledgebank/why-does-money-depend-on-trust.
Baughman, G., F. Carapella, J. Gerszten, and D. Mills. 2022. “The Stable in Stablecoins.”
https://www.federalreserve.gov/econres/notes/feds-notes/the-stable-instablecoins-20221216.html.
Bech, M., J. Hancock, T. Rice, and A. Wadsworth. 2020. “On the Future of Securities
Settlement.” BIS Quarterly Review, March. https://www.bis.org/publ/qtrpdf/r_
qt2003i.pdf.
Benetton, M., G. Compiani, and A. Morse. 2021. “When Cryptomining Comes to Town:
High-Electricity-Use Spillovers to the Local Economy.” http://dx.doi.
org/10.2139/ssrn.3779720.
Berentsen, A., and F. Schär. 2018. “A Short Introduction to the World of
Cryptocurrencies.” Review of the Federal Reserve Bank of St. Louis 100: 1–16.
https://files.stlouisfed.org/files/htdocs/publications/review/2018/01/10/a-shortintroduction-to-the-world-of-cryptocurrencies.pdf.
Borri, N., Y. Liu, and A. Tsyvinski. 2022. “The Economics of Non-Fungible Tokens.”
Working paper. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4052045.
Bowman, M. 2022. “Technology, Innovation, and Financial Services.” Federal Reserve Board.
https://www.federalreserve.gov/newsevents/speech/bowman20220817a.htm.
Briola, A., D. Vidal-Tomás, Y. Wang, and T. Aste. 2023. “Anatomy of a Stablecoin’s
Failure: The Terra-Luna Case.” Finance Research Letter 51. https://www.
sciencedirect.com/science/article/abs/pii/S1544612322005359.
Brown, K. 2002. “Announcing Approval of Federal Information Processing Standard
(FIPS) 180-2, Secure Hash Standard; a Revision of FIPS 180-1.” National
Institute of Standards and Technology. Federal Register. https://www.
federalregister.gov/documents/2002/08/26/02-21599/
announcing-approval-of-federal-information-processing-standard-fips-180-2secure-hash-standard-a.
References | 383

Brunnermeier, M., and L. Pedersen. 2007. Market Liquidity and Funding Liquidity.
NBER Working Paper 12939. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w12939.
BTC. 2022. “Difficulty.” btc.com/stats/diff.
Calma, J. 2022. “Texas’ Fragile Grid Isn’t Ready for Crypto Mining’s Explosive
Growth.” The Verge. https://www.theverge.com/2022/7/14/23206795/
bitcoin-crypto-mining-electricity-texas-grid-energy-bills-emissions.
Carapella, F., E. Dumas, J. Gerszten, N. Swem, and L. Wall. 2022. “Decentralized
Finance (DeFi): Transformative Potential & Associated Risks.” Federal Reserve
Board, Finance and Economics Discussion Series 2022-057. https://www.
federalreserve.gov/econres/feds/files/2022057pap.pdf.
Cassidy, J. 2008. “The Minsky Moment: Subprime Mortgage Crisis and Possible
Recession.” New Yorker, February 4. https://www.newyorker.com/
magazine/2008/02/04/the-minsky-moment.
Chaum, D. 1982. “Computer Systems Established, Maintained, and Trusted by Mutually
Suspicious Groups.” Ph.D. dissertation, University of California, Berkeley.
https://evervault.com/papers/chaum.pdf.
Cheng, E. 2017. “$24 Million Iced Tea Company Says It’s Pivoting to the Blockchain,
and Its Stock Jumps 200%.” CNBC. https://www.cnbc.com/2017/12/21/longisland-iced-tea-micro-cap-adds-blockchain-to-name-and-stock-soars.html.
Clearing House. No date. “Real-Time Payments for All Financial Institutions.” https://
www.theclearinghouse.org/payment-systems/rtp.
Coindesk. 2022. “Bitcoin.” https://www.coindesk.com/price/bitcoin/.
CoinMarketCap. 2022. “Helium.” https://coinmarketcap.com/currencies/helium/.
———. 2023. “Top Stablecoin Tokens by Market Capitalization.” https://coinmarketcap.
com/view/stablecoin/.
CPFB (Consumer Protection Financial Bureau). 2022. “Complaint Bulletin.” https://files.
consumerfinance.gov/f/documents/cfpb_complaint-bulletin_cryptoassets_2022-11.pdf.
CRS (Congressional Research Service). 2022 “The U.S. Dollar as the World’s Dominant
Reserve Currency.” https://crsreports.congress.gov/product/pdf/IF/IF11707.
Damalas, G., R. Abouseif, K. O’Brien, and A. Stafford. 2022. “Crypto Derivatives Are
Becoming a Major Digital Asset Class.” EY. https://www.ey.com/en_us/
financial-services/crypto-derivatives-are-becoming-a-major-digital-asset-class.
de Vries, A. and C. Stoll. 2021. “Bitcoin’s Growing E-Waste Problem.” Resources,
Conservation, and Recycling 175. https://www.sciencedirect.com/science/
article/abs/pii/S0921344921005103.
Diamond., D., and P. Dybvig. 1983. “Bank Runs, Deposit Insurance, and Liquidity.”
Journal of Political Economy 91, no. 3: 401–19. https://www.jstor.org/
stable/1837095.

384 |

References

Digiconomist. 2022. “Bitcoin Electronic Waste Monitor.” https://digiconomist.net/
bitcoin-electronic-waste-monitor/.
Dixon, C. 2021. “Why Web3 Matters.” Future. https://future.com/why-web3-matters/.
Dowling, M. 2022. “Fertile LAND: Pricing Non-Fungible Tokens.” Finance Research
Letters 44. https://doi.org/10.1016/j.frl.2021.102096.
Emerson, S., D. Jeans, and P. Liu. 2022. “Crypto Darling Helium Promised a ‘People’s
Network’; Instead, Its Executives Got Rich.” Forbes, September 23. https://
www.forbes.com/sites/sarahemerson/2022/09/23/
helium-crypto-tokens-peoples-network/?sh=234bb5087316.
Emsisoft Malware Lab. 2021. “The Cost of Ransomware in 2021: A Country-by-Country
Analysis.” https://www.emsisoft.com/en/blog/38426/
the-cost-of-ransomware-in-2021-a-country-by-country-analysis/.
Etherscan. 2022. “Ethereum Daily Transactions Chart.” https://etherscan.io/chart/tx.
Faverio, M., and N. Massarat. 2022. “46% of Americans Who Have Invested in
Cryptocurrency Say It’s Done Worse Than Expected.” Pew Research Center.
pewresearch.org/fact-tank/2022/08/23/46-of-americans-who-have-invested-incryptocurrency-say-its-done-worse-than-expected/.
FCA (U.K. Financial Conduct Authority). 2021. “First Supervisory Notice.” Reference
688849. https://www.fca.org.uk/publication/supervisory-notices/firstsupervisory-notice-binance-markets-limited.pdf.
FDIC (Federal Deposit Insurance Corporation). 2022. “2021 FDIC National Survey of
Unbanked and Underbanked Households.” https://www.fdic.gov/analysis/
household-survey/index.html.
Federal Register. 2019. “Notices.” Vol. 84, no. 154: 39297–322. https://www.govinfo.
gov/content/pkg/FR-2019-08-09/pdf/2019-17027.pdf.
Federal Reserve Board. 1988. “The Federal Reserve in the Payments Mechanism.”
https://www.federalreserve.gov/boarddocs/press/general/1998/199
80105/19980105.pdf.
———. 2020. “Federal Reserve Board: Automated Clearinghouse Services.” https://
www.federalreserve.gov/paymentsystems/fedach_about.htm.
———. 2022a. “Developments in Noncash Payments for 2019 and 2020: Findings from
the Federal Reserve Payments Study.” https://www.federalreserve.gov/
paymentsystems/december-2021-findings-from-the-federal-reserve-paymentsstudy.htm.
———. 2022b. “FedNow Service.” https://www.federalreserve.gov/paymentsystems/
fednow_about.htm.
———. 2022c. “FedNow Service: Frequently Asked Questions.” https://www.
federalreserve.gov/paymentsystems/files/fednow_faq.pdf.
———. 2022d. “Money and Payments: The U.S. Dollar in the Age of Digital
Transformation.” https://www.federalreserve.gov/publications/files/money-andpayments-20220120.pdf.

References | 385

———. 2023. “Policy Statement on Section 9(13) of the Federal Reserve Act.” https://
www.federalreserve.gov/newsevents/pressreleases/files/bcreg20230127a1.pdf.
———. No date. “Fostering Payment and Settlement System Safety and Efficiency.”
https://www.federalreserve.gov/aboutthefed/files/pf_6.pdf.
Federal Reserve Bank of Minneapolis. 1988. “Developing an Efficient Payments
System.” https://www.minneapolisfed.org/article/1988/developing-anefficient-payments-system.
Federal Reserve Bank of San Francisco. No date. “What Is the Fed? Payment Services.”
https://www.frbsf.org/education/teacher-resources/what-is-the-fed/
payment-services/.
Fletcher, E. 2021. “Crypto-Assets Buzz Drives Record Investment Scam Losses.” Federal
Trade Commission. https://www.ftc.gov/news-events/data-visualizations/dataspotlight/2021/05/cryptocurrency-buzz-drives-record-investment-scam-losses.
Flood, J. 2012. “Bank Runs.” New York, May 25. https://nymag.com/news/intelligencer/
topic/banks-2012-6/.
Ge Huang, V., A. Osipovich, and P. Kowsmann. 2022. “FTX Tapped into Customer
Accounts to Fund Risky Bets, Setting Up Its Downfall.” Wall Street Journal.
https://www.wsj.com/articles/
ftx-tapped-into-customer-accounts-to-fund-risky-bets-setting-up-itsdownfall-11668093732.
Gibson, M., and E. Belsky. 2022. “SR 22-6 / CA 22-6: Engagement in Crypto-AssetRelated Activities by Federal Reserve–Supervised Banking Organizations.”
Board of Governors of the Federal Reserve System. https://www.federalreserve.
gov/supervisionreg/srletters/SR2206.htm.
Goldstein, J., and D. Kestenbaum. 2010. “The Island of Stone Money.” NPR. https://
www.npr.org/sections/money/2011/02/15/131934618/the-island-ofstone-money.
Goldstein, M., A. Stevenson, M. Farrell, and D. Yaffe-Bellany. 2022. “How FTX’s Sister
Firm Brought the Crypto Exchange Down.” New York Times, November 18.
https://www.nytimes.com/2022/11/18/business/ftx-alameda-ties.html.
Gorton, G. 2012. Misunderstanding Financial Crises: Why We Don’t See Them Coming.
Oxford: Oxford University Press. https://global.oup.com/academic/product/
misunderstanding-financial-crises-9780199922901.
Gorton, G., and J. Zhang. 2021. “Taming Wildcat Stablecoins.” Working paper. https://
papers.ssrn.com/sol3/papers.cfm?abstract_id=3888752.
———. 2022. “Protecting the Sovereign’s Money Monopoly.” Working paper, University
of Michigan. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4162884.
Government Accountability Office. 2021. “Virtual Currencies: Additional Information
Could Improve Federal Agency Efforts to Counter Human and Drug
Trafficking.” https://www.gao.gov/assets/gao-22-105462.pdf.

386 |

References

Griffith, E. 2018. “Is Your Startup Stalled? Pivot to Blockchain.” Wired. https://www.
wired.com/story/is-your-startup-stalled-pivot-to-blockchain/.
Hoffman, A. No date. “Taking the Cryptic Out of Crypto Assets.” Federal Reserve Bank
of Dallas. https://www.dallasfed.org/-/media/Documents/educate/events/2022/2
2econsummit/22econsummit_Hoffman.pdf.
Howden. 2023. “The Great Realignment.” https://www.howdengroup.com/sites/g/files/
mwfley566/files/2023-01/the-great-realignment-report-2023.pdf.
Huang, J., C. O’Neill, and H. Tabuchi. 2021. “Bitcoin Uses More Electricity Than Many
Countries; How Is That Possible?” New York Times, September 3. https://www.
nytimes.com/interactive/2021/09/03/climate/bitcoin-carbon-footprint-electricity.
html.
Hudson’s Bay Company History Foundation. 2016. “Currency.” https://www.hbcheritage.
ca/history/fur-trade/currency.
Humphrey, T. 1975. “The Classical Concept of the Lender of Last Resort.” Federal
Reserve Bank of Richmond Economic Review 61: 2–9. https://www.
richmondfed.org/~/media/richmondfedorg/publications/research/economic_
review/1975/pdf/er610101.pdf.
Hyperledger Foundation. No date. “Case Study: DLT Labs and Walmart Canada
Transform Freight Invoice Management with Hyperledger Fabric.” https://
www.hyperledger.org/learn/publications/dltlabs-case-study.
Iansiti, M., and K. Lakhani. 2017. “The Truth About Blockchain.” Harvard Business
Review. https://hbr.org/2017/01/the-truth-about-blockchain.
Jordan, L. 1997. “Wampum: Introduction.” University of Notre Dame. https://coins.
nd.edu/colcoin/colcoinintros/Wampum.intro.html.
Kamin, D. 2021. “Investors Snap Up Metaverse Real Estate in a Virtual Land Boom.”
New York Times, November 30. https://www.nytimes.com/2021/11/30/business/
metaverse-real-estate.html.
Klein, A. 2019. “The Fastest Way to Address Income Inequality? Implement a Real Time
Payment System.” Brookings Institution. https://www.brookings.edu/research/
the-fastest-way-to-address-income-inequality-implement-a-real-time-paymentsystem/.
Laaper, S. No date. “Using Blockchain to Drive Supply Chain Transparency: Future
Trends in Supply Chain.” Deloitte. https://www2.deloitte.com/us/en/pages/
operations/articles/blockchain-supply-chain-innovation.html.
Liao, G., and J. Caramichael. “Stablecoins: Growth Potential and Impact on Banking.”
Federal Reserve Board, International Finance Discussion Paper 1334. https://
www.federalreserve.gov/econres/ifdp/files/ifdp1334.pdf.
Lankenau, S. 2007. “Smoke ’Em If You Got ’Em: Cigarette Black Markets in U.S.
Prisons and Jails.” National Library of Medicine, National Center for
Biotechnology Information 81, no. 2: 142–61. https://www.ncbi.nlm.nih.gov/
pmc/articles/PMC2117377/.

