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economic
re p ort
of the

president
transmitted to congress | april 2022
together with the annual report
of the council of economic advisers

economic
re p ort
of the

president

transmitted to congress | april 2022
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: The Public Sector’s Role in Economic Growth.................... 21
Chapter 2: The Year in Review and the Years Ahead............................. 45
Chapter 3: The U.S. Economy and the Global Pandemic....................... 97
Chapter 4: I nvesting in People: Education, Workforce Development,
and Health............................................................................ 123
Chapter 5: B
 arriers to Economic Equality: The Role of Monopsony,
Monopoly, and Discrimination............................................ 157
Chapter 6: Building Resilient Supply Chains........................................ 191
Chapter 7: Accelerating and Smoothing the Clean Energy Transition. 221
References.............................................................................................. 251
Appendix A: Report to the President on the Activities of the Council of
Economic Advisers during 2021...................................... 333
Appendix B: Statistical Tables Relating to Income, Employment, and
Production........................................................................ 347

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

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Economic Report
of the
President

Economic Report of the President | 1

Economic Report of the President
To the Congress of the United States:
When I took office on January 20, 2021, I looked out at a Nation that was in
the midst of the COVID-19 pandemic and experiencing a weak and uneven
economic recovery. There were roughly 4 million workers who had been
unemployed for more than 6 months. The Congressional Budget Office and
private sector forecasters predicted a slow decrease in the unemployment
rate throughout 2021.
Our Nation needed an economic policy that was nimble enough to
meet the significant and evolving challenges required to defeat a pandemic
and recover from the severe economic disruptions it had caused. Recovery
had to be swift and robust; it was not sufficient to return to where we had
been, we also had to build toward a better future.
Today, we look out at a markedly different America. Over 200 million
Americans have been fully vaccinated and are now protected from the worst
of COVID-19. Businesses have been able to resume activity. Schools and
childcare centers are open again. Our Nation’s economic recovery has been
strong, marked by dramatic increases in employment and GDP. Moreover,
our progress has been achieved with a $360 billion decline in the Nation’s
deficit in fiscal year (FY) 2021 and a historic $1.3 trillion projected decrease
in FY22.
This success was not preordained. It is the result of well-designed and
well-administered policies.
At the start of my Administration, the most important task was to free
ourselves from the grip of a deadly virus. Last year, I signed into law the
American Rescue Plan Act of 2021 (ARP), one of the most consequential
economic rescue packages in American history. The ARP provided an
insurance policy for businesses, workers, and families harmed by the virus.
It prioritized resources to get and keep economic recovery on track: aid to
State and local governments, checks in Americans’ pockets, support to allow
schools to reopen safely, and a robust vaccination program.
In addition to immediate assistance, the ARP provided scaffolding
for long-term recovery—enabling workers and businesses to avoid many
of the long-term, harmful effects that often follow an economic shock. We
saw success across nearly every metric. At the end of 2021, our economy
had created more than 6 million jobs, the largest number ever in 1 year, and
we experienced the fastest drop on record for the unemployment rate. The
United States saw the strongest economic growth since 1984, with GDP
expanding by almost 6 percent. Poverty is projected to have reached historic

Economic Report of the President | 3

lows, particularly for children. Real disposable income was up for the bottom half of the income distribution.
With money in their pockets, Americans were poised to spend—and
because the virus had depressed demand for travel, leisure, and other services, consumers largely turned to goods. This pent-up demand has added
to backlogs, but my Administration has been working with industry to ease
supply chain disruptions at every step in the process: the ports, the trains,
and the trucks. As a result, store shelves are well stocked, and the muchpredicted holiday supply chain crisis did not occur.
A pandemic-constrained economy, coupled with strong demand, has
resulted in increasing prices. This trend is not unique to the United States;
countries around the world are grappling with rising costs as the pandemic
recedes and demand builds. Adding to this, the war in Ukraine has incited
a supply shock that has increased energy and food prices around the globe.
While we tackle these immediate challenges, we must also expand
our productive capacity for the future. The pandemic exposed cracks in
the United States economy that had been widening for years: decades of
low and unequal economic growth that left Black and brown Americans
and Tribal Nations disproportionately vulnerable; inadequate investment
in research and infrastructure; increasing corporate consolidation and
decreasing competition; a hollowed-out manufacturing sector; and a lack of
support for America’s workers and middle-class families. We seek to build
an economy that delivers stronger and more equitable growth for America’s
families and workers.
The Bipartisan Infrastructure Law (BIL) that I signed on November
15, 2021, provides a historic opportunity to build that economy. The BIL,
which will create millions of new jobs, provides long-overdue investment
in our Nation’s physical infrastructure—resources to modernize roads and
bridges, ensure clean drinking water, deliver efficient and affordable broadband, and produce clean, reliable energy. These critical investments will
be especially transformative in rural America, creating jobs and building
wealth for these communities.
This past year, I also signed several Executive Orders that improve the
economy and increase the efficiency in Federal Government procurement.
Examples include an Executive Order to promote competition so that firms
cannot use concentrated market power to hurt workers or consumers, an
Executive Order that establishes a $15 per hour minimum wage for workers
on Federal contracts, and an Executive Order to address supply chain flaws.
In my first year in office, I have also put forward whole-of-government
approaches to combat climate change, strengthen worker organizing and
empowerment, and pursue gender and racial equity.
We must continue this important work. For decades, the United
States has underinvested in our families, in our communities, in American
4 |

Economic Report of the President

businesses, and in our Nation. When we put resources toward children and
families, and workers and United States businesses, we raise both the floor
and the ceiling of the economy for all of us.
We know, for example, that investments in education and training, particularly for young people, make economic sense. Universal access to highquality preschool is the norm in most other advanced economies. Providing
all children with access to high-quality preschool will pay for itself down the
road by producing benefits well into adulthood.
Further, nearly half of new jobs created over the next decade are
projected to require at least some postsecondary education or training at the
entry level. Just as early-20th-Century universal elementary and secondary
schooling helped create a highly skilled labor force, investments in higher
education today can help workers fill the higher-paying jobs of tomorrow.
The result will be broader and more robust economic growth.
We have seen the economic consequences of our failure to put in place
policies to help families balance work and family life. In 1999, labor force
participation for people between the ages of 25 and 54 peaked at more than
84 percent. Since that time, it has never again reached that level. We know
that workplace supports such as affordable, high-quality childcare and longterm care, as well as access to paid family and medical leave, can all lead to
higher labor force participation.
I have repeatedly said, and long maintained, that the middle class built
this country, and unions built the middle class. Without unions, workers
often lack bargaining power to secure higher wages, better working conditions, security for their families’ futures, and a voice in their workplaces.
However, we have seen worker power diminish for nearly 70 years. That is
why we must find ways to strengthen the United States labor force, always
the backbone of the American economy, by finding ways that workers can
gain strength by organizing.
While we empower workers, we must also pay attention to costs families face, the ones that get discussed at the kitchen table and keep parents
up at night: putting food on the table, caring for an aging parent, and ensuring their children are well cared for while they work. This past year, many
American households were able to strengthen their own balance sheets, but
we do not want to see cost increases erode the economic gains of 2021. From
gas prices to groceries to housing costs, I will continue to use all the tools
available to my Administration to address rising prices.
The backdrop to all of this is a planet heating up at rates that we simply
cannot sustain. The costs of climate change can be seen everywhere: damage
from an increasing number of devastating storms and fires, droughts and
flooding that hamper food production and make it more expensive, supply
chain disruptions that slow down our economy, and illness produced by
pollution.
Economic Report of the President | 5

Last year alone, extreme weather and climate disasters cost our communities $145 billion and claimed hundreds of lives. Getting to net zero
greenhouse gas emissions by 2050, while supporting American communities
and workers and expanding new American industries, is a priority of my
Administration. When I think of climate change, I think of jobs.
Moreover, the war in Ukraine reinforces the fact that the United States
must attain energy independence, which can happen by eliminating dependence on fossil fuels over the long term.
As I said in my 2022 State of the Union Address, America has lived
through 2 of the hardest years our Nation has ever faced. As I deliver this
economic report, I am confident that we are building a historic recovery, and
a better America.
I came into office promising to not only find a way to repair the harms
of the pandemic, but to turn the page on an economy that benefits only those
at the top and rewards wealth over work. My Administration is committed to
making critical investments in people, in innovative ideas, in 21st-Century
physical infrastructure, and in combating climate change. We have laid the
groundwork to build an economy from the bottom up and the middle out,
ensuring growth that benefits all Americans.

The White House
April 2022

6 |

Economic Report of the President

The Annual Report
of the
Council of Economic Advisers

Economic Report of the President | 7

Letter of Transmittal
Council of Economic Advisers
Washington, April 14, 2022

Mr. President:
The Council of Economic Advisers herewith submits its 2022 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

Economic Report of the President | 9

Contents
Chapter 1: The Public Sector’s Role in Economic Growth.................... 21
Before the Pandemic......................................................................... 25
Ensuring Macroeconomic Stability................................................... 29
Macroeconomic Stabilization during the Pandemic.................. 29
Addressing Market Failures.............................................................. 32
Market Failures during the Pandemic....................................... 33
Market Failures Beyond the Pandemic...................................... 35
Reducing Inequality.......................................................................... 38
Inequality Before and Beyond the Pandemic............................. 38
Inequality in the Pandemic......................................................... 41
Conclusion......................................................................................... 42
Chapter 2: The Year in Review and the Years Ahead............................. 45
Fiscal Policy in 2021......................................................................... 49
The Rise in Economic Uncertainty................................................... 53
Financial Markets...................................................................... 53
Consumer Sentiment................................................................... 56
The Economy during the Recession and Recovery: How Do
This Recession and Recovery Differ from Others?..........................
Consumer Spending....................................................................
Business and Residential Investment.........................................
Investment in Nonresidential Structures....................................
Investment in Equipment............................................................
Intellectual Property...................................................................
Residential Investment................................................................
State and Local Purchases.........................................................
Exports and Imports...................................................................

56
57
61
61
62
63
63
64
65

Global Supply Chain Disruptions..................................................... 66
Inventory Investment.................................................................. 68
Consumer Price Inflation........................................................... 69
Economic Report of the President | 11

Inflation Expectations................................................................. 70
The Labor Market.............................................................................
Ways in Which the Labor Market Appeared Tight in 2021........
Ways in Which the Labor Market Appeared Loose in 2021......
Labor Supply and Labor Force Participation...........................
The Historical Sluggishness of U.S. LFPR Recoveries..............
Caring for Family Members.......................................................
The Unemployment Rate............................................................
Reconciling the Paradox............................................................

71
72
79
80
86
86
87
88

The Forecast......................................................................................
Macroeconomic Forces during 2022.........................................
The Forecast over the Long Term..............................................
The Supply Side of the Long-Term Forecast..............................

89
90
92
94

Conclusion......................................................................................... 95
Chapter 3: The U.S. Economy and the Global Pandemic....................... 97
Recovery Amid Global Economic Challenges................................. 98
The Global Pandemic................................................................. 98
The United States’ Economic Recovery in the Global
Context...................................................................................... 100
The Challenge of Inflation....................................................... 102
International Trade, the Economic Recovery, and Lingering
COVID-19 Challenges....................................................................
The U.S. Trade Balance...........................................................
International Trade in Goods...................................................
International Trade in Services................................................

104
107
111
113

Policies to Build an Equitable International Economy...................
Broadening the Gains from Trade............................................
Leveling the International Economic Playing Field................
A Collaborative, Transparent Policymaking Process..............

116
116
118
121

Conclusion....................................................................................... 122
Chapter 4: Investing in People: Education, Workforce Development,
and Health.............................................................................................. 123
Human Capital Is Critical for Economic Growth and Individual
Well-Being...................................................................................... 125

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Economic Report of the President

Measuring the Stock of Human Capital.......................................... 128
Investing in Education and Skill Development..............................
Early Childhood Education and Care......................................
K-12 Education.........................................................................
Postsecondary Human Capital Development..........................

133
133
136
137

Investing in Health.......................................................................... 144
Deploying Human Capital..............................................................
Health.......................................................................................
Family Support Policies...........................................................
Employment Practices..............................................................
Occupational Licensing............................................................
Immigration..............................................................................
Incarceration............................................................................
Government Personnel Policies...............................................

148
148
150
151
152
154
154
156

Conclusion....................................................................................... 156
Chapter 5: Barriers to Economic Equality: The Role of Monopsony,
Monopoly, and Discrimination.............................................................. 157
Labor Market Inequality................................................................. 159
Racial, Ethnic, and Gender Wage Gaps.......................................... 161
Sources of Earnings Inequality....................................................... 168
A Lack of Competition in Labor and Product Markets............ 169
Racial and Gender Discrimination.......................................... 172
How Inequality Affects Economic Efficiency and Growth............
Monopsony Power Produces Inefficient Labor Market
Outcomes..................................................................................
Discrimination Misallocates Talent and Suppresses
Innovation.................................................................................
Discrimination Reduces Incentives for Human Capital
Investment.................................................................................
Policies to Address Sources of Labor Market Inequality...............
Promoting Competition............................................................
Unions and Labor Market Equity............................................
The Minimum Wage..................................................................
Full Employment and Tight Labor Markets.............................
Care Economy Policies............................................................

178
178
179
180
180
181
182
183
184
185

Economic Report of the President | 13

Progressive and Equitable Tax Policy..................................... 187
Conclusion....................................................................................... 189
Chapter 6: Building Resilient Supply Chains........................................ 191
21st-Century Supply Chains...........................................................
Vertical Integration with Isolated Industries...........................
Outsourcing with Isolated Industries.......................................
Offshoring and Outsourcing with Isolated Industries..............
Outsourcing with a Central Node............................................
Arm’s-Length and Collaborative Relationships.......................
Drivers of Change in Supply-Chain Structures.......................

192
193
194
194
195
196
199

Implications of Supply Chain Structures........................................ 204
Impact on Innovation............................................................... 204
Impact on the Macroeconomy.................................................. 207
The Rising Incidence of Supply-Chain-Related Disasters............. 210
Private Sector Incentives for Resilience.........................................
Visibility....................................................................................
Redundancy..............................................................................
Agility.......................................................................................

210
211
211
212

Public Sector Strategies for Promoting Resilience......................... 214
Aggregating and Disseminating Information........................... 215
National Security...................................................................... 216
Indirect Supply Chain Policy.......................................................... 219
Conclusion....................................................................................... 220
Chapter 7: Accelerating and Smoothing the Clean Energy
Transition............................................................................................... 221
Accelerating the Energy Transition................................................ 222
Global Efforts to Reduce Greenhouse Gas Emissions............. 224
Accelerating the Energy Transition in the United States......... 225
A Smooth Transition to Clean Energy............................................ 229
The First Challenge: Supporting Domestic Industries.................... 230
Strategies for Supporting Domestic Industries through the
Energy Transition............................................................................ 236

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Economic Report of the President

The Second Challenge: Supporting Communities That Rely
on a Carbon-Intensive Economy..................................................... 240
The Geographic Concentration of Fossil-Fuel-Dependent
Communities............................................................................. 240
The Inadequacy of Place-Neutral Policies.............................. 242
Strategies for Place-Based Policies......................................... 244
The Clean Energy Transition Provides Unique Opportunities
to Implement Successful Place-Based Policies........................ 246
Discussion and Conclusions........................................................... 249
References.............................................................................................. 251
A.
B.

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Appendixes

Report to the President on the Activities of the Council of
Economic Advisers during 2021................................................ 333
Statistical Tables Relating to Income, Employment, and
Production................................................................................... 347

Figures

Women’s Labor Force Participation Rate, 25 to 54 Years........... 26
Men’s Labor Force Participation Rate, 25 to 54 Years................ 26
Growth Rates in Economic Expansions....................................... 27
Growth in Average Family Income, by Income Group................ 28
Infant Mortality............................................................................ 29
Gaps in Annual Earnings by Race, Ethnicity, and Gender.......... 39
Gaps in Average Hourly Earnings by Race, Ethnicity, and
Gender.......................................................................................... 40
Poverty Rate by Racial Group...................................................... 42
Job Growth and Change in COVID-19 Deaths, September
2020–December 2021................................................................... 45
Daily COVID-19 Fatalities, February 2020–December 2021..... 46
Frequencies of Major SARS-CoV-2 Variants, 2021.................... 47
Level of Real GDP, 2021:Q4, versus Before the Pandemic........ 51
Federal Reserve Balance Sheet Composition, 2006–21.............. 52
The Standard & Poor’s 500 Index, 2006–21................................ 54
The U.S. Corporate Spread, 2006–21.......................................... 54
The CBOE’s VIX Index, 2006–21............................................... 55
University of Michigan Consumer Sentiment Index, 2006–21... 55
Total Spending on Goods: Cyclical Comparison......................... 57

Economic Report of the President | 15

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Total Spending on Services: Cyclical Comparison...................... 58
Personal Saving during the Pandemic Relative to Its Average
Pace, 2008–21............................................................................... 58
Business Fixed Investment: Cyclical Comparison....................... 61
Structures Investment: Cyclical Comparison............................... 62
Equipment Investment: Cyclical Comparison.............................. 62
Intellectual Property Investment: Cyclical Comparison.............. 63
Residential Investment: Cyclical Comparison............................. 64
State and Local Purchases: Cyclical Comparison........................ 64
Exports: Cyclical Comparison...................................................... 65
Imports: Cyclical Comparison...................................................... 66
Forty-Foot Container Shipping Benchmark Rates by Route,
2020–21........................................................................................ 67
Cass Trucking Index..................................................................... 67
Air Cargo Rates by Route............................................................ 68
Inventory-to-Sales Ratio (Private Inventories to Final Sales),
1997–21........................................................................................ 68
Consumer Price Index (CPI) Inflation, 2007–21......................... 69
Components of Core CPI Inflation, Commodities versus
Services, 2007–21......................................................................... 70
Job Openings per Unemployed Worker, 2000–2021................... 73
Median Hourly Wage Growth by Level of Education, 1998–21. 74
Median Hourly Wage Growth by Sex, 1998–21.......................... 75
Median Hourly Wage Growth by Workers Who Switch
Industry/Occupation, 2004–21..................................................... 76
Median Hourly Wage Growth by Age, 1998–21......................... 77
Median Hourly Wage Growth by Race/Ethnicity, 1998–21........ 77
Median Hourly Wage Growth by Wage Quantile, 1998–21........ 78
Real Market Income Growth, 2020–21........................................ 78
Real Disposable Income Growth, 2019–21.................................. 79
Payroll Employment, 2020–22..................................................... 80
Employment Changes by Industry Sector, 2020 and 2021.......... 80
The Labor Force Participation Rate, 2020–22............................. 81
U.S. Prime-Age (25–54) LFPR, 2020–22.................................... 82
Prime-Age LFPRs during Past Recessions and Recoveries......... 82
Change in U.S. Rate of Nonparticipation in the Labor Force,
February 2020–January 2022, by Reason for Nonparticipation.. 83
The Retirement Rate, 2010–22..................................................... 84

Economic Report of the President

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Retirement Flow Rates, 1998–22................................................. 84
Maternal LFPR versus the Same Calendar Month in 2019......... 87
The U.S. Unemployment Rate, 2020–22..................................... 88
Labor Supply and Demand, 2019–21........................................... 89
The Federal Fiscal Impetus by Quarter........................................ 91
International COVID Case Rates................................................. 99
International COVID Vaccination Rates.................................... 100
Real GDP by Country................................................................. 101
Discretionary Fiscal Response, 2020:Q1–2021:Q3................... 102
Consumer Price Level................................................................ 103
Recovery in Output and Inflation............................................... 104
Unemployment Rates................................................................. 105
International Employment.......................................................... 106
U.S. Trade Balance, 2001–21..................................................... 108
Trade in Petroleum Products...................................................... 109
Nominal Broad Dollar Index...................................................... 110
U.S. Trade in Goods................................................................... 111
Real Exports, Selected End-Use Categories.............................. 112
Real Imports, Selected End-Use Categories.............................. 113
Trade in Services........................................................................ 114
Trade in Travel Services............................................................. 115
Trade in Transportation Services................................................ 115
U.S. Gross Domestic Product per Person, 1870–2021.............. 126
Earnings Increase with Years of Schooling................................ 127
Average Years of Education by Age Group............................... 128
Life Expectancy, 1900–2019...................................................... 129
Percent Reporting Health as Fair or Poor, 1997–2018.............. 130
Share of COVID Deaths and Share of Population by Age........ 131
Degree or Certificate Completion Rates among Students Who
First Enroll at a Public, Two-Year Institution............................ 142
Life Expectancy at Birth for U.S. Counties, 2010–19............... 147
Infant Mortality Rates by Race or Ethnicity, 2018.................... 148
Percent of U.S.-Born People Employed in the United States,
by Age......................................................................................... 149
The Gap Between Productivity and Worker Compensation,
1948–2020.................................................................................. 159
Median and Average Wealth by Race and Ethnicity, 2019........ 160
Wage Gaps by Education, Race, and Ethnicity, 2021................ 162
Economic Report of the President | 17

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Gender Wage Gap by Level of Education, 2021....................... 163
Wage Gaps by Gender, Race, and Ethnicity, 2021..................... 163
Average Household Income among Asian American, Native
Hawaiian, and Pacific Islander Subgroups................................. 165
Mothers’ and Nonmothers’ Labor Force Participation Rates,
2021............................................................................................ 167
Average Income, Means-Tested Transfers, and Federal Taxes,
2018............................................................................................ 188
Common Types of Supply Chains.............................................. 193
Examples of Tier 1 and Tier 2 Supply Relationships................. 195
The Production Network Corresponding to U.S. Input-Output
Data in 2002............................................................................... 197
Sources of the Components of a Hot Tub.................................. 199
Frequency of Billion-Dollar Natural Disasters by Type,
United States............................................................................... 210
Domestic Business Ratios of Private Inventories to Final
Sales............................................................................................ 213
Atmospheric CO2 Levels Across the Millennia to 2019............ 223
Global Carbon Dioxide Emissions Projections, 2025–40.......... 225
Representative Pathway to Meet Net Zero Emissions in the
United States, 2005–50............................................................... 226
Changes in U.K. Greenhouse Gas Emissions and Real GDP
since 1990................................................................................... 228
U.S. Fossil Fuel Consumption for Selected Years..................... 231
The United States’ and China’s Percentages of the Market
Across Clean Energy Industries................................................. 235
Fossil Fuel Employment by County........................................... 241
Distressed Communities in the United States............................ 244

Tables

Fiscal Support from Coronavirus Relief Laws in Fiscal Years
2020–23........................................................................................ 50
Historical Episodes of Fiscal Expansion since 1941................... 50
Consumer Spending Growth since the Beginning of the
Pandemic...................................................................................... 59
Fixed Investment Components, 2019:Q4–2021:Q4..................... 60
Consumer Price Index Inflation Expectations.............................. 71
Economic Projections, 2020–32................................................... 92
Supply-Side Components of Actual and Potential Real Output
Growth, 1953–2032...................................................................... 95
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Global Clean Energy Deployments in 2020 and 2030
Consistent with Net Zero Emissions by 2050............................ 227
Selected BIL Programs That Target Energy Communities........ 247

Boxes

Unemployment Insurance during the Pandemic.......................... 31
Effective COVID-19 Vaccines as Public Goods.......................... 34
Historical Precedents for the COVID-19 Pandemic.................... 48
Monetary Policy in 2021.............................................................. 52
A Note on the Butterfly Figures................................................... 57
Fiscal Impetus by Quarter............................................................ 91
Lessons from Abroad for Labor Market Policy......................... 105
Trade in Oil and Petroleum Products......................................... 109
Greenhouse Gas Emissions and Trade....................................... 117
COVID and Health..................................................................... 131
COVID and Education............................................................... 138
Federal Investments in Lead Abatement and Rural Broadband. 145
Racial and Ethnic Wealth Gaps.................................................. 160
Improving Data Infrastructure for Equity Analysis................... 164
The Supply Chain of a Hot Tub................................................. 199
The Role of China in U.S. Supply Chains................................. 202
Outsourcing and Job Quality...................................................... 206
Low Inventories and Just-in-Time Production........................... 213
Policies to Improve the Functioning of Supply Chains............. 218
The United Kingdom’s Emissions Have Fallen Rapidly While
Its Economy Has Grown............................................................ 228
The History of U.S. Government Support for Domestic
Carbon-Intensive Energy Industries........................................... 232
Industrial Policy Successes and Failures.................................... 239
The Broader Issue of Distressed Local Economies................... 244
The Administration’s Actions on Place-Based Policies for
Energy Communities.................................................................. 247

Economic Report of the President | 19

Chapter 1

The Public Sector’s Role in
Economic Growth
The U.S. economy is among the world’s strongest and most productive, but
trends over the last several decades threaten to undermine its standing—and
to diminish the living standards of most Americans. Since the 2001 recession, the United States has seen relatively weak economic growth, with
income and wealth disparities at levels not seen in a century. Divisions along
lines such as race, ethnicity, and gender persist.
These economic challenges have many causes. A common theme among
them is the retreat of the U.S. public sector from its complementary role
vis-à-vis the private sector in economic growth. Over the last four decades,
neglect of critical physical infrastructure, from ports to the power grid, has
left the Nation with bottlenecks and vulnerabilities that restrict growth and
make the economy less resilient to shocks and shifts. The United States
has cast aside its history as a global leader in public funding for education—from the high school movement to the G.I. Bill—and now lags its peer
countries in early childhood education and job training. Underinvestment
has, in particular, diminished the pace of growth in U.S. economic capacity—that is, the maximum sustainable amount of goods and services our
economy can produce when unemployment is low and other resources are
being put to full use.
This transformation of the U.S. public sector’s role did not occur by accident. It reflected an economic philosophy which maintained that private
enterprise would thrive only if government got out of the way; otherwise,
public sector investment would “crowd out” the activity of the private

21

sector. Put to the test, these predictions did not deliver. Proponents of this
philosophy had ignored some of the economics discipline’s most celebrated
ideas—ones revealing situations where the private sector cannot and will not
substitute for the public sector. As a result, when the public sector stepped
back, economic growth diminished and became less evenly shared. The
private sector did not lose a rival; it lost a partner.
During the pandemic, infrastructure problems created by underinvestment
became crises. The absence of reliable broadband Internet, for example,
made remote education a challenge for millions of children and families,
setting them back (Auxier and Anderson 2020). The capacity constraints of
U.S. ports and other aspects of freight infrastructure snarled supply chains,
harming U.S. manufacturers (U.S. Department of Transportation 2022b).
Yet underinvestment had constrained U.S. economic capacity before the
pandemic, and it would have continued to do so if the pandemic had not
exposed these vulnerabilities.
When the public sector underinvested in people’s health and education, the
private sector was left with a weaker foundation on which to build, hire, and
invest. When the public sector underinvested in innovation and basic science, the private sector had fewer ideas and technologies that it could apply
to products in such industries as clean energy and biomedicine. By building
a large, healthy, and highly skilled workforce, and by fueling technological
progress, public investments can expand the capacity of the U.S. economy—
and thereby sustain the long-run advance of the American standard of living.
The payoffs from public investment, however, are rarely immediate. Ideas
take time to germinate into industries, as do children to mature into adults.
This has two implications. First, the U.S. government must invest today if
we are to benefit tomorrow, as the payoffs from investments take time to
emerge. And if the government waits until the signs of underinvestment are
fully revealed, it will have waited too long. There will be higher costs to
replace infrastructure beyond repair, a more tumultuous transition to clean

22 |

Chapter 1

energy, and a greater need for public assistance for adults instead of public
investment in disadvantaged children. Second, the government’s role in
increasing the aggregate capacity of our economy can be challenging and
requires sustained effort. Building bridges, running research labs, enhancing
the power grid, and educating children to become productive adults entail
complex, long-term investments. They require patient, capable institutions
that plan beyond budget horizons for the design and delivery of public
services. When the public sector’s role is neglected, these investment aspects
of the government’s capacity are likely to deteriorate the most.
A core aim of the Biden-Harris Administration’s economic policy agenda is
to restore the public sector as a partner in long-run growth, with a particular
focus on the economy’s supply side—from physical infrastructure to the
vitality of our workforce. This means, first, fixing what is broken in physical
infrastructure. The Bipartisan Infrastructure Law, signed by President Biden
in November 2021, makes a historic investment in transportation and utility
systems—spending that will address decades of deferred maintenance of
the infrastructure that keeps lights on, water clean, and people and goods
flowing across the country. This law also upgrades infrastructure in several
strategic areas—such as lead abatement, rural broadband, and electric
vehicles. Such investments are important to make growth more robust, more
widely shared, and more environmentally sustainable.
However, restoring the public sector to its full role in promoting growth
involves more than physical infrastructure investment. Long-run economic
growth also depends on the growth of productive skills and abilities
among workers—what economists call “human capital”—and the pace of
technological progress (Romer 2019). These factors together determine the
capacity of the U.S. economy. The U.S. government could also do much
more to support growth through investments in workers, children, and
families. For instance, though early childhood education is typically free or
available at very low cost in other developed countries, it remains financially

The Public Sector’s Role in Economic Growth | 23

burdensome for a large share of American children born into lower-income
families (Boushey, Barrow, and Rinz 2021). Investments in early childhood
education, like other public investments in human capital, would raise longrun productivity growth as children and students grow up to become workers
(Cascio 2021).
The fruits of economic growth must also be shared more broadly. Labor’s
share of income, once famously stable, has declined to historic lows in the
United States, and the distribution of labor income has become more skewed
to the top earners since the 1970s (Congressional Budget Office 2021).
Public investments in physical infrastructure and human capital also help
ensure that economic growth is more broadly shared by making sure that
people have access to economic opportunities.
Two other ways to make growth more inclusive are tax policy and labor
regulation. Some multinational corporations, for example, exploit the
absence of effective international tax cooperation to shift where they report
income and assets to tax havens, where tax rates are low and malleable.
Establishing international standards and minimums can stop the global race
to the bottom in corporate taxation, so that highly profitable companies pay
for their fair share of the public investments and services they use. Stronger
labor standards—such as a higher minimum wage, effective enforcement
of wage-and-hour and occupational-safety regulations, and protections for
workers’ right to organize—will also help to boost workers’ wages and
working conditions.
The Administration’s agenda could start to rebuild our economic capacity. According to an estimate by Moody’s Analytics, passing additional
legislation based on the President’s policies, along with the Bipartisan
Infrastructure Law and the American Rescue Plan, would lead to an economy that is about 1.5 percent larger in 2031 than it would have been without
any of this legislation (Zandi and Yaros 2021). Economic projections from

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the Administration’s Fiscal Year 2023 Budget find that passing it would raise
the long-run annual growth rate by about 0.4 percentage point.
This introductory chapter explains why a strong and effective public sector
is not only smart economics but also critical to putting the United States
back on the path of robust, inclusive economic growth. It begins with a brief
portrait of the U.S. economy before the COVID-19 pandemic—which, due
in part to a depleted public sector, struggled with disappointing growth in its
productive capacity. Each section then considers one of three complementary roles of the public sector: (1) ensuring macroeconomic stability; (2)
addressing areas where the private sector fails to deliver (market failures);
and (3) reducing inequality. It first explains, on a conceptual level, why
government has a role to play in each of these areas. Next, it describes how
the U.S. government performed in this role during the pandemic. Finally, it
discusses what role for government remains unfinished.

Before the Pandemic
How strong was the economy in the immediate years heading into the
COVID-19 pandemic? By some measures of economic performance, it was
stronger than it had been in many years. Unemployment was low, and stock
and home prices were soaring. Yet that sunny account of the late 2010s
ignores other weaknesses in the economic data, especially the warning signs
coming from measures that serve as economists’ best proxies for long-run
growth in U.S. economic capacity.
Among these warning signs: U.S. labor force participation rates have
dropped to some of the lowest in the developed world. Whereas in 1985, a
larger share of prime-age American women participated in the labor force
than their counterparts in Australia, Canada, the European Union, Japan, or
the United Kingdom, U.S. female labor force participation has since been
surpassed by all these countries or entities (figure 1-1).
The decline in labor force participation among men is similarly staggering. In 1960, work among men age 25 to 54 years was nearly universal,
with just 3 in 100 such men not working or looking for work (Krueger
2017). But, by 2019, nonparticipation among men of such ages had tripled,
with more than 1 in 10 out of the labor force (figure 1-2). While this decline
might have reflected changes in the gender division of household responsibilities, much of it appears unrelated to such shifts (White House 2016).
The Public Sector’s Role in Economic Growth | 25

Figure 1-1. Women’s Labor Force Participation Rate, 25 to 54 Years
Percent
90
85

Canada

United Kingdom

80
75

United States

70

Australia

65
60

European Union 27

1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

55
50

Japan

Source: OECD (2021).

Figure 1-2. Men’s Labor Force Participation Rate, 25 to 54 Years
Percent
99
97

Japan

95
93

European Union 27

91

Canada

89

Australia
United States

1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020

87
85

United Kingdom

Source: OECD (2021).

The weakness in both male and female rates of labor force participation has directly diminished the growth of the U.S. economy’s productive
capacity. With a smaller labor force, U.S. firms can hire fewer workers
domestically and thus can produce less in the United States than they would
if participation rates were higher.
The slow growth rates of output and productivity provide another
grim perspective on U.S. economic performance before the pandemic.
Comparing all U.S. economic expansions from start to end since 1950, there
is none with a weaker average growth rate than the recovery from the Great
Recession. Compared with the average for these expansions, growth in both
26 |

Chapter 1

Figure 1-3. Growth Rates in Economic Expansions
Percent growth (average annual rate)
6
5

Real output per capita
Real output per hour

4
3
2
1
0

Q4:1949– Q2:1954– Q1:1958– Q4:1960– Q4:1970– Q1:1975– Q3:1980– Q4:1982– Q1:1991– Q3:2001– Q2:2009–
Q3:1953 Q3:1957 Q1:1960 Q3:1969 Q2:1973 Q3:1979 Q3:1981 Q2:1990 Q4:2000 Q4:2007 Q4:2019

Sources: Bureau of Economic Analysis; CEA calculations.

real output per capita and productivity (real output per hour) during the
prepandemic expansion was less than half as fast (figure 1-3). Productivity
growth provides an especially clear view on the slowdown in U.S. capacity growth, given that it adjusts for cyclical changes in unemployment and
resource utilization.
Economic growth has not only slowed; it has also become less broadly
shared. From the end of World War II until the late 1970s, real incomes
roughly doubled for families in the bottom fifth of the income distribution
as well as families in the top 5 percent. Yet after the 1970s, the gains from
growth became far more concentrated at the top. Since 1973, the real median
income of households in the bottom fifth of the distribution has risen by less
than 15 percent, compared with growth of more than 100 percent for families in the top 5 percent (figure 1-4). Furthermore, other data from the U.S.
Federal Reserve and the World Inequality Database show that the share of
net wealth held by the top 1 percent of households is at or near record highs
(Federal Reserve 2021; World Inequality Database 2021).
Signs of economic underperformance also appear in an array of other
indicators. Over the last few decades, U.S. life expectancy at birth has
slowly fallen behind that in other high-income countries (OECD 2021). It is
now the lowest in the Group of Seven, with little net increase over the last
decade. Furthermore, inequality and underinvestment in health are linked to
infant mortality (Chen, Oster, and Williams 2016), which has also remained
higher in the United States than in its peer countries since the 1980s (figure
1-5). Maternal mortality rates are also higher in the United States than in any
other developed country (Declercq and Zephyrin 2020). Many analysts have
also blamed economic stagnation for a surge in so-called deaths of despair
related to alcohol, drugs, and suicide (Case and Deaton 2020).

The Public Sector’s Role in Economic Growth | 27

Figure 1-4. Growth in Average Family Income, by Income Group
Index (1979 level = 100)
250

Top 5 percent

200

80th - 95th
percentile
Fourth fifth
Middle fifth
Second fifth

150
100

Bottom fifth

0

1947
1950
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019

50

Sources: Census Bureau; CEA calculations.
Note: Income is in dollars adjusted by the Consumer Price Index for all Urban Consumers, retroactive series, using
current methods.

To account for simmering discontent beneath a seemingly booming
economy requires a more nuanced picture of the Nation’s economic health.
The prepandemic economy was indeed at or approaching full employment
for the first time in 20 years. But while the U.S. economy benefited from
cyclical gains, the structural foundations for long-run inclusive growth were
not being maintained. Accommodative macroeconomic policy could not
substitute for everything else that the public sector should do as a partner of
private enterprise.
What is needed now is an effective partnership between the public and
private sectors. The very existence of private business relies on functions
that only the public sector can provide, ranging from an institutional legal
framework to national security to reliable infrastructure. However, these
basic government functions do not exhaust the complementary roles of the
public sector in promoting economic growth through greater productive
capacity, and in ensuring that well-being flourishes alongside growth.
These functions are, in some ways, troublingly easy to neglect: The
damages wrought by underinvestment accumulate slowly, and the task of
public investment is inherently more demanding than a tax cut. But when
these functions are neglected, government becomes less capable and less
responsive to economic change. At the onset of the COVID-19 pandemic,
for example, the lack of administrative infrastructure to channel support to
businesses meant that the Paycheck Protection Program was far costlier and
less well-targeted toward businesses most in need of rescue than similar
programs in other high-income countries (Autor et al. 2022). The bill for
public sector underinvestment eventually comes due in the form of less
effective government.
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Chapter 1

Figure 1-5. Infant Mortality
Deaths per 1,000 live births

35
30
25

Average of other
Group of
Seven countries

20
15
10

United States

0

1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018

5

Source: World Bank.

Ensuring Macroeconomic Stability
Although the COVID-19 pandemic has been the worst global outbreak of
disease since the influenza pandemic of 1918, societies are also often hit by
other aggregate shocks, including recessions and swings in prices of critical
commodities such as oil and staple foods. These shocks are economy-wide,
sudden, and—especially in the case of epidemics—at once rare, costly, and
hard to forecast. As such, they may be difficult or impractical for individuals
themselves to prepare for.
An important function of government is to help insure society against
such risks. For example, countercyclical monetary and fiscal policies are
essential for boosting demand, output, and employment in depressed economies. Moreover, there are reasons to think that appropriate countercyclical
policies raise living standards on average, instead of purely stabilizing the
economy around its long-run growth path. When capacity is already being
underused, as in a recession, the private sector faces weaker incentives to
invest in more capacity, potentially limiting longer-run growth (DeLong
and Summers 2012). Even if these so-called hysteresis effects are weak or
absent, countercyclical policies may be able to raise the long-run level of
output by reducing the amount of time spent below the economy’s capacity
level, as in Milton Friedman’s famous “plucking” model of business cycles
(Dupraz, Nakamura, and Steinsson 2021; Friedman et al. 1964).

Macroeconomic Stabilization during the Pandemic
At the onset of the pandemic, the loss of jobs and income threatened
hardship for millions of families and bankruptcies for small businesses.
The Public Sector’s Role in Economic Growth | 29

A massive public policy response likely prevented the pandemic’s public
health crisis from creating a prolonged and spiraling economic one.
The government provided the equivalent of an economy-wide insurance policy against the pandemic—with expanded unemployment insurance,
support for temporarily shuttered businesses, aid to State and local governments, and Economic Impact Payments (EIPs, which were often referred
to as “stimulus checks”). This response, as former Council of Economic
Advisers Chair Christina Romer argued in a recent paper with David Romer,
can be thought of as roughly enacting the “pandemic insurance” policy that
families and businesses would have wanted to buy themselves, if such insurance had existed (Romer and Romer 2021).
Although there has been a larger public focus on discretionary fiscal
policies like EIPs, much of what the government “did” to prevent a catastrophic pandemic-induced economic crisis happened without Congress or
the executive branch taking any affirmative action, through a set of policies
known as “automatic stabilizers.” For instance, when workers are laid off,
they can file for unemployment benefits and can typically collect up to 26
weeks of assistance as they search for work. Such spending eases those
workers’ hardships and, when many workers lose their jobs at once (as in a
recession), has a macroeconomic impact of preventing a cascading decline
in income and spending (Kekre 2021). In crises, a program called Extended
Benefits automatically adds weeks in certain states when the unemployment
rate reaches certain metrics. As discussed in box 1-1, Congress did take
important actions to make unemployment insurance (UI) more generous and
more widely available during the pandemic, reflecting weaknesses in the
current system, but some of the UI system would have been triggered without Congressional action. For instance, almost 25 percent of the increase
in UI payments in 2020 relative to 2019 was due to “normal” UI programs
(regular benefits and extended benefits). Though this increase may not have
been enough to support workers during the pandemic, or even amid a normal
recession, it does speak to the importance of ensuring that future policy
includes robust “automatic stabilizers.”
Monetary policies adopted by the U.S. Federal Reserve System also
play a crucial role in macroeconomic stabilization. As reviewed in a recent
paper by former Fed Vice Chair Richard Clarida and coauthors Burcu
Duygan-Bump and Chiara Scotti (2021), the Fed’s efforts to halt and reverse
the economic crisis sparked by the pandemic took several forms. First, the
Fed implemented its conventional policy toolkit with unprecedented speed.
It cut its benchmark nominal interest rate to zero, provided forward guidance
that its zero-rate policy would remain until “the economy has weathered
recent events and is on track to achieve its maximum employment and price
stability goals,” and announced $700 billion in asset purchases of U.S.
Treasuries and mortgage-backed securities.
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Box 1-1. Unemployment Insurance during the Pandemic
Unemployment insurance (UI) is an important component of the U.S.
safety net, providing workers with income amid job loss that is out of
their control. With UI, workers can continue to receive a portion of
their wages and support their families as they search for new jobs. UI
is also an automatic stabilizer (Kekre 2021). When the economy suffers a downturn, increased UI payments lift the economy, preventing a
spiraling descent in consumption and output. Indeed, during the Great
Recession in 2008, UI kept millions of Americans out of poverty while
also saving millions of jobs (West et al. 2016).
However, the COVID-19 pandemic, and the unprecedented job
loss it precipitated, put the UI system to the test and exposed underlying
weaknesses. The current UI system is fragmented—jointly funded by
the Federal government and States, but primarily administered by States,
which, within broad standards, set their own eligibility criteria, benefit
levels, and benefit durations. And as the nature of work has evolved even
before the pandemic began, UI has not kept up. For instance, workers
who are self-employed, including independent contractors, are ineligible
for UI. As the labor force has changed and grown tremendously in
the past few decades, the UI taxable wage base has not grown with it
(Vroman and Woodbury 2014).
Expansions of UI enacted during the pandemic allowed the system
to provide appropriate relief during a widespread national crisis, while
strengthening the system’s ability to support workers and stabilize the
economy. The Federal Pandemic Unemployment Compensation and
Pandemic Emergency Unemployment Compensation programs set
nationwide standards in benefit amounts and durations that accounted
for the unprecedented labor market challenges the pandemic posed.
Meanwhile, at its peak, the Pandemic Unemployment Assistance program made benefits available to nearly 15 million workers ineligible for
traditional UI (Bivens and Banerjee 2021).
The pandemic also highlighted a need for investment in UI systems
and broader UI policy reforms (Bivens et al. 2021). In the summer of
2021, roughly 40 percent of workers receiving their first UI payment
reported having to wait at least 3 weeks for it (U.S. Department of Labor
2022). Delay times in application processing and distribution of safety
net polices can put financially vulnerable families in an even more
precarious situation. Future economic downturns may require again
extending UI benefits to currently excluded workers, suggesting a role
for reforms that would incorporate them.

In the subsequent weeks and months, the Fed established additional
programs to safeguard liquidity in financial markets and to encourage banks
to lend to small businesses and municipal governments, many of which
The Public Sector’s Role in Economic Growth | 31

found themselves unable to borrow just when they most needed credit to
survive. Finally, the Fed worked with banks to complete two rounds of stress
tests focused on understanding the impact of the pandemic on banks’ capital
positions, creating transparency that, as in the 2008 financial crisis, had the
goal of raising investor confidence about the readiness of U.S. financial
institutions to weather the crisis (Morgan, Peristiani, and Savino 2014).
These policy actions helped to prevent not only another Great
Depression but also another Great Recession. That is, through a response
that was responsive to the scale and nature of the pandemic-induced crisis,
the Fed’s actions helped to avert an even larger economic catastrophe and
to fuel a postcrisis recovery that to date has been far stronger than after the
2008 financial crisis.
The greatest challenges in years to come may arise with little warning.
Just as the government buttressed the macroeconomy during the pandemic,
so too must it be able to guide the economy through unanticipated shocks
in the future. Social insurance programs that protect workers, families,
and businesses from severe hardship play a central role in macroeconomic
stabilization (McKay and Reis 2016). Unemployment insurance and the
Supplemental Nutrition Assistance Program (SNAP) proved to be powerful
countercyclical policy levers, shoring up household resources throughout
the unforeseen demands of the pandemic (Rouse and Restrepo 2021). In the
face of historic spikes in joblessness and hunger, some government aid was
automatically assured. Recent updates to the Thrifty Food Plan will crucially
reinforce the stabilizing power of SNAP in future recessions (Bauer 2021).

Addressing Market Failures
Although the private market adequately provides goods and services in many
instances, there are textbook cases in which it does not. These situations
constitute “market failures,” which occur when individual actors—such as
households or businesses—do not achieve efficient outcomes on their own.
Market failures are a pervasive feature of real-world markets. Left unaddressed, they inhibit the efficiency and capacity of the economy.
One well-known example of a market failure is when the consequences of private decisions spill over onto people who were not party to
those decisions, a phenomenon economists call “externalities.” The choices
of industrial factories over how much to spend on equipment to reduce
their emissions, for example, matter for everyone who breathes the air and
drinks the water near these factories. And yet, when making such decisions, private firms have incentives to control emissions only to the extent
that they affect their bottom line, likely emitting more than is desirable for
society as a whole. Government involvement can improve outcomes through

32 |

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policies that compel factories to account for this social damage in their
decisionmaking.
Even the need for macroeconomic stabilization can be characterized as
a form of market failure that stems from price rigidities, incomplete insurance markets, and externalities from shocks to aggregate demand. Market
failures can also arise when people are credit-constrained. When these credit
constraints inhibit people’s ability to pay what something is worth, this
inability to meet costs may incorrectly signal that the good or service has no
long-term value. One notable example of this is childcare and education: just
because families cannot meet the true costs of these services at this point in
their lifecycle does not mean they are not valuable, hence motivating public
involvement.
Furthermore, efficient markets require buyers and sellers to be
informed about the quality and prices of the goods and services traded.
When participants are uninformed, markets struggle to yield mutually
beneficial trades between buyers and sellers. For instance, in the market for
health insurance, people buying it know more about their individual health
status than the insurance companies, which causes these markets to provide
inadequate coverage, out of fear that only unhealthy people will choose to
buy adequate coverage. Finally, markets may not reach efficient outcomes
when production and sales are highly concentrated in one or a handful of
companies. A dominant position gives such companies an incentive to price
their goods and services above their cost, to innovate less, and to take other
anticompetitive actions to entrench their position and to extract monopoly
rents from buyers.

Market Failures during the Pandemic
The pandemic has shown that people’s behaviors may accelerate or slow
the spread of the virus. Testing, mask-wearing, social distancing, and vaccination all benefit more than just the people doing those things, producing
beneficial health externalities for everyone with whom these people come in
contact. Governments have taken several steps to encourage or require these
pro-social behaviors during the pandemic, including the American Rescue
Plan’s funding for the national vaccination campaign and free COVID-19
tests. The Federal Government, and many State and local governments, also
mandated mask-wearing indoors to reduce COVID-19’s airborne spread. In
addition, many State and local governments put in place temporary indoor
capacity limits to encourage increased social distancing and implemented
vaccine mandates for certain activities. The Federal Government has also
funded the development and distribution of vaccines, given that vaccinations
benefit many beyond vaccinated individuals themselves (see box 1-2).

The Public Sector’s Role in Economic Growth | 33

Box 1-2. Effective COVID-19 Vaccines as Public Goods
The life-saving impact of COVID-19 vaccines illustrates the importance
of an important public good: basic scientific research. One consideration
that makes such research a public good is that one use of knowledge—for
example, to cure a given disease—does not take away from other potential
applications of the same knowledge. In the case of COVID-19 vaccines,
the central scientific breakthroughs were the result of decades of publicly
financed research against other viral threats, including Ebola, MERS,
Human Papillomavirus, and Human Immunodeficiency Virus (Harris
2021). The Biomedical Research and Development Authority, for example, was a key funder of research on messenger RNA, the vaccine platform
eventually used in the Moderna and Pfizer vaccines. Public investment
was also crucial in the final step of developing the COVID-19 vaccine:
Richard G. Frank, Leslie Dach, and Nicole Lurie conclude, reviewing a
variety of estimates, that the U.S. government invested between $18 and
$23 billion in COVID-19 vaccine research and development and spent
about $12 billion more on advance purchases of the vaccines. The United
States also spent $20 billion on the vaccination campaign, according to
analyses from the Kaiser Family Foundation (Kates 2021) and the U.S.
Federal Emergency Management Agency (2021).
Researchers have estimated that, without a vaccination program,
there would have been approximately 1.1 million additional deaths
and up to 10.3 million additional hospitalizations in the United States
from December 2020 through November 2021 (Galvani, Moghadas,
and Schneider 2021). Calculating the cost per life saved suggests that
public spending on vaccines was remarkably cost-effective. In particular,
assuming the COVID-19 vaccines would not have emerged without
public investment, the cost of this investment was between $45,000 and
$50,000 per American life saved.
By comparison, some U.S. government agencies typically consider
spending to be cost-effective if it costs around $11 million per life
saved—indicating that half of a cent of spending on COVID-19 vaccines saved as many lives as $1 of spending on other U.S. policies (U.S.
Department of Health and Human Services 2021; U.S. Department of
Transportation 2021). Such thresholds, referred to as the “value of a statistical life,” are widely used to evaluate life-saving regulatory policies,
from car safety to power-plant emissions (Viscusi 2018). Even these
estimates, however, greatly understate the true cost-effectiveness of
vaccine spending, as they do not account for the millions of lives saved
abroad, those saved after November 2021, and those yet to be saved by
COVID-19 vaccines, nor the avoided costs of hospitalizations, illnesses,
and work absences. Taken together, these considerations suggest that
public investments in COVID-19 vaccines were likely the single most
cost-effective policy response to the pandemic.

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An important special case of externalities relates to “public goods”—
goods and services, like national defense and some forms of infrastructure,
that cannot be depleted by one person’s use and that benefit people whether
or not they have paid for them. If left to the private sector to provide, public
goods are undersupplied, as people can individually opt not to pay and to
free ride on the willingness of others to pay. However, if everyone tries to
free ride, there are no public goods to enjoy. Government spending on public
goods can ensure that they are adequately provided and can thereby raise the
economy’s productive capacity (see box 1-2).
Emergency government assistance for small businesses during the
pandemic can also be viewed as a policy response to market failures, as
former Council of Economic Advisers Chair Joseph E. Stiglitz has argued
(Stiglitz 2021). Many small businesses, for example, have insurance policies
against “business interruption” to cover revenue losses due to fires, floods,
or other disasters that are no fault of their own. These policies largely do
not cover pandemics, which left the 41 percent of small businesses that
temporarily closed in late April 2020 without coverage against revenue
losses, putting them at risk of closing their doors forever (U.S. Census
Bureau 2022). Grants and loans to small businesses, such as the Paycheck
Protection Program and the Restaurant Revitalization Fund, addressed this
lack of insurance coverage by directly providing a form of business interruption insurance.

Market Failures Beyond the Pandemic
Market failure is a unifying theme in making the case for public investment
in infrastructure, child health and education, and clean energy. This subsection explores these areas of concern.
Infrastructure. There is much evidence that the United States lags far
behind its competitors in supplying the essential inputs to economic capacity. U.S. infrastructure provides several examples. The World Economic
Forum’s Global Competitiveness Report found in 2019 that, out of 141
countries, the United States ranked 13th in quality of overall infrastructure,
17th in quality of road infrastructure, 23rd in electricity supply quality, and
30th in reliability of water supply (Schwab 2019). A separate ranking of
global ports by the World Bank and IHS Markit found that no U.S. port
made it into the top 50 globally, and just 4 are in the top 100. By comparison, of the top 10 ports, several are in China. The Federal Communications
Commission (FCC 2018) has also ranked the United States 10th among
developed countries for broadband speed and connectivity. In transporting
goods and services, in connecting workers around the country and globe,
in transforming technological progress into productivity gains, the United
States is not at the frontier.

The Public Sector’s Role in Economic Growth | 35

The public sector has an important role to play in building and maintaining the stock of physical infrastructure, which complements private
capital investment. Though the private sector can adequately supply the
economy with most physical capital—factories and offices, for instance—
infrastructure projects, such as transportation systems, are far less suited to
private development. Their construction often requires legal authority to use
property to overcome holdups by individual landowners. Furthermore, some
of the social benefits of these projects may stem from increases in innovation, economies of scale, and labor mobility—factors that private developers
would not consider in their investment decisions, leading to underinvestment (Ramondo, Rodríguez-Clare, and Saborío-Rodríguez 2016; Perla,
Tonetti, and Waugh 2021).
The supply chain disruptions during 2021–22 have illustrated the critical importance of fast, efficient transportation for economic growth and have
highlighted the cost to the United States when government does not invest
adequately in transportation infrastructure. When these systems are strained,
they may become bottlenecks for the rest of the economy, causing cascading
shortages, delays, and price increases (Bernstein and Tedeschi 2021; Helper
and Soltas 2021). In mid-December 2021, 71 percent of U.S. manufacturing small businesses reported delays with their domestic suppliers (U.S.
Census Bureau 2022). Facing higher shipping costs, and unable to promise
timely deliveries, these manufacturers have been put at risk of losing sales
to international competitors and being forced to cut jobs and investment
(Hummels and Schaur 2013; Clark, Dollar, and Micco 2004; Hornbeck and
Rotemberg 2021).
Children. Another large body of evidence documents how investments
in children can have positive effects throughout the life cycle and on society
at large (Almond, Currie, and Duque 2018). Education boosts workers’
productivity and wages in the long run, while reducing adult mortality and
incarceration, thereby lifting the economy’s overall capacity (Card 1999;
Oreopoulos and Salvanes 2011). Child health interventions, such as the
provision of adequate nutrition, similarly have lasting effects on both medical and nonmedical aspects of well-being (Bailey et al. 2020). The returns
to such educational and health investments have been shown for children
of all ages, from newborns to young adults (Hendren and Sprung-Keyser
2020), suggesting broad benefits from investments in early education and
childhood programs as well as in elementary and secondary schools.
However, the private costs of childcare and health care are increasingly burdensome and must be paid upfront, even as the rewards are reaped
in the future (Council of Economic Advisers and Office of Management and
Budget 2021). Many of these benefits accrue in large part to society, rather
than just to the family itself—such as through higher tax receipts, less crime,
and lower spending on public assistance (Hendren and Sprung-Keyser
36 |

Chapter 1

2020). Furthermore, the quality of childcare is often variable and difficult
for parents to ascertain (Mocan 2007). These considerations point to the possibility that families are unable to invest in children relative to the long-run
benefits of these investments for society as a whole.
Government can help ensure that children receive high-quality education and care early in life through measures like direct public provision and
subsidies. Despite strong evidence for the benefits of early education, only
about half of three- and four-year-old Americans are enrolled in preschool,
and children of lower-income families are much less likely to be enrolled
in preschool than children of higher-income families (National Center for
Education Statistics 2021; Cascio 2017). Improving pay for caregivers and
instituting standards for care would raise quality across the country, which
may also raise the long-term payoff from these programs by increasing their
effectiveness (Banerjee, Gould, and Sawo 2021).
The past decades of underinvestment in children mean that the United
States is not well prepared for current and future demographic changes. The
aging workforce and the resulting increase in the number of retired workers
suggest that growth in human capital per worker, and by extension growth in
productive capacity, will slow unless the United States reverses underinvestment in our future human capital, as we discuss in chapter 4.
Climate change. Climate change caused by pollution presents another
economic challenge. Each polluting activity contributes to global warming
and environmental damage, but polluters do not individually bear the costs
associated with their pollution. Already the economic damages from storms,
floods, droughts, and wildfires have risen to over $100 billion per year in
the United States (National Centers for Environmental Information 2022).
The mirror image of this problem is underinvestment in clean energy,
as private actors bear the upfront costs of transition investments but cannot themselves capture all the long-term social benefits. Government can
correct these externalities by helping to ensure that the private costs of
carbon and other greenhouse gas emissions, as well as the private benefits
of clean energy, correspond to their long-term costs and benefits for society. Replacing subsidies for fossil fuels with subsidies for clean energy
investments, such as electric vehicles, helps align these private and social
incentives.
Adapting the Nation’s energy systems for the future is not a task that
can be achieved by individual households, businesses, or industries alone.
Consider a consumer in North Dakota wishing to purchase an electric
vehicle. According to the Department of Energy, North Dakota has a total of
138 public and private electric vehicle supply equipment ports (Alternative
Fuels Data Center n.d.). That is one charging station per 510 square miles,
which is equal to or beyond the distance that any electric vehicle currently
sold in the United States can drive on one charge (Wallace and Irwin 2021).
The Public Sector’s Role in Economic Growth | 37

Meanwhile, California has one charging station for each 4 square miles of
land in the state (Alternative Fuels Data Center n.d.). A key challenge in
electric vehicle infrastructure is coordination between vehicle buyers and
charging-station suppliers: Neither wants to be the first to invest, creating a
chicken-and-egg problem that delays the transition to electric vehicles (Li et
al. 2017). This suggests a role for government in undertaking upfront investments in infrastructure, and thus allowing all Americans to take part in the
energy transformation.

Reducing Inequality
Both economic efficiency and equity are important goals. But there
is no guarantee that efficient economic outcomes are equitable ones.
Governments have a role to play in ensuring that the benefits of economic
growth are shared when they would otherwise go to a fortunate few—and
in spreading the costs of economic dislocations, such as trade adjustment
and technological change, when they would otherwise wreak concentrated
harm on particular local economies and groups. Another important, if
difficult, task for government lies in confronting the ongoing legacies of
de jure discrimination that many minority groups face, from labor market
disadvantages to residential segregation (Rothstein 2017).

Inequality Before and Beyond the Pandemic
The U.S. economy has long featured substantial inequalities in income,
wealth, and other economic outcomes among individuals and families.
These inequalities reflect variations in opportunities, earnings ability, preferences, bargaining power, and luck—along with structural divisions by race,
ethnicity, class, gender, sexual orientation, and other markers of difference.
Income inequality can be explained by two economic trends: the
decline in labor’s share of national income, and rising earnings inequality
among workers. From 2000 to 2019, labor’s share of income in the U.S.
nonfarm business sector fell 6 percentage points, from 63 percent to 57
percent, according to Bureau of Labor Statistics data. In addition, labor
earnings growth since the 1970s has been strongly tilted toward the best-off
households (Congressional Budget Office 2021). Since the distribution of
nonlabor income (i.e., payments to capital and business owners) is even
more unequal than that of labor income, the decline in labor’s share and
the increase in earnings inequality have both contributed to rising inequality in overall income. The fall in the labor share and the rise in earnings
inequality reflect many contributing causes—among them, shifting relative
supply and demand for skills, changes in public policies like top tax rates
and antitrust enforcement, and changes in labor market institutions such as

38 |

Chapter 1

Figure 1-6. Gaps in Annual Earnings by Race, Ethnicity, and Gender
Percent
80
70
60

Female vs. male

50
40

Black vs. white

30
20

Hispanic vs. nonHispanic white

0

1948
1950
1952
1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020

10

Sources: Bureau of Labor Statistics; CEA calculations.

unions (Furman 2016). Collectively, these economic shifts and institutional
changes have undermined worker power, especially that of the most vulnerable workers, for the benefit of top earners and the owners of capital and
businesses.
At the same time, gaps by race and gender have been highly persistent.
There has been strikingly little progress in closing gaps in hourly or annual
earnings by race and ethnicity over the last 20 years, and progress in closing
gender gaps has slowed over the same period (figures 1-6 and 1-7).
While these economic disparities have proved persistent, policy action
and legal efforts against discrimination have been important in driving
the progress that did occur. Critically, the reduction in racial and ethnic
inequality has been “episodic” rather than “continual,” reflecting identifiable shifts such as the Civil Rights Act of 1964, the Fair Labor Standards
Act of 1966, and the tight labor market of the 1990s (Donohue and Heckman
1991; Derenoncourt and Montialoux 2021; Baker and Bernstein 2013).
Improvements in school quality after the landmark U.S. Supreme Court
decision in Brown v. Board of Education were another important contributor to the compression of racial and ethnic earnings gaps (Card and Krueger
1992).
Research also suggests that past antidiscrimination policies not only
benefited minorities but also expanded the overall capacity of the U.S.
economy, as discrimination prevented the economy from making full use of
the potential of all Americans. According to one analysis, between 20 and
40 percent of all U.S. economic growth from 1960 to 2010 can be explained
by reductions in discriminatory barriers by sex and race (Hsieh et al. 2019).
Although women and racial and ethnic minorities are now more able to enter
The Public Sector’s Role in Economic Growth | 39

Figure 1-7. Gaps in Average Hourly Earnings by Race, Ethnicity, and Gender
Percent
40
35

Female vs. male

30
25
20

Hispanic vs. white

15
10

2019

2017

2015

2013

2011

2009

2007

2005

2003

2001

1999

1997

1995

1991

1989

1987

1985

1983

1981

1979

0

1993

Black vs. white

5

Sources: Bureau of Labor Statistics; CEA calculations.

high-earning occupations like law and medicine, occupational segregation
remains an important contributor to income disparities by gender, race, and
ethnicity (Cortes and Pan 2018; Weeden 2019). Overall, occupation and
industry segregation account for about half of the gender pay gap as of 2011
(Blau and Kahn 2017). After the rapid advance of women in the workplace
during the 1970s and 1980s (figures 1-6 and 1-7), progress in reducing gender disparities in the labor market has been slow in recent years.
A key factor behind the remaining gender gaps, much recent research
has argued, is how household responsibilities are typically divided within
heterosexual couples, especially those with children. In the United States,
women’s employment and earnings fall immediately upon the birth of their
first child and remain 20 to 30 percent lower, even 10 years after childbirth.
Worldwide, larger “child penalties” occur in countries and regions of countries with more traditional gender norms (Kleven 2021). Other research
has suggested that the lack of fair and predictable work schedules may be
a barrier to maternal labor force participation. Women are less willing to
accept higher-paying jobs with longer commutes than men, likely because of
their greater home and care responsibilities, and gender pay gaps are smaller
in occupations that can accommodate flexible work hours (Barbanchon,
Rathelot, and Roulet 2021; Goldin 2014). Though norms and a fundamental
economic force—specialization in either paid or household work—push
women and men to make different life choices, government could do more
to accommodate caretakers, typically women, who want to manage both
family and career, such as through paid leave and subsidized child care
(Boushey 2016).

40 |

Chapter 1

Inequality in the Pandemic
The COVID-19 pandemic laid bare vast, alarming economic disparities.
Many higher-earning workers, for example, continued in their jobs through
telework, while 80 percent of job losses after the pandemic were concentrated in the lowest quarter of wage earners (Gould and Kandra 2021).
Women bore the brunt of school and childcare closures by picking up
additional care responsibilities, and labor supply among mothers of young
children remained depressed even two years into the pandemic (Goldin
2021). Furthermore, analyses that have parsed U.S. economic data by race,
sex, ethnicity, and education have found weaker pandemic recoveries in
labor force participation among women with compounding sources of
disadvantage, such as Hispanic and non-Hispanic Black mothers or mothers
with less than a bachelor’s degree (Tüzemen 2021).
The government’s pandemic response aimed to prevent its costs from
falling heavily on specific groups of workers. Several programs provided
targeted relief to pandemic-affected industries—such as air travel, hotels,
and restaurants—as well as to their workers. In addition, the government
patched several holes in the safety net that, if they had been left unaddressed,
would have exposed millions of families to pandemic-related hardships
(Wheaton, Giannarelli, and Dehry 2021).
One of these patches was the expansion of unemployment insurance
to cover “gig” workers and others who are typically ineligible for such
benefits, such as the self-employed and people with limited work histories,
through Pandemic Unemployment Assistance (see box 1-1). A second patch
to the safety net was in housing policy: The government forbade banks and
landlords from foreclosing upon or evicting families, and it provided relief
with the Emergency Rental Assistance Program and Homeowner Assistance
Fund. Third, school closures during the pandemic meant that the nearly 30
million children who received free or reduced-price school lunches before
the pandemic needed other forms of nutrition support—a safety-net hole
patched with the Pandemic Electronic Benefits Transfer program (Economic
Research Service 2022).
These safety net patches, along with other policies such as the
expanded Child Tax Credit, helped to reduce poverty to its lowest level on
record, despite the pandemic and recession. Official estimates for the year
2021 will not be released until late 2022, but in 2020, the poverty rate fell
to 9.6 percent from 11.8 percent in 2019, according to the Supplemental
Poverty Measure, which accounts for the resources that many low-income
households receive from the government (Fox and Burns 2021). Declines
in poverty were even larger for particular racial and ethnic groups, with the
supplemental poverty rate among Black and Hispanic Americans falling by
3.7 and 4.9 percentage points, respectively (figure 1-8). The decline in the

The Public Sector’s Role in Economic Growth | 41

Figure 1-8. Poverty Rate by Racial Group
Percentage of population
30

Hispanic
25

Black

20
15

Asian

10

White, not Hispanic

5
0

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

Source: Census Bureau.

child poverty rate was equally dramatic, dropping by almost 3 percentage
points and projected to fall even further in 2021 (Wheaton, Giannarelli, and
Dehry 2021). The data illustrate the importance of public assistance in preventing pandemic hardships, because the poverty rate, as measured by the
Official Poverty Measure—which does not reflect the increase in transfers—
rose by a full percentage point to 11.4 percent in 2020 (Shrider et al. 2021).

Conclusion
Economists have long understood the myriad ways in which government
action in the economy can promote growth and well-being, fulfilling the
public sector’s role as a partner of the private sector. Ensuring macroeconomic stability, investing in public goods, addressing market failures, and
reducing inequality are just some of the functions that markets cannot do
alone—or do too little in the absence of government. When governments
fulfill these roles, they are not interfering in the market or crowding out
private enterprise; they are creating, protecting, and expanding markets and
their potential to produce an inclusive and prosperous society.
These complementary functions of government were on prime display during the COVID-19 pandemic. The health costs and risks of viral
transmission meant that basic person-to-person interactions carried social
implications, motivating a host of U.S. government policies to reduce these
risks: physical distancing, subsidized testing, mask requirements, and public
investment in vaccines and treatments for COVID-19. And just behind the
public health crisis loomed a potential economic crisis, one that portended
hardship for tens of millions of people who had lost jobs or income—a crisis
that the U.S. government successfully alleviated with aggressive monetary
42 |

Chapter 1

and fiscal responses that sustained aggregate demand and strengthened the
safety net throughout the pandemic. The U.S. response to COVID-19 has
been intentional in recognizing and undoing the pandemic’s unequal effects
across our society—with progressive direct cash assistance, targeted support
for workers in the industries most affected by the pandemic, and investments
in broadband access and vaccine outreach to serve rural and other disadvantaged communities.
The partnership between public and private sectors worked during the
pandemic and has the potential to contribute to increased future economic
growth. As the remaining chapters of this Report discuss, understanding the
role of government is important in assessing economic policy options. A
policy agenda to fulfill these roles can improve U.S. economic outcomes and
expand U.S. productive capacity, both now and over generations to come.
Chapter 2 provides an overview of the economy over the past year,
focusing on how this recovery differs from past ones. The chapter discusses fiscal and monetary policy support, pandemic issues, inflation, and
labor force participation. The macroeconomic forecast underpinning the
Administration’s Budget is also presented.
Addressing the pandemic-induced economic downturn has been a
shared priority for countries around the world. Chapter 3 analyzes the U.S.
economy in a global context, examining other countries’ paths toward
recovery, inflation trends, and labor markets, as well as shifts in international trade and their impact on the U.S. trade deficit. The chapter then
discusses principles for a U.S. international economic policy that promotes
economic resilience and generates benefits that are shared broadly across
American society.
Human capital—or the knowledge, skills, health, and other valuable
resources embodied in an individual—is a critical component of economic
growth. However, the accumulation of human capital has slowed in recent
years. For instance, life expectancy only rose by less than half a year in the
decade before the pandemic, and the education levels of the current generation of young adults have grown only slightly compared with their parents’
generation. Chapter 4 discusses education, workforce development, and
health (several of the major components of human capital), and explores
public investments that would support the development of these forms of
human capital, and policy changes that could allow human capital to be used
more productively and expand U.S. economic capacity.
Even when people develop strong human capital, countervailing forces
can keep them from successfully utilizing it. For example, since the late
1990s, concentration has increased in about 75 percent of U.S. industries,
and research shows that about 60 percent of U.S. labor markets are highly
concentrated, likely reducing wages and the quality of working conditions
(Grullon, Larkin, and Michaely 2019; Azar et al. 2019). Chapter 5 discusses
The Public Sector’s Role in Economic Growth | 43

the forces that inhibit competition—and why it is critical for long-run
growth to address monopsonies (a lack of competition among employers or
other buyers of goods and services); monopolies; and racial, ethnic, and gender discrimination. In addition, chapter 5 examines how persistent inequality
may reduce economic efficiency and capacity growth, particularly through
its effects on labor market outcomes, talent allocation, innovation, and
incentives for human capital investment.
For decades, experts have warned that U.S. supply chains were fragile
and thus vulnerable to shocks like extreme weather and global disturbances.
However, it was not until the pandemic highlighted existing weaknesses that
“supply chain” became a household term. Chapter 6 describes the evolution
of the supply chain and discusses issues linked to firms’ increased reliance
on outsourcing and offshoring. In critical industries, supply chain resilience
has national security implications. In other industries, the complexity of
supply chains can make it difficult for firms to coordinate their private
planning and decisionmaking, suggesting a role for policies such as industry
standards and information aggregation and dissemination. The chapter then
provides examples of Administration proposals that would help to address
these issues, strengthening supply chains’ resilience and innovation.
Chapter 7 discusses climate risks and the global progress in mitigating
these risks by transitioning to clean energy. Then it outlines the factors holding back the energy transition and policies that can cost-effectively accelerate the transition. The chapter explains the economic rationale underlying
Federal climate policies to smooth the energy transition for U.S. domestic
industries and vulnerable communities. Specifically, the chapter describes
the opportunities and challenges of government interventions to support
domestic clean industries and place-based policies for economic development in fossil-fuel-dependent communities.

44 |

Chapter 1

Chapter 2

The Year in Review and the Years Ahead
The COVID-19 pandemic was the dominant factor steering the U.S.
economy in 2021, as it was in 2020. In early 2020, the paralyzing grip of
the pandemic drove the deepest macroeconomic shock to the United States
since the Great Depression; and, in 2021, more than a year after shutdowns
and masking began, almost every driver of the economic ebbs and flows
the United States experienced had stemmed directly or indirectly from this
virus.1 The growth of payroll employment, for example, varied inversely
with the rises and falls of the COVID-19 fatality rate (figure 2-1).
Figure 2-1. Job Growth and Change in COVID-19 Deaths, September 2020–
December 2021
Change in payroll employment (thousands)

Month-to-month change in deaths (thousands)

1,000

120
Payroll growth (left)

Change in COVID-19 deaths (right)

800

100

600

80

400

60

200

40

0

20

–200

Sep Oct Nov Dec Jan
Feb Mar Apr May Jun
Jul Aug Sep Oct Nov Dec
2020 2020 2020 2020 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021

0

Sources: Johns Hopkins University; Bureau of Labor Statistics; Haver Analytics.

Two broad and interweaving forces influenced COVID-19 dynamics in 2021. The first was continuing waves of infections; the second was
continued progress on vaccinations.2 The official start of the pandemic
in the United States was January 20, 2020, when the Centers for Disease
Control and Prevention (CDC) confirmed the first U.S. coronavirus case in
Washington State.3 By the end of 2021, deaths in the United States had accuFor historical quarterly U.S. output data, see Gordon (1986).
See 91-DIVOC (2022).
3
David J. Spencer CDC Museum (2022).
1
2

45

Figure 2-2. Daily COVID-19 Fatalities, February 2020–December 2021
Seven-day moving average of COVID-19 fatalities

4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0

Sources: Our World in Data; CEA calculations.

mulated to over 800,000,4 more than all the U.S. combat deaths combined
in every war including the American Revolution.5 In early January 2021,
at the height of the pandemic, measured cases spiked and fatalities averaged about 3,400 a day over seven days (figure 2-2). Cases and deaths fell
markedly throughout the winter and spring, as over 1.5 million people were
fully vaccinated each day on average. COVID-19’s more contagious Delta
variant, however, emerged in June; and by August, Delta accounted for 90
percent of U.S. cases (figure 2-3), driving an increase in hospitalizations
and deaths.6 The Delta wave may have been partially responsible for the
temporary weakening of growth in real gross domestic product (GDP) in
2021:Q3. Later in the year, the even-more-contagious Omicron variant of
COVID-19 displaced Delta. These variants served as sober reminders that
the pandemic—and the economic devastation it has wrought—was not over.
The second dynamic—the effort to vaccinate the population—began
after the Food and Drug Administration (FDA) gave Emergency Use
Authorization for the Pfizer/BioNTech vaccine on December 11, 2020;
and, a week later, Moderna’s vaccine also got the go-ahead.7 Before taking office, President Biden set a goal of administering 100 million shots in
his first 100 days in office and released a plan to accelerate the vaccination effort on his first full day in office, January 21, 2021.8 On March 11,
President Biden instructed States to make vaccines available to all adults
See 91-DIVOC (2022).
Department of Veterans Affairs (2021).
6
CDC (2022a).
7
American Journal of Managed Care (2021).
8
White House (2021a).
4
5

46 |

Chapter 2

Figure 2-3. Frequencies of Major SARS-CoV-2 Variants, 2021
Percent

100

Omicron variant

Other

90
80
70
60

Alpha variant

50

Delta variant

40
30
20
10
0

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Source: GISAID data via Nextrain.com, assembled by Hatfield et al., showing results of all sequence analyses in the United States,
without regard for regional weighting.

18+ by May 1.9 Driven by Federal efforts to increase vaccine supply, that
date was later pulled forward to April 19.10 The week ending April 12 saw
1.9 million new people each day become fully vaccinated—a pandemic
record.11 On his 92nd day in office, April 21, President Biden announced
that the United States had administered 200 million shots since he entered
office, doubling his initial target of 100 million shots in 100 days and doing
so eight days ahead of schedule.12
By midyear, 162 million people (49 percent of the population) had
been fully vaccinated; by the end of the year, this figure had risen to 207
million (62 percent of the population).13 Among seniors, 78 percent of the
population had been fully vaccinated by midyear, and 88 percent by year
end.14 Progress continued on broad vaccination of Americans, with FDA
authorization of the vaccine for children age 12 to 15 on May 10 and for
children age 5 to 11 on October 29.15 In September, the Biden Administration
announced vaccine requirements for Federal workers and contractors, as
White House (2021b).
Treisman (2021).
11
See 91-DIVOC (2022).
12
Naylor (2021).
13
See 91-DIVOC (2022).
14
This is from the CEA’s analysis of CDC data; see CDC 2022b.
15
See U.S. Food and Drug Administration (2021a, 2021b).
9

10

The Year in Review and the Years Ahead

| 47

Box 2-1. Historical Precedents for
the COVID-19 Pandemic
After the 2008 global financial crisis, the recovery started out slowly,
with job growth averaging only 173,000 a month during 2011—the first
full year of the recovery. Yet the United States went on to experience
steady economic growth, which evolved into the longest expansion in
the country’s recorded history. The COVID-19 pandemic, however,
upended society and halted economic activity, with devastating consequences for the well-being of countless Americans.
COVID-19 was not the first time that the United States had to
cope with a pandemic or a seismic shift in economic activity. The 1918
influenza pandemic—the most recent major pandemic to hit the United
States—had a devastating impact in lives lost. However, it did not have
an easily detectable impact on the macroeconomy. U.S. economic data at
the time were far more limited than in 2021, and often were only available on an annual basis, making precise measurement of the pandemic
shock difficult. Moreover, the substantial World War I effort likely
compensated for any macroeconomic impact, according to Benmelech
and Frydman (2020).
Unlike World War I, World War II did not see a pandemic outbreak
of similar magnitude. But the war and its aftermath offer an interesting
parallel to the current COVID-19 experience. World War II involved dramatic wartime shifts in industrial production, followed by a rapid pivot
back to regular economic activity after the peace. That shift in economic
activity produced supply chain disruptions that very much resemble the
disruptions witnessed in 2021. World War II shut down entire domestic
industries or conscripted them for the war production apparatus. Not
surprisingly, as a result of that shift in production capacity, supplies of
regular products ran low or were exhausted entirely during the war. For
instance, families had trouble buying cars and household appliances
because they were not being produced. According to the Bureau of Labor
Statistics, “[by] 1943, many durable goods, such as refrigerators and
radios, were also dropped from the domain of the consumer price index
as their stocks were exhausted” (BLS 2014). The lack of supplies put
severe upward pressure on prices by the end of the war.
In addition, the pent-up demand of consumers pushed up prices
after World War II. During the war, widespread rationing limited household purchases. The government rationed foods such as sugar, coffee,
meat, and cheese along with durable goods, including automobiles, tires,
gasoline, and shoes. Personal savings increased substantially and were
spent soon after the war ended. Between 1945 and 1949, the population
of roughly 140 million Americans purchased 20 million refrigerators,
21.4 million cars, and 5.5 million stoves. The supply chain disruptions
and pent-up demand that have occurred with the COVID-19 pandemic
are similar—but less severe—to those that occurred after World War II.
48 |

Chapter 2

well as a requirement for health care workers to get vaccinated.16 Workers
at private businesses with 100 or more employees were required to either
get vaccinated or be tested at least once a week.17 These requirements helped
drive additional progress on the vaccination effort through the second half
of 2021, with entities that implemented the requirements often seeing vaccination rates rise by 20 percentage points or more and compliance rates in
the high 90 percent range.18
The United States also made major progress in the fight against
COVID-19 in 2021 with new therapeutics, more and better testing, greater
understanding of the disease, and an improved public health surveillance
system. With increasing levels of immunity and more tools like tests and
treatments available, the pandemic is likely to progress to one with lower
mortality. That said, continued evolution of the virus is likely to require
additional vigilance and investments to prepare for future variants. (See box
2-1.)
The remainder of this chapter examines the COVID-19 recession and
the emerging recovery through the lenses of fiscal policy, monetary policy,
the rise in uncertainty, supply chain disruptions, and the expenditure components of GDP. The pandemic’s effects on the labor market are then assessed,
both on the supply and demand sides. The forecast for the post-COVID-19
economy that underpins the President’s Fiscal Year 2023 Budget is presented. Finally, the chapter concludes with a look back at the convulsions of
the past two years and makes an assessment for the years ahead.

Fiscal Policy in 2021
The fiscal response to COVID-19 in 2020 was swift and massive, as exemplified by the bipartisan Coronavirus Aid, Relief, and Economic Security
(CARES) Act, which was signed into law in March of that year. Fiscal support was strengthened even further in 2021. The major fiscal relief programs
enacted during the pandemic are shown in table 2-1.
One way to put the pandemic fiscal expansion into historical context
is to look at past fiscal support. Table 2-2 identifies periods of fiscal support—that is, years when the primary (noninterest) deficit-to-GDP ratio
was expanding. It then averages how much higher the primary deficit was
during each of those years relative to the final year before the expansionary
period. For example, during fiscal years 1941–43, the primary deficit was
higher than in fiscal year 1940 by an average of 13 percent of GDP per year.
Support during the two pandemic fiscal years has averaged 9.2 percent of
White House (2021c).
See U.S. Department of Labor (2021).
18
White House (2021d).
16
17

The Year in Review and the Years Ahead

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Table 2-1. Fiscal Support from Coronavirus Relief Laws in Fiscal Years 2020–23
% of nominal fiscal-year GDP
2020
2021
2022
2023

Date
4-Mar-2020

Coronavirus Preparedness and Response Supplemental Appropriations Act, 2020, H.R.
6074
Effect on Federal fiscal deficit
0.0
0.0
0.0
0.0

18-Mar-2020

Families First Coronavirus Response Act, Public Law 116127
Effect on Federal fiscal deficit
0.6

0.3

0.0

0.0

Coronavirus Aid, Relief, and Economic Security
(CARES) Act, Public Law 116-136
Effect on Federal fiscal deficit

7.7

2.0

–0.5

–0.6

Paycheck Protection Program and Health Care
Enhancement Act, H.R.266
Effect on Federal fiscal deficit

2.1

0.2

0.0

0.0

Coronavirus Response and Relief Supplemental Appropriationsa
Effect on Federal fiscal deficit
0.0
3.3

0.3

0.1

American Rescue Plan, HR 1319
Effect on Federal fiscal deficit

27-Mar-2020

21-Apr-2020

27-Dec-2020

6-Mar-2021

Total increase in the deficit

0.0

5.2

2.2

0.4

10.4

11.0

2.0

0.0

Source: Cost estimates are from the Congressional Budget Office.
Note: The nominal fiscal-year GDP is from the Administration’s economic forecast.
aDivisions M and N of the Consolidated Appropriations Act 2021, Public Law 116-260, enacted on December 27, 2020.

Table 2-2. Historical Episodes of Fiscal Expansion since 1941
Average Annual Support
(percentage of GDP)

Period

Episode of Fiscal Expansion

1941–43

World War II mobilization

2020–21

COVID-19 pandemic

9.2

2008–9

Great Recession

5.5

1949–50

1949 Recession / Korean War

4.9

2001–4

2001 Recession and aftermath

4.7

13.0

Sources: Office of Management and Budget; CEA calculations.
Note: This table shows the average annual increase in the primary deficit-to-GDP ratio, relative to the final year
before the expansion (it includes both new and expanded programs).

GDP per year higher than in 2019, making it the period with the largest support since the end of World War II.
Fiscal support in 2021 began early. In the first weeks of January 2021,
most households received a $600 economic impact payment for each adult
through the Consolidated Appropriations Act of 2021 (H.R. 133), which was
enacted in late December 2020. The legislation’s $900 billion in COVID-19

50 |

Chapter 2

Figure 2-4. Level of Real GDP, 2021:Q4, versus Before the Pandemic
Percentage of 2019:Q4 level

+3.2

+0.9

+0.7
+0.1

U.S. actual

France

U.S., ex.
ARP*

Canada

U.K.
–0.4

Japan

–0.4

Italy

–0.3

Germany

–1.1

U.S., ex. all
fiscal
response**

–2.1

Sources: OECD; BEA; CBO; Department of the Treasury; CEA calculations.
* CEA calculations using actual ARP spendout and CBO pandemic multipliers.
** CEA ARP calculations plus CBO calculations of GDP effects of 2020 fiscal policy response and Federal Reserve
credit facilities.

relief also reinstituted $300 per week in supplemental pandemic unemployment benefits, which the jobless began to see in January and which was
key to making their families whole as the labor market recovered. Also in
January, small businesses got an extension and expansion of the Paycheck
Protection Program, giving many of them access to additional funds to
maintain payroll and extend operations.
Beginning in March, Americans received additional fiscal pandemic
support in the $1.9 trillion American Rescue Plan (ARP). The ARP funded
the vaccination rollout and continued to fund the COVID-19 response,
both directly and by aiding States in their responses. Households received
$1,400-per-person (including children) economic impact payments soon
after enactment. Families with children started receiving monthly payments
from the expanded Child Tax Credit in July. These were the first refundable
tax credits to be automatically delivered this way; the payments maxed out
at $250 per child age 6–17 per month and $300 per child under 6 per month.
Because this credit was fully refundable, low-income families were, for
the first time, eligible for the full amount. Supplemental pandemic jobless
benefits were extended through early September, though some States chose
to end these benefits beginning in July. Aid to States’ education efforts were
designed to address educational challenges that arose during the pandemic,
such as school closings and staffing issues. Also, the Emergency Rental
Assistance program assisted households that were unable to pay rent or
utilities.
The upshot: the Federal fiscal response had a sizable effect on the economic recovery in 2021. The U.S. economy ended 2021 3.1 percent larger
in inflation-adjusted terms than its prepandemic level, the fastest recovery
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Box 2-2. Monetary Policy in 2021
In response to the sudden COVID-19 pandemic upheaval in March 2020,
the Federal Reserve and other central banks around the world slashed
interest rates and stepped into their role as lenders of last resort. In
addition to lowering the cost of borrowing through traditional bank channels, the Federal Reserve created “emergency lending facilities” under
Section 13(3) of the Federal Reserve Act to support certain segments
of the financial markets. In 2008, the Federal Reserve established six
emergency lending facilities over the span of nine months. In 2020, by
contrast, the Federal Reserve launched 13 emergency lending facilities
in just two months, some of which were direct real economy support
programs, not solely financial sector support programs.
In early 2021, the emergency lending facilities funded by the
CARES Act closed down. However, given the severity of the pandemic’s
economic impact, the Federal Reserve did not stop its asset purchases of
U.S. Treasury securities and mortgage-backed securities. The Federal
Reserve’s balance sheet was $4.1 trillion in February 2020 (figure 2-i).
Within three months, that shot up to $7.1 trillion and continued to grow
at a rapid pace. From the end of 2020 through the end of 2021, the
Federal Reserve’s holdings of U.S. Treasuries increased from $4.69
trillion to $5.65 trillion, and its holdings of mortgage-backed securities
increased from $2.04 trillion to $2.62 trillion. The Fed’s overall balance
sheet grew to $8.7 trillion by the end of 2021—more than double its size
before the pandemic.
Of note, in November 2021, the Federal Open Market Committee
(FOMC) voted to gradually reduce, or “taper,” its ongoing purchases of
Treasury and mortgage-backed securities. The FOMC planned to reduce
the $120-billion-a-month net asset purchase pace by $15 billion per
Figure 2-i. Federal Reserve Balance Sheet Composition, 2006–21

All other
Mortgage-backed
securities

Treasuries

Source: Federal Reserve Bank of Saint Louis.
Note: Excludes eliminations from consolidation.

52 |

Chapter 2

month beginning in late November until purchases reached $0, though
the FOMC also noted it was “prepared to adjust the pace of purchases if
warranted by changes in the economic outlook.” As of the end of 2021,
the Federal funds rate target remained at 0 to ¼ percent.

among Group of Seven nations (see figure 2-4). The CEA finds that the
ARP likely contributed at least 2½ points to this growth, using various
data on ARP spendout as well as demand and output multipliers from the
Congressional Budget Office (CBO).19 Previously published CBO analyses
of the 2020 fiscal relief packages, including the emergency Federal Reserve
credit facilities, suggest that together these pre-ARP packages accounted for
another 2.8 percentage points of real GDP growth during the pandemic.20
This extensive fiscal relief and monetary stimulus accomplished
many critical goals—disseminating vaccines, restoring jobs, advancing
the recovery, and reducing poverty. With the achievement of full employment, and with inflation rising as discussed in greater detail below, the
Federal Reserve reduced its asset purchases and signaled an intent to start
raising interest rates in 2022 (box 2-2).

The Rise in Economic Uncertainty
This section examines the rise in economic uncertainty, in the context of the
COVID-19 pandemic. It explores, in turn, financial markets and consumer
sentiment.

Financial Markets
Financial markets have fully recovered since the onset of the COVID-19
pandemic, supported by strong fiscal and monetary policy interventions.
With respect to equities, the Standard & Poor’s 500 Index was 26.9 percent
higher at the end of 2021 compared with the end of 2020; and it was 47.5
Based on data from OMB, the Department of the Treasury, BEA, and others, the CEA estimates
that roughly half of available ARP funds were spent out over the course of calendar year 2021. The
CEA applied the output multipliers from Seliski et al. (2020) to these spendout estimates. The CEA
chose to use the midpoints of the CBO multipliers under social-distancing assumptions, which were
lower than multipliers without social distancing, leading to the result that real GDP growth was 2½
percentage points faster than it would have been otherwise during the four quarters of 2021, due to
the ARP. If fiscal policy was in actuality more effective than the CEA assumes—e.g., because social
distancing was less binding over 2021 than in 2020—then the ARP would explain a larger share of
2021 GDP growth than is accounted for here.
20
Pre-ARP fiscal impact estimates are from Seliski et al. (2020) and the Congressional Budget
Office (2021). At the time of this chapter’s finalization, the second estimate of 2021:Q4 GDP was
the latest available.
19

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Figure 2-5. The Standard & Poor’s 500 Index, 2006–21
Index level: Jan. 2017 = 100
400
350
300
250
200
150
100

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

0

2006

50

Source: Haver Analytics.
Note: The red line denotes the start of the pandemic.

Figure 2-6. The U.S. Corporate Spread, 2006–21
Percentage points
7
6
5
4
3
2
1
0
2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Source: Federal Reserve Economic Data from the Federal Reserve Bank of Saint Louis.
Note: This series is a proxy of U.S. corporations’ borrowing costs, as measured by the Intercontinental Exchange Bank of
America U.S. Corporate Index Option-Adjusted Spread. The index tracks the performance of dollar-denominated,
investment-grade-rated corporate debt publicly issued in the U.S. domestic market. The red line denotes the start of the
pandemic.

percent higher at the end of 2021 compared with the end of 2019, before the
pandemic (figure 2-5).
The credit market has similarly recovered. Consider, for instance,
the U.S. corporate credit spread, a proxy for corporate borrowing costs. In
March 2020, this spread peaked at over 400 basis points (figure 2-6). (The
higher the spread, the worse the borrowing conditions for U.S. corporations.) After the rapid government and central bank interventions, the spread
fell dramatically and continued to fall through 2021. The spread averaged

54 |

Chapter 2

Figure 2-7. The CBOE’s VIX Index: 2006–21
VIX level (index value)
90
80
70
60
50
40
30
20
10
0

Source: Haver Analytics.
Note: This series is the Chicago Board Options Exchange’s Volatility Index (CBOE VIX), which
measures market expectation of near-term volatility conveyed by stock index option prices. The red line
denotes the start of the pandemic.

Figure 2-8. University of Michigan Consumer Sentiment Index, 2006–21
Index level: 1966:Q1 = 100
110
100
90
80
70
60
50
2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

Sources: Haver Analytics; CEA calculations.
Note: The red line denotes start of the pandemic.

approximately 94 basis points in 2021, compared with 156 basis points in
2020 and 124 basis points in 2019.
However, financial market volatility remained above pre-COVID-19
levels. Figure 2-7 shows a time series of the VIX, which measures the
market’s perception of its own riskiness as valued in options markets. In
March 2020, the VIX spiked to levels not seen since the 2008 global financial crisis. In the 21 months since then, including the 12 months of 2021,
The Year in Review and the Years Ahead

| 55

the measure has generally been on a downward trajectory. As of the end
of 2021, however, it still remained higher than its prepandemic levels—at
about 21 in December 2021, versus its 2019 average of 15—likely due to
uncertainty with respect to the future path of the pandemic.

Consumer Sentiment
Consumers’ perceptions of the U.S. economy became highly pessimistic
at the onset of the COVID-19 pandemic. According to the University of
Michigan’s Consumer Sentiment Index, sentiment fell to its lowest levels
since 2011.21 After a bounce-back in late 2020 and early 2021, consumer
sentiment peaked in 2021:Q2, before declining in the second half of the year
(figure 2-8). This decline in sentiment coincided with the onset of the Delta
and Omicron waves, along with a rise in measured inflation.

The Economy during the Recession and Recovery: How
Do This Recession and Recovery Differ from Others?
The 2020 U.S. recession was shorter than those in the past, and the recovery,
based on several metrics, has been stronger. From February through April
2020, consumer spending fell faster and deeper than in any recession after
World War II. However, the recovery has been faster than any other, and
it differs in important ways, as is demonstrated in figures 2-9 to 2-19. For
example, while the goods-consuming sector swiftly and completely recovered in 2020, the services-consuming sector has recovered only part of its
loss, with some subsectors remaining far below their prepandemic peaks.
As of the end of 2021, real goods consumption was almost 14 percent
above its prepandemic peak at the end of 2019, the fastest goods recovery
of any post-World War II recession, as seen in figure 2-9 (see box 2-3 for an
explanation of this “butterfly” figure and the 10 subsequent similar ones).22
In contrast, services spending recovered as slowly as any prior post–
World War II recession, as shown in figure 2-10. From peak to trough,
services spending fell more steeply than ever before, and more steeply than
purchases of goods, from peak to trough. And although services spending rebounded swiftly, the level of spending eight quarters after the peak
remained below what was experienced during any previous business cycle.
The low spending on services likely reflected social distancing
Another often-cited survey is the Conference Board’s Consumer Confidence Index. Consumer
confidence similarly showed a drop at the onset of the COVID-19 pandemic, followed by a bounceback in late 2020 and early 2021.
22
The National Bureau of Economic Research’s business cycle chronology names February
2020 and April 2020 as the monthly peak and trough of the 2020 recession, but in its quarterly
chronology, the peak occurred in 2019:Q4, and the trough occurred in 2020:Q2. See National Bureau
of Economic Research (2022).
21

56 |

Chapter 2

Box 2-3. A Note on the Butterfly Figures
The butterfly figures—figures 2-9 through 2-19—show how spending
on goods (or services or construction) compares with that in previous
business cycles. After indexing at 100 at each of the 12 post–World War
II business-cycle peaks, the orange line in these figures is the maximum
of the 11 previous business cycles; the blue line is the minimum of these
business cycles; and the gray area shows the range of historical variation.
The goods GDP concept comes from the National Income and Product
Accounts’ (NIPA) table 1.2.6 and aggregates spending on goods within
all GDP components (consumption, investment, government, exports,
and imports). Spending on goods GDP in NIPA table 1.2.6 differs
from the goods-producing sector in the GDP-by-industry accounts. For
example, the value added from automobile retailing is part of goods GDP
in NIPA table 1.2.6 but is part of the service-producing sector in the
GDP-by-industry accounts.

Figure 2-9. Total Spending on Goods: Cyclical Comparison
Index = 100 at business-cycle peak
120
115

Business-cycle
peak

110

2020–21 cycle
through 2021:Q4

Maximum of previous cycles

105
100
95
90

Minimum of previous
cycles

85
80

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.2.6, “Real GDP by Major Type of Product.”

and consumers’ avoidance of businesses and situations that involve faceto-face interactions, such as theater, medical, and personal services.
Consumer Spending
In 2021, consumer spending on goods increased rapidly, while consumer
spending on services had not yet regained its peak, as shown in table 2-3.

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| 57

Figure 2-10. Total Spending on Services: Cyclical Comparison
Index = 100 at business-cycle peak
120
Business-cycle
peak

115

Maximum of previous cycles

110
105
100

Minimum of previous
cycles

95
90
2020–21 cycle
through 2021:Q4

85
80

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.2.6, “Real GDP by Major Type of Product.”

Figure 2-11. Personal Saving during the Pandemic Relative to Its Average
Pace, 2008–21
During 2020–21, excess saving
implies an accumulation of $1.7
trillion excess savings.

Dollars (trillion, annual rate)
6
5

Actual quarterly saving

4
3

2021:Q1

2021:Q4

2020:Q1

2019:Q1

2018:Q1

2017:Q1

2016:Q1

2014:Q1

2013:Q1

Quarterly saving deviation relative to
average
2012:Q1

2011:Q1

2010:Q1

0

2008:Q1

1

2009:Q1

Quarterly saving at
last business cycle’s
average saving rate

2015:Q1

2

Sources: Data from Haver Analytics; CEA calculations.
Note: Quarterly saving at last cycle’s average saving rate is defined as disposable personal income times the average saving rate from
2008 to 2019 (6.8 percent).

Because real consumer spending data are available monthly, the table shows
real growth rates during the 22 months from the monthly (and prepandemic)
business-cycle peak in February 2020 through December 2021. Overall, real
consumer spending grew 1.6 percent at an annual rate during the 22-month

58 |

Chapter 2

Table 2-3. Consumer Spending Growth since the Beginning of the Pandemic
Type of Good or Service

February 2020 to December 2021
% change, Annual Rate
(1)
1.6

Contributiona
(2)
1.6

Goods
Motor vehicles and parts
Durables, ex. motor vehicles
Nondurables

6.5
2.2
10.1
5.8

2.10
0.09
0.76
1.22

Services
Housing and utilities
Health care
Transportation
Recreation

–0.5
1.2
–0.7
–5.3

–0.36
0.21
–0.11
–0.17
–0.27

Total

–7.2

Food services
Accommodations
Financial

0.9
–4.4
3.0
–2.0
–3.2

Otherb
NPISHc

0.06
–0.05
0.24
–0.16

–0.10

Source: Bureau of Economic Analysis, NIPA tables 2.3.5U and 2.3.6U.
a Contribution to the annual rate of growth of real consumer spending. These contributions
may not precisely sum to totals and subtotals because of approximations to the Fisher index
formulas used in the National Income and Product Accounts.
b Other services include communication, education, professional and other services;
personal pare and clothing services; social services and religious activities; household
maintenance; and net foreign travel.
c NPISH = net consumption of nonprofit institutions serving households.

pandemic period, which was slightly lower than the roughly 2 percent
annual rate of trend GDP growth.
Real consumer spending on goods grew at a 6.5 percent annual rate
during those 22 months, far in excess of the pace at which consumer spending growth could be maintained in the long run. This rapid growth came
even as motor vehicle sales were constrained by a worldwide chip shortage,
holding the growth rate down to 2.2 percent. Excluding motor vehicles,
consumer durables spending grew at a rapid 10.1 percent annual rate, while
nondurables grew at a 5.8 percent rate.
In contrast, consumer services spending fell at a 0.5 percent annual
rate during those 22 months, as shown in table 2-3. The consumer-spending
categories with notable declines include health care (–0.7 percent), transportation (–5.3 percent), recreation (–7.2 percent), and accommodation services
(–4.4 percent). Declines were also substantial among some of the categories
within the “other services category” (not shown in table 2-3), including
educational services (–2.4 percent), professional services (–1.8 percent), and
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| 59

Table 2-4. Fixed Investment Components, 2019:Q4–2021:Q4
Investment Component
Nonresidential
Nonresidential equipment
Information processing equipment
Industrial equipment
Transportation equipment
Other equipment
Nonresidential structures
Office
Health care
Multimerchandise shopping
Food and beverage establishments
Warehouses
Other commercial buildings
Manufacturing structures
Power/communication facilities
Mining exploration/shafts/wells
Other nonres. structures
Intellectual property
Software
Research and development
Entertainment/literary/artistic originals
Residential

Annual Growth Rate
1.3
3.0
12.8
7.7
–15.7
2.3
–11.9
–11.9
–6.3
–20.4
–19.5
3.2
–14.0
–7.3
–16.1
–9.0
–16.5
7.1
10.2
5.4
2.0
6.7

Source: Bureau of Economic Analysis, NIPA tables 1.5.6, 5.4.6U, and 5.5.6U.

personal care and clothing services (–16.0 percent). The spending categories
that remained below their prepandemic levels were those that require faceto-face interaction.
Income exceeded what consumers spent during 2020–21, with the
excess partly due (on the spending side) to the constrained services sector
and partly due (on the income side) to income support programs under the
CARES Act and the American Rescue Plan Act. Figure 2-11 shows actual
quarterly saving (in trillions of dollars) relative to the saving that would
have taken place if the saving rate had remained flat at its average during
the 2008–19 business cycle (6.8 percent). The blue shading in this figure
represents the deviation from average quarterly saving. By the end of 2021,
the stock of “excess” savings during the pandemic interval accumulated to
$2.7 trillion, or enough to sustain household outlays for 1.9 months.

60 |

Chapter 2

Figure 2-12. Business Fixed Investment: Cyclical Comparison
Index = 100 at business-cycle peak
120
Business-cycle
peak

115
110

2020–21 cycle through
2021:Q4

105
Maximum of previous cycles
100
95
90
85
80

Minimum of previous
cycles
-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.1.6.

Business and Residential Investment
Real business fixed (nonresidential) investment edged up at a 1.3 percent
annual rate from 2019:Q4 to 2021:Q4 (table 2-4). In comparison with the
previous 11 post–World War II business cycles, overall business investment
was stronger than the average cycle, but still within the previous range, as
shown by figure 2-12.

Investment in Nonresidential Structures
Investment in nonresidential structures—which made up 3.1 percent of
GDP in 2019—fell at an 11.9 percent annual rate (table 2-4) during the two
years 2020–21 and was tracking near the lower end of preceding cycles at
the end of 2021, as shown in figure 2-13. Sizable declines occurred in the
construction of office buildings (possibly reflecting the transition to remote
work). Construction also fell in those sectors that had been hurt by the
general reluctance to engage in face-to-face transactions: health care facilities, shopping centers, and food and beverage establishments. Construction
of manufacturing, power, and mining structures also fell. Most of these
declines occurred during the four quarters of 2020, but overall nonresidential structures investment continued to decline slowly during 2021, with the
major exception of petroleum and natural gas well drilling, which grew 40
percent, recovering from much of its year-earlier decline.

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Figure 2-13. Structures Investment: Cyclical Comparison
Index = 100 at business-cycle peak
120
Business-cycle
peak

115
110
105

Maximum of previous cycles

100
95
90
85
80

2020–21 cycle
through 2021:Q4

75
70

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

Minimum of previous
cycles
3

4

5

6

7

8

5

6

7

8

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.1.6.

Figure 2-14. Equipment Investment: Cyclical Comparison
Index = 100 at business-cycle peak
120
Business-cycle
peak

115
110

2020–21 cycle
through 2021:Q4

Maximum of previous cycles

105
100
95
90
85
80
75
70

Minimum of previous
cycles
-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.1.6.

Investment in Equipment
In contrast to structures investment, investment in equipment (which made
up 5.8 percent of GDP in 2019) grew at a 3.0 percent annual rate during the
eight quarters through 2021:Q4, which was as fast as during any preceding
business cycle (figure 2-14). During these two years, double-digit growth
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Chapter 2

Figure 2-15. Intellectual Property Investment: Cyclical Comparison
Index = 100 at business-cycle peak
120

2020–21 cycle
through 2021:Q4

Business-cycle
peak

110

Maximum of previous cycles

100
Minimum of previous
cycles

90

80

70

60

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.1.6.

occurred in information-processing equipment, while industrial equipment
investment grew at a 7.7 percent annual rate. In contrast, investment in
transportation equipment fell sharply, likely due to the chip shortage that
plagued motor vehicle manufacturing during 2021.

Intellectual Property
Investment in intellectual property, which made up 6.3 percent of GDP in
2019, grew 7.1 percent from 2019:Q4 to 2021:Q4, in the top half of the
range experienced during the preceding cycles (figure 2-15). The subsectors
of intellectual property diverged substantially: software investment skyrocketed, at a 10.2 percent annual rate; research and development rose at a 5.4
percent rate; and the category “entertainment, literary, and artistic originals”
recovered from its early losses, and edged up slightly.

Residential Investment
Residential investment, which made up 3.8 percent of GDP in 2019, grew at
a 6.7 percent annual rate from 2019:Q4 to 2021:Q4, which places it in the
top half of the historical record of this volatile sector (figure 2-16). Growth
was strong during the four quarters of 2020 (15.9 percent), but starts and
construction of single-family and multifamily homes appear to have been
restrained by supply constraints in 2021, which limited the pace of growth
in those construction components to more moderate gains. Manufactured
homes grew in both years, while dormitory construction fell sharply in both
years.
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Figure 2-16. Residential Investment: Cyclical Comparison
Index = 100 at business-cycle peak
150
Maximum of previous cycles

140

Business-cycle
peak

130
2020–21 cycle through
2021:Q4

120
110
100
90
80
70
60

Minimum of previous
cycles
-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.1.6.

Figure 2-17. State and Local Purchases: Cyclical Comparison
Index = 100 at business-cycle peak
140
Business-cycle
peak

130
120
Maximum of previous cycles
110

2020–21 cycle
through 2021:Q4

100

Minimum of previous
cycles

90
80
70

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.1.6.

State and Local Purchases
State and local purchases (in real dollars) increased only slightly (0.4 percent, at an annual rate) from 2019:Q4 to 2021:Q4 (figure 2-17), about 3 percentage points per year less than the average historical recovery experience

64 |

Chapter 2

Figure 2-18. Exports: Cyclical Comparison
Index = 100 at business-cycle peak
150
Business-cycle
peak

140
130

Maximum of previous cycles

120
110
100

2020–21 cycle
through 2021:Q4

90

Minimum of previous
cycles

80
70
60

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.1.6.

but only a bit less than during the preceding eight quarters through 2019:Q4.
Because tax collections increased faster than nominal GDP and because of
Federal grants-in-aid authorized during the pandemic-era spending programs
listed in table 2-1, the increase in overall State and local receipts exceeded
the increase in spending (including not only purchases, but also transfers and
subsidies). As a result, the overall State and local fiscal position was positive
(with net lending at $3.1 billion) in 2020 and likely will be positive again in
2021 (based on the first three quarters).23 These would be the first positive
annual fiscal positions for the State and local sector since 1946. These positive fiscal positions are consistent with the suggestion that some of the ARP
funds were not yet fully dispersed as of 2021:Q3.

Exports and Imports
Exports have fallen at a 3 percent annual rate during the eight pandemic
quarters, which places them at the lower end of the post–World War II
business-cycle experience (figure 2-18). As discussed in chapter 3 of this
Report, U.S. exports faced weak demand from abroad due to the severity
of the economic effects of the pandemic and slower recovery in major U.S.
trading partners as well as surging domestic demand for exportable goods.
Imports grew solidly in the upper half of the business-cycle record
measured relative to the average business-cycle experience or the median
At the time of this chapter’s finalization, NIPA data on State and local revenues went through
2021:Q3.
23

The Year in Review and the Years Ahead

| 65

Figure 2-19. Imports: Cyclical Comparison
Index = 100 at business-cycle peak
150
Business-cycle
peak

140
130
120

2020–21 cycle through
2021:Q4

Maximum of previous cycles

110
100
90
80

Minimum of previous
cycles

70
60

-8

-7

-6

-5

-4

-3

-2

-1

0

1

2

3

4

5

6

7

8

Quarters from business-cycle peak →
Source: BEA, NIPA table 1.1.6.

one (figure 2-19). The recovery in output was driven by an exceptionally
strong domestic demand for goods; in some sectors, imports contributed to
meeting that demand when supply constraints meant that domestic production could not. Because faster domestic growth pulls in more imports, the
strength of imports relative to exports reflected faster growth in the United
States compared with our trading partners. It also meant that the net exports
were increasingly negative and subtracted from real GDP growth.

Global Supply Chain Disruptions
The COVID-19 pandemic threw global supply chains into disarray. Many
of the problems that surfaced had their roots in growing U.S. reliance on
products assembled globally and transported, as discussed in chapter 6 on
supply chains. Delays for ships waiting to offload at the Port of Los Angeles
lengthened though the second half of 2021. Shipping costs increased
substantially in the supply chain, from trucking to air cargo, as shown in
figures 2-20, 2-21, and 2-22. Supply chain bottlenecks were evident for
motor vehicles, because a shortage of computer chips kept automakers from
increasing production to meet demand.
Data also suggest that shortages of other inputs held back business
activity in other sectors in 2021. For example, homebuilders surveyed by the
National Association of Homebuilders reported shortages of key materials

66 |

Chapter 2

Figure 2-20. Forty-Foot Container Shipping Benchmark Rates by Route, 2020–21
Route

Rate
$18,000

Composite
Shanghai to LA

$16,000

Shanghai to Rotterdam

$14,000

Hong Kong to LA

$12,000

Shanghai to NYC

$10,000

Rotterdam to Shanghai
LA to Shanghai

$8,000

Rotterdam to NYC
Shanghai to Genoa

$6,000
$4,000

Dec 2021

Nov 2021

Oct 2021

Sep 2021

Jul 2021

Aug 2021

Jun 2021

Apr 2021

May 2021

Mar 2021

Jan 2021

Feb 2021

Dec 2020

Oct 2020

Nov 2020

Sep 2020

Aug 2020

Jul 2020

Jun 2020

Apr 2020

May 2020

Mar 2020

Jan 2020

$0

Feb 2020

$2,000

Source: Data from Bloomberg.
Note: “Rate” refers to the benchmark rate for freight for a given shipping lane for a forty-foot container.

Figure 2-21. Cass Trucking Index
Index level: Feb. 2020 = 100
120
115
110
105
100

Dec 2021

Nov 2021

Oct 2021

Sep 2021

Aug 2021

Jul 2021

Jun 2021

May 2021

Apr 2021

Mar 2021

Feb 2021

Jan 2021

Dec 2020

Nov 2020

Oct 2020

Sep 2020

Aug 2020

Jul 2020

Jun 2020

May 2020

Apr 2020

Mar 2020

Jan 2020

90

Feb 2020

95

Source: Data from Bloomberg.

such as framing lumber, wallboard, and roofing.24 Homebuilders responded
to these shortages in part by delaying new construction, which was reflected
in the slowdown of permanent-site residential investment to 4.0 percent during the four quarters of 2021 from its 16.0 percent increase in 2020.

24

NAHB (2021).

The Year in Review and the Years Ahead

| 67

Figure 2-22. Air Cargo Rates by Route
Dollars per kilogram
18
16

Shanghai to North
America

14
12

Hong Kong to North
America

10
8
6
4

Dec 2021

Nov 2021

Oct 2021

Sep 2021

Aug 2021

Jul 2021

Jun 2021

Apr 2021

Mar 2021

Jan 2021

Feb 2021

Dec 2020

Oct 2020

Nov 2020

Sep 2020

Aug 2020

Jul 2020

Jun 2020

May 2020

Apr 2020

Feb 2020

Mar 2020

Jan 2020

0

May 2021

Frankfurt to North
America

2

Source: Data from Bloomberg.

Figure 2-23. Inventory-to-Sales Ratio (Private Inventories to Final Sales),
1997–21
Months’ supply of inventory
3.1
3.0
2.9
2.8
2.7
2.6
2.5
2.4

2021:Q4

2.3
1997

2001

2005

2009

2013

2017

2021

Sources: Bureau of Economic Analysis (NIPA table 5.8.6); National Bureau of Economic Research.

Inventory Investment
These supply chain problems, together with increasing consumer demand
for goods, led to declines in the stock of inventories during the first three
quarters of 2021, before a partial rebuilding in the fourth quarter. The stock
of inventories began 2021 at a low level, as stocks had been liquidated at a
rapid rate during the first two quarters of the pandemic in 2020. With the
rebound in real final sales, the inventory-to-sales ratio (real inventories to
real final sales) fell from the 2019:Q4 ratio of 2.56 to 2.41 months’ supply
68 |

Chapter 2

at the end of 2021:Q3, and the lowest on record, as shown in figure 2-23.
Rebuilding these inventories beginning in 2021:Q4—and shifting from
negative inventory investment in 2021:Q3—contributed 4.9 percentage
points to the annual rate of real GDP growth in 2021:Q4. The accumulation of inventories in 2021:Q4 rebuilt roughly one-third of stocks that were
drawn down during the preceding seven pandemic quarters.

Consumer Price Inflation
The concentrated demand for goods and the limited supply of these goods,
along with supply chain delays, elevated consumer price inflation. Headline
inflation—according to the Consumer Price Index (CPI)—rose to 7.0
percent during the 12 months of 2021, up from the prepandemic rate of 2.3
percent during the 12 months of 2019 (figure 2-24). Some of the increase
in inflation occurred in the volatile food and energy components; excluding
food and energy, however, the core CPI also rose substantially during 2021,
to 5.5 percent, from its prepandemic rate of 2.3 percent.
Within core inflation, most of the increase—since the pandemic
began—has been in core goods, where inflation increased to 10.7 percent
during the 12 months of 2021 from its 2019 prepandemic pace of 0.1 percent
(figure 2-25). In contrast, core services increased only to 3.7 percent during
the 12 months of 2021, up from a 3.0 percent rate.
Supply chain disruptions also had a material impact on consumer
goods prices, notably in the motor vehicles sector. Prices of motor vehicles
(new, used, leased, and rental) increased 21 percent during the 12 months
of 2021, and this increase accounted for 36 percent of 5.5 percent core
Figure 2-24. Consumer Price Index (CPI) Inflation, 2007–21
Twelve-month inflation rate
8%

6%

4%

Dec
Headline CPI
Core CPI

2%

0%

–2%

–4%

Sources: Bureau of Labor Statistics; National Bureau of Economic Research.

The Year in Review and the Years Ahead

| 69

Figure 2-25. Components of Core CPI Inflation, Commodities versus Services, 2007–21
Twelve-month change
12%

Core CPI goods

10%
8%
6%

Core CPI services

4%
2%

Dec

0%
–2%

Sources: Bureau of Labor Statistics; CEA calculations.
Note: CPI = Consumer Price Index.

CPI inflation in 2021, and also for 40 percent of its year-to-year increase.
That the rise in inflation was concentrated in goods suggests that the goods
economy was operating close to its potential output in 2021.

Inflation Expectations
Expectations about future inflation are important in macroeconomic theory
because they potentially create “self-fulfilling” outcomes; that is, when
households and firms believe inflation will be high in the future, they may
either ask for higher wages or raise their prices today.
Inflation expectations increased during 2021, but the magnitude of the
increase differed according to whose expectations were being followed and
the horizon over which expectations were surveyed. The increase in shortterm inflation expectations was substantial for consumers (2.2 percentage
points, to 4.8 percent, measured at the median, see row 1 of table 2-5), but
more moderate (0.6 percentage point) for professional forecasters (row 2).
To understand how inflation expectations for consumers and professional
forecasters are moving after the first year, the first year’s effect must be
extracted from the longer-term average expectation. Measured this way, the
increase in implicit long-term inflation expectations was relatively small in
2021, whether measured among consumers (row 5), professional forecasters
(row 6), or agents in the market for Treasury Inflation Projected Securities
(row 7). The relatively small increase in long-term inflation expectations—
even for consumers—is roughly consistent with the idea that agents viewed
the near-term increase in inflation as not permanent. The end-of-2021 expectations for CPI inflation were only slightly above what would be consistent
70 |

Chapter 2

Table 2-5. Consumer Price Index Inflation Expectations
Expectation

Term

1
2

Short term (1-year
ahead)
Consumers (median)
SPF

1 year
1 year

3
4

5
6
7

2019
Avg.

Date of Survey
Nov.–Dec. 2021

Increase

2.6
2.0

4.8
2.6

2.2
0.6

Long term (5–10 years, including year 1)
Consumers (median)
Next 5 to 10 years
SPF
Next 10 years

2.4
2.2

3.1
2.6

0.7
0.4

Long term (4–9 years) excluding year 1
Consumersa
4–9 years after year 1

2.4

2.8

0.4

2.2
1.8

2.5
2.4

0.3
0.6

SPFb
TIPS 5/5

9 years after year 1
5 years, 5 years forward

Sources: University of Michigan Surveys of Consumers;
Philadelphia Federal Reserve Bank; Survey of Professional Forecasters;
Treasury Inflation-Protected Securities (TIPS) are from Haver Analytics.
aCalculated from rows 1 and 3.
bCalculated from rows 2 and 4; SPF = Survey of Professional Forecasters.

with the Federal Reserve’s 2 percent target for a similar price index (the
Price Index for Personal Consumption Expenditures), which generally is
below CPI inflation by 0.3 percentage point a year.

The Labor Market
The labor market story in 2021 was complex and, at times, seemingly
contradictory. There were both historic successes and continuing challenges.
Some of the data suggest extraordinary tightness in the labor market, while
others indicate considerable remaining slack.
The U.S. economy added more than 6 million jobs on net over 2021;
yet the labor force still remained several million below the precrisis trend.
The labor force participation rate (LFPR) for prime-age (25–54 years) workers rose at its fastest December-to-December pace since 1979, but the LFPR
for workers 55 and older was little changed (though the reported 55+ LFPR
rate increased in January 2022, due to statistical adjustments by the Bureau
of Labor Statistics, BLS). Some metrics signaled that the labor market was
tighter in 2021 than before the pandemic, such as high rates of job openings,
quits, and wage growth. Other metrics were murkier: the unemployment
rate fell markedly in 2021 but was still somewhat elevated relative to prepandemic levels, and the rate of prime-age employment and the LFPR were

The Year in Review and the Years Ahead

| 71

still lower than in February 2020, though they were rising briskly by the end
of 2021.
With the exception of some prior structural trends that continued
throughout the year—most notably, the aging of the U.S. population—
COVID-19 was the dominant driver in the labor market. Whether in the
form of worker concern, weak demand for certain services, school closures,
workers absent or out of the labor force due to illness, long COVID, or other
mechanisms such as limited child care options, this virus was ultimately
responsible for the bulk of the labor force weakness starting in February
2020.

Ways in Which the Labor Market Appeared Tight in 2021
To illustrate how bifurcated the labor market was in 2021, imagine a simple
(and more than a little far-fetched) thought experiment. Suppose a labor
economist were frozen in 2019, thawed out in early 2022, and then immediately asked to assess the state of the labor market based solely on a handful
of economic charts laid out before her. No doubt, after catching up with the
events of the intervening years, she would be shocked at the magnitude of
the declines that happened in early 2020. But as she then focused on the
state of the economy in late 2021 and early 2022, what might she conclude?
At the very least, she would notice several measures suggesting a very tight
labor market—that is, one where labor demand was high relative to labor
supply.
Job openings and quits. Two such metrics come from the Job Openings
and Labor Turnover Survey (JOLTS): job openings and quits. In December
2021, there were 11.4 million open vacancies in the United States, the highest in the history of the data going back to late 2000, and about 50 percent
more than the prepandemic record of 7.6 million openings set in November
2018.25
Economists generally think of job openings as a measure of unmet
labor demand from firms; higher openings often suggest higher demand
among employers, although equilibrium job openings can shift over time
due to a number of different factors, such as the marginal cost of posting a
vacancy and changes in workers’ bargaining power.26
Even relative to the number of workers actively searching for a job,
vacancies were elevated. On average, in December 2021, there were 1.81
job openings per unemployed person, the highest in the history of the JOLTS
data and about 48 percent higher than just before the pandemic, in February
2020 (figure 2-26). A more permanent concept of unemployment can be seen
by stripping out temporarily furloughed workers from the denominator—in
25
26

BLS (2022).
On the latter, see, e.g., Figura and Ratner (2015).

72 |

Chapter 2

Figure 2-26. Job Openings per Unemployed Worker, 2000–2021
Ratio of job openings per unemployed worker

2.0

Openings per
unemployed

1.8
1.6
1.4
1.2

Openings per
permanently
uinemployed

1.0
0.8
0.6
0.4

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2000

0.0

2001

0.2

Sources: BLS; CEA calculations.
Note: The permanently unemployed are defined as the unemployed less the temporarily unemployed plus nonparticipants
who want a job.

principle, a company is not supposed to count a furloughed worker’s job as
a job opening in JOLTS—and by adding workers who are out of the labor
force but saying they want a job. This shifts the ratio to 1.02 openings per
permanent unemployed worker, still a record, and 45 percent above where
it was in February 2020.
In December 2021, the number of voluntary quits stood at 4.4 million,
about 3 percent of employment and second only to November 2021 as the
highest since JOLTS data began to be gathered in late 2000. Economists
generally view a voluntary quit as a sign of labor market confidence, given
other Census data suggest that people who voluntarily quit their jobs typically do so with another job already lined up, or are confident they can find
another one quickly.27
Wages. An increase in nominal wage growth can be a sign that labor
demand is outpacing labor supply. Several different wage measures accelerated in 2021. Average hourly earnings, a measure of the average wage of all
nonfarm payroll workers in the private sector, rose by 4.9 percent over the 12
months ending in December 2021, in nominal terms (i.e., without adjusting
for inflation).28 That is the largest nominal wage growth in any Decemberto-December period since data on all private sector workers began being
collected in 2006. Excluding managers and just looking at production and
nonsupervisory workers—who constitute about 80 percent of all workers,
and whose wage data stretch back to 1964—wages grew by 6.2 percent over
For analyses of direct job-to-job transitions, see U.S. Bureau of the Census (2022b); and Fujita,
Moscarini, and Postel-Vinay (2021).
28
BLS (2022).
27

The Year in Review and the Years Ahead

| 73

Figure 2-27. Median Hourly Wage Growth by Level of Education, 1998–21
7

Percent

6
5
4
< HS

3

HS
only

2

AA/
AS

1

BA/
BS+

0
1998

2003

2008

2013

2018

Sources: CPS; CEA calculations.
Note: Values are Kalman smoothed monthly values. HS = high school; AA/AS = associate degrees; BA/BS = bachelor’s degrees.

the same 12 months.29 Before the pandemic, one needs to look all the way
back to 1981 to find a single year when wage growth was so high. These
and other data suggest that the pandemic has driven particularly strong wage
growth for lower-wage workers, given that production and nonsupervisory
workers typically earn less than managers. As explained below, however,
overall nominal wage growth has not kept up with inflation.
There are three concerns when examining growth in average nominal wages: composition effects, distributional differences, and inflation.
Composition effects arise in average wage measures when shifts in who has
a job skew the average wage. For example, in the immediate wake of the
pandemic—the sharpest macroeconomic contraction in almost a century—
average hourly earnings increased. But this increase was not a signal of
labor market tightness or economic health. It occurred because pandemicrelated layoffs disproportionately hit lower-wage workers. As a result, the
remaining workforce was distorted toward higher-wage workers, so the
resulting average wage rose mechanically.
The Employment Cost Index (ECI), which is released by the BLS,
controls for many such compositional effects.30 It shows that nominal private
sector wages rose 5 percent from December 2020 to December 2021, a bit
higher than implied by average hourly earnings in that same period. This
represents the largest nominal ECI growth since 1984.
BLS (2022).
The ECI measures changes in hourly compensation, fixing the industry and occupational
composition of its sample to a base period to keep compositional shifts from affecting its results.
29
30

74 |

Chapter 2

Figure 2-28. Median Hourly Wage Growth by Sex, 1998–21
Percent

6

5

4

3

Men

2

Women

1

1998

2003

2008

2013

2018

Sources: CPS; CEA calculations.
Note: Values are Kalman smoothed monthly values.

Average wages can also hide important distributional differences by,
for example, education, race, and age. The average hourly earnings and ECI
data do not provide demographic breakdowns, but the Current Population
Survey (CPS) provides monthly data that can shed some light on how different groups saw their wages evolve.
The CPS suggests that year-on-year wage growth was not even across
different groups during the pandemic, and that some groups that are typically on the margins of the labor force saw stronger wage growth. Notably,
low-wage workers experienced some of the fastest median wage growth
during the pandemic (figure 2-32), and wage growth was been faster among
workers with only a high school education or less than it was for those with
college degrees (figure 2-27).31 Women saw faster growth during the pandemic than men, especially later on in 2021 (figure 2-28). Young workers
under age 25 typically saw stronger wage growth than older workers; this
The median wage growth is calculated in the CPS by comparing the same workers employed 12
months apart and noting the 50th-percentile change in hourly wages over the year for each worker.
This method partially controls for compositional effects, since it is calculated from of identical
workers 12 months apart. Because the sample of workers in the CPS changes each month, however,
it is not a traditional panel of workers, which would better control for compositional effects over
time.
31

The Year in Review and the Years Ahead

| 75

Figure 2-29. Median Hourly Wage Growth for Workers Who Switch
Industry/Occupation, 2004–21
Percent
6

5

4

3
Same industry/
occupation

2

Switched industry/
occupation

1
2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

Sources: CPS; CEA calculations.
Note: Values are Kalman smoothed monthly values.

was true even before the pandemic, due, in part, to the mechanical percentage effect of lower starting wages (figure 2-30). But during the pandemic,
youth wage growth further widened its lead over other age groups. Finally,
wage growth has accelerated across different race and ethnicities in recent
months (figure 2-31).
There is also some evidence that labor market churn—workers leaving
and entering jobs—was associated with stronger wage growth. While it is
not possible in the CPS data to fully identify workers who voluntarily quit
their jobs, it is feasible to look at workers who stayed employed but switched
industries or occupations—which captures many voluntary quits as well as
some workers who nonvoluntarily left their jobs and found new ones in different lines of work (figure 2-29).
Adjusting for inflation is the final factor to consider. While nominal
hourly wage growth increased in 2021, so did inflation. Real (inflationadjusted) average hourly earnings continued growing earlier in the pandemic
but fell on a year-on-year basis in the aggregate toward the end of 2021.32
There are two important other trends of note. First, in some specific
industries, like leisure and hospitality, nominal wage growth outpaced
overall consumer inflation. The second is that even though average hourly
wage growth fell short of inflation in 2021, average real income growth per
32

BLS (2022).

76 |

Chapter 2

Figure 2-30. Median Hourly Wage Growth by Age, 1998–21
Percent
12

9

6
Age (years)
16–24
25–39

3

40–54
55+

0
1998

2003

2008

2013

2018

Sources: CPS; CEA calculations.
Note: Values are Kalman smoothed monthly values.

Figure 2-31. Median Hourly Wage Growth by Race/Ethnicity, 1998–21
Percent
7

6

5

4

3
White non-Hispanic

2

Black non-Hispanic

1

Hispanic
Other race/ethnicity

0
1998

2003

2008

2013

2018

Sources: CPS; CEA calculations.
Note: Values are Kalman smoothed monthly values.

The Year in Review and the Years Ahead

| 77

Figure 2-32. Median Hourly Wage Growth by Wage Quantile, 1998–21
Percent
8
7
6
5
4
3
2

Lowest
Second

1

Third
Highest

0

1998

2003

2008

2013

2018

Sources: CPS; CEA calculations.
Note: Values are Kalman smoothed monthly values.

Figure 2-33. Real Market Income Growth, 2020–21
Percent change in average inflation-adjusted market income
per person since the fourth quarter of 2020
12
10

Bottom 50%

Next 40%

Top 10%

Total

8
6
4
2
0
2020:Q4

2021:Q1

2021:Q2

2021:Q3

2021:Q4

Source: Preliminary estimates by Blanchet, Saez, and Zucman (2022), via realtimeinequality.org.

adult across all sources was still often positive for the year. Preliminary data
from a recent analysis by Blanchet, Saez, and Zucman (2022) suggest that
average real market incomes—incomes from labor and capital before the
effects of taxes and government benefits—rose by 5.6 percent during 2021
overall, and by almost 11 percent for the bottom half of households (figure
2-33). Real disposable income—which includes the effects of taxes and
government benefits, including the recent fiscal response—was 5 percent

78 |

Chapter 2

Figure 2-34. Real Disposable Income Growth, 2019–21
Percent change in average inflation-adjusted disposable income
per person since the fourth quarter of 2019
45
Bottom 50%
Next 40%
40
Top 10%
Total
35
30
25
20
15
10
5
0
–5
–10
2019:Q4

2020:Q1

2020:Q2

2020:Q3

2020:Q4

2021:Q1

2021:Q2

2021:Q3

2021:Q4

Source: Preliminary estimates by Blanchet, Saez, and Zucman (2022), via realtimeinequality.org.

above prepandemic levels at the end of 2021, and 11 percent above for the
bottom half of adults (figure 2-34).

Ways in Which the Labor Market Appeared Loose in 2021
Our unfrozen economist would see much to suggest a tight labor market in
2021. But she would also quickly see several important measures suggesting
a meaningful amount of room for further growth.
Employment. First, while the economy added 6.7 million jobs between
December 2020 and December 2021, employment was still 3.3 million
below its prepandemic level (figure 2-35). It is even further away when measured against the prepandemic trend, which tries to estimate the pace of job
growth that would have prevailed without the pandemic. In its final prepandemic economic projections from January 2020, the Congressional Budget
Office (CBO) assumed that payroll employment would grow at an average
pace of about 97,000 a month during 2020 and 2021;33 this implies that
employment remained about 5.4 million below the trend at the end of 2021.
Even if one adjusts the CBO’s prepandemic projections for the mortality and
lower immigration rates seen during the pandemic, its adjusted January 2020
path grows by 53,000 a month, suggesting that current employment is about
4.5 million below the estimated trend.
The pain of the pandemic did not spread evenly across industries (figure 2-36). The leisure and hospitality subsector, for example, lost nearly half
its jobs between February and April 2020; in December 2021, its employment was 11 percent lower than before the pandemic. However, information, professional and business services, and transportation and warehousing
had fully recovered beyond their prepandemic employment levels by the end
of 2021.
33

CBO (2020).

The Year in Review and the Years Ahead

| 79

Figure 2-35. Payroll Employment, 2020–22
Millions
160
155
150
145
140
135

Actual
CBO Jan 2020 projections

130

Jan 2022

Dec 2021

Nov 2021

Oct 2021

Sep 2021

Jul 2021

Aug 2021

Jun 2021

May 2021

Apr 2021

Mar 2021

Jan 2021

Feb 2021

Dec 2020

Oct 2020

Nov 2020

Sep 2020

Jul 2020

Aug 2020

Jun 2020

Apr 2020

May 2020

Feb 2020

Mar 2020

CBO Jan 2020 (adjusted for actual population)
125

Sources: BLS; CBO.

Figure 2-36. Employment Changes by Industry Sector, 2020 and 2021
Net change, in millions, seasonally adjusted
9

Feb.–Apr. 2020 job losses
Apr. 2020–Dec. 2021 job gains

8

(8.2)

7

6.3

6
5
4

(3.2)

3.5

3

(2.3)

2
1
0
–1

(1.1) 1.0

(1.4)

(2.8)
2.2
(1.4)

1.1
(0.3) 0.3

(0.1) 0.0

Mining and
logging

2.7

Construction Manufacturing

Trade,
transportation,
and utilities

Information

1.1

(0.3) 0.3

Financial
activities

(1.0)
0.2

Professional
and business
services

Education
and health
services

Leisure
and
hospitality

Other services

Government

Sources: Bureau of Labor Statistics; CEA calculations.
Note: Parentheses denote negative values.

Labor Supply and Labor Force Participation
When the U.S. economy “shut down” due to the COVID-19 pandemic in
early 2020, not only did employment fall sharply and unemployment rise
quickly, but the Nation’s labor force—the number of people either working
or looking for work—also declined sharply. As figure 2-37 reveals, the labor
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Figure 2-37. The Labor Force Participation Rate, 2020–22
Percentage of population 16+ years of age
64.0

Feb. 2020 level

63.5

Feb. 2020 LFPR,
adjusted for sex
and age

63.0
62.5

Actual

62.0
61.5
61.0

Jan 2022

Dec 2021

Oct 2021

Nov 2021

Sep 2021

Jul 2021

Aug 2021

Jun 2021

Apr 2021

May 2021

Mar 2021

Jan 2021

Feb 2021

Dec 2020

Oct 2020

Nov 2020

Sep 2020

Jul 2020

Aug 2020

Jun 2020

Apr 2020

May 2020

Feb 2020

60.0

Mar 2020

60.5

Sources: BLS; CEA calculations.
Note: LFPR = labor force participation rate.

force as a share of the population age 16 and older—called the labor force
participation rate or LFPR, as mentioned above—fell by an unprecedented
3.2 percentage points in just two months. Since then, the LFPR has partially
recovered, and it rose by 0.4 percentage point over the course of 2021 alone.
In January 2022, the LFPR rose an additional 0.3 percentage point due to
new population controls from the BLS, noted earlier in this chapter. Still,
as of January 2022, it remains 1.1 percentage points below prepandemic
levels.34
It is important to note that even before the pandemic, the aging U.S.
labor force was putting downward pressure on the LFPR. Because people
of different ages have different degrees of attachment to the job market, the
age structure of the population is one determinant of the LFPR. In the years
running up to the pandemic, the aging of the large baby boom cohort into
retirement was cumulatively reducing the LFPR by about 25–30 basis points
(i.e., hundredths of a percentage point) each year.35 Many other determinants
were (and still are) also in play, including the strength of labor demand,
immigration trends, education levels (more highly educated persons tend to
have higher LFPRs), persistent labor market barriers to entry, inadequate
care options, and racial and gender discrimination.

Data in this section run through January 2022 rather than December 2021 due to the magnitude of
the adjustment from the Census Bureau’s 2022 population controls.
35
From CEA calculations.
34

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Figure 2-38. U.S. Prime-Age (25–54) LFPR, 2020–22
Percentage of population age 25–54

83.5

Feb 2020 Level

83.0
82.5
82.0
81.5
81.0

Actual

80.5

Jan 2022

Dec 2021

Oct 2021

Nov 2021

Sep 2021

Jul 2021

Aug 2021

Jun 2021

Apr 2021

May 2021

Mar 2021

Jan 2021

Feb 2021

Dec 2020

Oct 2020

Nov 2020

Sep 2020

Jul 2020

Aug 2020

Jun 2020

Apr 2020

May 2020

Feb 2020

Mar 2020

80.0
79.5

Sources: BLS; CEA calculations.
Note: LFPR = labor force participation rate.

Figure 2-39. Prime-Age LFPRs during Past Recessions and Recoveries
Index: 100 = cycle peak

101.0
1990-Jul

100.5
100.0

2007-Dec

99.5
2001-Mar

99.0
98.5

2020-Feb

98.0
97.5
97.0
96.5
96.0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Source: Data from Haver Analytics.
Note: The date denotes a month out from the monthly business-cycle peak (index level = 100).

But because of the exit of a large number of older workers (who are
not replaced by the same number of younger workers), it is unlikely that
the overall LFPR will revert back to its prepandemic peak (63.4 percent)
in the near future, even as temporary factors abate. (See the blue dotted
line showing adjustment for sex and age line in figure 2-37.) To put this in
perspective, if every age group returned to its February 2020 rate of participation, the overall LFPR would have been 62.9 percent in December 2021
rather than the 63.4 percent prepandemic rate, due to the older profile of the
American population today.

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Figure 2-40. Change in U.S. Rate of Nonparticipation in the Labor
Force, February 2020 – January 2022, by Reason for Nonparticipation
Percentage points of population, seasonally adjusted
4.0
3.5

Total rise in
nonparticiation

3.0

Demographic
age/sex shifts

2.5
2.0

“Something else”

1.0

Home/family
Care

0.5

Excess retirements
Shadow labor force

0.0

Disability

-0.5

School
enrollment

Dec 2021

Oct 2021

Aug 2021

Jun 2021

Apr 2021

Feb 2021

Dec 2020

Oct 2020

Aug 2020

Jun 2020

Apr 2020

Feb 2020

-1.0

DEMOGRAPHICALLY
ADJUSTED

1.5

Sources: CPS; CEA calculations.

A different way to adjust for aging is to omit both seniors and the
young and to look solely at prime-age participation. As figure 2-38 shows,
the prime-age LFPR gradually rose throughout 2021; at the same point in the
last two cycles, the prime-age LFPR was still falling (figure 2-39).
There is no single overriding factor explaining the change in the LFPR
between February 2020 and early 2022; rather, a variety of explanations are
at play. In January 2022, there were 3.2 million fewer workers in the labor
force relative to the size of the labor force if the LFPR had remained at its
prepandemic level. The information provided by respondents to the CPS
can be used to break down why these 3.2 million workers said they were not
looking for work (figure 2-40):
• Aging of the population: 880,000, explains 28 percent of the actual
LFPR decline (none of the adjusted decline). As noted above, the aging
of the population and retirement of the baby boomers is an ongoing
force putting downward pressure on the LFPR (see, e.g., Cooper et
al. 2021). Other population shifts have occurred during the pandemic,
including lower immigration and higher mortality due to COVID-19.
If the age profile of the U.S. population looked as it did in February
2020, in January 2022 the LFPR would have been about 35 basis points
higher. Most of the persons accounted for in this category take the form
of permanent retirements, though it is possible that a small portion may
eventually reenter the labor force.
• “Excess” retirements: 1.0 million, explains 33 percent of the actual
LFPR decline (46 percent of the adjusted decline). These are retirements

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Figure 2-41. The Retirement Rate, 2010–22
Percentage of 16+ population
20.0
19.5
19.0
18.5
2010–20 trend

18.0
17.5
17.0
16.5
16.0
15.5
15.0

2010
2011
2012
2013
2014
2015
2016
2017
2018
Sources: Data from the Integrated Public Use Microdata Series and CPS; CEA calculations.
Note: Seasonally adjusted, with CEA calculations.

2019

2020

2021

2022

Figure 2-42. Retirement Flow Rates, 1998–22
Percent, year-on-year
12
10
Outflow trend
prepandemic

8
Outflow rate
Percent of retired

6
4

Inflow trend
prepandemic
Inflow rate
Percent of nonretired

2
0
1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

2020

2022

Sources: Data from the Integrated Public Use Microdata Series and CPS; CEA calculations.
Note: Trends are calculated with a linear regression of flows on time trend, share of population.

beyond what one would expect, given aging (figure 2-41). The CEA
finds that this increase was driven not by an increase in the likelihood
of older workers entering retirement but by the diminished likelihood
of leaving retirement to reenter the workforce (figure 2-42). That is, in
the prepandemic course of retirement flows, an average share of about
9 percent of retirees each year left retirement status and reentered the
labor force or engaged in other activities. This share declined between
February 2020 and early 2021, but then began recovering. If this rise
in retirement exits continues, overall retirement rates would decline.

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People who are not in the labor force but who say they want a job:
730,000, explains 23 percent of the actual decline (32 percent of
the adjusted decline). Such workers, sometimes referred to as in the
“shadow labor force,” are not actively looking for a job, and thus are
definitionally not unemployed. Historically, they have higher labor
force reentry rates than other nonparticipants. The rise in the shadow
labor force during the pandemic over 2021 was roughly even by sex but
has been most acute among Hispanics.36
• Family or home care: 600,000, explains 19 percent of the actual
decline (26 percent of the adjusted decline). Below, this chapter further
explores the extent to which childcare and elder care responsibilities
held back the labor supply of these caretakers, who are disproportionately women and mothers.
• Enrollment and disability: –580,000, explains –18 percent of the actual
decline (–25 percent of the adjusted decline). Nonparticipation due to
school enrollment and disability slightly declined after February 2020,
meaning that fewer people were in school without a job or cited disability as a reason for not being in the labor force. Note that what is charted
here is “disability” as measured in the CPS: whether a respondent
who does not want a job believes that her disability is preventing her
from looking for work. This is an entirely separate concept from participating in disability benefit programs, like Social Security Disability
Insurance and Supplemental Security Income—though CPS disability
is strongly correlated with participation in these programs, which has
also declined during the pandemic and over the last year.37
• Something else: 560,000, explains 18 percent of the decline (25 percent
of the adjusted decline). This category captures rises in nonparticipation not explicitly accounted for in CPS questions.
In summary, about 61 percent of the 1.2-percentage-point shortfall in the
LFPR through January 2022 was due to either aging or excess retirements,
with the remainder roughly split between the shadow labor force and workers who were out of the labor force due to family or home care obligations.
There were other factors that decreased the labor force via their effects
on the population as a whole rather than on the LFPR. Such factors can
exacerbate a reduced labor supply in certain industries. Two examples are
COVID-19 mortality and immigration. The CEA estimates—based on the
age, sex, and the state of COVID-19 deaths to date—that the labor force was
about 250,000 smaller at the end of 2021 due to the direct effects of COVID19 mortality. The population in 2021 was also smaller due to a decrease
in immigration from the pre-2019 trend; this fall in immigration resulted
from a combination of the pandemic along with prepandemic policies. The
•

36
37

CEA calculations, using CPS microdata.
SSA (2022).

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| 85

CEA estimates that the labor force would have been about 550,000 larger in
January 2022 if immigration had followed its pre-2019 trend.

The Historical Sluggishness of U.S. LFPR Recoveries
It is also worth noting that, in recent decades, the LFPR appears to have
recovered more slowly than unemployment after recessions. Hobijn and
Sahin (2021) highlight this pattern, decomposing the growth in the
employment-to-population ratio into the part accounted for by falling
unemployment and the part explained by rising LFPRs. In at least the last
three business cycles, rising LFPRs lagged the falling unemployment rate,
typically by many years. For example, applying this decomposition to the
current period, employment-to-population ratios for prime-age workers
were up 9 percentage points since jobs began recovering in May 2020.
About one-fifth of this growth was due to the rising LFPR, with the rest due
to the falling unemployment rate. This is actually a relatively large LFPR
contribution compared with recent cycles. For example, if one investigates
a comparable period after the global financial crisis and Great Recession in
2008, employment-to-population ratios barely changed, and the components
due to the LFPR and the unemployment rate barely changed either.
The CEA also examined the same pandemic-cycle decomposition
by gender and race, finding that a rising LFPR explained 19 percent of
the increased employment rate for men, and 22 percent for women. Black,
Asian, and Hispanic employment rates were up 9, 10, and 12 percentage
points, respectively; the rising LFPR explains 37 percent of the gain for
Blacks, 30 percent for Asians, and 20 percent for Hispanics. Again, during the comparable period after the Great Recession, the LFPR had not
rebounded for any subgroup during this time, and thus held back employment rates for all groups.
In one sense, this difference between the pandemic recovery and
that of the Great Recession is not too surprising. The GDP and unemployment—and, to some extent, job growth—all bounced back faster in 2021
compared with slower, and initially more “jobless,” recoveries after other
recent downturns.

Caring for Family Members
Family members’ responsibility to care for their children or elderly parents
can also be a barrier to labor market entry or reentry, and the pandemic
exacerbated the role of this barrier at times for some caregivers. One way
to examine the potential role this of this barrier during the pandemic is to
compare the labor force participation of parents and nonparents, or, because
women disproportionately provide such care, between mothers and women
without children. Research by the CEA and others reveals that at times

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Figure 2-43. Maternal LFPR versus the Same Calendar Month in 2019
Percentage points, 95% confidence intervals
4
3

2
1
0
–1
–2
–3
–4
–5
Jan 2020

Apr 2020

Jul 2020

Oct 2020

Jan 2021

Apr 2021

Jul 2021

Oct 2021

Jan 2022

Sources: BLS; CEA calculations.
Note: LFPR = labor force participation rate. The graph shows mothers of young school-age (3–13) children versus otherwise
similar women without children. The data include controls for age, sex, race/ethnicity, education, marital status, foreign-born
status, State, and metro size.

during the pandemic, mothers were significantly less likely than otherwise
similar women without children to be in the labor force, especially during
the declines of 2020 and 2021, at the beginnings of school years. The CEA
finds that relative to patterns that prevailed in 2019, the maternal LFPR was
2.1 percentage points lower than that of otherwise similar women without
children in October 2021, but that this difference shrank and became insignificant in November and December 2021 (figure 2-43). There is some evidence that this reversal was due to schools and childcare centers reopening.

The Unemployment Rate
Just before the pandemic, the unemployment rate stood at 3.5 percent. The
official rate then peaked at 14.7 percent in April 2020, before beginning
a steady decline. Over the 12 months of 2021, it declined 2.8 percentage
points, the largest December–December fall on record.
But the official unemployment rate is still somewhat higher than prepandemic levels, suggesting some amount of remaining slack in the labor
market. Moreover, the decline in the LFPR over the course of the pandemic
put mechanical downward pressure on the measured unemployment rate
given that, holding employment constant, a lower LFPR lowers the measured unemployment rate.
The extent to which the official unemployment rate understates slack
depends crucially on the assumed underlying trend participation rate.
Assume for a moment, illustratively, that the LFPR recovered all the way
back to the level consistent with where it was in February 2020 in ageadjusted terms. This implies that the unemployment rate would have been
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| 87

Figure 2-44. The U.S. Unemployment Rate, 2020–22
Percentage of the labor force
25
20
15

Adjusted for
misclassification
and trend LFPR

10

Adjusted for
misclassification
Official

5
0

Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan
2020 2020 2020 2020 2020 2020 2021 2021 2021 2021 2021 2021 2022

Sources: BLS; CEA calculations.
Note: LFPR = labor force participation rate.

5.5 percent in January 2022 rather than 4.0 percent, with an extra 1.5 percentage points of slack in the unemployment rate space (figure 2-44). But if
one assumes the other extreme—that the LFPR will not rise any further than
current levels—then the official unemployment rate will not understate labor
market slack, at least due to participation.

Reconciling the Paradox
How, then, does the unfrozen economist imagined earlier in the chapter
reconcile these facts? How did the labor market seem to recover fully while
also being more than 5 million jobs short of the prepandemic trend? Like
so many other economic dynamics during the pandemic, a large part of the
answer is that the COVID-19 pandemic has created an extraordinary set of
circumstances in the U.S. labor market.
Labor supply—the number of workers with or wanting jobs—and
labor demand—the number of jobs employers want to have filled—were
still depressed at the end of 2021 in level terms relative to the prepandemic
(figure 2-45). Labor force participation was lower by 1.5 percentage points
overall, and, if one adjusts for aging, by 1 percentage point—representing
2.6 million people. Labor demand, in contrast, had almost recovered to its
prepandemic level by end of 2021; and in January 2022, it had grown further
to slightly exceed it.
Without question, demand for labor has recovered more quickly than
the supply of workers. This is not surprising; as discussed above, the LFPR
typically lags the unemployment rate in recovering during U.S. business
cycles. And whereas labor demand was once clearly the binding, limiting
factor in this pandemic, by the end of 2021 supply had become the more
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Chapter 2

Figure 2-45. Labor Supply and Demand, 2019–21
Percentage of population 16+
Index: Feb. 2020 = 100

102

Aging trend

100
98

Labor supply
(LF + NILF,
want a job)

96
94
92

Labor demand
(employment +
vacancies)

90
88

Jan 2022

Nov 2021

Jul 2021

Sep 2021

Mar 2021

May 2021

Jan 2021

Nov 2020

Jul 2020

Sep 2020

May 2020

Jan 2020

Mar 2020

Nov 2019

Jul 2019

Sep 2019

May 2019

Jan 2019

84

Mar 2019

86

Sources: BLS; CEA calculations.
Note: “LF + NILF” means those in the labor force plus those not in the labor force,

binding component. This creates tightness in two ways. First, the level of
tightness is high. Demand exceeds supply in the aggregate and in many
industries. Second, momentum is high. Even in industries where demand
still lagged supply at the end of 2021, demand often grew quickly over the
last year, and this could have created labor market friction.

The Forecast
The Biden-Harris Administration finalized the economic forecast that underpins the President’s Budget on November 10, 2021. By the third quarter of
2021, real GDP had recovered to a level that was 1.4 percent above its prepandemic level. That third-quarter level was, however, still 1.5 percent short
of a plausible counterfactual path of 2 percent annual growth. Consistent
with that shortfall from the counterfactual, and consistent with the consensus
of professional economic forecasters, the Administration believes that the
economy has additional room to grow during the next two years because
aggregate demand appears to have enough momentum to make this happen.
The Administration’s November 2021 forecast expected real GDP to
grow 5.1 percent during the four quarters of 2021, and slow to 3.8 percent
during 2022. In comparison, the consensus of private professional forecasters—the latest available at that time, published in October 2021—projected

The Year in Review and the Years Ahead

| 89

5.5 percent real GDP growth during the four quarters of 2021 and a slowing
to 3.5 percent growth in 2022.

Macroeconomic Forces during 2022
As this chapter has stressed, the ongoing pandemic generates unusually high
forecast uncertainty, which has been exacerbated by the Russian invasion of
Ukraine in February 2022. Nevertheless, the Administration must still present a central forecast. Among the expected manifestations of a supply-side
surge were, at the time of the budget forecast in November, the anticipated
resolution of supply chain problems, the gradual increase in the willingness
of workers to staff a wide range of service industries, and a rebound in the
LFPR.
The near-term prospects for demand growth depend on large but
competing forces. On the positive side, the supply of excess savings—accumulated during a period of large Federal transfers with limited opportunity
to spend those funds—will probably support continued growth of consumer
spending. Customers are expected to return to consumer-facing businesses
and those establishments that include crowds (bars, restaurants, theaters,
etc.). On the negative side, fiscal policy is now turning sharply negative,
reflecting the disappearance of the substantial Federal subsidies and transfers of the emergency pandemic programs (see figure 2-ii in box 2-4). The
Administration forecasts above-trend growth during the four quarters of
2022 and 2023 (at 3.8 and 2.5 percent, respectively, as shown in table 2-6)
reflecting the CEA’s view in November 2021 that these supply and demand
positives from emergence out of the COVID-restrained economy outweigh
the swing to negative fiscal impetus due to the sunsetting of the temporary
pandemic fiscal support. (See box 2-4.)
The Administration’s inflation forecast focuses on two of the many
price indices produced by the U.S. statistical agencies: the CPI and the
price index for GDP. The CPI is important because it measures prices faced
directly by consumers and because versions of it are used to escalate Social
Security benefits, Federal pensions, and the notches in the Federal tax code.
Based on the November forecast, the CPI is expected to rise 2.9 percent
during the four quarters of 2022, down from its 6.7 percent (actual) pace
during the four quarters of 2021 (which had been forecasted to be 6.6 percent when the forecast was finalized, as shown in table 2-6). This forecasted
2022 rate was higher than the consensus forecast available at the time the
Administration forecast was finalized. Based on the forecast, starting in
2023, CPI inflation is expected to fall to the 2.3 percent rate that is consistent with the Federal Reserve’s inflation target of 2.0 percent for a different
(but closely related) price index, the Price Index for Personal Consumption
Expenditures.

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Chapter 2

Box 2-4. Fiscal Impetus by Quarter
Positive effects on demand can follow an increase in Federal Government
purchases or transfers, or a temporary tax cut. But as spending programs
end, or temporary tax cuts expire, the subsequent quarters will exhibit
negative demand effects. At the end of 2021, the large fiscal supports
enacted during fiscal years 2020 and 2021 (see table 2-1 above) have
mostly ended, and this ending will depress economic demand during
2022. To estimate the growth effects of this stimulus, and the negative
effects of their termination, the CEA built an estimation system modeled
on the one maintained by the Brookings Institution, which itself was
modeled on one suggested by Federal Reserve staff. (See Kovalski et al.
2021; Brookings Institution 2019; Cohen et al. 1999; and Cashin et al.
2017.) The quarterly growth effects—both positive and negative—are
shown in figure 2-ii. As can be seen, the effects of fiscal policy on
growth are negative for 2022. These negative fiscal policy effects may
be offset by positive supply side shocks from the emergence out of the
pandemic-restrained economy, despite the uncertainty caused by the
invasion of Ukraine and possible future variants of COVID-19.
Figure 2-ii. The Federal Fiscal Impetus by Quarter
Contribution to real GDP growth, annual rate, percentage points
30
25
20
15
10
5
0
-5
-10
-15
2019

2020

2021

2022

2023

Source: CEA calculations.

The price index for GDP measures the price of everything produced
in the United States, and its measure of inflation differs from the CPI
because—in addition to consumer prices—it includes the price of investment, government purchases, and exports, while import prices are excluded.
When averaged over long intervals, GDP price-index inflation tends to run
slightly lower than the CPI, partially due to a different indexing formula. In
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| 91

Table 2-6. Economic Projections, 2020–32
Nominal
GDP

Year

2020
(Actual)
2021
2022
2023
2024
2025
2026
2027
2028
2029
2030
2031
2032

Percent Change (Q4 to Q4)
Consumer
GDP
Real
Price
Price
GDP
Index
Index

Unemployment
Rate
(percent)

Level (calendar year)
Interest Rate
Interest Rate
10-Year
91-day
Treasury Notes
Treasury
Bills
(percent)
(percent)

–1.0

–2.3

1.5

1.2

8.1

0.4

0.9

10.1
6.3
4.6
4.1
4.0
4.0
4.0
4.1
4.3
4.4
4.3
4.3

5.1
3.8
2.5
2.1
2.0
2.0
2.0
2.1
2.2
2.3
2.3
2.3

4.8
2.4
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0
2.0

6.6
2.9
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3
2.3

5.4
3.9
3.6
3.7
3.8
3.8
3.8
3.8
3.8
3.8
3.8
3.8

0.0
0.2
0.9
1.6
1.9
2.1
2.2
2.3
2.3
2.3
2.3
2.3

1.5
2.1
2.5
2.7
2.8
3.0
3.1
3.1
3.2
3.2
3.2
3.3

Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; Department of the Treasury; Office of Management and Budget;
Council of Economic Advisers.
Note: The forecast was based on data available as of November 10, 2020. The interest rate on 91-day T-bills is
measured on a secondary-market discount basis. GDP = gross domestic product.

the forecast, inflation—as measured by the price index for GDP—is projected to fall to 2.4 percent during the four quarters of 2022, from a projected
4.8 percent in 2021.
When the forecast was finalized, the October unemployment rate of
4.6 percent was the latest datum. The Administration expected it to fall
further, and thus to average 3.9 percent in 2022, and to fall to 3.7 percent
by the end of 2022, and then to average 3.6 percent in 2023. Subsequently,
the unemployment rate fell sharply further in November (4.2 percent) and to
3.9 percent in December. Even so, the 3.9 percent average for 2022 remains
plausible.

The Forecast over the Long Term
As described above, real GDP growth was forecast to edge down year by
year from 2021 to 2024 (2 percent), in large part because by the end of
2021, GDP had almost fully rebounded from the recession, so less room
remained for growth. Along this path, the unemployment rate descends to
3.6 percent by 2023:Q4, slightly overshooting the forecast estimate of the
unemployment rate consistent with stable inflation (3.8 percent). But the
unemployment rate edges back up to 3.8 percent by the end of 2024.
The consensus estimate (October 2021, the latest available when the
forecast was finalized) for potential real GDP growth in the medium term

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Chapter 2

appears to be about 2 percent annually. That is, the Blue Chip consensus
panel forecasts a 2.0 percent average annual rate of growth during the four
years 2024–27 while the unemployment rate is approximately constant.
The Administration believes that potential real GDP growth in the long
run could be modestly higher because of a range of policies supported in the
2021 Bipartisan Infrastructure Law (BIL) and the President’s other proposed
economic policies. These include increments to infrastructure investment
from the BIL, and a range of programs to enhance human capital formation
and labor force participation. Altogether, these policies could plausibly
boost real GDP growth by 0.3 or 0.4 percentage point a year during the
10-year budget window (2022–32).
In addition, real GDP growth is expected to increase during the last
four years of the forecast interval 2029–32 because the change in the
LFPR becomes less negative at that horizon. The retirement of the baby
boom cohort (those born from 1946 to 1962), is currently subtracting about
0.4 percentage point per year from the growth rate of the LFPR, and this
downward force is likely to continue for the next several years. However,
after 2028, after the last of these baby boomers (those born in 1962) reaches
the standard retirement age of 65–66, these retirements will diminish. The
negative contribution to real GDP growth from the retirement of the baby
boomers moderates from about –0.4 percentage point per year through 2027
to –0.3 percentage point per year in 2028–30, and to –0.2 percentage point
in 2031–32.
During the last six years of the forecast (2027–32), the Administration’s
forecast grows faster than the Blue Chip consensus (1.9 percent per year)
because of the possible combination of these two factors: the Blue Chip
consensus may not completely incorporate the growth-promoting aspects of
the President’s proposals, and the consensus does not appear to account for
the diminishment of baby boom retirements.
Interest rates are projected to slowly rise during the 11-year projection interval, following paths that are similar (but slightly steeper) than
those projected in the Blue Chip consensus panel’s October 2021 long-term
interest rate projection. The Administration focuses on two interest rates:
the rate on 91-day Treasury Bills, and the yield on 10-year Treasury notes.
These interest rate forecasts are key to projecting the cost of servicing the
Federal debt. The Treasury Bill rate is projected to creep up from an average
of 0.0 percent in 2021 to a 0.9 percent average in 2023, and eventually to
2.3 percent during the last five years of our projection interval (2028–32).
In comparison, the Blue Chip consensus panel’s October 2021 forecast of
the Treasury Bill rate plateaus at 2.1 percent. The Administration’s interest rate forecast is slightly higher than that of the consensus because the

The Year in Review and the Years Ahead

| 93

Administration also forecasts slightly higher real GDP growth during those
years, and higher growth is likely to boost interest rates.38

The Supply Side of the Long-Term Forecast
Real GDP is expected to grow at an average 2.2 percent annual rate during the 13-year interval through the Administration’s budget horizon in
2032. The six components of the supply-side identity that account for this
growth are shown in table 2-7, both over the forecast interval as well as
over relevant historical periods. Because the growth of these supply-side
components over short intervals is erratic and has cyclical patterns, growth
rates between business-cycle peaks are shown. For this reason, this table
shows the growth rates of these supply-side components starting from the
last business-cycle peak in 2019:Q4.
The Administration’s forecast of growth of the working-age (16+)
population comes from the latest Social Security Administration Trustees’
report. The 0.7 percent projected rate of growth (row 1, column 5 in table
2-7) is below the average growth rate during the 66 years through 2019 (row
1, column 1), and also below the growth rates in each of the three preceding
business cycles (columns 2, 3, and 4).
The LFPR is expected to decline further (row 2, column 5 in table 2-7)
over the forecast window, due to the continuing retirement of the baby boom
cohorts. But during the last five years of the projection interval, this decline
will become less steep as the retirements of those baby boom cohorts near
completion. In addition, the President’s proposed policies are expected to
promote higher labor force participation rates than would otherwise be the
case.
The employed share of the labor force (row 3, column 5, in table 2-7,
equal to 1 minus the unemployment rate) usually contributes little to GDP
growth because the employment rates are similar among business-cycle
peaks. The workweek in the nonfarm business sector (row 4, column 5)
is projected to remain flat, after falling at a 0.2 percent annual rate during
the 66-year interval shown in column 1. The workweek shortened during
that interval because of generally declining employment in manufacturing
(where workweeks are long) and the rise in the labor force participation of
women (who generally entered the workforce with shorter workweeks than
men). Looking ahead, the workweek is expected to stabilize at its 2019 level
because female participation is expected to plateau while the workweek of
women rises.
Labor productivity (output per hour in the nonfarm business sector)
is expected to grow at an average 1.8 percent annual rate, above the 1.4
38

Higher interest rates are expected with faster growth; see Council of Economic Advisers (2015).