References | 387

Levine, M. 2022. “Gary Gensler Wants to Regulate Crypto.” Bloomberg. https://www.
bloomberg.com/opinion/articles/2022-09-08/gary-gensler-wants-to-regulatecrypto#xj4y7vzkg?leadSource=uverify%20wall.
Locke, T. 2021. “Jack Dorsey Sells His First Tweet Ever as NFT for Over $2.9 Million.”
CNBC. https://www.cnbc.com/2021/03/22/jack-dorsey-sells-his-first-tweetever-as-an-nft-for-over-2point9-million.html.
Maniff, J. 2020. “Inclusion by Design: Crafting a Central Bank Digital Currency to Reach
All Americans.” Federal Reserve Bank of Kansas City. https://www.
kansascityfed.org/research/payments-system-research-briefings/
inclusion-by-design-crafting-central-bank-digital-currency/.
Marlinspike, M. 2022. “My First Impressions of Web3.” Blog. https://moxie.
org/2022/01/07/web3-first-impressions.html.
Mickens, J. 2018. “Blockchains Are a Bad Idea.” HBS Digital Initiative. https://www.
youtube.com/watch?v=15RTC22Z2xI.
Minsky, H. 1992. “The Financial Instability Hypothesis.” Working paper, Levy
Economics Institute. https://www.levyinstitute.org/pubs/wp74.pdf.
———. 2008. Stabilizing an Unstable Economy, rev. ed. New York: McGraw-Hill. Orig.
pub. 1986. https://www.amazon.com/Stabilizing-Unstable-Economy-HymanMinsky/dp/0071592997.
Mnuchin, S., and C. Phillips. 2018. “A Financial System That Creates Economic
Opportunities: Nonbank Financials, Fintech, and Innovation.” U.S. Department
of the Treasury. https://home.treasury.gov/sites/default/files/2018-08/AFinancial-System-that-Creates-Economic-Opportunities---Nonbank-FinancialsFintech-and-Innovation_0.pdf.
Modderman, G. 2022. “Who Accepts Bitcoin as Payment?” Coin Telegraph, June 5.
https://cointelegraph.com/explained/who-accepts-bitcoin-as-payment.
Moen, J., and E. Tallman. 2015. “The Panic of 1907.” Federal Reserve History. https://
www.federalreservehistory.org/essays/panic-of-1907.
Nacha. 2021. “Payments Myth Busting.” https://www.nacha.org/content/paymentsmyth-busting.
Nakamoto, S. 2008. “Bitcoin: A Peer-to-Peer Electronic Cash System.” https://bitcoin.org/
bitcoin.pdf.
National Association of Federally-Insured Credit Unions. 2022. “Re: Money and
Payments: The U.S. Dollar in the Age of Digital Transformation.” https://www.
nafcu.org/system/files/files/5.20.22%20Letter%20to%20Federal%20
Reserve%20re%20Central%20Bank%20Digital%20Currency.pdf.
NIST (National Institute of Standards and Technology). No date. “Non-Repudiation.”
https://csrc.nist.gov/glossary/term/non_repudiation.
Nogrady, B. 2016. “There’s Gold, Platinum and Other Valuable Materials in Every
Phone—the Hard Part Is Getting It Out.” https://www.bbc.com/future/
article/20161017-your-old-phone-is-full-of-precious-metals.

388 |

References

Noko, J. 2011. “Dollarization: The Case of Zimbabwe.” Cato Institute. https://www.cato.
org/sites/cato.org/files/serials/files/cato-journal/2011/5/cj31n2-9.pdf.
OCC (U.S. Office of the Comptroller of the Currency). 2020. “Federally Chartered Banks
and Thrifts May Provide Custody Services for Crypto Assets.” https://www.occ.
gov/news-issuances/news-releases/2020/nr-occ-2020-98.html.
OpenSea. 2022. “@jack 2006-03-21 20:50:14.” https://opensea.io/assets/matic/0x280098
81f0ffe85c90725b8b02be55773647c64a/20.
Oracle. 2022. “Permissioned Blockchain.” https://developer.oracle.com/learn/technicalarticles/permissioned-blockchain.
Orosz, G. 2022. Twitter post. https://twitter.com/GergelyOrosz/
status/1516422295186722824.
Outten, S. 2021. “Bitcoin Transaction Validation, What Exactly Goes on Under the
Hood?” Deltec Bank. https://www.deltecbank.com/2021/10/05/
bitcoin-transaction-validation-what-exactly-goes-on-under-the-hood/.
Pechman, M. 2021. “Here’s How Bitcoin’s Intraday Volatility Complicates Leverage
Trading.” CoinTelegraph. https://cointelegraph.com/news/
here-s-how-bitcoin-s-intraday-volatility-complicates-leverage-trading.
Putzier, K. 2021. “Metaverse Real Estate Piles Up Record Sales in Sandbox and Other
Virtual Realms.” Wall Street Journal, November 30. https://www.wsj.com/
articles/metaverse-real-estate-piles-up-record-sales-in-sandbox-andother-virtual-realms-11638268380.
Rathjen, J. 2022. “ACH Improvements, FedNow Represent Future of Electronic
Payments.” Bloomberg Tax, March 23. https://news.bloombergtax.com/payroll/
ach-improvements-fednow-represent-future-of-electronic-payments.
Roose, K. 2022. “Maybe There’s a Use for Crypto After All.” New York Times, February
6. https://www.nytimes.com/2022/02/06/technology/helium-cryptocurrencyuses.html.
Rybarczyk, R. 2020. “Understanding the Bitcoin Blockchain Header.” Medium. https://
medium.com/fcats-blockchain-incubator/
understanding-the-bitcoin-blockchain-header-a2b0db06b515.
Samford, H., and L. Domingo. “The Political Geography and Environmental Impacts of
Cryptocurrency Mining.” University of Washington. https://jsis.washington.edu/
news/the-political-geography-and-environmental-impacts-of-cryptocurrencymining/.
Schmidt, L., A. Timmermann, and R. Wermers. 2016. “Runs on Money Market Mutual
Funds.” American Economic Review 106, no. 9: 2625–57. https://pubs.aeaweb.
org/doi/pdfplus/10.1257/aer.20140678.
Schneier, B. 2019. “There’s No Good Reason to Trust Blockchain Technology.” Wired.
https://www.wired.com/story/theres-no-good-reason-to-trust-blockchaintechnology/.

References | 389

SEC (U.S. Securities and Exchange Commission). 2021a. “SEC Charges Global Crypto
Lending Platform and Top Executives in $2 Billion Fraud.” https://www.sec.
gov/news/press-release/2021-172.
———. 2021b. “SEC Charges Three Individuals with Insider Trading.” https://www.sec.
gov/news/press-release/2021-121.
———. 2022. “Crypto Assets and Cyber Enforcement Actions.” https://www.sec.gov/
spotlight/cybersecurity-enforcement-actions.
Siegel, R. 2008. “Zimbabwe’s Hyperinflation Poses Unique Challenges.” NPR. https://
www.npr.org/templates/story/story.php?storyId=89123990.
Seward, Z. 2021. “A16z Leads $111M Token Sale for Helium’s HNT.” CoinDesk. https://
www.coindesk.com/business/2021/08/10/
a16z-leads-111m-token-sale-for-heliums-hnt/.
Smilyanets, D. 2021. “‘I Scrounged Through the Trash Heaps; . . . Now I’m a
Millionaire: An Interview with REvil’s Unknown.” Record. https://therecord.
media/i-scrounged-through-the-trash-heaps-now-im-a-millionaire-an-interviewwith-revils-unknown/.
Tabuchi, H. 2022. “Cryptomining Capacity in U.S. Rivals Energy Use of Houston,
Findings Show.” New York Times, July 15. https://www.nytimes.
com/2022/07/15/climate/cryptocurrency-bitcoin-mining-electricity.html.
Tian, R. 2021. “A Deep Dive into Leverages in DeFi Borrowing, Margin Trading,
Leveraged Tokens and Options: FinNexus.” Alexandria. https://coinmarketcap.
com/alexandria/
article/a-deep-dive-into-leverages-in-defi-borrowing-margin-trading-leveragedtokens-and-options-finnexus.
Tikkanen, A. No date. “A Brief (and Fascinating) History of Money.” Britannica. https://
www.britannica.com/story/a-brief-and-fascinating-history-of-money.
Timón, J. 2016. “Amount.h.” Code commit. https://github.com/bitcoin/bitcoin/blob/08a73
16c144f9f2516db8fa62400893f4358c5ae/src/amount.h.
Tortorelli, P., and K. Rooney. 2022. “Sam Bankman-Fried’s Alameda Quietly Used FTX
Customer Funds for Trading, Say Sources.” CNBC. https://www.cnbc.
com/2022/11/13/sam-bankman-frieds-alameda-quietly-used-ftx-customer-fundswithout-raising-alarm-bells-say-sources.html.
University of Cambridge. 2022. “Cambridge Bitcoin Electricity Consumption Index.”
https://ccaf.io/cbeci/index/comparisons.
U.S. Commercial Code. No date. “§ 1-201: General Definitions.” https://www.law.
cornell.edu/ucc/1/1-201.
U.S. Department of Labor. 2022. “Compliance Assistance Release No. 2022-01.” https://
www.dol.gov/agencies/ebsa/employers-and-advisers/plan-administration-andcompliance/compliance-assistance-releases/2022-01.

390 |

References

U.S. Department of Justice. 2022. “The Role of Law Enforcement in Detecting,
Investigating, and Prosecuting Criminal Activity Related to Digital Assets.”
https://www.justice.gov/ag/page/file/1535236/download.
U.S. Department of the Treasury. 2022a. “Crypto-Assets: Implications for Consumers,
Investors, and Businesses.” https://home.treasury.gov/system/files/136/
CryptoAsset_EO5.pdf.
———. 2022b. “The Future of Money and Payments: Report Pursuant to Section 4(b) of
Executive Order 14067.” https://home.treasury.gov/system/files/136/Future-ofMoney-and-Payments.pdf.
———. 2022c. “Report on Digital Asset Financial Stability Risks and Regulation.”
https://home.treasury.gov/system/files/261/FSOC-Digital-Assets-Report-2022.
pdf.
———. 2022d. “Action Plan to Address Illicit Financing Risks of Digital Assets.” https://
home.treasury.gov/system/files/136/Digital-Asset-Action-Plan.pdf.
———. 2022e. “Fact Sheet: Framework for International Engagement on Digital Assets.”
https://home.treasury.gov/news/press-releases/jy0854.
Velde, F. 2012. “On the Origin of Specie.” Federal Reserve Bank of Chicago. https://
www.atlantafed.org/-/media/documents/news/conferences/2012/monetaryeconomics/papers/velde.pdf.
Venmo. No date. “Bank Transfer Timeline.” https://help.venmo.com/hc/en-us/
articles/221083888-Bank-Transfer-Timeline.
Vitasek, K., J. Bayliss, L. Owen, and N. Srivastava. 2022. “How Walmart Canada Uses
Blockchain to Solve Supply-Chain Challenges.” Harvard Business Review.
https://hbr.org/2022/01/
how-walmart-canada-uses-blockchain-to-solve-supply-chain-challenges.
Warren, E. 2019. “Warren, Van Hollen, Pressley, García Introduce Legislation to Require
the Fed to Act on Faster Payments.” Press release. https://www.warren.senate.
gov/newsroom/press-releases/
warren-van-hollen-pressley-garca-introduce-legislation-to-require-the-fed-toact-on-faster-payments.
Wheatley, J. 2013. “Six Things You Need to Know About Raghuram Rajan.” Financial
Times, August 6. https://www.ft.com/content/6b12ca6a-e993-3021-b7747228934ba322.
White House. 2022a. “Technical Evaluation for a U.S. Central Bank Digital Currency
System.” https://www.whitehouse.gov/wp-content/uploads/2022/09/09-2022Technical-Evaluation-US-CBDC-System.pdf.
———. 2022b. “Executive Order on Ensuring Responsible Development of Digital
Assets.” https://www.whitehouse.gov/briefing-room/presidentialactions/2022/03/09/executive-order-on-ensuring-responsible-developmentof-digital-assets/.