94 |

Chapter 2

Table 2-7. Supply-Side Components of Actual and Potential Real Output Growth, 1953–2032
Growth Rate (percentage points)
Component

1953:Q2 to
2019:Q4

1990:Q3 to
2001:Q1

2001:Q1 to
2007:Q4

2007:Q4 to
2019:Q4

2019:Q4 to
2032:Q4

(1)

(2)

(3)

(4)

(5)

1

Civilian noninstitutional population age 16+

1.4

1.2

1.1

1.0

2

Labor force participation rate

0.1

0.1

–0.3

–0.4

0.7
–0.2

3

Employed share of the labor force

0.1

0.1

0.1

0.0

4

Average weekly hours (nonfarm business)

0.0
–0.2

–0.2

–0.1

0.0

5

Output per hour (productivity, nonfarm business)
Output per worker differential: GDO vs.
nonfarma
Sum: Actual real GDOb

2.0

–0.1
2.4

2.4

1.4

1.8

–0.3
3.0

–0.3
3.5

–0.6
2.4

–0.4
1.7

–0.1
2.2

0.1
–0.3

0.3
–0.4

6
7

Memo:
8

Ratio of nonfarm business employment to
household employment

9

Ratio of real GDO to nonfarm business output

0.0

0.3

0.4

–0.3

–0.6

–0.2

Sources: Bureau of Labor Statistics; Bureau of Economic Analysis; Department of the Treasury; Office of Management and Budget; CEA
calculations.
aThe output-per-worker differential (row 6) is the difference between output-per-worker growth in the economy as a whole and output-per-worker
growth in the nonfarm business sector, and it is also equal to row 8 + row 9.
b Real GDO and real nonfarm business output are measured as the average of income- and product-side measures.
Note: All contributions are in percentage points at an annual rate. The forecast is made from data available on November 10, 2021. Totals
may not add up due to rounding. The quarters 1953:Q2, 1990:Q3, 2001:Q1, 2007:Q4, and 2019:Q4 are all quarterly business-cycle peaks.
Gross domestic output (GDO) is the average of GDP and gross domestic income. Population, labor force, and household employment have
been adjusted for discontinuities in the population series.

percent average annual rate during the preceding business cycle but below
the average 2 percent annual rate over the 66 years through 2019. Again,
productivity growth is expected to be boosted by the BIL, as well as the
human-capital-building aspects of the President’s other proposed policies.
Both the workweek and productivity are measured in the nonfarm
business sector, but the supply side identity adds up to GDP (which includes
the farm, government, and household sectors in addition to the nonfarm
sector), and the employment rate is measured (from the household survey)
for the economy as a whole. As a result, a conversion factor is needed to
translate from nonfarm business employment to total employment (row 8 of
table 2-7) and also from nonfarm business to GDP (row 9). The sum of these
two rows (row 6) is the difference between the growth rate of output per
person in the economy as a whole and the growth rate of output per person
in the nonfarm business sector. Because the National Income and Product
Accounts assume that productivity does not grow in the government and
household sectors, the nonfarm business is the sector where productivity
grows. As a result, the row 6 is negative over any long interval.

Conclusion
The story of the U.S. economy in 2021 was again one where COVID-19
was in the driver’s seat. But it was also one where the United States made
The Year in Review and the Years Ahead

| 95

enormous strides at recovery and normalization throughout the year, thanks
in large part to extraordinary fiscal and monetary policy support and a
historic campaign to research and distribute vaccines.
Pandemic-induced disruptions were still evident throughout the
economy at the end of 2021. The Omicron variant caused a spike in cases,
hospitalizations, and deaths. Consumers were still favoring goods more
than they had before the pandemic, to the detriment of services. The strong
demand for goods strained supply chains and put upward pressure on prices.
And labor markets were not fully recovered, with such key measures as the
unemployment rate, prime-age employment, and the prime-age labor force
still weaker than in 2019.
But the progress over 2021 was significant. The United States ended
the year with an economy more than 3 percent larger in real terms than just
before the pandemic—the fastest pandemic recovery among the Group of
Seven countries. The unemployment rate fell by its fastest December-toDecember pace since modern data began to be collected after World War II,
and the economy added 6.7 million jobs. Given the historic damage wrought
by the pandemic in early 2020, such progress was not preordained. This pace
of recovery raises hopes that, even while managing future COVID-19 variant risks and geopolitical upheavals, the United States will not just normalize but also emerge with a stronger, healthier, and more inclusive economy.

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Chapter 2

Chapter 3

The U.S. Economy and the
Global Pandemic
The COVID-19 pandemic has had repercussions for economies around the
globe. Although the U.S. economy suffered one of the sharpest contractions
in its history during 2020, the economic damage was even greater in many
foreign countries. Bolstered by an early and rapid vaccine rollout as well as
by strong fiscal support, the United States’ recovery has been robust, outpacing that of most of our major trading partners in 2021. Inflation emerged as
a challenge for the United States and nearly all our major trading partners,
as strong demand, skewed toward goods and away from services, interacted
with the supply chain stresses described in detail in chapter 6 of this Report.
As a result of the rapid U.S. recovery relative to the rest of the world, the
U.S. trade deficit has widened. The strength of the U.S. recovery has led to
increased imports, as goods have flowed in from abroad to satisfy resurgent
demand from firms and consumers. Although exports have hit record
highs, they have increased at a slower pace than imports because many of
the countries that buy U.S. goods have not recovered as fast. At the same
time, new waves of infection depressed international travel and weighed on
the recovery of some services that are important for U.S. exports, such as
tourism.
The pandemic highlighted the need to tackle long-standing economic issues,
including those resulting from global economic integration. Due to a lack
of supportive public policy in the past, many American workers and communities have borne the costs of shifting production around the world but
have not fully shared in its benefits, contributing to widening inequality.

97

Addressing these inadequacies requires policies that broaden the gains from
trade while leveling the international economic playing field by countering unfair trade practices and putting in place a more equitable global tax
system. Implementing such policy changes in a way that reduces uncertainty
and engages with the United States’ trade and commercial partners can
ensure that American consumers, workers, businesses, and investors benefit
from global trade.
The first section of this chapter places America’s economic experience during the pandemic in the global context by comparing it with that of our largest trading partners: the euro area, the United Kingdom, China, Canada, and
Mexico. The next section examines how international trade has recovered
from its sharp pandemic decline, discussing the causes of the widening U.S.
trade deficit and the effects of supply chain bottlenecks internationally on
traded inputs such as auto parts and capital goods. The last section discusses
how the Biden-Harris Administration is reorienting U.S. international economic policy to mitigate rather than exacerbate economic inequality and to
level the international economic playing field.

Recovery Amid Global Economic Challenges
Placing the U.S. recovery from the COVID-19 pandemic in the global
context highlights how our robust fiscal support resulted in a faster return to
a strong economy. The backdrop to this demand-driven recovery, however,
was a tragic loss of human lives and higher inflation.

The Global Pandemic
The path of the global economy over the past year is best understood
in the context of the coronavirus pandemic. The starkest measure of the
pandemic’s effect is the number of deaths attributed to COVID-19. By
the end of 2021, reported deaths due to the virus had exceeded 5 million
people globally, including more than 827,000 in the United States (OWID
2021). The true global toll is probably much higher, because data collection
challenges outside the United States suggest that many other countries may
have substantially underreported deaths. For example, some estimates put
the true death toll in India alone in excess of 4 million (Anand, Sandefur,

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Chapter 3

Figure 3-1. International COVID Case Rates
Cases per million
2,500

2,000

1,500

1,000

500

0
Jan-20

Apr-20
United States

Jul-20
Canada

Oct-20

Jan-21

Euro area

Apr-21
United Kingdom

Jul-21
Mexico

Oct-21
China

Source: Our World in Data.

and Subramanian 2021). With deaths measured as a share of the population,
many of the hardest-hit countries have been middle-income countries in
Latin America and Eastern Europe (Johns Hopkins 2022).
Looking at total deaths can obscure the fact that different countries
have been hit by waves of differing severity at different times. Which country is faring worst at any point in time has varied significantly. Official data
show that the United States, the United Kingdom, and the euro area have all
had the highest recorded cases per capita at some point in time (figure 3-1).
Early in the pandemic, the United States led in per capita cases while the
United Kingdom led in deaths. In the second half of 2021, the reverse was
true. And the euro area reported the highest per capita cases in the spring
of 2021. This variation demonstrates how nearly all major economies have
been severely affected at some point during the pandemic.
Progress and timeliness in vaccinating populations have also varied
across countries. Both the United States and United Kingdom managed rapid
vaccine rollouts that made them early leaders in the share of the population
vaccinated (figure 3-2). Rollouts in Canada and the euro area accelerated
dramatically in the summer of 2021, and vaccination rates in both places
have since reached higher levels than in other major U.S. trading partners.
During the second half of 2021, vaccination rates in many middle-income
countries, such as Mexico, approached that of the United States, while rates
in low-income developing countries (not shown) remain substantially lower
(OWID 2021).

The U.S. Economy and the Global Pandemic | 99

Figure 3-2. International COVID Vaccination Rates
Percent fully vaccinated
100
80
60
40
20
0
Jan. 1, 2021

April 1, 2021
United States

Canada

July 1, 2021
Euro area

Oct. 1, 2021
United Kingdom

Mexico

Source: Our World in Data.
Note: “Fully vaccinated” is defined as having received all doses prescribed by the initial vaccination
protocol. China does not report statistics on the share of its population that is fully vaccinated.

The United States’ Economic Recovery in the Global Context
The path of real gross domestic product (GDP) since the onset of the
COVID-19 pandemic provides the most basic measure of the virus’s economic impact. The pandemic was accompanied by historic drops in output
in almost all major economies. U.S. GDP fell by 8.9 percent in the second
quarter of 2020 (figure 3-3), the largest single-quarter contraction in more
than 70 years (BEA 2021c). Most other major economies fared even worse.
The GDP of the United Kingdom in 2020:Q2 was 21.4 percent below its
average in 2019 (ONS 2022). In the euro area, output fell by more than 12.4
percent (Eurostat 2022c). Closer to home, Canada’s GDP was down 12.4
percent, while Mexico’s GDP fell by 19 percent (Statistics Canada 2022;
INEGI 2022).
The U.S. recovery has outpaced that of all its major trading partners
except China. By the second quarter of 2021, U.S. real GDP exceeded its
prepandemic level, ahead of most other major economies. Output growth
picked up in the euro area and Canada in the third quarter of 2021; but at
the end of 2021, output in most major U.S. trading partners had only just
reached its prepandemic level, while U.S output was 3 percent higher than
before the pandemic (see figure 3-3). Though many effects of the pandemic
are not captured by GDP, measured by this most basic indicator, the United
States’ recovery remained farther along than those of nearly all its peers.
The initial drop in real output in China was of a very similar magnitude to that of the United States (see figure 3-3), but the initial recovery
was even faster. By the third quarter of 2020, China’s real GDP had not
only exceeded its prepandemic level but was also above what would have
100 |

Chapter 3

Figure 3-3. Real GDP by Country
Index level: 2019:Q4 = 100

120

110

100

90

80

70

Jan-19

Apr-19

Jul-19

United States

Oct-19

Jan-20

Canada

Apr-20
Euro area

Jul-20

Oct-20

Jan-21

United Kingdom

Apr-21

Jul-21

Mexico

Oct-21
China

Sources: National data organizations.
Note: Data are seasonally adjusted.

been expected based on its prepandemic trend. The Chinese government
did extend substantial support, primarily through infrastructure spending.
However, exports have been a key driver of China’s recovery, climbing to
more than 40 percent above their prepandemic level by the fourth quarter of
2021 (GACC 2021). As a result, the contribution of net exports to China’s
real GDP growth reached nearly 30 percent in 2020, its highest level in
more than 20 years (CNBS 2021a). In this way, China has benefited from
the pandemic-induced pivot of global consumption away from services and
toward goods, many of which are manufactured in China. Despite continuing support from strong demand for its exports, output growth in China
slowed in the second half of 2021 as government support for the economy
was withdrawn (CNBS 2021b).
Future research by economists will fully assess what enabled some
economies to weather the pandemic shock better or to bounce back more
quickly. Based on what we know now, there are two areas of policy where
the U.S. response stands out. The first is the speed of our vaccine rollout,
discussed above. The fact that more than 40 percent of the U.S. population
was fully vaccinated by May 2021, when vaccination rates in most European
countries were still less than half that, gave our economic rebound an important head start.
The other area where the United States stands apart is fiscal policy,
suggesting that this also played a role in accelerating the recovery beyond
those of most of our trading partners. U.S. Federal Government spending to
directly support firms and workers, as well as State and local governments,

The U.S. Economy and the Global Pandemic | 101

Figure 3-4. Discretionary Fiscal Response, 2020:Q1–2021:Q3

Percentage of 2020 GDP
30
25
20
15
10

5
0

Source: International Monetary Fund.

was substantially larger than comparable efforts in other major economies
(figure 3-4). As of the third quarter of 2021, the cumulative U.S. discretionary fiscal response (including not only additional spending but also revenue
forgone due to discretionary tax cuts) exceeded 25 percent of GDP. By
comparison, the U.K. response was under 20 percent of GDP, and average
spending in the euro area was 12 percent of GDP. The scale here helped to
ensure that, by the end of 2021, U.S. consumption had returned to its precrisis trend, while in the euro area, for example, consumption remained below
its precrisis level (Boone 2021).

The Challenge of Inflation
Inflation has proved a serious challenge for many countries during the
recovery. In the 12-month period ending December 2021, headline consumer price inflation in the euro area was 5.0 percent, well above its average
of about 1 percent in the five years before the pandemic (Eurostat 2022a), as
shown in figure 3-5. Canada and the United Kingdom have also seen substantially higher inflation than was the case before 2020. Inflation has also
risen here; indeed, U.S. inflation has run higher than that of most of its major
trading partners, although the gap narrowed in the second half of 2021.
The fact that inflation has accelerated in so many countries underscores its common drivers. Pandemic-induced changes in behavior led to
relatively more demand for goods than services. In many countries, the
balance of consumption remained unusually tilted toward goods throughout
2021, so demand for goods grew substantially faster than would have been
the case in a normal recovery (Bruce 2021; Boone 2022). As a result, the
world’s economic recovery put stress on the already-vulnerable global

102 |

Chapter 3

Figure 3-5. Consumer Price Level
Index level: Dec. 2019 = 100
115

110

105

100

95

Jan-19

Apr-19

Jul-19

United States

Oct-19

Jan-20

Canada

Apr-20
Euro area

Jul-20

Oct-20

Jan-21

United Kingdom

Apr-21

Jul-21

Mexico

Oct-21
China

Sources: National data organizations.
Note: Data are seasonally adjusted.

supply chains for consumer goods, as discussed further in chapter 6 of this
Report. This phenomenon of recovering demand for goods interacting with
supply constraints can help to explain the relatively higher inflation in the
United States, where the recovery was relatively stronger. Looking across
countries, inflation was higher where the gap between the real GDP and its
prepandemic level—a main measure of progress toward economic recovery—was smaller (figure 3-6).
Rising prices for motor vehicles were a key driver of U.S. inflation,
with prices of new cars nearly 12 percent higher at the end of 2021 than they
were a year earlier. Prices of used cars jumped by almost 40 percent during
the year (BLS 2022b). Though other countries also saw higher car prices,
their rise was not as dramatic. Indeed, the CEA calculates that consumer
prices, excluding those of new and used cars, rose by similar magnitudes in
the euro area (4.7 percent), for example, as in the United States (5.1 percent).
Globally, factors pushing up car prices included rebounding demand
and a shortage of semiconductors (Gross, Miller, and Inagaki 2021). Car
manufacturers both in the United States and abroad have faced production challenges due the semiconductor shortage, but during 2021, U.S.
auto production outpaced that of many peers. At the end of 2021, U.S.
auto production stood at just under 5 percent below its prepandemic level,
ahead of the recovery of German, French, and Japanese production (Federal
Reserve Board 2022; Eurostat 2022b; METI 2021). Thus, the greater rise
in U.S. prices came in spite of a faster recovery in production. The fact
that the rise in car prices has been larger here than abroad stems partly
from the particularly resilient demand created by the U.S. recovery passing
The U.S. Economy and the Global Pandemic | 103

Figure 3-6. Recovery in Output and Inflation
Annualized CPI growth, Feb. 2020–Sep. 2021

8.0

Brazil

6.0

Senegal

United States

4.0

Canada
2.0

Malaysia
0.0

–2.0

China

90

95

100

105

110

115

2021:Q3 real GDP (as % of 2019:Q4 GDP)
Sources: National data organizations.
Note: CPI = Consumer Price Index. Data are seasonally adjusted, except for Senegal’s CPI.

through to the auto sector—real consumer spending on new motor vehicles
rose 16 percent in 2021, a level reaching 18 percent above its prepandemic
level (BEA 2022b). Though higher vehicle prices do pose challenges for
American households and businesses, the strength of the recovery in the
U.S. auto sector relative to other major auto-producing countries highlights
the important benefits of the U.S. demand-driven recovery for workers and
businesses. (See box 3-1.)

International Trade, the Economic Recovery,
and Lingering COVID-19 Challenges
In 2021, international trade broadly recovered from the sharp decline that
followed the onset of the COVID-19 pandemic, with U.S. exports and
imports of goods exceeding prepandemic records. Import growth outpaced
export growth, widening the U.S. trade deficit. Though trade in goods
broadly recovered in 2021, supply bottlenecks slowed the recovery of both
imports and exports of such products as automotive and capital goods that
are at the heart of the global value chains that were disrupted by pandemicrelated challenges.
In contrast, waves of COVID-19 infections have weighed down the
recovery of cross-border trade in services. Although trade in services that
are less reliant on personal contact followed a recovery pattern similar to

104 |

Chapter 3

Box 3-1. Lessons from Abroad for Labor Market Policy
By some measures, the U.S. labor market appears to have recovered rapidly. America’s unemployment rate jumped at the onset of the pandemic,
but then fell steadily, and by the fourth quarter of 2021 was once again
lower than in the euro area, Canada, or the United Kingdom (figure 3-i).
However, though the number of people employed at the end of 2021 was
above its prepandemic level in most of our trading partners, this is not
true here (figure 3-ii). The reason: though labor force participation has
increased significantly over the past year, relatively more people left the
U.S. labor force early during the pandemic than in many other countries.
The discretionary fiscal response in the United States was larger
than that of most of our trading partners when considering the three
major pieces of fiscal legislation passed over the course of the pandemic,
and the government support associated with that response was delivered
to individuals and households in a very different way. As discussed
in chapter 2, pandemic support payments were generally received in
the form of unemployment insurance or as direct payments. By contrast, governments in the euro area and the United Kingdom adopted
or strengthened existing job retention programs, which subsidized
employed workers’ incomes. (OECD 2020).
These programs come in two forms: short-time work programs, in
which the government pays employees for hours not worked; and wage
subsidies, in which the government either subsidizes pay for hours the
employee actually works or raises employees’ pay to a minimum level,
regardless of time worked. These programs help explain why unemployment rates increased remarkably little in the euro area and the United
Figure 3-i. Unemployment Rates
Percent
16
14
12
10
8
6
4
2
0

Jan-19

May-19
United States

Sep-19

Jan-20

Canada

May-20
Euro area

Sep-20

Jan-21

United Kingdom

May-21
Mexico

Sep-21
China

Sources: National data organizations; OECD.
Note: Data are seasonally adjusted, except China. The United States measures age 16 and above, Canada
measures age 15+, and China measures urban area unemployment. Other metrics are total unemployment.

The U.S. Economy and the Global Pandemic | 105

Figure 3-ii. International Employment
Index level: 2019:Q4 = 100
105

100

95

90

85

80

Jan-19

Apr-19

Jul-19

Oct-19

Jan-20

Apr-20

Jul-20

Oct-20

Jan-21

Apr-21

Jul-21

Oct-21

United States
Canada
Euro area
United Kingdom
Mexico
Sources: National data organizations.
Note: Employment metrics vary slightly by source. The United States measures 16 years and above, Canada measures
15 years and above, and the United Kingdom measures a three-month rolling average for employment 16 years and
above. The euro area and Mexico measure total employment. All data are seasonally adjusted, except for Mexico.

Kingdom, both in absolute terms and relative to the change in the U.S.
unemployment rate. By design, job retention programs ensured that
many people working few or no hours remained on the payroll, receiving
paychecks from their employer that were almost entirely government
funded (OECD 2020).
The difference between the U.S. approach and these job retention
programs may seem semantic: workers were on the job dramatically less
in the spring and summer of 2020, whether or not they were technically
employed, and the magnitude of the drop was similar in the United States
and other major economies. However, in the United States, workers were
formally separated from their jobs and became unemployed (Boissay
et al. 2021). Unemployed workers leave the labor force (meaning they
stop looking for a job) at a rate almost 10 times greater than employed
workers, who exit the labor force if they leave their job and do not try
to find a new one (for details of what constitutes being in the labor
force, see BLS 2014). Once they leave the labor force, workers tend to
stay out (Hobijn and Şahin 2021). As the U.S. economy has recovered,
unemployed workers have found jobs and the unemployment rate has
fallen quickly. But unlike countries that adopted job retention programs,
in the United States there are also more workers who are no longer in the
labor force—meaning that they are neither working nor actively trying to
find a job; and this slows the rebound in the number of people employed
(BLS 2022a; CRS 2021).
Since 2012, the United States has had a job retention program—the
Short-Time Compensation Program—similar to efforts adopted else-

106 |

Chapter 3

where during the pandemic. Twenty-six States, which are home to 70
percent of the U.S. labor force, have active versions of the Short-Time
Compensation Program. However, participation in these local programs
is very low, in part due to the associated administrative burdens (Von
Wachter 2020). Viewed in light of the data on transitions in and out
of the labor force discussed above, the trajectory of U.S. employment
during 2021 suggests that reforms aimed at expanding participation in
this program could ensure a speedier labor market recovery after future
downturns. That said, in considering this policy option, a very important
open question is how European-style job retention programs are affecting the reallocation of workers across types of jobs during the economic
recovery.

goods, others—particularly travel and transportation services1—continue to
be impaired by the persistence of the virus. The sharp contraction of trade in
travel services was a notable drag on the U.S. trade balance in 2021. Exports
of these services in the form of foreign tourists, students, and business travelers are typically a significant contributor to the surplus in the U.S. trade
balance in services.

The U.S. Trade Balance
The strong domestic demand for goods that has characterized the economic
recovery in 2021 is reflected in the deepening deficit of the U.S. trade
balance—defined as the difference between the total value of goods and services that U.S. residents buy from abroad and the value of all the U.S. goods
and services sold abroad (BEA 2022a). At 4 percent of GDP, the 2021 trade
deficit is the largest since 2008 (measured as a share of GDP) (figure 3-7).
Deeper trade deficits in the United States over the past two decades have
been correlated with economic growth because they reflect strong demand;
2021 was no exception (BEA 2022b).
Over the past 20 years, the United States has typically maintained a
deficit in goods trade that is partially offset by a surplus in services trade.
The higher overall trade deficit in 2021 reflected a larger goods trade deficit
and a smaller services trade surplus relative to recent years. In particular,
the increase in the goods and services trade deficit from 2.8 percent of GDP
in 2019 to 4.0 percent in 2021 reflects a 0.5-percentage-point reduction in
the services surplus and a 0.7 percentage point increase in the goods trade
deficit (figure 3-7). Although both developments can be traced to challenges
stemming from COVID-19, the reasons for these outcomes are distinct.
In official U.S. data on services trade, this category is named “transport” rather than
“transportation.”
1

The U.S. Economy and the Global Pandemic | 107

Figure 3-7. U.S. Trade Balance, 2001–21
Percentage of GDP
2.0
1.0
0.0
–1.0
–2.0
–3.0
–4.0
–5.0
–6.0
–7.0

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Services

Goods

Total

Source: Bureau of Economic Analysis.

The increases in consumption and investment expenditures that drove
strong economic growth in 2021 entailed greater expenditures on both
domestically produced and imported goods and services. American producers of goods, challenged by pandemic-induced labor and input supply
obstacles, strained to keep pace with surging domestic demand for goods,
which reduced the available supply for exports (Furman and Powell 2021).
The dampening of growth in exports of U.S. goods was amplified by the
fact that America’s fiscal policy response was larger than most other major
economies (see figure 3-4). Though demand here exceeded its prepandemic
trend, demand abroad lagged. As a result, American firms and consumers
stepped up purchases of imported goods to a greater degree than their foreign counterparts, widening the U.S. trade deficit in goods (Milesi-Ferretti
2021). Also contributing to the widening goods trade deficit was the shift
in the balance of trade in oil and petroleum products from surplus to deficit,
which is discussed in box 3-2. Further, restrictions on foreign nationals
entering the United States and rising costs of maritime freight transportation,
a service that is primarily provided by foreign-owned firms, brought down
the surplus in services trade (BEA 2022a).
Macroeconomic developments here and abroad have contributed to the
widening trade deficit through another channel: exchange rate movements.
As the COVID-19 virus spread in early 2020, the U.S. dollar appreciated
9.7 percent from January to late March, reflecting the dollar’s status as a
safe asset (figure 3-8). In times of heightened economic uncertainty, investors around the world purchase dollar assets, which they view as a reliable
store of value (Jiang, Krishnamurthy, and Lustig 2021). From the end
of March 2020 through the end of 2020, the dollar depreciated as global
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Box 3-2. Trade in Oil and Petroleum Products
The United States is the world’s largest oil producer, and both an important exporter and a major importer of petroleum products (EIA 2021a).
These products constitute more than 10 percent of U.S. exports and about
7 percent of U.S. imports. Prices of oil and gas rose significantly during
the first 10 months of 2021, with West Texas Intermediate Crude prices
finishing the year more than 55 percent above its end-2020 level (EIA
2022) and global natural gas prices increasing almost sixfold between
November 2019 and November 2021 (IMF 2021). Higher prices, along
with rising volumes of imports and exports, meant that the dollar values
of U.S. petroleum products exports were almost 50 percent above their
2020 level, while imports were up more than 75 percent (figure 3-iii).
Foreign and domestic factors drove the rise in energy prices in
2021. In China, overall supply was constrained by ambitious government
efforts to rein in the burning of coal while manufacturing establishments’
energy demand jumped as production surged (Riordan 2021). As a
result, natural gas prices in Europe and Asia jumped due to the higher
Chinese demand for natural gas as a substitute for coal. Also pushing
up global energy prices was the OPEC+ (Organization of the Petroleum
Exporting Countries Plus) group of oil producers’ reluctance to more
rapidly expand oil production (Lawler, Ghaddar, and Astakhova 2021),
which they had cut by 10 million barrels per day (about 10 percent of
global production) in 2020 in response to the pandemic-induced drop
in demand (EIA 2020). In the United States, weak investments in new
energy sources during 2020 weighed on energy supply as the economy
recovered in 2021 (IEA 2021). Additionally, bad weather, including an
unusually cold winter in Texas and hurricanes Ida and Nicholas in the
Figure 3-iii. Trade in Petroleum Products
Dollars (billions)
25
20
15
10
5
0

Source: Census Bureau.
Note: Data are seasonally adjusted.

Balance

Exports

Oct-21

Jul-21

Apr-21

Jan-21

Oct-20

Jul-20

Apr-20

Jan-20

Oct-19

Jul-19

Apr-19

Jan-19

–5
–10

Imports

The U.S. Economy and the Global Pandemic | 109

Gulf of Mexico, also affected America’s oil production (EIA 2021b,
2021c).
On the demand side, the widespread availability of vaccines starting in the spring of 2021 meant the resumption of travel and some commuting, pushing up gasoline demand (EIA 2021d). Pandemic-induced
shifts in the modes of transportation used for travel and commuting
further boosted gasoline demand, as many opted to drive rather than use
mass transit or travel by plane (Bair, Guerra Luz, and Bradham 2021).

financial conditions began to normalize and the earlier flight to safety was
reversed. That depreciation also reflected the very aggressive action of the
Federal Reserve to support the U.S. economy by keeping interest rates low
(Economist 2021). This benefits American businesses and households that
borrow to purchase equipment or homes, but it makes U.S. financial assets
less attractive to global investors. Lower foreign demand for U.S. assets, in
turn, resulted in dollar depreciation from April through December 2020, as
seen in figure 3-8.
In 2021, the dollar resumed appreciating and ended the year up 3.6
percent against the currencies of its major trading partners, as measured by a
Federal Reserve Board index (figure 3-8). Expectations were that the Federal
Reserve would begin to tighten policy earlier than other central banks, and
that contributed to the rise in the dollar’s value (Rovnick, Rennison, and
Platt 2021). Such expectations reflected two aspects of America’s macroeconomic performance relative to our trading partners: the more rapid recovery in U.S. output, and the relatively larger rise in inflation. A strengthening
Figure 3-8. Nominal Broad Dollar Index

Index level: Jan. 2, 2020 = 100
110

↑ Dollar appreciation
105

100

95
Jan-19

Jul-19

Source: Federal Reserve Board.

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Chapter 3

Jan-20

Jul-20

Jan-21

Jul-21

dollar tends to widen the trade deficit by making imported goods cheaper
for American consumers, which boosts imports, and U.S. exports become
more expensive for foreign buyers, depressing exports (Gruber, McCallum,
and Vigfusson 2016).

International Trade in Goods
U.S. trade in goods rebounded relatively quickly after the sharp drop at the
onset of the COVID-19 pandemic in 2020, and continued to rise through
2021. Both exports and imports of goods broke nominal records set in 2018.
Goods imports breached record levels in real terms as well. This swift and
robust rebound stands in sharp contrast to the stagnation in trade that followed the Great Recession, beginning in 2008 (figure 3-9). From the start of
the Great Recession, goods exports did not recover from their precrisis peak
for more than two years, and goods imports did not systematically rise above
their precrisis peak for nearly 10 years.
As discussed in the previous section, 2021 growth in imports generally
outpaced that of exports. This has been true throughout the economic recovery. Even though goods imports had fully recovered in real terms to prepandemic levels by November 2020, U.S. exports did not achieve that feat
until more than a year later, in October 2021 (Census Bureau 2022b). The
faster recovery of imports relative to exports is a direct consequence of the
broader macroeconomic context discussed earlier in this chapter. However,
the effects of pandemic-related disruptions inhibited export recovery for
some products more than others.

Figure 3-9. U.S. Trade in Goods
Dollars (billions)

300
250
Imports

200
150
Exports

100
50
0

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021

Source: Census Bureau.
Note: Data are seasonally adjusted.

The U.S. Economy and the Global Pandemic | 111

Figure 3-10. Real Exports, Selected End-Use Categories
Index level: Feb. 2020 = 100
140
120
100
80
60

Capital goods

Consumer goods

Automotive goods

Oct-21

Jul-21

Apr-21

Jan-21

Oct-20

Jul-20

Apr-20

Jan-20

Oct-19

Jul-19

Apr-19

20

Jan-19

40

Food, feed, and beverage goods

Source: Census Bureau.
Note: Data are seasonally adjusted.

In real terms, U.S. exports of food, feed, and beverages were little
affected and exceeded their February 2020 levels for most of the second half
of that year. U.S. exports of consumer goods surpassed their prepandemic
level in November 2020 (figure 3-10).2 By contrast, exports of capital goods
did not exceed their prepandemic value until April 2021, and remained at
about that level for the rest of the year. Exports of autos and parts were more
than 10 percent below their prepandemic level for most of the year.
The relatively swift rebound in exports of consumer goods highlights
the global nature of the pandemic-induced switch from services to goods
consumption. The softer performance of capital goods and auto exports
reflects the flip side of the strong demand unleashed by the economic recovery. Supply challenges for critical inputs disrupted the global value chains
that characterize production in the automotive and other capital goods industries, inhibiting their ability to meet surging domestic and foreign demand
(see chapter 6 for a full discussion of supply chain challenges). The final
goods produced and exported by American businesses in these industries
are complex. Automotive exports often rely on semiconductors, the global
supply of which was notably stressed in 2021 (McKinsey & Company 2021;
Ewing and Boudette 2021). Civilian aircraft, engines, and parts represented
the largest share of the decline in exports of capital goods relative to 2019,
reflecting diminished demand by airlines after COVID-19 dramatically
reduced air traffic (Census Bureau 2022a; Kuzmanovic and Rassineux n.d.).
The BEA end-use category “food, feed, and beverages” consists of agricultural commodities,
including those used for animal feed, as well as fish and shellfish, prepared foods, and alcoholic and
nonalcoholic beverages.
2

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Chapter 3

Figure 3-11. Real Imports, Selected End-Use Categories
Index level: Feb. 2020 = 100
160
140
120
100
80
60
40

Capital goods

Consumer goods

Automotive goods

Oct-21

Jul-21

Apr-21

Jan-21

Oct-20

Jul-20

Apr-20

Jan-20

Oct-19

Jul-19

Apr-19

Jan-19

20

Food, feed, and beverage goods

Source: Census Bureau.
Note: Data are seasonally adjusted.

The composition of U.S. imports growth in 2021 highlights the
strength with which U.S. demand has recovered and the challenges economies around the world continue to face. U.S. goods imports dipped across
the board during the initial months of the pandemic, but to a lesser extent
than exports, and then rapidly exceeded their pre-COVID-19 level (figure
3-11). Consistent with the increased consumption of goods relative to services, imports of consumer goods showed a striking increase in 2021, rising
to 16.6 percent above their 2019 level. Imports of capital goods, such as
machinery used in factories, also rose notably in 2021, to 11.3 percent in
real terms above their 2019 level, as domestic American firms expanded to
satisfy booming U.S. demand.
The trajectory of automotive imports illustrates the global nature of
the supply chain stresses that emerged during 2021. Though automotive
imports initially rebounded, they subsequently declined as global supply
chains were disrupted (Ewing and Boudette 2021). Imports in this category
were 9.6 percent below their 2019 level in 2021. This category includes both
motor vehicles and parts, but the decline was entirely due to falling imports
of finished vehicles, while parts were slightly above their 2019 level (Census
Bureau 2022a). As discussed previously in this chapter, the recovery of the
U.S. automotive sector outpaced that of other major auto-manufacturing
countries in 2021.

International Trade in Services
In contrast to the relatively swift recovery of trade in goods, the exigencies
of containing the spread of COVID-19 continue to suppress global demand
The U.S. Economy and the Global Pandemic | 113

Figure 3-12. Trade in Services
Dollars (billions)

80
70
60
50
40
30
20

Balance

Exports

Oct-21

Jul-21

Apr-21

Jan-21

Oct-20

Jul-20

Apr-20

Jan-20

Oct-19

Jul-19

Apr-19

0

Jan-19

10

Imports

Source: Bureau of Economic Analysis.
Note: Data are seasonally adjusted.

for services. The overall decline in both exports and imports of services at
the onset of the pandemic (figure 3-12) is primarily due to a steep drop in
trade in travel services (figure 3-13). Total exports and imports of services
other than travel and transportation services—which covers finance, insurance, maintenance, construction, information, personal and government
services, intellectual property, and other services—exceeded their 2019
value in 2021.
Figure 3-13 illustrates that neither imports nor exports of travel services have approached their prepandemic levels. However, while imports
of travel services have increased relatively steadily since the pandemic first
hit the United States, exports saw only a minimal increase until November
2021, when the Biden-Harris Administration eased travel restrictions that
had prevented many foreign tourists, students, and business travelers from
traveling to the United States, resuming revenue from travel exports (White
House 2021c).3 By contrast, most other countries were open to U.S. travelers
for much of 2021 (Schengen Visa Info 2021; Ponczuk 2021).
Trade in transportation services has likewise been shaped by the
exigencies of the pandemic and economic recovery. The dramatic increase
in the deficit for the transportation services balance depicted in figure 3-14
directly reflects the challenges faced by shippers of goods in 2021. The rise
in maritime freight services imports largely drove the increased value of
imported transportation services (BEA 2022a). The skyrocketing cost of
moving goods from abroad to the United States meant that U.S. importers
paid dramatically more to shipping companies (Harper Petersen 2022).
Exports of travel services include goods and services acquired by foreign visitors, including foreign
students, while visiting the United States. Similarly, imports of travel services cover goods and
services acquired by U.S. residents visiting foreign countries.
3

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Figure 3-13. Trade in Travel Services
Dollars (billions)

18
16
14
12
10

Exports

Imports

8
6
4
2

Jan-21

Apr-21

Jul-21

Oct-21

Jan-21

Apr-21

Jul-21

Oct-21

Oct-20

Jul-20

Apr-20

Jan-20

Oct-19

Jul-19

Apr-19

Jan-19

0
-2

Source: Bureau of Economic Analysis.
Note: Data are seasonally adjusted.

Figure 3-14. Trade in Transportation Services
Dollars (billions)
12
10

8

Imports

6
4

Exports

2
0

–2

Oct-20

Jul-20

Apr-20

Jan-20

Oct-19

Jul-19

Apr-19

–6

Jan-19

–4

Source: Bureau of Economic Analysis.
Note: Data are seasonally adjusted. In official U.S. data on services trade, this category is named “transport”
rather than “transportation.”

Because nearly all major shipping firms are foreign-owned (Marine Digital
2021), these costs register as U.S. service imports. In contrast, U.S. exports
of transportation services are dominated by passenger air transportation,
which, like travel services, were suppressed by restrictions on foreign travel
to the United States until the end of 2021 (BEA 2022a).
Categories of services that saw a robust recovery included finance and
insurance trade and other business services imports. Because they do not
rely as heavily on in-person interaction, both imports and exports increased

The U.S. Economy and the Global Pandemic | 115

year-on-year relative to their 2019 levels throughout the pandemic and
recovery. Similarly, trade in intellectual property, telecommunications, and
other business services recovered quickly and is now above 2019 levels.

Policies to Build an Equitable International Economy
U.S. participation in the global economy has yielded important benefits,
including lower prices for consumers, lower costs for American manufacturing inputs, and access to a greater variety of products as well as larger
markets for American-made goods and services. However, global economic
integration has also increased the exposure of American businesses and their
workforces to import competition, which has meant loss of livelihoods for
some American workers, thus contributing to the troubling rise in inequality
documented in chapter 1 of this Report (Clausing 2019; Autor, Dorn, and
Hanson 2013, 2016, 2021). Other factors have also pushed up inequality, ranging from the declining progressivity of the tax system (Antràs,
de Gortari, and Itskhoki 2017) to increased automation in manufacturing
production (Moll, Rachel, and Restrepo 2021). Nonetheless, the effects of
U.S. international trade and investment policies on American workers and
communities, and thus on economic inequality, have also played a role.
The COVID-19 pandemic provided an opportunity to refocus domestic
and international policies to alleviate the disruptions that participation in the
global economy can inflict on American workers and increase the opportunities that it can offer them. This means seeking a better balance between,
on one hand, reducing costs for American businesses and lowering prices
for consumer products and, on the other hand, ensuring that workers whose
livelihoods are at risk from global competition are not disproportionately
harmed. Ensuring that U.S. participation in the global economy supports
the Biden-Harris Administration’s goal of a more equitable economy at
home also requires policies that level the international economic playing
field by improving labor standards abroad, confronting unfair practices by
our trading partners, and making the international tax system fairer. Trade
policy can also support another fundamental policy goal, the reduction in
greenhouse gas emissions; box 3-3 describes how this can be accomplished.

Broadening the Gains from Trade
The uneven effects of the COVID-19 pandemic demonstrated inequalities within American society, showcasing how negative economic shocks
can be disproportionately concentrated among individuals who are more
economically vulnerable.4 Similarly, the job and income losses that have
accompanied rising import competition have often fallen disproportionately
4

See, e.g., Mongey et al. 2021; Chetty et al. 2020; Liu and May 2020; and Hardy and Logan 2020.

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Box 3-3. Greenhouse Gas Emissions and Trade
As an example of how trade policy can support a broader set of goals,
consider international trade policy oriented toward incentivizing the
reduction of greenhouse gas emissions. Effectively combating climate
change requires policies that reduce global emissions of greenhouse
gases and increase resilience to the climate changes that have already
happened. However, those very policies can put domestic production at
a competitive disadvantage relative to production in countries with less
stringent mitigation policies (Dechezlepretre and Sato 2017). Further,
local policies that reduce emissions by producers in one country are
ineffective—from a global perspective—if their primary effect is to shift
emissions elsewhere.
To create a level playing field in domestic markets with strong climate policies and ensure maximal decarbonization from those policies,
scholars and policymakers have suggested introducing trade rules based
on the carbon content of traded goods and services. Such a policy could,
for example, impose a carbon fee on goods imported from countries with
less ambitious climate policies that offsets the climate regulatory costs
that producers face in the domestic market. Research suggests that these
policies can help accelerate decarbonization globally and protect the
domestic industry in the countries enacting them (Campbell, McDarris,
and Pizer 2021). For example, the United States and European Union
reached an agreement in late 2021 to negotiate a global arrangement
for trade in steel and aluminum that takes the carbon intensity of these
industries into account and that aims to drive industrial decarbonization
around the globe (White House 2021b).
These emissions-based trade policies need not favor any one
mechanism for incentivizing decarbonization, recognizing that domestic
mitigation policies can take many forms—from regulations to tax
incentives to a carbon price. Instead, these trade tools can retain the
flexibility for countries to enact a range of climate policy tools, as long
as emissions are decreasing. As discussed in chapter 7 of this Report,
policies that encourage domestic industries to shift toward clean energy
could, for example, take the form of regulations, tax incentives, and other
similar provisions.

on low-skilled workers, exacerbating inequality (Clausing 2019). A large
body of economic research focused on the effects of the dramatic increase
in import competition from China in the early 2000s, the so-called China
Shock, has demonstrated that, though the gains from international trade
have been substantial, the costs have outweighed these gains for some U.S.
communities. Increased import competition from China has had adverse
effects on employment and incomes in labor markets that are more exposed

The U.S. Economy and the Global Pandemic | 117

to competition from China, and these adverse effects have persisted long
after the initial shock (Autor, Dorn, and Hanson 2013, 2016, 2021).
In the future, U.S. policy should aim to mitigate and indeed reverse
the effects that greater exposure to import competition has had on inequality in America. This requires rebalancing the objectives of trade policy to
give greater weight to its impact on individuals and communities that are
negatively affected. To effectively incorporate these interests, policymaking
must become more inclusive, and thus must be informed not only by the
views of American firms directly engaged in international trade and workers
competing with imports but also by the views of affected communities and
other stakeholders.
In addition, economic scholarship has consistently called for complementary domestic policies to increase American workers’ competitiveness
and address the disruptions experienced by those affected by negative trade
shocks. Basic economic policies focused on workers would better equip
them to adapt to changes in the economy, including those that are transmitted through international trade (Clausing 2019; Rodrik 1996; Hanson 2021;
Dixit and Norman 1986). The investments in transportation infrastructure
that have been made possible by the Bipartisan Infrastructure Law will make
it easier for U.S. goods exports to reach markets overseas. Greater exports, in
turn, promote economic growth and support well-paying jobs, especially for
blue-collar workers (Riker 2015). Along with the other policy proposals to
fortify America’s supply chains discussed in chapter 6 of this Report, these
investments will also bolster U.S. competitiveness both at home and abroad,
and more broadly distribute the gains from the country’s participation in the
global economy. Looking ahead, the investments in human capital outlined
in chapter 4 of this Report would equip American workers with skills and
education that would enlarge their share in the benefits of international trade
and investment.

Leveling the International Economic Playing Field
Key to broadening the gains from trade is ensuring that American workers
are competing on a level playing field. Too often, the competitiveness of
American workers and firms has been eroded by other countries’ inadequate labor standards and unfair trade policies and practices, and also by
international tax competition.5 Economic analyses that ignore the negative
effects of these practices provide only a narrow and potentially misleading
view of the gains from trade and how they are distributed domestically and
internationally.
Labor standards. An important component of modern trade agreements between countries are provisions to improve labor conditions. These
5

Chapter 5 of this Report discusses the importance of fair competition in domestic markets.

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Chapter 3

are intended to ensure that workers are appropriately compensated and
protected during their work, and that relative competitiveness is not driven
by differences in labor standards between the countries. Twice in 2021, the
United States invoked the rapid response mechanism included in the United
States–Mexico–Canada Agreement to respond to allegations that workers
in Mexico were being denied the rights of free association and collective
bargaining. The first time was in response to corruption uncovered during
a worker vote on a collective bargaining agreement at an automotive plant,
which resulted in the United States and Mexico negotiating a plan to address
the violations and provide for a free and fair vote on the agreement (USTR
2021b). The second responded to a petition filed by the AFL-CIO and others
alleging violations during a union organizing campaign at an auto parts company (USTR 2021a). The resulting agreement with the company in question
not only secured compensation for the adversely affected workers but also
put in place mechanisms to protect workers’ rights.
Labor standards are also crucial when some producers resort to practices that are not only unfair but also inhumane, in that they rely on forced
labor. The International Labor Organization (ILO) estimates that 25 million
individuals on any given day are subjected to forced labor (ILO 2017), and
that this forced labor generates large profits for the firms involved (ILO
2014). Though some have argued that market forces on their own will drive
coercive employers out of the labor market, recent theoretical modeling calls
this result into question (Acemoglu and Wolitzky 2011). Indeed, the tragic
persistence of forced labor suggests that policy actions are needed to combat the practice. To this end, Group of Seven leaders, including the United
States, made combating forced labor a priority starting at their June 2021
meeting (Group of Seven 2021). After discussions of conditions in China’s
Xinjiang Uyghur Autonomous Region (White House 2021a), the Group of
Seven called for strengthened cooperation and collective efforts to eradicate
the use of all forms of forced labor in global supply chains.
Responding to unfair trade policies and practices. One of the most
significant challenges for the United States’ ability to realize broadly distributed gains from trade is the direct and indirect support for targeted industries
used by some foreign governments to promote their own domestic producers at the expense of other producers, including the United States. Foreign
governments implement such policies using a variety of tools, including
taxes, subsidies, preferential regulatory treatment for domestic enterprises,
broad support for state-owned enterprises or other state-affiliated entities,
and formal and informal restrictions on the ability of foreign enterprises to
compete in the domestic market. At a minimum, these interventions create
economic distortions that disadvantage foreign producers in the domestic
market and often in third-country markets as well, diminishing the benefits
of the commitments they have made under multilateral and preferential trade
The U.S. Economy and the Global Pandemic | 119

agreements. In more egregious circumstances, they can concentrate market
power in the country that uses them, stifling global competition, limiting
innovation, and creating opportunities for economic coercion (Sykes 2003;
Hart 2020; Autor et al. 2020; Bown 2022).
Global markets for industries such as steel, aluminum, and solar panels
bear the hallmarks of government policies designed to secure market power.
Over time, China’s array of government support and policy directives, which
experts have argued amount to sizable subsidies, have led China to become
the dominant global supplier in each of these industries (Bown and Hillman
2019). Public statements of policy suggest that China is using continued,
targeted government support for specific high-tech manufacturing industries
aimed at promoting its dominance at the expense of its trading partners
(CRS 2020; Creemers et al. 2021). Unchecked, the effects of China’s capture of these industries can be expected to give Chinese firms substantial
market power, further concentrating crucial aspects of global manufacturing
in a single country, at the expense of producers of competing goods in the
United States (Bown and Hillman 2019). Such policies can also hinder the
adoption of critical innovations, because the subsidies that facilitate market
dominance are not necessarily directed toward the best technology available (Hart 2020). Importantly, the burdens associated with China’s system
of targeted industrial policies fall not only on the United States but on all
countries whose producers compete with China in global markets (McBride
and Chatzky 2019). As such, efforts to counter the use of these policies are
most effective when pursued collaboratively and in concert with U.S. allies
and partners (Mattoo and Staiger 2020).
Reform of the international corporate tax system. Leveling the playing
field for American workers and businesses requires reform of the international corporate taxation system to curtail a race to the bottom in corporate
taxation, whereby countries lower their tax rates to attract mobile multinational activities (Azemar et al. 2020). This practice distorts businesses’
decisionmaking, including production decisions, while also generating less
tax revenue than could be obtained if countries engaged with one another
cooperatively (Cobham and Jansky 2018). Large multinational firms have
taken advantage of this tax competition among countries by shifting profits
and economic activities to minimize their tax burdens (Guvenen et al. 2019).
In 2021, world leaders reached a historic agreement that will address
these challenges and stabilize the international tax system. The plan to
reform international tax practices was agreed to by the overwhelming majority of the world’s economies—representing over 90 percent of world GDP.
The agreement includes a global minimum tax of 15 percent that would
apply to profits of multinational firms that have more than €750 million
(about $822 million) in sales globally. It also includes provisions that would
reallocate some taxing rights over certain residual profits of multinational
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Chapter 3

firms to the markets where products are consumed, regardless of whether
these firms have a physical presence in these markets (OECD 2021).
These reforms respond to concerns that businesses generate value from
profits in certain jurisdictions while paying minimal taxes there. As such,
the agreement addresses existing international tax tensions by incorporating
commitments from several countries to withdraw digital services taxes that
would have fallen disproportionately on multinationals headquartered in the
United States (Giles 2021). The reforms would generate additional revenue
that could help countries address the myriad challenges they face, including
rising inequality.

A Collaborative, Transparent Policymaking Process
Reorienting policy to ensure that the United States’ participation in the
global economy does not exacerbate rising inequality requires important
changes, but experience shows that the benefits of such policy shifts are
greater when they happen after consultation with our trading partners and
through a process that is transparent for those affected. Through trade agreements and through entities such as the World Trade Organization, the United
States has long cooperated with its trading partners to establish and enforce
global trade rules (Bagwell, Bown, and Staiger 2016). In addition to providing reliable market access for U.S. exporters, such institutions limit the use
of beggar-thy-neighbor policies, which advance one country’s targeted economic outcomes at the expense of those of other countries (Ossa 2014). An
approach to addressing the flaws in current U.S. trade policy and in global
trade rules that ignores the commitments the United States has made weakens these institutions and diminishes the benefits that they bring to American
firms and workers. This is exemplified by the retaliatory measures taken
by many of our trading partners in response to U.S. trade policy actions in
2018 and 2019 that they judged to be in violation of commitments made
by the United States under the World Trade Organization’s rules (Mattoo
and Staiger 2020). These retaliatory measures cost U.S. manufacturing jobs
(Flaaen and Pierce 2019), exports (Morgan et al. 2022), incomes, and more
broadly economic welfare in the period immediately after their imposition
(Amiti, Redding, and Weinstein 2019; Cavallo et al. 2021).
Fundamentally, the global system of trade rules benefits not only
domestic producers directly engaged in international trade as importers
or exporters but also buyers of goods and services for which prices are
influenced by global markets. A large body of research has established that
uncertainty negatively affects economic outcomes (Bloom 2014), and more
recent work makes clear that this is also true of trade policy uncertainty
(Caldara et al. 2020; Heise et al. 2021). Global trade rules limit uncertainty
about future changes in tariffs or the imposition of other trade restrictions,

The U.S. Economy and the Global Pandemic | 121

which can in turn foster investment and employment. Although changes to
U.S. trade policy are needed, elevated uncertainty about how trade policy
might alter prices and availability along global value chains pose a particular
challenge in the wake of the COVID-19 pandemic’s supply chain disruption
(Miroudot 2020).
Making the necessary changes to U.S. international economic policy
to ensure the benefits from trade are more broadly distributed and that
competition takes place on a level playing field demands rethinking some
of the existing rules and norms governing international economic relations.
The practical difficulties of making changes within existing institutions creates a complex challenge for governments seeking to develop sustainable
international economic policy. However, implementing changes noncooperatively could ultimately leave the United States worse off if its trading
partners no longer feel constrained to respect their own commitments
(Mattoo and Staiger 2020; Bown and Hillman 2019). Trade policy that is
long on combative rhetoric and indifference to trade partners’ interests, but
short on substance and consistency, puts American firms at a disadvantage.
It dissuades our partners and allies from working with the United States to
tackle common challenges. Importantly, it cannot deliver on creating jobs,
reducing inequality, or promoting economic growth more generally. Since
2021, the Biden-Harris Administration has been renewing strong relationships with our trading partners, working to resolve outstanding trade issues
and to establish cooperative frameworks to address emerging challenges.

Conclusion
Comparing the performance of the United States’ economy during 2021 with
that of our trading partners demonstrates this country’s resilience at a time
of daunting challenges. Supported by a strong fiscal response and a rapid
vaccine rollout, the GDP of the United States exceeded its prepandemic
level before those of other major advanced economies. However, as the
recovery got under way, demand continued to tilt toward goods and away
from services. This shift in global consumption patterns interacted with
stressed supply chains to generate inflation in the United States and most of
our major trading partners, although this effect was particularly pronounced
here due to the relative strength of our recovery. The faster pace of the U.S.
economic recovery has also resulted in a widening trade deficit.
Openness to international commerce provides substantial benefits to
the U.S. economy. However, these benefits have at times come at the cost
of wider domestic inequality. We must engage with our partners and allies to
make international economic engagement work for all Americans, by ensuring that the global rules are aligned with domestic objectives and values, and
that these rules are rigorously enforced.
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Chapter 4

Investing in People: Education,
Workforce Development, and Health
To increase productivity and growth, we must invest in the American
people. U.S. investments in universal primary and secondary education in
the early 20th century, combined with medical advances in such areas as
vaccines and antibiotics, contributed to strong growth throughout most of
that century (Goldin and Katz 2008; Goldin 2016). Life expectancy at birth
increased by nearly 30 years between 1900 and 2000 in the United States
(CDC 2017), and we developed a highly skilled labor force (Goldin and
Katz 2008). These gains contributed to economic growth and rising living
standards across the Nation. However, increases in educational attainment
and life expectancy have slowed in recent decades, and the United States is
now falling behind other peer countries.
When society invests in people, the economy has more capacity to grow. In
the first half of the 20th century, for example, the United States led the world
in high school enrollment (Goldin and Katz 2008) and ranked among the top
10 in life expectancy.1 In contrast, by 2017, the country had slipped to 12th
in the share of 25- to 34-year-olds having completed some postsecondary
education and to 29th in life expectancy at birth among members of the
Organization for Economic Cooperation and Development (OECD) and
its partner countries.2 These slips in rank are not simply a matter of other

CEA calculations, based on life expectancy data from Roser, Ortiz-Ospina, and Ritchie (2013).
CEA calculations, based on tertiary education data from the OECD (2022a) and life expectancy
data from the OECD (2022b). Tertiary education data are not available for India or China in 2017.
1
2

123

countries catching up, but rather of the United States falling further behind.3
This suggests that the United States may be underinvesting in people,
potentially dampening economic progress. Further, there are widespread and
long-standing disparities in the United States by race, ethnicity, and gender
in measures of human capital investment and accumulation. For example,
in 2019, 82 percent of Asian young adults immediately enrolled in college
after high school completion, compared with 58 percent of Black recent high
school graduates (de Brey et al. 2021, table 302.20); and in 2018, life expectancy at birth for a Hispanic infant was seven years longer than for a nonHispanic Black infant (Arias and Xu 2020). Inequitable access to relevant
resources exacerbates the persistence of these issues. For more discussion
of the structural nature of such racial and gender disparities, see chapter 5.
Economists analyze investments in people in terms of the “human capital”
they produce—a concept that captures the knowledge, skills, health, and
other valuable resources embodied in a person. Just as investments in physical or financial capital can reap benefits, well-timed investments in people
can generate payoffs to individuals, employers, and society. Education and
job training are classic examples of inputs to human capital. Other crucial
investments include mental and physical health and work experience. The
contributions these investments in people make to economic well-being
and growth depend on how effectively the human capital they produce is
developed and deployed.4
This chapter focuses on what is known about key investments in human
capital—education, workforce development, and health—as well as policies
to ensure that individuals, society, and the economy can fully benefit from
In 1992, the United States ranked second in the percentage of young adults with postsecondary
education, 3 percentage points behind Canada. By 2019, just over 50 percent of U.S. young adults
had completed some form of postsecondary school, roughly 13 percentage points behind Canada and
more than 19 percentage points below number-one-ranked South Korea. Further, in 1975, the U.S.
life expectancy at birth was within three years of the top-ranked OECD country (Iceland); but by
2019, U.S. life expectancy at birth was four years below Iceland and five and a half years below that
of top-ranked Japan.
4
See Jacobs and Hipple (2018) for a discussion of inequality and intergenerational mobility with a
similar frame.
3

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these investments. The first section explains why human capital plays such
an important role in economic growth. The second section discusses ways
in which additional investments in education, workforce development, and
health are needed to improve the development of human capital and reverse
the course of the past 20 years. And the third and final section highlights several areas where changes to government policy or institutional and societal
practices could help people deploy their human capital more productively.

Human Capital Is Critical for Economic
Growth and Individual Well-Being
In thinking about how human capital affects individuals and the economy,
researchers focus on both macroeconomic and microeconomic perspectives.
From the macroeconomic perspective, human capital improvements are a
key factor in generating economic growth; and, ultimately, long-run economic growth helps determine living standards. Generally, economists look
for output to grow at least as fast as population growth to maintain living
standards and to grow faster than population growth to improve them; thus,
they often rewrite total output in terms of output per person.
Figure 4-1 shows the time series of per capita U.S. gross domestic
product (GDP)—the most popular measure of economic output—on a ratio
scale from 1870 through 2021. The ratio scaling means that the slope of the
fitted line (shown in orange dots) represents the average annual growth rate
over the period. As shown in figure 4-1, the growth rate was remarkably
stable over this time, despite large deviations from that trend during and
after the Great Depression. Over the roughly 150-year period, per capita
U.S. GDP grew at an average rate of 1.8 percent a year.
In a simple model of the economy, output per person can be written
in terms of four factors—the (physical) capital-output ratio, human capital
per person, research intensity (idea generation), and the number of people in
the economy. When Fernald and Jones (2014) decompose per capita GDP
growth into growth from these four components over the 1950–2007 period,
they estimate that 20 percent of growth came from increases in human capital, nearly 60 percent can be attributed to increases in research intensity, and
the remaining roughly 20 percent was due to a growing population.5
From a microeconomic perspective, human capital accumulation is
associated with various benefits to individuals, their families, and their
communities. Although many benefits of human capital investment accrue
Since capital and output grew at roughly the same rate, the capital-output ratio added 0 percent to
per capita GDP growth over this period.
5

Investing in People: Education, Workforce Development, and Health | 125

Figure 4-1. U.S. Gross Domestic Product per Person, 1870–2021
Gross domestic product per capita (2012 dollars), ratio scale
64,000

32,000

16,000

8,000

4,000

2,000

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

2020

Source: Updated and reproduced from Fernald and Jones (2014). Data for 1870 to 1929 are from Madison (2008). Data for 1929 to 2021 are
from the Bureau of Economic Analysis.
Note: Orange dots represent the fitted line.

directly to individuals in the form of buying power or the ability to enjoy
life, this chapter primarily focuses on individuals as workers in the economy
and how human capital investments contribute to U.S. productivity and
growth.
The relationship between additional years of education and earnings is
among the most extensively documented in economics. Figure 4-2 illustrates
this relationship, reflecting that, on average, more highly educated workers
both earn higher wages and enjoy higher employment rates. Researchers
find positive returns to additional education at the elementary and secondary levels as well as the postsecondary level (Angrist and Krueger 1991;
Card 1995; Kane and Rouse 1995; Ashenfelter and Rouse 1998; Card 1999;
Zimmerman 2014). Additional years of education increase wages, on average, because education increases worker productivity in the labor market,
which increases output growth. Similarly, work experience is associated
with higher earnings as workers develop valuable skills through on-the-job
training.
Researchers find that more education also reduces adult mortality rates
(Buckles et al. 2016) and incarceration rates (Lochner and Moretti 2004) and
raises civic engagement (Milligan, Moretti, and Oreopolous 2004). Research
also finds that maternal education has a positive effect on infant health

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Figure 4-2. Earnings Increase with Years of Schooling
Annual wage and salary income (2019 dollars)
80,000
70,000

Total Personal Income

60,000
$100,000
$90,000

50,000
$80,000
$70,000

40,000
$60,000
$50,000

30,000
$40,000
$30,000

20,000
$20,000
$10,000

10,000
$0
0

0

1

2

0

1

2

3

3

4

4

5

5

6

6

7

8

9

10 11 12 13 14 15 16 17 18

7 8 9 10 11 12 13 14 15 16 17 18
Years of Schooling

Source: 2015–19, American Community Survey, 5-year sample.
Note: The sample is limited to individuals 25 and above.

(Currie and Moretti 2003). As such, investments in education can even raise
the human capital of the next generation.
Although health is prominent as one of human capital’s key elements
(along with education, migration, labor market information, and job training)
in the original formulation of human capital by Schultz (1962), fewer studies have explored health within this framework. Vaccines and public safety
measures, through reductions in death and work-hampering disability, can
increase the size and productivity of the workforce (Bleakley 2010; Hamory
et al. 2021). Other health investments increase productivity by improving
workers’ mental health or quality of daily living. At the macroeconomic
level, cross-country regressions suggest that health is a robust predictor of
economic growth, with a one-year increase in life expectancy predicting an
increase in GDP per capita of about 2 to 4 percent (Sharma 2018; Bloom
and Canning 2003).

Investing in People: Education, Workforce Development, and Health | 127

Measuring the Stock of Human Capital
Researchers would, ideally, like to study all forms of human capital but
remain limited to those aspects that can be easily or consistently measured.
For example, the World Bank’s index of the stock of human capital in different countries is constructed using measures of childhood survival and health
in addition to quality-adjusted educational attainment, which are combined
with estimates of how these dimensions affect productivity (Kraay 2018).
Measures of human capital used to estimate potential GDP growth in the
United States depend largely on educational attainment, work experience,
and estimates of how both affect productivity. Educational attainment and
years of work experience are only proxies for human capital. Notably, years
of completed education are not adjusted for differences in quality and do not
reflect job-training programs, such as apprenticeships, that operate outside
a school-based setting. Further, any systematic change in human capital that
is unmeasured, such as improvements in the quality of education or declines
in health, can bias estimates of human capital and potential output growth.
Rising U.S. educational attainment was a main driver of measured
human capital growth over the second half of the 20th century (Aaronson
and Sullivan 2001). However, though recent cohorts of Americans are the
most educated ever, their average years of completed education only modestly exceed what the prior generation attained. Figure 4-3 displays average
years of education over time for individuals age 25–34 years and age 55–64.
In 1960, individuals in the 55–64 range had completed 9.2 years of education on average, while the younger group, 25–34, averaged 11.3 years of
education, a difference of just over 2 years. By 1990 this gap had closed to
Figure 4-3. Average Years of Education by Age Group
Average years of education
16
14
12
10
8
6
4
2
0

1960

1970

1980

1990
Age 25–34

2000

2010

2019

Age 55–64

Sources: Census Bureau; CEA calculations. Data for 1960 and 1970 are from the 1 percent sample; data for 1980, 1990, and 2000 are from
the 5 percent sample; and data for 2010 and 2019 are from the American Community Survey.
Note: If educational attainment is nursery school to grade 4, the observation is coded as 4 years of education. If educational attainment is
grade 5, 6, 7, or 8, the observation is coded as 8 years of education. Observations with 5+ years of college are coded as 17 years of education.

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Figure 4-4. Life Expectancy, 1900-2019
Years
85

80

75

70

65

60

55

50

45

40

35

1900

1910

1920

1930

1940

Females, age 45

1950

1960

Males, age 45

1970

1980

Females at birth

1990

2000

2010

Males at birth

Source: Our World in Data 1900-2014, Social Security Administration 1915-2019.

just over 1 year, and by 2019 the gap was only half a year. When younger
cohorts far exceed their elders in years of completed education, the average
education level of the workforce increases rapidly. However, as that gap
closes, retirees are replaced by new entrants with roughly the same level of
education. This slowing in the growth of educational attainment corresponds
to a slowing in the growth of human capital per worker, all else being equal.
When measuring health as a contributor to human capital, life expectancy at birth is a common metric used in the United States and other
developed countries. Figure 4-4 illustrates life expectancy at birth and at
age 45 for both males and females between 1900 and 2019. In that period,
life expectancy at birth rose about 30 years for both sexes. Most of the
gains occurred between 1900 and 1955, largely due to reductions in infant
and child mortality (Crimmins, Preston, and Cohen 2011). As a result, life
expectancy at age 45 increased by a more modest 13 years for females and
10 years for males. In the decade before the COVID-19 pandemic, life
expectancy at birth rose by less than half a year, compared with gains averaging about 4 years per decade between 1900 and 1950 and 1.7 years per
decade between 1950 and 2010.
The COVID-19 pandemic has directly destroyed human capital
through death. The virus has also reduced and delayed investments in

Investing in People: Education, Workforce Development, and Health | 129

Figure 4-5. Percent Reporting Health as Fair or Poor, 1997–2018
Percent

25

20

15

10

Age 25–44

Age 45–54

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

0

1997

5

Age 54–65

Source: National Health Interview Survey.

education, experience, and health. Of note, COVID-19 accounted for sizable
reductions in estimates of provisional life expectancy between 2019 and
2020 (Arias et al. 2021), even though children who were born in 2020 are
unlikely to experience the same conditions at older ages that led to the estimated decline. This is one example of why changes in life expectancy can
be less meaningful as a reflection of health human capital than alternatives
such as disease prevalence, work-limiting disabilities, or indices of activities
of daily living. (For more on the effect of the COVID-19 pandemic on health
human capital, see box 4-1.)
That said, the plateauing of gains in life expectancy before the pandemic is consistent with evidence from other measures of health that are only
available for more recent years. Self-reported health, for example, can be
predictive of subsequent mortality, even after controlling for socioeconomic
status and comorbidities (McGee et al. 1999). Figure 4-5 presents data from
the National Health Interview Survey on the percentage of respondents by
age group reporting that their self-assessed health status was either fair or
poor between 1997 and 2018. For adults in all three age groups, the percentage rating their health as fair or poor has held steady or even increased over
this period. These findings suggest that the growth in the stock of health
human capital among working-age adults has slowed.
Demographic change, driven by the current and upcoming retirements
among the large baby boom generation, also has implications for growth
in human capital per person. When baby boomers first started entering

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Box 4-1. COVID and Health
Through the end of 2021, there were over 820,000 reported deaths from
COVID-19 in the United States (CDC 2022). One measure of excess
deaths, which includes unreported pandemic deaths along with deaths
from related causes, suggests the true COVID-19 death toll through 2021
might be 15 percent higher than reported (Giattino et al. 2020). About
75 percent of reported deaths from COVID-19 have occurred among
those over age 65 (CDC 2021a). As shown in figure 4-i, deaths from
COVID-19 are more concentrated among older people, especially those
age 85 and above.
However, deaths do not tell the whole story; communities of color
saw higher rates of hospitalization and greater losses in life expectancy
between 2019 and 2020, largely due to the effects of COVID-19 (CDC
2021c; Arias et al. 2021). Further, there were over 54 million reported
COVID-19 infections through the end of 2021, and tens of thousands of
patients were hospitalized with the virus during a typical week in 2021
(CDC 2022; Johns Hopkins University 2022). These consequences of
the pandemic are causes for concern from a human capital perspective:
COVID-19 can cause many health complications aside from death, and
those complications may be occurring in people who have much of their
working lives ahead of them.
The effects of COVID-19 on health are not limited to those who
become infected, however. Secondary consequences of the pandemic
have created a series of health challenges. Primary among them has
been an overall decline in mental health. More than half of women and
a third of men reported worsening mental health after the beginning of
the pandemic, with about a fifth saying the pandemic had a major impact

Figure 4-i. Share of COVID Deaths and Share of Population by Age
Share
30

Share of COVID
deaths

25

Share of
population

20
15
10
5
0

0–17

18–29

30–39

40–49

Years

50–64

65–74

75–84

85+

Source: Centers for Disease Control and Prevention.
Note: Data are current as of January 18, 2022.

Investing in People: Education, Workforce Development, and Health | 131

(Frederiksen et al. 2021). One study estimates the risk of depression
among college students in spring of 2020 was 50 percent higher than
prepandemic rates (Giuntella et al. 2021); another finds that the average
share of adults reporting symptoms of anxiety disorder and/or depressive
disorder were up nearly fourfold in January 2021 (Panchal et al. 2021).
The problem has also been worse for groups that are already socially
marginalized. Women with children, Hispanic and Black people, the
unemployed, and essential workers were more likely to report mental
health issues during the pandemic (Panchal et al. 2021).
Declines in mental health during the pandemic exacerbated other
negative outcomes. A late 2020 survey found that 15 percent of adults
in the United States reported starting or increasing substance use as a
way of dealing with the pandemic (Czeisler et al. 2021). In November
2021, the Centers for Disease Control and Prevention (CDC) estimated
that there were 100,306 overdose deaths in the 12-month period ending
in April 2021, up nearly 30 percent from the previous 12 months and
the highest count on record (CDC 2021b). Domestic partner violence
also increased globally by about a third in 2020 as compared with 2019
(Newman 2021).
The pandemic also created difficulties in receiving medical care
for other conditions. In the initial phases of the pandemic, 29 percent
of adults reported forgoing medical care due to fears of catching
COVID-19, while another 7 percent missed care due to COVID-related
financial concerns (Anderson et al. 2021). This number was still about
10 percent in April 2021, with Black and Hispanic adults, those with
low incomes, and people with chronic conditions the most likely to miss
care (Gonzalez, Karpman, and Haley 2021). Another study finds that,
among adults reporting that they missed or delayed health care due to
the pandemic, one-third reported that doing so negatively affected their
health, ability to work, or ability to perform other activities (Gonzalez
et al. 2021). Declines were particularly acute in the use of mental health
services, substance use treatment, primary care, childhood vaccinations,
and dental visits (CMS 2021). Uses of many types of care had not fully
rebounded as of mid-2021. Hospital admissions were still about 20
percent below the prepandemic trend in April, while health spending
remained 7 percent below trend in June (Gallagher et al. 2021). Some of
these changes may be due to longer-lasting responses to the pandemic by
medical professionals. Two percent of physicians in one survey reported
closing their practice due to COVID-19, while 32 percent cut back on
staff (Physicians Foundation 2021).

the labor market in the late 1960s, the average age of the labor force (and
therefore the number of years of expected work experience) declined. This
decline continued until the last of the baby boom generation entered the
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labor force in the mid-1980s (Aaronson and Sullivan 2001), at which point
the average age of the labor force began to increase, marking a positive
effect on average human capital. As the baby boom generation retires, the
U.S. labor force is losing a large population of highly experienced workers.
Slowing growth in educational attainment and health improvements,
combined with the retirement of baby boomers, results in slower overall
growth in human capital per worker. These factors are reflected in economic
forecasts of slower potential growth (see, e.g., Woodward 2013; Fernald
2016; and Fernald and Li 2019). There is scope for increasing human capital
through targeted investments, and additional scope for increasing effective
human capital through policies that help individuals deploy their human capital more efficiently. Such investments help bolster future economic growth.

Investing in Education and Skill Development
Long-term trends point to future cohorts having similar years of educational
attainment but no more years of experience than current workers. This raises
the question: how else can we develop more human capital during the time
in life typically devoted to education? One promising strategy is working to
close existing inequities between children in different circumstances—such
as different racial or ethnic groups, urban and rural communities, and moreand less-advantaged economic backgrounds—through interventions starting
with early childhood.

Early Childhood Education and Care
Although the terms used may differ based on the age of the children
involved, all forms of care in early childhood present opportunities for
important cognitive, social, and emotional development. Indeed, a National
Academy of Sciences review notes that “virtually every aspect of early
human development, from the brain’s evolving circuitry to the child’s
capacity for empathy, is affected by the environments and experiences that
are encountered in a cumulative fashion, beginning early in the prenatal
period and extending throughout the early childhood years” (Shonkoff and
Phillips 2000, 6).
Both theoretical models and empirical evidence indicate that access
to high-quality early childhood care and education improves human capital.
Cunha and Heckman (2007) develop a model of human capital production
in which early investments in human capital are complements to investments
made later in life. In this model, early investments make later investments
more productive; conversely, early investments only have limited productivity if not backed up by later investments. This theory, referred to as dynamic

Investing in People: Education, Workforce Development, and Health | 133

complementarity, is an important basis for supporting investments in highquality early childhood care and education.
Children from low-income families often begin kindergarten at an academic disadvantage. Though there are also disparities at entry by race and
ethnicity, these differences are smaller than those by family income. Based
on a nationally representative sample of children entering kindergarten in
the fall of 2010, mathematics and reading skills for children from families in
the bottom income quintile were, on average, more than 1 standard deviation
below math and reading skills for children from families in the top income
quintile;6 by the spring of fifth grade, these gaps were largely unchanged.7
These large differences in early skills are predictive of worse later outcomes
in educational attainment and even arrest rates (Duncan and Magnuson
2011). As a result, expanding access to high-quality early care and education has long been viewed as having the potential to improve outcomes for
children from low-income families.
In the short run, many early childhood programs have been shown to
increase student achievement, particularly for children from low-income
families (Cascio 2015, forthcoming; Yoshikawa, Christina, and BrooksGunn 2016). These early test-score advantages often fade out in the medium
term (e.g., see Puma et al. 2012; Durkin et al. 2022). In contrast, high-quality
early childhood programs have a long track record of improving a broad
array of longer-term outcomes ranging from educational attainment and
earnings to criminal activity. For example, a study of the cohorts of children
who benefited from heavily subsidized universal child care as a result of
the Lanham Act during World War II finds that they were more likely to
be employed, had higher earnings, and received less cash assistance during
adulthood than the cohorts of children born just after those exposed to the
Lanham Act funding (Herbst 2017). Similarly, another study finds that a
large-scale expansion of subsidized childcare in Norway during the mid1970s had large positive effects on children’s educational attainment and
labor force participation as adults and reduced their welfare dependency
(Havnes and Mogstad 2011). Further, studies of Head Start, the program
established as part of President Lyndon B. Johnson’s “War on Poverty” to
boost services to low-income children and their families, find long-term
benefits of these investments for several human capital and labor market
outcomes (Ludwig and Miller 2007; Deming 2009; Bailey, Sun, and Timpe
2021). More recently, students who were randomly selected for preschool
Researchers often measure differences in outcomes in standard deviation units in order to
be comparable across different outcomes such as graduation rates and test scores. In a normal
distribution, 68 percent of the observations are within 1 standard deviation of the mean, meaning that
only 16 percent of all observations are more than 1 standard deviation below the mean. Thus, lowincome students scoring, on average, more than 1 standard deviation below high-income students is
a large difference.
7
CEA calculations, based on de Brey et al. (2021, tables 220.40 and 220.41).
6

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slots in Boston were more likely than students who were randomized out of
preschool access to take the SAT, graduate from high school, and enroll in
college (Gray-Lobe, Pathak, and Walters 2021).
The fact that high-quality early education and care programs have
long-term effects on outcomes such as high school graduation and college
enrollment suggests that they can generate long-run improvements in children’s human capital. Building noncognitive skills (sometimes called soft
or social skills) is particularly relevant because of their importance in the
current labor market. In this computer age, the tasks that prove difficult to
automate are those that rely on personal interactions (Autor 2015). Deming
(2017) finds that, between 1980 and 2010, occupations requiring social
skills grew by nearly 12 percentage points; wages also grew more rapidly
for these types of jobs. This evidence reinforces the role early childhood
education can play in increasing human capital and in providing the skills
necessary for a modern economy.
However, access to high-quality early care and education differs by
family income and race or ethnicity. For example, Hispanic and American
Indian / Alaska Native populations are more likely to live in neighborhoods
without adequate childcare availability, as are families in the lowest-income
neighborhoods (Malik et al. 2018). In Georgia, Bassok and Galdo (2016)
found that state preschool classrooms in low-income and high-minority
communities were rated significantly lower in quality, even though Georgia
is considered a national leader in high-quality early education and care.
Children from low-income families are also less likely to be enrolled
in preschool. In 2019, 42 percent of three- and four-year-old children living
in households below the poverty threshold were enrolled in preschool, compared with 54 percent of those living in households at or above 185 percent
of the poverty threshold (de Brey et al. 2021, table 202.20). Thus, greater
access to public preschool programs may help close gaps in kindergarten
readiness between lower- and higher-income children. However, results
vary between universal preschool programs, which serve all children, and
ones that are means-tested, which serve only children from families with
low enough incomes to qualify. Cascio (forthcoming) finds that state-funded
universal preschool programs generate large test score gains, particularly for
children from low-income families. Indeed, Cascio estimates a cost/benefit
ratio of $3.52 for universal preschool programs. Universal preschool for all
three- and four-year-old children, combined with investments in childcare
provisions, could help ensure equal access to high quality early education
and care for all children.

Investing in People: Education, Workforce Development, and Health | 135

K-12 Education
Despite a long-standing debate on the question of whether increased school
spending improves student outcomes, modern quasi-experimental research
on the topic suggests that increased school spending has a positive causal
effect on students’ future education and labor market outcomes (Card and
Payne 2002; Jackson, Johnson, and Persico 2016; Hyman 2017; Lafortune,
Rothstein, and Schanzenbach 2018).
However, as in early childhood education and care, access to highquality K-12 schools differs by family income and race or ethnicity. Rouse
and Barrow (2006) and Barrow and Schanzenbach (2012) find that though
some resource measures may be quite similar or even somewhat higher in
districts with greater shares of disadvantaged children, children from more
advantaged backgrounds arguably attend higher-quality public elementary
and secondary schools. For example, students from families of low socioeconomic status are more likely to have teachers with less than three years
of experience and to attend schools with inadequate facilities or temporary
buildings. Similarly, high-poverty schools are more likely to employ
teachers who do not have a certificate or major in the field they teach.
Additionally, differences in academic achievement by race and ethnicity
widen between kindergarten entry and fifth grade, suggesting that there may
be systematic differences in elementary school quality by student race and
ethnicity.8 As such, policy interventions aimed at improving school quality for children from disadvantaged families and communities of color are
likely to be important for increasing human capital growth.
Although there is little consensus about effective education policies,
several themes have emerged from the literature beyond the basic finding
that resources matter. Barrow and Rouse (2007) review evidence on several inputs in K-12 education, including class size, teacher quality, time in
school, and technology. Several studies find that class size matters, particularly for students in the early grades (Angrist and Lavy 1999; Krueger 1999;
Krueger and Whitmore 2001), though class size reduction is expensive and
implementation at scale can be a challenge (Bohrnstedt and Stecher 2002).
Not surprisingly, researchers also find strong evidence that teachers matter
(Aaronson, Barrow, and Sander 2007; Rivkin, Hanushek, and Kain 2005;
Chetty, Friedman, and Rockoff 2014), and many school reform efforts in
the early 2010s included the adoption of teacher performance rating systems
that combined measures of teachers’ effects on student achievement (value
added) and classroom observation (National Council on Teacher Quality
2017). Researchers find that these types of reforms can improve average
teacher quality by leading the lowest-performing teachers to exit teaching
at higher rates (Sartain and Steinberg 2016; Dee, James, and Wycoff 2021).
8

CEA calculations, based on de Brey et al. (2021, tables 220.40 and 220.41).

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There is also some evidence that teacher performance evaluation can lead to
improvements in teacher practice (Taylor and Tyler 2012).
Instructional time has also been shown to have a positive effect
on student achievement, through evidence that a longer school year can
improve student outcomes (Pischke 2007), as can longer school days (Figlio,
Holden, and Ozek 2018; Atteberry, Bassok, and Wong 2019). The evidence on accountability policies and technology is somewhat more mixed.
Though accountability policies have been shown to cause schools to change
instructional practices in meaningful ways, leading to increased test score
performance (Rouse et al. 2013), in other settings test score improvements
have been shown to come from gaming the system rather than from generating improvements in educational practices that benefit all students (e.g., see
Neal and Schanzenbach 2010; Booher-Jennings 2005; and Hout and Elliott
2011). Finally, research on the use of technology in the classroom continues
to find mixed results (Bulman and Fairlie 2016), even though the potential
for computer-aided instruction to allow for more self-paced learning remains
promising (Barrow, Markman, and Rouse 2009).
The COVID-19 pandemic has disrupted instruction at all levels of
education, with potentially serious consequences for students. For more
discussion of this issue, see box 4-2.