References | 391

———. 2022c. “Fact Sheet: White House Releases First-Ever Comprehensive
Framework for Responsible Development of Digital Assets.” https://www.
whitehouse.gov/briefing-room/statements-releases/2022/09/16/fact-sheetwhite-house-releases-first-ever-comprehensive-framework-for-responsibledevelopment-of-digital-assets/.
———. 2022d. “Energy Implications of Crypto-Assets in the United States.” https://
www.whitehouse.gov/wp-content/uploads/2022/09/09-2022-Crypto-Assets-andClimate-Report.pdf.
———. 2022e. “Policy Objectives for a U.S. Central Bank Digital Currency System.”
https://www.whitehouse.gov/wp-content/uploads/2022/09/09-2022-PolicyObjectives-US-CBDC-System.pdf.
Williams. 2022. “A Neighborhood’s Cryptocurrency Mine: ‘Like a Jet That Never
Leaves.’” Washington Post. https://www.washingtonpost.com/business/
interactive/2022/cryptocurrency-mine-noise-homes-nc/.
Wong, R. 2022. Why Stablecoins Fail: An Economist’s Post-Mortem on Terra.” Federal
Reserve Bank of Richmond, Economic Brief 22-24. https://www.richmondfed.
org/publications/research/economic_brief/2022/eb_22-24.
World Bank. No date. “Tamper-Proof Logs.” https://id4d.worldbank.org/guide/
tamper-proof-logs.
Yaffe-Bellany, D. 2022. “They Made Millions on Luna, Solana and Polygon: Crypto’s
Boom Beyond Bitcoin.” New York Times, February 7. https://www.nytimes.
com/2022/02/07/technology/cryptocurrency-luna-solana-polygon.html.
Yaga, D., P. Mell, N. Roby, and K. Scarfone. 2018. “Blockchain Technology Overview.”
National Institute for Standards and Technology, Report 8202. https://nvlpubs.
nist.gov/nistpubs/ir/2018/nist.ir.8202.pdf.
Yang, Y. 2022. “JPMorgan Finds New Use for Blockchain in Trading and Lending.”
https://www.bloomberg.com/news/articles/2022-05-26/
jpmorgan-finds-new-use-for-blockchain-in-collateral-settlement.

Chapter 9
Acharya, V., T. Johnson, S. Sundaresan, and T. Tomunen. 2022. Is Physical Climate Risk
Priced? Evidence from Regional Variation in Exposure to Heat Stress. NBER
Working Paper 30445. Cambridge, MA: National Bureau of Economic
Research. https://doi.org/10.3386/w30445.
Agarwala, M., and D. Coyle. 2021. “Natural Capital in Climate Models.” Nature
Sustainability 4: 81–82. https://doi.org/10.1038/s41893-020-00618-x.
Agarwala, M., and D. Zenghelis. 2020. “Natural Capital Accounting for Sustainable
Macroeconomic Strategies.” U.N. Department of Economic and Social Affairs,
System of Environmental and Economic Accounting. https://seea.un.org/
content/natural-capital-accounting-sustainable-macroeconomic-strategies.

392 |

References

Ahmedzade, T., J. Horton, P. Mwai, and W. Song. 2022. “China, Europe, U.S. Drought: Is
2022 the Driest Year Recorded?” British Broadcasting Center, September 17.
https://www.bbc.com/news/62751110.
Akerlof, G. 1970. “The Market for ‘Lemons’: Quality Uncertainty and the Market
Mechanism.” Quarterly Journal of Economics 84, no. 3: 488–500. https://doi.
org/10.1016/B978-0-12-214850-7.50022-X.
American Society of Civil Engineers. 2018. Climate-Resilient Infrastructure: Adaptive
Design and Risk Management, edited by B. Ayyub. Reston, VA: American
Society of Civil Engineers.
———. 2021. “Investment Gap 2020–2029.” 2021 Report Card for America’s
Infrastructure. https://infrastructurereportcard.org/resources/investmentgap-2020-2029/.
Annan, F., and W. Schlenker. 2015. “Federal Crop Insurance and the Disincentive to
Adapt to Extreme Heat.” American Economic Review 105, no. 5: 262–66.
https://doi.org/10.1257/aer.p20151031.
Arellano-Gonzalez, J., A. AghaKouchak, M. Levy, Y. Qin, J. Burney, S. Davis, and F.
Moore. 2021. “The Adaptive Benefits of Agricultural Water Markets in
California.” Environmental Research Letters 16, no. 4. https://doi.
org/10.1088/1748-9326/abde5b.
Ash, K., and N. Obradovich. 2019. “Climatic Stress, Internal Migration, and Syrian Civil
War Onset.” Journal of Conflict Resolution 64, no. 1: 3–31. https://doi.
org/10.1177/0022002719864140.
Auffhammer, M. 2022. “Climate Adaptive Response Estimation: Short- and Long-Run
Impacts of Climate Change on Residential Electricity and Natural Gas
Consumption.” Journal of Environmental Economics and Management 114.
https://doi.org/10.1016/j.jeem.2022.102669.
Bakkensen, L., and L. Barrage. 2022. “Going Underwater? Flood Risk Belief
Heterogeneity and Coastal Home Price Dynamics.” Review of Financial Studies
35, no. 8: 3666–709. https://doi.org/10.1093/rfs/hhab122.
Baldauf, M., L. Garlappi, and C. Yannelis. 2020. “Does Climate Change Affect Real
Estate Prices? Only If You Believe in It.” Review of Financial Studies 33, no. 3:
1256–95. https://doi.org/10.1093/rfs/hhz073.
Barrage, L. 2020. “The Fiscal Costs of Climate Change.” AEA Papers and Proceedings
110: 107–12. https://doi.org/10.1093/restud/rdz055.
Barrage, L., and J. Furst. 2019. “Housing Investment, Sea Level Rise, and Climate
Change Beliefs.” Economics Letters 177: 105–8. https://doi.org/10.1016/j.
econlet.2019.01.023.
Barreca, A., and J. Schaller. 2020. “The Impact of High Ambient Temperatures on
Delivery Timing and Gestational Lengths.” Nature Climate Change 10: 77–82.
https://doi.org/10.1038/s41558-019-0632-4.

References | 393

Bastien-Olvera, B., F. Granella, and F. Moore. 2022. “Persistent Effect of Temperature on
GDP Identified from Lower Frequency Temperature Variability.” Environmental
Research Letters 17, no. 8. https://doi.org/10.1088/1748-9326/ac82c2.
Baylis, P., and J. Boomhower. 2023. “The Economic Incidence of Wildfire Suppression in
the United States.” American Economic Journal: Applied Economics. https://
www.aeaweb.org/articles?id=10.1257/app.20200662.
Beasley, W., and S. Dundas. 2021. “Hold the Line: Modeling Private Coastal Adaptation
Through Shoreline Armoring Decisions.” Journal of Environmental Economics
and Management 105: 102397. https://doi.org/10.1016/j.jeem.2020.102397.
Benveniste, H., M. Oppenheimer, and M. Fleurbaey. 2020. “Effect of Border Policy on
Exposure and Vulnerability to Climate Change.” Proceedings of the National
Academy of Sciences 117, no. 43: 26692–702. https://doi.org/10.1073/
pnas.2007597117.
Benz, S., and J. Burney. 2021. “Widespread Race and Class Disparities in the Surface
Urban Heat Extremes Across the United States.” Earth’s Future 9. https://
agupubs.onlinelibrary.wiley.com/doi/pdf/10.1029/2021EF002016.
Bercos-Hickey, E., T. O’Brien, M. Wehner, L. Zhang, C. Patriciola, H. Huang, and M.
Risser. 2022. “Anthropogenic Contributions to the 2021 Pacific Northwest
Heatwave.” Geophysical Research Letters 49, no. 23. https://doi.
org/10.1029/2022GL099396.
Berrang-Ford, L., et al. 2021. “A Systematic Global Stocktake of Evidence on Human
Adaptation to Climate Change.” Nature Climate Change 11: 989–1000. https://
www.nature.com/articles/s41558-021-01170-y.
Bernstein, A., M. Gustafson, and R. Lewis. 2019. “Disaster on the Horizon: The Price
Effect of Sea Level Rise.” Journal of Financial Economics 134, no. 2: 253–72.
https://doi.org/10.1016/j.jfineco.2019.03.013.
Bhutta, N., A. Chang, L. Dettling, and J. Hsu. 2020. “Disparities in Wealth by Race and
Ethnicity in the 2019 Survey of Consumer Finances.” Finance and Economics
Discussion Series Notes, Board of Governors of the Federal Reserve System.
https://www.federalreserve.gov/econres/notes/feds-notes/disparities-in-wealthby-race-and-ethnicity-in-the-2019-survey-of-consumer-finances-20200928.html.
Billings, S., E. Gallagher, and L. Ricketts. 2022. “Let the Rich Be Flooded: The
Distribution of Financial Aid and Distress After Hurricane Harvey.” Journal of
Financial Economics. https://doi.org/10.1016/j.jfineco.2021.11.006.
Borunda, A. 2021. “‘Megadrought’ Persists in Western U.S., as Another Extremely Dry
Year Develops.” National Geographic. https://www.nationalgeographic.com/
environment/article/megadrought-persists-in-western-us-as-another-extremelydry-year-develops.
Bradsher, K., and J. Dong. 2022. “China’s Record Drought Is Drying Rivers and Feeding
Its Coal Habit.” New York Times, August 29. https://www.nytimes.
com/2022/08/26/business/economy/china-drought-economy-climate.html.

394 |

References

Bradt, J., C. Kousky, and O. Wing. 2021. “Voluntary Purchases and Adverse Selection in
the Market for Flood Insurance.” Journal of Environmental Economics and
Management 110: 102515. https://doi.org/10.1016/j.jeem.2021.102515.
Braun, A., and C. Kousky. 2021. “Catastrophe Bonds.” Wharton Risk Center Primer,
July. https://riskcenter.wharton.upenn.edu/wp-content/uploads/2021/07/
Cat-Bond-Primer-July-2021.pdf.
Brunetti, C., J. Caramichael, M. Crosignani, B. Dennis, G. Kotta, D. Morgan, C. Shin, and
I. Zer. 2022. “Climate-Related Financial Stability Risks for the United States:
Methods and Applications.” Finance and Economics Discussion Series, Board of
Governors of the Federal Reserve System. https://www.federalreserve.gov/
econres/feds/climate-related-financial-stability-risks-for-the-united-states.htm.
Brunetti, C., B. Dennis, D. Gates, D. Hancock, D. Ignell, E. Kiser, G. Kotta, A. Kovner,
R. Rosen, and N. Tabor. 2021. “Climate Change and Financial Stability.” FEDS
Notes, Board of Governors of the Federal Reserve System. https://www.
federalreserve.gov/econres/notes/feds-notes/climate-change-and-financialstability-20210319.html.
Bureau of Reclamation. 2022. “About Us: Fact Sheet.” https://www.usbr.gov/main/about/
fact.html.
Burke, M., A. Driscoll, S. Heft-Neal, and M. Wara. 2021. “The Changing Risk and
Burden of Wildfire in the United States.” Proceedings of the National Academy
of Sciences 118, no. 2. https://doi.org/10.1073/pnas.2011048118.
Burke, M., F. González, P. Baylis, S. Heft-Neal, C. Baysan, S. Basu, and S. Hsiang. 2018.
“Higher Temperatures Increase Suicide Rates in the United States and Mexico.”
Nature Climate Change 8: 723–29. https://doi.org/10.1038/s41558-018-0222-x.
Burke, M., S. Heft-Neal, J. Li , A. Driscoll, P. Baylis, M. Stigler , J. Weill, J. Burney , J.
Wen , M. Childs, and C. Gould. 2022. “Exposures and Behavioral Responses to
Wildfire Smoke.” Nature Human Behavior 6: 1351–61. https://doi.org/10.1038/
s41562-022-01396-6.
Burke, M., S. Hsiang, and E. Miguel. 2015. “Global Non-Linear Effect of Temperature
on Economic Production.” Nature 527, no. 7577: 235–39. https://doi.
org/10.1038/nature15725.
Carleton, T., A. Jina, M. Delgado, M. Greenstone, T. Houser, S. Hsiang, A. Hultgren, R.
Kopp, K. McCusker, I. Nath, J. Rising, A. Rode, H. Seo, A. Viaene, J. Yuan,
and A. Zhang. 2022. “Valuing the Global Mortality Consequences of Climate
Change Accounting for Adaptation Costs and Benefits.” Quarterly Journal of
Economics 137, no. 4: 2037–2105. https://doi.org/10.1093/qje/qjac020.
Carlson, C., G. Albery, C. Merow, C. Trisos, C. Zipfel, E. Eskew, K. Olival, N. Ross, and
S. Bansal. 2022. “Climate Change Increases Cross-Species Viral Transmission
Risk.” Nature 607: 555–62. https://doi.org/10.1038/s41586-022-04788-w.
CBO (Congressional Budget Office). 2018. “Federal Support for Financing State and
Local Transportation and Water Infrastructure.” http://www.cbo.gov/
publication/54549.