Postsecondary Human Capital Development
The development of universal and compulsory primary and secondary
education in the United States during the 20th century meant that, by 2019,
more than 90 percent of adults age 25 years and above had completed at least
high school (de Brey et al. 2021, table 104.10). After high school, Americans
take many paths to further develop their human capital. Some enter the
labor force directly and develop their skills through on-the-job training and
experience. Others pursue apprenticeship opportunities, military service,
or gap-year programs. The majority (66 percent in 2019), however, pursue
further academic or vocational training—including certificate programs—at
a college or university (de Brey et al. 2021, table 302.20). And over a lifetime, many workers find the need or desire to go back to school or enter a
workforce training program to further their career or switch tracks entirely.
Access to postsecondary education has expanded over time, such that
two out of three recent high school graduates enrolled in a two-year or
four-year college in 2019, up from one out of two in 1965 (de Brey et al.
2021, table 302.10). Community colleges, also known as two-year public
colleges, are open enrollment and tend to cost less than programs at public
and private nonprofit four-year colleges and private for-profit institutions.
They also offer flexibility that allows working adults to attend college. As
a result, community colleges enroll nearly one in three first-time degree- or

Investing in People: Education, Workforce Development, and Health | 137

Box 4-2. COVID and Education
The COVID-19 pandemic disrupted all levels of formal education in
the United States, and exacerbated existing disparities in educational
opportunities and outcomes (U.S. Department of Education 2021a). By
the end of March 2020, leaders of 48 States, 4 territories, and the District
of Columbia ordered or recommended building closures in K-12 schools
for the remainder of the academic year, affecting over 50 million public
school students (Decker et al. 2020). Many school districts, students,
and families were not prepared for online learning, particularly in rural
communities (Hampton et al. 2020), low-income communities, and
communities of color. In 2019, over 10 percent of school-age children
in families in the bottom income quartile did not have Internet access
at home, and an additional 14 percent only had access through a smartphone (Irwin 2021). Before the pandemic, only 75 percent of Black and
Hispanic children lived in a house with a computer, compared with 91
and 96 percent of white and Asian children, respectively (KewalRamani
et al. 2018). Online school exacerbated barriers to good educational
opportunities and outcomes, especially for children who lived in a home
without a computer.
The challenges of switching to remote education thwarted many
students’ involvement in education. School districts saw attendance
decline, and educators expressed concerns about adequate student
engagement (Carminucci, Rickles, and Garet 2021; Chambers, Scala,
and English 2020). The result has been higher rates of chronic absenteeism (Dorn et al. 2021), which has been shown to have negative effects
on the absent students’ grades, graduation rates, and college success
(Allensworth and Evans 2016).
COVID-19 changed education in a way that also likely affected
children’s development of noncognitive/social and emotional skills.
Students could not interact with their classmates and teachers in the same
way and, in many cases, they were cut off from services they accessed at
school, such as physical and mental health services, or the support of a
social worker. Further, many extracurricular activities were canceled or
moved online, limiting social interactions with peers. By limiting these
activities, COVID-19 may have disrupted development of students’
social and emotional skills, which are associated with future academic
achievement (Blake et al. 2014).
School districts, which were largely unprepared for the transition
to remote learning, were forced to make changes that will likely have an
impact on human capital accumulation. Though some teachers, schools,
and districts were ultimately able to effectively transition to remote
learning, others were unable to develop a plan to deliver classroom
work in a form that would be most effective for students, particularly in
the short run. Before the pandemic (2011–12), only about one-third of

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teachers reported having training in the use of computers for instruction
(Garcia and Weiss 2020). A nationally representative survey of school
districts found that in the spring of 2020, 85 percent of school districts
expected students to spend less than four hours daily on instructional
activities during the pandemic (Rickles et al. 2020). The prepandemic
daily instructional time average was five hours (U.S. Department of
Education 2021a).
These changes to formal K-12 school have resulted in academic
learning loss. One study finds that by the end of the 2020–21 school year,
students were, on average, five months behind in math and four months
behind in reading (Dorn et al. 2021); another study estimates that by the
fall of 2021, students were scoring below expected performance levels
based on historical trends by 9 to 11 percentile points in math and 3 to
7 percentile points in reading (Lewis et al. 2021). In both studies, estimated learning losses were larger for historically marginalized students
and those enrolled in high-poverty schools.
The COVID-19 pandemic has also affected higher education. Fall
enrollment in postsecondary institutions peaked in 2010 and had been
declining at an average annual rate of 0.8 percent, primarily driven by
declines in enrollment at sub-baccalaureate institutions and all levels
of for-profit institutions (CEA calculations, based on de Brey et al.
2021, table 303.25). However, enrollment fell more precipitously with
the pandemic, particularly at the sub-baccalaureate level. According to
National Student Clearinghouse (2021) data, there was an approximately
8 percent drop in undergraduate enrollment between the fall of 2019 and
the fall of 2021 (roughly 4 percent each year), with community colleges
losing 14.8 percent of students over the two years. However, enrollments
in graduate and professional certificate and degree programs rose, suggesting that, though fewer students were taking initial steps in higher
education, more degree holders were returning for additional credentials.
Many students enrolled in higher education during the pandemic
have seen disruptions to the mode of instruction, which may affect their
learning and ultimately their completion. Prepandemic research found
that taking a course online instead of in person reduced student success
in the course and mitigated progress in college (Bettinger et al. 2017).
Research conducted at the U.S. Military Academy during the pandemic
also found that students randomly assigned to online instruction performed worse than those randomly assigned to in-person instruction covering the same material (Kofoed et al. 2021). Adverse effects of online
instruction were largest for students who were academically at risk.
Learning loss associated with the pandemic is likely to lower the
educational attainment of the future workforce by reducing the share
of college-educated adults (Blagg 2021; Fuchs-Schündeln et al. 2020).
Using estimates of the decline in the educational attainment of the

Investing in People: Education, Workforce Development, and Health | 139

future workforce from Fuchs-Schündeln and others (2020), Fernald, Li,
and Ochse (2021) estimate that the pandemic learning disruption will
decrease average yearly output over the next 70 years by 0.23 percentage point, peaking at a gap of half a percentage point (just below $150
billion, inflation-adjusted) from 2045 to 2050. Similar estimates at the
microeconomic level translate learning losses into lifetime earnings
losses. Goldhaber, Kane, and McEachin (2021) use the decline in math
achievement found by Lewis et al. (2021) to estimate that these losses, if
permanent, equal $43,800 in lifetime earnings for each student, or over
$2 trillion across the 50 million public school students currently enrolled
in grades K to 12.
In order to support educational equity and address these losses,
the American Rescue Plan Act of 2021 included $122 billion in the
Elementary and Secondary School Emergency Relief Fund to help
schools safely reopen and address the academic, social, emotional, and
mental health needs of their students (White House 2021a). The act
further required States and districts to spend a combined minimum of
24 percent of the total funds on evidence-based practices to address lost
instructional time and the coronavirus’s impact on underserved students.
The funding has been used for such activities as implementing summer
learning and enrichment programs and hiring nurses and counselors
(U.S. Department of Education 2021b).

certificate-seeking students.9 Importantly, research shows that community
colleges increase the earnings of their students (Kane and Rouse 1995;
Marcotte 2010; Jepsen, Troske, and Coomes 2014; Bahr et al. 2015; Minaya
and Scott-Clayton 2022).
However, college enrollment rates differ by family income and by
race and ethnicity (as noted in the introduction). For example, in 2016, 83
percent of high school graduates from families in the top income quintile
immediately enrolled in college after high school graduation, compared with
only 67 percent of high school graduates from families in the bottom income
quintile (Snyder, de Brey, and Dillow 2017, table 302.30). These differential
enrollment rates suggest that some students may face more barriers than others in making the transition to college.
Students who decide to continue their education at a college or university must first navigate complex application, enrollment, and financial
aid processes. These hurdles can deter students from continuing to develop
their skills through formal education, and those from more advantaged
families are likely to have access to better information about how to enroll in
higher education than students from less advantaged families. For example,
9

CEA calculations, based on two-year public institutions, given by de Brey et al. 2019, table 305.10.

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students whose parents attended college are well situated to receive firsthand advice on navigating the college enrollment process and information
on what to expect as a college student.
In addition, many students and their families have struggled to complete the Free Application for Federal Student Aid (FAFSA), a financial aid
application necessary to access Federal postsecondary student aid, including
Pell Grants and Direct Loans (Bettinger et al. 2012). Unclear and/or incorrect expectations about the cost of attending selective four-year institutions
may dissuade low-income students from applying and attending schools
where they would qualify for aid (Hoxby and Turner 2015; Dynarski et
al. 2018). The FAFSA Simplification Act of 2021 aims to make applying
for aid easier and the award amount more transparent and predictable for
students (Congressional Research Service 2022b). These changes, combined
with more readily available information on the net price a student faces (as
opposed to the overall “sticker price”), can help reduce barriers in the transition to college.
Free community college is another proposal aimed at increasing access
to postsecondary education. Although some of the increased enrollment
may come from students who would have otherwise enrolled in a four-year
college or a private two-year junior college, there is strong evidence that
making community college tuition free may also increase enrollment among
individuals who otherwise would not have enrolled at all (Carruthers and
Fox 2016; Mountjoy 2019; Nguyen 2020). Despite the fact that community
college tuition is effectively free for many low-income students due to the
availability of Federal Pell Grants and other State and local grant aid (Ma and
Pender 2021), a recent study in Michigan finds that students are particularly
responsive to a clear, upfront offer of free tuition (Dynarski et al. 2018). In
this study, low-income, high-achieving students were randomly selected to
receive a promise of free tuition and fees if they applied and were admitted
to the University of Michigan in Ann Arbor.10 Notably, the intervention did
not change the probable costs for the students but rather guaranteed grant
aid for which the students were likely already eligible.11 The likelihood of
applying to the university more than doubled, and the researchers find that
students in the treatment group were 4 percentage points more likely than
the control group to attend any postsecondary institution.
However, many students who do enroll in college still fail to complete
any degree or certificate program (Chen et al. 2019), and completion rates at
two-year public colleges are particularly low. Five years after enrolling, only
39 percent of first-time college students who started at a public two-year
Randomization was at the school level, and parents and principals were also notified.
The offer was unconditional, e.g., students were not required to fill out the FAFSA form, and the
offer was guaranteed for four years. This was prominently stated in the mailing, but students were
also encouraged to fill out the FAFSA because they would likely qualify for even more aid.
10
11

Investing in People: Education, Workforce Development, and Health | 141

Figure 4-6. Degree or Certificate Completion Rates among Students Who
First Enroll at a Public, Two-Year Institution
Percent

45

Women

Hispanic
or Latino

White

40

Asian

Men

35

Black or
African
American

30

25
20
15
10

5
0
.

By gender

By race or ethnicity

Source: U.S. Department of Education (2019).

institution in 2011–12, with the expectation of completing a four-year
bachelor’s degree, had received any degree or certificate, compared with 68
percent of students who started that year at a public four-year institution.12
Further, as shown in figure 4-6, these completion rates differ by sex and race
or ethnicity, ranging from 34 percent for men to 42 percent for women and
27 percent for Black or African American students to 41 percent for Asian
students.
Investments aimed at encouraging higher education institutions—and
community colleges in particular—to adopt evidence-based strategies for
improving student completion are important for increasing human capital
accumulation, particularly for students from backgrounds historically marginalized in higher education. These supports include wraparound services,
ranging from childcare and mental health services to faculty mentoring.
Community college students often live complicated lives, which may be one
reason why completion rates are relatively low. Research on initiatives such
as the Accelerated Study in Associate Programs has shown that enhanced
student services combined with additional financial supports can double
graduation rates (Scrivener et al. 2015).
Workforce development programs help create opportunities for displaced workers, new entrants, and current workers seeking higher-paying
and more fulfilling work. Having workers with the right skills can raise labor
productivity, which in turn increases economic growth. As Holzer (2021, 4)
notes, “Workforce development policies, programs, and practices are critical
to any effort to improve economic productivity, income mobility, and equity
This computation was by PowerStats, from the National Center for Educational Statistics, using
data from the U.S. Department of Education (2019).
12

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among American workers.” Such programs can be important alternatives for
those not pursuing more formal education after high school. For example,
registered apprenticeship programs—including many that are cooperatively
run by employers and labor organizations—offer opportunities for individuals to earn industry-recognized credentials through a combination of
on-the-job paid training and classroom-based instruction. These programs
have been shown to be effective at increasing workers’ earning potential. A
study of apprenticeships in 10 States finds that individuals who completed
their training earned an average of $240,037 more over their lifetime than
nonparticipants.13 Further, the study’s conservative estimate of the net social
benefits is $49,000 over the course of the apprentice’s career (Reed et al.
2012).
That said, apprenticeships remain relatively rare. In a 2016 survey of
adults focusing on participation in “work experience” programs—internships, externships, co-ops, practicums, and apprenticeships—a little over 20
percent reported having completed any type of work experience program,
and only 3 percent reported having ever completed an apprenticeship
program.14 Even among apprenticeships, which many think of as being noncollege-track work experiences, participation was highest among those with
a bachelor’s degree or higher (5.4 percent) and was lowest among those with
no postsecondary enrollment (1.0 percent).
Other sector-focused training programs aimed at preparing disadvantaged workers for employment in high-demand occupations have also been
shown to be successful. Examples of promising sector-focused training programs include the Wisconsin Regional Training Partnership, an association
of unions and employers in Milwaukee concentrating on two- to eight-week
training programs in construction, manufacturing, and health care (Maguire
et al. 2010); Year Up, a year-long training program for young adults (18–24)
focusing on employment in information technology and business and financial services; Project Quest, a one- to three-year program serving early- to
mid-career adults (largely Hispanic women) targeting jobs in the health care
sector; as well as programs evaluated under MDRC’s WorkAdvance program, which targeted employment in information technology, health care,
manufacturing, and transportation (Katz et al. 2020). Katz and others (2020)
review these and other programs and investigate the mechanisms whereby
programs affect participant outcomes. Their findings indicate that sectoral
training programs increase earnings by getting participants into higher-wage
jobs and higher-earning occupations, rather than simply increasing employment. They also find that programs that produce the largest and most persistent earnings gains offer a combination of upfront screening of participants
This finding controls for demographic differences at the time of enrollment.
This computation was by PowerStats, from the National Center for Educational Statistics, using
data from the U.S. Department of Education (2016).
13
14

Investing in People: Education, Workforce Development, and Health | 143

on basic skills and motivation, wraparound support services for participants,
and strong connections to employers.15

Investing in Health
A multitude of studies link early-life conditions to human capital accumulation, though many lack definitive explanations of the mechanisms driving
these links (Almond and Currie 2011). As Mushkin (1962) highlights, health
and education are interrelated in many ways. She notes that formal education is impossible if a child is unable to attend school and learn due to poor
health. Lengthening life expectancy by improving health increases the return
to education.
The relationship between health and human capital development
through school attendance is well documented. One early and important
study finds that the eradication of hookworm in the southern U.S. States in
the early 20th century increased school attendance, enrollment, and literacy.
These changes resulted in higher income about 30 years later (Bleakley
2007). Investments in lead abatement have similar potential today. Other
studies link poor childhood health and malnutrition to lower levels of educational attainment (Alderman, Hoddinott, and Kinsey 2006; Case, Fertig,
and Paxson 2005; Haas, Glymour, and Berkman 2011). (For discussion of
some of the recent Federal infrastructure investments with the potential to
improve human capital, see box 4-3.)
Even if children are able to attend school, physical and mental health
problems can hinder educational progress. For example, children in the
United States and Canada with symptoms of Attention Deficit Hyperactivity
Disorder (ADHD)—the most-common chronic neurodevelopmental disorder in young children—performed less well than their siblings without
ADHD symptoms on such school-related outcomes as test scores and grade
promotion (Currie and Stabile 2006), suggesting that children with ADHD
symptoms may accumulate less human capital.
A relationship can also be drawn between health and the development
of cognitive and noncognitive skills beyond the classroom. One recent
study finds that childhood illnesses lead to poor financial management later
in life (Luik 2016). Other studies show similar findings, noting that low
income—and the poor early childhood health that comes along with it—is
associated with lower socioemotional skills in later childhood (Fletcher and
Wolfe 2016). That poor formation of noncognitive skills is associated with
Minimum skill requirements applied to all participants before random assignment for treatment.
As noted by Katz et al. (2020), whether these programs can provide a successful career pathway
for individuals who do not meet the minimum skill requirements—high school diploma or General
Educational Development certificate and testing at the 6th- to 10th-grade level in math and
reading—remains an open question.
15

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Box 4-3. Federal Investments in Lead
Abatement and Rural Broadband
Recent Federal legislation, including the Bipartisan Infrastructure
Law (BIL), provides funding for lead abatement and rural broadband
development, both of which would be expected to have positive effects
on human capital development. In particular, the BIL invests $55 billion
in clean drinking water (White House 2021b); this increases the size
of the Clean Water State Revolving Fund (CWSRF) and the Drinking
Water State Revolving Fund (DWSRF) to nearly six times their previous
appropriation levels, with $15 billion for lead service line replacement
as well as a combined $5 billion to address emerging contaminants
(Congressional Research Service 2022a).
Reducing lead exposure through abatement methods is one of the
key provisions of the BIL (White House 2021b). The Centers for Disease
Control and Prevention recognizes that there is no safe blood lead level in
children and that lead service pipes can be one source of lead in a child’s
environment (CDC n.d.). Situations like the Flint, Michigan, water crisis
are a potent example of the nearly 10 million households that lack safe
drinking water. Lead abatement also has important equity implications
as Black children are at greater risk for elevated blood lead levels than
white or Hispanic children, even after controlling for risk factors such as
living in pre-1950s housing (Yeter, Banks, and Aschner 2020). Lower
blood lead levels are associated with improved health, educational, and
economic outcomes. Prenatal lead exposure has been linked to reduced
gestational age, lower birth weight, and potential fetal loss (Schwartz
1992a; National Research Council 1993), and childhood exposure has
been shown to increase adolescent impulsivity, anxiety, depression, and
body mass index (Winter and Sampson 2017). Educationally, lower
average blood lead level reduces the probability of suspension and detention among boys (Aizer and Currie 2019) and increases test scores (Aizer
et al. 2018). Despite the potentially long-lasting nature of elevated blood
lead levels, lead abatement interventions have shown promise in reversing many of the negative consequences of early childhood exposure,
demonstrating the potential benefits of the BIL lead abatement funding
even for somewhat older children (Billings and Schnepel 2017).
In addition, since the beginning of the COVID-19 pandemic, investments investments in rural broadband connection have been included in
the Consolidated Appropriations Act (CAA), the American Rescue Plan
(ARP), and the BIL. While the BIL funds $65 billion in broadband
investments for all States (White House 2021b), the other bills include
programs aimed at digital equity. The Emergency Broadband Benefit
($3.2 billion, CAA), the Emergency Connectivity Fund ($7.2 billion,
ARP), and the Capital Projects Fund ($10 billion, ARP) all provide
exclusive funding for expanding and discounting broadband to address

Investing in People: Education, Workforce Development, and Health | 145

education and health gaps. The ARP includes an additional nine provisions amounting to $388.1 billion in flexible funding that could apply to
rural broadband, as well (Tomer and George 2021).
The investments in broadband help address digital equity and
geographic disparities in Internet access. According to FCC estimates,
about $80 billion in investments are necessary for ubiquitous broadband
access (FCC 2017). Given that population density is a major determinant of both service provision and lower prices (Ribiero Pereira 2016),
these investments will likely be heavily concentrated in rural areas.
The economic benefits of broadband access are well documented. A
10-percentage-point increase in broadband penetration has been found
to increase per capita economic growth by 0.9–1.5 percentage points
(Czernich et al. 2011). Counties gaining broadband access in the early
2000s were found to have an increase in employment rates by 1.8 percentage points (Atasoy 2013). The benefits of broadband access likely
expand to health and education benefits as well. A prepandemic survey
of community-based health centers found that, among those not using
telehealth, those located in rural areas were much more likely to report
broadband as a barrier to adoption (Lin et al. 2018). And survey data
show that students in rural school districts with high-speed Internet at
home had higher grades and standardized test scores than their peers
without access (Hampton et al. 2020). The investments in broadband
in rural communities will help spur economic growth and help provide
more equitable services to those previously left behind by the digital
divide.

lower probabilities of employment in adulthood suggests the connection
with human capital accumulation (Carneiro, Crawford, and Goodman 2007).
Interactions between health, life expectancy, and decision-making also
affect human capital development. As shown in figure 4-7, life expectancy
varies dramatically across geographic areas. In 2010–15, life expectancy
at birth for a person born in Mississippi was 74.9 years (and was less than
70 in some areas), while those born in Hawaii could expect to live for 82
years (Tejada-Vera et al. 2020). Similarly, life expectancy at birth in 2018
across the United States was 81.8 years for Hispanic people and only 74.7
for non-Hispanic Black people (Arias and Xu 2020). And the difference
in life expectancy between the richest and poorest 1 percent of individuals
was 14.6 years (Chetty et al. 2016). Some of these variations are driven by
differences in infant mortality rates. As shown in figure 4-8, the infant mortality rate for non-Hispanic Black babies is more than double the rates for
Hispanic, white, and Asian babies (Ely and Driscoll 2021). Reducing these

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Figure 4-7. Life Expectancy at Birth for U.S. Counties, 2010–19

Sources: Centers for Disease Control and Prevention, National Center for Health Statistics; CEA calculations.

geographic, racial, and socioeconomic differences could improve average
life expectancy without requiring scientific or medical advances.
Focusing on policies that improve health care access and equity could
be one path toward improving human capital development. Becker (2007)
notes that if an individual expects to live for fewer years, the return on
investment in healthy decisions, such as exercising or avoiding addiction,
is lower. In other words, he argues that it may not simply be that nonsmokers and people who exercise and eat well are healthier, but rather that the
causality runs in the opposite direction. Namely, good health causes people
to choose healthier habits.
Expansion of public health insurance coverage could boost the development of human capital. Studies of health insurance coverage during childhood have found many positive benefits, including improvements in school
performance. For example, one study finds that eligibility for Medicaid
reduced the probability of children being below grade for age (Qureshi
and Gangopadhyaya 2021). These early human capital effects can be longlasting; children with more years of Medicaid eligibility during childhood
had higher college enrollment, delayed fertility, increased wages, and lower
mortality as adults (Brown, Kowalski, and Lurie 2019).
Policies focusing on maternal health by expanding coverage for pregnancy and postpartum care could also lessen inequalities in human capital
development. Expansions in postpartum Medicaid coverage under the
Investing in People: Education, Workforce Development, and Health | 147

Figure 4-8. Infant Mortality Rates by Race or Ethnicity, 2018
Rate per 1,000 live births

12
Black

10

Native Hawaiian or
other Pacific Islander
American Indian
or Alaska Native

8
6

Hispanic

White
Asian

4
2
0
Source: Centers for Disease Control and Prevention.

Affordable Care Act increased outpatient visits for mothers, likely improving health outcomes (Gordon et al. 2020). Adequate prenatal care can also
create better health habits for mothers, with one study finding that firsttrimester prenatal care led to decreases in parental smoking and increases in
well-child visits after birth (Reichman et al. 2010).

Deploying Human Capital
Deploying human capital effectively—putting a worker’s skills to more
productive use—is an important component of economic output. Although
the deployment of human capital is often straightforward, the real world can
present workers with obstacles to making the most of the skills they have
rigorously developed. Health problems of their own—or those of a child,
parent, or other loved one—can prevent an individual from putting their
human capital to work. Sometimes entire groups of workers are legally prevented from working or have their options significantly limited. Other times,
as shown in chapter 5, illegal discrimination in the labor market can keep
affected workers from realizing their full potential, as can anticompetitive
practices that limit workers’ ability to change jobs. These obstacles create a
smaller, less equitable economy and a less prosperous country.

Health
Better health allows people to deploy their existing human capital more
effectively. In his canonical 1972 paper, Michael Grossman (1972) creates
a model of “good health” that parallels other models of human capital. He
assumes that such inputs as diet, exercise, and health care spending produce
health stock, which provides a person with a time allocation of “healthy

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*$!$
 (+'*4(
)/Ҋ +*+0'/$*)-/$*4"
ѶршупѶршхпѶ
Figure 4-9. Percent
of U.S.-Born
People
Employed in the United
States, by Age
ршчпѶспппѶ)спрш

Percent

100%

90

80%

80

60%
40%

70

20%

60

0%

50

18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80

Age

40
30

Share employed, 1940

Share employed, 1960

Share employed, 1980

Share employed, 2000

Share employed, 2019

Share employed, 1940

10

Share employed, 1960

Share employed, 1980

Share employed, 2000

0

Share employed, 2019

20

18

22

26

30

34

38

42

46

50

54

58

62

66

70

74

78

Age (years)
1940

1960

1980

2000

2019

Sources: CEA calculations from the Decennial Census; Bureau of Labor Statistics, American Community Survey;
Statista data.
Note: This figure shows U.S.-born people employed in the United States as a percentage of all people born in the
United States, by age. The denominator for the percentage in this figure includes people who have died or have
emigrated from the United States, along with those living in the country. To estimate the total number of births for
each age and year, we multiplied an estimate of the birth rate by population estimates for that year, interpolating
where necessary.

days.” People with more healthy days can readily take part in labor and
leisure activities; however, those who are sick are more limited. Grossman’s
model implicitly underlies research showing that health is crucial in the
deployment of human capital.
People in better health are more likely to enter and stay in the labor
force. Krueger (2017) finds that 40 percent of men not in the labor force
report that pain prevented them from doing jobs for which they were otherwise qualified. Further, adults with a serious mental illness are twice as
likely to be out of the labor force as adults with no mental illness and are
also less likely to be employed full time (Luciano and Meara 2014). Even
for those who do enter the labor force, those with poorer health are often
prevented from working a full number of days and hours. Multiple studies
show that missed workdays due to mental and physical health problems,
which result in significant payroll losses, are top causes of work absenteeism each year (Dewa et al. 2004; Luciano and Meara 2014; Currie 2008).
Finally, being in good health allows workers to work more intensely on days
when they are present, allowing them to more fully expend their human
capital (Goldin 2016).
Good health also facilitates deployment of human capital through
longer life expectancy; those who live longer and are in better health can
work for more years. One analysis shows that “working life expectancies”
Investing in People: Education, Workforce Development, and Health | 149

have grown as healthy life expectancies and life expectancies have increased
across Europe (Loichinger and Weber 2016). To illustrate how this has
played out over time in the United States, figure 4-9 shows the likelihood
that a U.S.-born person is alive and working, by age, from 1940 to 2019. At
every adult age below 60, this likelihood has increased substantially since
1940. Gains in this age range were especially large between 1940 and 1980.

Family Support Policies
Family responsibilities can sometimes pose an obstacle to human capital
deployment—a reality made all too clear during the COVID-19 pandemic.
Short-term family priorities such as caring for a child or elderly relative
may conflict with longer-term priorities, like maintaining a job or career that
is necessary to support the family. Without external supports, people may
be forced to make decisions that result in underutilization of their human
capital. Evidence from settings ranging from recessions and mass layoffs
(e.g., Jacobson, LaLonde, and Sullivan 1993; Sullivan and Von Wachter
2009; Oreopoulos, Wachter, and Heisz 2012; Yagan 2019; Stuart 2022;
Rinz, forthcoming) to the birth of a child (Bertrand, Goldin, and Katz 2010;
Angelov, Johansson, and Lindahl 2016; Goldin and Mitchell 2017; Kleven
et al. 2019) to routine job separations (Fallick et al. 2021) indicates that
spending time out of work can have persistent adverse effects on earnings
and, more broadly, on accumulated human capital.
Providing financial support to keep people connected to their jobs
while they address family-related needs can facilitate their return to work.
Studies of State programs that provide paid family and medical leave suggest that access to leave can increase mothers’ longer-term labor supply after
the birth of a child (Baum and Ruhm 2016; Byker 2016; Jones and Wilcher
2020; Saad-Lessler 2020). Leave can also increase the likelihood that a
mother returns to her prior employer after having a child (Baum and Ruhm
2016), which can be particularly beneficial when her job made good use
of her human capital. Evidence suggests that paid leave may also produce
labor supply benefits when used for other purposes, such as caring for a
spouse with a work-limiting disability or chronic health condition (Anand,
Dague, and Wagner 2021). Though evidence to date finds limited use of
paid leave among fathers in U.S. programs (Baum and Ruhm 2016) and little
role for paid paternity leave in mitigating gender earnings gaps that tend
to emerge after the birth or adoption of a child (Andresen and Nix 2019),
available research also suggests that these earnings gaps are driven largely
by gender norms and preferences about the allocation of care responsibilities rather than biology (Andresen and Nix, forthcoming; Kleven, Landais,
and Sogaard 2019). See chapter 5 for further discussion of paid leave and
gender norms.

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Paid leave helps with situations where family members want to take
care of a new child or ill family member and also retain their job. In other
cases, the family member may want to go to work but needs care for their
child or disabled or infirm family member. Both childcare and care for the
elderly and disabled can be prohibitively expensive. But research indicates
that public childcare and preschool programs can help parents of young children, particularly mothers, remain engaged in the workforce. This evidence
is based on State programs (Cascio and Schanzenbach 2013), Head Start
(Wikle and Wilson 2021), the expansion of kindergarten access to slightly
older children (Gelbach 2002; Cascio 2009), and historical experience
with childcare provided in the United States from 1943 to 1946 under the
Lanham Act (Herbst 2017), as well as various programs in other countries
(Bauernschuster and Schlotter 2015; Finseraas, Hardoy, and Schøne 2017).
Likewise, programs that provide care for elderly or disabled people can
also increase their relatives’ ability to work. One recent study finds that for
every 2.4 to 3 women whose parents gained access to formal home care as
the result of Medicaid covering that service in some States, one additional
daughter worked full time (Shen 2021).

Employment Practices
Working conditions can also influence how effectively human capital is
deployed. Certain employment practices, sometimes called “high road”
practices, for which labor unions have long been important advocates, support employees’ success on the job by meeting the needs they have in life.
They can also increase workers’ productivity and reduce turnover, benefiting
both workers and businesses. Higher compensation is an important element
of these practices. One recent study based on general compensation policies
at a large online retail company indicates that higher wages for warehouse
and call center workers increased productivity more than dollar for dollar
(Emanuel and Harrington 2020). Another study finds that minimum wage
increases led to increased productivity and reduced termination rates among
department store sales workers (Coviello, Deserranno, and Persico 2021).
Other studies find that increases in compensation driven by changes in the
minimum wage reduce separations more generally (Reich, Hall, and Jacobs
2004; Dube, Lester, and Reich 2016; Bassier, Dube, and Naidu, forthcoming). Compensation in the form of benefits like paid sick leave or the ability
to work remotely can improve employee health and reduce workplace
infection (DeRigne, Stoddard-Dare, and Quinn 2016; Pichler and Ziebarth
2017; Stearns and White 2018; Zhai et al. 2018) or allow them to work
under conditions they find most conducive to success (Bloom et al. 2015;
Choudhury, Foroughi, and Larson 2021).

Investing in People: Education, Workforce Development, and Health | 151

Maintaining a safe and respectful workplace also allows workers to
get the most from their human capital. Workplace injuries and illnesses
reduce productivity by decreasing the quantity and/or effectiveness of time
spent at work. A study of randomized inspections by California’s Division
of Occupational Safety and Health suggests that attention to safety in
high-injury industries can reduce injury rates and associated costs without
reducing employment, sales, or business survival rates (Levine, Toffel, and
Johnson 2012). Treating workers fairly and respectfully can also contribute
to higher productivity. For example, one study indicates that the average
worker would be willing to give up a substantial share of their wages to
avoid having their employer set their schedule on short notice (Mas and
Pallais 2017). Avoiding this practice can both improve workers’ well-being
(Harknett, Schnieder, and Irwin 2021) and increase their productivity. For
example, when Gap, Inc., experimentally implemented consistent, predictable scheduling practices at its stores in San Francisco and Chicago, productivity increased by about 5 percent (Kesavan et al. 2021).
Skilled and experienced workers can be tapped to help businesses
respond to changing economic conditions in ways that promote resilience
and growth. When workers are invested in their jobs and unlikely to leave,
managers can reorient business processes and adapt job content to get more
from their employees. A wide variety of jobs could incorporate more satisfying tasks if, for example, workers were cross-trained in different types
of work or allowed to make certain types of decisions. Setting up processes
to reduce errors and eliminate waste can also ensure that employees are
as productive as possible. Case studies indicate that, when implemented
thoughtfully, these high-road approaches can succeed in sectors ranging
from manufacturing (Helper 2009) to retail (Ton 2012). Because the adjustments are broad and largely depend on generating the desired response from
workers to be worthwhile, a comprehensive implementation of high-road
employment and managerial practices may be more effective than trying to
change particular practices on a one-off basis.

Occupational Licensing
Occupational licensing policies are often introduced to ensure safe, highquality services from professionals, like dentists and electricians, whose
safety and quality are difficult for consumers to ascertain themselves.
These policies frequently establish minimum standards for workers’ human
capital investments—such as by mandates to acquire specific credentials or
to pursue continuing education. Kleiner and Soltas (2019) show that these
standards induce workers who enter these occupations to invest more than
they otherwise would, especially in occupation-specific forms of human
capital such as vocational associate degrees and master’s degrees.

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However, occupational licensing can make it more difficult for workers
to enter fields or move to places where their human capital would be more
productive by increasing the cost of mobility in terms of fees for obtaining
a license or time to complete required training or other licensing requirements. Research finds that licensing requirements decrease employment and
churn within an occupation (Blair and Chung 2019; Kleiner and Soltas 2019;
Kleiner and Xu 2020). On the positive side, licensing increases wages and
wage growth within licensed occupations (Kleiner and Krueger 2010, 2013;
Gittleman, Klee, and Kleiner 2017; Kleiner and Soltas 2019; Kleiner and
Xu 2020). One analysis suggests that the magnitude of the licensing wage
premium is comparable to the premium associated with union membership
(Kleiner and Krueger 2010). Though licensed workers may benefit from
higher wages, other similarly skilled workers who lack the resources to
acquire a license may be prevented from moving into jobs where they would
be more productive and better paid. There is also evidence that occupational
licensing reduces interstate migration (Johnson and Kleiner 2020), making it
more difficult for workers to relocate and deploy their human capital where
it would be most beneficial for them. This especially affects mobile populations such as military spouses, who are 10 times more likely to have moved
across State lines in the last year than their civilian counterparts and experience persistently high unemployment due to relocations (U.S. Department
of the Treasury and U.S. Department of Defense 2012).
Although many occupations require licenses in some jurisdictions,
relatively few require licenses in all jurisdictions (Council of Economic
Advisers et al. 2015), suggesting that there is substantial scope to tailor
occupational licensing to balance interests in quality, safety, and effective
human capital deployment. In 2019, Current Population Survey data show
that just under 20 percent of California’s labor force held a professional
certification or State or industry license, the lowest share for any State; at
the other extreme, in Wyoming, that share was just over 30 percent. In the
average State that year, about 84 percent of workers with licenses needed
them to do their jobs.16 In some cases, States have taken steps to reduce
barriers associated with occupational licensing, such as creating reciprocity
arrangements or interstate compacts related to licensing in certain occupations (National Conference of State Legislatures 2020). For example, during
the COVID-19 pandemic, some States waived or modified requirements
associated with telehealth to allow providers licensed in other States to serve
their residents (Federation of State Medical Boards 2022). As more licensed
occupations are deemed well-suited for remote work, further adoption of
additional allowances will help reduce barriers for workers to deploy their
human capital effectively.
16

CEA calculations, based on Current Population Survey data.

Investing in People: Education, Workforce Development, and Health | 153

Immigration
There are about 11 million undocumented immigrant residents of the United
States, a group of people who are not able to fully deploy their human
capital because they lack legal authorization to work or are authorized to
work only temporarily. Research suggests that granting these immigrants
permanent legal status would increase the productivity of their human capital. Unauthorized immigrants in the workforce experience a wage penalty
relative to what native-born and authorized immigrant workers earn, even
after controlling for educational attainment. The gap in wages can largely
be explained by differences in the industrial and occupational composition
of employment between unauthorized immigrants and other workers. This
suggests that allowing these workers to move to different jobs that better
utilize their skills could increase their productivity and wages (Rouse et al.
2021). Legal status would enable greater job mobility, a key channel through
which workers find more productive job matches during their careers
(Engbom 2022). Research also suggests that access to permanent legal
status for undocumented immigrants could facilitate the development of
additional human capital, because studies have found that legal status leads
to increases in labor force attachment, education attainment, and other types
of skill development (Gathmann and Keller 2018; Liscow and Woolston
2017; Cortes 2013).
Increasing authorized immigration can also lead to more human capital
being deployed in the United States, boosting growth without waiting for
a new generation of workers to complete the entirety of their education.
Immigrants supply labor to produce a wide variety of goods and services,
from agricultural products to medical services. Immigrants also consume a
wide variety of goods and services, and this demand creates opportunities
for other workers to deploy their human capital productively. On top of this,
research identifies innovation and entrepreneurship benefits associated with
immigration, which make use of the human capital of both the innovator/
entrepreneur immigrants and the U.S. workers employed by their ventures
(Hunt and Gauthier-Loiselle 2010; Fairlie and Lofstrom 2015).

Incarceration
A highly carceral criminal justice system as we have in the United States
incapacitates a substantial amount of human capital; people cannot work
to their full capacity while they are imprisoned. Even after they have
served their time, the formerly incarcerated face barriers to being hired in
jobs for which they may be fully qualified. About 1.4 million people were
incarcerated in Federal or State prisons at the end of 2019, a population
that is disproportionately male and nonwhite. About one-third were Black,
and nearly another quarter were Hispanic (Carson 2020). Including people
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Chapter 4

incarcerated in local jails, who are typically incarcerated for shorter periods,
would likely bring the total closer to 2 million.17 People who are incarcerated are generally not available to participate in the labor market, and they
have very limited opportunities to put their human capital to use. This fact
is sometimes overlooked because commonly used labor market indicators
like the employment-population ratio and the labor force participation rate
exclude people who are incarcerated.
Producing employment-population ratio measures that include the
incarcerated population reveals lower levels of human capital utilization,
especially for Black men, and larger gaps between races. In December
2019, the white employment-population ratio was 61.2 percent, while the
Black employment-population ratio was 59.3 percent. If people who were
incarcerated in Federal or State prisons were included in these estimates,
the Black ratio would fall by about 0.8 percentage point, to 58.5 percent,
and the white ratio would fall by only about 0.1 percentage point, to 61.0
percent—increasing the difference between the two races to 2.5 percentage
points. Including people incarcerated in local jails in this exercise would
likely increase this gap further.
Laws that limit post-incarceration employment opportunities create
longer-term obstacles to effectively deploying human capital for the formerly incarcerated. Having been incarcerated renders workers ineligible
for certain types of employment, licenses, or credentials, regardless of their
qualifications. Federal, State, and territorial governments collectively apply
over 40,000 restrictions and requirements to people who have been convicted of crimes, 72 percent of which affect the employment opportunities
available to them (Umez and Gaines 2021). For example, some of the incarcerated people who helped fight wildfires in California in recent years found
themselves ineligible to be hired as firefighters after being released from
prison because they were not eligible to receive certification as emergency
medical technicians (Romo 2020). Though California has since passed a
law that attempts to address this, the law requires that formerly incarcerated
people petition to have their convictions expunged, a process that can be
burdensome (Smith 2021). Reducing incarceration and post-incarceration
employment restrictions could mitigate the extent to which the criminal
justice system limits the deployment of human capital, as could improving
and increasing access to programs designed to help formerly incarcerated
people return to work.
A total of 734,500 people were incarcerated in local jails in 2019, and 549,000 people were
incarcerated in local jails in 2020 (Minton and Zeng 2021, table 2). A total of 1,379,786 people were
incarcerated in State or Federal corrections facilities in 2019, and 1,182,166 were incarcerated in
2020 (Minton and Zeng 2021, table 3) for total incarcerated populations of 2.1 million in 2019 and
1.7 million in 2020.
17

Investing in People: Education, Workforce Development, and Health | 155

Government Personnel Policies
In certain fields, the government plays an important role in determining how
human capital is managed and/or compensated. Decisions about how much
Medicare and Medicaid pay for various medical procedures, for example,
have a direct impact on physicians’ earnings (Gottlieb et al. 2020). The
government’s role extends to other areas of health, such as home health care
services.
Nursing homes are one area where government payment policies have
particular significance. In 2019, Medicaid accounted for around 29 percent
of all spending on nursing care facilities and continuing care retirement
communities, and Medicare covered another 22 percent (MACPAC 2021).
Evidence suggests that the introduction of State Medicaid policies designed
to increase wages in nursing homes was associated with increased staffing of
certified nurse aides (Feng et al. 2010). Other evidence on wages in nursing
homes also suggests that higher pay keeps workers in the industry. Ruffini
(2021) finds that higher minimum wages increased retention among lowwage workers in nursing homes. She also finds that higher wages improved
the quality of service provided by nursing homes, as reflected in reduced
inspection violations; adverse, preventable health conditions; and mortality.
This suggests that increasing compensation not only helps direct human
capital toward an industry where it is needed but also induces workers to
deploy their human capital more productively.

Conclusion
Increases in human capital accumulation contribute to faster economic
growth and improved standards of living. Yet human capital accumulation
has slowed over the past two decades, and the United States has fallen
behind many other countries in both educational attainment and life expectancy. Further, many long-standing discrepancies remain in human capital
accumulation and in deployment between individuals by income, race, and
ethnicity. Thus, the Nation can benefit from investing more in education,
workforce development, and health, and from exploring policies that can
help individuals deploy existing human capital more effectively. These policies range from improving early childhood education and care to ensure that
children get a strong start in life to lifting barriers to permit unauthorized
immigrants and the formerly incarcerated to employ their human capital in
its most effective form. Investments in people expand the productive capacity of the U.S. economy, boost living standards, and ensure that our workforce has the skills and education needed to compete in this dynamic world.

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Chapter 5

Barriers to Economic Equality:
The Role of Monopsony, Monopoly,
and Discrimination
Markets function well when firms must compete for employees or customers. In competitive product markets, the right amounts of goods are produced
to meet demand, with prices that accurately reflect value. In a well-operating
labor market, workers are able to switch jobs, wages reflect productivity, and
differences in earnings only reflect such factors as ability, effort, education,
experience, and random chance.
However, empirical economics research has documented the many ways
in which this ideal does not reflect reality. Perfect competition does not
describe most labor markets, for example, and not all workers are able to
easily move through the labor force to obtain more satisfactory compensation. Two concrete examples are (1) the market power of employers, which
allows for unfair hiring and compensation practices; and (2) discrimination,
which has exacerbated persistent forms of inequality in earnings across
racial and gender lines. Nearly 20 percent of U.S. workers report being
bound by noncompete agreements, which limit an employee’s ability to
join or start up a competing firm (Starr, Prescott, and Bishara 2021). Also,
in general, employer market power is responsible for wages that are at least
15 percent lower than they would be in a perfectly competitive market
(U.S. Department of the Treasury 2022). In addition, Federal government
statistics show that, on average, Hispanic and Black employees earn less
than 80 percent of what white employees earn (BLS 2021). Women earn,
on average, roughly 83 percent of what men earn, and the disparities are

157

even greater for most nonwhite women (Department of Labor 2022a).
These earnings differences remain even after accounting for such factors as
educational attainment and experience (Blau and Kahn 2017; BorowczykMartins, Bradley, and Tarasonis 2017). Although many groups can be
targeted by such discrimination—including those with disabilities; lesbian,
gay, bisexual, transgender, and queer (LGBTQ+) people; and members of
religious minorities—this chapter focuses on discrimination by race, ethnicity, and gender.
Noncompetitive labor markets are not completely devoid of competitive
forces, though they generally feature fewer job options, reducing the wellbeing of workers, and discriminatory barriers, resulting in a misallocation
of talented workers. Broader costs for the overall economy include lower
productivity and slower economic growth. New Deal labor reform laws
sought to protect workers by establishing the right to bargain collectively,
establishing a floor for wages, and providing protection from overwork,
while the Civil Rights Act sought to break through discriminatory barriers
across all kinds of economic activity, including in the labor market (Boone
2015). Emblematic of these laws’ success, Hsieh and others (2019) estimate
that the removal of barriers to higher-income occupations for women and
people of color accounted for 20 to 40 percent of growth in output from 1960
to 2010; this was driven by an improvement in the allocation of talented
workers within the economy.
Despite this progress, barriers to equality in the workplace remain today, in
no small part due to the market power of employers. The opening section
of this chapter provides a summary of current levels of inequality in wages,
income, and wealth. The next sections document the forces that inhibit
workers from being fully rewarded for their skills in labor markets—such
as excessive wage-setting power by employers and racial and gender discrimination—and discuss how these forces impede economic growth. The
final section discusses several policies, including legal measures designed
to protect workers and members of disadvantaged groups and more general
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economic policies with the potential to counteract the adverse effects of
a lack of competition—thereby, reducing inequality as well as boosting
economic growth. The chapter finishes with a discussion of tax reforms
that can help to offset inequality that may remain even if barriers to healthy
competition are removed.

Labor Market Inequality
Research reveals the significant scope of economic inequality—in wages,
incomes, and wealth—in the United States (Gould 2019; Congressional
Budget Office 2021; Piketty 2014; Wolff 2021). These inequities across
demographic groups cannot be fully explained by differences in such
characteristics as education or experience that provide an indication of their
productivity, suggesting that people may not be equitably rewarded for their
economic contributions. This section reviews current patterns of inequality,
with a primary focus on wage inequality by race, ethnicity, and gender.
For most households, earnings account for most of their income; thus,
wage inequality translates to income inequality. Wealth inequality reflects
earnings and income inequality—as well as disparities in access to capital,
returns on those assets, and transmission of wealth across generations (see
box 5-1).
Figure 5-1 shows that, while net productivity has grown by nearly 62
percent over the past four decades, average hourly pay for the typical worker

Figure 5-1. The Gap Between Productivity and Worker Compensation,
1948–2020

Cumulative change (index: 1979 = 100)
180

162

160
140

118

120
100
80
Net total economic productivity

60

Nonsupervisory compensation

40
20
0

1948

1958

1968

1978

1988

1998

2008

2018

Source: Economic Policy Institute, analysis of data from the Bureau of Labor Statistics and the Bureau of Economic Analysis.

Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 159

Box 5-1. Racial and Ethnic Wealth Gaps
Although differences in income across groups typically provide an
account of inequality in resources on an annual basis, wealth disparities
track how these income flows can contribute to divergences in accumulated resources across longer time periods and even over multiple generations. A household’s net worth, measured as the difference between
its assets and its debts, has many components. For most American
families, the largest single asset is their home; thus, the largest portion of
net worth is often tied to the value of one’s home minus the mortgage or
the other debts against it. Net worth also includes savings and retirement
accounts, stocks and or other property, and inheritances and gifts from
family members. Sources of debt also include credit card balances and
loans for education, vehicles, or durable goods.
In the United States, there are substantial racial wealth gaps, as
shown in figure 5-i. In 2019, the net worth of the median white family
was $199,498, almost eight times higher than that of the median Black
family and five times higher than that of the median Hispanic family
(Bhutta et al. 2020). The average net worth within each group is higher
than the median, because the average incorporates information about the
ultrawealthy, who account for a large proportion of overall wealth: The
average white family has nearly seven times more wealth than the average Black family and almost six times more than the average Hispanic
family.
The causes of current wealth inequality are complex, as today’s net
worth reflects the accumulation of differences in past income between
racial groups, differences in savings rates for households with similar
incomes, differences in the return to savings for households with similar
savings rates, differences in transfers of wealth between generations, and
the possibility of individual-level and/or structural discrimination at any
Figure 5-i. Median and Average Wealth by Race and Ethnicity, 2019
Wealth (2021 dollars)
$1,000,000

$1,042,323
Black

Hispanic

White

$800,000
$600,000
$400,000
$199,498

$200,000
$25,544

$0

$151,038

$175,417

$38,263

Median net worth

Average net worth

Sources: 2019 Federal Reserve Board Survey of Consumer Finances; Haver analytics; CEA calculations.

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of these stages. In this regard, civil and legal rights play an important
role. For example, after Emancipation, the promise of land for Black
freedmen in the South did not materialize, meaning that Black freedmen
exited slavery without land they could farm and pass on to their children.
This lack of land ownership has been documented to have affected asset
accumulation (Miller 2020).
The lack of access to assets continued throughout much of the 20th
century, as Jim Crow policies and practices limited access and mobility
for Black Americans. Further, systemic disinvestment and exclusion from
federally subsidized homeownership opportunities in Black neighborhoods, collectively referred to as “redlining,” were associated with lower
property values decades later (Aaronson, Hartley, and Mazumder 2021;
Fishback et al. 2021). Moreover, Derenoncourt (2022) shows that the
attempts of Black Americans to migrate to neighborhoods with greater
opportunity were often met with “white flight” and disinvestment, limiting the potential for escape from segregated economic fortunes. Given
the large role played by homeownership wealth on modern-day balance
sheets, this history provides just one example of how racial wealth gaps
are sustained over time.

has increased by just under 18 percent (Economic Policy Institute 2021).
The divergence between the two trends suggests that there may be forces
suppressing the pay of workers relative to their productivity.

Racial, Ethnic, and Gender Wage Gaps
There are substantial differences in the wages paid to white women, and to
Black, Hispanic, American Indian, and Alaska Native workers of any gender, relative to white men, and some differences remain even after accounting for differences in education, occupation, and experience. Focusing just
on differences in educational levels, as shown in figure 5-2, reveals the
basic pattern. In 2021, Black workers were paid less than white workers,
on average at every education level, with the Black/white wage ratio ranging from 76 percent to 91 percent. Hispanic, American Indian, and Alaska
Native workers were paid less than white workers at all but the lowest level
of education (less than a high school degree). The patterns suggest that differences in earnings between these groups are driven by more than simply
such differences as educational attainment and level of experience.
The wage profile of Asian American, Native Hawaiian, and Pacific
Islander workers (AANHPI, or “Asian” for short) is distinct from that of
other nonwhite groups. Asian workers earn more than white workers, on
average, at most education levels. However, the overall group average

Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 161

Figure 5-2. Wage Gaps by Education, Race, and Ethnicity, 2021
Average hourly wages (2021 dollars)
$13.60
$16.92
$16.30
$15.25
$15.00
$18.20
$19.50
$19.47
$19.77
$21.85
$19.65
$21.17
$21.61
$23.05
$24.34

Less than high school

High school

Some college

Black
American Indian / Alaska Native
Hispanic
AANHPI
White

$30.22

$37.18
$32.65
$42.01
$39.88
$39.67
$45.74
$45.00

College

Advanced degree

$49.65

$0

$10

$20

$30

$40

$50

$58.15

$60

$70

Sources: Economic Policy Institute; Current Population Survey extracts; CEA calculations.
Note: AANHPI = Asian American, Native Hawaiian, and Pacific Islanders.

masks a substantially higher within-race wage inequality among Asian people than that found within other groups. This can be captured by comparing
the wage of the worker at the 90th percentile in earnings, including earnings
among salaried workers, with the wage of the worker at the 10th percentile.
In 2021, among Asian people, the worker at the 90th percentile earned $81
an hour, 6.4 times more than the worker in the 10th percentile, who made
almost $13 an hour. Meanwhile, among the other racial and ethnic groups,
the wage of the 90th percentile worker was only 3.5 to 4.8 times as large
as that of the wage of the 10th percentile worker. The varied experiences
of Asian workers are further demonstrated by comparisons across different
ethnic subgroups within the larger group (see box 5-2).
There are also earnings differences by gender: women are paid less,
on average, than men. Although the wages of both men and women increase
with education, figure 5-3 shows that the gender wage gap is even larger
for those with more education. Among those with an advanced degree, the
average wage for women is 70 percent of that for men.
As laid out by Crenshaw (1989), examining inequality along one
dimension of identity at a time may obscure the specific experiences that lay
at the intersection of race and gender identities. Figure 5-4 therefore presents
wages separately by race and gender. On average, Black women’s wages
are 62 percent of white men’s wages, while Hispanic and American Indian /
Alaska Native women’s wages are 59 and 62 percent of white men’s wages,
respectively. The average wages of Asian women are higher than those of
women in the other racial and ethnic groups, though still below those of
white men. In addition, Asian women experience a larger within-race gender
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Figure 5-3. Gender Wage Gap by Level of Education, 2021
Average hourly wages (2021 dollars)
$13.30

Less than high school

Women

$16.98

Men

$17.93

High school

$22.55
$20.35

Some college

$25.89
$32.49

Bachelor’s degree

$44.29
$41.46

Advanced degree

$58.82
$0

$10

$20

$30

$40

$50

$60

$70

Source: Economic Policy Institute, Current Population Survey extracts.

Figure 5-4. Wage Gaps by Gender, Race, and Ethnicity, 2021
Average hourly wages (2021 dollars)
$22.42

Black

$24.73

Women
Men

$22.33

American Indian / Alaska Native

$25.59
$21.21

Hispanic

$24.49
$33.01

AANHPI

$45.43
$27.68

White

$35.91
$0

$10

$20

$30

$40

$50

Sources: Economic Policy Institute, Current Population Survey extracts; CEA calculations.
Note: AANHPI = Asian American, Native Hawaiian, and Pacific Islanders.

gap than women in any of the other racial and ethnic groups, earning 73 percent of the average wage of Asian men. It is important to note that, as seen
in figure 5-4, the lower gender wage gap among Black, Hispanic, American
Indian, and Alaska Native workers is partly due to the relatively low wages
earned by men in these groups.

Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 163

Box 5-2. Improving Data Infrastructure
for Equity Analysis
Understanding the mechanisms underlying the inequality discussed in
this chapter involves gathering evidence, both quantitative and qualitative. Research plays an important role in uncovering these patterns,
and shedding light on issues related to equity across different groups
requires adequate information and data on the many dimensions of an
individual’s identity. However, many barriers remain to collecting the
information needed for such equity analysis.
First, the existing set of questions typically asked on household
surveys may not be detailed enough to capture certain important subpopulations. This may prevent the discovery of unique outcomes for
important subgroups and can reduce the accuracy of equity analyses
by lowering rates of self-identification among respondents who do not
see themselves represented in the available categories (Census Bureau
2021). Members of Asian American, Native Hawaiian, and Pacific
Islander racial/ethnic communities, for example, are commonly grouped
together, masking the greater economic challenges faced by some
subgroups within the broader category. This is demonstrated in figure
5-ii, which shows a great deal of variation in average income across
subgroups of this population. In addition, survey respondents of Middle
Eastern and North African origin generally do not have a satisfying
option to select in the standard list of racial and ethnic categories, which
may result in higher rates of nonresponse to these questions. Likewise,
the concepts of sex and gender are often collapsed into binary categories
that exclude a number of gender identities and expressions.
Moreover, even when surveys do have questions that capture
key aspects of identity, the survey sample size may be too small to be
representative of certain groups in the population, and privacy concerns
may require suppression of statistics for those groups to prevent tracing the information back to a specific respondent. For example, before
February 2022, labor force statistics from the Current Population Survey
for American Indian and Alaska Native respondents were not reported
as a separate category, due to small sample sizes. Likewise, statistics on
wealth and net worth from the Survey of Consumer Finances are released
publicly for Black, white, and Hispanic respondents, separately, but
not for Asian, Native Hawaiian, Pacific Islander, American Indian, or
Alaska Native respondents (Bhutta et al. 2020).
A second concern is that many key economic indicators are
measured using administrative data; that is, data are collected for the
purposes of implementing a program, and not necessarily with the
primary purpose of facilitating general research analysis. In these cases,
it may not be necessary to collect demographic information, and may be
counterproductive or illegal to do so. For example, administrative tax

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Figure 5-ii. Average Household Income among Asian American, Native
Hawaiian, and Pacific Islander Subgroups
AANHPI average
U.S. average
household income household income

Asian Indian
Taiwanese
Malaysian
Chinese
Pakistani
Filipino
Sri Lankan
Japanese
Fijian
South Korean
Indonesian
Other Asian
Vietnamese
Guamanian/Chamorro
Thai
Hawaiian
Cambodian
Laotian
Samoan
Hmong
Tongan
Bangladeshi
Nepalese
Bhutanese
Mongolian
Other Pacific Islander
Burmese
Other Micronesian

$0

$50,000

$100,000

$150,000

$200,000

2021 dollars
Sources: American Community Survey, 2017–19; Haver analytics; CEA calculations.
Note: AANHPI = Asian American, Native Hawaiian, and Pacific Islanders.

data have proven useful in analyses of income inequality by incorporating the incomes of the ultrarich, but the Internal Revenue Service does
not collect many demographic characteristics on the 1040 tax return
(Huang and Taylor 2019). Such demographic data are also not typically
collected for other key programs that generate useful data for tracking
economic outcomes, such as the Unemployment Insurance (Kuka and
Stuart 2021) programs across different states, and the Supplemental
Nutritional Assistance Program (Prell 2016).
There are possible solutions to the issues outlined above, and some
efforts are under way to facilitate equity analysis. The Biden-Harris
Administration’s Executive Order on Advancing Racial Equity and
Support for Underserved Communities Through the Federal Government
established an Equitable Data Working Group, an interagency committee, to explore ways to make available data disaggregated by race,
ethnicity, gender, and other key demographic variables (Nelson and
Wardell 2021; White House 2021a). These include a comprehensive
review of race, ethnicity, and gender-related questions on Federal

Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 165

surveys, and exploration of the possibility of merging Federal datasets
to append demographic information to administrative data. An example
of the type of analysis possible is the ongoing collaboration between the
U.S. Treasury and U.S. Census Bureau to merge individual-level data
on race and ethnicity with tax data to study when members of different
racial groups received their first Economic Impact Payment as a part of
the 2020 CARES Act (Adeyemo and Batchelder 2021; U.S. Congress
2020).
The Administration’s National Strategy on Gender Equity and
Equality calls for the collection of gender-disaggregated data to better
track outcomes such as gender gaps in the labor market and entrepreneurship, financial outcomes, including within households, and
gender-based violence (White House 2021b). In another case, the U.S.
Census Bureau’s Household Pulse Survey, designed to provide real-time
tracking of outcomes during the COVID-19 pandemic, for the first
time introduced separate questions about sexual orientation and gender
identity on a Census Bureau survey in July 2021 (File and Lee 2021).
In terms of data by income group, the 2022 Green Book included
proposed funding to share data between the Treasury and Bureau of
Economic Analysis (BEA), which would aid in the estimate of the
distribution of income growth across different income percentiles (U.S
Department of the Treasury 2021a, 101). BEA has explored prototype
estimates of the distribution of personal income, which covers outcomes
as recently as two years in the past; and recent developments, such as the
Realtime Inequality project (Blanchet, Saez, and Zucman 2022), demonstrate the potential for even more timely estimates at a higher frequency
from BEA (U.S. Bureau of Economic Analysis 2021).

These wage gaps reflect the fact that women—particularly nonwhite
women—and most nonwhite men are overrepresented among the low-wage
workforce. For example, in 2021, nonwhite men made up 39 percent of all
men in the workforce, but over half (51 percent) of low-wage men in the
workforce. Likewise, nonwhite women made up 39 percent of all women in
the workforce and 45 percent of low-wage women in the workforce.
The gender pay gap has narrowed over time, partially as a result of
women increasing their skills through educational attainment and greater
labor market experience. Women are now better educated than men—being
more likely than men to graduate from college and earn graduate degrees
(National Center for Education Statistics 2022). The share of women in the
labor force (either working or actively looking for work) nearly doubled
from 1950 to 2000, from 33.8 percent to 59.9 percent (BLS 2022a). Boustan
and Collins (2014) show that these historical trends have varied across racial
groups: the labor force participation rate for Black women, for example, was
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Figure 5-5. Mothers’ and Nonmothers’ Labor Force Participation Rates, 2021
Labor force participation rate (percent), prime-age women
90
Nonmothers
80

Mothers

70
60
50
40
30
20
10
0

White

Black

American Indian /
Alaska Native

AANHPI

Hispanic

Sources: 2021 Current Population Survey; CEA calculations.
Note: AANHPI = Asian American, Native Hawaiian, and Pacific Islanders.

14 percent higher than that of white women in 1950, and the two rates did
not converge until about 1990.
However, the increase in women’s labor force participation has stalled
since 2000, and the gap between the share of men and women in the labor
force has remained fairly steady since that time in the United States, while
such gaps continued to shrink across many other countries that belong to the
Organization for Economic Cooperation and Development (OECD) (Blau
and Kahn 2013). In 2019, before the COVID-19 pandemic, 58 percent of
women and 69 percent of men were in the U.S. labor force. One general
factor at play is parenthood; on average, prime-age (age 25 to 54 years)
women with children have lower labor force participation rates than those
without children, as shown in figure 5-5. However, there is variation in participation patterns across women of different racial and ethnic backgrounds,
and the relationship between parenthood and participation does not hold for
Black and American Indian women and for Alaska Native women, whose
participation rates do not substantially differ by motherhood status. This differential pattern may in part be driven by a greater share of women in these
groups being the breadwinners for their household (Institute for Women’s
Policy Research 2016) and therefore less able to afford to exit the labor
force.
A number of studies have also documented a concentration of income
among the richest households. This is the result of the wage inequality discussed above, including relatively high rates of compensation for executives
(Mishel and Kandra 2021), and the fact that the highest-income households
receive a disproportionately high share of capital income earned from assets
Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 167

and savings. The most recent estimates show that, in 2021, the top 1 percent
received 19.5 percent of pretax income, as compared with only 11.4 percent
for the bottom 50 percent of the population (Blanchet, Saez, and Zucman
2022). Although there is some variation in such estimates due to differences
in data and methods, various studies find that between 14 and 20 percent
of income has been accrued by the top 1 percent of households in recent
years (Piketty, Saez, and Zucman 2018; Auten and Splinter 2020; Internal
Revenue Service 2021; Congressional Budget Office 2021). There is also
considerable income inequality among households below the top 1 percent.
For example, in 2018, U.S. households at the 90th percentile of the income
distribution earned 12.6 times more than households at the 10th percentile
(Horowitz, Igielnik, and Kochhar 2020), a ratio that is among the highest for
OECD countries (OECD 2022).

Sources of Earnings Inequality
This section explores how earnings inequality can arise from noncompetitive market forces and discriminatory barriers. A robust and growing body
of evidence shows that some degree of economic inequality stems from
forces inconsistent with competitive markets. In a noncompetitive market,
barriers emerge that prevent some individuals from realizing the gains from
their productivity. This chapter focuses on two specific aspects of noncompetitive markets: the market power of employers, and discrimination. New
empirical research provides evidence that many firms have some power
to set wages, violating the core tenet of a competitive labor market (Card
2022), and allowing for persistent differences in outcomes across racial and
gender lines.
These are not the only sources of earnings inequality; nor does the
presence of inequality necessarily imply that labor markets are not competitive. For example, even a random event such as a serious illness could
have implications for an individual’s potential earnings. Earnings inequality
can also appear within competitive markets due to differences in worker
productivity. A worker’s skills and experience—that is, their human capital—affects their marginal productivity, as discussed more fully in chapter
4. A large body of research has focused on productivity-related explanations
for inequality, examining the roles of technological change, innovation,
and trade policy that have increased the productivity of some workers
while replacing other workers whose jobs could be outsourced or automated (Autor, Levy, and Murnane 2003; Autor, Katz, and Kearney 2006;
Acemoglu and Autor 2012; Autor 2010). Recent work has found evidence
that import competition from China and other developed economies has had
adverse effects on U.S. employment in manufacturing and per-capita income
in more trade-exposed labor markets, particularly among workers with less
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than a college degree (Autor, Dorn, and Hanson 2013, 2016; Hakobyan and
McLaren 2016). Further, these adverse effects spill over to overall employment and persist long after the initial severe loss of manufacturing jobs
(Autor, Dorn, and Hanson 2021).

A Lack of Competition in Labor and Product Markets
Noncompetitive markets can emerge under many conditions, such as when
mergers result in dominant firms that can use their consolidated market
power to charge higher prices, offer decreased quality, and block potential
competitors from entering the market (Boushey and Knudsen 2021). A
distinguishing feature of noncompetitive markets is the existence of “economic rents,” which are profits derived from prices that are higher than
needed to cover the investment and production costs of goods. In a perfectly
competitive market, neither workers nor firms earn such rents in the long
run; if there are excess economic rents in a product market, for example, this
would create an incentive for new firms to enter the market, which in turn
would drive down prices and rents. A critical question in noncompetitive
markets is how the economic rents are split between employer profits and
employee wages. When firms use their market power to capture a greater
share of economic rents, the outcome can be “suboptimal”; meaning that,
from society’s point of view, workers are paid too little or firms charge too
much for their products. Another implication of noncompetitive markets
is that they provide an incentive for firms to do less, not more. If the firm
has labor market power, theory says it will restrain hiring to maintain low
wages, because adding more employees would mean paying higher wages
to lure new applicants. Similarly, a firm with product market power will
restrain production in order to charge higher prices than it would if it had
competitors. This subsection explains how a lack of competition not only
affects efficiency but also can exacerbate labor market inequality.
Labor market monopsony. The classic form of a noncompetitive labor
market is a monopsony. In the case of a pure monopsony, a concept first
developed by Joan Robinson (1933), there is a single employer that uses
its market power to set wages below what the competitive rate would be;
that is, the firm has the power to set such wages. Robinson’s theoretical
model of a single employer has been extended to incorporate the concept
that an employer’s monopsony power can come from representing a larger
share of the labor market, which limits the options of employees to push
toward competitive wages. Employers may also derive monopsony power
from situations where it is difficult for workers to switch jobs due to issues
of commuting distance or workplace scheduling flexibility, which give
employers greater power to set wages (Manning 2020a). Stelzner and Bahn
(2021) argue that, because female and nonwhite workers may be more likely

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to experience these difficulties, monopsony power can translate into greater
gender and racial inequality.
A number of studies focus on a direct measure of monopsony power
by estimating a firm’s ability to adjust the wages it offers, as opposed to
offering a market wage that a competitive market would demand. Using job
applications data, Azar, Berry, and Marinescu (2019) find strong evidence
of this monopsony power in many markets, and they conclude that workers’
productivity is 17 percent higher than the wage they receive. There is similar
evidence of monopsony power even in online, on-demand labor markets
where the costs of searching for and switching jobs should be relatively low
(Dube et al. 2020). A meta-analysis of 53 studies concludes that, overall,
the literature provides strong evidence for monopsony power among many
employers, implying sizable markdowns in wages (Sokolova and Sorenson
2020). Importantly, two studies find that the degree of monopsony power is
substantially larger in low-wage labor markets (Bassier, Dube, and Naidu
2021; Webber 2015). Moreover, research by Webber (2015, 2016) shows
that the negative effect of a firm’s market power on wages is strongest in the
lower half of the earning distribution and among female workers, suggesting
that monopsony power amplifies both overall and gender wage inequality.
One way that a firm can derive monopsony power is from providing a large share of the jobs available in a local labor market. Economic
research has found a link between higher labor market concentration and
lower wages (Azar, Marinescu, and Steinbaum 2019; Benmelech, Bergman,
and Kim 2020, CEA 2016; Philippon 2019; Qiu and Sojourner 2019; Rinz
2020). Two recent studies find that wages are lower when concentration
in local labor markets increases due to mergers and acquisitions (Arnold
2019; Benmelech, Bergman, and Kim 2020). A third study focuses on
hospital mergers and finds that they decrease the wage growth of workers
whose skills are specific to their industry (Prager and Schmitt 2021). Recent
research has raised the question of whether employers are able to gain or
maintain a greater share of the labor market through actions that may violate
antitrust laws (Naidu, Posner, and Weyl 2018; Posner 2021).
Monopsony power can also arise from practices that reduce the outside options of workers (Manning 2020b). One such practice is the use of
noncompete agreements, which prohibit employees from joining or starting
competing businesses, typically within a specified time frame or geographic
boundary. Starr, Prescott, and Bishara (2021) find that almost 20 percent of
U.S. workers were bound by a noncompete agreement in 2014, including 12
percent of workers with annual income less than $20,000. Such agreements
are increasingly used by employers in low-wage industries, such as fast food
chains and home health agencies (Quinton 2017). A recent study found that
when Oregon initiated a ban on noncompete agreements, wages rose by 2 to
3 percent, with larger effects in occupations where noncompete agreements
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were more common (Lipsitz and Starr 2021). Johnson, Lavetti, and Lipsitz
(2021) examine this relationship in the national context, and find that greater
enforcement of noncompete agreements reduces earnings, with stronger
negative effects on the earnings of female and nonwhite workers.
Some employer practices hamper worker mobility by impeding their
ability to gain information about important characteristics of potential jobs,
such as expected compensation and working conditions. For example,
nondisclosure agreements (NDAs), which are often bundled with noncompete agreements in employment contracts, prevent an employee or former
employee from disclosing information about employers. Though NDAs can
be used to protect confidential business information, some are much more
broadly applied and can reduce the ability of workers to share information
about the work environment. Research suggests that overly broad NDAs can
reduce the reporting of workplace harassment (Sockin, Sojourner, and Starr
2021). Workers may also lack information on the wages offered at other
jobs, partly due to employer practices that promote pay secrecy. Research
has shown that workers, especially those with low incomes, are unaware of
potential higher-paying job options (Jäger et al. 2021), and that reducing pay
secrecy could reduce the gender wage gap (Baker et al. 2021).
Another practice that can reduce workers’ mobility are no-poach
agreements, which are compacts made between employers agreeing to not
hire workers from each other for a specified period of time. Employees
may not even be aware that these agreements are in effect, and because nopoaching agreements between separate employers are illegal per se under
antitrust laws, and therefore hard to discover, it is difficult to know how
common they are. In a slightly different context, Krueger and Ashenfelter
(2021) documented that in 2016 almost 60 percent of franchise agreements,
including for some major fast-food chains, contained no-poaching clauses.
The study also found that no-poaching clauses were more common for franchises in low-wage and high-turnover industries, though a number of fastfood franchises have already dropped them from their franchisee contracts in
response to public pressure and legal challenges (Abrams 2018).
Product market monopoly. Whereas a pure monopsony refers to a
market with a single buyer, a pure monopoly refers to a market with a single
seller. Accordingly, a firm gains greater monopoly power when the market
in which it sells products is more concentrated—what is often referred to
as an oligopolistic market—with just a handful of sellers. This allows the
firm to charge higher prices and leads them to produce less than it would if
it faced greater competition. In addition, Boushey and Knudsen (2021) cite
growing evidence that market concentration has reduced innovation and
economy-wide investment in the United States.
Product market concentration may also contribute to economic
inequality. This can occur when firms with market power are able to set
Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 171

prices above what they would be in a competitive market. This pricing
power harms consumers but improves the payoffs to shareholders, as
explored in recent research (Gans et al. 2018; Philippon 2019). This phenomenon can exacerbate inequality, since consumers are spread across the
income distribution, while the shareholders who benefit are more likely to be
near the top of the income distribution. Research has also shown that higher
levels of market concentration are associated with workers receiving a lower
share of the income generated by economic output (Barkai 2020; Autor et al.
2020; Eggertsson, Robbins, and Wold 2021).
Joining the two strands of the literature on market concentration, Qiu
and Sojourner (2019) note how product and labor market concentration may
interact. They use the example of a town with two nursing homes, which
may be the only employers of nurses and the only providers of nursing
care in the local market, giving them power in both the labor and product
markets. They find that the negative effect of labor market concentration on
wages is stronger in more concentrated product markets. Chapter 6 explores
additional cases where varying levels of competition and market power at
different points along the supply chain create similar dynamics, as discussed
here in the context of labor market inequality.

Racial and Gender Discrimination
Racial and gender inequality can arise from discrimination that occurs
both at the individual level and under broader, more structural conditions.
This section explores the extensive evidence on how discrimination has
exacerbated inequality, along with how such inequality can be sustained and
worsened by employer market power.
Not all differences in earnings by race, ethnicity, and gender are the
result of a lack of competition or discrimination, because they can emerge
in competitive labor markets due to differences in characteristics such as
educational attainment that enhance a person’s work productivity. There are
notable disparities in educational achievement by race and ethnicity. For
example, while 35.8 percent of white, non-Hispanic people have earned a
bachelor’s degree, the shares are lower for Black (21.6 percent), Hispanic
(16.4 percent), Native Hawaiian and Pacific Islander (17.8 percent), and
American Indian and Alaska Native (15.0 percent) people (McElrath and
Martin 2021). Asian Americans have the highest educational attainment,
with 54.3 percent earning a bachelor’s degree or higher. There is a large literature on the extent to which differences in productivity-related characteristics, known as “human capital,” can explain racial and gender earnings gaps.
Residual gaps in wages and earnings by race, ethnicity, and gender
remain even after accounting for differences in educational attainment and a
wide range of other productivity-enhancing characteristics (Burnette 2017;

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Kamara 2015; Borowczyk-Martins, Bradley, and Tarasonis 2017). For
example, recent research finds that—even after accounting for factors such
as education, occupation, work experience, and unionization status—40 to
60 percent of the gender wage gap remains unexplained (Blau and Kahn
2017; Foster et al. 2020). In fact, given that educational attainment of
women is now higher, on average, than that of men, accounting for gender
differences in education increases the unexplained portion of the gender
wage gap. This unexplained portion is even larger for Black and Hispanic
women, who face wage gaps that are greater than the sum of the gender
wage gap and the racial wage gap. (Paul et al. 2018; Bahn and McGrew
2018). Moreover, while educational disparities can explain some of the
differences in economic outcomes across racial and ethnic groups, these
disparities can also result from discrimination that occurs before individuals
enter the workforce.
Individual-level discrimination. One leading explanation for “residual”
inequality is individual-level discrimination in labor markets on the basis
of race or gender. A large literature in the field of economics homes in
on two leading models of discrimination in the labor market, (1) so-called
taste-based discrimination (Becker 1971), where some employers individually have a distaste for hiring workers of a certain group; and (2) statistical
discrimination (Phelps 1972; Arrow 1973), which occurs when employers
that do not have full information about a potential worker’s skills use the
average characteristics of their racial or gender group to make wage offers
(for a review of theory and empirical evidence, see Guryan and Charles
2013). Regardless of intent, both forms of discrimination have disparate
negative effects on the group against which the discrimination is occurring.
These forms of discrimination in the labor market take place during individual transactions between workers and employers, and they are
theoretically unlikely to persist in well-functioning markets. In the case of
taste-based discrimination, differential treatment should decline as discriminatory employers are driven from the competitive market by those whose
employment decisions reflect only the productive capacity of their workers.
Meanwhile, statistical discrimination may potentially decline over time as
employers gather more accurate information about workers (Altonji and
Pierret 2001). However, Sarsons (2019) shows that this need not be the case,
finding that after the death of a patient, female surgeons experience a greater
drop in referrals from primary physicians than their male counterparts,
which suggests that the same kind of information may be interpreted less
favorably for women doctors as compared with men.
Evidence on individual-level discrimination by race or gender has
been found through the use of experimental methods such as résumé studies, where résumés with identical qualifications, but with different racial or
gender identities, are sent to employers. Bertrand and Mullainathan (2004)
Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 173

find that résumés with white-sounding names were called back at a 50 percent higher rate than those with Black-sounding names. Quillian and others
(2017) conducted a meta-analysis of all such experimental studies of racial
and ethnic discrimination, and find that white applicants got 36 percent more
callbacks than Black applicants and 24 percent more callbacks than Latino
applicants. The study also finds no change in the levels of discrimination
against Black applicants between 1990 and 2015, but a modest decline
in discrimination against Latino applicants. Related research focusing on
discrimination against Hispanic and Latino workers in the housing market,
which can reduce overall labor market mobility, finds that immigration and
assimilation play an important role. An experimental study using email correspondence by Hanson and Santas (2014) finds that 6.9 percent of landlords
discriminate against seemingly recent Hispanic immigrants, with little to
no discrimination against applicants who appear assimilated, suggesting
significant barriers to mobility for marginalized Hispanic and Latino people.
Experimental studies also find individual-level labor market discrimination against women. Qualified women are less likely to be hired or
promoted compared with men (for a case study of symphony orchestras, see
Goldin and Rouse 2000), and the hiring discrepancy is particularly strong
for positions where expected income is higher (Neumark et al. 1996). More
recent résumé studies shed light on how gender discrimination is concentrated among particular firms and is stronger in certain industries (Kline,
Rose, and Walters 2021), and find evidence that it can be particularly acute
among employers in male-dominated professions (Hangartner, Kopp, and
Siegenthaler 2021) and those seeking to fill jobs that require a major in
science, technology, engineering, and/or mathematics (Kessler, Low, and
Sullivan 2019).
Beyond individual-level discrimination: structural racism. A growing
body of research documents how theories of individual-level discrimination
are incomplete, particularly in explaining the persistent gaps in outcomes
between racial groups, because they do not adequately incorporate the
legacy of historic forms of discrimination in the United States. For example,
current Black/white gaps in economic outcomes can be partially explained
by periods throughout U.S. history ranging from the era of chattel slavery,
to Jim Crow regimes of segregation, to the present era of mass incarceration
(Cook and Logan 2020).
To establish a theory capable of explaining these persistent gaps,
William Darity Jr. developed the subfield of “stratification economics”
(Darity 2005; Darity, forthcoming; Chelwa, Hamilton, and Stewart, forthcoming), in which he argues that economic gaps have persisted because
of the material incentive to maintain distinct group identities. With these
group identities in place and entrenched within a hierarchy, theories such as
Acemoglu and Wolitzky’s (2011) model of coercion can be used to show
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how “structural” forms of racism can take hold in labor markets.1 Under this
theory, employers have an economic incentive to coerce workers into undesirable, low-wage work arrangements that maximize profits, in the extreme
using force or violence, or, under softer versions of coercion, weakening
workers’ bargaining power by limiting their mobility and outside options.
Naidu (2010) provides evidence of this, showing that enticement fines that
prevented employers in the postbellum U.S. South from recruiting alreadyemployed agricultural workers reduced the labor market mobility and wages
of Black sharecroppers.
A second key insight regarding structural racism is that discrimination
by a subset of actors can spill over to others in the same setting or market,
or in other parts of the economy, generating more pervasive disparities. For
example, discrimination in law enforcement and legal systems exacerbates
disproportionate rates of incarceration across racial groups. Though there
are 233 people in State or Federal prisons per every 100,000 white U.S.
residents, Hispanic people have a 50 percent higher rate, at 351 per 100,000,
American Indian and Alaska Native people have more than twice the rate, at
565 per 100,000, and Black people have nearly five times the rate, at 1,160
per 100,000. And though those who identify as Asian American alone have
a much lower imprisonment rate, of 39 per 100,000, people identified as
Native Hawaiian and Pacific Islander have a rate more than 12 times as high,
at 497 per 100,000 (Carson 2021). In addition, there is substantial evidence
of labor force discrimination against formerly incarcerated people, both due
to concerns about recidivism and gaps in work experience, and also due to
a general stigma above and beyond productivity-related factors (Agan and
Starr 2018). This discrimination is at times codified in restrictions that keep
them from working in certain sectors; a number of States deny occupational
licenses to those with a prior arrest or conviction (Sibilla 2020). Chapter 4
provides further detail on some of the obstacles that limit the employment
opportunities of formerly incarcerated people. Even if the barriers faced
by the formerly incarcerated were not racially targeted by design, higher
rates of incarceration for certain racial groups mean that these employment
barriers disproportionately block members of these groups, resulting in a
structural form of racial discrimination.
In some cases, the long-run impact of historical racial discrimination can result in economic indicators that might naively be interpreted as
evidence that discrimination has been overcome. Suzuki (1995) examines
the improvement in economic outcomes for Japanese immigrants between
1920 and 1930, as measured by a greater share employed in “professional”

For further discussion of this application, see the notes on structural economic racism by Acemoglu
and Wolitzky (2011).
1

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and higher-paid occupations during this period.2 These patterns are cited
by some as an example of exceptionalism among Asian American families,
which continue to have some of the highest levels of earnings among different racial and ethnic groups. Suzuki (1995) challenges this common narrative, pointing out that during that 1920–30 period, nine States passed laws
banning the purchase of farmland by Japanese immigrants, the Supreme
Court deemed Japanese people ineligible for naturalization as they were
neither white nor of African descent, and the U.S. government passed a
law excluding Japanese immigrants. The author also shows that the laws
were associated with a significant return of these immigrants to Japan, and
that this outflow was disproportionately made up of those in lower-earning
occupations. Thus, the apparent economic success story of Japanese immigrants may have actually been driven by highly discriminatory policies that
resulted in selection bias among those who remained here.
One of the most notable cases of historic economic stratification
involves the widespread dispossession of land from indigenous people and
nations during the expansion of U.S. territory that began in the late 1700s.
Carlos, Feir, and Redish (2021) argue that though historians often highlight
the key roles of abundant land, property rights, and the rule of law in U.S.
economic development, these discussions erase the simultaneous erosion
of these very same inputs and institutions for members of existing Native
groups and entities. In addition to the direct types of harm caused by the
often-violent process of relocation and geographic restriction, the centurieslong process helped give rise to adverse economic outcomes for present-day
American Indians and Alaska Natives. As just one example, Akee (2020)
studies the Nelson Act of 1889, which took collectively held property of the
Minnesota Anishinabe reservations and allotted parcels to individual owners, allowing them to sell lands to non-Indian buyers (U.S. Congress 1889).
While increased private ownership of land might be expected to support a
more productive use of land, Akee (2020) finds, compared with reservations
not affected by the allotment, a rapid reduction in land ownership, home
ownership, and self-employed farming, along with an increase in renting and
wage labor in the timber industry. These reductions in land and capital ownership likely resulted in lower wealth levels and poorer economic outcomes
for subsequent Anishinabe generations.
Gender-based occupational segregation and bias. Beyond employer
discrimination in hiring and promotion, economists have also considered
broader sources of gender inequality in the labor market, such as occupational segregation and employers’ assumptions about the division of labor
in the household. Occupational segregation plays a major role in the gender
wage gap. Research finds that differences in the types of occupation and
Although income itself may be considered a better measure, it was not captured by the Census
surveys used for this analysis.
2

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industries in which men and women work are some of the largest contributors to the wage gap, accounting for one-third to one-half of the gap (Blau
and Kahn 2017; Foster et al. 2020). There is also evidence that the gender
wage gap is linked to the disproportionate rewards for long hours and weekend work in some occupations (Goldin 2014; Foster et al. 2020). Although
occupational segregation by gender has been decreasing over time, progress
has stalled in recent decades (del Río and Alonso-Villar 2015). In the years
2011–15, more than 40 percent of workers were in occupations in which
more than three-fourths of workers were of one gender, with women more
likely to be in low-paying occupations (Gould, Schieder, and Geier 2016).
Women are more likely to enter occupations that entail caring for others. For example, 94 percent of workers in the childcare sector and 89 percent of workers in home health care are women; of those, Black, Hispanic,
and Asian American / Pacific Islander women are overrepresented relative
to their share in the overall workforce (Gould, Sawo, and Banerjee 2021).
Average wages in these sectors are roughly half the average among workers
overall. Furthermore, research has documented a wage penalty associated
with certain caregiving occupations that persists after controlling for the
education and skills required for these jobs (England, Budig, and Folbre
2002; Barron and West 2011; Pietrykowski 2017; Budig, Hodges, and
England 2019; Folbre and Smith 2017). This “care penalty” means that even
highly skilled care workers may be paid less than they would be in jobs that
require similar qualifications but do not involve caregiving. Estimates of the
care penalty vary across studies, but the most comprehensive recent study
finds a 15 percent wage penalty for female childcare workers, nursing aides,
and health aides (Budig, Hodges, and England 2019). The study also finds
a 6 percent wage penalty among men in these fields, consistent with other
studies that find that the wage penalties in these caregiving occupations are
not confined to women. Recent research has found evidence that stereotypes
about gender-specific skills and gender-specific roles can explain at least
some of this occupational segregation (Bertrand 2020; Levanon, England,
and Allison 2009; Pan 2015). The predominance of women in relatively
low-paying occupations translates into greater gender wage inequality.
Another source of gender inequality relates to the division of labor
in the household, as well as employers’ assumptions about it. Though
the increase in women’s labor force participation has been accompanied
by a decrease in their average time spent on household labor (including
housework and child care), research shows that women spend a higher
fraction of their hours in unpaid family care and that men spend a higher
fraction of their hours in paid work (Bianchi et al. 2012). In 2019, mothers spent almost double the amount of time as fathers caring for children
in the household (BLS 2020). This is true regardless of a woman’s wages
relative to those of her spouse, as Siminski and Yetsenga (2021) find even
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at the extreme—where women’s wages are more than double those of their
spouses—women do 44 percent more household work. A potential result of
imbalances within the household is that mothers experience long-term wage
penalties related to the reduction in labor supply and loss of work experience
that occurs when a child is added to their household (Kleven et al. 2019).
In addition to the direct effect of this period of labor force exit on
mothers’ long-term earnings, experimental evidence shows that employers’
expectations of women’s greater childcare responsibilities can influence
women’s labor market outcomes. A résumé study modeled on the research
of Bertrand and Mullainathan (2004) found that prospective employers
were almost twice as likely to call back women without children as they
were women with children, while their callbacks of men were unaffected by
fatherhood status (Correll, Benard, and Paik 2007). Petit (2007) similarly
uses a résumé study to find significant hiring discrimination against young
women for high-skill positions in the French finance industry, where time
off for dependent care may be particularly penalized.

How Inequality Affects Economic Efficiency and Growth
Although part of the motivation for addressing imperfect competition in
labor markets and discrimination is rooted in the spirit of fairness and
justice, there is also an important case to be made that such measures can
contribute to overall economic output and growth. When the policies that
reduce inequality also serve to curtail costly rent seeking, economic efficiency and productivity are improved. Similarly, when the inequality stems
from barriers that have kept some from fully taking part in the economy,
removal of these barriers supports economic growth.

Monopsony Power Produces Inefficient Labor Market Outcomes
As explained above, firms with monopsony power in the labor market can
set lower wages and employ fewer workers than they would under more
competitive conditions, contributing to wage inequality. These inefficiently
low levels of employment also directly hurt economic output.3 A recent
study estimates that monopsony power in the U.S. economy reduces overall
economic output by 13 percent (Naidu, Posner, and Weyl 2018). In addition,
noncompete clauses and no-poach agreements, along with nondisclosure
agreements and pay secrecy practices, can harm workers throughout the
wage distribution. By reducing competition among employers and limiting
workers’ mobility, these restrictive employment practices reduce economic
In addition, lower levels of employment and lower wages mean that there are fewer workers and
that these workers have less money to spend, thereby reducing consumer demand. This reduction in
consumer demand will, in turn, create a drag on overall economic growth in the long term (Caldwell
and Naidu 2020).
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efficiency by preventing some workers from finding the job that best
matches their qualifications.

Discrimination Misallocates Talent and Suppresses Innovation
A number of empirical studies argue that various forms of racial and gender
discrimination can sideline talented workers, resulting in slower economic
growth. For example, a recent study by Buckman and others (2021) estimates that if employment, education, and earnings were equalized across
racial and ethnic groups over the period from 1990 to 2019, gross domestic
product would have increased by $22.9 trillion. These gains emerge both by
allowing current workers to fully realize their potential, and also by signaling a more reliable return to investments in skills among underrepresented
racial groups, which yields growth in the future. Likewise, Hsieh and others
(2019) show that increased access to high-income occupations for underrepresented groups, over the period from 1960 to 2021, accounted for 20
to 40 percent of growth in aggregate output. Bucknor and Barber (2016)
estimate an $80 billion cost to gross domestic product due to lower levels
of employment among those who are formerly incarcerated, which is in part
driven by discrimination and disproportionately affects Black, Hispanic,
American Indian, and Alaska Native communities. Finally, research by
Cook (2014) finds that racist violence led to hundreds of fewer patents by
African American inventors in the late 19th and early 20th centuries, and a
study by Cook and Gerson (2019) shows how closing the gaps in patenting
for women and underrepresented minorities can increase economic growth.
As a concrete example, research shows that alleviating entrenched
racism in the South was associated with greater regional economic growth.
The brief period of increased Black political power in the South during
Reconstruction saw increases in taxation and spending on public education
(Logan 2020). Likewise, the Great Mississippi Flood of 1927, which forced
the migration of Black workers to industrial cities and reduced the coercive
powers of southern landowners, resulted in a greater reliance on capital
investment and technology adoption (Hornbeck and Naidu 2014) in the
region. Subsequent economic growth in these regions suggest that private
gains from coercive labor practices had come at the expense of more socially
valuable investment and efficient production. Most notably, Wright (2013)
argues that the revolutionary changes brought about by the Civil Rights
Movement led to improvements in access to jobs, education, and health care
that yielded benefits not only for Black southerners but also for the entire
southern economy, helping to partially undo decades of underdevelopment.
Overall, the moments in history where entrenched racism in the South was
partially dislodged have tended to be times where the region has best been
able to catch up with the more industrialized northern economy.

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Discrimination Reduces Incentives for Human Capital Investment
Discrimination and monopsony power can also have large, long-term
negative effects on economic growth if they reduce the extent to which
the affected individuals invest in their education and skill development.
A worker who expects to be paid a wage lower than their productivity,
whether due to discrimination or an employer’s monopsony power, may
have less incentive to engage in activities like training that could increase
their productivity, compounding already-existing barriers to such training.
For example, in one study, Latina high school students who anticipated
future career barriers due to their immigration status were found more likely
to plan to attend a two-year college than a four-year college (McWhirter,
Ramos, and Medina 2013). The benefits of greater human capital development for economic growth are discussed in more detail in chapter 4.

Policies to Address Sources of Labor Market Inequality
Addressing inequality is important for ensuring that people are rewarded
fairly for their efforts and contributions to productivity as well as for fostering stronger productivity and growth. Because this occurs in so many ways,
there are no one-size-fits-all solutions. Instead, there are a number of specific
policies designed to address the inequality that stems from noncompetitive
and discriminatory market outcomes, as well as policies that address larger,
structural problems.
Core to addressing inequality is increased enforcement of current labor
protection and antidiscrimination laws. The 1935 National Labor Relations
Act (U.S. Congress 1935), which established the National Labor Relations
Board; the 1938 Fair Labor Standards Act (U.S. Congress 1938), which
led to the Wage and Hour Division at the Department of Labor; and the
1964 Civil Rights Act (U.S. Congress 1964), which established the Equal
Employment and Opportunity Commission, are each important to ensuring
that workers are treated fairly. More recent policies, such as the Americans
with Disabilities Act of 1990 (U.S. Congress 1990) and the Family and
Medical Leave Act of 1993 (U.S. Congress 1993), have focused on particular equity concerns. The proposed Equality Act, if passed, would prohibit
additional forms of discrimination, including on the basis of sexual orientation and gender identity in settings beyond the realm of employment (U.S.
Congress 2021e).
Research on the effects of laws prohibiting discrimination against
workers generally finds positive effects on outcomes for the intended beneficiaries (for studies of specific groups, see Collins 2003; Neumark and
Stock 2006; and Neumark et al. 2019). These results also underscore the
need to address workers’ misclassification, whereby workers who should be

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classified as employees, and therefore receive coverage of the above laws,
are instead treated as independent contractors. More general economic policies have the potential to further counteract the forces that underlie wage
inequality and racial/gender discrimination. Though far from an exhaustive
list, this section surveys several such policies.

Promoting Competition
Healthy market competition is fundamental to a well-functioning U.S.
economy. Basic economic theory demonstrates that when firms must compete for customers, it generally leads to lower prices, higher-quality goods
and services, greater variety, and more innovation. In 2021, President Biden
signed the Executive Order on Promoting Competition in the American
Economy, establishing a multiagency approach to push back on decades of
decline in competition. The Executive Order not only calls on the traditional
antitrust agencies—the Department of Justice (DOJ) and the Federal Trade
Commission (FTC)—to enforce existing laws vigorously and to consider
updating their merger guidelines; it also directs all agencies and departments to use their detailed knowledge and expertise to ensure that their
work clearly supports competition in the markets they regulate (White
House 2021c). This whole-of-government approach is designed to address
the concern that antitrust agencies are limited both by resources and the
current judicial interpretation of the antitrust laws. It also relies on the fact
that Congress has delegated authority to police anticompetitive conduct and
oversee mergers to many agencies—not just the DOJ and the FTC. The
Executive Order therefore directs or encourages roughly a dozen agencies
to engage in more than 70 specific actions that will remove barriers to entry
and encourage more competition.
Increased enforcement of antitrust laws would also alleviate labor
market monopsony and therefore its negative effects on wages, equality, and
race- and gender-based pay gaps (Marinescu and Posner 2019). Antitrust law
has been used to combat no-poaching agreements, noncompete agreements,
and related contractual restrictions on workers’ mobility. It can also be used
to block mergers that would concentrate labor markets excessively and to
penalize large employers that use illegal methods to obtain or maintain
labor monopsonies. Though some of these uses of antitrust law have been
rare until recently, the Executive Order on Promoting Competition calls
for agencies to make greater use of antitrust law to promote competition in
labor markets. For example, the DOJ and the FTC have begun the process
for revising the merger guidelines, and have called for public comment on
labor market implications (Federal Trade Commission 2022).

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Unions and Labor Market Equity
Unions can provide workers the increased leverage to bargain with their
employer, serving as a counterweight to the power that employers have to
set wages and working conditions. Numerous studies support this notion,
including research showing that unions’ negotiating power increases wages
(Card 1996; Chava, Danis, and Hsu 2020), and that union representation
also increases worker satisfaction and job tenure (Freeman and Medoff
1984). Unions also give workers a voice, which can improve productivity
(Cai and Wang 2020). In the presence of employer monopsony power, the
compensation gains achieved by unions may shift economic rents from
employers to employees, reducing inequality without significant efficiency
costs. Consistent with this view, higher rates of unionization have been
shown to mitigate the negative effect of monopsony on wages (Benmelech,
Bergman, and Kim 2020; Qiu and Sojourner 2019; Prager and Schmitt 2021;
Dodini, Salvanes, and Willen 2022), and there has been, historically, an
inverse relationship between the degree of union membership and income
inequality (Farber et al. 2021).
Unions also have the potential to foster equitable pay and working
conditions for people of different genders and racial and ethnic backgrounds. For example, higher rates of union membership among Black
workers have led to increased wages; and, for Black women, have led to
a substantial reduction in the gap in their wages relative to white women
(Rosenfeld and Kleykamp 2012). Also, collective bargaining is associated
with lower gender wage gaps among teachers (Biasi and Sarsons 2022).
This has not always been the case in U.S. history: some unions have, in
the past, supported exclusionary, anti-Asian immigration policies (Frymer
and Grumbach 2020), and major unions have at times faced criticism for
discriminatory practices against Black workers (Hill 1959) or limited representation of women among leadership roles (Ledwith 2012). Nonetheless,
labor unions were important proponents of the Civil Rights Act of 1964
(Collier and Grumbach 2022), and later waves of unionization in the United
States have been associated with greater representation for women in these
organizations (Milkman 1990). In 2021, union membership was quite
diverse; more than a third of unionized workers are Black, Hispanic, Asian,
or members of another nonwhite group, and almost half are women (BLS
2022b). And among white workers, Frymer and Grumbach (2020) find that
union membership leads to lower racial resentment and greater support for
policies that benefit African Americans.
Despite declining union membership since the 1960s, almost half of
nonunionized workers report interest in joining a union if one were available at their workplace (Hertel-Fernandez 2020), suggesting that there is a
valuable role for policy efforts that support the right to union organizing. To

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support these efforts, President Biden signed Executive Order 14025, which
established the Task Force on Worker Organizing and Empowerment (White
House 2021d). The Task Force, charged with identifying how the executive
branch could support worker power and collective bargaining, released 70
recommendations focusing on how the Federal government can serve as a
model employer and support workers by sharing information and improving
transparency when it comes to organizing rights (Harris and Walsh 2022).
In addition to the executive branch’s efforts, key legislation related to
worker empowerment includes the Protecting the Right to Organize (PRO)
Act (U.S. Congress 2021a). The PRO Act aims to protect workers’ right to
join a union by introducing penalties for companies that violate workers’
rights, expanding workers’ collective bargaining rights, and ensuring access
to fair union elections. The Public Service Freedom to Negotiate Act (U.S.
Congress 2021b) similarly provides support to workers in the public sector,
while the National Domestic Workers’ Bill of Rights (U.S. Congress 2021c)
proposes to expand coverage of labor protections to domestic workers,
providing greater regulation of labor standards for a sector that is disproportionately home to women, workers of color, and immigrants.

The Minimum Wage
The Fair Labor Standards Act was first signed into law over 80 years ago,
and subsequent amendments have extended coverage to a broader range of
workers. In addition, 30 States and the District of Columbia currently have
a minimum wage that is higher than the Federal minimum (Department
of Labor 2022b), and 40 localities have adopted minimum wages above
their State minimum wage (Economic Policy Institute 2022). Mandating a
minimum wage can decrease inequality by ensuring that those with the least
earnings potential receive at least a minimum level of compensation for
each hour they work. The potential for minimum wages to—on net—make
low-paid workers better off depends on several factors, including whether
employers have to compete for workers. A minimum wage could cause
employers in a perfectly competitive labor market to cut back on hiring
workers at the higher hourly rate. However, when workers’ wages are low
due to a lack of competition or discrimination, minimum wage legislation
may not be distortionary because employers are setting wages lower than a
worker’s productivity and hiring fewer workers than they would under more
competitive conditions. Though debate continues on the employment effects
of minimum wage laws (Neumark and Shirley 2021; Dube 2019; Cengiz et
al. 2019; Card and Krueger 1994), recent empirical evidence indicates that
they do not materially reduce employment in concentrated labor markets
and may even increase employment as market concentration increases (Azar
et al. 2019). This suggests that policies like the minimum wage can reduce

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wage inequality without reducing employment or sacrificing economic
output.
The minimum wage has been shown to reduce inequality by increasing
growth in earnings, with effects that persist over several years (Rinz and
Voorheis 2018). When the Fair Labor Standards Act was amended in 1966
(U.S. Congress 1966) to extend Federal minimum wage coverage to some
of the country’s lowest-paid sectors, wages increased and racial earnings
gaps were reduced (Bailey, DiNardo, and Stuart 2020; Derenoncourt and
Montialoux 2021). Derenoncourt and Montialoux (2021) estimate that the
minimum wage law accounted for 20 percent of the reduction in the Black/
white earnings gap during the Civil Rights Era.
Although legislation is required to increase the Federal minimum wage
from its current level of $7.25 per hour, the Biden-Harris Administration’s
Executive Order 14026 establishes a new hourly minimum wage of $15.00
for workers performing work on or in connection with covered Federal
contracts (White House 2021e). In addition to directly lifting the wages
of hundreds of thousands of contract workers, this Executive Order could
have broader effects, as competitors in the same labor markets as Federal
contractors may increase wages, too, as they seek to compete for workers (Derenoncourt et al. 2021). In addition, President Biden has endorsed
several other adjustments to minimum wage policy, including raising the
Federal minimum wage to $15 for all workers, indexing future increase to
inflation, phasing out the lower minimum wage that applies to some workers
who receive tips, and expanding coverage of the Federal minimum wage to
teens and workers with disabilities, all of which are features of the proposed
Raise the Wage Act of 2021 (U.S. Congress 2021d).

Full Employment and Tight Labor Markets
Although minimum wage legislation and support for unionization efforts can
directly help to reduce overall wage inequality, fiscal and monetary policies
to support full employment conditions can play a strong underlying role as
well. Full employment—the lowest rate of unemployment possible without
spurring inflation—can put workers in a position to demand pay increases
in accordance with their productivity. This can both offset the market power
of employers and limit their ability to engage in discriminatory practices.
When the number of job openings relative to workers seeking jobs is high,
there are improved outside options for all workers, which may be especially
important for those subject to discrimination. For example, the American
Rescue Plan, crafted both to address the COVID-19 pandemic and support
the economy, contributed to much higher growth than anticipated, with over
6 million jobs added to the U.S. economy in 2021, the largest percentage
rise during a calendar year since 1978. However, the world has learned

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that expansionary fiscal policy can become challenging when the supply of
goods and services is constrained, as has been the case during the pandemic.
Research by Dahl and Knepper (2021) supports the idea that full
employment can protect workers from discriminatory practices. They find
that tighter labor markets and more generous unemployment insurance
benefits, which allow job seekers greater ability to search for jobs, increase
the reporting of sexual harassment by workers who may otherwise avoid
reporting out of fear of retaliation. Beyond the substantial moral considerations, policies that support tighter labor markets and help limit gender
discrimination in the workplace may also improve economic efficiency by
allowing bad actors to be identified and held accountable, rewarding good
employers, and ensuring better matches between employers and employees.
Dahl and Knepper (2021) find similar evidence from discrimination claims
that tighter labor markets reduce age-related discrimination.
There is also evidence that tighter labor markets can reduce the gender
wage gap, as shown by Biddle and Hamermesh (2013). In contrast, however,
the authors find that Black/white gaps in wages are actually larger during
tighter labor markets, though that may be partially due to the fact that more
low-wage Black workers are able to enter the workforce when unemployment is low (Ashenfelter 1970; Freeman et al. 1973). Indeed, other research
finds that the Black/white gap in unemployment tends to fall during tighter
labor markets (Rodgers 2008; Hoynes, Miller, and Schaller 2012; Cajner et
al. 2017). This smaller Black/white gap in unemployment during tight labor
markets does not appear to operate through lower levels of racial discrimination in callbacks to job applicants, however. A number of résumé studies
have shown that the gap in callbacks between these groups persists through
periods of both high and low unemployment (Bertrand and Mullainathan
2004; Nunley et al. 2015; Quillian et al. 2017).

Care Economy Policies
The provision of affordable childcare and early childhood education in
the United States has the potential to reduce gender wage inequality by
helping to support the paid labor force participation of women in families
with children and reducing care-related discrimination by employers. The
pandemic highlighted the importance of the availability of care, as school
and childcare closures exacerbated existing shortages in the availability
of care (Carson and Mattingly 2020). Childcare and universal preschool
can ease the trade-offs that families with children must make between
care responsibilities and paid work. But many families find the prices for
high-quality childcare and early childhood education on the private market
unaffordable, and credit constraints may keep them from accessing needed
childcare at a time in their lives when their earnings and savings are lowest

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(U.S. Department of the Treasury 2021b). Subsidizing childcare and providing universal public preschool, therefore, can help many families access
otherwise unaffordable options. In addition, there may be positive economic
spillovers that parents do not completely factor in when deciding whether to
purchase childcare or early childhood education. As discussed in chapter 4,
high-quality childcare provides long-lasting benefits for children, especially
those who are more economically disadvantaged (Herbst 2017), thereby
benefiting the rest of society by fostering economic growth. Moreover,
viable options for childcare and preschool, by providing parents with the
option to remain in the paid workforce, can mitigate the motherhood penalty
associated with a labor force exit and reduce the likelihood of employer
discrimination related to expectations of childcare responsibilities that arise
even for women without children.
Much research on past childcare and preschool programs has found
positive effects on maternal labor force participation and household income
(Blau and Kahn 2013; Davis et al. 2018; Herbst 2017; Morrissey 2017;
Bauernschuster and Schlotter 2015; Wikle and Wilson 2021). Olivetti and
Petrongolo (2017) examine cross-country differences and find that the
provision of early education and childcare are particularly beneficial to
women’s employment and earnings. In contrast, Kleven and others (2021)
find that the expansion of parental leave and subsidized childcare in Austria
had no effect on gender inequality in the labor market. This suggests that the
provision of generous family policies is necessary, but not always sufficient,
to reduce motherhood penalties in the labor market. Whether or not they are
sufficient to reduce motherhood penalties, generous family policies do allow
parents to ensure that their children will receive high-quality care while they
have the option to participate in the labor force.
In addition, policies that support the care industry also have the potential to disrupt the “low road” equilibrium of low wages and difficult working
conditions in this sector. Subsidies that bolster the wages of childcare workers, one of the lowest-paid occupations in the U.S. economy, can increase
their earnings and expand employment. Moreover, given that the care sector
is home to a disproportionate share of women—especially Black, Hispanic,
and Asian American and Pacific Islander women—childcare subsidies can
also directly reduce both gender and racial wage inequality.
Another policy that could help families manage care responsibilities
is the establishment of a national paid family and medical leave program,
building on the 1993 Family and Medical Leave Act, which requires covered
employers to provide employees with 12 weeks of unpaid leave to care for
a new child, care for a seriously ill family member, or recover from the
worker’s own serious illness. Paid family and medical leave programs have
been enacted in nine U.S. States and the District of Columbia (Kaiser Family
Foundation 2021). Paid leave used at the time of the birth of a child has been
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shown to increase the mother’s attachment to the labor force (Byker 2016;
Rossin-Slater, Ruhm, and Waldfogel 2013), which can potentially increase
long-term earnings. Along with other policies that maintain their labor force
participation, moderate lengths of parental leave can reduce motherhood
wage penalties (Budig, Misra, and Boeckmann 2016). Paid leave may also
produce labor supply benefits when used for other purposes, such as caring
for a spouse with a work-limiting disability or a chronic health condition
(Anand, Dague, and Wagner 2021).
The structure of parental leave in the United States differs markedly
from that of other countries, where parental leave is often tied to a child,
and family members can choose who takes the leave. In contrast, leave in
the United States is tied to the worker, and cannot be transferred between
family members. This means that parents of a new child can maximize
their combined parental leave by having more than one parent take it. This
nontransferable leave has the potential to reduce care-based discrimination
against women by creating an incentive for both men and women to use
it. Research has shown that when other countries have introduced policies
designed to increase fathers’ use of parental leave, the labor supply and
earnings of mothers have increased, though the persistence of the effects
has varied (Dunatchik and Ozcan 2020; Druedahl, Ejrnaes, and Jorgensen
2019). Such polices have also had positive health effects on mothers as well
as long-lasting effects on the division of labor in the household (Patnaik
2019; Persson and Rossin-Slater 2019).

Progressive and Equitable Tax Policy
A progressive system of taxation, where higher-income households pay a
greater share of their income in taxes, can play an important role in reducing
inequality, including that which is driven by differences in skills and luck,
or other forces that remain even when barriers to competition have been
addressed. Figure 5-6 demonstrates how the combination of means-tested
transfers and Federal income taxes increased incomes of the lowest quintile
by 68 percent, and reduced incomes in the highest quintile by 24 percent.
Using an alternative summary measure of income inequality, the Gini coefficient was reduced by 8 percent by taxes and transfers in 2018. And given
that white women and Black, American Indian, Alaska Native, and Hispanic
people of any gender are overrepresented in the low-wage workforce, progressive taxation can also reduce racial, ethnic, and gender inequality.
Tax credits that provide direct transfers to middle- and lower-income
households can support the goals of reducing inequality and enhancing
equity. The Child Tax Credit has emerged as a key lever in this area. While
this credit traditionally accrued to largely middle-income households, the
American Rescue Plan Act temporarily increased the credit and made it fully

Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 187

Figure 5-6. Average Income, Means-Tested Transfers, and Federal Taxes, 2018
Thousands of 2021 dollars
$400
Highest
quintile
Income
$350

before
taxes and
transfers

$300

+

-

Meanstested
transfers

Federal
taxes

=

Income
after
taxes and
transfers

$250
$200
$150
$100

Lowest
quintile

$50
$0

Income quintiles

Sources: Congressional Budget Office; Haver analytics; CEA calculations.

refundable in 2021, allowing all households at the lower end of the income
distribution to receive the maximum credit, even if they had no tax liability.
The most direct impact of these changes was to reduce poverty, especially
for children in recipient households, with the greatest estimated reductions
in poverty for Black and Latino children (Center on Poverty and Social
Policy 2021). These credits also support investments in human capital, such
as educational attainment, as discussed in chapter 4, and the associated longrun increases in employment, earnings, and longevity.
A key challenge to progressivity is the preferential tax treatment of
capital income—such as dividends generated from an investment or the
gain in the value of stocks or other assets (Tax Policy Center 2020). Capital
income is generally taxed at lower rates than wage and salary income, and
the increase in the market value of stocks and many other assets is not taxed
until the gain is “realized” when the asset is sold. Thus, these capital gains
are allowed to accrue and compound for years before being taxed, and, if
passed on at death without being sold, the gains in the value of the asset over
the lifetime of the holder will escape taxation completely. Recent research
shows that when capital income is instead counted as income in the year
it accrues, the 400 wealthiest households pay between 6 and 12 percent of
their income in taxes (Leiserson and Yagan 2021). This is a much lower
rate than would be paid by households that had received all their earnings
through labor income, and because capital income is concentrated among
higher income households, these factors tend to exacerbate inequality in
after-tax income.

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In addition, households with significant capital income are more likely
to get away with tax evasion. It is estimated that nearly 99 percent of income
taxes on labor wages and salary are paid, while a much lower percentage of
taxes owed are collected on the forms of income, such as short-term capital
gains, that are more likely to be accrued by higher-income households. (U.S.
Department of the Treasury 2021c; Internal Revenue Service 2019). Recent
research suggests that highly sophisticated forms of tax evasion, including
through offshore accounts and pass-through businesses, go undetected and
account for nearly one-third of evasion (Guyton et al. 2021). Moreover,
while audits by the Internal Revenue Service (IRS) have decreased in
general in recent years, they have decreased more rapidly among higherincome earnings, skewing enforcement toward a group with lower rates
of underpayment (Sarin 2021). One reason for a decline in audits among
higher-income taxpayers is that audits among this group are costly—they
have access to advanced forms of evasion—and the IRS has been underfunded during the last decade.
Policies that achieve greater parity in tax rates on capital income relative to labor income, and greater funding for the IRS to enhance taxpayer
compliance, can therefore improve the progressivity of the tax code. This
includes taxing capital income at ordinary income tax rates and taxing
the capital gains on assets transferred at death, both of which were proposed, with some progressive exclusions, as a part of the revenue policies
in President Biden’s Fiscal Year 2022 budget (U.S. Department of the
Treasury 2021a). On the tax compliance side, this budget also outlined a
number of improvements to the IRS’s enforcement capability, including
additional funding to help combat sophisticated forms of tax evasion, better information from third-party reporters on capital income, technological
upgrades at the IRS, and improved regulation of paid tax preparers. This
combination of policies would likely increase the effective tax rate faced by
those with capital income, which, given the concentration of capital income
among the richest households and the underrepresentation of marginalized
groups among this category, would facilitate greater progressivity and racial
and ethnic equity in the tax code.

Conclusion
This chapter has explored and defined the scope of forces that keep labor and
product markets from being truly competitive, and that prevent individuals
from reaching their full potential. These include a lack of competition in
markets affecting a broad range of workers, and racial and gender discrimination more specifically. The costs of ignoring these structural forces
are increased inequality and reduced economic growth and output. These
societal and economic costs stem from inefficient labor market outcomes,
Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 189

misallocated talent, suppressed innovation, and reduced incentives for
human capital investment. Government actions can curtail these forces
by enforcing existing antidiscrimination laws and promoting competition
in the economy—at large, and in labor markets in particular. Policies that
establish a minimum wage or protect the rights of workers to join a union
are examples of actions that counterbalance employers’ market power, while
government support for the care economy can bolster wages and increase
employment in that sector. These and other polices can begin relieving the
historical burdens on disadvantaged groups of workers, helping to reduce
inequality and bolster economic output and growth.

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Chapter 6

Building Resilient Supply Chains
The year 2021 was when supply chains—the networks of producers,
transportation companies, and distribution centers that develop and move
products and services—entered dinner table conversations. Though this term
has certainly been part of the lexicon going back to the 1980s, and has been
a part of doing business for centuries, COVID-19 highlighted supply chains’
vulnerabilities, which became front-page news. Supply chains have become
more complex, interconnected, and global than they were in decades past.
The share of world trade that crossed at least two borders increased from 37
percent in 1970 to nearly 50 percent in 2014 (World Bank 2020a, 2020b).
This increasing segmentation of the production process has reduced prices in
the United States, while also raising productivity and aggregate incomes in
many of the low-income countries that are integral to global supply chains
(World Bank 2020a). However, the globalization of production has also
made supply chains more vulnerable to disruption. This fragility has been
exacerbated as firms have removed excess capacity (e.g., extra inventory,
or reserves of people with the time and skills to solve problems), making
supply chains less resilient. That is, they have less ability to recover quickly
from unexpected events. Thus, though modern supply chains have driven
down consumer prices for many goods, they can also easily break (Brede
and de Vries 2009; Baldwin and Freeman 2021; Miroudot 2020; de Sá et al.
2019; White House 2021a).
Though it was not inevitable, movement toward this more fragile configuration has been happening for decades, as public and private policies
have undermined firms’ incentives to invest in such capacity to ensure

191

resilience. The COVID-19 pandemic is not the first time that supply chains
have been disrupted; the production and distribution of goods have been
regularly snarled by natural disasters, cyberattacks, labor strikes, supplier
bankruptcies, industrial accidents, and climate-induced weather emergencies
(de Sá et al. 2019). The pandemic simply exposed just how complex and
interconnected modern supply chains have become. These highly publicized
disruptions and product shortages made the public painfully aware of the
many steps involved in getting a product produced, transported, and placed
on shelves or doorsteps.
The first section of this chapter describes modern supply chains and explains
their evolution, focusing on manufacturing. Supply chains are shaped by
a complex network of relationships; these relationships affect not just the
movement of supplies from place to place but also the incentives of lead
firms and suppliers to invest in producing new products, in providing good
jobs, and in achieving resilience. The second section describes how increasingly frequent disruptions of the economy suggest that supply chain fragility
will continue to be a problem. The third section outlines the private sector’s
incentives to become more resilient in the face of these challenges. Finally,
the fourth section suggests vital roles for government in helping to shape
supply chains and overcome market failures.

21st-Century Supply Chains
Supply chains are the linkages in the production process that facilitate the
transformation of raw materials into finished goods or services. A supply
chain is made up of producers and logistics providers that move inputs from
one stage to the next, and also of participants in the distribution channels for
the finished product, including wholesalers, distributers, and retailers. This
chapter primarily focuses on manufacturing supply chains that facilitate the
production of physical products from unprocessed materials.1
Figure 6-1 depicts some of the ways supply chains are commonly
organized. Even within the same industry, firms have different supply chain
In addition to goods, services are also part of supply chains and often face some of the same issues
that are discussed in this chapter.
1

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Figure 6-1. Common Types of Supply Chains
B

C

D

Country B

Country A

A

Source: Adapted from Cavalho (2014).
Note: From left to right: A, vertical integration with isolated industries; B, outsourcing with isolated
industries; C, outsourcing and offshoring with isolated industries; D, outsourcing with a central node (starshaped). Arrows denote flows of products, information, and the like between companies.

configurations (Kamalahmadi and Parast 2016; Lund et al. 2020). This
figure gives four stylized examples of how supply chain relationships could
be formed:

Vertical Integration with Isolated Industries
Panel A of figure 6-1 illustrates a three-firm configuration, where each
firm (shown by the dots in the figure) is self-sufficient—that is, completely
vertically integrated. Thus each firm produces everything, starting from raw
materials and ending with the finished product. In this configuration, supply
chains are completely internal to a firm. A prototypical example of this is
the automaker Ford’s River Rouge Plant, which in the 1930s included a steel
mill, glass factory, power plant, rubber factory, foundries, machine shops,
stamping plants, assembly lines, a cement plant, a paper mill, a leather
Building Resilient Supply Chains | 193

plant, and a textile mill (Weber 2019). Ford also owned a rubber plantation
in Brazil, coal mines in Kentucky and West Virginia, and railway cars to
transport raw materials. This allowed Ford to maintain direct control over
the entire manufacturing process. However, this complete vertical integration also made it difficult for Ford to cut costs during the sharp decrease in
demand for cars during the Great Depression, as the automaker continued to
bear the fixed costs of component production. In contrast, Chrysler, which
was much less vertically integrated during this time period, did not need to
bear these fixed capital and administrative costs; Chrysler’s suppliers did
(Chandler 1962, 1992). A firm’s decision to vertically integrate depends in
part on whether the costs of transacting in different markets outweighs the
cost of managing these activities internally (Coase 1937).

Outsourcing with Isolated Industries
Panel B of figure 6-1 represents three industries, each with significant
supply-chain relationships. Here, inputs travel “downstream,” where they
are transformed into a final good. The lead firm typically designs products
and directs production by multiple tiers of suppliers in many locations,
but it does not own most of these suppliers. This is called outsourcing.
Outsourcing allows the lead firm to contract with firms that may have lower
production costs due to lower wages or other competitive advantages (see
box 6-3 below).
The chain includes direct suppliers of the lead firm (tier 1 suppliers),
as well as suppliers to those suppliers (tier 2 suppliers), and so on—all the
way back to the raw materials used to produce the good. A firm can have
hundreds of tier 1 suppliers and thousands of tier 2 suppliers, as shown in
figure 6-2 (Lund et al. 2020).2 Looking at the publicly disclosed lists of
suppliers for 668 companies, the McKinsey Global Institute found that the
number of direct suppliers was large and that the network of indirect suppliers was even larger, often numbering in the thousands (Lund et al. 2020). As
discussed below, the degree of coordination between the firms, represented
by the arrows in figure 6-1, can vary between two extremes: arm’s-length
transactions and collaborative relationships.

Offshoring and Outsourcing with Isolated Industries
If lead firms choose suppliers across national boundaries, this is called
offshoring, as shown in panel C of figure 6-1. Offshoring gives companies
expanded scope to locate production in areas with lower wages, or that
have other competitive advantages not available in their home country, such
Note that, due to data limitations, the tier 2 suppliers in figure 6-2 may not be supplying inputs into
the lead firms’ products; rather, they are suppliers of the tier 1 suppliers, which usually produce for
more than one lead firm.
2

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Figure 6-2. Examples of Tier 1 and Tier 2 Supply Relationships
General
Motors

18,000+

Airbus

Apple

12,000+
7,400+

856

Nestlé

1,676

Publicly disclosed tier 1 suppliers

638

5,000+
717

Tier 2 suppliers and below

Source: Adapted from Lund et al. (2020), relying on the Bloomberg Supply Chain Database.

as access to natural resources or better technology (Antràs 2020; World
Bank 2020b). Competitive advantage may be the result of naturally occurring endowments or developed by government or private sector policies
(Mazzucato 2016; Lee 1995). In the past, internationally traded goods were
largely either raw materials, such as cotton, or finished goods, such as clothing. Since the early 1990s, there has been a large rise in trade of “intermediate goods” or components, such as fabric that has been cut but not sewn.
In both panels B and C of figure 6-1, no connections exist between the
blue industry and the parallel orange and black industries. In this diagram,
nodes are industries with few overlapping suppliers, such as electronics and
autos in the past.

Outsourcing with a Central Node
In contrast to the isolated industries depicted in panels B and C of figure 6-1,
supplier firms usually sell to more than one lead firm and may sell to several
different industries, as shown in panel D (Carvalho and Tahbaz-Salehi 2019;
Carvalho 2014). One example is a star-shaped configuration, with one central node (the green node) that is used in production by all other nodes. Firms
in this general-purpose industry supply a wide number of other industries
and often also use inputs from the industries they supply (Carvalho 2014).3
These types of supplier relationships allow firms to take advantage of
In practice, some suppliers, and even the central node of panel D, may be offshored as well as
outsourced; for simplicity, this configuration is not depicted.
3

Building Resilient Supply Chains | 195

economies of scale, where per-unit costs decrease as the number of units
produced increases and the supplier is able to sell to multiple firms.
Firms’ decisions regarding the design of their supply chains lead to a
complex web of connections. Aggregating firm-to-firm supply chain connections, industry A has supply chain connections to industry B when firms
in industry A purchase inputs from firms in industry B. Though comprehensive data on firm-to-firm supply relationships are lacking for the United
States, the network structure of the U.S. economy can be visualized at an
industry level. This industry-level analysis can shed light on which industries supply inputs to many other industries and the structure of network connections between industries. These connections can amplify microeconomic
disruptions.
The U.S. economy is complex and interconnected, with several central
hub industries that have connections to most other sectors. Using the most
disaggregated, publicly available sectoral data—the Bureau of Economic
Analysis’s (BEA) Input-Output Accounts Data—it is possible to see the
supply chain connections between 417 different industry sectors, as depicted
in figure 6-3. Each node is a sector, and the connections between them represent flows of inputs from one supplying sector to another. The network is
sparsely connected; on average, each narrowly defined industry is connected
to only 11 other industries (Carvalho 2014). However, a small number of
hub industries are highly connected to many others in the network. Although
most industry pairs are not directly linked, they are indirectly connected by
a small number of steps through these hub industries (Carvalho 2014). The
most-connected input supply sectors (the numbered nodes) in 2002 included
real estate, electricity generation and distribution, iron and steel mills,
depository and credit intermediation, petroleum refineries, and truck transportation (Carvalho 2014). The CEA’s analysis of the 2012 input-output
tables shows that semiconductors have become a highly connected industry,
while truck transportation has dropped from the top 10 list (Carvalho 2014;
Bureau of Economic Analysis 2012). Other countries also have similar patterns of central hub industries, though the central industries may be different
(Carvalho and Tahbaz-Salehi 2019; Fadinger, Ghiglino, and Teteryatnikova
2015; McNerney, Fath, and Silverberg 2013).

Arm’s-Length and Collaborative Relationships
The arrows in figure 6-1 represent connections between the nodes in the supply chain. The nature of these connections can vary between two extremes:
arm’s-length transactions and collaborative relationships.
In an arm’s-length transaction, a firm purchases a standard input from
an unaffiliated firm, often choosing from a large set of possible sellers. In
this case, the connection is very simple: the seller provides an off-the-shelf

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Figure 6-3. The Production Network Corresponding to U.S. Input-Output
Data in 2002

Source: Carvalho (2014). Copyright the American Economic Association; reproduced with permission of the Journal
of Economic Perspectives.

product to the buyer, which sends payment. If there is a problem with a
supplier (e.g., the price is too high or a disaster causes it to be unable to
produce), the buyer can easily find another supplier. Lead firms may benefit
from these relationships because they are able to easily change suppliers,
creating competition that requires suppliers to reduce their prices to win
business.
In collaborative relationships, firms in a supply chain communicate frequently about the product and production process; performance
requirements (e.g., price, quality, specifications, and delivery schedule) are
customized for a particular product, and are usually set by the lead firm
(Gereffi 2020). In some instances, these are transactions between affiliates of a large company, while others involve a lead firm and financially
independent suppliers. For instance, companies such as Nike do not own the
facilities in which their products are manufactured; instead, they provide the
design, product specifications, advertising, distribution, and coordination
of the complex network of contractors that make the shoes (Gereffi and
Korzeniewicz 1994).

Building Resilient Supply Chains | 197

Suppliers in collaborative relationships provide these highly customized inputs on a repeated basis, usually without complete or easily enforceable contracts (Hart and Moore 1990). Both the buyer and supplier invest in
capital, equipment, or knowledge that is useful only with a particular partner
(Antràs 2020). These relationship-specific investments increase the cost of
finding and switching to a new supplier, but often pay off in components that
better fit the lead firm’s needs and in quicker responses to unexpected situations (Antràs 2020; Helper 1991; Gibbons and Henderson 2011). A large literature describes the potential benefits to lead firms of having collaborative
relations with suppliers, such as reduced costs, defect rates, and lead times;
and increased investment, responsiveness, innovation, and problem solving
(Delbufalo 2012; Gibbons and Henderson 2011; Aoki and Wilhelm 2017).
A key reason for the long-term profitability of firms such as Toyota
and Honda is their investment in collaborative relationships with their
suppliers (Aoki and Wilhelm 2017; Liker 2004; Lieberman, Helper, and
Demeester 1999). The rise of these sticky buyer–seller relationships is a distinctive aspect of the recent rise in global value chains (World Bank 2020a).
Understanding why some firms adopt collaborative relationships and others
do not is an area of active research in many disciplines, including economics, management, and sociology (Bernstein 2015; Gil and Zanarone 2018;
Schrank and Whitford 2009). Box 6-1 provides an example of how one firm
currently combines domestic production, offshoring, vertical integration,
and offshoring to make its products.
However, there is no single optimal way to organize a supply chain.
Even within the same industry, firms often choose different strategies. For
example, on average, automakers producing in the United States have 4.7
suppliers for each product category, a financial stake in 22 percent of transactions, and relationships with suppliers that last 2.4 years. However, there
are substantial differences among automakers in these practices. Japanese
vehicle manufacturers engage more in collaborative outsourcing than do
their U.S. counterparts; therefore, Japanese relationships with suppliers last
70 percent longer, and they have fewer than half as many suppliers for each
part as do U.S. automakers. These differences persist even when automakers are selling similar products in the same market, and after controlling for
component volume and mix (Helper and Munasib 2022). Automakers differ
in their offshoring strategies as well. For example, in 2020, Ford had 24
percent more production offshore than did Stellantis.4

These data, from the American Automobile Labeling Act (AALA), do not allow the separation of
U.S. and Canadian content (Center for Automotive Research 2020).
44

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Box 6-1. The Supply Chain of a Hot Tub
The M9 hot tub is made by Bullfrog Spas in Utah, where 500 workers
assemble almost 1,850 parts from 7 countries and 14 states (see figure
6-i). The hot tub top shell starts as a flat acrylic sheet from Kentucky,
which is then combined with a different type of plastic in Nevada and
sprayed with an industrial chemical from Georgia. Parts of the frame
shell of the hot tub are driven in by trucks from Idaho several times a
week. Many of the electric motors come from China and are assembled
into water pumps in Mexico and then driven to Utah. Additional material
for exterior cabinets is transported from Shanghai on container ships
through the ports of Long Beach or Oakland. Water-spraying jets are
made in Guangzhou, China; are sent through the Panama Canal and
Eastern ports to the supplier’s warehouse in Cleveland, Tennessee;
and then are sent on to Utah. Once fully assembled, the finished hot
tubs are placed on trucks or trains and delivered to retailer warehouses.
This example illustrates both the extent of outsourcing, which increases
the number of individual companies involved in the production of a
single good, and the geographic distance traveled by each component,
estimated to total nearly 900,000 miles, as well as the dependence on
transportation and logistics this entails.

Figure 6-i. Sources of the Components of a Hot Tub

Herriman, Utah

Component

Frame
Seat backs

Acrylic sheet
Urethane

Water jets
Touch screens

Electric motors
Cabinet pieces

Source: Adapted from Hufford, Kim, and Levinson (2021).

Drivers of Change in Supply-Chain Structures
Global supply chains that involve offshoring, and often outsourcing, multiplied rapidly from 1990 to 2008, though their growth slowed after the 2008

Building Resilient Supply Chains | 199

global financial crisis (World Bank 2020a). Manufacturing firms also outsource services, including logistics, cleaning, and security. That is, workers
providing these services are no longer direct employees of manufacturers;
instead, they work for financially independent contractors. For example, in
food, cleaning, security, and logistics services, the share of those working
for such contractors in the United States rose from about 5 percent to about
30 percent between 1950 and 2015 (Dorn, Schmieder, and Spletzer 2018).
Two key changes have increased the attractiveness of outsourcing
and offshoring. The first change is increased access to foreign suppliers,
making offshoring more cost-effective for firms, largely due to advances
in information technology (IT) and reductions in trade barriers since the
1990s. Advances in IT allow firms to convey detailed information about
product and process specifications across long distances, while improvements in transportation, such as containerization, allow goods to be moved
more quickly and consistently (Grossman and Rossi-Hansberg 2006). These
developments make it possible to segment the production process, keeping
highly skilled functions, such as research and development and management,
in more advanced economies, while moving others, such as production of
components or assembly, to countries with lower wages (Gereffi 2020).
Major trading nations have signed agreements that reduced barriers to
trade, such as the 1994 North American Free Trade Agreement. These trade
pacts contain strong protections for property rights of corporations, but far
weaker protections for labor rights. This disparity increased the attractiveness to multinational firms of offshoring production to low-wage countries
(Drake 2018). The result has been increased availability of cheaper goods
for American consumers, but also significant pressure on wages and benefits that have often driven workers from the middle class (Hakobyan and
McLaren 2016).
Finally, widespread international government subsidization of manufacturing industries has lowered prices that lead firms pay for inputs, and
has oriented many nations’ domestic industry toward global supply chain
participation (Hauge 2020). For instance, in the past few decades, the
Taiwan Industrial Technology Research Institute has facilitated relationships between young, domestic semiconductor manufacturers and multinational buyers. The institute helped organize two firms—the Taiwan
Semiconductor Manufacturing Company and the United Microelectronics
Corporation—and gave them intellectual property. By 2020, these two companies accounted for 60 percent of global semiconductor revenue (Lee 2021;
Breznitz 2005). Taiwan and China have extensively subsidized their semiconductor industries, with subsidies often approaching nearly 30 percent of
a company’s revenue, according to the U.S. Department of Defense (2022,
36). “Made in China 2025,” China’s 10-year plan to transform itself into a
world leader in high-tech industries, promotes policies that increase Chinese
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firms’ market share and builds globally competitive industries in key sectors
without relying on foreign firms (Congressional Research Service 2020).
(See box 6-2.)
The second key change is the growing role of financial criteria and
institutions in corporate decisionmaking. This “financialization” of the
economy has encouraged outsourcing and offshoring because of savings in
costs that are easily measurable. Firms increasingly tie executive compensation to such financial measures as earnings per share, stock prices, and return
on equity. Before the 1970s, only 16 percent of the chief executive officers
in Standard & Poor’s 500 companies had compensation based on such measures; by the 1990s, 47 percent did, and in 2021, the vast majority employed
by large corporations did (Admati 2017). Such incentives have encouraged
managers to focus more on these financial statement numbers than on less
easily measurable metrics, such as resilience.
However, financial metrics can be misleading. Although an outside or
offshore supplier may offer a lower unit price, these savings may be eaten
away by hidden costs, such as longer lead times, increased vulnerability
to disruption, and reduced access to ideas for innovation due to linguistic
and geographic distance (Gray, Helper, and Osborn 2020). Such hard-toestimate costs are often ignored, even though they may negate the estimated
savings from outsourcing (Barthelemy 2001). These less easily measurable
metrics are often characterized as “soft” information, which, in contrast
to “hard” information, may require knowledge of the environment and/or
personal relationships to collect and understand.
Soft information includes operational measures that use physical units,
such as defect rates or downtime, and involve such intangibles as the value
of research and development or of employee training (Liberti and Peterson
2019; Edmans, Heinle, and Huang 2016). It is often difficult to convert soft
information into dollars. For example, it is not easy to measure how much
an investment in training will improve quality, and how much this improvement in quality will flow to the bottom line. Such investments are also hard
for outsiders or those without experience with a given product to verify.
Thus, the pursuit of favorable performance as measured by financial indicators may induce firms to act in ways that could be detrimental to long-term
performance, essentially trading longer-term resilience and sustainability for
nearer-term profitability (Edmans, Heinle, and Huang 2016).
For firms increasingly driven by short-term investors’ demands, the
temptation to ignore these costs has often been great. A survey of senior U.S.
financial executives found a willingness to sacrifice long-term shareholder
value to meet Wall Street earnings targets or smooth reported earnings. For
example, when managers were asked if they would “accept a sacrifice in
value . . . to avoid volatile earnings,” 78 percent said yes; 55 percent would
“delay starting a new project, even if this entails a small sacrifice in value”
Building Resilient Supply Chains | 201

Box 6-2. The Role of China in U.S. Supply Chains
A significant factor in the recent evolution of global supply chains has
been the rise of China, which is now the largest source of U.S. imports.
China’s manufacturing began exploding in the 1990s, and its share of
world manufacturing exports rose from 3.1 percent in 1991 to 17.6
percent in 2015, before dipping to 14.2 percent in 2018 (Autor, Dorn,
and Hansen 2021).
Initially, China specialized in simply assembling products from
imported components and designs. For example, it is estimated that in
2010 China provided less than 2 percent of the value added of the Apple
iPhone 4; the product was designed in the United States, and the components were made in places like Japan and South Korea; no Chinese
suppliers contributed components (Linden, Kraemer, and Dedrick
2011). However, China learned quickly, and for the iPhone X in 2018, it
contributed more than 25 percent of the value added, including assembly
and high-value components such as the battery pack and touch screen
(Linden, Kraemer, and Dedrick 2007; Xing 2019).
China’s entry into global supply chains was facilitated not only
by technological advance in transportation and communication but
also by changes in institutions. Particularly important were the United
States’ granting of Permanent Normal Trading Relations (PNTR)
to China in 2000 and the admission of China to the World Trade
Organization in 2001, steps that gave imports from China permanent
access to the relatively low tariff rates reserved for members of the
World Trade Organization. These steps did not require China to change
its labor policies, which banned workers from joining independent trade
unions, involved reprisals against workers who sought higher wages,
and involved forced labor. These policies suppressed wages in China,
increasing the competitiveness of firms, including multinational firms,
that produced there.
China’s competitiveness was also facilitated by large subsidies,
and requirements that multinationals transfer technology to Chinese
firms. As the Congressional Research Service concluded, China aims
to advance its national development goals and future global economic
position through industrial policies that seek global civilian and military
leadership in advanced and emerging technologies. China’s policies feature a heavy government role in directing and funding Chinese firms to
obtain foreign expertise and intellectual property in strategic industries,
including aerospace, semiconductors, microelectronics, pharmaceuticals,
and electric vehicles (Congressional Research Service 2020). Through
these policies, and aided by U.S. companies pursuing asset-light strategies, China gained large degrees of market power in a variety of critical
supply chains. For example, China has 97 percent global market share
of the ingots and wafers used to make solar panels (U.S. Department of

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Energy 2022). It also produces 73 percent of global lithium-ion batteries,
which are the primary source of energy for electric cars (Henze 2022).
These policies may have contributed to the decrease in extreme
poverty in China, which fell by over 90 percent between 1990 and
2016 (the latest year for which data are available) (World Bank 2016;
Goodman 2021). These policies had significant effects on U.S. consumer
prices; one estimate is that prices for consumer tradables fell 0.19 percentage point annually between 2004 and 2015 due to trade with China
(Bai and Stumpner 2019).
However, these policies had negative effects on U.S. factories’
vitality and innovation; increased exposure to Chinese imports significantly reduced sales, profitability, and research-and-development
expenditures at U.S. firms facing import competition (Autor et al. 2020).
The China shock also had adverse effects on workers. In the decade
from 2000 to 2010, one-third of U.S. manufacturing workers lost their
jobs; at least a quarter of this effect was due to China’s accession to the
World Trade Organization (Autor, Dorn, and Hanson 2016). Workers
in affected industries saw rapid job loss after the United States granted
PNTR status to China (Pierce and Schott 2016). Communities more
exposed to Chinese imports had reduced earnings for low-wage workers, housing prices, and tax revenues; and larger increases in childhood
and adult poverty, single-parenthood, and mortality related to drug and
alcohol abuse, as well as greater uptake of government transfers (Pierce
and Schott 2016; Autor, Dorn, and Hansen 2021).

to avoid missing an earnings target (Graham, Harvey, and Rajgopal 2019,
8). Underlying this willingness is a view that stock market investors lack the
information to properly value long-term investments (Asker, Farre-Mensa,
and Ljungqvist 2015; Poterba and Summers 1995).
This financialization of the economy has been an important driver
of U.S. lead firms’ supply chain strategies. Outsourcing of production and
other capital-intensive activities is prescribed by consulting firms promoting
an “asset-light” strategy. These firms note that, all else held equal, a lower
amount of capital makes a given amount of revenue yield a higher measured
return on assets (Kachaner and Whybrew 2014); the importance of the “all
else held equal” assumption is discounted. Offshoring to suppliers with a
low quoted price is also attractive. Chinese subsidies and wage suppression
have yielded very low unit costs for Chinese suppliers; often, the price from
a Chinese manufacturer of a finished manufactured component has been
less than the price of raw materials for a U.S. supplier (U.S. Department of
Defense 2022, 27). The disadvantages of such purchasing strategies are hard

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to quantify; in a financialized environment, where many purchasing agents
are rewarded exclusively for driving down quoted prices, these disadvantages have typically been assumed (without much evidence) to be small
(Gray, Helper, and Osborn 2020).

Implications of Supply Chain Structures
This section examines the relationship between supply chains and innovation, and the role of supply chains in the business cycle. Both outsourcing
and offshoring have significant effects on innovation, some positive and
some negative.

Impact on Innovation
Outsourcing can lead to the development of highly specialized and innovative suppliers. Take the example of semiconductors. The particular trajectory
of innovation in this industry has led to a production process with very large
economies of scale; for instance, a new fabrication plant (fab) now costs at
least $12 billion to build. Because of the significant overhead involved, the
more semiconductor chips a fab produces, the lower the average cost of each
chip. And, with more sales, a fab’s owner can invest more in research and
development, enabling it to produce even more sophisticated chips (White
House 2021; Jie, Yang, and Fitch 2021). In part for this reason, it has become
more advantageous for firms to purchase semiconductors from a specialized
semiconductor firm than to make them in house (Breznitz 2005).5
This semiconductor example illustrates how a buyer can obtain the
benefits of suppliers’ innovation simply by buying the product; as semiconductors improved and their prices fell, manufacturers were able to dramatically increase the computing power of products ranging from refrigerators to
computers. Though many firms buy generic semiconductors from distributors, innovation often results from the interaction between a buyer’s needs
and a supplier’s capabilities (Batra et al. 2016; von Hippel 1988). Apple’s
cutting-edge products often result from significant interaction between its
designers and the producers of its semiconductors (Owen 2021; Jie, Yang,
and Fitch 2021).
Although collaborative relationships have many benefits, as described
above, they may also have costs, particularly in lost flexibility (Levin
2002). To minimize the costs of switching suppliers, a lead firm may use
arm’s-length relationships and design its production processes to enable it
to outsource to firms with weak bargaining power. Though this flexibility
has benefits for lead firms, it may cause their suppliers to invest less in both
As discussed above, government subsidies for semiconductor manufacturers were also an important
reason why firms reduced their vertical integration into this industry.
5

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innovation and workers due to uncertainty about the continuing demand
for their products, because these investments often have customer-specific
elements (see box 6-3) (Baker, Gibbons, and Murphy 1995; Helper and
Henderson 2014).
The use of semiconductors in the auto industry illustrates this point.
Although semiconductors became key to the operation of modern vehicles
more than a decade ago, many automakers did not begin to communicate
directly with semiconductor manufacturers until late 2021. Rather, they
bought chips indirectly, through distributors or first-tier suppliers, and did
not commit to purchases more than a few weeks out. Thus, although their
product plans included more intensive use of semiconductors in future
vehicles, automakers had not been credibly signaling this intention to manufacturers. Without this commitment, semiconductor manufacturers were
unwilling to build new fabs for automotive-grade chips, since fabs must
maintain very high capacity utilization to be profitable. Further, they did not
devote resources to innovating on the dimensions important to automakers,
such as reduced cost and increased reliability. In contrast, Apple has long
paid to reserve capacity in advance at fabs, and has worked with semiconductor manufacturers and design firms to innovate on the dimensions important to them—speed and power (Burkacky, Lingemann, and Pototzky 2021;
Ewing and Boudette 2021; Fogarty 2020; Lawrence and VerWey 2019).6
Innovation is affected by offshoring as well. In some cases, foreign
purchases increase the ability of U.S. firms to innovate by allowing access
to innovative technology developed abroad. For example, companies such
as Apple, Qualcomm, and Advanced Micro Devices rely on semiconductor designs from a U.K. firm called Arm; firms such as Intel rely on the
Dutch company ASML for its advanced lithography equipment (Associated
Press 2022a). And some scholars have argued that offshoring of production
increases U.S. firms’ innovation by allowing them to focus on high-value
tasks.
However, there is evidence suggesting that geographically separating production and innovation impedes innovation. Engineers overseeing
production are exposed to the capabilities and problems of existing technology, helping them to generate new ideas both for improving processes
and for applying a given technology to new markets. Losing this exposure
reduces the opportunity to generate such innovative ideas. For example,
when production of consumer electronics migrated to Asia in the 1980s, the
United States lost the potential to later compete in the burgeoning market
for follow-on products like flat-panel displays, LED lighting, and advanced
batteries (Pisano and Shih 2012; Berger 2015; Fuchs and Kirchain 2010).
As discussed below, U.S. automakers have recently announced significant changes in the way they
purchase semiconductors.
6

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Box 6-3. Outsourcing and Job Quality
Overall, 43 percent of U.S. workers are in supply-chain industries,
employed either at lead firms or their suppliers (Delgado and Mills
2020). The structure of supply chains has significant implications for job
quality for these workers.
As mentioned above, sometimes outsourcing is efficient. However,
in other cases, lead firms use outsourcing to gain access to suppliers
with weak bargaining power, adopting a strategy that David Weil has
called “fissuring.” In these cases, supplier firms have little ability to
compete except by aggressively holding down wages (Weil 2017). For
example, firms that sell to a small number of buyers pay lower wages
than do similar firms with more customers; this greater dependence on
large buyers lowers suppliers’ wages and has accounted for 10 percent
of wage stagnation in nonfinancial firms since the 1970s, according to
one estimate (Wilmers 2018).
Research suggests that jobs that are outsourced from lead firms
to suppliers are often worse for most workers, for several reasons
(Handwerker and Spletzer 2015; Goldschmidt and Schmieder 2017;
Helper 2021). As summarized by Helper (2021), these reasons include:
• Design for supplier interchangeability. Many lead firms structure
their supply chains to make contractors easily replaceable. For
instance, U.S. automakers in the past brought product design
and complex subassemblies in house, making it possible to have
contractors compete on making small, predesigned components
under short-term contracts. This strategy lowered barriers to entry
for suppliers, meaning that suppliers did not capture many rents
(Helper and Henderson 2014). This style of production has led
many lead firms in the apparel industry to employ long chains of
anonymous subcontractors. Walmart Corporation, for example,
was surprised when goods marked with its label were found in
the aftermath of the horrific fire at the Rana Plaza complex, in
which over 1,100 Bangladeshi apparel workers were killed due to
subcontractors’ poor safety practices (White 2017).
• Monitoring without accountability. Some lead firms specify in
detail the actions to be taken by workers in their supply chains,
even those who are not their employees (Davis-Blake and Broschak
2009). That is, lead firms can control workers without taking
responsibility for paying them benefits. Tight monitoring from lead
firms means that one of the few profit-making strategies available
to subcontractors is to keep wages low. Sometimes these workers
are misclassified as independent contractors, even though they
lack the autonomy of running their own business. When firms
misclassify workers in this way, “they offload labor costs and risks
onto workers—for example, by avoiding unemployment insurance

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taxes and workers’ compensation premiums—and make it difficult
for workers to organize or join a union and bargain collectively
for better wages and conditions” (U.S. Department of the Treasury
2022, i).
• Low supplier capability. When lead firms maintain tight control
over suppliers’ work methods, subcontractors’ ability to create or
capture value is low. Even though investments might yield productivity improvements, contractors often do not make them because
they lack the capability to do so or they would not capture much
of the benefit due to fierce competition. As a result, subcontractors
often cannot increase pay without risking bankruptcy. Suppliers
to lead firms that adopt financialized metrics also have difficulty
adopting management practices that have been shown to be effective. Fewer than half of second-tier auto suppliers have adopted
practices such as quality circles, in which production employees
gather regularly to explore ways to improve quality; one-third
report that they do not consistently do preventive maintenance, and
one-quarter employ no engineers. In contrast, suppliers that report
a collaborative relationship with customers were more likely to
adopt high-road policies such as cross-training of workers, and had
higher productivity (Helper and Martins 2020).
• Weak ecosystems. Not only do U.S. suppliers lack support from
lead firms; they are isolated in other ways as well (Berger 2015).
The reason: There are few institutions to help with innovation,
training, or finance (Ezell and Atkinson 2011). In contrast,
Germany’s Mittelstand, which are medium-sized firms, are the
backbone of the German manufacturing sector due to the help they
get from community banks, applied research institutes, training
institutions, and unions (Berger 2015).

Impact on the Macroeconomy
The structure of production networks, as described in figure 6-1, has
important effects on the macroeconomy. The location of supply relative to
consumers, the degree of interconnection and substitutability among firms
and industries, the geographic concentration of supply, and the amount of
collaboration and trust between buyers and suppliers all affect the degree
to which a shock to one firm or industry propagates through the entire
economy.
Distinct configurations of supply chain structures carry distinct exposure profiles. For example, offshoring, or openness to international trade,
can reduce exposure to domestic shocks by broadening supply or hedging against concentrated disruption (Caselli et al. 2020; Miroudot 2020).
Building Resilient Supply Chains | 207

However, the greater distance that imported inputs must travel increases
risks associated with transportation. For example, 40 percent of U.S. containerized imports go through the ports of Los Angeles and Long Beach,
where the rise in demand for goods induced by the COVID-19 pandemic
caused significant delays (Karlamangla 2021). Even supply chains that had
no production problems suffered from the shipping bottlenecks. In addition,
risks to a supply chain can grow with more global connections, because a
disruption in one country will affect suppliers in all other countries. For
instance, Bonadio and others (2020) estimated that one-quarter of pandemicrelated gross domestic product declines across 64 countries were related to
global supply chain shock transmission. When disasters occur with supply
chains abroad, as with the 2011 earthquake in Japan, recovery takes longer
than if the supply chain was local due to the longer lead time involved in
shipping.
Dependence on a single supplier or a single location also carries risk.
This is true even if the suppliers are domestic; for example, a severe 2021
freeze in Texas led to months-long disruptions in U.S. and global supplies
of plastics because of the concentration of petrochemical companies there
(Wiseman and Krisher 2021). These risks can be greater in industries important to national security that are located abroad, because decisions about supply would be affected by the policies of another country, as discussed below.
If firms within an industry share suppliers with skills that are hard to
replace, the bankruptcy of a few such suppliers can also take down other
suppliers, and even lead firms, with them. Fear of this “cascading bankruptcy” in 2008, when auto sales suddenly fell 45 percent, led the CEO of
Ford, Alan Mulally (2008), to ask for a government rescue of his major
competitors, noting that 90 percent of Ford’s suppliers were shared with
other automakers. Auto suppliers have hard-to-replace skills that include
the ability to maintain high quality standards (e.g., to control variation in the
size of parts produced to no more than 1/1000th of an inch, thinner than the
width of a human hair), consistently over millions of parts that sell for a few
dollars each. If these firms fail, other firms cannot easily enter the market
to replace them. Dependence on shared suppliers is not uncommon; the
computer giants Dell and Lenovo have more than 70 percent commonality
in their top 20 suppliers (Lund et al. 2020). In contrast, if each downstream
firm were vertically integrated (i.e., produced its own inputs), some firms
might be affected by a disruption, but it is likely others would still be able
to produce.
Some of these potential vulnerabilities carry offsetting benefits.
For example, geographic clustering of suppliers is common, and often is
efficient, because suppliers can share skilled labor, specialized inputs, and

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innovative ideas (Marshall 1919; Delgado, Porter, and Stern 2015).7 In addition, repeated dealings and face-to-face contact build the trust required for
collaborative supplier–buyer relationships (Bernstein 2015). As discussed
above, close relationships among firms in the supply chain could speed
recovery from disruptions (Baldwin and Freeman 2021; Alfaro and Chen
2018). That is, the reduced ability to seek new suppliers is often offset
by suppliers’ increased incentive to pitch in to help others. If firms could
quickly recover from supply disruptions, then the macroeconomy would not
be affected as much by global supply chains’ increasing exposure to shocks
and dependence on other firms (Carvalho and Tahbaz-Salehi 2019).
However, in the absence of such collaboration, shocks to supplier–
buyer relationships can have persistent effects on the macroeconomy, especially if networks are highly connected (e.g., star-shaped) and frequently
use hard-to-substitute inputs (Carvalho 2014). For instance, Barrot and
Sauvagnat (2016) found that if a supplier was hit by a major U.S. natural
disaster between 1978 and 2013, its key customers (those accounting for
more than 10 percent of the supplier’s sales) experienced an average drop of
2 to 3 percentage points in sales growth for one to two years afterward. If the
disrupted supplier produced hard-to-substitute inputs, this disruption further
propagated to suppliers that were not exposed to the original shock (Barrot
and Sauvagnat 2016). Bigio and La’O (2020) estimate that the input-output
structure of the U.S. economy amplified financial distortions by a factor of
2 during the 2008 global financial crisis.8
As the consulting firm McKinsey noted in 2011, “Many global supply chains are not equipped to cope with the world we are entering. Most
were engineered, some brilliantly, to manage stable, high-volume production by capitalizing on labor-arbitrage opportunities available in China and
other low-cost countries” (Malik, Niemeyer, and Ruwadi 2011, 1). These
conditions are less prevalent now. As networks become more connected,
and climate change worsens, the frequency and size of supply-chain-related
disasters rises. For this reason and others, understanding how to promote
quick recovery is increasingly important. It is also vital for companies to
have the incentive to make sufficient investments in resiliency, even when
they may not be able to monetize all the benefits of these expenditures
because of spillovers to other parts of the networked system.

Sometimes the clusters are near where natural resources required for production are or once were
concentrated (e.g., steel in Cleveland). Other times “clusters” of suppliers develop near where an
invention happened to occur (e.g., floor coverings in Dalton, Georgia; see Krugman 1991).
8
The authors compared the effects of the current star-shaped structure of the U.S. economy (panel
D of figure 6-1) to what they would have been if the economy looked more like panel A (vertically
integrated firms).
7

Building Resilient Supply Chains | 209

Figure 6-4. Frequency of Billion-Dollar
Natural Disasters by Type, United States
Number of disasters
25

20

15

10

Droughts

Flooding

Tropical cyclones

Year
Severe storms

Freezes

Wildfires

2020

2015

2010

2005

2000

1995

1990

1985

0

1980

5

Winter storms

Sources: NCEI (2021, 2022).
Note: Disaster costs are adjusted for inflation using the Consumer Price Index for All Urban Consumers.

The Rising Incidence of Supply-Chain-Related Disasters
Although the pandemic has been a particularly dramatic example of a
supply-side disruption, the global frequency of natural disasters increased
almost threefold between 1975–84 and 2005–14 (Vinod and López 2015),
mostly due to increases in climate-related events (NCEI 2021). Lund and
others (2020) found that supply chain shocks affecting global production
lasting at least a month occur on average every 3.7 years.
The magnitude of damage from these events is also growing; the
number of billion-dollar disasters has risen from an average of 5 annually
to 20 over the past 40 years (figure 6-4). The frequency of such events is
likely to continue to rise in the future, according to the United Nations
Intergovernmental Panel on Climate Change (IPCC 2022).

Private Sector Incentives for Resilience
As supply chains have increased in complexity, firms’ need for risk management has also grown (Baldwin and Freeman 2021). When unable to
produce due to lack of inputs, firms lose revenue, providing some incentive
to invest in resilience (Miroudot 2020). Practices include understanding the
structure of their supply chains (visibility), investing in backup capacity
(redundancy), and improving their ability to solve problems and substitute
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between inputs (agility), as well as vertically integrating components of
the production process (Christopher and Peck 2004; de Sá and de Souza
Miguel 2019). However, these strategies, especially redundancy, increase
costs (Baldwin and Freeman 2021). Thus, it is not cost-effective for firms
to invest in completely avoiding all disasters. Instead, these practices are
designed to reduce firms’ risks, such that the perceived expected value of
additional revenue during a disruption compensates for the cost of minimizing production issues (Miroudot 2020; Baldwin and Freeman 2021).
One consequence of a firm underinvesting in resiliency is that it
increases the exposure of other firms in the networked system to the negative spillover effects of a disruption. This type of market failure is likely
when the firm’s investment decisions consider only its private costs and
benefits, and it is unable to monetize the spillover benefits of its investment
decisions for the rest of the system. Under certain conditions, the private
sector can achieve an efficient level of investment. For example, if parties
can bargain without high transaction costs, an efficient market outcome
may be achieved through private contracting, or through self-policing,
cooperative arrangements (Bernstein 2015). However, these approaches
are infeasible when there is a large number of entities and/or contingencies involved, because these raise the transaction costs of negotiating and
enforcing contracts (Coase 1960). In this case, there is an important role for
government to play, as discussed below.

Visibility
A first step toward achieving resilience is for firms to learn more about their
suppliers’ production and inventory levels. This allows firms to monitor the
capability of their supply chain to meet demand, even if the suppliers do
not directly supply the lead firm. Visibility into supply chain relationships
is necessary to identify vulnerabilities in supply chains, so that firms can
properly plan for disruptive events (Fujimoto and Park 2014). Gaining this
knowledge is not just a technical challenge but also depends on trust between
buyer and supplier (MacDuffie, Heller, and Fujimoto 2021). One reason is
that if a buyer learns that a supplier has a lot of extra production capacity, the
buyer could push for a lower price. Beyond being able to identify suppliers,
key metrics include “time to survive”—how long demand for a particular
component could be met from inventory or another supplier if the regular
supplier was unavailable—and suppliers’ “time to recover” in case of an
emergency (Simchi-Levi 2020; Simchi-Levi and Simchi-Levi 2020).

Redundancy
Firms may also invest in developing relationships with additional suppliers.
Finding alternative suppliers for an input is time-consuming, and suppliers

Building Resilient Supply Chains | 211

must often go through quality verification. If firms proactively invest in
building relationships with several suppliers, the lead firm has ready alternatives. Even if one supplier is unable to produce, another one can step in as
a replacement.
Firms can also hold additional inventory, particularly if suppliers’ lead
time, or how long it takes to make their products, cannot be brought down
below their time to recover from a shock (Michaelman 2007). For example,
Toyota learned that its semiconductor suppliers’ lead time was four months,
so the automaker has kept four months of inventory of these products
(Shirouzu 2021). Though redundancy generally increases costs, it can also
increase profits during periods of supply chain stress by allowing production
to continue. However, holding inventory may not always be effective, given
that the stored parts may not be the parts needed in a crisis (Sheffi 2022).
(See box 6-4.)

Agility
Firms can invest in their and their workers’ ability to solve problems, thus
enabling them to pivot quickly to alternative products or processes or react
to abnormal situations (Baldwin and Freeman 2021; MacDuffie, Heller, and
Fujimoto 2021; Helper 2021). The new process may be one that allows use
of a different raw material to replace one that is unavailable, or it may be
a product and process very different from what the firm has traditionally
made. Another option is to increase the flexibility of their production process so that the firm can use a less specialized input. A variety of techniques
promote such flexibility, including:
• Reducing lead times, by identifying the critical path and working to
speed it up (Ericksen 2021).
• Investing in surge capacity, for example, by maintaining more generalpurpose equipment (such as 3D printers), and more generally trained
workers.
• Maintaining collaborative relationships between suppliers and customers, to identify problems early and provide incentives to fix them.
• Building problem-solving capability, including for front-line workers
(see the “high-road” discussion in chapter 4, on human capital).
• Maintaining real options, or the ability to postpone decisionmaking
until more information is available; for example, by producing domestically rather than enduring long shipping lead times (de Treville and
Trigeorgis 2010).
Agility may require upfront investment by firms in a supply chain, but
over time may reduce costs and enhance efficiency. Investing in problemsolving capability that reduces lead time can improve performance in normal
times as well as in emergencies.

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Box 6-4. Low Inventories and Just-in-Time Production
In addition to moving production across firm and national boundaries,
companies have been holding less inventory of both final and intermediate goods. Figure 6-ii graphs the ratio of private inventories to final sales
from 1947 to 2021, for establishments operating in the United States. It is
clear that, over the past 30 years, this ratio has decreased. Holding extra
inventory for production increases storage costs; the lower their inventory, the less working capital is needed and the lower the probability the
firm gets stuck with inputs that may become obsolete or spoil. However,
if supply is disrupted and the firm has a low ratio of inventory to final
sales, it has less inventory to fall back on, perhaps requiring it to shut
down production until its supplier can recover its ability to produce or
another supplier can be found.
As originally envisioned by Taichi Ohno at Toyota, just-in-time
production combines low inventories with additional policies that offset
the dangers discussed above, by speeding up the supply chain’s ability
to recover from disruption. These policies include localizing production
near consumers and increasing operational “agility,” as discussed above
(Liker 2004; Handfield 2021). In contrast, many U.S. firms have combined reduced inventory with longer supply lines, often of 4 to 6 weeks
(Buchholz 2020), and with workforce policies that limit their ability
to respond to shocks, as discussed above. That is, low inventories by
themselves do not necessarily lead to fragility; problems arise when low
inventories are combined with low agility.

Figure 6-ii. Domestic Business Ratios of Private Inventories to
Final Sales
6.5
6.0
5.5
5.0
4.5
4.0
3.5
3.0

2017

2007

1997

1987

1977

1967

1957

2.0

1947

2.5

Sources: Bureau of Economic Analysis; FRED Database of the Saint Louis Federal Reserve Bank.
Note: Shaded areas indicate U.S. recessions.

Building Resilient Supply Chains | 213

Collaborative relationships with suppliers are key to agile supply
chains. For example, in February 1997, a fire at Aisin Seiki, the sole source
for proportioning valves used in all Toyota vehicles, could have halted all
Toyota production for weeks. However, assembly plants were reopened after
only two days through collaboration between Toyota and its suppliers; more
than 200 firms set up alternative valve production. This collaboration was
orchestrated with limited direction from Toyota, haggling over intellectual
property, or worry about repayment for expenses incurred. Long previous
relationships, implicit competition for future contracts, pressure to maintain
relationships, and trust with the Toyota group promoted the effectiveness
and speed of the collaboration (Nishiguchi and Beaudet 1998).
Increasing domestic production may also make a firm more agile.
Because proximity leads to reduced transportation time and increases the
potential for better communication, domestic production helps firms develop
build-to-order capability. Reduced lead time also allows decisionmakers to
forecast for a smaller range of outcomes (de Treville and Trigeorgis 2010;
MacDuffie, Heller, and Fujimoto 2021).

Public Sector Strategies for Promoting Resilience
The public sector can play an important role in promoting supply chain
resilience, especially in helping to incentivize private sector decisions that
align with broader geostrategic and economic priorities. A supply chain that
crosses national boundaries means that production depends on the decisions
and activities of other nations, adding uncertainty to supply. In addition,
many aspects of supply chains have externalities; that is, decisions affect not
only the direct decisionmakers but also other actors in the supply chain. In
the presence of public goods, such as national security, government policy
can improve national welfare.
The government’s role in promoting robust supply chains is particularly important in two types of industries: those that provide inputs
into many individual supply chains with large spillover effects, such as
energy production, semiconductors, or transportation; and those that are
important for national security, including climate and health security, where
the assured supply provided by domestic production is especially valuable.
Specifically, public sector interventions to build robust supply chains can
address challenges related to aggregating and disseminating information,
and to assuring that we have the products and goods essential to effective
national security. Each is discussed here.