References | 395

———. 2019. “Expected Costs of Damage from Hurricane Winds and Storm-Related
Flooding.” https://www.cbo.gov/system/files/2019-04/55019-ExpectedCosts
FromWindStorm.pdf.
———. 2022a. “Discretionary Spending in Fiscal Year 2021: An Infographic.” https://
www.cbo.gov/publication/58269.
———. 2022b. “Wildfires.” https://www.cbo.gov/publication/58212.
Centers for Medicare & Medicaid Services. 2022. “NHE Fact Sheet.” https://www.cms.
gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/
NationalHealthExpendData/NHE-Fact-Sheet.
Chapman, J., A. Lu, and L. Timmerhoff. 2020. “By the Numbers: A Look at Municipal
Bankruptcies Over the Past 20 Years.” Pew Research Institute. https://www.
pewtrusts.org/en/research-and-analysis/articles/2020/07/07/
by-the-numbers-a-look-at-municipal-bankruptcies-over-the-past-20-years.
Chediak, M. 2019. “Unprecedented California Blackout Ending as PG&E Restores
Power.” Bloomberg News, October 9. https://www.bloomberg.com/news/
articles/2019-10-10/unprecedented-california-blackout-spreads-with-millionsin-dark?leadSource=uverify%20wall#xj4y7vzkg.
Childs, M., J. Li, J. Wen, S. Heft-Neal, A. Driscoll, S. Wang, C. Gould, M. Qiu, J.
Burney, and M. Burke. 2022. “Daily Local-Level Estimates of Ambient
Wildfire Smoke PM2.5 for the Contiguous U.S.” Environmental Science &
Technology 56, no. 19: 13607–21. https://doi.org/10.1021/acs.est.2c02934.
Climate Central and Zillow. 2018. “Ocean at the Door: New Homes and the Rising Sea:
Recent Housing Growth Rates are Faster in High Flood Risk Zones for Most
Coastal States.” http://assets.climatecentral.org/pdfs/Nov2018_Report_
OceanAtTheDoor.pdf?pdf=OceanAtTheDoor-Report.
CMRA. No date. “Climate Mapping for Resilience and Adaptation.” https://resilience.
climate.gov/#assessment-tool.
Cong, S., D. Nock, Y. Qiu, and B. Xing. 2022. “Unveiling Hidden Energy Poverty Using
the Energy/Equity Gap.” Nature Communications 13. https://doi.org/10.1038/
s41467-022-30146-5.
Congressional Research Service. 2022. “National Flood Insurance Program Risk Rating
2.0: Frequently Asked Questions.” https://crsreports.congress.gov/product/pdf/
IN/IN11777.
Conte, M., and D. Kelly. 2018. “An Imperfect Storm: Fat-Tailed Tropical Cyclone
Damages, Insurance, and Climate Policy.” Journal of Environmental Economics
and Management 92: 677–706. https://doi.org/10.1016/j.jeem.2017.08.010.
Cook, L. 2014. “Violence and Economic Activity: Evidence from African American
Patents, 1870–1940.” Journal of Economic Growth 19: 221–57. https://doi.
org/10.1007/s10887-014-9102-z.
CoreLogic. 2022. “CoreLogic Analysis Shows Final Estimated Insured and Uninsured
Damages for Hurricane Ian to Be Between $41 Billion and $70 Billion.” https://

396 |

References

www.corelogic.com/press-releases/
corelogic-analysis-shows-final-estimated-insured-and-uninsured-damages-forhurricane-ian-to-be-between-41-billion-and-70-billion/.
Coyle, D. 2015. GDP: A Brief but Affectionate History. Princeton, NJ: Princeton
University Press. https://press.princeton.edu/books/paperback/9780691169859/
gdp.
Dasgupta, P. 2021. “The Economics of Biodiversity: The Dasgupta Review.” Her
Majesty’s Treasury. https://www.gov.uk/government/publications/
final-report-the-economics-of-biodiversity-the-dasgupta-review.
Davenport, F., M. Burke, and N. Diffenbaugh. 2021. “Contribution of Historical
Precipitation Change to U.S. Flood Damages.” Proceedings of the National
Academy of Sciences 118, no. 4. https://doi.org/10.1073/pnas.2017524118.
Dell, M., B. Jones, and B. Olken. 2012. “Temperature Shocks and Economic Growth:
Evidence from the Last Half Century.” American Economic Journal:
Macroeconomics 4, no. 3: 66–95. http://dx.doi.org/10.1257/mac.4.3.66.
Deng, H., D. Aldrich, M. Danziger, J. Gao, N. Phillips, S. Cornelius, and Q. Wang. 2021.
“High-Resolution Human Mobility Data Reveal Race and Wealth Disparities in
Disaster Evacuation Patterns.” Humanities and Social Sciences
Communications 8. https://doi.org/10.1057/s41599-021-00824-8.
Denholm, P., P. Brown, W. Cole, T. Mai, B. Sergi, M. Brown, P. Jadun, J. Ho, J.
Mayernik, C. McMillan, and R. Sreenath. 2022. “Examining Supply-Side
Options to Achieve 100% Clean Electricity by 2035.” National Renewable
Energy Laboratory. https://www.nrel.gov/docs/fy22osti/81644.pdf.
Derenoncourt, E., C. Kim, M. Kuhn, and M. Schularick. 2022. Wealth of Two Nations:
The U.S. Racial Wealth Gap, 1860–2020. NBER Working Paper 30101.
Cambridge, MA: National Bureau of Economic Research. https://doi.
org/10.3386/w30101.
Deryugina, T. 2017. “The Fiscal Cost of Hurricanes: Disaster Aid Versus Social
Insurance.” American Economic Journal: Economic Policy 9, no. 3: 168–98.
https://doi.org/10.1257/pol.20140296.
Deschênes, O. 2022. The Impact of Climate Change on Mortality in the United States:
Benefits and Costs of Adaptation. NBER Working Paper 30282. Cambridge,
MA: National Bureau of Economic Research. https://doi.org/10.3386/w30282.
Deschênes, O., and M. Greenstone. 2011. “Climate Change, Mortality, and Adaptation:
Evidence from Annual Fluctuations in Weather in the U.S.” American
Economic Journal: Applied Economics 3, no. 4: 152–85. https://doi.
org/10.1257/app.3.4.152.
Deschênes, O., M. Greenstone, and J. Guryan. 2009. “Climate Change and Birth Weight.”
American Economic Review: Papers and Proceedings 99, no. 2: 211–17.
https://doi.org/10.1257/aer.99.2.211.

References | 397

Diaz, D. 2016. “Estimating Global Damages from Sea Level Rise with the Coastal
Impact and Adaptation Model (CIAM).” Climatic Change 137: 143–56. https://
doi.org/10.1007/s10584-016-1675-4.
Dietz, T., E. Ostrom, and P. Stern. 2003. “The Struggle to Govern the Commons.”
Science 302, no. 5652: 1907–12. https://doi.org/10.1126/science.1091015.
Diffenbaugh, N., D. Swain, and D. Touma. 2015. “Anthropogenic Warming Has
Increased Drought Risk in California.” Proceedings of the National Academy of
Sciences 112, no. 13: 3931–36. https://doi.org/10.1073/pnas.1422385112.
Dixon, L., N. Clancy, B. Bender, A. Kofner, D. Manheim, and L. Zakaras. 2013. Flood
Insurance in New York City Following Hurricane Sandy. Santa Monica, CA:
RAND Corporation. https://www.rand.org/pubs/research_reports/RR328.html.
Dundas, S., and R. von Haefen. 2020. “The Effects of Weather on Recreational Fishing
Demand and Adaptation: Implications for a Changing Climate.” Journal of the
Association of Environmental and Resource Economists 7, no. 2. https://doi.
org/10.1086/706343.
Electric Power Research Institute. 2022. “Costs and Benefits of Proactive Adaptation in
the Electric Sector.” https://www.epri.com/research/products/0000000030
02025872.
Elliott, D. 2022. “Insurance Woes in Coastal Louisiana Make Hurricane Recovery
Difficult.” National Public Radio, July 27. https://www.npr.
org/2022/07/27/1113639292/
insurances-woes-in-coastal-louisiana-make-hurricane-recovery-difficult.
Environmental and Energy Study Institute. 2017. “The National Security Impacts of
Climate Change.” https://www.eesi.org/files/IssueBrief_Climate_Change_
Security_Implications.pdf.
Environmental Law Institute. 2022. Law of Environmental Protection. Blue Springs, MO:
Aspen Publishing. https://www.eli.org/law-environmental-protection.
Evans, D., C Webb, E. Braunstein, J. Glowacki, A. Netter, B. Katz, and D. Lohmann.
2020. “Residential Flood Risk in the United States: Quantifying Flood Losses,
Mortgage Risk and Sea Level Rise.” Society of Actuaries. https://www.soa.org/
globalassets/assets/files/resources/research-report/2020/soa-flood-report.pdf.
Farrell, J., P. Burow, K. McConnell, J. Bayham, K. Whyte, and G. Koss. 2021. “Effects
of Land Dispossession and Forced Migration on Indigenous Peoples in North
America.” Science 374, no. 6567. https://doi.org/10.1126/science.abe4943.
Federal Emergency Management Agency. 2010. “Flooding: Our Nation’s Most Frequent
and Costly Natural Disaster.” https://www.fbiic.gov/public/2010/mar/
FloodingHistoryandCausesFS.PDF.
———. 2022. “Flood Risk Disclosure: Model State Requirements for Disclosing Flood
Risk During Real Estate Transactions.” https://www.fema.gov/sites/default/files/
documents/fema_state-flood-risk-disclosure-best-practices_07142022.pdf.

398 |

References

———. No date. “The Watermark: National Flood Insurance Program Financial
Statements.” https://www.fema.gov/flood-insurance/work-with-nfip/
watermark-financial-statements.
Federal Insurance and Mitigation Administration. 2022. “The Watermark: Fiscal Year
2022, Second Quarter, Volume 18.” Federal Emergency Management Agency.
https://www.fema.gov/sites/default/files/documents/fema_fy22-q2-watermark.
pdf.
Fiedler, T., A. Pitman, K. Mackenzie, N. Wood, C. Jakob, and S. Perkins-Kirkpatrick.
2021. “Business Risk and the Emergence of Climate Analytics.” Nature Climate
Change 11: 87–94. https://doi.org/10.1038/s41558-020-00984-6.
Financial Stability Board. 2022. “Climate Scenario Analysis by Jurisdictions: Initial
Findings and Lessons.” https://www.ngfs.net/sites/default/files/medias/
documents/climate_scenario_analysis_by_jurisdictions_initial_findings_and_
lessons.pdf.
Fisher, E., S. Sippel, and R. Knutti. 2021. “Increasing Probability of Record-Shattering
Climate Extremes.” Nature Climate Change 11: 689–95. https://doi.
org/10.1038/s41558-021-01092-9.
Fitch Ratings. 2022. “Fitch Downgrades Pakistan to ‘CCC+.’” https://www.fitchratings.
com/research/sovereigns/fitch-downgrades-pakistan-to-ccc-21-10-2022.
Fleenor, W., E. Hanak, J. Lund, and J. Mount. 2008. “Delta Hydrodynamics and Water
Salinity with Future Conditions: Technical Appendix C.” Public Policy Institute
of California. https://www.ppic.org/wp-content/uploads/content/pubs/
other/708EHR_appendixC.pdf.
Florida Citizens. 2023. “Policies in Force.” https://www.citizensfla.com/policies-in-force.
Fothergill, A., and L. Peek. 2004. “Poverty and Disasters in the United States: A Review
of Recent Sociological Findings.” Natural Hazards 32: 89–110. https://doi.
org/10.1023/B:NHAZ.0000026792.76181.d9.
Frame, D., M. Wehner, I. Noy, and S. Rosier. 2020. “The Economic Costs of Hurricane
Harvey Attributable to Climate Change.” Climatic Change 160: 271–81. https://
doi.org/10.1007/s10584-020-02692-8.
George Washington University. 2018. “Ascertainment of the Estimated Excess Mortality
for Hurricane Maria in Puerto Rico.” Milken Institute School of Public Health.
https://publichealth.gwu.edu/content/gw-report-delivers-recommendations-aimedpreparing-puerto-rico-hurricane-season.
Gibson, M., and J. Mullins. 2020. “Climate Risk and Beliefs in New York Floodplains.”
Journal of the Association of Environmental and Resource Economists 7, no. 6.
https://doi.org/10.1086/710240.
Goldsmith-Pinkham, P., M. Gustafson, and R. Lewis. 2021. “Sea Level Rise Exposure
and Municipal Bond Yields.” Working paper, Jacobs Levy Equity Management
Center for Quantitative Financial Research. http://dx.doi.org/10.2139/
ssrn.3478364.