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Aggregating and Disseminating Information
The public sector can play a role in disseminating information that helps
markets work more efficiently. As noted above, firms often share suppliers
with other firms, making them dependent on these other firms’ actions.
Because supply chain information can be a competitive advantage, firms
may be unwilling to disclose certain data to other firms. For example, when
there is a shortage of a product, such as personal protective equipment
(PPE), individual hospitals are likely to overorder and hold more inventory
because they want to ensure their supply. PPE suppliers in this situation do
not know if they should increase capacity because they do not know if the
new level of demand will continue or whether hospitals are accumulating
inventory that will cause them to reduce the quantity they demand in the
future. Yet hospitals would not be willing to share information about their
true demand with suppliers because they could then be downgraded in priority or receive a smaller quantity of PPE.
The government has the capability to strategically collect sensitive
data and release aggregate information to market participants in ways that
can improve market functioning. For instance, the U.S. Department of
Health and Human Services has taken on an important role in providing an
accurate demand signal for PPE. The department’s Supply Chain Control
Tower receives near-daily data from distributors that represent more than
80 percent of the volume for the commodities it is tracking, along with
supply status from 5,000 hospitals. This dashboard alleviates hospitals’ fear
of shortages, so they do not need to incur extra costs of holding inventory.
The dashboard also allows distributors to receive a truer demand signal
by reducing excessive ordering that exacerbates supply constraints (U.S.
Department of Health and Human Services 2022, 13). In cases such as these,
the public sector is well positioned to collect, aggregate, and disseminate
this information.
The government also has a role to play in convening and coordinating
private sector actors. For example, standards bodies, such as the National
Institute for Standards and Technology in the Department of Commerce,
have played a key role in developing standard interfaces, such as for USB
ports, that allow many firms to easily participate in electronic supply chains,
which promotes innovation and cost reduction.
In addition, major innovations in decentralized supply chains can suffer from a chicken-and-egg problem, in that upstream firms will not supply
something until they see a demand for it, but downstream firms will not
invest in products requiring that input unless there is a ready supply. A past
success in resolving this dilemma was the 1990s development of a semiconductor industry road map by Sematech, a public–private partnership. The
group came together to agree on common equipment needs and innovation

Building Resilient Supply Chains | 215

direction, and to fund such equipment. Sematech’s convening helped equipment manufacturers make products that were compatible with what chip
designers were thinking, and, conversely, helped chip designers understand
the directions where equipment makers might go. Over the seven years that
Sematech received Federal funding, more than $1.65 in benefits was generated for each $1 in Federal spending (Link, Teece, and Finan 1996).9
During the chip shortage arising from the pandemic, in the fall of
2021 the Department of Commerce convened CEOs of leading companies,
enabling automakers and chip leaders to meet each other for the first time,
and to discuss supply chain bottlenecks and identify common solutions. One
such meeting led to a partnership between Ford Motor Company and Global
Foundries. This partnership will focus on increasing the production capacity
for Ford’s existing product lines and on facilitating joint research on future
chip technologies that will be critical to the next generation of vehicles.
Ford’s CEO announced that the company will also act to give chip producers “more confidence in future production” by buying directly from them,
rather than buying chips indirectly through other suppliers (Hicks 2021, 1).
General Motors recently announced a similar partnership with seven semiconductor producers. Advances in supply chain management will be crucial
for auto manufacturers in the next several years, given that new vehicles,
especially electric vehicles, could lead to a doubling of semiconductor
requirements (Colias and Foldy 2021).

National Security
Dependence on a foreign supplier in times of geopolitical conflict makes
supply chains fragile, particularly for a good that has few close substitutes.
Foreign control of a key resource is a valuable geopolitical bargaining
chip (Sanger and Schmitt 2022). Currently, the United States is heavily
dependent on foreign suppliers for semiconductors and batteries, which are
key inputs for much military technology. In 2021, 92 percent of the world’s
supply of advanced semiconductors came from one company, TSMC, in one
location, Taiwan (Lee, Shirouzu, and Lague 2021). Similarly, key parts of
battery supply chains are largely located in China, which refines 60 percent
of the world’s lithium and 80 percent of the world’s cobalt—two core inputs
to high-capacity batteries, without close substitutes. Access to these inputs
critical for defense is more assured if the goods are produced domestically
(White House 2021a).
The rationale for this calculation is as follows: “The unweighted ratio [of benefits to costs] for fully
burdened cost is 3.3. Of course, when Federal dollars are added to the cost basis, all of the ratios in
table 4 are reduced in half”; Link, Teece, and Finan (1996, 748). So, 3.33/ 2 = 1.65; these are private
benefits only; the paper did not estimate public benefits (hence “more than” $1.65).
9

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Profit-maximizing firms do not take full account of this spillover benefit to domestic production. National defense is an example of a public good;
it is both nonrival—that is, consumption does not diminish others’ ability to
consume the good—and nonexcludable, which means that those that do not
pay cannot be blocked from using the good. Because people can use public
goods without paying for them, the private sector will undersupply these
goods. For this reason, governments typically provide for national security.
Having at least some domestic production of critical goods also means
that, in the event of a natural disaster, U.S. firms are not dependent on the
policy choices of other countries. China’s COVID-19 policies that locked
down whole cities or ports for a small number of cases disrupted production for firms in countries with different policy approaches and different
case counts (Kuttner 2022). Though the United States has underinvested in
a variety of industries, moving toward 100 percent domestic production is
not necessarily the best response to these risks, given that allies and partners
may have a competitive advantage in some goods, and may allow diversification in case of domestic disruption (White House 2021a).
In addition to inputs directly used in defense production, governments
spend significant amounts of time and money to protect electricity and communication networks from supply chain disruptions. These sectors are hub
industries; as such, they are part of the production process for almost all
economic activity. To protect the power grid from cyberattacks, the Federal
Energy Regulatory Commission mandates minimum cybersecurity standards for systems necessary for operating the electric transmission network,
and the Department of Energy provides cybersecurity training and guidance
(GAO 2021). Though power generation companies have an incentive to protect against shutdowns that would decrease their revenue, disruption of the
power sector could cause economywide disruption far larger than the impact
on electricity industry revenue. In these types of industries, where disruptions affect the ability of other industries to produce, particularly industries
that are important to the Nation’s health and safety, the private sector does
not internalize the full costs of disruption to society.
Public sector intervention can be beneficial in these cases. In critical
sectors, the public may be willing to pay a higher cost than would the private
sector to avoid shortages. For example, the United States maintains large
stocks of food and keeps defense capabilities ready even during peacetime,
because the possibility of insufficient supply is so costly (Baldwin and
Freeman 2021). In these cases, the public sector must intervene to reach the
socially optimal level of resilience. Such intervention could include investments in U.S. manufacturing, using public procurement to stabilize demand
for U.S. supply chains, and helping small business invest in upgraded capabilities (see box 6-5).

Building Resilient Supply Chains | 217

Box 6-5. Policies to Improve the
Functioning of Supply Chains
The Biden-Harris Administration has been taking a number of steps to
help improve the functioning of supply chains. The focus has been on
strengthening critical supply chains, including those necessary to tackle
the climate crisis. The Administration has taken steps such as the following:
• Signed an executive order that directs agencies to fortify our
Nation’s supply chains and industrial bases, including focusing
attention on the supply chains of products critical to our economic
and national security (White House 2021a; Executive Order 14017
2021).
• Established the Supply Chain Disruptions Task Force to address
the challenges arising from the pandemic-affected economic recovery (White House 2021c).
• Directed seven Cabinet agencies to publish reports identifying key
weaknesses in some of the Nation’s most crucial supply chains, and
devised multiyear strategies to address these weaknesses (White
House 2022b).
• Enacted the Bipartisan Infrastructure Law, which is our Nation’s
most significant investment ever in modernizing the transportation
systems on which our supply chains depend (White House 2021d).
• Enacted the American Rescue Plan, which, among other programs,
authorized the $10 billion State Small Business Credit Initiative,
which will catalyze more than $70 billion in lending and investment in small businesses—including small manufacturers—during
the decade (White House 2021e).
• Issued the new Buy American rule that increases required U.S.
content in Federal procurement, and will create a new category of
critical products that are eligible for enhanced price preferences to
ensure that Federal spending supports American businesses (White
House 2021f).
• Proposed a new domestic financing initiative through the ExportImport Bank to strengthen U.S. manufacturing exports.
In addition to their direct effects, policies such as these have the
potential to catalyze private sector investments, consistent with the argument in chapter 1 that the public sector can be a partner of the private
sector, rather than a rival.

National security includes not only direct inputs into military security
but also inputs critical to citizens’ health, climate, and economic security.
As such, developing new supply chains is key to U.S. efforts to address
climate change (see chapter 7 on climate). In general, private firms invest
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too little in addressing climate change due to the fact they do not capture
all of the benefits, providing a rationale for government intervention, as
discussed in chapter 7. Decentralized supply chains face an additional issue
in making these investments: coordination of demand and supply (Samford
and Breznitz 2022).
For example, firms will not invest in making components for electric
vehicles unless they think there will be demand for them. Conversely, automakers will slow their investments in electric vehicles if they think components will be hard to obtain. The Biden-Harris Administration’s actions will
help to overcome these chicken-and-egg problems that make it hard to establish new industries. For example, the Bipartisan Infrastructure Law invests
billions of dollars in establishing mining and recycling programs for batteries. The White House has also convened automakers, unions, environmental
groups, and suppliers to coordinate plans to make and sell electric cars and
trucks that would use these batteries. The Administration learned from these
meetings what level of electric vehicle penetration might be feasible, before
publicly announcing the goal that 50 percent of U.S. light vehicle sales
should be “zero emission vehicles” by 2030 (White House 2021b). The
certainty provided by these actions has unlocked billions in private sector
investments in battery production that will employ thousands of people in
states like Tennessee and Michigan (Associated Press 2022b; Eggert 2022).
Similarly, the Administration has announced the goal that solar energy will
produce 45 percent of U.S. electricity by 2050, with tax credits targeted at
each stage of the solar panel supply chain (Fears 2021).

Indirect Supply Chain Policy
Many other government policies have implications for the structure of
modern supply chains. This section provides examples of economic policies
that are broader than supply chains but nevertheless have implications for
their structure.
The price of shipping intermediate goods thousands of miles during
the production process does not incorporate the social cost of emissions.
Transportation contributes about 29 percent of all U.S. greenhouse gas
emissions, which have been rising (EPA 2021). For example, international
shipping currently accounts for about 3 percent of total global greenhouse
gas emissions. If treated as a country, international shipping would have
been the sixth-largest emitter of energy-related carbon dioxide in 2015—
more than Germany (Chen, Fei, and Wan 2019; Gallucci 2021; IMO 2021;
Olivier et al. 2016; Rose et al. 2021; Olner et al. 2017). Pricing in the true cost
of moving goods—that is, to include greenhouse gas emissions—would
incentivize firms to reduce their use of transportation services; for example,
by producing closer to where their customers live, or investing in new lowcarbon fuels. (See chapter 7, on climate.)
Building Resilient Supply Chains | 219

Trade policy also has enormous implications for the structure of
supply chains. As discussed above, China’s entry into the World Trade
Organization led to a significant increase in offshoring, which has reduced
consumer prices but also has harmed U.S. innovation, employment, and
wages for decades. The North American Free Trade Agreement (NAFTA)
has been found to have had similar employment and wage effects, albeit on
a smaller scale (Hakobyan and McLaren 2016), although a 2020 revision
to NAFTA, the United States–Mexico–Canada Agreement, has somewhat
addressed these issues. Newer emissions-based policies—like the global
arrangement for steel and aluminum trade between the United States and the
European Union—promise to further reshape supply chains by incentivizing
production of lower-emissions goods. These newer policies offer the promise that global supply chains can be designed in a way that benefits people
in rich and poor nations alike.

Conclusion
Because of outsourcing, offshoring, and insufficient investment in resilience, many supply chains have become complex and fragile, with central
nodes that lack agility and have few substitutes. Some of this change has
been driven by advances in technology, which have beneficial effects. For
example, because more of today’s products are electronic, semiconductors
have become a central node in the economy.10 However, this evolution has
also been driven by shortsighted assumptions about cost reduction that have
ignored important costs that are hard to turn into financial measures, or that
spilled over to affect others. The validity of these assumptions is reduced in
a world where disruptions have become more prevalent and firms are more
tightly interconnected.
The COVID-19 pandemic has made these issues salient to the general
public, which has experienced frustrating waiting times for the delivery of
goods ranging from personal protective equipment to appliances. Though
supply chains have performed well in the aggregate, with over 20 percent
more goods flowing through the economy in 2021 compared with pre-pandemic times, it is still important to address supply chain fragility, given that
disruptions are likely to continue. As disruptions become more common,
private firms are beginning to increase their resilience through visibility,
redundancy, and agility. The Federal Government has acted, and will continue to act, to build resilience in critical supply chains—for example, by
providing clear signals of demand and supply that are already transforming
sectors critical for the Nation’s military, climate, and health security.
Note that this change was also significantly promoted by U.S. government supply chain policy
over many decades, as described in the text; see also Council of Economic Advisers (2021).
10

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Chapter 7

Accelerating and Smoothing the
Clean Energy Transition
Responding to the severe risks of climate change ranks among the most
important and difficult challenges facing the United States. Levels of
heat-trapping carbon dioxide in the atmosphere are higher than they have
been in millions of years, causing gradually increasing temperatures and
sea levels and worsening the catastrophic consequences of hurricanes,
wildfires, and other extreme events. Along with the governments of other
major greenhouse-gas-emitting countries, the Biden-Harris Administration
has declared the United States’ intention to rapidly reduce greenhouse gas
emissions to avoid the worst consequences of climate change.
Because three-quarters of human-caused U.S. greenhouse gas emissions
come from burning fossil fuels for energy, the most important step in reducing emissions is to shift from carbon-intensive to clean sources of energy
(U.S. Energy Information Administration 2021a)—in short, to pursue a
clean energy transition. A large and robust economics literature shows how
policies can accelerate this energy transition by encouraging cost-effective
emissions reductions. Completing this transition by mid-century would
constitute a transformation of the energy system at a pace without precedent,
and mark a giant achievement in human history, given the scale of the
avoided damage to current and future generations (Newell and Raimi 2018).
President Joseph R. Biden has also committed to build a clean energy supply
chain stamped “Made in America,” reflecting the considerable economic
opportunities and associated challenges presented by the energy transition.
One challenge is how to support America’s continued industrial strength

221

and energy security. Doing so will require government actions that enable
U.S. firms to compete on a level playing field in emerging global industries,
especially given the degree to which other countries are supporting their
own domestic firms.
Another challenge presented by the transition is how to best support the
communities across the United States that depend on carbon-intensive
industries for jobs and tax revenue. In the past, when American communities
have faced employment losses due to economic shocks—such as recessions,
trade with China, and automation—workers and their families largely have
not moved to communities where jobs are more plentiful, raising the important policy question of how to help people in the places where they are.
This chapter highlights what economics can tell us about effective policy
strategies to accelerate and smooth the United States’ clean energy transition. The first section provides background on climate risks, global progress
in mitigating these risks, and the policies that will accelerate the transition.
The second section describes the opportunities and challenges of supporting those domestic industries and communities that are most affected by
the transition. The chapter concludes by highlighting the interdependency
between the strategies to accelerate and to smooth the transition.

Accelerating the Energy Transition
The widespread adoption of fossil fuel energy technologies powered the
steamships and factories that made the Industrial Revolution possible,
and has helped spur economic growth for over a century (U.S. Energy
Information Administration 2011; Friedrich and Damassa 2014). The burning of fossil fuels has also led to the rise in human-made carbon dioxide
(CO2) emissions, which is changing the composition of the atmosphere and,
with it, environments around the globe. Over the 800,000 years before the
20th century, the atmospheric concentration of CO2 vacillated between 150
and 300 parts per million, creating a climate hospitable for the world’s development, as detailed in figure 7-1. In early 2022, CO2 concentration levels are
well above 400 parts per million and are continuing to grow. Because CO2
is a heat-trapping greenhouse gas, rising levels in the atmosphere have led

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Figure 7-1. Atmospheric CO2 Level Across the Millennia to 2019
CO2 level (parts per million)
450

Current
level

400

350

1950
level

300

250

200

150
–800,000

–700,000

–600,000

–500,000

–400,000

–300,000

–200,000

–100,000

0

100,000

Years before today
Source: NASA (2021).
Note: CO2 = carbon dioxide.

to increasing temperatures, higher sea levels, more acidic oceans, and more
frequent and severe cases of extreme weather and climate events (Zickfield,
Solomon, and Gilford 2017; Bijma et al. 2013; Stott 2016).
Climate change poses considerable risks to the global economy.
Climate-driven extreme events and biodiversity loss can result in cascading damage to such critical and interconnected systems as energy, public
health, water, and food (Garcia et al. 2018; Porter et al. 2021). In the United
States, estimated damage from storms, floods, wildfires, and other extreme
weather events has grown to about $120 billion a year over the past five
years (Smith 2021). Climate change disproportionately harms low-income
and historically marginalized populations, because vulnerable individuals
lack the resources to adequately prepare for or cope with extreme weather
and climate events (U.S. Global Change Research Program 2018).
Because the rapid increase in greenhouse gases in the atmosphere is
an ongoing planetary experiment, future damage from climate change is
difficult to forecast precisely, and empirical estimates cover only a subset
of likely effects. A 2017 meta-analysis finds that an increase in global temperatures of 5.4 degrees Fahrenheit (3 degrees Celsius) over preindustrial
levels—a threshold that could be surpassed later in this century absent strong
policy interventions—could cause economic damage equivalent to 7 to 11
percent of global gross domestic product (GDP) (Howard and Sterner 2017).
In addition, studies that estimate the economic effects of climate change
often fail to account for important aspects of climate change’s impact on
public health, including temperature-related mortality (Bressler 2021) and
the deaths and sicknesses caused by local pollution from fossil-fuel-related
emissions (Shindell et al. 2018; Scovronick et al. 2019).

Accelerating and Smoothing the Clean Energy Transition | 223

Global Efforts to Reduce Greenhouse Gas Emissions
Average global temperatures have already risen about 1 degree Celsius
above preindustrial levels (NASA 2021). CO2 remains in the atmosphere for
centuries, so our continued emissions will cause temperatures to continue to
increase (Archer et al. 2009).
We can slow the pace of temperature increases by reducing global
emissions, but halting global warming requires achieving net zero CO2 emissions (Net Zero Climate 2022). Considerable momentum toward this goal
is building worldwide. The world’s major countries committed in the 2015
Paris Agreement to keep global warming well below 2 degrees Celsius above
preindustrial temperatures, which is likely to require net zero emissions at
the global level between 2050 and 2070 (UNFCCC 2021). Many countries,
including the United States, have coalesced around a goal of net zero emissions by 2050. President Biden has additionally committed the United States
to halve its net greenhouse gas emissions by 2030 (using a 2005 baseline)
(McCarthy and Kerry 2021). In the European Union, the United Kingdom,
and Japan, mid-century net zero emissions targets are stipulated by law
(European Commission 2021a; Climate Change Committee 2021; Jiji Press
2021). The world’s largest emitter of greenhouse gases—China—has committed to net zero emissions by 2060 (Myers 2020). Many of the world’s
largest companies have also made pledges to cut emissions to net zero,
including financial institutions responsible for over $130 trillion in assets
(Glasgow Financial Alliance for Net Zero 2022).
Global annual CO2 emissions have begun to level off after centuries of
increasing, partially as a consequence of this momentum (Our World in Data
2020). A recent United Nations report declares that the peaking of annual
global emissions by 2030 is within reach (UNFCCC 2021). The projections
of future global CO2 emissions by the International Energy Agency (IEA),
displayed in figure 7-2, also show annual global emissions peaking and then
beginning to decline in the decades ahead.
But to achieve the climate goals specified seven years ago in the Paris
Agreement, the energy transition will need to accelerate markedly from current trends: a recent study estimates that without additional policy actions,
there is less than a 10 percent probability that temperatures will stay below
2 degrees Celsius above preindustrial temperatures by 2100 (Ou et al. 2021).
Figure 7-2 shows that in 2040, global emissions under currently announced
or implemented policies are projected to be seven times higher than emissions under a scenario in which the world is on pace to achieve net zero
emissions by mid-century (IEA 2021b).

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Figure 7-2. Global Carbon Dioxide Emission Projections, 2025–40
Million metric tons
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
2025

2026

2027

2028

WEO 2014

2029

2030

2031

2032

WEO 2021

2033

2034

2035

2036

2037

2038

2039

2040

Sustainable development scenario

Source: International Energy Agency (IEA 2014, 2021), World Energy Outlook (WEO).
Note: The WEO 2014 and WEO 2021 scenarios reflect projections that assume existing policy frameworks and
announced policy intentions. The IEA’s Sustainable Development Scenario outlines how the world can deliver on the three main energyrelated goals: achieving universal access to energy, reducing the severe health effects of air pollution, and tackling climate change.

Accelerating the Energy Transition in the United States
An effective response to climate change requires policy actions around the
globe, starting here at home. The United States’ annual greenhouse gas
emissions are surpassed only by those of China, and our cumulative emissions are larger than those of any other country (Ritchie and Roser 2020;
Our World in Data 2020).
Shifting from carbon-intensive to carbon-free energy systems is the
major challenge to achieving net zero emissions in the United States (see
figure 7-3). While reducing deforestation and other actions outside the
energy sector are also critical to slowing climate change, the production
and consumption of energy are responsible for about three-quarters of U.S.
emissions (Ge, Friedrich, and Vigna 2020; Climate Watch 2021).
Successfully transitioning the U.S. economy to clean energy necessitates a large shift in economic activity. Americans spend over $1 trillion
annually on energy, or about 5 to 10 percent of U.S. GDP in recent decades
(U.S. Energy Information Administration 2018). Natural gas- and coal-fired
power plants produce the majority of U.S. electricity, while petroleum
products are the dominant fuel to transport people and products. Houses and
buildings are often heated with furnaces and boilers that burn natural gas and
oil, and the products Americans buy, the food we eat, and the sidewalks we
walk on have carbon embedded in their production processes (White House
2021a). In 2019, 83 percent of the country’s energy demand was satisfied
by coal, oil, and natural gas, down from about 87 percent in 2000 (Ritchie
and Roser 2020).
Meeting domestic and global climate targets means substantially
stepping up the pace of clean energy deployments over the next decades,
Accelerating and Smoothing the Clean Energy Transition | 225

Figure 7-3. Representative Pathway to Meet Net Zero Emissions in the
United States, 2005–50

Reductions in net emissions (gigatons of CO2-equivalent per year)
7
6
5
4
3
2
1

Energy transition

0

Total

Energy
efficiency

Decarbonizing
Transition to
electricity
low-carbon fuels

Non-CO₂
reductions

Land sink CO₂
removal

CO₂ removal
technologies

Source: U.S. Long-Term Climate Strategy.
Note: CO₂ = carbon dioxide.

as shown by a recent IEA analysis that details a pathway to net zero emissions by 2050 (see table 7-1) (Bouckaert et al. 2021). Though the world is
not decarbonizing at the pace of this IEA scenario, recent trends and expert
forecasts do tell a story of an explosive growth of clean energy technologies.
In the United States, wind turbine technicians and solar energy installers
are two of the five fastest-growing occupations, and over 80 percent of new
electricity generation capacity built here in the first three quarters of 2021
was wind or solar (U.S. Bureau of Labor Statistics 2021a; Shahan 2021).
Although many details about the energy transition are impossible to
know in advance, the road map to meeting the energy demands of a growing economy with clean energy has become much clearer in recent years.
Dozens of “deep decarbonization” studies point to a similar recipe: produce
electricity with carbon-free sources and shift energy uses to this carbon-free
electricity and other low-carbon fuels (National Academies 2021).
A rapid energy transition will not occur without the implementation of
a host of policy measures. If market prices fail to account for the damage
caused by emissions, then consumers and producers will continue buying
and selling too many artificially inexpensive, carbon-intensive goods and
services. Carefully designed policies can change this behavior by raising the
relative price of carbon-intensive goods and services compared with cleaner
alternatives, which provides a financial incentive to shift away from the
carbon-intensive products (Serrano and Feldman 2012).
Such carbon prices could be implemented directly via carbon taxes,
indirectly through a cap on emissions and tradable permits, or through other
similar policy tools. Government revenues from the carbon price can be used

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Table 7-1. Global Clean Energy Deployments in 2020 and 2030 Consistent with Net Zero
Emissions by 2050
Type of Clean Energy
2020
2030
Global wind installations
114 GW per year
390 GW per year
Global solar energy installations

134 GW per year

Electric vehicles

5% of global car sales 60% of global car sales

Heat pump installations

180 million per year

600 million per year

Captured carbon

40 mt per year

1670 mt per year

Source: Bouckaert et al. (2021, tables 2.5, 2.6, 2.9).
Note: GW = gigawatts; mt = metric tons.

630 GW per year

to compensate consumers for increases in energy prices or to invest in other
societal priorities.
Carbon prices of some form exist at the national level in 45 countries,
including those that have been successful at sustaining emissions reductions,
such as the United Kingdom (see box 7-1) (World Bank 2021). Canada’s
federal carbon price is scheduled to increase from 50 Canadian dollars per
metric ton of CO2 in 2022 to 170 dollars in 2030 (Government of Canada
2021). However, many countries have failed to implement carbon prices at
the scale and scope needed to achieve large emissions cuts (OECD 2021).
In the United States, Federal-level carbon pricing proposals have stalled in
Congress for over 30 years, including legislation that passed in the House
of Representatives in 2009 but failed in the Senate (Center for Climate and
Energy Solutions 2021).
Even in the absence of these political challenges, carbon prices are
just one of many policy measures needed to cost-effectively accelerate the
energy transition. After all, in addition to the failure of market prices to
account for the damages caused by emissions, various other barriers stand
in the way of a rapid, equitable, and low-cost transition. Complementary
policies can make it cheaper or easier to conserve energy or to shift away
from carbon-intensive products.
Policy measures are needed for situations in which consumers cannot or do not fully respond to price signals; for example, tenants are often
responsible for paying utility bills but have no control over what landlords
could do to effectively reduce energy consumption (Ryan et al. 2011). Welldesigned incentives and standards can encourage broader use of energyefficient products and other energy-conserving actions.
Measures that foster innovation are also necessary to reduce the costs
of the clean energy transition. Private firms are likely to underinvest in
technological progress because the benefits of their investments in emerging
technologies partially accrue to society writ large. In addition, new products
struggle to compete on a level playing field with established products due to

Accelerating and Smoothing the Clean Energy Transition | 227

Box 7-1. The United Kingdom’s Emissions Have
Fallen Rapidly While Its Economy Has Grown
The United Kingdom passed a major climate change law in 2008 and
implemented a combination of emissions pricing, regulations, subsidies,
and spending on clean energy (London School of Economics 2020). Its
emissions fell by about 20 percent between 2009 and 2019, as shown in
figure 7-i; the trends shown are not due to swapping domestic production
of carbon-intensive products for imports (i.e., “offshoring” emissions); in
fact, between 2009 and 2019, emissions from imported goods decreased
by more than emissions from exported goods (Ritchie and Roser 2020).
Figure 7-i. Changes in U.K. Greenhouse Gas Emissions and
Real GDP since 1990
Percent change since 1990
80

Change in real GDP

60
40
20

0
–20

Change in emissions

–40
–60

1990

1992

1994

1996

1998

2000

2002

2004

2006

2008

2010

2012

2014

2016

2018

Sources: Climate Watch; U.K. Office for National Statistics; CEA calculations.
Note: Real GDP is reported in chained 2019 pounds. Greenhouse gases are reported in megatons and use productionbased accounting

a host of competitive disadvantages, which include access to capital and the
difficulty of acquiring the talent, materials, and customer bases necessary to
scale up production. Well-designed policies can help encourage investments
at all stages of the innovation process, from research to demonstration projects to initial commercialization (Gundlach, Minsk, and Kaufman 2019).
Finally, even with these policies in place, the widespread adoption of
cost-effective clean energy solutions requires building the necessary public
infrastructure and regulatory structures that enable them to compete with
more established products. For example, regulators can require financial
institutions to assess climate risks in their investments, and Federal agencies can set guidelines to ensure that emerging technologies, such as carbon
capture and storage, are deployed effectively and equitably (White House
2021b; Council on Environmental Quality 2021).

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More broadly, policies that accelerate the transition can be designed
to prioritize equity. Currently, lower-income households are often disproportionately harmed by higher energy bills. Further, energy infrastructure
investments have historically led to environmental degradation in marginalized communities. Policies can be designed to lessen rather than exacerbate
these equity concerns; for example, the Biden Administration has committed
to devoting a substantial portion of Federal investments in clean energy
development to disadvantaged communities through the Justice40 Initiative
(White House 2021c). In many places that have implemented carbon prices
(e.g., Canada’s federal carbon pollution pricing system), the revenues are
returned to lower-income households so that they receive more in government payments than they pay in higher prices of goods and services
(Government of Canada 2022).

A Smooth Transition to Clean Energy
The need to shift to clean energy is paramount to lessen the severe threats of
climate change. However, an equitable transition to a clean energy economy
requires more than efforts to reduce emissions. This section highlights the
need for public policies that support certain domestic industries and vulnerable communities in response to two key challenges posed by the energy
transition.
First, domestic clean energy industries will become increasingly
important for the Nation’s security and global economic position. Currently,
the United States’ energy industry is carbon-intensive and a source of economic productivity and stability (U.S. Environmental Protection Agency
2021). For example, our domestic production of natural gas helps to keep
costs low for American consumers and firms (U.S. Energy Information
Administration 2021b). However, as the global energy transition progresses,
the innovation and production of clean technologies will grow in importance. Fortunately, the United States has the needed resources, institutions,
and workforce to support globally competitive clean industries. However,
other nations are rapidly ramping up investments in clean energy and support for their domestic industries. Without strong and sustained Federal
Government support, U.S. firms that can supply a clean economy are likely
to struggle to compete in global markets.
The second portion of this section describes the challenges the energy
transition poses to communities across the United States where jobs,
income, and tax revenues depend on carbon-intensive industries, such as the
production of fossil fuels or downstream products like automobiles. Fossil
fuel-dependent communities across the country are already facing economic
challenges, and the energy transition poses additional risks to communities that are not well prepared and supported (Interagency Working Group
Accelerating and Smoothing the Clean Energy Transition | 229

2021). In the past, workers and their families largely have not moved to
find jobs when faced with the loss of major employers in their communities. Strategies to support these groups of Americans through the energy
transition therefore require policies that target fossil fuel-dependent local
economies.
Although economists largely agree on the policy recipe for accelerating
the energy transition, no similar playbook exists on how to smooth the transition for U.S. firms and communities. In fact, economists have long pointed
to the risks of government interventions that advantage certain industries or
geographic regions over others. However, the economic literature highlights
ways to minimize policy risks and capitalize on the economic opportunities
of creating global-leading firms and revitalizing local economies.

The First Challenge: Supporting Domestic Industries
This subsection describes the need for policy measures that support domestic
clean industries, and the opportunities and risks of government interventions
that can enable U.S. firms to compete in global markets that are growing
rapidly during this energy transition.
The domestic energy sector is important to the U.S. economy. Energy
production is an important component of U.S. economic strength and
stability. The United States is the world’s largest producer of petroleum
and natural gas, surpassing Saudi Arabia in petroleum production in 2018
and Russia in natural gas production in 2011 (U.S. Energy Information
Administration 2019). Despite being the world’s largest consumer of oil and
natural gas, American producers are also now large exporters of these fuels
(U.S. Energy Information Administration 2021c). Net imports of petroleum
products (about three-fourths of which come from crude oil) fell from about
10 million barrels a day in 2000 (roughly half of U.S. consumption) to below
zero by 2019; meanwhile, net imports of natural gas fell from about 4 trillion cubic feet in 2000 to about -2 trillion cubic feet in 2019 (U.S. Energy
Information Administration 2021b, 2021c).
The United States is also the world’s largest exporter of refined
petroleum products and liquefied national gas (Observatory of Economic
Complexity; U.S. Energy Information Administration 2021d). The value of
fuel exports as a fraction of the total value of merchandise exports increased
from about 2 percent in 2000 to 13 percent in 2020, indicating that fuel
exports alone account for about 1 percent of U.S. GDP (World Bank 2020)
(figure 7-4).
In addition to fossil fuels, American firms are large producers and
exporters of many other energy- and carbon-intensive products, including
chemicals and steel (DeCarlo 2017; U.S. International Trade Administration
2020; IEA 2022a). The carbon-intensive auto industry makes up 3 percent
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Figure 7-4. U.S. Fossil Fuel Consumption for Selected Years
Trillions of cubic feet

Trillions of barrels per day

A

25

B

20

40
35
30

15

25
20

10

15
5

10
5

0

0

Consumption

C

Production

Imports

Exports

Net imports

Production

Consumption

2019

2016

2013

2010

2007

2004

2001

1998

1995

1992

1989

1986

1983

1980

1977

1974

1971

1968

1965

1962

1959

1956

1953

-5
1950

2019

2016

2013

2010

2007

2004

2001

1998

1995

1992

1989

1986

1983

1980

1977

1974

1971

1968

1965

1962

1959

1956

1953

1950

-5

Net imports

Percent
16
14
12
10
8
6
4
2
2020

2017

2014

2011

2008

2005

2002

1999

1996

1993

1990

1987

1984

1981

1978

1975

1972

1969

1966

1963

1960

0

Sources: U.S. Energy Information Administration; World Bank.
Note: Figure panels, from left to right: A, U.S. petroleum consumption, production, imports, exports, and net imports, 1950–2020; B, U.S. natural
gas consumption, dry production, and net imports, 1950–2020; C, U.S. fuel exports as a share of merchandise exports, 1960–present.

of GDP, more than any other manufacturing sector (American Automotive
Policy Council 2020).
Despite the harmful effects of the United States’ reliance on fossil
fuels, the reality is that we currently benefit in certain ways from our domestic energy production. In the winter of 2021–22, Europe was immersed in
an energy crisis, including historically high natural gas prices caused by a
series of shocks that led to increased demand and constrained supply, due
in part to the continent’s dependence on natural gas from Russia (Cohen
2021; Stapczynski 2021; Sabadus 2021). The United States is somewhat
insulated from turmoil in natural gas markets abroad due to our domestic
production and the lack of a fully integrated global market—natural gas
prices in Europe rose to over 10 times higher than prices in the United States
in December 2021 (Reed 2021).
In contrast, the global oil market is highly integrated, with a group of
countries that essentially set prices (Fattouh 2007) and a mixture of stateowned and private producers with widely varying costs of production (Wall
Street Journal 2016). American consumers of oil are therefore vulnerable to
geopolitical turmoil and the decisions of policymakers in petrostates. The
uninterrupted availability of affordable energy is a national security concern
for the United States (IEA 2022b). Ensuring the security of our energy
supply will require policy measures that diversify our energy sources and
supply chains, and that build resilience into the energy system as a buffer
against future shocks (Yergin 2006).
The energy transition is an economic opportunity, but policies
are needed to help build strong domestic clean industries. American oil

Accelerating and Smoothing the Clean Energy Transition | 231

Box 7-2. The History of U.S. Government Support
for Domestic Carbon-Intensive Energy Industries
As industry and consumers ramped up their use of fossil fuels in the early
20th century, experts became concerned that the country would run out
of oil unless new oil fields were found and brought online (Olien and
Olien 1993). In 1913, the Federal Government added the intangible drilling oil and gas deduction into the tax code, which allowed companies to
deduct from their taxes most of the costs of drilling new wells, reducing
the high up-front expenses that could discourage exploration (Center for
a Responsible Federal Budget 2013). This deduction remains in place
today; at $2.3 billion a year, it is the single largest production tax benefit
for the fossil fuel industry (Roberts 2018).
The U.S. government has periodically intervened in markets to
ensure stable prices in the face of turmoil. For example, in 1930 in
East Texas, an enormous new oil field known as the “Black Giant” was
discovered by the oilman Dad Joiner (Loeterman 1992). Thousands of
independent producers (known as wildcatters) flocked to the area, flooding the market with supply and driving the price of oil down to as low as
$0.02 a barrel, well below the cost of production. Faced with a possible
collapse of the oil industry, the Governors of Texas and Oklahoma
declared martial law in 1931, halting production and stabilizing the price
(Goodwyn 1996). President Franklin D. Roosevelt’s Secretary of the
Interior, Harold Ickes, led an effort to work out quotas and regulations
with producers in the area. Three decades later, the founders of OPEC
would look to that system as their model (Loeterman 1992). In 1959,
President Dwight D. Eisenhower imposed a quota system restricting
oil imports that would remain in place until 1973 (Council on Foreign
Relations 2021).
The U.S. government has also intervened to help American
companies access energy sources around the world. For example, in the
1940s and 1950s, the U.S. Department of State worked with U.S. oil
companies to negotiate profit-sharing agreements with oil-producing
nations, including Venezuela and Saudi Arabia, to be as favorable as
was feasible to U.S. companies (Council on Foreign Relations 2021).
In a 1950 agreement with Saudi Arabia, negotiators cut a deal in which
oil companies increased the taxes they paid to Saudi Arabia while
reducing the taxes they paid in the United States (Ross 1950). This
agreement allowed money to flow to Saudi Arabia outside the formal
Congressional approval process. When the Mossadeq government in Iran
nationalized the Anglo-Iranian Oil Company, the U.S. and U.K. governments launched Operation Ajax, which helped overthrow Mossadeq in
1953 (Allen-Ebrahimian 2017). In the aftermath, the five major U.S.
oil companies, along with British and French companies, were given
access to Iranian oil fields as part of the Iranian Consortium Agreement

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of 1954; the companies were also given control over production levels
(Heiss 1994).
Government support comes in the form of boosting energy infrastructure and supply chains as well. A notable example is the Federal
Highway Act of 1956, which built the networks necessary for fossil
fuels to dominate personal and freight transportation in the United States,
while potentially crowding out lower-carbon alternatives such as rail.

producers are also vulnerable to decisions made in petrostates. Though
the United States is currently the world’s largest oil producer, if the world
moves to rapidly limit carbon and therefore reduce oil demand, state-owned
oil producers in countries like Saudi Arabia may increasingly find it in their
interest to maintain their production levels by setting prices closer to production costs than they are now, at the expense of higher-cost producers that
include U.S. firms (U.S. Energy Information Administration 2021f). This
means that while global oil demand may decrease only gradually in the coming decades, the effect on the U.S. oil industry may be more abrupt. Indeed,
two recent projections show the oil market shares of the members of the
Organization of the Petroleum Exporting Countries (OPEC) increasing from
roughly one-third in 2021 to about one-half or two-thirds by 2050 in a net
zero scenario (Bouckert et al. 2021; Mercure, Salas, and Vercoulen 2021).
At the same time, the rapid growth of the demand for carbon-free
products globally creates massive—but possibly fleeting—opportunities for
U.S. firms. A key question is how the economic productivity and energy
security of the United States will be affected as countries transition to clean
energy. Will U.S. firms be able to compete in emerging global carbon-free
industries? If not, the energy transition could lead to our reliance on imports
of the batteries, heat pumps, low-carbon steel, and other critical inputs to a
clean energy economy.
Consider the transition from internal combustion engine (ICE) vehicles
to electric vehicles (EVs). Cars are a major source of greenhouse gas emissions, and President Biden has announced a goal to increase the share of new
passenger vehicle sales that are EVs and other zero emissions vehicles from
2.4 percent in 2020 to 50 percent in 2030 (Bui, Slowik, and Lutsey 2021).
There are nearly 1 million workers in the U.S. automotive industry, and over
3 million in the car dealer industry (U.S. Bureau of Labor Statistics 2021b).
The motor vehicle and parts industry has an annual output of over $500 billion (U.S. Bureau of Economic Analysis 2022). Reducing harmful emissions
from vehicles will entail the reduction in output and employment related
to ICE vehicles, but enormous growth in EVs—the value of the global EV

Accelerating and Smoothing the Clean Energy Transition | 233

market is expected to grow from $163 billion in 2020 to over $800 billion by
2030, according to one expert’s forecast (Jadhav and Mutreja 2020).
Over the past century, the combination of automaker innovations,
workers’ unions, and labor laws have made ICE vehicles a staple of middleclass families—and in the process creating good jobs, new methods of production, and a strong domestic automobile industry. The United States has
the resources and capital required to rapidly scale up a domestic EV industry
that can satisfy the growing and changing nature of transportation needs.
But this will not occur at a pace consistent with our climate goals without a
policy strategy that encourages the redirection of capital and workers across
the auto industry supply chain.
More broadly, the United States is well positioned to incubate leadingedge clean energy firms (Rodrik 2014; Cleary et al. 2018)—with a highly
educated population (National Center for Education Statistics 2021) and
institutions that have enabled global leaders in Silicon Valley, biotech, pharmaceuticals, and other industries. Further, a unique endowment of natural
resources makes certain United States’ geographic regions ideally suited to
become hubs of carbon-free energy production (National Academies 2021).
However, U.S. firms will require support to compete in emerging
global markets for clean products. The inability to capture the full societal
benefits of innovation has led to insufficient private sector investments in
emerging clean technologies, inhibiting the expansion of clean industries
(Council of Economic Advisers 2021). For example, a first-of-its-kind
demonstration facility for low-carbon cement production may provide large
societal benefits but also have a cost and risk profile that the private sector
is unwilling to take on without government support.
Even after a new technology has been successfully developed and
demonstrated, its producers often face additional barriers competing with
more established technologies. Established firms receive a range of benefits from the existence of a mature industry with extensive supply chains,
agglomeration effects (i.e., interactions between innovation and production),
and networks of consumers, whereas chicken-and-egg problems hinder
emerging technologies. For example, the uptake of EVs is slowed by a lack
of a nationwide charging network, and a nationwide charging network has
not been built because there are not enough EVs on the roads (Wei et al.
2021).
The robust industrial policy strategies of other countries can also be
an obstacle to emerging clean industries in the United States. In an efficient
global market, each country would provide its domestic firms with only the
support required to overcome the types of hurdles described above, which
should enable the most productive firms worldwide to become market
leaders. In reality, if the U.S. government fails to provide domestic firms
with sufficient support, or if other governments overcompensate their own
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Figure 7-5. The United States’ and China’s Percentages of the
Market across Clean Technology Industries
Percent
90

China

United States

80
70
60
50
40
30
20
10

0

Solar module
manufacturing
(crystalline
silicon)

Battery cell
manufacturing

Battery cathode
Passenger
manufacturing electric vehicle
sales

Wind turbine
manufacturing

Lithium mining Lithium refining
capacity

Source: BloombergNEFѵ

domestic firms, American firms may not be able to compete in global markets, regardless of their potential competitive advantages.
The Chinese government has made a concerted and successful effort
to build domestic industries that can supply a global clean energy economy
(Liu and Urpelainen 2021). Therefore, Chinese firms dominate clean energy
manufacturing worldwide. Chinese companies produce about 60 percent of
the world’s wind turbines and about 80 percent of its solar module cells (see
figure 7-5).
In addition, China now produces over 80 percent of the world’s battery cells used to power EVs. Ceding such industries to China is not only a
lost opportunity for U.S. firms but also a risk to U.S. consumers, given the
potential for the monopolization of important supply chains (see also chapter
6). Building a domestic battery industry—as well as other components of the
EV supply chain, such as key critical minerals—that can compete with firms
in China and other countries is a key challenge for the U.S. economy over
the next decade—and a major economic opportunity, given the growing
global demand for EVs.
China and Russia are also making large bets on nuclear energy,
another source of clean energy with the potential to grow rapidly in a global
energy transition (Berthélemy and Cameron 2021). A recent study by the
International Atomic Energy Agency projects nuclear energy capacity
could grow between 17 and 94 percent worldwide by 2030 (IAEA 2013).
In contrast, the growth of nuclear energy has stalled in the United States
due to concerns related to costs, safety, and waste, although the Bipartisan
Infrastructure Law and other Biden-Harris Administration proposals include
substantial incentives to support the domestic nuclear energy industry

Accelerating and Smoothing the Clean Energy Transition | 235

(Bordoff 2022; U.S. Energy Information Administration 2021g). Ceding the
global-leading positions in the nuclear industry to China and Russia, whose
companies are now supplying reactor technologies to other parts of the
world, would forgo not only economic opportunities for U.S. firms but also
the potential for the U.S. government to influence nonproliferation efforts in
other countries with nuclear energy facilities (Bordoff 2022).
Our allies are developing industrial policy strategies as well. For
example, the European Union is the world’s leader in subsidizing renewable electricity generation (Taylor 2020), and it recently introduced a new
strategy to support domestic industries with increased access to financing,
reduced regulatory burdens, and capacity building for the transition to
sustainability and digitization (European Commission 2020). The EU has
also provided substantial support to key emerging technologies such as
batteries and clean hydrogen, positioning European clean energy firms to
be the global leaders in potentially game-changing technologies (European
Commission 2021b, 2022).

Strategies for Supporting Domestic Industries
through the Energy Transition
The world’s most advanced economies, including the United States, have
implemented policy measures with the aim of industrial development
(Goodman 2020). For over a century, U.S. policymakers have provided
support to the fossil fuel industry, recognizing that a strong domestic energy
industry is important for economic competitiveness and national security
(Johnson 2011). Yet government interventions are not without risk; after all,
market forces can improve the economic efficiency of decisions. The challenge for policymakers, then, is to design a fulsome strategy that maximizes
the economic opportunities of the clean energy transition while minimizing
the risks.
Although there is no established playbook for green industrial policy,
economists have offered numerous general principles (Vogel 2021; Rodrik
2014; Mazzucato, Kattel, and Ryan-Collins 2019). First, the government
should provide domestic industries with transparent, high-level goals.
National governments can launch national missions to confront the largest
challenges facing societies, including climate change (Mazzucato, Kattel,
and Ryan-Collins 2019). For example, during the Space Race of the 1960s,
funding for the U.S. National Aeronautics and Space Administration reached
nearly 4.5 percent of Federal spending, which fueled domestic industries
like computer chip production and spawned a new generation of engineers
and scientists (Chatzky, Siripurapu, and Markovich 2021). In contrast to
high-level missions, supporting specific companies or technologies over

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others comes with demanding informational requirements on policymakers,
and government actors do not have complete information on the potential
benefits, costs, and risks of each investment (Schultze 1983). Instead,
the government may (at least partially) let political considerations influence investment decisions, which raises the odds of wasteful government
spending.
Another recommendation is that government should focus support on
technologies that are not fully mature—from research and development to
demonstration projects to initial commercialization. Without government
support, firms that produce emerging technologies often cannot compete
with firms that produce mature technologies. Many of the largest industrial
policy success stories have come from investing in innovative technologies
that exhibit a wide range of potential (and often unforeseen) applications
(Goodman 2020). In contrast, subsidies for fully mature technologies can
cause long-term declines in allocative efficiency, largely by untethering
prices and output allocations from underlying economic conditions (Kim,
Lee, and Shin 2021). Importantly, it may not be possible or desirable to
avoid supporting specific emerging clean energy technologies, despite the
associated challenges noted above.
Governments need to balance the potentially conflicting needs to foster collaborations with industry while avoiding its undue influence on the
policy process. Successful public policies often require considerable interaction between government officials and industry stakeholders, so that the
government officials understand the businesses and technologies on which
public policies focus (Rodrik 2014). Such interactions naturally heighten the
concerns of political capture—whereby government officials put their own
interests and the interests of industry stakeholders who lobby them above
the interests of their constituents—because policy decisions are made by
political actors (Gregg 2020). Indeed, whenever subsidies and tariffs are on
the table, moneyed interests will lobby for the adoption and retention of their
preferred policies, making these policies difficult to eliminate when they
become unnecessary or counterproductive. For example, fossil fuel subsidies were first paid in the 1910s, and agriculture subsidies were first paid
in the 1930s (Center for a Responsible Federal Budget 2013; Comparative
Food Politics n.d.); in both cases, the subsidies have lasted to the present day
due in large part to interests that benefit from them. Approaches to balance
the needs to collaborate with industry, while avoiding their undue influence,
include government institutions with some degree of independence from the
political process and restrictions on a revolving door between government
service and industry.
Another way to maximize the effectiveness of government interventions is to make the regulatory environment as certain as possible. Ensuring
that the parameters and duration of government support are clear and
Accelerating and Smoothing the Clean Energy Transition | 237

concrete will give firms confidence about future technological and market
opportunities, catalyzing investment and innovation that would not otherwise occur. In contrast, uncertain regulatory environments are not conducive
to attracting private sector investments. For example, the periodic expiration
(or near-expiration) of the production tax credit for renewables in the United
States has inhibited investments in wind and other clean energy technologies
and thus has inhibited the growth of these emerging industries (Sivaram and
Kaufman 2019).
Finally, just as an investor may be wise to consider a diversified
portfolio rather than a concentrated set of individual stocks, the government
should invest in a broad portfolio of clean energy solutions (Rodrik 2014).
An important role of government is to take on risks that the private sector
will not bear; a diverse portfolio accommodates such risks, even in the presence of the inevitable failed investments. For example, the Department of
Energy’s Loan Programs Office was established to provide financing for
innovative energy projects in the United States, including access to debt
capital that private lenders cannot or will not provide (U.S. Department of
Energy, Loan Programs Office 2017). The program has funded a few companies that went bankrupt—most notably the solar producer Solyndra—but
those bankruptcies have not prevented the formation of a highly successful
overall portfolio of investments (Rodrik 2014). The program has propelled
the growth of game-changing companies, including Tesla (U.S. Department
of Energy, Loan Programs Office 2017). The Federal Government should
be willing to lose money to achieve such benefits; but instead, the monetary
losses from the Loan Program have been less than one-third of the interest
paid to the government on the loans to date (U.S. Department of Energy,
Loan Programs Office 2021).
Following this playbook, President Biden has announced a goal for 50
percent of passenger vehicle sales by 2030 to be EVs, along with helping
to build a domestic supply chain to support EV production (White House
2021d). Moreover, the Federal Government is investing in the infrastructure
needed to entice consumers to purchase EVs; there are currently only about
5,000 of the fastest EV chargers in the United States for public use, and these
chargers are clustered in a few regions, including in the Northeast and on
the West Coast. The 2021 Bipartisan Infrastructure Law is investing billions
of dollars in building a domestic supply chain for batteries and nationwide
network of EV charging stations (White House 2021d, White House 2021e).
Previous attempts to support domestic industries in global markets
have mixed track records (see box 7-3). Many failed investments might
have been avoided with better processes for strategically targeting industrial
policy opportunities. Perhaps more important than avoiding failed investments is creating the conditions where failures are expected and accepted as
a learning experience, including with data collection, information sharing,
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Box 7-3. Industrial Policy Successes and Failures
Governments worldwide have had many successes and failures supporting domestic industries. Perhaps the most prominent examples are in
the context of economic development. South Korea is an often-lauded
success story, due to its subsidies for a targeted set of industries that
helped build a series of large, family run business conglomerates called
the Chaebol, including well-known brands like Hyundai and Samsung
(Albert 2018; Westphal 1990). One study found that targeted industries
grew more than 80 percent more than nontargeted ones from 1973 to
2017 (Lane 2017). In contrast, several industrial policy pushes in SubSaharan Africa, North Africa, and the Middle East have been largely
unsuccessful, with corruption, existing distortions, and weak government capacity limiting their effectiveness (Devarajan 2016). Even in
cases where industrial policy has been successful in the development
context, such as Japan, it is difficult to disentangle industry support from
other factors that influence economic growth, such as favorable domestic
economic conditions or high savings rates (Goodman 2020).
The anecdotal evidence of developed countries supporting domestic producers in emerging high growth industries offers notable successes
and failures. Denmark has successfully leveraged a national strategy to
build world-leading capabilities in offshore wind energy, while the billions of dollars spent by France, Germany, and the European Union in
the early 2000s to fund search engines that could compete with Google
were unsuccessful (Lewis 2021; Goodman 2020).
Efforts by the U.S. government to support domestic industry
have similarly produced mixed results. Some of the largest anecdotal
successes of government interventions have come in the face of threats,
like the Space Race or the War Production Board during World War II
(Chatzky, Siripurapu, and Markovich 2021). Facing intense competition
from Japan in the 1980s, subsidization of the semiconductor industry
created a globally competitive industry by the 1990s (Hof 2011). In
contrast, the United States has provided strong support to the domestic
shipping industry for a century—yet U.S. ships still cannot compete
on cost with foreign vessels, in part due to poor labor standards in
the industry abroad (which is also a highly relevant concern for clean
energy production abroad) (Frittelli 2003, 2019; Ha et al. 2020; Kaplan,
Buckley, and Plumer 2021).

and impact evaluations. This will enable policymakers to experiment with
policy design, figure out what works, and take sufficient risks to reap the
rewards of economy-boosting investments.

Accelerating and Smoothing the Clean Energy Transition | 239

The Second Challenge: Supporting Communities
That Rely on a Carbon-Intensive Economy
The geographic concentration of many of the industries most affected by the
energy transition, including fossil fuel extraction and the manufacturing of
high-carbon products, implies disproportionate risks for the regions of the
country that rely on these industries for jobs and tax revenue, and important
opportunities for public policies to mitigate these risks and invest in the
residents of these same regions.
There is considerable overlap between the dual challenges of smoothing the energy transition for domestic economic sectors and for local communities. After all, clean energy-related investments in fossil fuel-dependent
local economies can serve to boost both the industries and places most
affected by the energy transition.
However, these two challenges also differ in marked ways. As
described above, supporting domestic industries most effectively entails
a national strategy that will lead to investments across the entire country,
including but not limited to local economies that currently depend on fossil
fuels. Similarly, effectively supporting fossil fuel-dependent communities
will involve a commitment to these local economies with measures that are
not limited to clean energy investments.
The remainder of this section describes the rationale for government
interventions to support fossil fuel-dependent communities and the lessons
learned from prior experience with place-based policies.

The Geographic Concentration of Fossil-Fuel-Dependent Communities
As a case study, consider the automobile industry’s shift away from ICE
vehicles. Certain industry jobs, including vehicle assembly and sales, may
translate to jobs on the EV line relatively seamlessly. However, many of
the jobs specific to ICE components and supply chains will decline. For
example, the ICE and EV powertrains—the system by which the engine and
motor deliver power to the wheels—require different parts. Of the 140,000
workers in the U.S. powertrain sector, 70 percent are mostly concentrated
in small communities in Michigan, Ohio, and Indiana. In Monroe County,
Michigan, more than one-quarter of employment relates to ICE vehicle
powertrains (Raimi et al. 2021).
The risks of the energy transition may be even more acute for communities dependent on the extraction and combustion of fossil fuels. The U.S.
fossil fuel industry is highly geographically concentrated, as shown in figure
7-6. The coal extraction industry (panel A) is largely located in Appalachia
and portions of the Mountain West—about 90 percent of U.S. coal production takes place in 50 counties (U.S. Energy Information Administration

240 |

Chapter 7

Figure 7-6. Fossil Fuel Employment by County
A

B

Fraction of
county WOfkforce

•0.10-026

•0.05-0.10

o.o, -0.05
0.00-0.01
No data

...,

Fraction of
counly WOf1d0tce
ao.10-0.2•
■0.05-0.10

o.o, -o.05

0.00-0.01
No data

C

•

......

1/�

Fraction of
county wOfkforee
ao.,o-o:ia
■0.05-0.10

o.o, -0.05
0.00-0.01

No date

Sources: Quarterly Census of Employment and Wages; Bureau of Labor Statistics (BLS); CEA calculations.
Note: Figure panels, from left to right: A, coal mining; B, oil and gas extraction; C, support services for the mining and quarrying of minerals and for the
extraction of oil and gas. Industries are defined by NAICS codes 211 (oil and gas extraction), 2121 (coal mirung), and 213 (support activities for mining and
oiVgas extraction). Each panel displays the fraction of the county's workforce in the NAICS industry. Cells with small employment are suppressed by the BLS.

2021h). In some counties, fossil fuel employment is as high as 30 to 50
percent of all employment (panels A, B, and C); these figures are higher
when including jobs directly supported by the region’s dominant industry,
such as in the service sector, supply chain, and local government (Tomer,
Kane, and George 2021).
Employment and economic activity associated with fossil fuel production is already declining in many regions of the country. Coal-mining jobs
have decreased by about three-quarters since 1980, and employment in
the oil and gas sector has declined by about 30 percent in the last decade
(Interagency Working Group 2021; Federal Reserve Bank of Saint Louis
2022). The underlying reasons are myriad: automation; cheap natural gas
causing a shift away from coal-fired electricity; lower prices of renewable
energy; resource decisions that account for the damage caused by climate
change and air pollution; volatility in oil markets; and weak international
demand, which may continue to fall as countries seek to meet their Paris
Agreement commitments (Look et al. 2021; Bowen et al. 2018).
Fossil fuel-dependent communities that are unprepared for the energy
transition risk further reductions in employment and economic activity
(Larson et al. 2020). These areas are often rural, undiversified, and have preexisting economic challenges—poverty rates are higher in fossil fuel-reliant
communities than in neighboring counties and the Nation as a whole, as are
mortality rates due to such issues as opioid abuse and black lung disease
(Interagency Working Group 2021; Bowen et al. 2018; Metcalf and Wang
2019; National Institute for Occupational Safety and Health 2018). Large
Accelerating and Smoothing the Clean Energy Transition | 241

populations in coal communities depend on pensions and other benefit funds
with questionable solvency (Randles 2019).
More broadly, rural locations often lack both the basic infrastructure
(e.g., roads and broadband Internet) and the financial infrastructure (e.g.,
easily accessible credit) necessary to transition to new industries (Raimi et
al. 2021). Many rural locations also suffer from a dearth of opportunities,
with undiversified economies and workers that are specialized for the jobs
in the region. For instance, workers in Appalachia are 25 percent less likely
than the national average to have a college degree (Appalachian Regional
Commission 2022).
The loss of dominant employers can precipitate fiscal spirals from
which jurisdictions struggle to recover, as previously shown in the experiences of steel towns in Pennsylvania, coal-producing regions of the United
Kingdom, and the automobile-dominated economy of Detroit, among others. When major industrial firms depart, the supporting service sectors and
nearby supply chains shrivel in size. Reduced economic activity leads to
reduced government revenues from property and sales taxes, which often
results in cuts to government services. Combined with reduced employment
opportunities, these factors make it difficult for distressed communities to
attract new businesses and for dislocated workers to find new job opportunities (Morris, Kaufman, and Doshi 2021).

The Inadequacy of Place-Neutral Policies
The geographic concentration of the risks of the energy transition does
not, by itself, imply that government support should specifically target
these regions. Instead of targeting economically distressed regions, policies
could target struggling people, regardless of where they live. Indeed, many
government programs already support people in communities that face economic shocks, even though they are often not targeted at specific communities. For example, Federal and State governments have implemented trade
adjustment assistance programs to directly compensate workers who lose
their jobs because of increased exposure to trade,1 and assistance programs
such as the Supplemental Nutrition Assistance Program (formerly known as
Food Stamps) and Medicaid help people during times of economic hardship
(Higdon and Robertson 2020).2
Multiple reports have found limited effectiveness of trade adjustment assistance (TAA) programs
at transitioning workers to new, higher-paying lines of work (Rodrik 2017; U.S. Government
Accountability Office 2012a, 2012b). While TAA has a large, positive causal effect on employment
and earnings, take-up of TAA is low, so some of the limited effectiveness of TAA may be explained
by how few people use it (Hyman 2018; Autor et al. 2014).
2
Social safety net programs may be especially important for aiding fossil-fuel-reliant communities,
given preexisting economic challenges and the growing concerns about the solvency of industryfunded pension programs (Higdon and Robertson 2020; Walsh 2019).
1

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However, new evidence suggesting that people largely do not move
in response to economic shocks has challenged the argument for targeting
people rather than places for transition assistance. For example, researchers
who have studied the effect on U.S. communities of increased trade with
China have found that trade-induced manufacturing job losses led to nearly
one-to-one decreases in the employment-to-population ratio in affected
communities, indicating that workers were not migrating to other communities or sectors (Autor, Dorn, and Hanson 2021). Similarly, Hershbein and
Stuart (2021) find persistent decreases in employment-to-population ratios
after severe recessions. Over half of Americans spend most of their career
in their childhood metropolitan area (Bartik 2009). The reasons people do
not move in response to shocks likely include their attachment to local communities (including support from family and neighbors), the falling housing
prices in declining communities, and lower wages for noncollege workers
in high-income cities (Notowidigdo 2020; Autor, Dorn, and Hanson 2021).
What often sparks migration is opportunity elsewhere, not the shock in
one’s community. Monras (2020) finds that the local differences to migration in response to recessions are driven by differences in in-migration, not
in out-migration. In other words, conditional on deciding to move, people
respond to local economic conditions when choosing a new location. The
workers most likely to stay behind are those with lower earnings capacity
(Notowidigdo 2011; Bound and Holzer 2000). For minority households,
housing discrimination has also restricted mobility (Neumark and Simpson
2015).
This tendency to remain in economically distressed communities and
the inadequacy of assistance programs alone in ameliorating long-standing
economic hardships (see box 7-4) implies the need for policies that help
people where they are. This has led to an increase in scholarship on placebased, economic development policies aimed at improving the well-being
of individuals in particular areas. Though the earlier literature highlighted
the potential for inefficiencies, more recent findings focus on the conditions that may justify place-based policies. These include the invariance of
location choices to local economic conditions; geographically segregated
income groups that make investing in regions a reasonable proxy for investing in lower-income individuals (Akerlof 1978; Fajgelbaum and Gaubert
2021); the desire for insurance against location-specific economic shocks
(Neumark and Simpson 2015), which may become more important as
temperatures continue to rise; differences in the optimal hiring subsidies
across regions based on local productivity levels (Kline and Moretti 2013);
heterogeneity in local public goods provisions (Bartik 2020); and the desire
to take advantage of agglomeration effects (Kline 2010).

Accelerating and Smoothing the Clean Energy Transition | 243

Box 7-4. The Broader Issue of
Distressed Local Economies
A proactive energy transition could prevent exacerbating problems in
already distressed areas. The economies of many local communities are
struggling, and some local economies have been distressed for a long
time. Before the COVID-19 pandemic, in 2019, about 14 percent of U.S.
counties had an unemployment rate above 8 percent (see figure 7-ii).
Distressed local economies are concentrated in portions of the Black
Belt, Appalachia, industrial Midwestern cities, and rural Western areas.
The causes of these struggles vary—the “China trade shock”
(Autor et al. 2013), migration to urban centers, and technological change
(Acemoglu and Restrepo 2020), to name a few—and the struggles are
often persistent: about one-third of the counties with unemployment
rates above 8 percent in 2019 also had unemployment rates in the worst
quartile of U.S. counties in 1980, 1990, 2000, and 2010. Similarly,
Kline and Moretti (2013) find that a plot of unemployment rates in 1990
and 2008 across 239 metropolitan areas shows “a remarkable degree of
persistence,” with a regression coefficient of 0.509 (.045) and R2 of 0.35;
they note that European labor markets show a similar (perhaps larger)
degree of persistence.
Figure 7-ii. Distressed Counties in the United States
A

..

B

Unemployment
rate

I

��n���
Oumbte

l(

•5
.4
•3
2
1

a

No data

►

•7.5-34.1
•6.0- 7.5
■ 5.0-6.0
4.1 - 5.0
3.1 -4.1
3.1

a.a -

No data

Source: American Community Survey, Census Bureau.
Note: Figure panels, from left to right: A, number of decennial censuses in \\11ich the comity is the highest quintile of unemployment rate; B,
unemployment rate in 2019.

Strategies for Place-Based Policies
While there is no established playbook for policymakers to follow in designing policies to support local economic development (Rodrik 2014), the
following are general principles, drawn from the literature, for the design
of place-based policies to support communities affected by the energy
transition.
First, revitalizing communities requires a sustained commitment from
the Federal Government to forming partnerships with local communities to

244 |

Chapter 7

fund suitable opportunities for economic development—a type of high-level
national mission called out above in the context of industrial policy design.
Indeed, perhaps the most important cause of our limited understanding
of successful place-based policies is how few resources have been devoted
to these efforts at the Federal level. According to Bartik (2020), the U.S.
government spends about $10.1 billion a year on Federal programs and
tax credits that could fall under the umbrella of place-based policies. Such
spending is a drop in the bucket compared with the resources spent on other
Federal Government priorities, such as the annual grants of $417 billion
to States and localities for Medicaid and the Children’s Health Insurance
Program (Shambaugh and Nunn 2018). If the Federal Government committed to providing communities with opportunities to rebuild after economic
shocks, the subsequent policy experimentation would likely lead to a far
better understanding of the most successful strategies for implementing
place-based policies.
State and local governments spend above five times more per year
than the Federal Government on place-based policies (Bartik 2020), and
some State governments are an important source of support for distressed
communities within their jurisdictions. However, for struggling regions
facing binding budget constraints, economic development programs come
in lieu of other public services—or, even worse, create a race to the bottom,
in which local governments outbid one another to attract new businesses,
depleting government coffers (Mast 2018). The Federal Government is the
sole entity that can fund and implement a nationwide strategy to revitalize
distressed areas.
A second principle for the design of place-based policies is to target
the communities that will benefit most from the support. Austin, Glaeser,
and Summers (2019) note that spending to boost employment is more effective in areas where unemployment is high. Bartik (2020) estimates that the
benefits of added jobs are at least 60 percent greater in distressed regions
than in booming local economies. Designing effective place-based policies therefore requires a process of selecting which communities to target.
Avoiding political influence in making such decisions will be important for
a program’s success and credibility.
A third common recommendation for successful place-based policies
is to avoid one-size-fits-all solutions. Place-based policies can be designed
so that the same measure will be applied to any eligible region; or, at the cost
of additional complexity, measures can be differentiated to accommodate
local conditions and the relative strengths, needs, and existing assets of individual communities. Forming partnerships with communities and catering to
local circumstances may be especially important for fossil fuel communities,
given their distinctive characteristics noted above. For example, ReImagine
Appalachia is a think tank that has proposed a blueprint for expanding
Accelerating and Smoothing the Clean Energy Transition | 245

opportunities for high-quality jobs with public investments that aim to match
the skills of fossil fuel workers and contribute to sustainable economic
development in the region (ReImagine Appalachia 2021).
Other recommendations for successful policy design include encouraging hubs of research and development activity, including in distressed communities, to take advantage of agglomeration effects (Gruber and Johnson
2019); and directing place-based policies toward industries for which
investments create larger boosts in economic activity, which are referred to
as higher-multiplier industries. For example, Bartik (2020) argues that multipliers in high-technology industries are especially large because the ideas
and workers of one high-tech firm boost the productivity of nearby high-tech
firms (Rodrik 2014, 2020; Mast 2018). (See box 7-5.)

The Clean Energy Transition Provides Unique Opportunities to
Implement Successful Place-Based Policies
Place-based policies largely have not followed the principles described
above (Bartik 2020), so it is perhaps unsurprising that the empirical evidence
evaluating previous attempts at place-based policies is mixed. Bartik (2020)
finds evidence supportive of the potential for place-based policies to generate large long-run benefits. He points to numerous examples of successful
local economic development policies, including experiences involving the
Tennessee Valley Authority and the Appalachian Regional Commission. At
the same time, Neumark and Simpson (2015) conclude that, though placebased policies may increase economic activity when they are in effect, it is
not clear from the evidence that place-based policies typically achieve their
goal of jump-starting lasting economic development.
While support for struggling communities cannot focus only on clean
energy investments, there are various reasons to believe that the energy transition will provide opportunities to improve the track record of place-based
policies. The first reason is scale. Climate action requires large investments
in a diverse set of emerging clean energy technologies. A recent National
Academies panel estimated that roughly $2 trillion in incremental capital
investments needs to be mobilized over the next decade to put the United
States on track to achieve the goal of net zero emissions by 2050 (National
Academies 2021). Princeton University’s Net Zero America report estimates
the need for 0.5 to 1 million additional jobs in the U.S. energy sector in the
2020s (Larson et al. 2020).
Indeed, many clean energy investments will vastly exceed the scale
of the typical place-based policies of the past. For example, the Bipartisan
Infrastructure Law includes money for large-scale demonstration projects
for low-carbon hydrogen production and carbon capture retrofits for large
steel, cement, and chemical production (see box 7-5) (White House 2021g).

246 |

Chapter 7

Box 7-5. The Administration’s Actions on PlaceBased Policies for Energy Communities
The Biden-Harris Administration has taken actions in its first year that
are intended to help energy communities. On January 27, 2021, President
Biden signed Executive Order 14008, which established the Interagency
Working Group (IWG) on Coal and Power Plant Communities and
Economic Revitalization. The IWG’s initial report identifies $37.9
billion in existing Federal funding that could be used to help energy
communities; so far, IWG member agencies have delivered more than
$2.8 billion in direct Federal funding to 25 priority energy communities
across the country.
The American Rescue Plan Act of 2021 allocates $3 billion to the
Economic Development Administration (EDA) to benefit underserved
communities affected by COVID-19. The EDA has allocated $300
million to support communities that are dependent on the coal industry
through Build Back Better Regional Challenge grants and Economic
Adjustment Assistance grants.
The Bipartisan Infrastructure Law (BIL) includes a number of
place-based investment provisions for which energy communities are
prioritized (see table 7-i). Over the next five years, the BIL will allocate
more than $27 billion to these programs—which includes $8 billion for
regional clean hydrogen hubs, $3.5 billion for regional direct air capture
hubs, and $2.5 billion for carbon capture demonstration projects.
The BIL also includes programs that target support to communities
in other ways, including $55 billion for clean drinking water and eliminating lead pipes, $65 billion to ensure universal access to high-quality
broadband, $110 billion to repair roads and bridges, and $21 billion for
cleaning up legacy pollution by reclaiming mines and plugging orphaned
oil and gas wells (White House 2021f).
Table 7-i. Selected BIL Programs That Target Energy Communities
BIL Program Name
Regional Clean Hydrogen Hubs

Total (thousand
dollars)
8,000,000

Regional Direct Air Capture Hubs

3,500,000

Battery Material Processing Grants

3,000,000

Battery Manufacturing and Recycling Grants

3,000,000

Carbon Capture Demonstration Projects Program

2,537,000

Carbon Storage Validation and Testing

2,500,000

Advanced Reactor Demonstration Program

2,477,000

Carbon Dioxide Transportation Infrastructure
Finance and Innovation
Clean Hydrogen Electrolysis Program

2,100,000
1,000,000

Source: U.S. House of Representatives (2022).
Note: BIL = Bipartisan Infrastructure Law. This table only includes programs
with at least $1 billion in funding.

Accelerating and Smoothing the Clean Energy Transition | 247

Such projects can involve many millions of dollars in investments in local
economies (Jones and Lawson 2021).
Though place-based policies have not historically been well targeted
to individual distressed communities, the diversity of clean energy solutions
provides an opportunity to tailor investments to a community’s strengths
and needs, including characteristics related to geography, workforce skills,
education levels, and preexisting infrastructure (Bartik 2020; Tomer, Kane,
and George 2021). Importantly, the employment opportunities created by
the energy transition may not, absent policy intervention, arise in fossil
fuel-dependent communities that often support more extractive and laborintensive industries. Yet place-based policies can channel investment to
these communities. Some are well suited for a carbon capture project, while
others are better suited for projects involving wind, solar, geothermal,
nuclear, or other climate solutions. In many cases, policies can leverage the
existing infrastructure and workforce skills in fossil fuel-dependent communities, including measures to repurpose retired power plants or equip
facilities with the ability to sequester carbon underground (Tomer, Kane,
and George 2021).
The energy transition also presents a unique opportunity to implement
measures that raise the quality of jobs for American workers in the energy
industry. Though roughly 30 percent of the clean energy workforce will
require at least a bachelor’s degree, 70 percent will require fewer than four
years of related work experience (Larson et al. 2020). And though some
clean energy jobs are already high paying, policy measures that incentivize high-quality clean energy jobs can help to ensure that opportunities in
clean industries are suitable replacements for the relatively high-paying
blue-collar jobs that constitute much of the employment in fossil fuel-reliant
communities (Muro et al. 2019).
Once again, the growing EV industry provides an important example.
The existing auto industry presents a unique economic opportunity to build a
successful domestic EV industry in many of the same locations. For instance,
Ford recently announced that it is converting its Van Dyke Transmission
Plant in Sterling, Michigan, into the Van Dyke Electric Powertrain Center
(Ford Motor Company 2021). Though market forces alone may be sufficient
to incentivize such conversions in certain instances, policy support will
often be needed to encourage automakers to take advantage of opportunities
to shift to EVs in the communities where they currently operate.
Finally, it is worth reemphasizing that clean energy investments often
carry atypical growth potential. The world needs clean energy solutions
to rapidly scale up to successfully address the risks of climate change.
Though clean energy investments are not devoid of risk, the likelihood that
the demand for clean products will rapidly increase in coming decades is a
major advantage compared with a generic, place-based investment.
248 |

Chapter 7

Discussion and Conclusions
This chapter has emphasized that carefully designed policies are needed
to accelerate the United States’ transition to a clean energy economy. The
host of market failures inhibiting this transition justifies the implementation
of policies that reduce the relative prices of low-carbon products, offer
incentives for innovation and energy efficiency, and provide public goods
and regulatory measures that effectively support the development of a clean
energy economy. These policies should be designed to ensure that they help
to mitigate rather than exacerbate preexisting inequities in the economy.
Policies are also needed to smooth the transition to clean energy by
lessening the risks to U.S. competitiveness in global markets and by supporting vulnerable communities. The literature points to numerous principles
for how government can successfully intervene to boost domestic industries
by setting transparent and high-level goals, providing regulatory certainty,
creating a diversified portfolio of government investments, focusing on
nonmature technologies, and pursuing measures that avoid having industry
stakeholders exercise undue influence on the policy process.
Governments can also make sustained commitments to supporting and
diversifying fossil fuel-dependent regional and local economies, by forming
partnerships with these communities for measures that fit their particular
characteristics, strengths, and challenges.
Fortunately, the energy transition provides opportunities for bolstering
domestic firms in emerging carbon-free industries and for economic development in the communities that are most vulnerable to the transition’s risks.
Taking advantage of these opportunities is at the core of the Biden-Harris
Administration’s economic and climate strategies.
Given the lack of an established playbook for green industrial policies
and place-based policies, policymakers need to be open to experimentation
and must expect failures—along with lessons learned from these failures—
as necessary aspects of what will become a successful portfolio of policies
and investments.
The stakes are high. Although this chapter has separated the discussion
of policies that accelerate the transition to clean energy from policies that
smooth it, the fates of these two policy strategies are very much intertwined.
The transition to clean energy has begun, but its pace is difficult to predict.
Climate policies have long faced political opposition, partly because their
costs are localized and front-loaded while their benefits accrue around
the entire globe and for generations into the future. Failing to smooth the
transition for workers, firms, and communities could erode public support
for policies that can accelerate it and, most critically, can help us avoid the
ever-worsening threats to our planet as it continues to warm.

Accelerating and Smoothing the Clean Energy Transition | 249

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Appendix A

Report to the President
on the Activities of the
Council of Economic Advisers
during 2021

333

Letter of Transmittal
Council of Economic Advisers
Washington, December 31, 2021
Mr. President:
The Council of Economic Advisers submits this report on its activities
during calendar year 2021 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 2021 | 335

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

336 |

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 2021 | 337

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

338 |

Appendix A

June 22, 2020
January 6, 2021

Report to the President on the
Activities of the Council of
Economic Advisers during 2021
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
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

Activities of the Council of Economic Advisers during 2021 | 339

2011 to 2020. From 2009 to 2011, he was Chief Economist and Economic
Adviser to then–Vice President Biden.
Heather Boushey was appointed to the Council by the President on
January 20, 2021. Before assuming this position, Boushey co-founded 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 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.

Areas of Activity
A central function of the Council is to advise the President on all economic
issues and developments. Over the past year, the priorities of the Council
have included analysis on policies to spur economic growth and job creation
while recovering from the global COVID-19 pandemic.
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 areas on which the Council focused this year include economic
stimulus and pandemic recovery; income inequality and inclusive growth;
investment in resilient infrastructure and supply chains; innovation and
competition, including in the labor market; inflation and unemployment;
climate-related risks; and the cost of care, housing, and other household
necessities.
The Council prepares almost-daily memos for the President, the Vice
President, and White House senior staff on key economic data releases and
policy issues.
The Council, the Department of Treasury, and the Office of Management
and Budget—the Administration’s economic “troika”—are responsible for
producing the economic forecasts that underlie the Administration’s budget
proposals. The Council initiates the forecasting process twice each year,
consulting with an array of outside sources, including leading private sector
forecasters and other government agencies. The Council provides analysis
and opinions on a range of trade-related issues involving the enforcement of
existing trade agreements and the analysis of proposed trade policies.
The Council is a leading participant in the Organization for Economic
Cooperation and Development (OECD), an important forum for economic
cooperation among high-income industrial economies. The Council chairs
the Economic Policy Committee, coordinating—including the Departments
of Commerce, State, Treasury, and Labor, as well as the Office of
340 |

Appendix A

Management and Budget—to provide information for the OECD’s review
of the U.S. economy. Council Members and staff economists participate in
working meetings on macroeconomic policy and contribute to the OECD’s
research agenda.
The Council produces economic analyses in a series of blogs and issue
briefs. This past year, these included:
• An issue brief on how the economic stimulus of the American Rescue
Plan (ARP) could help launch an equitable pandemic recovery (February 2021).
•

A blog assessing the pandemic’s effect on wage growth, employment,
and prices, outlining the role of composition and base effects in wage
volatility (April 2021).

•

A blog describing the economic downturn caused by the COVID-19
pandemic and the potential for higher inflation driven by base effects,
supply chain disruptions, and pent-up service demand (April 2021).

•

A blog on how government support during the pandemic helped boost
personal income and spending, thus contributing to economic growth
(April 2021).

•

An issue brief on the barriers that inhibit private sector investment in
clean energy innovation and the importance of public-private partnerships (April 2021).

•

An issue brief on the role of public sector investment in promoting sustained and equitable economic growth, highlighting the importance of
investing in innovation, social programs, and physical and human infrastructure (May 2021).

•

A blog outlining how supports that meet the needs of workers’ families—such as affordable, high-quality childcare, home health care, and
paid family and medical leave—can increase the U.S. labor supply and
boost economic growth (May 2021).

•

A blog describing pandemic-borne supply chain disruptions, ways in
which supply chains have adjusted to disruptions in the past, and possible solutions (May 2021).

•

A blog on the harm of exclusionary zoning laws and the potential of
proposed policies to address persistent inequities in the housing market
(June 2021).

•

A blog on data volatility and the need to examine trends and a wide
range of indicators rather than data from any single month or source
Activities of the Council of Economic Advisers during 2021 | 341

(June 2021).
•

An issue brief on the effects of earlier Medicaid expansions on qualifying individuals’ insurance coverage, health, food and housing security,
financial well-being, and the like (June 2021).

•

A blog outlining how additional Federal aid in the form of stimulus
checks and supplemental Unemployment Insurance benefits was followed by marked improvements in food insecurity among households
facing financial hardship (July 2021).

•

A blog that examines historical parallels during periods of heightened
inflation (July 2021).

•

A blog on the importance of product and labor market competition in
the American economy (July 2021).

•

A blog on the importance of voting rights to secure economic wellbeing (August 2021).

•

A blog on how the President’s proposed policies can reduce inflationary pressure and increase economic capacity through long-term investments in physical infrastructure, human capital, clean energy, housing,
and health care (August 2021).

•

An issue brief, cowritten with the Office of Management and Budget
(OMB), laying out how the rising prices of necessities—such as prescription drugs, childcare, and education—has have made an impact on
U.S. families’ budgets (August 2021).

•

A blog on price increases and supply constraints in the housing market
(September 2021).

•

A blog on the relationship between rent, housing prices, and measured
inflation (September 2021).

•

A blog, cowritten with OMB, on estimating the average Federal individual income tax rate paid by America’s 400 wealthiest families (September 2021).

•

A blog on how the President’s policy proposals can reduce greenhouse
gas emissions while keeping energy costs low for consumers (September 2021).

•

A blog on the economic benefits of extending permanent legal status to
unauthorized immigrants (September 2021).

•

A blog on the debt ceiling and the services that would be affected should

342 |

Appendix A

Congress not vote to raise the debt limit (October 2021).
•

An issue brief on the economic benefits of investing in modern, climateresilient physical infrastructure and the risks that continued disinvestment pose to the nation’s economy (November 2021).

•

An issue brief on the importance of incorporating climate change into
the economic projections that underlie assessments of financial risk and
government finances (November 2021).

•

An issue brief analyzing disaggregated data provided by the U.S. Small
Business Administration as a way for the Federal government to review
its current procurement practices (December 2021).

•

A monthly publication of Economic Indicators (January–December
2021).

•

A monthly blog analyzing the employment situation to correspond to
the monthly Jobs Report (January–December 2021).

The Council also contributed to the public’s understanding of economic
issues and the Administration’s policies through briefings and interviews
with the economic and financial press, speeches, discussions with outside
economists, Congressional testimony, and regular updates on major data
releases. The Chair and Members also regularly met to exchange views on
the economy with the Chair and Members of the Board of Governors of the
Federal Reserve System.

Public Information
The Economic Report of the President, together with the Annual Report of
the Council of Economic Advisers, is an important vehicle for presenting
the Administration’s domestic and international economic policies. It is
available for purchase through the Government Publishing Office, and is
viewable at no cost at www.gpo.gov/erp. All the Council’s written materials
noted above can be found at www.whitehouse.gov/cea.

Activities of the Council of Economic Advisers during 2021 | 343

The Staff of the Council of Economic Advisers
Front Office
Elisabeth Hirschhorn Donahue	����������
Chief of Staff & General Counsel
Martha Gimbel 	����������������������������������
Senior Adviser
Saharra Griffin 	����������������������������������
Special Assistant to the Chair
Abaigeal O’Shea 	������������������������������
Special Assistant to the Members
Zehra Khan 	��������������������������������������
Communications Specialist
Senior Economists
Lisa Barrow	����������������������������������������Education, Labor
Steven Braun 	������������������������������������Director of Macroeconomic
Forecasting
Nathan Converse	��������������������������������Macroeconomics, International
Finance
Gopi Shah Goda 	��������������������������������Health, Long-term Care, Social
Insurance
Kari Heerman 	������������������������������������International Trade
Susan Helper 	������������������������������������Supply Chains, Manufacturing
Damon Jones 	������������������������������������Social Insurance, Inequality, Racial
Equity
Noah Kaufman 	����������������������������������Climate
Helen Knudsen 	����������������������������������Industrial Organization, Small
Business, Health
Greg Leiserson 	����������������������������������Tax, Regulation
Kevin Rinz 	����������������������������������������Education, Labor
Ernie Tedeschi 	����������������������������������Macroeconomics
Laura Tiehen 	������������������������������������Poverty, Rural Issues
Jeffery Zhang 	������������������������������������Macroeconomics, Finance, Housing
National Security Economist
Meghan Greene	����������������������������������Senior Adviser for National Security
Staff Economists
R. Daniel Bressler 	����������������������������Climate
Elliot Charette 	����������������������������������Macroeconomics, Trade, Finance
Ryan Cummings 	������������������������������Macroeconomics, Finance, Energy
Brandon Enriquez	������������������������������Climate, Rural Issues
Victoria Lee 	��������������������������������������Education, Labor
Lindsey Raymond 	����������������������������Industrial Organization, Supply
Chains, Innovation
Evan Soltas 	��������������������������������������Education, Labor

344 |

Appendix A

Research Assistants
Bradley Clark 	������������������������������������Climate, Finance, Housing
Matthew Maury 	��������������������������������Climate, Finance, Housing
Stephen Nyarko 	��������������������������������Health, Supply Chains, Small
Business
Anna Pasnau 	������������������������������������Climate, Social Insurance, Inequality,
Infrastructure
Sarah Robinson 	��������������������������������Macroeconomics
Safia Sayed 	��������������������������������������Tax, Regulation, Social Insurance
Sarah Wheaton 	����������������������������������Education, Labor
Statistical Office
Brian Amorosi	������������������������������������Director of Statistical Office
Administrative Office
Megan Packer	������������������������������������Director of Finance and
Administration
Interns
Malhaar Agrawal, Umang Bansal, Prosser Cathey, Aditya Dhar, Jay
Philbrick, Dylan Saez, and Shoshana Singer
ERP Production
Alfred Imhoff 	������������������������������������Editor
Susan Kellam 	������������������������������������Editor

Activities of the Council of Economic Advisers during 2021 | 345

Appendix B

Statistical Tables Relating to Income,
Employment, and Production

347

Contents
National Income or Expenditure
B–1.

Percent changes in real gross domestic product, 1971–2021��������������

354

B–2.

Contributions to percent change in real gross domestic product,
1971–2021�������������������������������������������������������������������������������������������

356

B–3.

Gross domestic product, 2006–2021���������������������������������������������������

358

B–4.

Percentage shares of gross domestic product, 1971–2021������������������

360

B–5.

Chain-type price indexes for gross domestic product, 1971–2021�����

362

B–6.

Gross value added by sector, 1971–2021��������������������������������������������

364

B–7.

Real gross value added by sector, 1971–2021�������������������������������������

365

B–8.

Gross domestic product (GDP) by industry, value added, in current
dollars and as a percentage of GDP, 1997–2020���������������������������������

366

B–9.

Real gross domestic product by industry, value added, and
percent changes, 1997–2020��������������������������������������������������������������

368

B–10. Personal consumption expenditures, 1971–2021��������������������������������

370

B–11. Real personal consumption expenditures, 2002–2021������������������������

371

B–12. Private fixed investment by type, 1971–2021�������������������������������������

372

B–13. Real private fixed investment by type, 2002–2021�����������������������������

373

B–14. Foreign transactions in the national income and product accounts,
1971–2021�������������������������������������������������������������������������������������������

374

B–15. Real exports and imports of goods and services, 2002–2021�������������

375

B–16. Sources of personal income, 1971–2021���������������������������������������������

376

B–17. Disposition of personal income, 1971–2021���������������������������������������

378

B–18. Total and per capita disposable personal income and personal
consumption expenditures, and per capita gross domestic
product, in current and real dollars, 1971–2021����������������������������������

379

B–19. Gross saving and investment, 1971–2021�������������������������������������������

380

B–20. Median money income (in 2020 dollars) and poverty status of
families and people, by race, 2013–2020��������������������������������������������

382

B–21. Real farm income, 1957–2022������������������������������������������������������������

383

Contents

| 349

Labor Market Indicators
B–22. Civilian labor force, 1929–2021����������������������������������������������������������

384

B–23. Civilian employment by sex, age, and demographic characteristic,
1976–2021�������������������������������������������������������������������������������������������

386

B–24. Unemployment by sex, age, and demographic characteristic,
1976–2021�������������������������������������������������������������������������������������������

387

B–25. Civilian labor force participation rate, 1976–2021�����������������������������

388

B–26. Civilian employment/population ratio, 1976–2021����������������������������

389

B–27. Civilian unemployment rate, 1976–2021��������������������������������������������

390

B–28. Unemployment by duration and reason, 1976–2021��������������������������

391

B–29. Employees on nonagricultural payrolls, by major industry,
1976–2021�������������������������������������������������������������������������������������������

392

B–30. Hours and earnings in private nonagricultural industries,
1976–2021�������������������������������������������������������������������������������������������

394

B–31. Employment cost index, private industry, 2004–2021������������������������

395

B–32. Productivity and related data, business and nonfarm business
sectors, 1971–2021������������������������������������������������������������������������������

396

B–33. Changes in productivity and related data, business and nonfarm
business sectors, 1971–2021���������������������������������������������������������������

397

Production and Business Activity
B–34. Industrial production indexes, major industry divisions,
1976–2021�������������������������������������������������������������������������������������������

398

B–35. Capacity utilization rates, 1976–2021�������������������������������������������������

399

B–36. New private housing units started, authorized, and completed
and houses sold, 1976–2021����������������������������������������������������������������

400

B–37. Manufacturing and trade sales and inventories, 1979–2021���������������

401

Prices
B–38. Changes in consumer price indexes, 1979–2021��������������������������������

402

B–39. Price indexes for personal consumption expenditures, and
percent changes, 1972–2021��������������������������������������������������������������

403

350 |

Appendix B

Money Stock, Credit, and Finance
B–40. Money stock and debt measures, 1982–2021��������������������������������������

404

B–41. Consumer credit outstanding, 1970–2021�������������������������������������������

405

B–42. Bond yields and interest rates, 1950–2021������������������������������������������

406

B–43. Mortgage debt outstanding by type of property and of financing,
1960–2021�������������������������������������������������������������������������������������������

408

B–44. Mortgage debt outstanding by holder, 1960–2021������������������������������

409

Government Finance
B–45. Federal receipts, outlays, surplus or deficit, and debt, fiscal years
1958–2023�������������������������������������������������������������������������������������������

410

B–46. Federal receipts, outlays, surplus or deficit, and debt, as percent
of gross domestic product, fiscal years 1953–2023�����������������������������

411

B–47. Federal receipts and outlays, by major category, and surplus or
deficit, fiscal years 1958–2023������������������������������������������������������������

412

B–48. Federal receipts, outlays, surplus or deficit, and debt, fiscal years
2018–2023�������������������������������������������������������������������������������������������

413

B–49. Federal and State and local government current receipts and
expenditures, national income and product accounts (NIPA) basis,
1971–2021�������������������������������������������������������������������������������������������

414

B–50. State and local government revenues and expenditures,
fiscal years 1956–2019������������������������������������������������������������������������

415

B–51. U.S. Treasury securities outstanding by kind of obligation,
1980–2021�������������������������������������������������������������������������������������������

416

B–52. Estimated ownership of U.S. Treasury securities, 2007–2021������������

417

Corporate Profits and Finance
B–53. Corporate profits with inventory valuation and capital
consumption adjustments, 1971–2021������������������������������������������������

418

B–54. Corporate profits by industry, 1971–2021�������������������������������������������

419

B–55. Historical stock prices and yields, 1949–2003������������������������������������

420

B–56. Common stock prices and yields, 2000–2021�������������������������������������

421

Contents

| 351

International Statistics
B–57. U.S. international transactions, 1971–2021����������������������������������������

422

B–58. U.S. international trade in goods on balance of payments (BOP)
and Census basis, and trade in services on BOP basis, 1992–2021����

424

B–59. U.S. international trade in goods and services by area and country,
2000–2020�������������������������������������������������������������������������������������������

425

B–60. Foreign exchange rates, 2000–2021����������������������������������������������������

426

B–61. Growth rates in real gross domestic product by area and country,
2003–2022�������������������������������������������������������������������������������������������

427

352 |

Appendix B

General Notes
Detail in these tables may not add to totals due to rounding.
Because of the formula used for calculating real gross domestic product (GDP),
the chained (2012) dollar estimates for the detailed components do not add to the
chained-dollar value of GDP or to any intermediate aggregate. The Department
of Commerce (Bureau of Economic Analysis) no longer publishes chained-dollar
estimates prior to 2002, except for selected series.
Because of the method used for seasonal adjustment, the sum or average of seasonally adjusted monthly values generally will not equal annual totals based on
unadjusted values.
Unless otherwise noted, all dollar figures are in current dollars.
Symbols used:
p Preliminary.
... Not available (also, not applicable).
NSA Not seasonally adjusted.
Data in these tables reflect revisions made by source agencies through
March 8, 2022.
Excel versions of these tables are available at www.gpo.gov/erp.

General Notes

| 353

National Income or Expenditure
Table B–1. Percent changes in real gross domestic product, 1971–2021
[Percent change, fourth quarter over fourth quarter; quarterly changes at seasonally adjusted annual rates]
Personal consumption
expenditures

Year or quarter

1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

4.4
6.9
4.0
–1.9
2.6
4.3
5.0
6.7
1.3
.0
1.3
–1.4
7.9
5.6
4.2
2.9
4.5
3.8
2.7
.6
1.2
4.4
2.6
4.1
2.2
4.4
4.5
4.9
4.8
2.9
.2
2.0
4.3
3.4
3.0
2.6
2.2
–2.5
.1
2.8
1.5
1.6
2.5
2.6
1.9
2.0
2.7
2.3
2.6
–2.3
5.6
3.1
3.4
1.9
.9
2.4
3.2
2.8
1.9
–5.1
–31.2
33.8
4.5
6.3
6.7
2.3
7.0

Fixed investment
Nonresidential
Total

5.4
7.3
1.8
–1.6
5.1
5.4
4.2
4.0
1.7
.0
.1
3.5
6.6
4.3
4.8
4.4
2.8
4.6
2.4
.8
.9
4.9
3.3
3.8
2.8
3.4
4.5
5.6
5.2
4.3
2.5
2.0
3.8
3.8
2.8
3.2
2.0
–1.5
–.2
2.8
1.0
1.5
1.9
3.5
2.6
2.3
2.8
2.6
2.3
–2.4
7.0
2.4
3.5
2.7
1.7
.6
3.6
3.2
1.7
–6.9
–33.4
41.4
3.4
11.4
12.0
2.0
3.1

See next page for continuation of table.

354 |

Appendix B

Gross private domestic investment

Goods

6.6
8.5
.4
–5.6
6.1
6.4
4.9
3.5
.3
–2.5
–.2
3.6
8.3
5.3
4.6
6.5
.4
4.5
1.8
–1.6
–.8
5.3
4.4
5.5
2.3
4.8
5.3
8.1
6.6
4.0
4.9
1.7
6.6
4.3
3.0
4.6
1.8
–6.8
.6
4.3
.9
2.4
3.5
5.0
3.8
3.4
5.1
2.7
3.7
7.7
7.4
1.4
4.2
2.9
2.1
1.3
7.0
4.9
1.8
.3
–10.0
49.5
–.3
27.4
13.0
–8.8
1.5

Services

4.3
6.2
3.2
2.4
4.1
4.5
3.7
4.4
2.9
2.2
.3
3.4
5.3
3.6
5.0
3.0
4.6
4.7
2.7
2.3
2.0
4.7
2.7
2.8
3.0
2.7
4.0
4.3
4.5
4.5
1.3
2.1
2.3
3.6
2.7
2.5
2.0
1.2
–.6
2.1
1.0
1.1
1.1
2.7
2.1
1.8
1.8
2.5
1.6
–6.9
6.9
2.9
3.1
2.6
1.5
.3
2.0
2.4
1.7
–10.0
–42.4
37.5
5.3
3.9
11.5
8.2
3.9

Total

13.1
15.0
10.2
–10.4
–9.8
15.2
14.9
14.3
–3.4
–7.2
6.7
–17.3
31.3
14.2
1.9
–4.1
9.8
–.5
.7
–6.5
2.1
7.7
7.6
11.5
.8
11.2
11.4
9.7
8.5
4.3
–11.1
4.4
8.7
8.0
6.1
–1.5
–1.8
–15.3
–9.2
12.1
10.4
4.0
9.3
5.3
2.3
1.8
4.2
5.2
.8
2.4
8.9
8.5
.7
9.7
2.2
6.4
2.6
1.1
–6.5
–5.3
–48.8
82.1
24.7
–2.3
–3.9
12.4
33.5

Total

10.5
12.0
3.5
–9.9
–2.6
12.1
12.1
13.1
1.1
–4.8
1.5
–8.0
18.3
11.3
3.7
.6
1.5
3.7
1.5
–4.2
–1.9
8.7
8.4
6.6
5.5
9.9
8.3
11.5
7.2
5.9
–4.7
–1.5
8.6
6.5
5.8
.0
–1.1
–11.1
–10.5
6.1
9.2
7.2
5.7
7.0
1.7
2.8
4.7
3.8
2.9
.5
4.4
6.7
6.0
.8
1.8
3.7
6.1
3.1
–1.1
–2.3
–30.4
27.5
17.7
13.0
3.3
–.9
2.6

Total
4.7
11.5
10.6
–3.9
–5.9
7.8
11.9
16.0
5.5
–.9
9.0
–9.5
10.4
13.9
3.2
–3.2
2.2
5.1
4.5
–.9
–3.4
7.1
7.6
8.5
7.4
11.3
9.7
11.6
8.4
8.5
–6.8
–5.1
6.8
6.5
6.1
8.1
7.3
–7.0
–10.3
8.9
10.0
5.6
5.4
6.9
–.1
2.5
4.7
6.1
3.1
–3.8
6.6
10.2
6.8
2.8
4.8
4.7
6.7
2.9
–1.7
–8.1
–30.3
18.7
12.5
12.9
9.2
1.7
3.1

Structures
–1.1
5.1
7.9
–6.4
–8.1
3.8
5.7
21.7
8.8
2.7
14.1
–13.5
–3.9
15.7
3.3
–14.3
4.9
–3.3
3.3
–3.2
–12.8
1.0
.2
1.6
4.7
10.9
4.4
4.3
–.1
10.8
–10.6
–15.7
1.9
.3
1.5
9.0
17.7
–.8
–27.1
–3.6
8.6
4.0
6.7
9.3
–7.3
3.6
.0
1.8
5.8
–20.0
–2.9
20.2
7.1
–4.2
–12.8
4.4
14.3
14.0
–8.0
–.9
–46.8
–15.3
–8.2
5.4
–3.0
–4.1
–9.4

Equipment
8.5
17.0
13.5
–3.7
–6.7
9.0
17.2
14.5
2.7
–4.4
4.6
–10.0
19.9
13.4
1.7
.8
.1
8.2
2.5
–2.7
–3.2
11.3
13.1
12.5
8.1
11.1
10.7
14.8
9.5
8.5
–7.7
–3.7
9.6
9.8
8.7
7.1
3.9
–15.9
–8.4
22.6
12.7
7.8
5.4
5.6
1.5
–2.2
6.4
6.0
–.9
–.3
6.3
5.6
3.0
5.4
10.3
4.4
2.5
–5.1
–4.9
–21.3
–36.2
55.9
26.4
14.1
12.1
–2.3
2.4

Intellectual
property
products
4.8
6.2
5.1
1.6
2.8
11.8
4.8
10.3
9.4
4.7
12.1
3.4
13.0
12.6
7.7
5.4
4.2
9.8
11.3
6.2
7.2
4.8
2.9
5.8
8.3
12.1
12.4
11.5
13.3
6.6
–2.1
.9
5.8
5.7
5.1
9.3
4.0
.9
3.8
1.6
7.2
3.7
4.5
6.9
3.3
8.4
5.8
9.2
6.3
2.5
11.9
9.6
11.6
4.6
11.0
5.4
7.2
6.0
6.7
3.8
–10.6
8.1
10.2
15.6
12.5
9.1
10.6

Residential

25.2
12.9
–10.5
–24.6
7.8
23.8
12.6
6.8
–9.1
–15.3
–22.0
–1.7
49.7
3.7
5.2
11.8
–.5
.1
–6.5
–13.6
2.9
13.6
10.6
1.6
.1
5.6
4.0
11.3
3.5
–1.5
2.0
8.1
12.7
6.6
5.2
–15.2
–21.2
–24.7
–11.5
–5.7
5.3
15.4
7.1
7.7
9.2
4.0
4.5
–3.9
2.2
15.7
–1.7
–4.2
3.3
–5.8
–8.3
.1
4.1
3.6
1.1
20.4
–30.7
59.9
34.4
13.3
–11.7
–7.7
1.0

Change
in
private
inventories
����������������
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����������������
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����������������
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����������������
����������������
����������������
����������������
����������������
����������������
����������������
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Table B–1. Percent changes in real gross domestic product, 1971–2021—Continued
[Percent change, fourth quarter over fourth quarter; quarterly changes at seasonally adjusted annual rates]
Net exports of
goods and services
Year or quarter

1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Government consumption expenditures
and gross investment
Federal

Net
exports

Exports

Imports

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–4.5
19.5
18.4
3.1
1.6
4.3
–1.4
18.8
10.5
3.9
.7
–12.2
5.5
9.1
1.5
10.6
12.8
14.0
10.2
7.4
9.2
4.5
4.4
10.8
9.4
10.1
8.3
2.6
6.2
6.0
–12.2
4.0
7.2
7.2
7.4
9.9
9.2
–2.0
1.4
10.6
4.7
3.0
5.2
2.4
–1.5
1.3
5.9
.2
.3
–10.7
5.2
1.8
5.0
–6.1
.5
3.1
–2.2
–.8
1.2
–16.3
–59.9
54.5
22.5
–2.9
7.6
–5.3
23.6

1.3
17.9
–.5
–1.0
–5.6
19.2
5.7
9.9
.9
–9.3
6.2
–3.9
24.6
18.9
5.6
7.9
6.3
3.8
2.6
–.2
5.7
6.5
9.9
12.2
4.8
11.1
14.2
11.0
12.4
11.1
–7.6
9.6
5.9
10.9
6.1
4.0
1.6
–5.4
–5.1
11.5
3.3
.5
2.9
6.5
3.3
2.2
5.1
3.4
–2.0
.3
9.6
2.6
1.4
5.9
3.9
.0
1.7
–1.1
–8.5
–13.1
–53.1
89.2
31.3
9.3
7.1
4.7
17.6

Total
–2.4
–.1
–.3
3.0
3.0
–1.3
1.9
4.4
.9
.3
2.5
2.6
1.9
6.3
6.1
4.7
3.0
1.4
2.5
2.6
.0
1.3
–.7
.0
–.6
2.6
1.7
2.8
3.9
.5
4.9
3.8
1.8
.9
.9
1.9
2.3
2.6
3.1
–1.5
–3.4
–2.1
–2.4
.3
2.2
1.6
.7
1.0
3.2
1.2
.1
.9
2.8
1.0
–.8
2.7
5.0
2.1
3.0
3.7
3.9
–2.1
–.5
4.2
–2.0
.9
–2.6

Total
–7.3
–2.6
–3.6
3.7
.8
–1.0
2.3
3.5
1.2
4.0
6.0
4.5
2.7
7.1
6.7
5.3
3.6
–1.4
.5
1.5
–2.3
1.6
–4.5
–4.2
–4.8
1.1
.2
–.3
3.3
–1.9
5.5
8.1
6.5
2.6
1.8
2.4
3.6
6.4
6.2
1.8
–3.6
–2.6
–6.1
–1.0
1.2
.1
1.3
3.0
4.3
3.1
–1.1
1.8
5.1
3.4
1.9
1.4
8.9
3.6
3.5
2.4
20.6
–5.4
–3.1
11.3
–5.3
–5.1
–4.5

National Nondefense defense
–11.5
–5.8
–5.0
1.2
.5
–2.1
.1
2.9
2.4
3.7
7.9
7.3
6.5
5.6
8.2
4.7
5.3
–.8
–1.3
.0
–4.9
–.4
–5.4
–6.7
–5.0
.3
–.8
–2.4
3.9
–3.3
4.7
8.1
8.9
2.8
1.8
3.1
3.9
7.4
4.9
1.3
–3.6
–4.7
–6.5
–3.4
–.4
–.6
2.2
4.2
5.0
2.3
–3.7
–1.2
7.9
3.5
6.8
5.2
4.2
4.5
6.0
–.7
3.2
1.7
5.3
–5.8
–1.1
–1.7
–6.1

5.6
6.1
–.3
9.5
1.4
1.3
6.8
4.8
–1.1
4.6
2.0
–1.6
–6.6
11.5
2.8
6.8
–1.0
–3.0
5.8
5.4
4.3
6.2
–2.5
1.1
–4.3
2.6
1.9
3.3
2.4
.4
6.8
8.2
2.6
2.3
1.9
1.3
3.1
4.5
8.9
2.7
–3.5
1.2
–5.4
2.8
3.7
1.1
.0
1.4
3.4
4.4
2.7
6.3
1.1
3.4
–5.0
–3.9
16.2
2.2
.0
7.4
50.1
–14.3
–14.1
40.8
–10.7
–9.5
–2.2

State
and
local
2.8
2.3
2.9
2.4
4.9
–1.6
1.7
5.2
.7
–2.9
–.7
.8
1.1
5.4
5.5
4.1
2.4
4.1
4.3
3.6
1.9
1.1
2.2
3.1
2.2
3.6
2.7
4.6
4.2
1.8
4.6
1.5
–.8
–.2
.3
1.6
1.5
.3
1.1
–3.7
–3.2
–1.7
.2
1.2
2.8
2.5
.4
–.3
2.5
.0
.9
.3
1.5
–.5
–2.4
3.5
2.7
1.1
2.7
4.4
–5.5
.1
1.2
–.1
.2
4.9
–1.4

Final
Final
Gross sales to Gross
sales of domestic private domestic Average
of GDP
domestic pur- domestic income and
GDI
pur(GDI) 3
product chases 1
chasers 2
4.0
6.4
2.8
–1.7
3.9
3.8
4.5
6.4
2.2
.4
.3
.4
6.0
5.0
4.6
3.9
3.0
4.6
2.9
1.0
.5
4.5
2.7
3.3
3.0
4.2
3.9
5.2
4.6
3.2
1.5
.9
4.3
3.1
2.9
2.9
2.3
–1.8
–.2
2.0
1.3
2.0
1.9
2.8
1.8
2.2
2.8
2.1
2.9
–2.6
4.7
2.8
4.3
.4
.8
1.9
3.8
3.1
2.9
–4.6
–27.6
25.9
3.4
9.1
8.1
.1
2.0

4.7
6.8
2.8
–2.3
2.0
5.4
5.6
6.0
.5
–1.4
1.8
–.7
9.5
6.5
4.5
2.9
4.1
3.0
2.1
–.1
.9
4.6
3.2
4.3
1.8
4.6
5.2
5.9
5.6
3.7
.4
2.7
4.2
4.0
3.0
2.1
1.3
–3.1
–.9
3.2
1.4
1.2
2.2
3.2
2.5
2.1
2.7
2.7
2.2
–1.0
6.1
3.2
2.9
3.5
1.4
2.0
3.6
2.6
.5
–4.9
–30.8
37.8
6.1
7.7
6.7
3.5
6.8

6.5
4.8
4.6
8.3
7.1
7.0
2.2
3.8
3.9
–3.5
–2.9
–2.4
3.4
2.7
2.6
6.7
3.8
4.1
5.9
6.0
5.5
6.1
5.4
6.0
1.5
.8
1.0
–1.2
1.3
.6
.4
1.2
1.2
.8
–1.3
–1.3
9.1
6.6
7.3
5.9
6.7
6.1
4.6
3.4
3.8
3.5
2.7
2.8
2.5
5.5
5.0
4.4
4.7
4.2
2.2
1.0
1.9
–.3
1.0
.8
.3
.7
.9
5.6
3.9
4.1
4.3
3.0
2.8
4.4
4.3
4.2
3.3
2.9
2.6
4.8
4.8
4.6
5.3
5.5
5.0
6.9
4.9
4.9
5.7
4.4
4.6
4.7
3.6
3.2
.9
–.4
–.1
1.3
3.2
2.6
4.8
2.7
3.5
4.4
3.8
3.6
3.4
4.2
3.6
2.5
2.5
2.6
1.3
–.3
.9
–3.5
–2.6
–2.6
–2.1
.6
.3
3.4
3.3
3.1
2.4
2.0
1.8
2.5
3.1
2.3
2.6
1.3
1.9
4.2
4.0
3.3
2.5
1.2
1.5
2.4
1.2
1.6
3.2
2.9
2.8
2.8
2.9
2.6
2.4
1.8
2.2
–1.8
–.2
–1.2
6.5 ������������� ���������������
3.3
4.0
3.6
4.0
.8
2.1
2.3
5.1
3.5
1.7
1.5
1.2
1.2
2.3
2.3
4.1
.8
2.0
3.2
.9
1.9
1.1
3.0
2.4
–6.0
–.8
–3.0
–32.8
–32.7
–32.0
38.4
24.4
29.0
6.2
19.6
11.9
11.8
6.3
6.3
10.1
4.3
5.5
1.4
6.4
4.3
3.0 ������������� ���������������

1 Gross domestic product (GDP) less exports of goods and services plus imports of goods and services.
2 Personal consumption expenditures plus gross private fixed investment.
3 Gross domestic income is deflated by the implicit price deflator for GDP.

Note: Percent changes based on unrounded GDP quantity indexes.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 355

Table B–2. Contributions to percent change in real gross domestic product, 1971–2021
[Percentage points, except as noted; annual average to annual average, quarterly data at seasonally adjusted annual rates]
Personal consumption
expenditures

Year or quarter

1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product
(percent
change)

3.3
5.3
5.6
–.5
–.2
5.4
4.6
5.5
3.2
–.3
2.5
–1.8
4.6
7.2
4.2
3.5
3.5
4.2
3.7
1.9
–.1
3.5
2.8
4.0
2.7
3.8
4.4
4.5
4.8
4.1
1.0
1.7
2.8
3.9
3.5
2.8
2.0
.1
–2.6
2.7
1.5
2.3
1.8
2.3
2.7
1.7
2.3
2.9
2.3
–3.4
5.7
3.1
3.4
1.9
.9
2.4
3.2
2.8
1.9
–5.1
–31.2
33.8
4.5
6.3
6.7
2.3
7.0

Fixed investment
Nonresidential
Total

2.29
3.66
2.97
–.50
1.36
3.41
2.59
2.68
1.44
–.19
.85
.88
3.51
3.30
3.20
2.58
2.15
2.65
1.86
1.28
.12
2.36
2.24
2.51
1.91
2.26
2.45
3.42
3.49
3.29
1.63
1.70
2.13
2.54
2.38
1.95
1.63
.10
–.88
1.31
1.16
.94
1.01
1.82
2.20
1.67
1.65
1.96
1.48
–2.55
5.30
1.64
2.34
1.79
1.16
.43
2.37
2.12
1.13
–4.79
–24.10
25.51
2.26
7.44
7.92
1.35
2.13

See next page for continuation of table.

356 |

Appendix B

Gross private domestic investment

Goods

1.23
1.90
1.52
–1.08
.20
2.03
1.26
1.19
.45
–.72
.33
.19
1.69
1.91
1.38
1.45
.47
.96
.64
.16
–.49
.76
.99
1.26
.71
1.06
1.12
1.54
1.83
1.23
.72
.92
1.15
1.21
.98
.87
.65
–.71
–.70
.62
.49
.48
.70
.89
1.03
.73
.82
.84
.71
.96
2.70
.30
.89
.61
.44
.29
1.42
.99
.35
.04
–1.89
9.92
–.07
5.69
2.99
–2.21
.36

Services

1.06
1.76
1.45
.58
1.16
1.38
1.33
1.49
.99
.53
.52
.69
1.82
1.39
1.83
1.13
1.67
1.69
1.21
1.12
.61
1.60
1.26
1.26
1.20
1.20
1.33
1.88
1.66
2.06
.92
.78
.98
1.34
1.40
1.08
.98
.81
–.18
.68
.68
.46
.31
.93
1.18
.94
.83
1.13
.78
–3.52
2.60
1.34
1.45
1.18
.72
.14
.95
1.13
.77
–4.83
–22.21
15.59
2.34
1.75
4.93
3.57
1.76

Total

1.63
1.90
1.95
–1.24
–2.91
2.91
2.47
2.22
.72
–2.07
1.64
–2.46
1.60
4.73
–.01
.03
.53
.45
.72
–.45
–1.09
1.11
1.24
1.90
.55
1.49
2.01
1.76
1.62
1.31
–1.11
–.16
.76
1.64
1.26
.60
–.48
–1.52
–3.51
1.85
.94
1.64
1.10
.95
.95
–.18
.68
.98
.60
–.99
1.69
1.45
.14
1.64
.39
1.13
.48
.22
–1.18
–.92
–9.64
11.71
4.01
–.37
–.65
2.05
5.38

Total

1.08
1.85
1.47
–.98
–1.68
1.54
2.23
2.10
1.11
–1.18
.50
–1.16
1.32
2.83
1.02
.34
.11
.59
.55
–.25
–.84
.83
1.17
1.29
.99
1.48
1.49
1.82
1.65
1.34
–.27
–.64
.77
1.23
1.33
.50
–.24
–1.05
–2.69
.43
.99
1.47
.87
1.06
.64
.35
.69
.82
.55
–.47
1.37
1.14
1.03
.15
.31
.64
1.06
.54
–.19
–.41
–5.63
4.88
2.92
2.25
.61
–.16
.48

Total
–0.01
.97
1.51
.10
–1.13
.66
1.26
1.72
1.34
.00
.87
–.43
–.06
2.18
.91
–.24
.01
.63
.71
.14
–.48
.33
.84
.91
1.15
1.13
1.38
1.44
1.36
1.31
–.31
–.94
.30
.67
.92
1.00
.89
.08
–1.95
.52
.99
1.16
.53
.95
.32
.12
.53
.85
.59
–.73
.98
1.31
.90
.38
.65
.63
.90
.40
–.23
–1.14
–4.28
2.72
1.57
1.65
1.21
.22
.43

Structures
–0.06
.12
.30
–.08
–.42
.09
.15
.52
.51
.26
.39
–.09
–.56
.58
.31
–.49
–.11
.02
.07
.05
–.38
–.18
–.01
.05
.16
.15
.21
.16
.01
.24
–.04
–.56
–.09
.00
.06
.22
.42
.23
–.71
–.50
.07
.34
.04
.33
–.03
–.14
.13
.12
.06
–.39
–.23
.57
.22
–.13
–.42
.13
.42
.42
–.26
–.02
–1.77
–.46
–.22
.14
–.08
–.11
–.25

Equipment
0.05
.75
1.12
.14
–.73
.39
1.01
1.08
.62
–.35
.28
–.47
.32
1.29
.39
.08
.03
.43
.35
–.14
–.28
.34
.73
.75
.78
.65
.76
.91
.89
.71
–.31
–.35
.26
.49
.60
.57
.25
–.29
–1.21
.91
.69
.62
.28
.42
.19
–.11
.16
.36
.19
–.48
.69
.32
.18
.31
.57
.25
.15
–.31
–.29
–1.30
–1.99
2.73
1.29
.75
.66
–.13
.14

Intellectual
property
products
0.01
.11
.08
.05
.01
.18
.11
.12
.20
.09
.21
.12
.17
.30
.21
.17
.10
.18
.29
.22
.18
.17
.12
.11
.20
.33
.41
.37
.45
.36
.04
–.03
.14
.18
.26
.21
.23
.14
–.02
.11
.24
.20
.22
.20
.16
.37
.25
.36
.33
.14
.52
.42
.51
.21
.49
.25
.34
.29
.32
.18
–.51
.45
.50
.76
.62
.46
.53

Residential

1.08
.87
–.04
–1.08
–.54
.88
.97
.38
–.22
–1.19
–.37
–.72
1.38
.65
.11
.58
.10
–.05
–.16
–.38
–.35
.49
.32
.38
–.15
.35
.11
.38
.29
.03
.04
.29
.47
.57
.41
–.50
–1.13
–1.14
–.74
–.08
.00
.31
.33
.12
.33
.23
.15
–.02
–.04
.26
.39
–.17
.13
–.24
–.34
.00
.15
.14
.04
.73
–1.36
2.16
1.34
.60
–.60
–.38
.05

Change
in
private
inventories
0.56
.06
.48
–.26
–1.24
1.37
.24
.12
–.40
–.89
1.13
–1.31
.28
1.90
–1.03
–.31
.41
–.13
.17
–.21
–.26
.28
.07
.61
–.44
.02
.52
–.07
–.03
–.03
–.84
.48
–.02
.41
–.07
.10
–.25
–.46
–.82
1.42
–.05
.17
.23
–.12
.31
–.53
–.01
.16
.05
–.52
.32
.31
–.89
1.50
.08
.49
–.57
–.32
–.99
–.51
–4.01
6.84
1.10
–2.62
–1.26
2.20
4.90

Table B–2. Contributions to percent change in real gross domestic product,
1971–2021—Continued
[Percentage points, except as noted; annual average to annual average, quarterly data at seasonally adjusted annual rates]
Government consumption expenditures
and gross investment

Net exports of goods and services
Year or quarter

1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Net
exports
–0.18
–.19
.80
.73
.86
–1.05
–.70
.05
.64
1.64
–.15
–.59
–1.32
–1.54
–.39
–.29
.17
.81
.51
.40
.62
–.04
–.56
–.41
.12
–.15
–.31
–1.14
–.90
–.85
–.24
–.67
–.49
–.63
–.31
–.06
.52
1.04
1.07
–.43
.12
.12
.20
–.31
–.78
–.17
–.16
–.27
–.18
–.29
–1.39
–.16
.40
–1.66
–.51
.39
–.50
.07
1.43
–.05
1.53
–3.25
–1.65
–1.56
–.18
–1.26
–.07

Exports
Total
0.10
.42
1.08
.56
–.05
.36
.19
.80
.80
.95
.12
–.71
–.22
.61
.24
.53
.77
1.23
.97
.78
.61
.66
.31
.84
1.02
.86
1.26
.26
.52
.86
–.59
–.19
.19
.88
.67
.95
.94
.67
–1.00
1.43
.90
.54
.40
.52
.04
.05
.49
.35
–.01
–1.57
.48
.24
.62
–.78
.05
.36
–.26
–.08
.17
–1.95
–8.34
4.64
2.07
–.30
.80
–.59
2.35

Goods
0.00
.43
1.05
.49
–.14
.34
.12
.64
.69
.88
–.05
–.63
–.21
.41
.20
.27
.62
.99
.72
.56
.45
.52
.22
.65
.83
.68
1.10
.17
.32
.72
–.49
–.24
.19
.58
.52
.71
.53
.48
–1.00
1.13
.65
.37
.27
.41
–.03
.05
.32
.34
.00
–.76
.52
.14
.89
–.75
.13
.31
–.41
.10
–.04
–.32
–6.24
4.75
1.59
–.10
.48
–.39
1.63

Imports
Services
0.10
–.01
.02
.08
.09
.02
.07
.17
.11
.07
.17
–.08
.00
.20
.05
.25
.15
.24
.26
.22
.16
.14
.09
.19
.19
.18
.16
.08
.20
.13
–.10
.05
.01
.30
.15
.24
.41
.19
.00
.30
.26
.17
.13
.11
.07
.00
.17
.01
.00
–.81
–.04
.10
–.27
–.03
–.08
.05
.15
–.18
.21
–1.63
–2.09
–.11
.49
–.20
.32
–.19
.72

Total
–0.28
–.61
–.28
.17
.91
–1.41
–.89
–.76
–.16
.69
–.26
.12
–1.10
–2.16
–.63
–.82
–.60
–.41
–.46
–.37
.01
–.70
–.87
–1.25
–.90
–1.01
–1.57
–1.39
–1.42
–1.71
.35
–.48
–.68
–1.51
–.98
–1.01
–.42
.37
2.07
–1.86
–.79
–.42
–.20
–.84
–.81
–.22
–.65
–.62
–.17
1.28
–1.87
–.40
–.22
–.88
–.57
.02
–.24
.15
1.26
1.90
9.87
–7.89
–3.73
–1.26
–.99
–.68
–2.42

Goods
–0.32
–.55
–.33
.17
.85
–1.31
–.82
–.66
–.13
.66
–.18
.20
–.98
–1.78
–.50
–.80
–.39
–.35
–.37
–.25
–.04
–.76
–.82
–1.15
–.84
–.91
–1.40
–1.18
–1.31
–1.45
.39
–.41
–.67
–1.28
–.88
–.81
–.27
.47
2.10
–1.73
–.74
–.38
–.28
–.75
–.75
–.14
–.53
–.62
–.07
.65
–1.61
–.54
–.04
–.87
–.29
.01
.01
.19
1.16
.85
7.27
–7.37
–3.04
–1.21
–.51
.04
–2.11

Federal
Services
0.04
–.06
.05
.00
.06
–.10
–.07
–.10
–.02
.03
–.09
–.08
–.12
–.38
–.13
–.02
–.21
–.07
–.09
–.13
.05
.05
–.05
–.10
–.06
–.10
–.17
–.21
–.11
–.26
–.04
–.07
–.01
–.22
–.09
–.20
–.15
–.10
–.03
–.13
–.05
–.04
.07
–.09
–.07
–.08
–.12
.00
–.11
.63
–.26
.14
–.18
–.01
–.27
.01
–.25
–.03
.10
1.05
2.59
–.52
–.69
–.05
–.48
–.72
–.31

Total
–0.45
–.12
–.07
.47
.49
.12
.26
.60
.36
.36
.20
.37
.79
.74
1.37
1.14
.62
.26
.58
.65
.25
.10
–.17
.02
.10
.18
.30
.44
.59
.33
.67
.82
.39
.30
.15
.30
.34
.49
.72
–.02
–.67
–.42
–.47
–.17
.33
.35
.09
.24
.38
.43
.09
.15
.49
.17
–.14
.47
.86
.36
.52
.63
.97
–.19
–.09
.77
–.36
.17
–.45

Total
–0.80
–.37
–.39
.06
.05
.01
.21
.23
.20
.38
.43
.35
.65
.33
.78
.61
.38
–.15
.15
.20
.01
–.15
–.32
–.31
–.21
–.09
–.06
–.06
.12
.02
.24
.47
.45
.31
.15
.17
.14
.46
.48
.34
–.23
–.16
–.44
–.19
.00
.03
.02
.20
.25
.33
.04
.12
.32
.22
.12
.09
.57
.23
.23
.16
1.42
–.32
–.22
.78
–.38
–.35
–.30

National Nondefense defense
–0.97
–.60
–.40
–.07
–.07
–.04
.06
.04
.15
.22
.40
.47
.51
.38
.62
.52
.38
–.04
–.02
.02
–.06
–.31
–.32
–.28
–.21
–.08
–.13
–.09
.06
–.04
.13
.30
.35
.26
.11
.07
.13
.33
.29
.16
–.12
–.18
–.33
–.19
–.09
–.02
.04
.13
.20
.11
–.04
–.05
.29
.13
.26
.20
.16
.18
.23
–.03
.16
.11
.22
–.25
–.04
–.07
–.24

0.17
.22
.01
.14
.13
.06
.15
.19
.05
.16
.03
–.11
.14
–.04
.16
.09
.01
–.12
.18
.18
.07
.16
.00
–.02
.00
–.01
.07
.03
.06
.06
.12
.18
.10
.05
.04
.10
.01
.14
.20
.18
–.12
.02
–.10
.00
.09
.06
–.01
.07
.04
.21
.08
.16
.03
.09
–.14
–.11
.40
.06
.00
.20
1.26
–.43
–.44
1.02
–.34
–.29
–.06

State
and
local
0.35
.25
.32
.41
.43
.10
.05
.37
.16
–.02
–.23
.01
.14
.41
.59
.53
.24
.42
.43
.45
.24
.25
.15
.32
.31
.27
.36
.50
.47
.31
.43
.35
–.06
–.01
.00
.13
.20
.03
.24
–.36
–.44
–.26
–.03
.02
.33
.31
.07
.04
.14
.10
.04
.03
.17
–.05
–.26
.38
.29
.12
.28
.47
–.45
.13
.14
–.01
.02
.52
–.15

Final
sales of
domestic
product
2.74
5.20
5.16
–.28
1.03
4.01
4.38
5.42
3.56
.63
1.41
–.50
4.31
5.34
5.20
3.77
3.05
4.31
3.51
2.09
.15
3.24
2.68
3.41
3.13
3.76
3.92
4.55
4.82
4.11
1.80
1.21
2.81
3.45
3.56
2.68
2.26
.58
–1.77
1.29
1.60
2.11
1.61
2.41
2.40
2.20
2.26
2.76
2.24
–2.89
5.36
2.78
4.26
.44
.82
1.92
3.78
3.09
2.88
–4.60
–27.23
26.95
3.44
8.90
7.99
.10
2.09

Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 357

Table B–3. Gross domestic product, 2006–2021
[Quarterly data at seasonally adjusted annual rates]
Personal consumption
expenditures

Year or quarter

Gross private domestic investment
Fixed investment

Gross
domestic
product

Nonresidential
Total

Goods

Services

Total

Total

Total

Structures

Equipment

Intellectual
property
products

Residential

Change
in
private
inventories

Billions of dollars
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

13,815.6
14,474.2
14,769.9
14,478.1
15,049.0
15,599.7
16,254.0
16,843.2
17,550.7
18,206.0
18,695.1
19,479.6
20,527.2
21,372.6
20,893.7
22,997.5
20,143.7
20,492.5
20,659.1
20,813.3
21,001.6
21,289.3
21,505.0
21,694.5
21,481.4
19,477.4
21,138.6
21,477.6
22,038.2
22,741.0
23,202.3
24,008.5

9,277.2
9,746.6
10,050.1
9,891.2
10,260.3
10,698.9
11,047.4
11,363.5
11,847.7
12,263.5
12,693.3
13,239.1
13,913.5
14,428.7
14,047.6
15,746.9
13,667.4
13,864.8
14,002.6
14,119.3
14,155.6
14,375.7
14,529.5
14,653.9
14,439.1
12,989.7
14,293.8
14,467.6
15,005.4
15,681.7
15,964.9
16,335.5

3,239.7
3,367.0
3,363.2
3,180.0
3,317.8
3,518.1
3,637.7
3,730.0
3,863.0
3,923.0
3,991.8
4,158.6
4,353.7
4,478.9
4,653.8
5,482.8
4,298.4
4,354.4
4,373.2
4,388.8
4,382.8
4,479.4
4,512.7
4,540.8
4,530.9
4,349.9
4,867.2
4,867.3
5,245.0
5,529.8
5,500.1
5,656.2

6,037.6
6,379.6
6,686.9
6,711.2
6,942.4
7,180.7
7,409.6
7,633.6
7,984.8
8,340.5
8,701.4
9,080.6
9,559.8
9,949.8
9,393.7
10,264.1
9,369.0
9,510.3
9,629.4
9,730.5
9,772.7
9,896.3
10,016.8
10,113.2
9,908.2
8,639.8
9,426.6
9,600.4
9,760.4
10,151.9
10,464.8
10,679.2

2,701.0
2,673.0
2,477.6
1,929.7
2,165.5
2,332.6
2,621.8
2,826.0
3,044.2
3,237.2
3,205.0
3,381.4
3,637.8
3,826.3
3,637.8
4,113.4
3,550.8
3,603.2
3,679.6
3,717.5
3,801.9
3,843.0
3,858.2
3,801.9
3,752.4
3,167.0
3,708.8
3,923.2
3,928.0
3,925.1
4,099.6
4,501.1

2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

15,315.9
15,623.9
15,643.0
15,236.3
15,649.0
15,891.5
16,254.0
16,553.3
16,932.1
17,390.3
17,680.3
18,079.1
18,606.8
19,032.7
18,384.7
19,428.4
18,436.3
18,590.0
18,679.6
18,721.3
18,833.2
18,982.5
19,112.7
19,202.3
18,952.0
17,258.2
18,560.8
18,767.8
19,055.7
19,368.3
19,478.9
19,810.6

10,386.2
10,638.7
10,654.7
10,515.6
10,716.0
10,898.3
11,047.4
11,211.7
11,515.3
11,892.9
12,187.7
12,483.7
12,845.0
13,126.3
12,629.9
13,629.4
12,707.6
12,816.4
12,900.6
12,955.5
12,975.1
13,088.8
13,192.3
13,249.0
13,014.5
11,756.4
12,820.8
12,927.9
13,282.7
13,665.6
13,732.4
13,836.7

3,509.7
3,607.6
3,498.9
3,389.8
3,485.7
3,561.8
3,637.7
3,752.2
3,905.1
4,090.9
4,231.7
4,395.2
4,569.3
4,723.0
4,942.5
5,545.1
4,511.9
4,558.8
4,591.4
4,615.2
4,630.6
4,709.1
4,765.5
4,786.9
4,790.2
4,665.8
5,158.9
5,155.0
5,476.6
5,646.7
5,518.3
5,538.8

6,873.1
7,027.0
7,154.9
7,125.8
7,230.4
7,336.7
7,409.6
7,460.3
7,613.2
7,809.8
7,968.5
8,110.1
8,305.7
8,443.7
7,808.5
8,261.4
8,223.8
8,287.3
8,339.7
8,371.8
8,377.8
8,420.2
8,471.0
8,505.9
8,284.4
7,217.3
7,815.2
7,917.0
7,993.4
8,214.3
8,378.5
8,459.4

2,752.4
2,684.1
2,462.9
1,942.0
2,216.5
2,362.1
2,621.8
2,801.5
2,959.2
3,121.8
3,089.9
3,212.5
3,394.8
3,510.6
3,316.2
3,634.3
3,346.3
3,352.5
3,430.9
3,449.6
3,503.4
3,526.0
3,535.9
3,477.1
3,430.1
2,901.9
3,371.0
3,561.9
3,541.3
3,506.0
3,609.7
3,880.2

2,632.0
2,639.1
2,506.9
2,080.4
2,111.6
2,286.3
2,550.5
2,721.5
2,960.2
3,100.4
3,168.8
3,351.9
3,579.1
3,752.6
3,697.4
4,139.4
3,505.0
3,579.0
3,602.5
3,629.9
3,683.4
3,754.5
3,791.2
3,781.4
3,773.0
3,456.9
3,693.8
3,865.9
4,022.2
4,099.4
4,159.8
4,276.1

1,793.8
1,948.6
1,990.9
1,690.4
1,735.0
1,907.5
2,118.5
2,211.5
2,400.1
2,466.6
2,469.3
2,591.6
2,780.6
2,938.7
2,799.6
3,053.9
2,716.0
2,770.1
2,798.1
2,838.1
2,886.9
2,946.1
2,969.3
2,952.6
2,900.1
2,659.1
2,776.6
2,862.7
2,956.7
3,029.2
3,073.9
3,155.7

425.2
510.3
571.1
455.8
379.8
404.5
479.4
492.5
577.6
584.4
560.4
599.3
633.3
672.6
597.2
579.7
627.2
641.6
638.2
626.1
639.9
669.4
696.0
685.3
687.1
585.9
563.5
552.3
565.0
572.8
581.9
599.1

862.3
893.4
845.4
670.3
777.0
881.3
983.4
1,027.0
1,091.9
1,119.5
1,087.8
1,117.4
1,190.5
1,231.3
1,123.9
1,274.5
1,166.4
1,175.7
1,195.7
1,224.2
1,240.2
1,247.4
1,227.4
1,210.3
1,141.9
1,020.6
1,135.5
1,197.5
1,244.5
1,270.4
1,277.2
1,306.1

506.3
544.8
574.4
564.4
578.2
621.7
655.7
691.9
730.5
762.7
821.2
875.0
956.7
1,034.8
1,078.5
1,199.7
922.3
952.7
964.2
987.7
1,006.7
1,029.3
1,046.0
1,057.0
1,071.1
1,052.6
1,077.6
1,112.9
1,147.2
1,186.0
1,214.9
1,250.5

838.2
690.5
516.0
390.0
376.6
378.8
432.0
510.0
560.2
633.8
699.4
760.3
798.5
813.9
897.8
1,085.5
789.0
808.9
804.3
791.8
796.5
808.5
821.9
828.8
872.9
797.8
917.2
1,003.2
1,065.5
1,070.2
1,085.9
1,120.4

69.0
34.0
–29.2
–150.8
53.9
46.3
71.2
104.5
84.0
136.8
36.3
29.5
58.7
73.6
–59.6
–25.9
45.9
24.2
77.1
87.7
118.5
88.4
67.0
20.6
–20.6
–289.9
15.0
57.3
–94.2
–174.3
–60.2
225.1

501.7
568.6
605.4
492.2
412.8
424.1
479.4
485.5
538.8
534.1
511.0
532.5
553.6
565.0
494.2
454.3
554.2
563.7
557.7
538.9
544.7
563.2
582.0
570.0
568.8
485.8
466.0
456.1
462.1
458.6
453.8
442.7

832.6
865.8
824.4
649.7
781.2
886.2
983.4
1,029.2
1,101.1
1,134.6
1,114.6
1,145.5
1,218.8
1,258.8
1,154.0
1,304.4
1,196.6
1,205.4
1,221.3
1,251.7
1,265.2
1,273.1
1,256.4
1,240.6
1,168.3
1,044.0
1,166.6
1,237.1
1,278.5
1,315.7
1,307.9
1,315.6

521.5
554.3
575.3
572.4
588.1
624.8
655.7
691.4
724.8
752.4
818.8
865.2
935.5
1,002.9
1,031.3
1,136.1
905.2
930.3
940.8
965.8
978.5
995.7
1,010.5
1,027.1
1,036.6
1,008.0
1,027.7
1,053.0
1,091.9
1,124.6
1,149.3
1,178.5

818.9
665.8
504.6
395.3
383.0
382.5
432.0
485.5
504.1
555.4
592.1
615.9
612.3
606.7
648.0
707.3
616.5
621.5
612.2
599.0
599.1
605.2
610.6
612.2
641.2
584.9
657.8
708.2
730.6
708.2
694.2
696.0

87.1
40.6
–32.7
–177.3
57.3
46.7
71.2
108.7
86.3
137.6
35.7
33.6
65.7
75.1
–42.3
–38.1
63.5
11.1
101.0
87.3
131.7
84.3
68.3
16.3
–30.4
–252.8
25.3
88.8
–88.3
–168.5
–66.8
171.2

Billions of chained (2012) dollars

See next page for continuation of table.

358 |

Appendix B

2,686.8
2,653.5
2,499.4
2,099.8
2,164.2
2,317.8
2,550.5
2,692.1
2,869.2
2,979.0
3,041.0
3,164.3
3,316.2
3,421.3
3,329.4
3,587.5
3,273.2
3,321.2
3,327.9
3,342.6
3,372.8
3,423.2
3,449.3
3,439.9
3,419.6
3,123.0
3,318.5
3,456.6
3,564.1
3,593.0
3,585.0
3,607.8

1,854.2
1,982.1
1,994.2
1,704.3
1,781.0
1,935.4
2,118.5
2,206.0
2,365.3
2,420.3
2,442.0
2,541.4
2,704.4
2,822.0
2,671.1
2,868.8
2,654.0
2,698.0
2,716.7
2,749.0
2,780.7
2,826.0
2,846.5
2,834.7
2,775.5
2,535.7
2,646.9
2,726.2
2,810.4
2,873.1
2,884.8
2,907.0

Table B–3. Gross domestic product, 2006–2021—Continued
[Quarterly data at seasonally adjusted annual rates]
Net exports of
goods and services
Year or quarter

Government consumption expenditures
and gross investment
Federal

Net
exports

Exports

Imports

Total

–786.5
–735.9
–740.9
–419.2
–532.3
–579.6
–551.6
–479.4
–510.0
–526.2
–506.3
–539.9
–596.2
–596.3
–651.2
–915.9
–580.1
–539.8
–624.0
–640.9
–606.4
–632.3
–614.0
–532.4
–541.7
–538.9
–725.7
–798.4
–872.5
–881.7
–947.0
–962.2

1,470.2
1,659.3
1,835.3
1,582.8
1,857.2
2,115.9
2,217.7
2,287.0
2,377.4
2,268.7
2,232.1
2,383.8
2,533.5
2,519.7
2,123.4
2,479.9
2,504.4
2,568.3
2,534.2
2,527.1
2,524.6
2,533.4
2,512.1
2,508.7
2,385.5
1,807.9
2,079.6
2,220.7
2,311.9
2,461.5
2,485.2
2,661.1

2,256.6
2,395.2
2,576.2
2,001.9
2,389.6
2,695.5
2,769.3
2,766.4
2,887.4
2,794.9
2,738.4
2,923.7
3,129.7
3,116.0
2,774.6
3,395.8
3,084.5
3,108.1
3,158.2
3,168.1
3,131.0
3,165.7
3,126.1
3,041.1
2,927.3
2,346.7
2,805.3
3,019.1
3,184.5
3,343.2
3,432.3
3,623.2

2,623.8
2,790.6
2,983.0
3,076.3
3,155.6
3,147.9
3,136.5
3,133.0
3,168.8
3,231.6
3,303.1
3,399.1
3,572.0
3,713.9
3,859.5
4,053.0
3,505.5
3,564.3
3,600.9
3,617.4
3,650.5
3,702.9
3,731.3
3,771.0
3,831.6
3,859.6
3,861.7
3,885.3
3,977.3
4,015.9
4,084.9
4,134.0

Total

National Nondefense defense

State
and
local

Final
sales to
Gross
Final
Gross
sales of domestic private domestic Average
of GDP
domestic income and
domestic
purGDI
pur(GDI) 3
product chases 1
chasers 2

Billions of dollars
2006 ������������������
2007 ������������������
2008 ������������������
2009 ������������������
2010 ������������������
2011 ������������������
2012 ������������������
2013 ������������������
2014 ������������������
2015 ������������������
2016 ������������������
2017 ������������������
2018 ������������������
2019 ������������������
2020 ������������������
2021 p ����������������
2018: I ��������������
      II �������������
      III ������������
      IV ������������
2019: I ��������������
      II �������������
      III ������������
      IV ������������
2020: I ��������������
      II �������������
      III ������������
      IV ������������
2021: I ��������������
      II �������������
      III ������������
      IV p ���������

1,001.2
1,051.0
1,152.0
1,220.8
1,300.2
1,299.8
1,287.0
1,227.2
1,216.0
1,221.8
1,234.5
1,262.8
1,339.0
1,414.9
1,501.8
1,565.0
1,305.8
1,331.7
1,350.8
1,367.7
1,387.0
1,406.9
1,424.1
1,441.7
1,454.7
1,525.0
1,515.1
1,512.3
1,568.6
1,563.3
1,562.0
1,566.1

640.8
679.3
750.3
787.6
828.0
834.0
814.2
764.2
743.4
729.7
727.9
746.5
792.8
847.5
881.3
905.3
767.4
788.1
799.4
816.2
829.3
840.4
852.5
868.0
868.3
872.4
883.9
900.8
897.1
904.1
910.9
909.0

360.4
371.8
401.6
433.2
472.2
465.8
472.8
462.9
472.6
492.0
506.6
516.3
546.2
567.4
620.5
659.7
538.4
543.7
551.4
551.4
557.6
566.6
571.7
573.7
586.4
652.6
631.3
611.5
671.6
659.2
651.1
657.1

1,622.7
1,739.5
1,831.1
1,855.6
1,855.4
1,848.2
1,849.5
1,905.9
1,952.8
2,009.8
2,068.5
2,136.3
2,233.0
2,299.0
2,357.8
2,488.0
2,199.7
2,232.6
2,250.1
2,249.7
2,263.5
2,296.0
2,307.2
2,329.2
2,376.9
2,334.6
2,346.5
2,373.0
2,408.7
2,452.6
2,522.9
2,567.9

13,746.6
14,440.3
14,799.1
14,628.8
14,995.1
15,553.5
16,182.8
16,738.7
17,466.7
18,069.2
18,658.8
19,450.1
20,468.4
21,299.0
20,953.3
23,023.4
20,097.9
20,468.3
20,582.0
20,725.7
20,883.1
21,200.8
21,438.0
21,673.9
21,502.0
19,767.4
21,123.6
21,420.3
22,132.5
22,915.3
23,262.5
23,783.4

14,602.0
15,210.2
15,510.7
14,897.2
15,581.3
16,179.3
16,805.6
17,322.6
18,060.7
18,732.2
19,201.4
20,019.6
21,123.3
21,968.8
21,544.9
23,913.4
20,723.8
21,032.3
21,283.1
21,454.2
21,608.0
21,921.6
22,119.0
22,226.8
22,023.1
20,016.3
21,864.3
22,276.0
22,910.8
23,622.6
24,149.4
24,970.6

11,909.2
12,385.7
12,556.9
11,971.7
12,371.8
12,985.2
13,597.9
14,085.0
14,807.9
15,363.9
15,862.0
16,591.0
17,492.6
18,181.3
17,745.0
19,886.2
17,172.4
17,443.7
17,605.0
17,749.2
17,839.0
18,130.3
18,320.7
18,435.3
18,212.0
16,446.7
17,987.6
18,333.5
19,027.7
19,781.1
20,124.7
20,611.5

14,019.9 13,917.8
14,454.4 14,464.3
14,572.9 14,671.4
14,276.0 14,377.0
14,966.4 15,007.7
15,612.0 15,605.9
16,442.8 16,348.4
16,958.0 16,900.6
17,807.9 17,679.3
18,440.5 18,323.3
18,788.5 18,741.8
19,598.5 19,539.1
20,652.6 20,589.9
21,442.2 21,407.4
21,064.3 20,979.0
��������������� ���������������
20,276.4 20,210.0
20,497.4 20,494.9
20,823.8 20,741.5
21,012.9 20,913.1
21,195.6 21,098.6
21,361.6 21,325.4
21,481.9 21,493.4
21,729.8 21,712.2
21,755.9 21,618.6
19,620.2 19,548.8
20,908.5 21,023.6
21,972.6 21,725.1
22,547.9 22,293.1
23,132.7 22,936.8
23,833.2 23,517.8
��������������� ���������������

16,246.6
16,476.2
16,332.6
15,757.9
16,238.4
16,462.7
16,805.6
17,073.1
17,505.4
18,100.1
18,423.5
18,857.5
19,443.0
19,910.1
19,306.6
20,632.5
19,238.6
19,376.1
19,545.4
19,611.8
19,706.9
19,883.1
20,013.2
20,036.9
19,787.6
18,046.1
19,551.0
19,841.7
20,211.1
20,540.9
20,716.4
21,061.4

13,104.7
13,317.3
13,169.7
12,613.3
12,878.7
13,215.8
13,597.9
13,903.7
14,384.4
14,871.9
15,228.6
15,647.9
16,161.0
16,547.3
15,959.0
17,216.6
15,980.7
16,137.4
16,228.3
16,297.9
16,347.6
16,511.6
16,641.2
16,688.7
16,433.7
14,879.0
16,139.0
16,384.1
16,846.3
17,258.3
17,317.3
17,444.4

15,542.5 15,429.2
15,602.5 15,613.2
15,434.4 15,538.7
15,023.6 15,130.0
15,563.2 15,606.1
15,904.1 15,897.8
16,442.8 16,348.4
16,666.2 16,609.8
17,180.2 17,056.1
17,614.3 17,502.3
17,768.6 17,724.5
18,189.4 18,134.3
18,720.5 18,663.6
19,094.7 19,063.7
18,534.8 18,459.7
��������������� ���������������
18,557.7 18,497.0
18,594.4 18,592.2
18,828.5 18,754.1
18,900.8 18,811.0
19,007.2 18,920.2
19,047.0 19,014.8
19,092.1 19,102.4
19,233.6 19,218.0
19,194.2 19,073.1
17,384.7 17,321.5
18,358.8 18,459.8
19,200.3 18,984.0
19,496.4 19,276.0
19,701.9 19,535.1
20,008.5 19,743.7
��������������� ���������������

Billions of chained (2012) dollars
2006 ������������������
2007 ������������������
2008 ������������������
2009 ������������������
2010 ������������������
2011 ������������������
2012 ������������������
2013 ������������������
2014 ������������������
2015 ������������������
2016 ������������������
2017 ������������������
2018 ������������������
2019 ������������������
2020 ������������������
2021 p ����������������
2018: I ��������������
      II �������������
      III ������������
      IV ������������
2019: I ��������������
      II �������������
      III ������������
      IV ������������
2020: I ��������������
      II �������������
      III ������������
      IV ������������
2021: I ��������������
      II �������������
      III ������������
      IV p ���������

–927.6
–847.9
–685.7
–516.3
–589.4
–571.0
–551.6
–519.3
–575.3
–721.7
–757.1
–799.5
–864.2
–905.3
–942.7
–1,282.2
–826.4
–807.2
–896.9
–926.5
–906.7
–935.3
–931.5
–847.6
–841.9
–774.8
–1,021.3
–1,132.8
–1,226.1
–1,244.5
–1,316.6
–1,341.7

1,670.5
1,816.9
1,921.9
1,762.5
1,989.5
2,132.1
2,217.7
2,283.6
2,372.3
2,378.7
2,388.4
2,485.8
2,555.6
2,554.0
2,207.6
2,309.0
2,551.6
2,582.9
2,542.5
2,545.6
2,565.3
2,551.3
2,545.9
2,553.3
2,442.1
1,943.0
2,166.3
2,279.0
2,262.3
2,304.2
2,273.0
2,396.6

2,598.2
2,664.8
2,607.6
2,278.8
2,578.9
2,703.1
2,769.3
2,802.9
2,947.6
3,100.4
3,145.4
3,285.2
3,419.9
3,459.2
3,150.3
3,591.3
3,378.0
3,390.1
3,439.4
3,472.1
3,472.0
3,486.6
3,477.4
3,400.9
3,283.9
2,717.7
3,187.5
3,411.8
3,488.4
3,548.7
3,589.6
3,738.3

3,061.8
3,116.9
3,195.8
3,310.7
3,308.0
3,202.7
3,136.5
3,060.7
3,033.2
3,088.4
3,148.8
3,165.2
3,208.8
3,279.5
3,360.2
3,376.5
3,189.7
3,212.2
3,220.0
3,213.4
3,235.2
3,274.9
3,291.7
3,316.3
3,346.3
3,378.1
3,360.2
3,356.0
3,390.9
3,373.8
3,381.6
3,359.7

1,125.3
1,147.3
1,220.0
1,296.0
1,348.4
1,312.0
1,287.0
1,215.8
1,184.7
1,184.5
1,190.5
1,194.7
1,231.0
1,277.2
1,340.7
1,348.7
1,213.0
1,228.1
1,238.5
1,244.2
1,248.7
1,275.5
1,286.8
1,298.0
1,305.8
1,368.4
1,349.6
1,338.8
1,375.2
1,356.7
1,339.1
1,323.9

719.8
740.3
791.5
836.7
861.3
842.9
814.2
759.6
728.4
713.1
709.1
715.7
739.9
778.5
800.9
793.5
723.2
737.1
743.4
755.8
765.4
773.4
781.9
793.4
791.9
798.2
801.6
812.0
799.9
797.8
794.3
781.9

405.6
407.0
428.6
459.4
487.0
469.1
472.8
456.2
456.1
471.0
480.8
478.5
490.7
498.7
539.0
554.0
489.2
490.5
494.6
488.3
483.5
501.9
504.7
504.7
513.7
568.6
547.0
526.7
573.7
557.7
543.9
540.9

1,939.6
1,972.7
1,977.6
2,015.9
1,959.8
1,890.8
1,849.5
1,844.4
1,847.6
1,902.2
1,956.3
1,968.5
1,976.4
2,001.5
2,019.9
2,028.0
1,974.9
1,982.5
1,980.2
1,968.1
1,985.4
1,998.7
2,004.3
2,017.6
2,039.7
2,011.0
2,011.4
2,017.6
2,017.1
2,017.9
2,042.1
2,035.1

15,240.9
15,586.7
15,678.0
15,400.3
15,596.8
15,847.4
16,182.8
16,444.1
16,842.3
17,248.3
17,630.6
18,030.4
18,528.8
18,944.4
18,395.9
19,382.0
18,363.5
18,558.0
18,577.9
18,615.8
18,704.8
18,881.4
19,027.1
19,164.4
18,940.1
17,471.0
18,508.0
18,664.8
19,076.1
19,449.3
19,453.4
19,549.0

1 Gross domestic product (GDP) less exports of goods and services plus imports of goods and services.
2 Personal consumption expenditures plus gross private fixed investment.
3 For chained dollar measures, gross domestic income is deflated by the implicit price deflator for GDP.

Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 359

Table B–4. Percentage shares of gross domestic product, 1971–2021
[Percent of nominal GDP]
Personal consumption
expenditures

Year or quarter

1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product
(percent)

100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0

Fixed investment
Nonresidential
Total

60.1
60.1
59.6
60.2
61.2
61.3
61.2
60.5
60.3
61.3
60.3
61.9
62.8
61.7
62.5
63.0
63.4
63.6
63.4
63.9
64.0
64.4
64.9
64.8
65.0
65.0
64.5
64.9
65.2
66.0
66.8
67.2
67.6
67.4
67.3
67.2
67.3
68.0
68.3
68.2
68.6
68.0
67.5
67.5
67.4
67.9
68.0
67.8
67.5
67.2
68.5
67.8
67.7
67.8
67.8
67.4
67.5
67.6
67.5
67.2
66.7
67.6
67.4
68.1
69.0
68.8
68.0

See next page for continuation of table.

360 |

Appendix B

Gross private domestic investment

Goods

29.4
29.2
29.2
29.2
29.2
29.2
28.8
28.2
28.1
28.0
27.1
26.9
26.8
26.3
26.2
26.1
25.9
25.5
25.2
25.0
24.3
24.0
23.9
24.0
23.8
23.8
23.4
23.3
23.7
23.9
23.9
23.8
23.8
23.8
23.6
23.4
23.3
22.8
22.0
22.0
22.6
22.4
22.1
22.0
21.5
21.4
21.3
21.2
21.0
22.3
23.8
21.3
21.2
21.2
21.1
20.9
21.0
21.0
20.9
21.1
22.3
23.0
22.7
23.8
24.3
23.7
23.6

Services

30.7
30.8
30.4
31.0
32.0
32.1
32.4
32.3
32.3
33.3
33.2
35.0
36.0
35.4
36.3
36.9
37.5
38.1
38.2
38.9
39.7
40.4
41.0
40.8
41.2
41.2
41.2
41.6
41.5
42.1
43.0
43.5
43.8
43.6
43.6
43.7
44.1
45.3
46.4
46.1
46.0
45.6
45.3
45.5
45.8
46.5
46.6
46.6
46.6
45.0
44.6
46.5
46.4
46.6
46.8
46.5
46.5
46.6
46.6
46.1
44.4
44.6
44.7
44.3
44.6
45.1
44.5

Total

16.9
17.8
18.7
17.8
15.3
17.3
19.1
20.3
20.5
18.6
19.7
17.4
17.5
20.3
19.1
18.5
18.4
17.9
17.7
16.7
15.3
15.5
16.1
17.2
17.2
17.7
18.6
19.2
19.6
19.9
18.3
17.7
17.7
18.7
19.4
19.6
18.5
16.8
13.3
14.4
15.0
16.1
16.8
17.3
17.8
17.1
17.4
17.7
17.9
17.4
17.9
17.6
17.6
17.8
17.9
18.1
18.1
17.9
17.5
17.5
16.3
17.5
18.3
17.8
17.3
17.7
18.7

Total

16.2
17.1
17.6
16.9
15.6
16.3
18.0
19.2
19.9
18.8
18.8
17.8
17.7
18.7
18.6
18.4
17.8
17.5
17.2
16.4
15.3
15.3
15.8
16.4
16.8
17.4
17.8
18.5
19.0
19.4
18.6
17.5
17.6
18.1
19.0
19.1
18.2
17.0
14.4
14.0
14.7
15.7
16.2
16.9
17.0
16.9
17.2
17.4
17.6
17.7
18.0
17.4
17.5
17.4
17.4
17.5
17.6
17.6
17.4
17.6
17.7
17.5
18.0
18.3
18.0
17.9
17.8

Total
11.2
11.5
12.1
12.4
11.7
11.7
12.4
13.4
14.2
14.2
14.7
14.5
13.3
14.0
14.0
13.3
12.7
12.6
12.7
12.4
11.8
11.4
11.7
11.9
12.6
12.9
13.4
13.8
14.2
14.6
13.8
12.4
12.0
12.0
12.4
13.0
13.5
13.5
11.7
11.5
12.2
13.0
13.1
13.7
13.5
13.2
13.3
13.5
13.7
13.4
13.3
13.5
13.5
13.5
13.6
13.7
13.8
13.8
13.6
13.5
13.7
13.1
13.3
13.4
13.3
13.2
13.1

Structures
3.7
3.7
3.9
4.0
3.6
3.5
3.6
4.0
4.5
4.8
5.2
5.3
4.2
4.4
4.5
3.9
3.6
3.5
3.4
3.4
3.0
2.6
2.6
2.6
2.7
2.8
2.9
3.0
3.0
3.1
3.2
2.6
2.5
2.5
2.7
3.1
3.5
3.9
3.1
2.5
2.6
2.9
2.9
3.3
3.2
3.0
3.1
3.1
3.1
2.9
2.5
3.1
3.1
3.1
3.0
3.0
3.1
3.2
3.2
3.2
3.0
2.7
2.6
2.6
2.5
2.5
2.5

Equipment
5.9
6.2
6.7
6.8
6.4
6.5
7.1
7.7
7.9
7.6
7.5
7.0
6.8
7.2
7.1
6.9
6.6
6.6
6.6
6.2
5.9
5.9
6.2
6.5
6.9
7.0
7.1
7.3
7.4
7.5
6.7
6.0
5.9
5.9
6.1
6.2
6.2
5.7
4.6
5.2
5.6
6.1
6.1
6.2
6.1
5.8
5.7
5.8
5.8
5.4
5.5
5.8
5.7
5.8
5.9
5.9
5.9
5.7
5.6
5.3
5.2
5.4
5.6
5.6
5.6
5.5
5.4

Intellectual
property
products
1.6
1.6
1.6
1.7
1.7
1.7
1.7
1.7
1.8
1.9
2.0
2.2
2.2
2.4
2.4
2.5
2.5
2.5
2.7
2.8
2.9
2.9
2.9
2.8
3.0
3.1
3.4
3.5
3.8
4.0
3.9
3.7
3.7
3.6
3.6
3.7
3.8
3.9
3.9
3.8
4.0
4.0
4.1
4.2
4.2
4.4
4.5
4.7
4.8
5.2
5.2
4.6
4.6
4.7
4.7
4.8
4.8
4.9
4.9
5.0
5.4
5.1
5.2
5.2
5.2
5.2
5.2

Residential

5.0
5.7
5.5
4.5
4.0
4.6
5.5
5.9
5.6
4.5
4.0
3.3
4.4
4.7
4.6
5.1
5.1
4.9
4.5
4.0
3.6
3.9
4.2
4.4
4.2
4.4
4.4
4.6
4.8
4.7
4.8
5.1
5.6
6.1
6.6
6.1
4.8
3.5
2.7
2.5
2.4
2.7
3.0
3.2
3.5
3.7
3.9
3.9
3.8
4.3
4.7
3.9
3.9
3.9
3.8
3.8
3.8
3.8
3.8
4.1
4.1
4.3
4.7
4.8
4.7
4.7
4.7

Change
in
private
inventories
0.7
.7
1.1
.9
–.4
.9
1.1
1.1
.7
–.2
.9
–.4
–.2
1.6
.5
.1
.6
.4
.5
.2
.0
.3
.3
.9
.4
.4
.8
.7
.6
.5
–.4
.2
.1
.5
.4
.5
.2
–.2
–1.0
.4
.3
.4
.6
.5
.8
.2
.2
.3
.3
–.3
–.1
.2
.1
.4
.4
.6
.4
.3
.1
–.1
–1.5
.1
.3
–.4
–.8
–.3
.9

Table B–4. Percentage shares of gross domestic product, 1971–2021—Continued
[Percent of nominal GDP]
Government consumption expenditures
and gross investment

Net exports of goods and services
Year or quarter

1971 �����������������������
1972 �����������������������
1973 �����������������������
1974 �����������������������
1975 �����������������������
1976 �����������������������
1977 �����������������������
1978 �����������������������
1979 �����������������������
1980 �����������������������
1981 �����������������������
1982 �����������������������
1983 �����������������������
1984 �����������������������
1985 �����������������������
1986 �����������������������
1987 �����������������������
1988 �����������������������
1989 �����������������������
1990 �����������������������
1991 �����������������������
1992 �����������������������
1993 �����������������������
1994 �����������������������
1995 �����������������������
1996 �����������������������
1997 �����������������������
1998 �����������������������
1999 �����������������������
2000 �����������������������
2001 �����������������������
2002 �����������������������
2003 �����������������������
2004 �����������������������
2005 �����������������������
2006 �����������������������
2007 �����������������������
2008 �����������������������
2009 �����������������������
2010 �����������������������
2011 �����������������������
2012 �����������������������
2013 �����������������������
2014 �����������������������
2015 �����������������������
2016 �����������������������
2017 �����������������������
2018 �����������������������
2019 �����������������������
2020 �����������������������
2021 p ���������������������
2018: I �������������������
      II ������������������
      III �����������������
      IV �����������������
2019: I �������������������
      II ������������������
      III �����������������
      IV �����������������
2020: I �������������������
      II ������������������
      III �����������������
      IV �����������������
2021: I �������������������
      II ������������������
      III �����������������
      IV p ��������������

Net
exports
0.1
–.3
.3
–.1
.9
–.1
–1.1
–1.1
–.9
–.5
–.4
–.6
–1.4
–2.5
–2.6
–2.9
–3.0
–2.1
–1.5
–1.3
–.5
–.5
–1.0
–1.3
–1.2
–1.2
–1.2
–1.8
–2.7
–3.7
–3.6
–4.0
–4.6
–5.2
–5.7
–5.7
–5.1
–5.0
–2.9
–3.5
–3.7
–3.4
–2.8
–2.9
–2.9
–2.7
–2.8
–2.9
–2.8
–3.1
–4.0
–2.9
–2.6
–3.0
–3.1
–2.9
–3.0
–2.9
–2.5
–2.5
–2.8
–3.4
–3.7
–4.0
–3.9
–4.1
–4.0

Exports
Total
5.4
5.5
6.7
8.2
8.2
8.0
7.7
7.9
8.8
9.8
9.5
8.5
7.6
7.5
7.0
7.0
7.5
8.5
8.9
9.3
9.7
9.7
9.5
9.9
10.6
10.7
11.1
10.5
10.3
10.7
9.7
9.1
9.0
9.6
10.0
10.6
11.5
12.4
10.9
12.3
13.6
13.6
13.6
13.5
12.5
11.9
12.2
12.3
11.8
10.2
10.8
12.4
12.5
12.3
12.1
12.0
11.9
11.7
11.6
11.1
9.3
9.8
10.3
10.5
10.8
10.7
11.1

Goods
4.0
4.1
5.3
6.7
6.7
6.5
6.2
6.4
7.1
8.1
7.6
6.7
5.9
5.7
5.2
5.1
5.5
6.3
6.6
6.8
7.0
7.0
6.8
7.1
7.8
7.8
8.2
7.6
7.4
7.8
7.0
6.5
6.4
6.8
7.1
7.6
8.0
8.7
7.3
8.5
9.4
9.4
9.3
9.2
8.2
7.7
7.9
8.1
7.7
6.8
7.6
8.1
8.3
8.1
8.0
7.9
7.7
7.6
7.5
7.4
5.9
6.7
7.1
7.3
7.6
7.5
7.8

Imports
Services
1.4
1.4
1.4
1.5
1.6
1.5
1.5
1.6
1.6
1.8
1.9
1.8
1.7
1.8
1.7
2.0
2.0
2.1
2.3
2.5
2.7
2.7
2.7
2.8
2.9
3.0
3.0
2.9
2.9
2.9
2.7
2.7
2.6
2.9
2.9
3.1
3.5
3.7
3.6
3.9
4.2
4.2
4.3
4.3
4.2
4.2
4.3
4.2
4.1
3.4
3.2
4.3
4.2
4.2
4.1
4.1
4.2
4.1
4.1
3.7
3.4
3.2
3.3
3.2
3.2
3.2
3.3

Total
5.4
5.8
6.4
8.2
7.3
8.1
8.8
9.0
9.6
10.3
9.9
9.1
9.0
10.0
9.6
9.9
10.5
10.6
10.5
10.6
10.1
10.2
10.5
11.2
11.8
11.9
12.3
12.3
13.0
14.4
13.3
13.2
13.6
14.8
15.7
16.3
16.5
17.4
13.8
15.9
17.3
17.0
16.4
16.5
15.4
14.6
15.0
15.2
14.6
13.3
14.8
15.3
15.2
15.3
15.2
14.9
14.9
14.5
14.0
13.6
12.0
13.3
14.1
14.4
14.7
14.8
15.1

Goods
4.0
4.5
5.0
6.8
5.9
6.7
7.3
7.5
8.1
8.7
8.4
7.5
7.5
8.3
7.9
8.1
8.5
8.6
8.6
8.5
8.1
8.4
8.6
9.3
9.9
10.0
10.3
10.3
10.9
12.2
11.1
11.0
11.3
12.4
13.2
13.8
13.8
14.5
11.0
12.9
14.3
14.1
13.6
13.6
12.6
11.9
12.2
12.5
11.8
11.1
12.4
12.6
12.4
12.5
12.4
12.1
12.0
11.7
11.3
11.1
10.0
11.2
11.8
12.2
12.4
12.3
12.6

Federal
Services
1.4
1.4
1.4
1.5
1.4
1.4
1.4
1.5
1.5
1.6
1.6
1.6
1.5
1.7
1.7
1.8
1.9
1.9
1.9
2.0
2.0
1.9
1.9
1.9
1.9
1.9
2.0
2.0
2.1
2.2
2.1
2.2
2.3
2.4
2.4
2.6
2.7
2.9
2.9
2.9
3.0
2.9
2.8
2.8
2.8
2.8
2.8
2.8
2.8
2.2
2.4
2.8
2.8
2.8
2.8
2.8
2.8
2.8
2.8
2.5
2.1
2.1
2.2
2.2
2.3
2.5
2.5

Total
23.0
22.4
21.4
22.1
22.6
21.6
20.9
20.3
20.0
20.6
20.4
21.3
21.1
20.5
21.0
21.3
21.2
20.6
20.4
20.8
21.1
20.6
19.9
19.2
19.0
18.5
18.0
17.8
17.9
17.8
18.4
19.1
19.3
19.1
19.0
19.0
19.3
20.2
21.2
21.0
20.2
19.3
18.6
18.1
17.8
17.7
17.4
17.4
17.4
18.5
17.6
17.4
17.4
17.4
17.4
17.4
17.4
17.4
17.4
17.8
19.8
18.3
18.1
18.0
17.7
17.6
17.2

Total
11.5
11.1
10.3
10.3
10.3
9.9
9.6
9.3
9.2
9.6
9.8
10.4
10.5
10.2
10.4
10.5
10.4
9.8
9.5
9.4
9.5
9.0
8.5
7.9
7.5
7.2
6.8
6.5
6.3
6.2
6.3
6.8
7.2
7.3
7.3
7.2
7.3
7.8
8.4
8.6
8.3
7.9
7.3
6.9
6.7
6.6
6.5
6.5
6.6
7.2
6.8
6.5
6.5
6.5
6.6
6.6
6.6
6.6
6.6
6.8
7.8
7.2
7.0
7.1
6.9
6.7
6.5

National
defense

Nondefense

8.4
7.9
7.2
7.1
7.0
6.7
6.5
6.2
6.1
6.4
6.7
7.3
7.5
7.4
7.6
7.7
7.7
7.3
6.9
6.8
6.7
6.2
5.7
5.2
4.9
4.7
4.3
4.1
4.0
3.8
3.9
4.2
4.5
4.7
4.7
4.6
4.7
5.1
5.4
5.5
5.3
5.0
4.5
4.2
4.0
3.9
3.8
3.9
4.0
4.2
3.9
3.8
3.8
3.9
3.9
3.9
3.9
4.0
4.0
4.0
4.5
4.2
4.2
4.1
4.0
3.9
3.8

3.1
3.2
3.1
3.2
3.3
3.2
3.2
3.1
3.0
3.2
3.1
3.1
3.0
2.8
2.8
2.8
2.7
2.5
2.5
2.6
2.7
2.8
2.7
2.6
2.6
2.5
2.5
2.4
2.4
2.3
2.4
2.6
2.7
2.6
2.6
2.6
2.6
2.7
3.0
3.1
3.0
2.9
2.7
2.7
2.7
2.7
2.7
2.7
2.7
3.0
2.9
2.7
2.7
2.7
2.6
2.7
2.7
2.7
2.6
2.7
3.4
3.0
2.8
3.0
2.9
2.8
2.7

State
and
local
11.4
11.3
11.1
11.8
12.3
11.7
11.2
10.9
10.8
11.0
10.6
10.9
10.6
10.3
10.5
10.8
10.9
10.8
11.0
11.3
11.6
11.6
11.4
11.4
11.4
11.3
11.2
11.3
11.5
11.6
12.1
12.3
12.1
11.8
11.7
11.7
12.0
12.4
12.8
12.3
11.8
11.4
11.3
11.1
11.0
11.1
11.0
10.9
10.8
11.3
10.8
10.9
10.9
10.9
10.8
10.8
10.8
10.7
10.7
11.1
12.0
11.1
11.0
10.9
10.8
10.9
10.7

Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 361

Table B–5. Chain-type price indexes for gross domestic product, 1971–2021
[Index numbers, 2012=100, except as noted; quarterly data seasonally adjusted]
Personal consumption expenditures

Gross private domestic investment
Fixed investment

Year or quarter

1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

22.761
23.745
25.045
27.292
29.827
31.469
33.424
35.775
38.741
42.251
46.240
49.099
51.018
52.860
54.533
55.638
57.004
59.018
61.331
63.636
65.777
67.278
68.874
70.342
71.819
73.132
74.399
75.219
76.272
78.016
79.814
81.013
82.635
84.842
87.490
90.212
92.653
94.397
95.019
96.164
98.157
100.000
101.769
103.662
104.662
105.703
107.742
110.326
112.279
113.740
118.490
109.312
110.156
110.647
111.191
111.502
112.142
112.524
112.947
113.397
112.969
113.984
114.611
115.826
117.546
119.259
121.329

Nonresidential
Total

21.798
22.542
23.756
26.229
28.415
29.974
31.923
34.145
37.178
41.182
44.871
47.363
49.378
51.243
53.031
54.184
55.855
58.038
60.572
63.231
65.345
67.087
68.758
70.193
71.671
73.204
74.478
75.070
76.164
78.090
79.656
80.702
82.398
84.443
86.876
89.322
91.614
94.325
94.062
95.747
98.170
100.000
101.354
102.887
103.116
104.148
106.051
108.318
109.922
111.225
115.529
107.557
108.184
108.546
108.986
109.100
109.835
110.141
110.612
110.958
110.505
111.507
111.928
112.989
114.772
116.277
118.078

See next page for continuation of table.