References | 399

Gopalakrishnan, S., D. McNamara, M. Smith, and B. Murray. 2017. “Decentralized
Management Hinders Coastal Climate Adaptation: The Spatial-Dynamics of
Beach Nourishment.” Environmental and Resource Economics 67: 761–87.
https://doi.org/10.1007/s10640-016-0004-8.
Goss, M., D. Swain, J. Abatzoglou, A. Sarhadi, C. Kolden, A. Williams, and N.
Diffenbaugh. 2020. “Climate Change Is Increasing the Likelihood of Extreme
Autumn Wildfire Conditions Across California.” Environmental Research
Letters 15, no. 9. https://doi.org/10.1088/1748-9326/ab83a7.
Graff Zivin, J., and M. Neidell. 2014. “Temperature and the Allocation of Time:
Implications for Climate Change.” Journal of Labor Economics 32, no. 1.
https://doi.org/10.1086/671766.
Green, R., R. Basu, B. Malig, R. Broadwin, J. Kim, and B. Ostro. 2010. “The Effect of
Temperature on Hospital Admissions in Nine California Counties.”
International Journal of Public Health 55: 113–21. https://doi.org/10.1007/
s00038-009-0076-0.
Gulev, S., P. Thorne, J. Ahn, F. Dentener, C. Domingues, S. Gerland, D. Gong, D.
Kaufman, H. Nnamchi, J. Quaas, J. Rivera, S. Sathyendranath, S. Smith, B.
Trewin, K. von Schuckmann, and R. Vose. 2021. “Changing State of the
Climate System.” In Climate Change 2021: The Physical Science Basis, edited
by V. Masson-Delmotte, P. Zhai, A. Pirani, S. Connors, C. Péan, S. Berger, N.
Caud, Y. Chen, L. Goldfarb, M. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.
Matthews, T. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou, 287–422.
Cambridge: Cambridge University Press. https://doi.
org/10.1017/9781009157896.004.
Gutiérrez, J., R. Jones, G. Narisma, L. Alves, M. Amjad, I. Gorodetskaya, M. Grose, N.
Klutse, S. Krakovska, J. Li, D. Martínez-Castro, L. Mearns, S. Mernild, T.
Ngo-Duc, B. van den Hurk, and J. Yoon. 2021. “Atlas.” In Climate Change
2021: The Physical Science Basis, edited by V. Masson-Delmotte, P. Zhai, A.
Pirani, S. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.
Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. Matthews, T. Maycock, T.
Waterfield, O. Yelekçi, R. Yu, and B. Zhou, 1927–2058. Cambridge: Cambridge
University Press. https://doi.org/10.1017/9781009157896.021.
Hagerty, N., and B. Leonard. 2022. “Interior’s Plan Won’t Solve the Colorado River
Crisis. Here’s What Will.” High Country News, October 19. https://www.hcn.
org/articles/opinion-colorado-river-interiors-plan-wont-solve-the-coloradoriver-crisis-heres-what-will.
Hallegatte, S., J. Hourcade, and P. Dumas. 2007. “Why Economic Dynamics Matter in
Assessing Climate Change Damages: Illustration on Extreme Events.”
Ecological Economics 62, no. 2: 330–40. https://doi.org/10.1016/j.
ecolecon.2006.06.006.

400 |

References

Hartwig, R., and C. Wilkinson. 2016. “Residual Market Property Plans: From Markets of
Last Resort to Markets of First Choice.” Insurance Information Institute. https://
www.iii.org/sites/default/files/docs/pdf/residual_markets_wp_051616.pdf.
Hashida, Y., J. Withey, D. Lewis, T. Newman, and J. Kline. 2020. “Anticipating Changes
in Wildlife Habitat Induced by Private Forest Owners’ Adaptation to Climate
Change and Carbon Policy.” Public Library of Science. https://doi.org/10.1371/
journal.pone.0230525.
Hausfather, Z., and F. Moore. 2022. “Net-Zero Commitments Could Limit Warming to
Below 2°C.” Nature 604: 247–48. https://doi.org/10.1038/d41586-022-00874-1.
Hino, M., S. Belanger, C. Field, A. Davies, and K. Mach. 2019. ‘High-Tide Flooding
Disrupts Local Economic Activity.” Science Advances 5, no. 2. https://doi.
org/10.1126/sciadv.aau2736.
Hino, M., and M. Burke. 2021. “The Effect of Information About Climate Risk on
Property Values.” Proceedings of the National Academy of Sciences 118, no.
17. https://doi.org/10.1073/pnas.2003374118.
Hippe, A., A. Becker, M. Fischer, and B. Schwegler. 2015. “Estimation of Cost Required
to Elevate U.S. Ports in Response to Climate Change: A Thought Exercise for
Climate Critical Resources.” Working paper, Stanford University. https://stacks.
stanford.edu/file/druid:rm328fb1292/WP138.pdf.
Hoffman, J., V. Shandas, and N. Pendleton. 2020. “The Effects of Historical Housing
Policies on Resident Exposure to Intra-Urban Heat: A Study of 108 U.S. Urban
Areas.” Climate 8, no. 1. https://doi.org/10.3390/cli8010012.
Howe, C., K. Anderson, A. R. Siders, B. Ristroph, K. Spidalieri, J. Li, W. Burns, E.
Kronk Warner, H. Tanana, H. Vizcarra, N. Kuyumjian, and Z. Schiffer. 2002.
“Chapter 24: Climate Change.” In Law of Environmental Protection, 651–822.
Washington: Environmental Law Institute.
Howell, J., and J. Elliott. 2019. “Damages Done: The Longitudinal Impacts of Natural
Hazards on Wealth Inequality in the United States.” Social Problems 66, no. 3.
448–67. https://doi.org/10.1093/socpro/spy016.
Hsiang, S., R. Kopp, A. Jina, J. Rising, M. Delgado, S. Mohan, D. Rasmussen, R. MuirWood, P. Wilson, M. Oppenheimer, K. Larsen, and T. Houser. 2017.
“Estimating Economic Damage from Climate Change in the United States.”
Science 356, no. 6345: 1362–69. https://doi.org/10.1126/science.aal4369.
Insurance Information Institute. No date–a. “Current Table.” https://www.iii.org/
table-archive/20793.
———. No date–b. “Facts + Statistics: Catastrophe Bonds and Other Insurance-Linked
Securities.” https://www.iii.org/fact-statistic/facts-statistics-catastrophe-bonds.
Insurance Journal. 2022. “Florida Citizens Tops 1 Million Policies, Making It Largest in
State by a Third.” August 12. https://www.insurancejournal.com/news/
southeast/2022/08/12/680306.htm.

References | 401

IPCC (Intergovernmental Panel on Climate Change). 2021. “Summary for Policymakers.”
In Climate Change 2021: The Physical Science Basis, edited by V. MassonDelmotte, P. Zhai, A. Pirani, S. Connors, C. Péan, S. Berger, N. Caud, Y. Chen,
L. Goldfarb, M. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J. Matthews, T.
Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou, 1927–2058.
Cambridge: Cambridge University Press. https://doi.
org/10.1017/9781009157896.001.
Issler, P., R. Stanton, C. Vergara-Alert, and N. Wallace. 2020. “Mortgage Markets with
Climate-Change Risk: Evidence from Wildfires in California.” Working paper.
http://dx.doi.org/10.2139/ssrn.3511843.
James, I. 2022. “As Drought Persists, Minimal Water Deliveries Announced for the
Central Valley Project.” Los Angeles Times, February 24. https://www.latimes.
com/california/story/2022-02-24/
minimal-water-allocations-for-the-central-valley-project.
Jenkins, J., J. Farbes, R. Jones, N. Patankar, and G. Schivley. 2022. “Electricity
Transmission Is Key to Unlock the Full Potential of the Inflation Reduction
Act.” Rapid Energy Policy Evaluation and Analysis Toolkit. https://
repeatproject.org/docs/REPEAT_IRA_Transmission_2022-09-22.pdf.
Jerch, R., M. Kahn, and G. Lin. 2023. “Local Public Finance Dynamics and Hurricane
Shocks.” Journal of Urban Economics 134. doi.org/10.1016/j.jue.2022.103516.
Kahn, M. 2021. Adapting to Climate Change: Markets and the Management of an
Uncertain Future. New Haven, CT: Yale University Press. https://yalebooks.
yale.edu/book/9780300246711/adapting-climate-change/.
Kao, S., M. Ashfaq, D. Rastogi, S. Gangrade, R. Martinez, A. Fernandez, G. Konapala,
N. Voisin, T. Zhou, W. Xu, H. Gao, B. Zhao, and G. Zhao. 2022. The Third
Assessment of the Effects of Climate Change on Federal Hydropower. Oak
Ridge, TN: National Laboratory. https://info.ornl.gov/sites/publications/Files/
Pub168510.pdf.
Keenan, J., and J. Bradt. 2020. “Underwaterwriting: From Theory to Empiricism in
Regional Mortgage Markets in the U.S.” Climatic Change 162: 2043–67.
https://doi.org/10.1007/s10584-020-02734-1.
Kelley, C., S. Mohtadi, M. Cane, R. Seager, and Y. Kushnir. 2015. “Climate Change in
the Fertile Crescent and Implications of the Recent Syrian Drought.”
Proceedings of the National Academy of Sciences 112, no. 11: 3241–46. https://
doi.org/10.1073/pnas.1421533112.
Keys, B., and P. Mulder. 2020. Neglected No More: Housing Markets, Mortgage Lending,
and Sea Level Rise. NBER Working Paper 27930. Cambridge, MA: National
Bureau of Economic Research. https://doi.org/10.3386/w27930.
Kiewiet, R., and K. Szakaty. 1996. “Constitutional Limitations on Borrowing: An
Analysis of State Bonded Indebtedness.” Journal of Law, Economics, and
Organization 12, no. 1: 62–97.

402 |

References

Kolstad, C., and F. Moore. 2020. “Estimating the Economic Impacts of Climate Change
Using Weather Observations.” Review of Environmental Economics and Policy.
https://scholar.google.com/citations?view_op=view_citation&hl=en&user=ysvd
9c0AAAAJ&citation_for_view=ysvd9c0AAAAJ:L8Ckcad2t8MC.
Kopp, R. 2021. “Land-Grant Lessons for Anthropocene Universities.” Climatic Change
165, no. 28. https://doi.org/10.1007/s10584-021-03029-9.
Kousky, C. 2019. “The Role of Natural Disaster Insurance in Recovery and Risk
Reduction.” Annual Review of Resource Economics 11: 399–418.
———. 2022. Understanding Disaster Insurance. Washington: Island Press. https://
islandpress.org/books/understanding-disaster-insurance.
Kousky, C., H. Kunreuther, B. Lingle, and L. Shabman. 2018. “The Emerging Private
Residential Flood Insurance Market in the United States.” Wharton Risk
Management and Decision Processes Center, University of Pennsylvania.
https://riskcenter.wharton.upenn.edu/wp-content/uploads/2018/07/EmergingFlood-Insurance-Market-Report.pdf.
Kousky, C., M. Palim, and Y. Pan. 2020. “Flood Damage and Mortgage Credit Risk: A
Case Study of Hurricane Harvey.” Journal of Housing Research 29, no. 1:
86–120. https://doi.org/10.1080/10527001.2020.1840131.
Landers, J. 2021. “California Utility to Underground 10,000 Miles of Power Lines.”
American Society of Civil Engineers. https://www.asce.org/publications-andnews/civil-engineering-source/civil-engineering-magazine/article/2021/09/
california-utility-to-underground-10000-mi-of-power-lines.
Leefeldt, E. 2022. “Why Is Homeowners Insurance in Florida Such a Disaster?” Forbes,
November 22. https://www.forbes.com/advisor/homeowners-insurance/
why-is-homeowners-insurance-in-florida-such-a-disaster/.
Lipton, D., M. Rubenstein, S. Weiskopf, S. Carter, J. Peterson, L. Crozier, M. Fogarty, S.
Gaichas, K. Hyde, T. Morelli, J. Morisette, H. Moustahfid, R. Muñoz, R.
Poudel, M. Staudinger, C. Stock, L. Thompson, R. Waples, and J. Weltzin,
2018. “Ecosystems, Ecosystem Services, and Biodiversity.” In Impacts, Risks,
and Adaptation in the United States: Fourth National Climate Assessment,
Volume II, edited by D. Reidmiller, C. Avery, D. Easterling, K. Kunkel, K.
Lewis, T. Maycock, and B. Stewart, 268–321. Washington: U.S. Global Change
Research Program. https://doi.org/10.7930/NCA4.2018.CH7.
Lu, C. 2022. “The Lingering Impact of Pakistan’s Floods.” Foreign Policy, September
15. https://foreignpolicy.com/2022/09/15/
pakistan-floods-humanitarian-disease-food-crisis-climate/.
Lustgarten, A. 2020. “The Great Climate Migration.” New York Times Magazine, July 23.
https://www.nytimes.com/interactive/2020/07/23/magazine/climate-migration.
html.
Mach, K., C. Kraan, W. Adger, H. Buhaug, M. Burke, J. Fearon, C. Field, C. Hendrix, J.
Maystadt, J. O’Loughlin, P. Roessler, J. Scheffran, K. Schultz, and N. von

References | 403

Uexkull. 2019. “Climate as a Risk Factor for Armed Conflict.” Nature 571:
193–97. https://doi.org/10.1038/s41586-019-1300-6.
Mack, K., C. Kraan, M. Hino, E. Johnston, and C. Field. 2019b. “Managed Retreat
Through Voluntary Buyouts of Flood-Prone Properties.” Science Advances 5,
no. 10. https://doi.org/10.1126/sciadv.aax8995.
Marder, J. 2020. “Beating Back the Tides.” National Aviation and Space Administration.
https://sealevel.nasa.gov/news/203/beating-back-the-tides.
Martinich, J., J. Neumann, L. Ludwig, and L. Jantarasami. 2013. “Risks of Sea Level
Rise to Disadvantaged Communities in the United States.” Mitigation and
Adaptation Strategies for Global Change 18: 169–85. https://doi.org/10.1007/
s11027-011-9356-0.
McGee, K. 2021. “A Place Worth Protecting: Rethinking Cost-Benefit Analysis under
FEMA’s Flood-Mitigation Programs.” University of Chicago Law Review 88,
no. 8: 1925–70. https://heinonline.org/HOL/Page?handle=hein.journals/
uclr88&div=64&g_sent=1&casa_token=nFrduvaQRJUAAAAA:M3P21KHCgk
oPC6eTZJHTRHIf8L6uHFSy-vSGXG8GbJAntCQ9mAa34WUOPQ9OvtQ5F0
u8sGjb&collection=journals.
Meinshausen, M., J. Lewis, C. McGlade, J. Gütschow, Z. Nicholls, R. Burdon, L. Cozzi,
and B. Hackmann. 2022. “Realization of Paris Agreement Pledges May Limit
Warming Just Below 2°C.” Nature 604: 304–9. https://doi.org/10.1038/
s41586-022-04553-z.
Mendelsohn, R. 2000. “Efficient Adaptation to Climate Change.” Climatic Change 45:
583–600. https://doi.org/10.1023/A:1005507810350.
Mendelsohn, R., W. Nordhaus, and D. Shaw. 1994. “The Impact of Global Warming on
Agriculture: A Ricardian Analysis.” American Economic Review 84, no. 4:
753–71. https://doi.org/10.1257/aer.89.4.1046.
Milly, P., J. Betancourt, M. Falkenmark, R. Hirsch, Z. Kundzewicz, D. Lettenmaier, and
R. Stouffer. 2018. “Stationarity Is Dead: Whither Water Management?” Science
319: 573–74. https://www.science.org/doi/10.1126/science.1151915
Missirian, A., and W. Schlenker. 2017. “Asylum Applications Respond to Temperature
Fluctuations.” Science 358, no. 6370: 1610–14. https://doi.org/10.1126/science.
aao0432.
ModEx. No date. “The Catastrophe Risk Modeling Market: From Laggards to Leaders:
Barriers, Consequences and Strategic Considerations.” https://www.nasdaq.
com/docs/1617-Q19%20ModEx%20Brochure%20rebrand%20to%20
Nasdaq_MT_v2.pdf.
Moore, F., and D. Diaz. 2015. “Temperature Impacts on Economic Growth Warrant
Stringent Mitigation Policy.” Nature Climate Change 5: 127–35. https://doi.
org/10.1038/nclimate2481.
Moore, F., A. Stokes, M. Conte, and X. Dong. 2022. “Noah’s Ark in a Warming World:
Climate Change, Biodiversity Loss, and Public Adaptation Costs in the United

404 |

References

States.” Journal of the Association of Environmental and Resource Economists
9, no. 5: 981–1015. https://doi.org/10.1086/716662.
Mukherjee, A., and N. Sanders. 2021. The Causal Effect of Heat on Violence: Social
Implications of Unmitigated Heat Among the Incarcerated. NBER Working
Paper 28987. Cambridge, MA: National Bureau of Economic Research. https://
www.nber.org/system/files/working_papers/w28987/w28987.pdf.
National Advisory Council. 2020. “Report to the FEMA Administrator.” Federal
Emergency Management Agency. https://www.fema.gov/sites/default/files/
documents/fema_nac-report_11-2020.pdf.
National Intelligence Council. 2021. “Climate Change and International Responses
Increasing Challenges to U.S. National Security through 2040.” https://www.
dni.gov/files/ODNI/documents/assessments/NIE_Climate_Change_and_
National_Security.pdf.
National Oceanic and Atmospheric Administration. 2021. “What Percentage of the
American Population Lives Near the Coast?” https://oceanservice.noaa.gov/
facts/population.html.
———. No date. “Weather-Related Fatality and Injury Statistics.” https://www.weather.
gov/hazstat/.
Natural Resources Defense Council. No date. “Climate Resilience: How States Stack Up
on Flood Disclosure.” https://www.nrdc.org/flood-disclosure-map.
Neumann, J., P. Chinowsky, J. Helman, M. Black, C. Fant, K. Strzepek, and J. Martinich.
2021. “Climate Effects on U.S. Infrastructure: The Economics of Adaptation for
Rail, Roads, and Coastal Development.” Climatic Change 167, no. 44. https://
doi.org/10.1007/s10584-021-03179-w.
New York District, North Atlantic Division of the U.S. Army Corps of Engineers, New
Jersey Department of Environmental Protection, New York State Department of
Environmental Conservation, New York State Department of State, and New
York City Mayor’s Office of Climate and Environmental Justice. 2022. “New
York–New Jersey Harbor and Tributaries Coastal Storm Risk Management
Feasibility Study: Draft Integrated Feasibility Report and Tier 1 Environmental
Impact Statement.” https://www.nan.usace.army.mil/Portals/37/NYNJHATS%20
Draft%20Integrated%20Feasibility%20Report%20Tier%201%20EIS.pdf.
Newell, R., B. Prest, and S. Sexton. 2021. “The GDP-Temperature Relationship:
Implications for Climate Change Damages.” Journal of Environmental
Economics and Management 108. https://doi.org/10.1016/j.jeem.2021.102445.
NGFS-INSPIRE. 2022. “Central Banking and Supervision in the Biosphere: An Agenda
for Action on Biodiversity Loss, Financial Risk, and System Stability.” NGFS
Occasional Paper. https://www.ngfs.net/sites/default/files/medias/documents/
central_banking_and_supervision_in_the_biosphere.pdf.
Nordhaus, W. 1991. “To Slow or Not to Slow: The Economics of The Greenhouse
Effect.” Economic Journal 101, no. 407: 920–37. https://doi.org/10.2
307/2233864.

References | 405

———. 2015. “Climate Clubs: Overcoming Free-Riding in International Policy.”
American Economic Review 105, no. 4: 1339-70. https://www.aeaweb.org/
articles?id=10.1257/aer.15000001.
Ortiz-Bobea, A., T. Ault, C. Carrillo, R. Chambers, and D. Lobell. 2021. “Anthropogenic
Climate Change Has Slowed Global Agricultural Productivity Growth.” Nature
Climate Change 11: 306–12. https://doi.org/10.1038/s41558-021-01000-1.
Otto, C., K. Kuhla, T. Geiger, J. Schewe, and K. Frieler. 2023. “Better Insurance Could
Effectively Mitigation the Increase in Economic Growth Losses from U.S.
Hurricanes Under Global Warming.” Science Advances 9. https://www.science.
org/doi/pdf/10.1126/sciadv.add6616.
Ouazad, A., and M. Kahn. 2022. “Mortgage Finance and Climate Change: Securitization
Dynamics in the Aftermath of Natural Disasters.” Review of Financial Studies
35, no. 8: 3617–65. https://doi.org/10.1093/rfs/hhab124.
Painter, M. 2020. “An Inconvenient Cost: The Effects of Climate Change on Municipal
Bonds.” Journal of Financial Economics 135, no. 2: 468–82. https://doi.
org/10.1016/j.jfineco.2019.06.006.
Panjwani, A. 2022. “Underwater: The Effect of Federal Policies on Households’ Exposure
to Climate Change Risk.” Working paper. https://static1.squarespace.com/
static/595f21a5f7e0abb30f55b693/t/6324bffe703add302692
d00f/1663352831153/Panjwani_Ahyan_Underwater.pdf.
Pankratz, N., and C. Schiller. 2022. Climate Change and Adaptation in Global SupplyChain Networks. Finance and Economics Discussion Series, Working Paper 56.
Washington: Board of Governors of the Federal Reserve System. https://doi.
org/10.17016/FEDS.2022.056.
Paquette, D., and M. Kornfield. 2022. “Ian Is Florida’s Deadliest Hurricane Since 1935;
Most Victims Drowned.” Washington Post, October 5. https://www.
washingtonpost.com/nation/2022/10/05/hurricane-ian-florida-victims/.
Park, J., J. Goodman, M. Hurwitz, and J. Smith. 2020. “Heat and Learning.” American
Economic Journal: Economic Policy 12, no. 2: 306–39. https://doi.org/10.1257/
pol.20180612.
Park Williams, A., B. Cook, and J. Smerdon. 2022. “Rapid Intensification of the
Emerging Southwestern North American Megadrought in 2020–2021.” Nature
Climate Change 12: 232–34. https://doi.org/10.1038/s41558-022-01290-z.
Pitman, A., T. Fiedler, N. Ranger, C. Jakob, N. Ridder, S. Perkins-Kirkpatrick, N. Wood,
and G. Abramowitz. 2022. “Acute Climate Risks in the Financial System:
Examining the Utility of Climate Model Projections.” Environmental Research:
Climate 1, no. 2. https://doi.org/10.1088/2752-5295/ac856f.
Plume, K. 2022. “U.S. Barge Backlog Swells on Parched Mississippi River.” Reuters.
https://www.reuters.com/world/us/
us-barge-backlog-swells-parched-mississippi-river-2022-10-04/.
Querolo, N., and B. Sullivan. 2019. “California Fire Damage Estimated at $25.4 Billion.”
Bloomberg News, October 28. https://www.bloomberg.com/news/
406 |

References

articles/2019-10-28/california-fire-damages-already-at-25-4-billion-andcounting#xj4y7vzkg.
Rennert, K., F. Errickson, B. Prest, L. Rennels, R. Newell, W. Pizer, C. Kingdon, J.
Wingenroth, R. Cooke, B. Parthum, D. Smith, K. Cromar, D. Diaz, F. Moore,
U. Müller, R. Plevin, A. Raftery, H. Sevčíková, H. Sheets, J. Stock, T. Tan, M.
Watson, T. Wong, and D. Anthoff. 2022. “Comprehensive Evidence Implies a
Higher Social Cost of CO2.” Nature 610: 687–92. https://doi.org/10.1038/
s41586-022-05224-9.
Robles, F., and J. Bidgood. 2017. “Three Months After Maria, Roughly Half of Puerto
Ricans Still Without Power.” New York Times, December 29. https://www.
nytimes.com/2017/12/29/us/puerto-rico-power-outage.html.
Rosa, L., D. Chiarelli, M. Sangiorgio, A. Beltran-Peña, M. Rulli, P. D’Odorico, and I.
Fung. 2020. “Potential for Sustainable Irrigation Expansion in a 3°C Warmer
Climate.” Proceedings of the National Academy of Sciences 117, no. 47:
29526–34. https://doi.org/10.1073/pnas.2017796117.
Rossi, C. 2020. “Assessing the Impact of Hurricane Frequency and Intensity on Mortgage
Delinquency.” Journal of Risk Management in Financial Institutions 14, no. 4:
426–42. https://www.newswise.com/pdf_docs/159438407991822_
Assessing%20the%20Impact%20of%20Hurricane%20Frequency%20and%20
Intensity%20on%20Mortgage%20Default%20Ris1%20(2).pdf.
Rothstein, R. 2017. The Color of Law: A Forgotten History of How Our Government
Segregated America. New York: Liveright. https://www.epi.org/publication/
the-color-of-law-a-forgotten-history-of-how-our-government-segregatedamerica/.
Royal, A., and M. Walls. 2018. “Flood Risk Perceptions and Insurance Choice: Do
Decisions in the Floodplain Reflect Overoptimism?” Risk Analysis 39, no. 5.
1088–1104. https://doi.org/10.1111/risa.13240
Sadasivam, N. 2020. “Holding the Bill.” Grist, March 4. https://grist.org/climate/
insurance-companies-and-lenders-are-responding-to-climate-change-byshifting-risk-to-taxpayers/.
Sastry, P. 2022. “Who Bears Flood Risk? Evidence from Mortgage Markets in Florida.”
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4306291.
Schlenker, W., and M. Roberts. 2009. “Nonlinear Temperature Effects Indicate Severe
Damages to U.S. Crop Yields Under Climate Change.” Proceedings of the
National Academy of Sciences 106, no. 37: 15594–98. https://doi.org/10.1073/
pnas.0906865106.
Schuppe, J. 2022. “Louisiana Faces an Insurance Crisis, Leaving People Afraid They
Can’t Afford Their Homes.” U.S. News & World Report, September 16. https://
www.nbcnews.com/news/us-news/louisiana-homeowners-insurance-crisishurricanes-rcna46746.
Seneviratne, S., X. Zhang, M. Adnan, W. Badi, C. Dereczynski, A. Di Luca, S. Ghosh, I.
Iskander, J. Kossin, S. Lewis, F. Otto, I. Pinto, M. Satoh, S. M.

References | 407

Vicente-Serrano, M. Wehner, and B. Zhou. 2021. “Weather and Climate
Extreme Events in a Changing Climate.” In Climate Change 2021: The
Physical Science Basis. Contribution of Working Group 1 to the Sixth
Assessment Report of the Intergovernmental Panel on Climate Change, edited
by V. Masson-Delmotte, P. Zhai, A. Pirani, S. Connors, C. Pean, S. Berger, N.
Caud, Y. Chen, L. Goldfarb, M. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.
Matthews, T. Maycock, T. Waterfield, O. Yelekci, R. Yi, and B. Zhou, 1513–
1766. Cambridge: Cambridge University Press. https://doi.
org/10.1017/9781009157896.013.
Severen, C., C. Costello, and O. Deschênes. 2018. “A Forward-Looking Ricardian
Approach: Do Land Markets Capitalize Climate Change Forecasts?” Journal of
Environmental Economics and Management 89: 235–54. https://doi.
org/10.1016/j.jeem.2018.03.009.
State of California, Governor’s Office of Planning and Research. 2018. “California’s
Changing Climate 2018: A Summary of Key Findings from California’s Fourth
Climate Change Assessment.” California Natural Resources Agency and
California Energy Commission. https://www.energy.ca.gov/sites/default/
files/2019-11/20180827_Summary_Brochure_ADA.pdf.
Stern, N. 2006. The Economics of Climate Change: The Stern Review. Cambridge:
Cambridge University Press. https://www.lse.ac.uk/granthaminstitute/
publication/the-economics-of-climate-change-the-stern-review/.
Stevens, H., and J. Samenow. 2022. “Maps Show Where Extreme Heat Shattered 7,000
Records This Summer.” Washington Post, September 13. https://www.
washingtonpost.com/climate-environment/interactive/2022/
temperature-records-summer/.
Svartzman, R., E. Espagne, J. Gauthey, P. Hadji-Lazaro, M. Salin, T. Allen, J. Berger, J.
Calas, A. Godin, and A. Vallier. 2021. “A ‘Silent Spring’ for the Financial
System? Exploring Biodiversity-Related Financial Risks in France.” Working
Paper 826, Banque of France. https://publications.banque-france.fr/en/
silent-spring-financial-system-exploring-biodiversity-related-financial-risksfrance.
Thomas, K. 2017. “U.S. Hospitals Wrestle with Shortages of Drug Supplies Made in
Puerto Rico.” New York Times, October 23. https://www.nytimes.
com/2017/10/23/health/puerto-rico-hurricane-maria-drug-shortage.html.
Tilman, D., M. Clare, D. Williams, K. Kimmel, S. Polasky, and C. Packer. 2017. “Future
Threats to Biodiversity and Pathways to their Prevention.” Nature 546: 73–81.
https://doi.org/10.1038/nature22900.
Tran, B., and D. Wilson. 2022. The Local Economic Impact of Natural Disasters.
Working Paper 34. San Francisco: Federal Reserve Bank of San Francisco.
https://doi.org/10.24148/wp2020-34.

408 |

References

UN Framework Convention on Climate Change. 2021. Adaptation Communication of the
United States. https://unfccc.int/sites/default/files/resource/USA%20Full%20
Adaptation%20Communication%202021.11.2%209am_.pdf.
Urban Institute. 2022. “Housing Finance at a Glance: A Monthly Chartbook.” https://
www.urban.org/sites/default/files/2022-11/Housing%20Finance-%20At%20
A%20Glance%20Monthly%20Chartbook%2C%20November%202022.pdf.
U.S. Army Corps of Engineers. No date. “Flood Risk Management.” https://www.usace.
army.mil/Missions/Civil-Works/Flood-Risk-Management/.
U.S. Department of Agriculture, Office of Communications. 2022. “Secretary Vilsack
Announces New 10-Year Strategy to Confront the Wildfire Crisis.” U.S. Forest
Service. https://www.fs.usda.gov/news/releases/
secretary-vilsack-announces-new-10-year-strategy-confront-wildfire-crisis.
U.S. Department of Commerce. 2022. “Biden Administration Announces Historic Coastal
and Climate Resilience Funding.” June 29. https://www.noaa.gov/news-release/
biden-administration-announces-historic-coastal-and-climate-resilience-funding.
U.S. Department of Defense. 2021a. DOD Installation Exposure to Climate Change at
Home and Abroad. Office of the Deputy Assistant Secretary of Defense,
Environment and Resilience. https://media.defense.gov/2021/
Apr/20/2002624613/-1/-1/1/DOD-INSTALLATION-EXPOSURE-TOCLIMATE-CHANGE-AT-HOME-AND-ABROAD.PDF.
———. 2021b. Department of Defense Climate Risk Analysis. Office of the
Undersecretary for Policy–Strategy, Plans, and Capabilities. https://media.
defense.gov/2021/Oct/21/2002877353/-1/-–1/0/DOD-CLIMATE-RISKANALYSIS-FINAL.PDF.
U.S. Department of Energy. 2022. “The Inflation Reduction Act Drives Significant
Emissions Reductions and Positions America to Reach Our Climate Goals.”
Office of Policy. https://www.energy.gov/sites/default/files/2022-08/8.18%20
InflationReductionAct_Factsheet_Final.pdf.
U.S. Department of Homeland Security. 2018. “An Affordability Framework for the
National Flood Insurance Program.” Federal Emergency Management Agency.
https://www.fema.gov/sites/default/files/2020-05/Affordability_april_2018.pdf.
———. 2022. “Summary of Proposed Reforms.” Federal Emergency Management
Agency. https://www.fema.gov/sites/default/files/documents/fema_floodinsurance-reform-proposal_5242022.pdf.
U.S. Department of the Interior. 2022. “Biden-Harris Administration Makes $135 Million
Commitments to Support Relocation of Tribal Communities Affected by
Climate Change.” https://www.doi.gov/pressreleases/
biden-harris-administration-makes-135-million-commitment-support-relocationtribal.
U.S. Department of State and Executive Office of the President. 2021. “The Long-Term
Strategy of the United States: Pathways to Net-Zero Greenhouse Gas Emissions

References | 409

by 2050.” https://www.whitehouse.gov/wp-content/uploads/2021/10/US-LongTerm-Strategy.pdf.
U.S. Department of Transportation. 2022. “Bipartisan Infrastructure Law: Promoting
Resilient Operations for Transformative, Efficient, and Cost-Saving
Transportation (PROTECT) Formula Program.” Fact sheet, Federal Highway
Administration, July 29. https://www.fhwa.dot.gov/bipartisaninfrastructure-law/protect_fact_sheet.cfm.
U.S. Environmental Protection Agency. 2021. “Climate Change and Social Vulnerability
in the United States: A Focus on Six Impacts.” https://www.epa.gov/cira/
social-vulnerability-report.
———. 2022. “Greenhouse Gas Reduction Fund.” https://www.epa.gov/inflationreduction-act/greenhouse-gas-reduction-fund.
———. No date. “About the Office of Environmental Justice and External Civil Rights.”
https://www.epa.gov/aboutepa/
about-office-environmental-justice-and-external-civil-rights.
U.S. Federal Reserve. 2023. “Pilot Climate Scenario Analysis Exercise: Participant
Instructions.” https://www.federalreserve.gov/publications/files/
csa-instructions-20230117.pdf.
U.S. Global Change Research Program. 2018. “Fourth National Climate Assessment.”
https://nca2018.globalchange.gov.
U.S. Government Accountability Office. 2022. “National Security Snapshot: Climate
Change Risks to National Security.” https://www.gao.gov/products/
gao-22-105830.
U.S. Securities and Exchanges Commission. 2022. “SEC Proposes Rules to Enhance and
Standardize Climate-Related Disclosures for Investors.” https://www.sec.gov/
news/press-release/2022-46.
U.S. Small Business Administration. No date. “Disaster Assistance.” https://www.sba.
gov/funding-programs/disaster-assistance.
United Nations Climate Change. No date. “Paris Agreement: Status of Ratification.”
https://unfccc.int/process/the-paris-agreement/status-of-ratification.
Urban, M. 2015. “Accelerating Extinction Risk from Climate Change.” Science 348, no.
6234: 571–73. https://doi.org/10.1126/science.aaa4984.
van der Wiel, K., S. Kapnick, G. vsn Oldenborgh, K. Whan, S. Philip, G. Vecchi, R.
Singh, J. Arrighi, and H. Cullen. 2017. “Rapid Attribution of the August 2016
Flood-Inducing Extreme Precipitation in South Louisiana to Climate Change.”
Hydrology and Earth System Sciences 21: 897–921. https://hess.copernicus.org/
articles/21/897/2017/hess-21-897-2017.pdf.
Vose, R., D. Easterling, A. LeGrande, and M. Wehner. 2017. “Temperature Changes in
the United States.” In Climate Science Special Report: Fourth National Climate
Assessment, edited by D. Wuebbles, D. Fahey, K. Hibbard, D. Dokken, B.

410 |

References

Stewart, and T. Maycock, 185–206. Washington: U.S. Global Change Research
Program. https://doi.org/10.7930/J0N29V45.
Wagner, K. 2020. “Why Is Reforming Natural Disaster Insurance Markets So Hard?”
Policy Brief, Stanford Institute for Economic Policy Research. https://siepr.
stanford.edu/publications/policy-brief/
why-reforming-natural-disaster-insurance-markets-so-hard.
———. 2022. “Adaptation and Adverse Selection in Markets for Natural Disaster
Insurance.” American Economic Journal: Economic Policy 14, no. 3: 380–421.
https://doi.org/10.1257/pol.20200378.
Wheeler, K., B. Udall, J. Wang, E. Kuhn, H. Salehabadi, and J. Schmidt. 2022. “What
Will It Take to Stabilize the Colorado River?” Science 377, no. 6604: 373–75.
https://doi.org/10.1126/science.abo4452.
White House. 2015a. “Findings from Select Federal Reports: The National Security
Implications of a Changing Climate.” https://obamawhitehouse.archives.gov/
sites/default/files/docs/National_Security_Implications_of_Changing_Climate_
Final_051915.pdf.
———. 2015b. “National Security Strategy.” https://obamawhitehouse.archives.gov/sites/
default/files/docs/2015_national_security_strategy_2.pdf.
———. 2021. “Fact Sheet: Historic Bipartisan Infrastructure Deal.” https://www.
whitehouse.gov/briefing-room/statements-releases/2021/07/28/
fact-sheet-historic-bipartisan-infrastructure-deal/.
———. 2022a. “Fact Sheet: The Inflation Reduction Act Supports Workers and
Families.” https://www.whitehouse.gov/briefing-room/statementsreleases/2022/08/19/
fact-sheet-the-inflation-reduction-act-supports-workers-and-families/.
———. 2022b. “Federal Budget Exposure to Climate Risk.” Office of Management and
Budget. https://www.whitehouse.gov/wp-content/uploads/2022/04/ap_21_
climate_risk_fy2023.pdf.
———. 2022c. “National Strategy to Develop Statistics for Environmental-Economic
Decisions: A U.S. System of Natural Capital Accounting and Associated
Environmental-Economic Statistics.” Office of Science and Technology Policy,
Office of Management and Budget, and Department of Commerce. https://
www.whitehouse.gov/wp-content/uploads/2022/08/Natural-Capital-AccountingStrategy.pdf.
———. 2022d. “OMB Analysis: The Social Benefits of the Inflation Reduction Act’s
Greenhous Gas Emission Reductions.” Office of Management and Budget.
https://www.whitehouse.gov/wp-content/uploads/2022/08/OMB-AnalysisInflation-Reduction-Act.pdf.
———. 2022e. “National Security Strategy.” https://www.whitehouse.gov/wp-content/
uploads/2022/10/Biden-Harris-Administrations-National-SecurityStrategy-10.2022.pdf.

References | 411

———. 2022f. “Climate-Related Macroeconomic Risks and Opportunities.” https://www.
whitehouse.gov/wp-content/uploads/2022/04/CEA_OMB_Climate_Macro_
WP_2022-430pm.pdf.
———. 2022g. “Fact Sheet: 10 Ways the Biden-Harris Administration Is Making
America Resilient to Climate Change.” https://www.whitehouse.gov/briefingroom/statements-releases/2022/08/01/fact-sheet-10-ways-the-biden-harrisadministration-is-making-america-resilient-to-climate-change/.
———. 2022h. “Biden-Harris Administration Announces New Actions to Lower Energy
Costs for Families.” https://www.whitehouse.gov/briefing-room/statementsreleases/2022/11/02/fact-sheet-biden-harris-administration-announcesnew-actions-to-lower-energy-costs-for-families/.
———. 2022i. “Building a Clean Energy Economy: A Guidebook to the Inflation
Reduction Act’s Investments in Clean Energy and Climate Action.” https://
www.whitehouse.gov/wp-content/uploads/2022/12/Inflation-Reduction-ActGuidebook.pdf.
———. 2022j. “Executive Order on Catalyzing Clean Energy Industries and Jobs
through Federal Sustainability.” https://www.whitehouse.gov/briefing-room/
presidential-actions/2021/12/08/executive-order-on-catalyzing-clean-energyindustries-and-jobs-through-federal-sustainability/.
———. No date. “Justice40.” https://www.whitehouse.gov/environmentaljustice/
justice40/.
Wing, O., W. Lehman, P. Bates, C. Sampson, N. Quinn, A. Smith, J. Neal, J. Porter, and
C. Kousky. 2022. “Inequitable Patterns of U.S. Flood Risk in the
Anthropocene.” Nature Climate Change 12: 156–62. https://doi.org/10.1038/
s41558-021-01265-6.
Winter, J., H. Huang, E. Osterberg, and J. Mankin. 2020. “Anthropogenic Impacts on the
Exceptional Precipitation of 2018 in the Mid-Atlantic United States.” In
Explaining Extreme Events of 2018 from a Climate Perspective, edited by S.
Herring, N. Christidis, A. Hoell, M. Hoerling, and P. Stott, S5–S16.
Washington: American Meteorological Society. https://doi.org/10.1175/
BAMS-ExplainingExtremeEvents2018.1.
Woetzel, J., D. Pinner, H. Samandari, H. Engel, M. Krishnan, C. Kampel, and J. Graabak.
2020a. “Could Climate Become the Weak Link in Your Supply Chain?”
McKinsey Global Institute. https://www.mckinsey.com/capabilities/
sustainability/our-insights/
could-climate-become-the-weak-link-in-your-supply-chain.
Woetzel, J., D. Pinner, H. Samandari, H. Engel, M. Krishnan, C. Kampel, and M. Vasmel.
2020b. “Will Mortgages and Markets Stay Afloat in Florida?” McKinsey
Global Institute. https://www.mckinsey.com/~/media/mckinsey/business%20
functions/sustainability/our%20insights/will%20mortgages%20and%20
markets%20stay%20afloat%20in%20florida/mgi_climate%20risk_case%20
studies_florida_may2020.pdf.

412 |

References

Wondmagegn, B., J. Xiang, K. Dear, S. Williams, A. Hansen, D. Pisaniello, M. Nitschke,
J. Nairn, B. Scalleye, A. Xiao, L. Jian, M. Tong, H. Bambrick, J. Karnonh, and
P. Bia. 2021. “Increasing Impacts of Temperature on Hospital Admissions,
Length of Stay, and Related Healthcare Costs in the Context of Climate Change
in Adelaide, South Australia.” Science of the Total Environment 773, no. 15.
https://doi.org/10.1016/j.scitotenv.2021.145656.
World Weather Attribution. 2017. “Climate Change Fingerprints Confirmed in Hurricane
Harvey’s Rainfall, August 2017.” https://www.worldweatherattribution.org/
hurricane-harvey-august-2017/.
Xu, E., C. Webb, and D. Evans. 2019. “Wildfire Catastrophe Models Could Spark the
Changes California Needs.” Milliman. https://www.milliman.com/-/media/
milliman/importedfiles/uploadedfiles/wildfire_catastrophe_models_could_
spark_the_changes_california_needs.ashx.
Yu, Y., J. Dunne, E. Sheviakova, P. Ginoux, S. Malyshev, J. John, and J. Krasting. 2021.
“Increased Risk of the 2019 Alaskan July Fires Due to Anthropogenic Activity.”
In Explaining Extreme Events of 2019 from a Climate Perspective, edited by S.
Herring, N. Christidis, A. Hoell, M. Hoerling, and P. Stott, S1–S8. Washington:
American Meteorological Society. https://doi.org/10.1175/BAMS-D-20-0154.1.

References | 413

Appendix A

Report to the President
on the Activities of the
Council of Economic Advisers
during 2022

415

Letter of Transmittal
Council of Economic Advisers
Washington, December 31, 2022
Mr. President:
The Council of Economic Advisers submits this report on its activities
during calendar year 2022 in accordance with the requirements of Congress,
as set forth by Section 10(d) of the Employment Act of 1946, as amended
by the Full Employment and Balanced Growth Act of 1978.
Sincerely yours,

Cecilia Elena Rouse
Chair

Jared Bernstein
Member

Heather Boushey
Member

Activities of the Council of Economic Advisers during 2022 | 417

Council Members and Their Dates of Service
Name

Position

Oath of office date

Separation date

Edwin G. Nourse
Leon H. Keyserling

Chairman
Vice Chairman
Acting Chairman
Chairman
Member
Vice Chairman
Member
Member
Chairman
Member
Member
Member
Chairman
Member
Member
Member
Member
Chairman
Member
Member
Member
Chairman
Member
Member
Member
Chairman
Member
Member
Member
Chairman
Member
Member
Chairman
Member
Member
Member
Member
Chairman
Member
Member
Chairman
Member
Member
Member
Member
Chairman
Member
Member

August 9, 1946
August 9, 1946
November 2, 1949
May 10, 1950
August 9, 1946
May 10, 1950
June 29, 1950
September 8, 1952
March 19, 1953
September 15, 1953
December 2, 1953
April 4, 1955
December 3, 1956
May 2, 1955
December 3, 1956
November 1, 1958
May 7, 1959
January 29, 1961
January 29, 1961
January 29, 1961
August 3, 1962
November 16, 1964
May 17, 1963
September 2, 1964
November 16, 1964
February 15, 1968
February 2, 1966
February 15, 1968
July 1, 1968
February 4, 1969
February 4, 1969
February 4, 1969
January 1, 1972
September 9, 1971
March 13, 1972
July 23, 1973
October 31, 1973
September 4, 1974
June 13, 1975
July 22, 1975
January 22, 1977
March 18, 1977
March 18, 1977
June 6, 1979
August 20, 1980
February 27, 1981
June 12, 1981
July 14, 1981

November 1, 1949

John D. Clark
Roy Blough
Robert C. Turner
Arthur F. Burns
Neil H. Jacoby
Walter W. Stewart
Raymond J. Saulnier
Joseph S. Davis
Paul W. McCracken
Karl Brandt
Henry C. Wallich
Walter W. Heller
James Tobin
Kermit Gordon
Gardner Ackley
John P. Lewis
Otto Eckstein
Arthur M. Okun
James S. Duesenberry
Merton J. Peck
Warren L. Smith
Paul W. McCracken
Hendrik S. Houthakker
Herbert Stein
Ezra Solomon
Marina v.N. Whitman
Gary L. Seevers
William J. Fellner
Alan Greenspan
Paul W. MacAvoy
Burton G. Malkiel
Charles L. Schultze
William D. Nordhaus
Lyle E. Gramley
George C. Eads
Stephen M. Goldfeld
Murray L. Weidenbaum
William A. Niskanen
Jerry L. Jordan

418 |

Appendix A

January 20, 1953
February 11, 1953
August 20, 1952
January 20, 1953
December 1, 1956
February 9, 1955
April 29, 1955
January 20, 1961
October 31, 1958
January 31, 1959
January 20, 1961
January 20, 1961
November 15, 1964
July 31, 1962
December 27, 1962
February 15, 1968
August 31, 1964
February 1, 1966
January 20, 1969
June 30, 1968
January 20, 1969
January 20, 1969
December 31, 1971
July 15, 1971
August 31, 1974
March 26, 1973
August 15, 1973
April 15, 1975
February 25, 1975
January 20, 1977
November 15, 1976
January 20, 1977
January 20, 1981
February 4, 1979
May 27, 1980
January 20, 1981
January 20, 1981
August 25, 1982
March 30, 1985
July 31, 1982

Council Members and Their Dates of Service
Name

Position

Oath of office date

Separation date

Martin Feldstein
William Poole
Beryl W. Sprinkel
Thomas Gale Moore
Michael L. Mussa
Michael J. Boskin
John B. Taylor
Richard L. Schmalensee
David F. Bradford
Paul Wonnacott
Laura D’Andrea Tyson
Alan S. Blinder
Joseph E. Stiglitz

Chairman
Member
Chairman
Member
Member
Chairman
Member
Member
Member
Member
Chair
Member
Member
Chairman
Member
Member
Chair
Member
Member
Chairman
Member
Member
Chairman
Member
Member
Chairman
Member
Member
Chairman
Chairman
Member
Member
Chairman
Member
Chair
Member
Chairman
Member
Member
Member
Chairman
Member
Chairman
Member
Member
Member
Member

October 14, 1982
December 10, 1982
April 18, 1985
July 1, 1985
August 18, 1986
February 2, 1989
June 9, 1989
October 3, 1989
November 13, 1991
November 13, 1991
February 5, 1993
July 27, 1993
July 27, 1993
June 28, 1995
June 30, 1995
January 29, 1996
February 18, 1997
April 23, 1997
October 22, 1998
August 12, 1999
August 12, 1999
May 31, 2000
May 11, 2001
July 25, 2001
November 30, 2001
May 29, 2003
November 21, 2003
November 21, 2003
February 23, 2005
June 21, 2005
November 18, 2005
November 18, 2005
February 27, 2006
July 17, 2008
January 29, 2009
March 11, 2009
September 10, 2010
March 11, 2009
April 19, 2011
April 19, 2011
November 7, 2011
February 7, 2013
August 4, 2013
August 6, 2013
July 21, 2014
August 10, 2015
August 31, 2015

July 10, 1984
January 20, 1985
January 20, 1989
May 1, 1989
September 19, 1988
January 12, 1993
August 2, 1991
June 21, 1991
January 20, 1993
January 20, 1993
April 22, 1995
June 26, 1994

Martin N. Baily
Alicia H. Munnell
Janet L. Yellen
Jeffrey A. Frankel
Rebecca M. Blank
Martin N. Baily
Robert Z. Lawrence
Kathryn L. Shaw
R. Glenn Hubbard
Mark B. McClellan
Randall S. Kroszner
N. Gregory Mankiw
Kristin J. Forbes
Harvey S. Rosen
Ben S. Bernanke
Katherine Baicker
Matthew J. Slaughter
Edward P. Lazear
Donald B. Marron
Christina D. Romer
Austan D. Goolsbee
Cecilia Elena Rouse
Katharine G. Abraham
Carl Shapiro
Alan B. Krueger
James H. Stock
Jason Furman
Betsey Stevenson
Maurice Obstfeld
Sandra E. Black
Jay C. Shambaugh

February 10, 1997
August 30, 1996
August 1, 1997
August 3, 1999
March 2, 1999
July 9, 1999
January 19, 2001
January 12, 2001
January 19, 2001
February 28, 2003
November 13, 2002
July 1, 2003
February 18, 2005
June 3, 2005
June 10, 2005
January 31, 2006
July 11, 2007
March 1, 2007
January 20, 2009
January 20, 2009
September 3, 2010
August 5, 2011
February 28, 2011
April 19, 2013
May 4, 2012
August 2, 2013
May 19, 2014
January 20, 2017
August 7, 2015
August 28, 2015
January 20, 2017
January 20, 2017

Activities of the Council of Economic Advisers during 2022 | 419

Council Members and Their Dates of Service
Name

Position

Oath of office date

Separation date

Kevin A. Hassett
Richard V. Burkhauser
Tomas J. Philipson

Chairman
Member
Member
Acting Chairman
Vice Chairman
Member
Acting Chairman
Vice Chairman
Chair
Member
Member

September 13, 2017
September 28, 2017
August 31, 2017
July 1, 2019
July 24, 2019
May 22, 2019
June 23, 2020
June 23, 2020
March 2, 2021
January 20, 2021
January 20, 2021

June 30, 2019
May 18, 2019

Tyler B. Goodspeed
Cecilia Elena Rouse
Jared Bernstein
Heather Boushey

420 |

Appendix A

June 22, 2020
January 6, 2021

Report to the President on the
Activities of the Council of
Economic Advisers during 2022
Established by the Employment Act of 1946, the Council of Economic
Advisers is charged with advising the President on economic policy based
on data, research, and evidence. The Council is composed of three members:
a Chair, who is appointed by the President with the advice and consent of the
Senate; and two members, who are appointed by the President. Along with a
team of economists, they analyze and interpret economic developments and
formulate and recommend economic policies that advance the interests of
the American people.

The Chair of the Council
Cecilia Elena Rouse was confirmed by the Senate on March 2, 2021, as the
30th Chair of the Council of Economic Advisers. She is the first African
American to hold this position. In this role, she serves as President Biden’s
Chief Economist and a Member of the Cabinet. She is the Katzman-Ernst
Professor in the Economics of Education and Professor of Economics and
Public Affairs at Princeton University.
From 2012 to 2021, Rouse was Dean of Princeton University’s School
of Public and International Affairs. Rouse served as a Member of President
Barack Obama’s Council of Economic Advisers from 2009 to 2011. She
also worked at the National Economic Council in the Clinton Administration
as a Special Assistant to the President from 1998 to 1999. Her academic
research has focused on the economics of education, including the economic
benefits of community college attendance and impact of student loan debt on
postgraduation outcomes, as well as other issues in labor economics, such
as discrimination.

The Members of the Council
Heather Boushey was appointed to the Council by the President on
January 20, 2021. Before assuming this position, Boushey cofounded the
Washington Center for Equitable Growth, where she was President and
CEO from 2013 to 2020. She previously served as Chief Economist for
Secretary Hillary Clinton’s 2016 transition team and as an economist at the
Activities of the Council of Economic Advisers during 2022 | 421

Center for American Progress, the Joint Economic Committee of the U.S.
Congress, the Center for Economic and Policy Research, and the Economic
Policy Institute.
Jared Bernstein was appointed to the Council by the President on
January 20, 2021. Before this appointment, Bernstein spent 16 years in
senior roles at the Economic Policy Institute, and worked at the Department
of Labor. He was a Senior Fellow at the Center on Budget and Policy
Priorities from 2011 to 2020. From 2009 to 2011, he was Chief Economist
and Economic Adviser to then–Vice President Biden.

Areas of Activity
A central function of the Council is to advise the President on all economic
issues and developments, including preparing almost-daily memos for the
President, the Vice President, and White House senior staff on key economic
data releases and policy issues. The Council works closely with officials at
various government entities—including the National Economic Council,
the Domestic Policy Council, the Office of Management and Budget, and
Administrative Agencies—to engage in discussions on numerous policy
matters. The Council, the Department of the Treasury, and the Office of
Management and Budget are responsible for producing the economic
forecasts that underlie the Administration’s Budget proposals. Finally,
the Council is a leading participant in the Organization for Economic
Cooperation and Development (OECD), historically chairing the Economic
Policy Committee and participating in OECD working meetings.
The Council produces economic analyses in a series of blogs and issue
briefs. This past year, these included:
•

“The Employment Situation,” a monthly blog analyzing the employment situation that corresponds to the monthly Jobs Report (January–
December 2022).

•

“Looking Back, Moving Forward: Year One of President Biden’s
Economic Agenda,” a blog analyzing how government support during
the pandemic helped boost personal income and spending, thus contributing to economic growth (January 2022).

•

“New Data Show that Economic Growth Was Broadly Shared in
2021,” a