362 |

Appendix B

Goods

33.079
33.926
35.949
40.436
43.703
45.413
47.837
50.773
55.574
61.797
66.389
68.198
69.429
70.742
71.877
71.541
73.842
75.788
78.704
81.927
83.930
84.943
85.681
86.552
87.361
88.321
88.219
86.893
87.349
89.082
89.015
88.166
88.054
89.292
91.084
92.306
93.331
96.122
93.812
95.183
98.773
100.000
99.407
98.920
95.896
94.332
94.615
95.281
94.832
94.160
98.892
95.270
95.516
95.247
95.092
94.647
95.120
94.697
94.863
94.597
93.243
94.361
94.437
95.790
97.948
99.690
102.141

Services

16.733
17.441
18.284
19.833
21.533
23.027
24.770
26.674
28.911
31.918
35.187
37.949
40.280
42.376
44.450
46.276
47.660
49.939
52.293
54.690
56.829
58.850
60.885
62.540
64.288
66.051
67.914
69.351
70.731
72.740
75.063
77.004
79.574
82.018
84.774
87.844
90.786
93.458
94.182
96.017
97.875
100.000
102.322
104.880
106.796
109.197
111.965
115.100
117.836
120.302
124.213
113.930
114.763
115.471
116.236
116.656
117.536
118.253
118.900
119.604
119.713
120.624
121.267
122.109
123.593
124.904
126.244

Total

31.092
32.388
34.153
37.559
42.059
44.384
47.655
51.517
56.141
61.395
67.123
70.679
70.896
71.661
72.548
74.178
75.723
77.627
79.606
81.270
82.648
82.647
83.627
84.875
86.240
86.191
86.241
85.608
85.690
86.815
87.555
87.841
88.561
91.148
94.839
98.176
99.656
100.474
99.331
97.687
98.704
100.000
100.979
102.922
103.535
103.516
105.230
107.186
108.906
110.212
113.822
106.370
107.029
107.506
107.840
108.443
108.918
109.128
109.134
109.632
109.726
110.490
111.000
111.777
112.574
114.256
116.680

Total

30.134
31.420
33.169
36.449
40.874
43.232
46.550
50.444
54.977
60.105
65.624
69.311
69.575
70.253
71.277
73.021
74.506
76.586
78.561
80.278
81.683
81.728
82.711
83.983
85.378
85.450
85.599
85.133
85.277
86.486
87.241
87.500
88.265
90.843
94.597
97.958
99.456
100.296
99.076
97.568
98.641
100.000
101.091
103.172
104.075
104.202
105.928
107.926
109.684
111.052
115.386
107.086
107.765
108.254
108.597
109.212
109.680
109.915
109.930
110.340
110.701
111.316
111.850
112.864
114.105
116.042
118.532

Total
37.997
39.297
40.882
44.857
50.766
53.562
57.111
60.930
65.830
71.641
78.453
82.911
82.774
83.036
83.893
85.365
86.339
88.514
90.572
92.516
94.267
93.960
94.161
94.904
95.849
95.267
94.735
93.248
92.314
92.718
92.346
91.863
91.156
92.055
94.443
96.745
98.310
99.832
99.184
97.416
98.559
100.000
100.251
101.469
101.909
101.119
101.977
102.815
104.137
104.813
106.433
102.341
102.676
103.001
103.243
103.823
104.251
104.313
104.160
104.487
104.864
104.895
105.005
105.203
105.429
106.549
108.550

Structures Equipment
12.757
13.674
14.734
16.770
18.773
19.692
21.401
23.468
26.194
28.629
32.566
35.136
34.241
34.540
35.361
36.039
36.618
38.171
39.666
40.948
41.689
41.699
42.922
44.437
46.362
47.540
49.355
51.612
53.198
55.283
58.178
60.603
62.769
67.416
75.733
84.749
89.748
94.335
92.613
92.006
95.362
100.000
101.455
107.198
109.403
109.670
112.545
114.391
119.058
120.852
127.655
113.167
113.793
114.422
116.182
117.497
118.881
119.610
120.243
120.805
120.615
120.919
121.071
122.237
124.882
128.200
135.302

63.848
64.686
65.780
70.713
81.484
86.486
91.800
96.900
103.167
112.249
120.463
125.415
125.776
124.748
124.748
127.254
128.083
129.854
132.337
135.042
137.330
137.121
135.518
135.277
133.796
130.762
127.156
121.451
116.763
114.224
110.858
108.531
105.725
104.841
104.598
103.560
103.191
102.542
103.169
99.471
99.447
100.000
99.787
99.169
98.671
97.592
97.542
97.684
97.816
97.388
97.683
97.480
97.539
97.905
97.811
98.035
97.987
97.689
97.552
97.727
97.737
97.309
96.780
97.318
96.536
97.626
99.253

Intellectual
property
products
39.318
40.490
42.494
46.461
50.190
52.408
54.709
57.557
61.382
66.123
71.058
75.093
77.898
80.081
81.413
82.047
83.518
86.129
87.240
88.147
90.271
89.373
89.998
90.468
93.134
93.544
94.052
93.595
95.105
97.814
97.684
96.376
95.647
95.335
95.952
97.088
98.284
99.834
98.589
98.306
99.517
100.000
100.081
100.791
101.374
100.302
101.125
102.266
103.172
104.574
105.591
101.894
102.410
102.485
102.273
102.881
103.373
103.515
102.920
103.331
104.425
104.853
105.688
105.069
105.464
105.712
106.119

Residential

16.943
17.975
19.571
21.593
23.590
25.117
27.683
31.082
34.593
38.325
41.425
43.646
44.680
46.003
47.267
49.351
51.486
53.278
55.020
56.288
57.021
57.723
60.074
62.247
64.473
65.856
67.444
69.223
71.816
75.004
78.564
80.510
84.325
90.243
96.706
102.355
103.708
102.249
98.671
98.317
99.049
100.000
105.054
111.118
114.114
118.127
123.454
130.417
134.145
138.541
153.768
127.975
130.152
131.373
132.167
132.937
133.586
134.626
135.430
136.224
136.528
139.594
141.817
146.010
151.291
156.609
161.163

Table B–5. Chain-type price indexes for gross domestic product, 1971–2021—Continued
[Index numbers, 2012=100, except as noted; quarterly data seasonally adjusted]
Exports and imports
of goods and
services

Government consumption
expenditures and
gross investment
Federal

Year or quarter
Exports

1971 �����������������
1972 �����������������
1973 �����������������
1974 �����������������
1975 �����������������
1976 �����������������
1977 �����������������
1978 �����������������
1979 �����������������
1980 �����������������
1981 �����������������
1982 �����������������
1983 �����������������
1984 �����������������
1985 �����������������
1986 �����������������
1987 �����������������
1988 �����������������
1989 �����������������
1990 �����������������
1991 �����������������
1992 �����������������
1993 �����������������
1994 �����������������
1995 �����������������
1996 �����������������
1997 �����������������
1998 �����������������
1999 �����������������
2000 �����������������
2001 �����������������
2002 �����������������
2003 �����������������
2004 �����������������
2005 �����������������
2006 �����������������
2007 �����������������
2008 �����������������
2009 �����������������
2010 �����������������
2011 �����������������
2012 �����������������
2013 �����������������
2014 �����������������
2015 �����������������
2016 �����������������
2017 �����������������
2018 �����������������
2019 �����������������
2020 �����������������
2021 p ���������������
2018: I �������������
      II ������������
      III �����������
      IV �����������
2019: I �������������
      II ������������
      III �����������
      IV �����������
2020: I �������������
      II ������������
      III �����������
      IV �����������
2021: I �������������
      II ������������
      III �����������
      IV p ��������

Imports

Total

Total

National Nondefense defense

State
and
local

30.837 21.114 17.353 20.673 19.817 22.531 15.199
32.187 22.593 18.664 22.488 21.883 23.589 16.163
36.430 26.520 19.938 24.054 23.484 25.028 17.246
44.865 37.942 21.854 25.975 25.404 26.916 19.158
49.453 41.100 23.872 28.258 27.545 29.497 21.000
51.076 42.338 25.183 30.016 29.345 31.137 22.025
53.158 46.068 26.742 31.863 31.268 32.796 23.395
56.391 49.315 28.510 34.012 33.561 34.627 24.915
63.184 57.753 30.856 36.571 36.216 36.968 27.115
69.594 71.945 34.048 40.104 39.919 40.124 30.082
74.748 75.834 37.428 43.849 43.747 43.662 33.228
75.104 73.281 39.973 46.950 47.039 46.309 35.403
75.410 70.535 41.520 48.506 48.778 47.418 36.966
76.116 69.925 43.322 50.644 51.013 49.300 38.546
73.850 67.628 44.663 51.719 51.872 50.929 40.115
72.618 67.627 45.413 51.964 51.894 51.770 41.271
74.222 71.715 46.640 52.325 52.267 52.099 43.198
78.022 75.146 48.181 54.033 53.904 53.997 44.642
79.315 76.789 50.021 55.542 55.365 55.629 46.754
79.762 78.991 52.118 57.258 57.162 57.118 49.156
80.651 78.332 54.010 59.317 58.964 59.813 50.955
80.259 78.396 55.647 60.832 60.678 60.851 52.692
80.391 77.795 56.958 62.159 61.615 63.021 54.004
81.325 78.526 58.468 63.870 63.229 64.926 55.397
83.143 80.677 60.128 65.847 65.027 67.252 56.874
82.039 79.271 61.361 66.946 66.114 68.373 58.180
80.593 76.516 62.566 67.981 67.035 69.621 59.474
78.685 72.396 63.630 68.850 67.871 70.548 60.633
78.091 72.827 65.753 70.532 69.559 72.218 62.963
79.592 76.013 68.577 72.898 71.908 74.616 65.989
78.968 74.046 70.558 74.249 73.270 75.947 68.258
78.287 73.164 72.386 76.648 75.714 78.272 69.792
79.531 75.377 75.044 80.025 79.505 80.946 72.063
82.435 78.971 78.169 82.777 82.263 83.689 75.382
85.289 83.618 82.132 86.222 86.011 86.586 79.631
88.006 86.854 85.695 88.969 89.022 88.858 83.659
91.328 89.887 89.530 91.609 91.750 91.340 88.181
95.493 98.795 93.334 94.397 94.801 93.647 92.590
89.803 87.854 92.921 94.193 94.126 94.308 92.045
93.350 92.655 95.391 96.425 96.128 96.951 94.674
99.237 99.716 98.289 99.069 98.946 99.284 97.747
100.000 100.000 100.000 100.000 100.000 100.000 100.000
100.148 98.697 102.363 100.933 100.609 101.482 103.332
100.216 97.961 104.470 102.643 102.056 103.621 105.698
95.373 90.144 104.638 103.143 102.334 104.466 105.656
93.458 87.058 104.899 103.695 102.650 105.370 105.739
95.897 88.996 107.389 105.702 104.306 107.902 108.524
99.135 91.515 111.319 108.776 107.150 111.325 112.984
98.660 90.078 113.246 110.781 108.865 113.775 114.863
96.188 88.075 114.861 112.018 110.039 115.108 116.725
107.549 94.611 120.041 116.058 114.106 119.123 122.671
98.161 91.314 109.902 107.655 106.122 110.063 111.383
99.440 91.684 110.965 108.447 106.927 110.835 112.615
99.674 91.823 111.832 109.073 107.538 111.484 113.632
99.264 91.239 112.576 109.928 108.011 112.920 114.307
98.390 90.169 112.842 111.078 108.361 115.320 114.014
99.277 90.789 113.070 110.303 108.669 112.858 114.878
98.676 89.904 113.358 110.673 109.024 113.253 115.114
98.295 89.448 113.713 111.068 109.408 113.669 115.447
97.783 89.194 114.503 111.400 109.650 114.143 116.536
93.181 86.424 114.252 111.444 109.303 114.766 116.093
96.164 88.098 114.921 112.269 110.264 115.403 116.659
97.622 88.585 115.768 112.959 110.939 116.118 117.611
102.383 91.385 117.292 114.065 112.152 117.070 119.416
107.030 94.312 119.031 115.228 113.335 118.207 121.544
109.539 95.720 120.796 116.643 114.686 119.720 123.541
111.244 97.027 123.045 118.294 116.254 121.494 126.182

Personal
consumption
Final
expenGross
sales of ditures domestic
Gross
domestic excludpurproduct
ing
chases 1 domestic
product
food
and
energy
22.627
23.609
24.907
27.136
29.661
31.305
33.262
35.614
38.566
42.056
46.016
48.889
50.803
52.637
54.335
55.456
56.814
58.851
61.165
63.477
65.621
67.125
68.722
70.194
71.676
73.009
74.297
75.152
76.221
77.983
79.785
80.978
82.609
84.814
87.470
90.195
92.645
94.392
94.990
96.142
98.146
100.000
101.791
103.707
104.760
105.832
107.874
110.468
112.429
113.902
118.783
109.448
110.297
110.791
111.337
111.649
112.289
112.676
113.101
113.535
113.154
114.143
114.775
116.034
117.833
119.593
121.673

23.112
23.856
24.764
26.726
28.958
30.718
32.694
34.861
37.403
40.840
44.419
47.306
49.727
51.789
53.893
55.752
57.548
59.994
62.484
65.016
67.338
69.384
71.269
72.864
74.451
75.863
77.201
78.183
79.210
80.625
82.153
83.526
84.874
86.544
88.440
90.558
92.578
94.393
95.270
96.651
98.184
100.000
101.535
103.187
104.487
106.138
107.935
110.096
111.959
113.553
117.320
109.292
109.943
110.320
110.829
111.136
111.783
112.269
112.647
113.135
112.919
113.904
114.255
115.010
116.731
118.045
119.493

22.158
23.147
24.469
26.954
29.417
31.033
33.079
35.431
38.539
42.551
46.476
49.154
50.864
52.585
54.149
55.278
56.839
58.850
61.166
63.586
65.583
67.108
68.623
70.062
71.575
72.820
73.893
74.386
75.518
77.480
78.987
80.070
81.807
84.151
87.083
89.885
92.327
94.947
94.534
95.951
98.272
100.000
101.478
103.181
103.464
104.187
106.157
108.648
110.326
111.682
116.014
107.769
108.473
108.939
109.411
109.635
110.242
110.527
110.898
111.346
111.024
111.924
112.434
113.523
115.130
116.708
118.694

5.1
4.3
5.5
9.0
9.3
5.5
6.2
7.0
8.3
9.1
9.4
6.2
3.9
3.6
3.2
2.0
2.5
3.5
3.9
3.8
3.4
2.3
2.4
2.1
2.1
1.8
1.7
1.1
1.4
2.3
2.3
1.5
2.0
2.7
3.1
3.1
2.7
1.9
.7
1.2
2.1
1.9
1.8
1.9
1.0
1.0
1.9
2.4
1.8
1.3
4.2
2.4
3.1
1.8
2.0
1.1
2.3
1.4
1.5
1.6
–1.5
3.6
2.2
4.3
6.1
6.0
7.1

Percent change 2
Personal
consumption
expenditures

Total

4.2
3.4
5.4
10.4
8.3
5.5
6.5
7.0
8.9
10.8
9.0
5.6
4.3
3.8
3.5
2.2
3.1
3.9
4.4
4.4
3.3
2.7
2.5
2.1
2.1
2.1
1.7
.8
1.5
2.5
2.0
1.3
2.1
2.5
2.9
2.8
2.6
3.0
–.3
1.8
2.5
1.9
1.4
1.5
.2
1.0
1.8
2.1
1.5
1.2
3.9
2.7
2.4
1.3
1.6
.4
2.7
1.1
1.7
1.3
–1.6
3.7
1.5
3.8
6.5
5.3
6.3

Gross
domestic
Excludpuring chases 1
food
and
energy
4.7
3.2
3.8
7.9
8.4
6.1
6.4
6.6
7.3
9.2
8.8
6.5
5.1
4.1
4.1
3.4
3.2
4.3
4.2
4.1
3.6
3.0
2.7
2.2
2.2
1.9
1.8
1.3
1.3
1.8
1.9
1.7
1.6
2.0
2.2
2.4
2.2
2.0
.9
1.4
1.6
1.8
1.5
1.6
1.3
1.6
1.7
2.0
1.7
1.4
3.3
2.4
2.4
1.4
1.9
1.1
2.3
1.8
1.4
1.7
–.8
3.5
1.2
2.7
6.1
4.6
5.0

5.2
4.5
5.7
10.2
9.1
5.5
6.6
7.1
8.8
10.4
9.2
5.8
3.5
3.4
3.0
2.1
2.8
3.5
3.9
4.0
3.1
2.3
2.3
2.1
2.2
1.7
1.5
.7
1.5
2.6
1.9
1.4
2.2
2.9
3.5
3.2
2.7
2.8
–.4
1.5
2.4
1.8
1.5
1.7
.3
.7
1.9
2.3
1.5
1.2
3.9
2.9
2.6
1.7
1.7
.8
2.2
1.0
1.3
1.6
–1.2
3.3
1.8
3.9
5.8
5.6
7.0

1 Gross domestic product (GDP) less exports of goods and services plus imports of goods and services.
2 Quarterly percent changes are at annual rates.

Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 363

Table B–6. Gross value added by sector, 1971–2021
[Billions of dollars; quarterly data at seasonally adjusted annual rates]
Business 1
Year or quarter

1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

1,164.9
1,279.1
1,425.4
1,545.2
1,684.9
1,873.4
2,081.8
2,351.6
2,627.3
2,857.3
3,207.0
3,343.8
3,634.0
4,037.6
4,339.0
4,579.6
4,855.2
5,236.4
5,641.6
5,963.1
6,158.1
6,520.3
6,858.6
7,287.2
7,639.7
8,073.1
8,577.6
9,062.8
9,631.2
10,251.0
10,581.9
10,929.1
11,456.5
12,217.2
13,039.2
13,815.6
14,474.2
14,769.9
14,478.1
15,049.0
15,599.7
16,254.0
16,843.2
17,550.7
18,206.0
18,695.1
19,479.6
20,527.2
21,372.6
20,893.7
22,997.5
20,143.7
20,492.5
20,659.1
20,813.3
21,001.6
21,289.3
21,505.0
21,694.5
21,481.4
19,477.4
21,138.6
21,477.6
22,038.2
22,741.0
23,202.3
24,008.5

Total

882.5
972.5
1,094.0
1,182.8
1,284.8
1,443.3
1,616.2
1,838.2
2,062.8
2,225.8
2,502.0
2,568.6
2,801.9
3,136.7
3,369.6
3,539.3
3,735.2
4,019.3
4,326.7
4,542.0
4,645.0
4,920.2
5,177.4
5,523.7
5,795.1
6,159.5
6,578.8
6,959.2
7,401.8
7,875.9
8,057.7
8,256.0
8,642.9
9,249.3
9,911.0
10,524.7
10,997.8
11,061.8
10,659.6
11,137.8
11,614.9
12,206.4
12,689.6
13,279.8
13,804.8
14,168.5
14,803.1
15,643.7
16,298.1
15,666.4
17,552.0
15,341.2
15,633.8
15,747.4
15,852.3
16,001.0
16,247.2
16,406.9
16,537.3
16,249.0
14,318.1
15,899.9
16,198.6
16,726.8
17,361.1
17,702.2
18,417.9

Nonfarm 1

857.2
942.9
1,047.2
1,138.5
1,239.2
1,400.2
1,572.7
1,787.5
2,002.7
2,174.4
2,437.0
2,508.2
2,757.0
3,072.6
3,305.9
3,479.4
3,673.2
3,957.9
4,252.8
4,464.2
4,574.7
4,840.4
5,106.2
5,440.1
5,726.7
6,066.9
6,490.6
6,880.2
7,330.9
7,799.9
7,979.6
8,181.7
8,551.1
9,129.1
9,805.4
10,427.2
10,880.7
10,943.6
10,557.4
11,021.6
11,464.5
12,058.5
12,506.4
13,113.8
13,659.6
14,038.6
14,663.8
15,506.6
16,175.1
15,531.7
17,348.9
15,201.2
15,489.6
15,615.8
15,719.7
15,880.1
16,127.8
16,283.2
16,409.3
16,106.4
14,214.2
15,766.9
16,039.4
16,554.2
17,148.0
17,484.5
18,208.9

Households and institutions

Farm

25.4
29.7
46.8
44.2
45.6
43.0
43.5
50.7
60.1
51.4
65.0
60.4
44.9
64.2
63.7
59.9
62.0
61.4
73.9
77.8
70.4
79.9
71.3
83.6
68.4
92.6
88.1
79.0
70.9
76.0
78.1
74.3
91.8
120.2
105.6
97.5
117.1
118.2
102.2
116.2
150.4
148.0
183.3
166.0
145.2
129.9
139.4
137.1
123.0
134.7
203.1
140.0
144.1
131.6
132.6
120.9
119.4
123.7
128.0
142.6
103.9
133.0
159.2
172.6
213.1
217.6
209.0

Total

104.5
114.0
124.6
137.2
151.6
164.9
179.9
202.1
226.3
258.2
291.6
323.8
352.5
383.8
411.8
447.0
489.5
539.8
586.0
636.3
677.3
720.3
772.8
824.7
877.8
923.2
975.9
1,040.6
1,111.2
1,190.7
1,271.7
1,344.7
1,408.8
1,489.2
1,572.8
1,658.9
1,749.5
1,886.9
1,934.9
1,965.0
2,012.0
2,058.4
2,114.2
2,182.9
2,260.2
2,344.1
2,436.0
2,551.4
2,669.0
2,755.5
2,884.9
2,505.8
2,536.9
2,564.4
2,598.5
2,627.4
2,653.2
2,680.8
2,714.5
2,753.7
2,716.4
2,756.9
2,794.8
2,812.1
2,845.7
2,909.6
2,972.1

Households

67.2
72.7
78.5
85.5
93.7
101.7
110.7
124.8
139.5
158.8
179.2
198.2
213.6
230.9
248.2
268.4
289.8
316.4
341.4
367.6
386.6
407.1
437.6
472.7
506.9
534.6
565.7
601.6
644.0
692.3
748.9
781.6
814.1
862.6
922.3
976.2
1,035.9
1,125.2
1,136.8
1,150.7
1,164.0
1,168.8
1,196.5
1,228.3
1,258.8
1,299.3
1,352.6
1,416.5
1,479.6
1,528.0
1,579.3
1,389.8
1,407.7
1,425.3
1,443.4
1,459.1
1,472.8
1,485.8
1,500.6
1,517.8
1,527.1
1,533.3
1,533.9
1,545.7
1,566.2
1,589.0
1,616.4

Nonprofit
institutions
serving
households 2
37.4
41.4
46.1
51.7
58.0
63.2
69.2
77.3
86.9
99.3
112.4
125.6
138.9
152.8
163.6
178.6
199.7
223.4
244.6
268.8
290.7
313.2
335.1
352.0
370.9
388.7
410.2
439.0
467.2
498.4
522.8
563.0
594.6
626.6
650.5
682.8
713.6
761.7
798.2
814.3
848.0
889.6
917.7
954.6
1,001.4
1,044.8
1,083.4
1,134.9
1,189.4
1,227.4
1,305.6
1,116.1
1,129.2
1,139.1
1,155.1
1,168.3
1,180.4
1,195.0
1,213.9
1,235.8
1,189.4
1,223.6
1,260.9
1,266.3
1,279.6
1,320.6
1,355.8

General government 3

Total

177.8
192.6
206.8
225.3
248.4
265.3
285.7
311.3
338.2
373.4
413.5
451.4
479.7
517.1
557.5
593.3
630.4
677.4
728.8
784.9
835.8
879.8
908.3
938.8
966.9
990.3
1,022.9
1,063.0
1,118.1
1,184.3
1,252.6
1,328.4
1,404.8
1,478.7
1,555.4
1,631.9
1,726.9
1,821.2
1,883.5
1,946.1
1,972.9
1,989.1
2,039.3
2,088.0
2,141.0
2,182.5
2,240.4
2,332.1
2,405.5
2,471.9
2,560.6
2,296.7
2,321.8
2,347.3
2,362.5
2,373.2
2,388.9
2,417.3
2,442.7
2,478.7
2,442.9
2,481.8
2,484.2
2,499.4
2,534.1
2,590.6
2,618.5

Federal

87.5
92.4
96.4
102.5
110.5
117.3
125.2
135.8
145.4
159.8
178.3
195.7
207.1
225.3
240.0
250.6
261.0
278.5
292.8
306.7
323.5
329.6
331.5
332.6
333.0
331.8
333.5
336.8
345.0
360.3
370.3
397.8
434.7
459.4
488.4
509.9
535.7
569.1
603.0
640.0
659.8
663.7
658.4
666.8
673.7
683.9
699.3
726.0
749.4
782.5
817.9
715.1
722.9
730.2
735.8
741.7
745.7
752.5
757.8
765.6
776.7
792.2
795.5
803.0
812.9
822.4
833.5

State
and
local
90.3
100.2
110.4
122.8
138.0
148.0
160.6
175.5
192.8
213.5
235.2
255.6
272.6
291.9
317.6
342.7
369.4
398.8
436.1
478.2
512.2
550.2
576.9
606.2
633.9
658.6
689.3
726.2
773.1
824.0
882.3
930.6
970.1
1,019.3
1,067.0
1,122.1
1,191.2
1,252.1
1,280.5
1,306.1
1,313.1
1,325.5
1,380.9
1,421.1
1,467.3
1,498.6
1,541.2
1,606.1
1,656.1
1,689.4
1,742.7
1,581.6
1,598.9
1,617.1
1,626.7
1,631.4
1,643.2
1,664.8
1,684.8
1,713.1
1,666.2
1,689.6
1,688.7
1,696.4
1,721.2
1,768.2
1,785.0

Addendum:
Gross
housing
value
added
86.4
93.9
101.4
110.4
121.3
130.9
144.2
160.2
177.7
204.0
231.6
258.6
280.6
303.1
333.8
364.5
392.1
424.2
452.7
487.0
515.3
545.2
578.4
619.6
662.6
695.0
731.9
774.8
825.1
880.6
947.7
983.5
1,014.8
1,074.1
1,149.7
1,209.4
1,279.3
1,388.7
1,415.5
1,443.9
1,471.0
1,493.6
1,536.3
1,582.8
1,633.1
1,691.5
1,753.8
1,835.7
1,921.1
1,988.9
2,053.8
1,801.0
1,824.0
1,847.1
1,870.6
1,891.9
1,911.8
1,930.4
1,950.3
1,973.1
1,986.8
1,996.2
1,999.5
2,013.8
2,037.7
2,064.8
2,099.0

1 Gross domestic business value added equals gross domestic product excluding gross value added of households and institutions and of general
government. Nonfarm value added equals gross domestic business value added excluding gross farm value added.
2 Equals compensation of employees of nonprofit institutions, the rental value of nonresidential fixed assets owned and used by nonprofit institutions serving
households, and rental income of persons for tenant-occupied housing owned by nonprofit institutions.
3 Equals compensation of general government employees plus general government consumption of fixed capital.
Source: Department of Commerce (Bureau of Economic Analysis).

364 |

Appendix B

Table B–7. Real gross value added by sector, 1971–2021
[Billions of chained (2012) dollars; quarterly data at seasonally adjusted annual rates]
Business 1
Year or quarter

1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Gross
domestic
product

5,117.6
5,386.7
5,690.9
5,660.1
5,648.5
5,952.8
6,228.1
6,572.8
6,780.9
6,763.5
6,935.2
6,810.1
7,122.3
7,637.7
7,956.2
8,231.7
8,516.4
8,872.2
9,198.0
9,371.5
9,361.3
9,691.1
9,957.7
10,358.9
10,637.0
11,038.3
11,529.2
12,045.8
12,623.4
13,138.0
13,263.4
13,488.4
13,865.5
14,399.7
14,901.3
15,315.9
15,623.9
15,643.0
15,236.3
15,649.0
15,891.5
16,254.0
16,553.3
16,932.1
17,390.3
17,680.3
18,079.1
18,606.8
19,032.7
18,384.7
19,428.4
18,436.3
18,590.0
18,679.6
18,721.3
18,833.2
18,982.5
19,112.7
19,202.3
18,952.0
17,258.2
18,560.8
18,767.8
19,055.7
19,368.3
19,478.9
19,810.6

Total

3,397.4
3,619.3
3,870.6
3,811.6
3,775.4
4,030.5
4,261.3
4,533.1
4,694.1
4,651.7
4,787.4
4,650.0
4,896.4
5,330.7
5,579.3
5,782.0
5,989.5
6,246.0
6,485.1
6,589.0
6,548.8
6,826.1
7,020.7
7,359.3
7,585.5
7,937.6
8,354.3
8,813.8
9,323.0
9,741.3
9,799.7
9,966.6
10,281.2
10,733.3
11,154.8
11,520.2
11,766.1
11,663.6
11,234.6
11,597.6
11,825.7
12,206.4
12,506.7
12,874.0
13,313.2
13,564.1
13,926.2
14,410.8
14,791.5
14,164.4
15,186.8
14,258.6
14,398.2
14,474.9
14,511.3
14,621.2
14,749.3
14,863.7
14,931.8
14,670.6
13,080.9
14,346.5
14,559.8
14,848.7
15,143.8
15,211.1
15,543.6

Nonfarm 1

3,351.5
3,577.2
3,837.0
3,779.4
3,717.7
3,984.3
4,213.0
4,494.3
4,646.4
4,606.8
4,711.8
4,567.7
4,850.7
5,261.1
5,492.8
5,700.6
5,907.8
6,176.9
6,403.9
6,499.6
6,458.7
6,721.2
6,928.5
7,247.4
7,496.4
7,833.7
8,237.5
8,700.2
9,206.0
9,607.5
9,672.8
9,835.1
10,139.9
10,578.9
10,992.0
11,357.8
11,617.4
11,514.9
11,071.4
11,436.4
11,670.5
12,058.5
12,328.6
12,695.2
13,123.8
13,364.9
13,728.1
14,206.7
14,595.9
13,952.9
14,981.5
14,057.5
14,191.7
14,270.2
14,307.7
14,427.6
14,554.7
14,668.2
14,733.1
14,454.4
12,885.1
14,129.8
14,342.4
14,635.8
14,936.7
15,010.8
15,342.8

Households and institutions

Farm

48.2
48.2
47.7
46.6
55.5
52.8
55.6
53.5
58.6
56.9
75.2
78.8
54.5
72.7
86.1
82.4
83.2
73.9
84.0
90.7
91.2
105.3
93.4
113.0
90.0
104.1
116.7
112.6
115.5
136.6
126.6
132.3
144.4
159.2
168.6
165.5
145.4
145.7
168.1
162.7
155.3
148.0
177.9
178.3
190.0
203.0
198.2
203.6
186.2
221.0
207.6
200.3
207.7
204.2
202.2
184.2
184.8
185.4
190.4
223.0
204.9
228.8
227.4
218.4
209.4
201.5
201.3

Total

691.1
718.4
742.5
772.8
799.6
810.0
816.4
846.9
870.4
896.6
913.8
941.5
980.4
1,002.9
1,020.3
1,052.2
1,091.6
1,147.8
1,194.3
1,232.6
1,257.9
1,289.8
1,356.2
1,401.9
1,443.7
1,472.5
1,517.7
1,537.4
1,573.1
1,633.7
1,674.3
1,698.4
1,734.8
1,798.6
1,858.1
1,888.4
1,922.7
2,007.0
1,994.8
2,035.1
2,058.7
2,058.4
2,071.4
2,088.0
2,104.4
2,126.4
2,152.0
2,185.2
2,212.5
2,182.8
2,207.1
2,172.0
2,181.2
2,189.9
2,197.7
2,205.9
2,210.4
2,213.8
2,219.9
2,223.4
2,144.6
2,176.1
2,187.0
2,188.0
2,199.1
2,216.9
2,224.5

Households

408.9
425.8
439.5
459.1
472.2
478.4
478.3
501.3
511.5
526.1
531.8
539.1
560.2
570.7
583.6
595.3
610.4
635.7
655.5
668.2
678.5
693.9
727.5
764.4
790.9
807.1
829.9
851.5
878.8
917.7
951.4
956.4
984.2
1,020.5
1,068.7
1,097.0
1,123.2
1,185.0
1,161.9
1,186.5
1,186.4
1,168.8
1,174.6
1,182.5
1,180.9
1,186.6
1,201.1
1,220.8
1,235.0
1,235.1
1,245.3
1,212.0
1,218.1
1,223.9
1,229.4
1,234.4
1,234.8
1,234.7
1,236.1
1,237.0
1,236.9
1,234.5
1,232.2
1,236.1
1,242.4
1,249.5
1,253.3

Nonprofit
institutions
serving
households 2
279.5
289.6
300.0
310.4
324.2
328.4
335.3
342.2
355.7
367.4
379.3
401.1
419.0
431.4
435.4
456.5
481.9
513.6
541.4
568.4
583.9
600.8
634.0
641.5
656.4
669.0
691.8
688.8
696.4
717.7
723.7
743.6
751.6
779.3
789.8
791.4
799.3
821.5
832.8
848.4
872.2
889.6
896.7
905.5
923.2
939.5
950.6
964.1
977.2
947.8
961.6
959.7
962.8
965.8
968.0
971.3
975.3
978.8
983.4
985.9
909.0
941.9
954.6
951.9
956.6
967.2
970.9

General government 3

Total

1,228.7
1,226.9
1,232.9
1,257.1
1,276.0
1,286.8
1,300.3
1,325.1
1,339.9
1,359.9
1,369.5
1,385.7
1,397.7
1,418.3
1,461.1
1,500.5
1,537.5
1,580.7
1,619.4
1,659.8
1,676.7
1,683.9
1,687.9
1,689.5
1,691.9
1,695.2
1,708.1
1,726.8
1,742.1
1,770.3
1,801.4
1,835.6
1,858.5
1,871.5
1,888.4
1,903.9
1,930.9
1,970.9
2,006.7
2,016.3
2,007.2
1,989.1
1,975.7
1,971.9
1,977.2
1,995.5
2,009.6
2,024.3
2,046.0
2,043.5
2,064.2
2,017.8
2,024.1
2,028.8
2,026.5
2,022.0
2,039.9
2,053.4
2,068.8
2,070.3
2,015.2
2,050.2
2,038.3
2,043.5
2,055.6
2,079.6
2,078.1

Federal

506.6
487.2
473.6
473.8
472.1
473.3
475.2
481.5
482.5
490.3
498.5
507.7
520.6
534.1
551.1
564.4
582.2
593.4
602.4
612.9
616.4
606.3
596.3
579.7
561.2
547.8
538.8
533.1
528.9
531.7
533.2
542.6
557.0
565.1
572.3
576.7
584.6
606.3
636.6
658.0
664.3
663.7
652.0
646.9
642.5
645.4
646.2
649.5
656.4
674.4
679.7
647.3
649.7
651.6
649.4
644.2
657.7
661.0
662.8
666.4
672.6
680.8
678.0
678.5
680.5
680.1
679.7

State
and
local
700.2
724.6
750.1
777.4
801.0
811.7
824.3
843.7
859.1
871.1
871.0
876.9
873.5
879.0
904.3
930.7
949.1
981.6
1,011.9
1,042.2
1,055.9
1,073.9
1,088.7
1,107.7
1,129.6
1,147.1
1,169.7
1,194.6
1,214.4
1,240.0
1,269.6
1,294.4
1,302.8
1,307.5
1,317.0
1,328.3
1,347.3
1,365.3
1,370.5
1,358.5
1,343.0
1,325.5
1,323.7
1,324.7
1,334.2
1,349.5
1,362.6
1,373.9
1,388.7
1,369.1
1,384.5
1,369.6
1,373.5
1,376.3
1,376.2
1,376.8
1,381.4
1,391.6
1,405.0
1,402.9
1,343.1
1,369.7
1,360.7
1,365.3
1,375.3
1,399.4
1,398.2

Addendum:
Gross
housing
value
added
522.1
546.8
564.2
591.9
610.8
616.9
625.7
648.2
660.8
684.1
697.5
713.7
741.3
755.5
786.8
808.2
827.0
854.3
872.1
889.6
907.8
929.9
963.2
1,004.4
1,040.2
1,058.1
1,083.6
1,109.0
1,140.3
1,179.4
1,216.8
1,215.6
1,236.4
1,280.1
1,341.3
1,367.7
1,394.1
1,467.0
1,452.4
1,492.0
1,500.9
1,493.6
1,505.6
1,516.6
1,520.7
1,528.9
1,537.4
1,559.8
1,578.6
1,582.1
1,596.5
1,548.9
1,556.5
1,563.5
1,570.2
1,576.5
1,577.9
1,578.8
1,581.2
1,582.8
1,583.5
1,581.8
1,580.5
1,585.9
1,593.5
1,601.1
1,605.6

1 Gross domestic business value added equals gross domestic product excluding gross value added of households and institutions and of general
government. Nonfarm value added equals gross domestic business value added excluding gross farm value added.
2 Equals compensation of employees of nonprofit institutions, the rental value of nonresidential fixed assets owned and used by nonprofit institutions serving
households, and rental income of persons for tenant-occupied housing owned by nonprofit institutions.
3 Equals compensation of general government employees plus general government consumption of fixed capital.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 365

Table B–8. Gross domestic product (GDP) by industry, value added, in current dollars and
as a percentage of GDP, 1997–2020
[Billions of dollars; except as noted]
Private industries
Gross
domestic
product

Year

Total
private
industries

Agriculture,
forestry,
fishing,
and
hunting

Manufacturing
Construction

Mining

Total
manufacturing

Durable
goods

Nondurable
goods

Utilities

Wholesale
trade

Retail
trade

Value added
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������

8,577.6
9,062.8
9,631.2
10,251.0
10,581.9
10,929.1
11,456.5
12,217.2
13,039.2
13,815.6
14,474.2
14,769.9
14,478.1
15,049.0
15,599.7
16,254.0
16,843.2
17,550.7
18,206.0
18,695.1
19,479.6
20,527.2
21,372.6

7,432.0
7,871.5
8,378.8
8,927.9
9,189.0
9,454.7
9,904.1
10,585.9
11,328.9
12,023.6
12,587.2
12,788.3
12,433.0
12,941.0
13,462.7
14,094.5
14,630.7
15,279.3
15,866.6
16,310.9
17,032.6
17,980.9
18,750.8

108.6
99.8
92.6
98.3
99.8
95.9
114.6
143.8
129.5
126.4
145.5
146.0
129.1
144.9
179.2
178.7
214.3
198.9
180.1
165.8
175.9
175.6
162.6

95.1
81.7
84.6
110.5
123.9
112.4
138.9
166.4
225.4
273.1
314.1
392.5
275.4
306.4
357.8
360.5
387.8
417.0
261.7
218.1
276.9
320.1
295.7

339.6
379.8
417.7
461.2
486.4
493.5
525.2
584.6
651.6
697.1
715.7
649.3
565.4
525.7
525.6
554.9
588.7
637.7
695.3
747.7
800.6
847.1
903.6

1,382.9
1,430.6
1,489.6
1,549.8
1,473.5
1,468.3
1,524.0
1,607.8
1,692.5
1,793.5
1,845.8
1,802.1
1,700.8
1,799.8
1,873.6
1,934.7
1,997.3
2,053.5
2,131.0
2,102.9
2,199.3
2,334.1
2,370.9

823.8
850.7
875.2
924.6
833.3
832.8
863.1
905.0
956.3
1,004.2
1,031.0
1,000.2
880.4
965.5
1,017.9
1,065.2
1,104.5
1,135.6
1,184.4
1,187.8
1,235.4
1,298.9
1,327.9

559.1
579.9
614.4
625.2
640.3
635.6
660.9
702.8
736.2
789.3
814.7
801.8
820.5
834.3
855.7
869.5
892.8
917.9
946.6
915.1
963.8
1,035.3
1,043.0

171.5
163.7
180.0
180.1
181.3
177.6
183.9
199.1
197.9
226.7
231.9
241.7
257.8
279.1
288.3
280.7
286.9
298.3
299.2
302.0
311.6
319.0
333.3

527.5
563.7
584.2
622.5
613.7
613.1
641.4
697.1
754.7
811.4
858.2
884.8
833.8
890.0
937.1
1,000.3
1,042.2
1,089.6
1,143.6
1,135.8
1,165.7
1,217.4
1,275.0

579.9
626.9
652.8
685.4
709.4
732.6
769.5
795.5
840.6
869.8
869.4
848.8
827.3
852.1
873.1
910.0
950.6
975.1
1,020.3
1,053.0
1,081.6
1,119.7
1,166.7

20,893.7

18,223.1

174.5

182.1

895.9

2,272.0

1,268.8

1,003.1

341.7

1,243.3

1,202.2

2.0
1.8
1.9
1.8
1.7
1.6
1.6
1.6
1.5
1.6
1.6
1.6
1.8
1.9
1.8
1.7
1.7
1.7
1.6
1.6
1.6
1.6
1.6
1.6

6.2
6.2
6.1
6.1
5.8
5.6
5.6
5.7
5.8
5.9
5.9
6.0
5.8
5.9
6.0
6.2
6.2
6.2
6.3
6.1
6.0
5.9
6.0
6.0

6.8
6.9
6.8
6.7
6.7
6.7
6.7
6.5
6.4
6.3
6.0
5.7
5.7
5.7
5.6
5.6
5.6
5.6
5.6
5.6
5.6
5.5
5.5
5.8

Percent
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������

100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0
100.0

Industry value added as a percentage of GDP (percent)
86.6
86.9
87.0
87.1
86.8
86.5
86.4
86.6
86.9
87.0
87.0
86.6
85.9
86.0
86.3
86.7
86.9
87.1
87.2
87.2
87.4
87.6
87.7
87.2

1.3
1.1
1.0
1.0
.9
.9
1.0
1.2
1.0
.9
1.0
1.0
.9
1.0
1.1
1.1
1.3
1.1
1.0
.9
.9
.9
.8
.8

1.1
.9
.9
1.1
1.2
1.0
1.2
1.4
1.7
2.0
2.2
2.7
1.9
2.0
2.3
2.2
2.3
2.4
1.4
1.2
1.4
1.6
1.4
.9

4.0
4.2
4.3
4.5
4.6
4.5
4.6
4.8
5.0
5.0
4.9
4.4
3.9
3.5
3.4
3.4
3.5
3.6
3.8
4.0
4.1
4.1
4.2
4.3

16.1
15.8
15.5
15.1
13.9
13.4
13.3
13.2
13.0
13.0
12.8
12.2
11.7
12.0
12.0
11.9
11.9
11.7
11.7
11.2
11.3
11.4
11.1
10.9

9.6
9.4
9.1
9.0
7.9
7.6
7.5
7.4
7.3
7.3
7.1
6.8
6.1
6.4
6.5
6.6
6.6
6.5
6.5
6.4
6.3
6.3
6.2
6.1

6.5
6.4
6.4
6.1
6.1
5.8
5.8
5.8
5.6
5.7
5.6
5.4
5.7
5.5
5.5
5.3
5.3
5.2
5.2
4.9
4.9
5.0
4.9
4.8

1 Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing.
2 Consists of utilities; wholesale trade; retail trade; transportation and warehousing; information; finance, insurance, real estate, rental, and leasing;
professional and business services; educational services, health care, and social assistance; arts, entertainment, recreation, accommodation, and food services;
and other services, except government.
Note: Data shown in Tables B–8 and B–9 are consistent with the 2021 annual revision of the industry accounts released in September 2021. For details see
Survey of Current Business, October 2021.
See next page for continuation of table.

366 |

Appendix B

Table B–8. Gross domestic product (GDP) by industry, value added, in current dollars and
as a percentage of GDP, 1997–2020—Continued
[Billions of dollars; except as noted]
Private industries—Continued

Year

Transportation
and
warehousing

Finance,
insurance,
real
estate,
Information
rental,
and
leasing

Professional
and
business
services

Arts,
Educational entertainservices,
ment,
health
recreation,
care,
accommoand
dation,
social
food
assistance and
services

Other
services,
except
government

Government

Private
Private
servicesgoodsproducing producing
industries 1 industries 2

Value added
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������

257.3
280.0
290.1
307.8
308.0
305.6
321.4
352.0
375.6
410.3
414.0
427.0
404.4
433.5
452.5
473.3
492.1
522.5
566.1
582.4
609.4
648.1
685.7
572.0

394.1
434.6
485.3
471.2
502.3
550.5
564.8
620.3
642.0
651.9
707.5
743.8
721.4
754.9
763.0
762.7
831.4
844.4
907.8
970.3
1,004.7
1,064.6
1,134.5
1,167.9

1,612.4
1,710.1
1,835.4
1,974.7
2,129.4
2,210.0
2,294.2
2,392.8
2,611.4
2,745.2
2,865.6
2,816.1
2,903.1
2,990.4
3,080.8
3,289.2
3,362.0
3,560.7
3,713.8
3,883.2
4,020.2
4,257.8
4,451.5
4,592.1

840.6
914.0
997.4
1,104.9
1,155.3
1,189.8
1,247.4
1,340.9
1,446.0
1,546.5
1,667.3
1,777.9
1,688.1
1,768.5
1,860.0
1,968.9
2,020.1
2,120.2
2,237.7
2,306.2
2,434.3
2,586.9
2,731.3
2,689.8

590.6
615.8
654.1
695.4
749.8
807.0
862.7
927.2
970.2
1,035.3
1,088.0
1,185.0
1,267.0
1,311.3
1,356.2
1,409.3
1,448.4
1,492.6
1,571.2
1,652.6
1,710.7
1,784.2
1,871.4
1,798.6

301.8
322.1
354.2
386.5
390.7
413.5
432.1
461.1
481.1
511.4
533.6
542.9
533.0
556.2
581.9
622.7
652.3
691.9
747.0
790.5
828.2
869.6
914.2
672.1

230.3
248.7
260.9
279.7
265.5
284.9
283.8
297.2
310.6
325.0
330.5
330.3
326.4
328.2
333.5
348.6
356.7
376.8
391.6
400.5
413.5
436.5
454.4
419.0

1,145.6
1,191.3
1,252.3
1,323.0
1,392.9
1,474.4
1,552.3
1,631.3
1,710.3
1,792.0
1,887.1
1,981.6
2,045.1
2,108.0
2,137.1
2,159.5
2,212.5
2,271.4
2,339.4
2,384.2
2,447.1
2,546.3
2,621.8
2,670.6

1,926.1
1,991.8
2,084.5
2,219.9
2,183.7
2,170.2
2,302.8
2,502.7
2,698.9
2,890.1
3,021.1
2,989.8
2,670.7
2,776.8
2,936.1
3,028.8
3,188.0
3,307.1
3,268.2
3,234.5
3,452.7
3,677.0
3,732.8
3,524.4

5,505.9
5,879.7
6,294.3
6,708.0
7,005.3
7,284.6
7,601.3
8,083.2
8,630.0
9,133.5
9,566.0
9,798.4
9,762.3
10,164.2
10,526.5
11,065.7
11,442.7
11,972.2
12,598.4
13,076.4
13,579.9
14,303.9
15,018.0
14,698.7

13.4
13.1
13.0
12.9
13.2
13.5
13.5
13.4
13.1
13.0
13.0
13.4
14.1
14.0
13.7
13.3
13.1
12.9
12.8
12.8
12.6
12.4
12.3
12.8

22.5
22.0
21.6
21.7
20.6
19.9
20.1
20.5
20.7
20.9
20.9
20.2
18.4
18.5
18.8
18.6
18.9
18.8
18.0
17.3
17.7
17.9
17.5
16.9

64.2
64.9
65.4
65.4
66.2
66.7
66.3
66.2
66.2
66.1
66.1
66.3
67.4
67.5
67.5
68.1
67.9
68.2
69.2
69.9
69.7
69.7
70.3
70.3

Industry value added as a percentage of GDP (percent)
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������

3.0
3.1
3.0
3.0
2.9
2.8
2.8
2.9
2.9
3.0
2.9
2.9
2.8
2.9
2.9
2.9
2.9
3.0
3.1
3.1
3.1
3.2
3.2
2.7

4.6
4.8
5.0
4.6
4.7
5.0
4.9
5.1
4.9
4.7
4.9
5.0
5.0
5.0
4.9
4.7
4.9
4.8
5.0
5.2
5.2
5.2
5.3
5.6

18.8
18.9
19.1
19.3
20.1
20.2
20.0
19.6
20.0
19.9
19.8
19.1
20.1
19.9
19.7
20.2
20.0
20.3
20.4
20.8
20.6
20.7
20.8
22.0

9.8
10.1
10.4
10.8
10.9
10.9
10.9
11.0
11.1
11.2
11.5
12.0
11.7
11.8
11.9
12.1
12.0
12.1
12.3
12.3
12.5
12.6
12.8
12.9

6.9
6.8
6.8
6.8
7.1
7.4
7.5
7.6
7.4
7.5
7.5
8.0
8.8
8.7
8.7
8.7
8.6
8.5
8.6
8.8
8.8
8.7
8.8
8.6

3.5
3.6
3.7
3.8
3.7
3.8
3.8
3.8
3.7
3.7
3.7
3.7
3.7
3.7
3.7
3.8
3.9
3.9
4.1
4.2
4.3
4.2
4.3
3.2

2.7
2.7
2.7
2.7
2.5
2.6
2.5
2.4
2.4
2.4
2.3
2.2
2.3
2.2
2.1
2.1
2.1
2.1
2.2
2.1
2.1
2.1
2.1
2.0

Note (cont’d): Value added is the contribution of each private industry and of government to GDP. Value added is equal to an industry’s gross output minus
its intermediate inputs. Current-dollar value added is calculated as the sum of distributions by an industry to its labor and capital, which are derived from the
components of gross domestic income.
Value added industry data shown in Tables B–8 and B–9 are based on the 2012 North American Industry Classification System (NAICS).
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 367

Table B–9. Real gross domestic product by industry, value added, and percent changes,
1997–2020
Private industries
Gross
domestic
product

Year

Total
private
industries

Agriculture,
forestry,
fishing,
and
hunting

Manufacturing
Mining

Construction

Total
manufacturing

Durable
goods

Nondurable
goods

Utilities

Wholesale
trade

Retail
trade

Chain-type quantity indexes for value added (2012=100)
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������

70.931
74.110
77.663
80.830
81.601
82.985
85.305
88.592
91.678
94.229
96.123
96.241
93.739
96.278
97.770
100.000
101.842
104.172
106.991
108.775
111.229
114.475
117.096
113.109

70.046
73.402
77.229
80.653
81.255
82.670
84.993
88.565
91.949
94.880
96.595
96.324
93.332
95.883
97.540
100.000
101.880
104.617
107.828
109.748
112.474
116.061
119.008
114.529

77.781
75.893
78.203
89.714
86.605
89.789
97.128
104.991
109.754
111.588
99.272
99.372
110.461
107.225
103.127
100.000
116.130
116.724
124.160
131.263
129.287
133.135
124.990
142.510

72.719
75.656
73.421
65.086
75.515
77.598
68.794
69.198
70.313
81.229
87.622
84.835
97.011
85.963
89.386
100.000
103.744
114.972
125.161
118.502
120.941
121.136
135.434
121.647

124.354
130.050
135.421
140.845
138.221
133.748
136.061
140.907
141.526
138.689
134.513
121.342
103.961
98.810
97.298
100.000
102.401
104.349
109.037
113.193
117.231
119.956
121.588
117.208

73.447
76.469
80.746
86.510
83.058
83.793
88.483
94.727
97.591
103.211
106.720
104.522
94.688
100.081
100.599
100.000
102.945
104.601
105.768
105.458
108.976
113.457
115.469
112.050

54.511
58.994
63.131
70.473
66.103
67.503
72.557
77.761
83.117
89.557
93.800
94.301
80.569
90.953
97.223
100.000
102.358
103.902
105.519
105.707
110.096
115.413
116.330
110.615

107.960
106.119
109.889
110.880
109.949
109.179
112.634
120.408
118.265
122.110
124.218
117.744
114.123
112.119
104.835
100.000
103.671
105.467
106.067
105.121
107.530
110.982
114.334
113.857

81.637
77.993
90.800
91.942
76.548
79.187
77.791
82.510
78.281
83.357
85.179
89.539
84.369
94.906
98.679
100.000
98.755
94.883
94.918
100.030
101.224
100.875
102.103
106.381

67.499
74.131
76.606
80.289
81.714
82.739
87.144
91.106
95.261
98.148
101.407
101.651
89.214
94.469
96.638
100.000
102.072
106.032
110.553
109.159
109.730
110.950
110.431
108.354

76.759
84.135
87.240
90.127
93.502
97.602
102.667
104.409
107.790
108.680
105.184
101.255
96.810
99.023
99.257
100.000
103.046
104.956
108.872
112.919
116.680
120.389
123.395
119.834

3.1
–1.7
3.6
.9
–.8
–.7
3.2
6.9
–1.8
3.3
1.7
–5.2
–3.1
–1.8
–6.5
–4.6
3.7
1.7
.6
–.9
2.3
3.2
3.0
–.4

–5.2
–4.5
16.4
1.3
–16.7
3.4
–1.8
6.1
–5.1
6.5
2.2
5.1
–5.8
12.5
4.0
1.3
–1.2
–3.9
.0
5.4
1.2
–.3
1.2
4.2

10.9
9.8
3.3
4.8
1.8
1.3
5.3
4.5
4.6
3.0
3.3
.2
–12.2
5.9
2.3
3.5
2.1
3.9
4.3
–1.3
.5
1.1
–.5
–1.9

7.5
9.6
3.7
3.3
3.7
4.4
5.2
1.7
3.2
.8
–3.2
–3.7
–4.4
2.3
.2
.7
3.0
1.9
3.7
3.7
3.3
3.2
2.5
–2.9

Percent change from year earlier
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������

4.4
4.5
4.8
4.1
1.0
1.7
2.8
3.9
3.5
2.8
2.0
.1
–2.6
2.7
1.5
2.3
1.8
2.3
2.7
1.7
2.3
2.9
2.3
–3.4

4.9
4.8
5.2
4.4
.7
1.7
2.8
4.2
3.8
3.2
1.8
–.3
–3.1
2.7
1.7
2.5
1.9
2.7
3.1
1.8
2.5
3.2
2.5
–3.8

8.5
–2.4
3.0
14.7
–3.5
3.7
8.2
8.1
4.5
1.7
–11.0
.1
11.2
–2.9
–3.8
–3.0
16.1
.5
6.4
5.7
–1.5
3.0
–6.1
14.0

3.7
4.0
–3.0
–11.4
16.0
2.8
–11.3
.6
1.6
15.5
7.9
–3.2
14.4
–11.4
4.0
11.9
3.7
10.8
8.9
–5.3
2.1
.2
11.8
–10.2

0.5
4.6
4.1
4.0
–1.9
–3.2
1.7
3.6
.4
–2.0
–3.0
–9.8
–14.3
–5.0
–1.5
2.8
2.4
1.9
4.5
3.8
3.6
2.3
1.4
–3.6

6.6
4.1
5.6
7.1
–4.0
.9
5.6
7.1
3.0
5.8
3.4
–2.1
–9.4
5.7
.5
–.6
2.9
1.6
1.1
–.3
3.3
4.1
1.8
–3.0

9.1
8.2
7.0
11.6
–6.2
2.1
7.5
7.2
6.9
7.7
4.7
.5
–14.6
12.9
6.9
2.9
2.4
1.5
1.6
.2
4.2
4.8
.8
–4.9

1 Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing.
2 Consists of utilities; wholesale trade; retail trade; transportation and warehousing; information; finance, insurance, real estate, rental, and leasing;

professional and business services; educational services, health care, and social assistance; arts, entertainment, recreation, accommodation, and food services;
and other services, except government.
See next page for continuation of table.

368 |

Appendix B

Table B–9. Real gross domestic product by industry, value added, and percent changes,
1997–2020—Continued
Private industries—Continued

Year

Transportation
and
warehousing

Finance,
insurance,
estate,
Information realrental,
and
leasing

Arts,
Educational entertainservices,
ment,
health
recreation,
care,
accommoand
dation,
social
food
assistance and
services

Professional
and
business
services

Other
services,
except
government

Government

Private
Private
servicesgoodsproducing producing
industries 1 industries 2

Chain-type quantity indexes for value added (2012=100)
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������

84.687
88.991
89.731
89.480
83.650
80.670
83.579
90.509
94.860
100.505
99.790
98.863
92.702
97.391
99.295
100.000
101.389
104.448
107.144
108.740
113.484
117.549
119.611
103.584

45.514
50.255
56.341
55.253
58.676
64.338
66.391
74.041
78.974
81.801
89.943
95.681
93.038
98.573
100.202
100.000
108.867
111.584
123.147
134.001
142.545
153.585
164.264
169.990

64.047
66.832
71.072
74.812
78.601
79.078
79.608
81.082
86.517
88.398
90.254
88.297
92.938
94.383
95.951
100.000
99.486
101.652
102.770
103.834
104.288
106.100
108.176
108.494

63.505
66.440
69.573
73.644
75.849
76.729
79.196
81.122
84.732
87.160
90.088
94.308
88.066
91.902
95.650
100.000
101.208
105.828
109.409
111.630
116.872
123.568
129.199
126.159

1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������

4.4
5.1
.8
–.3
–6.5
–3.6
3.6
8.3
4.8
6.0
–.7
–.9
–6.2
5.1
2.0
.7
1.4
3.0
2.6
1.5
4.4
3.6
1.8
–13.4

–1.3
10.4
12.1
–1.9
6.2
9.6
3.2
11.5
6.7
3.6
10.0
6.4
–2.8
5.9
1.7
–.2
8.9
2.5
10.4
8.8
6.4
7.7
7.0
3.5

4.2
4.3
6.3
5.3
5.1
.6
.7
1.9
6.7
2.2
2.1
–2.2
5.3
1.6
1.7
4.2
–.5
2.2
1.1
1.0
.4
1.7
2.0
.3

7.3
4.6
4.7
5.9
3.0
1.2
3.2
2.4
4.5
2.9
3.4
4.7
–6.6
4.4
4.1
4.5
1.2
4.6
3.4
2.0
4.7
5.7
4.6
–2.4

65.087
65.370
67.564
70.038
71.815
74.683
77.657
81.355
82.879
86.244
86.927
92.430
95.531
96.650
98.364
100.000
101.220
103.045
106.918
109.940
111.886
114.997
118.248
110.728

78.592
80.744
85.167
90.255
87.220
89.599
91.907
96.013
96.269
98.946
98.432
96.222
90.582
94.163
97.550
100.000
102.051
105.746
109.018
110.917
113.644
115.686
118.019
82.895

115.380
120.186
120.996
123.725
111.631
114.679
111.481
112.927
113.691
114.286
111.693
107.558
101.117
99.308
98.489
100.000
99.174
102.044
102.620
101.787
102.423
105.804
105.811
92.653

87.664
88.684
89.743
91.570
92.529
94.176
95.338
96.193
97.080
97.638
98.590
100.494
100.556
101.076
100.755
100.000
99.296
99.081
99.199
100.179
101.164
102.015
102.629
101.667

81.001
84.104
88.164
93.389
91.021
91.172
94.634
100.195
102.577
107.155
108.829
104.591
97.383
98.450
98.726
100.000
103.727
106.526
109.624
110.207
113.558
117.308
119.773
116.329

67.082
70.519
74.287
77.217
78.624
80.375
82.397
85.441
89.093
91.577
93.305
94.115
92.247
95.186
97.215
100.000
101.376
104.095
107.334
109.587
112.156
115.701
118.767
114.042

4.7
4.2
.7
2.3
–9.8
2.7
–2.8
1.3
.7
.5
–2.3
–3.7
–6.0
–1.8
–.8
1.5
–.8
2.9
.6
–.8
.6
3.3
.0
–12.4

1.3
1.2
1.2
2.0
1.0
1.8
1.2
.9
.9
.6
1.0
1.9
.1
.5
–.3
–.7
–.7
–.2
.1
1.0
1.0
.8
.6
–.9

5.4
3.8
4.8
5.9
–2.5
.2
3.8
5.9
2.4
4.5
1.6
–3.9
–6.9
1.1
.3
1.3
3.7
2.7
2.9
.5
3.0
3.3
2.1
–2.9

4.7
5.1
5.3
3.9
1.8
2.2
2.5
3.7
4.3
2.8
1.9
.9
–2.0
3.2
2.1
2.9
1.4
2.7
3.1
2.1
2.3
3.2
2.6
–4.0

Percent change from year earlier
1.8
0.4
3.4
3.7
2.5
4.0
4.0
4.8
1.9
4.1
.8
6.3
3.4
1.2
1.8
1.7
1.2
1.8
3.8
2.8
1.8
2.8
2.8
–6.4

5.7
2.7
5.5
6.0
–3.4
2.7
2.6
4.5
.3
2.8
–.5
–2.2
–5.9
4.0
3.6
2.5
2.1
3.6
3.1
1.7
2.5
1.8
2.0
–29.8

Note: Data are based on the 2012 North American Industry Classification System (NAICS).
See Note, Table B–8.
Source: Department of Commerce (Bureau of Economic Analysis).

National Income or Expenditure | 369

Table B–10. Personal consumption expenditures, 1971–2021
[Billions of dollars; quarterly data at seasonally adjusted annual rates]
Goods

Year or quarter

1971 ����������������������
1972 ����������������������
1973 ����������������������
1974 ����������������������
1975 ����������������������
1976 ����������������������
1977 ����������������������
1978 ����������������������
1979 ����������������������
1980 ����������������������
1981 ����������������������
1982 ����������������������
1983 ����������������������
1984 ����������������������
1985 ����������������������
1986 ����������������������
1987 ����������������������
1988 ����������������������
1989 ����������������������
1990 ����������������������
1991 ����������������������
1992 ����������������������
1993 ����������������������
1994 ����������������������
1995 ����������������������
1996 ����������������������
1997 ����������������������
1998 ����������������������
1999 ����������������������
2000 ����������������������
2001 ����������������������
2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ����������������������
2007 ����������������������
2008 ����������������������
2009 ����������������������
2010 ����������������������
2011 ����������������������
2012 ����������������������
2013 ����������������������
2014 ����������������������
2015 ����������������������
2016 ����������������������
2017 ����������������������
2018 ����������������������
2019 ����������������������
2020 ����������������������
2021 p ��������������������
2018: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2019: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2020: I ������������������
      II �����������������
      III ����������������
      IV ����������������
2021: I ������������������
      II �����������������
      III ����������������
      IV p �������������

Personal
consumption
expenditures

699.9
768.2
849.6
930.2
1,030.5
1,147.7
1,274.0
1,422.3
1,585.4
1,750.7
1,934.0
2,071.3
2,281.6
2,492.3
2,712.8
2,886.3
3,076.3
3,330.0
3,576.8
3,809.0
3,943.4
4,197.6
4,452.0
4,721.0
4,962.6
5,244.6
5,536.8
5,877.2
6,283.8
6,767.2
7,073.8
7,348.9
7,740.7
8,232.0
8,769.1
9,277.2
9,746.6
10,050.1
9,891.2
10,260.3
10,698.9
11,047.4
11,363.5
11,847.7
12,263.5
12,693.3
13,239.1
13,913.5
14,428.7
14,047.6
15,746.9
13,667.4
13,864.8
14,002.6
14,119.3
14,155.6
14,375.7
14,529.5
14,653.9
14,439.1
12,989.7
14,293.8
14,467.6
15,005.4
15,681.7
15,964.9
16,335.5

Durable

Total

342.1
373.8
416.6
451.5
491.3
546.3
600.4
663.6
737.9
799.8
869.4
899.3
973.8
1,063.7
1,137.6
1,195.6
1,256.3
1,337.3
1,423.8
1,491.3
1,497.4
1,563.3
1,642.3
1,746.6
1,815.5
1,917.7
2,006.5
2,108.4
2,287.1
2,453.2
2,525.6
2,598.8
2,722.6
2,902.0
3,082.9
3,239.7
3,367.0
3,363.2
3,180.0
3,317.8
3,518.1
3,637.7
3,730.0
3,863.0
3,923.0
3,991.8
4,158.6
4,353.7
4,478.9
4,653.8
5,482.8
4,298.4
4,354.4
4,373.2
4,388.8
4,382.8
4,479.4
4,512.7
4,540.8
4,530.9
4,349.9
4,867.2
4,867.3
5,245.0
5,529.8
5,500.1
5,656.2

Services
Nondurable

Total 1

Motor
vehicles
and
parts

102.4
116.4
130.5
130.2
142.2
168.6
192.0
213.3
226.3
226.4
243.9
253.0
295.0
342.2
380.4
421.4
442.0
475.1
494.3
497.1
477.2
508.1
551.5
607.2
635.7
676.3
715.5
779.3
855.6
912.6
941.5
985.4
1,017.8
1,080.6
1,128.6
1,158.3
1,188.0
1,098.8
1,012.1
1,049.0
1,093.5
1,144.2
1,189.4
1,242.1
1,307.6
1,345.2
1,396.6
1,469.2
1,513.3
1,616.4
2,026.6
1,449.4
1,471.3
1,478.2
1,477.8
1,473.3
1,509.2
1,531.4
1,539.2
1,484.9
1,468.3
1,753.3
1,759.2
1,957.8
2,092.2
1,995.2
2,061.3

43.2
49.4
54.4
48.2
52.6
68.2
79.8
89.2
90.2
84.4
93.0
100.0
122.9
147.2
170.1
187.5
188.2
202.2
207.8
205.1
185.7
204.8
224.7
249.8
255.7
273.5
293.1
320.2
350.7
363.2
383.3
401.3
401.5
409.3
410.0
394.9
400.6
343.3
318.6
344.5
365.2
396.6
417.5
442.0
475.3
484.3
501.3
519.5
514.5
541.3
700.3
514.7
519.4
522.8
521.0
500.1
512.6
519.0
526.6
482.0
485.2
595.8
602.1
674.9
758.1
667.9
700.5

Total 1

239.7
257.4
286.1
321.4
349.2
377.7
408.4
450.2
511.6
573.4
625.4
646.3
678.8
721.5
757.2
774.2
814.3
862.3
929.5
994.2
1,020.3
1,055.2
1,090.8
1,139.4
1,179.8
1,241.4
1,291.0
1,329.1
1,431.5
1,540.6
1,584.1
1,613.4
1,704.8
1,821.4
1,954.3
2,081.3
2,179.0
2,264.5
2,167.9
2,268.9
2,424.6
2,493.5
2,540.6
2,620.9
2,615.4
2,646.7
2,761.9
2,884.5
2,965.6
3,037.4
3,456.2
2,849.1
2,883.2
2,895.0
2,911.0
2,909.5
2,970.1
2,981.3
3,001.6
3,046.0
2,881.7
3,113.9
3,108.1
3,287.2
3,437.6
3,505.0
3,595.0

Food and
beverages Gasoline
purchased and
for offother
premises energy
congoods
sumption
107.1
114.5
126.7
143.0
156.6
167.3
179.8
196.1
218.4
239.2
255.3
267.1
277.0
291.1
303.0
316.4
324.3
342.8
365.4
391.2
403.0
404.5
413.5
432.1
443.7
461.9
474.8
487.4
515.5
540.6
564.0
575.1
599.6
632.6
668.2
700.3
737.3
769.1
772.9
786.9
819.5
846.2
864.0
896.9
921.0
940.6
973.1
1,000.3
1,030.9
1,146.7
1,235.5
992.8
997.7
1,002.3
1,008.6
1,013.4
1,027.4
1,041.3
1,041.5
1,125.1
1,152.1
1,159.5
1,150.0
1,201.5
1,223.4
1,245.3
1,271.7

Addendum:
Personal
consumption
expendiFinancial
tures
excluding
Health services
and
food
care
insurand
ance
energy 2

Household consumption
expenditures

27.6
29.4
34.3
43.8
48.0
53.0
57.8
61.5
80.4
101.9
113.4
108.4
106.5
108.2
110.5
91.2
96.4
99.9
110.4
124.2
121.1
125.0
126.9
129.2
133.4
144.7
147.7
132.4
146.5
184.5
178.0
167.9
196.4
232.7
283.8
319.7
345.5
391.1
287.0
336.7
413.8
421.9
418.2
403.3
309.4
275.7
309.9
350.4
337.6
246.8
359.3
343.2
352.9
354.2
351.3
323.9
350.1
332.8
343.7
305.2
188.5
245.4
247.9
300.3
345.5
376.2
415.2

Total
Total 1

357.8
346.1
394.3
381.5
432.9
419.2
478.6
463.1
539.2
522.2
601.4
582.4
673.6
653.0
758.7
735.7
847.5
821.4
950.9
920.8
1,064.6 1,030.4
1,172.0 1,134.0
1,307.8 1,267.1
1,428.6 1,383.3
1,575.2 1,527.3
1,690.7 1,638.0
1,820.0 1,764.3
1,992.7 1,929.4
2,153.0 2,084.9
2,317.7 2,241.8
2,446.0 2,365.9
2,634.3 2,546.4
2,809.6 2,719.6
2,974.4 2,876.6
3,147.1 3,044.7
3,326.9 3,216.9
3,530.3 3,424.7
3,768.8 3,645.0
3,996.7 3,858.5
4,314.0 4,156.0
4,548.2 4,369.1
4,750.1 4,551.8
5,018.2 4,812.6
5,329.9 5,123.6
5,686.1 5,475.9
6,037.6 5,798.4
6,379.6 6,130.8
6,686.9 6,399.6
6,711.2 6,422.0
6,942.4 6,648.0
7,180.7 6,868.9
7,409.6 7,068.1
7,633.6 7,281.0
7,984.8 7,619.2
8,340.5 7,968.9
8,701.4 8,300.0
9,080.6 8,662.6
9,559.8 9,115.1
9,949.8 9,509.9
9,393.7 8,872.9
10,264.1 9,781.0
9,369.0 8,940.9
9,510.3 9,071.4
9,629.4 9,184.8
9,730.5 9,263.5
9,772.7 9,336.7
9,896.3 9,459.1
10,016.8 9,571.5
10,113.2 9,672.3
9,908.2 9,387.7
8,639.8 8,062.8
9,426.6 8,932.1
9,600.4 9,109.0
9,760.4 9,281.7
10,151.9 9,684.8
10,464.8 9,984.4
10,679.2 10,173.0

Housing
and
utilities

120.0
131.2
143.5
158.6
176.5
194.7
217.8
244.3
273.4
312.5
352.1
387.5
421.2
457.5
500.6
537.0
571.6
614.4
655.2
696.5
735.2
771.1
814.9
863.3
913.7
962.4
1,009.8
1,065.5
1,123.1
1,198.6
1,287.5
1,329.5
1,391.1
1,466.6
1,580.1
1,665.7
1,759.6
1,872.7
1,900.0
1,947.9
1,983.3
2,014.7
2,083.5
2,151.4
2,206.6
2,280.8
2,363.2
2,472.1
2,571.5
2,668.1
2,777.4
2,431.8
2,460.2
2,479.4
2,517.0
2,534.7
2,554.1
2,585.3
2,611.8
2,622.6
2,667.7
2,682.6
2,699.7
2,727.2
2,753.4
2,792.6
2,836.5

53.7
59.8
67.2
76.1
89.0
101.8
115.7
131.2
148.8
171.7
201.9
225.2
253.1
276.5
302.2
330.2
366.0
410.1
451.2
506.2
555.8
612.8
648.8
680.5
719.9
752.1
790.9
832.0
863.6
918.4
996.6
1,082.9
1,154.0
1,238.9
1,320.5
1,391.9
1,478.2
1,555.3
1,632.7
1,699.6
1,757.1
1,821.3
1,858.2
1,940.5
2,057.3
2,159.4
2,238.8
2,339.6
2,458.2
2,308.4
2,548.0
2,300.2
2,326.3
2,364.4
2,367.4
2,408.7
2,449.7
2,469.9
2,504.3
2,406.9
2,000.3
2,369.2
2,457.2
2,464.2
2,534.4
2,574.5
2,618.8

33.1
37.1
39.9
44.1
51.8
56.8
65.1
76.7
83.6
91.7
98.5
113.7
141.0
150.8
178.2
187.7
189.5
202.9
222.3
230.8
250.1
277.0
314.0
327.9
347.0
372.1
408.9
446.1
484.6
541.9
529.3
539.0
574.2
619.3
676.8
719.5
762.7
777.5
720.5
768.0
811.1
830.9
869.3
922.9
974.4
996.1
1,069.0
1,151.9
1,171.6
1,196.3
1,270.3
1,128.5
1,147.2
1,161.3
1,170.5
1,156.3
1,166.6
1,177.0
1,186.5
1,193.9
1,168.7
1,200.5
1,222.0
1,244.9
1,256.4
1,276.3
1,303.8

548.5
605.8
668.5
719.7
797.3
894.7
998.6
1,122.4
1,239.7
1,353.1
1,501.5
1,622.9
1,817.2
2,008.1
2,210.3
2,391.3
2,566.6
2,793.1
3,002.1
3,194.9
3,314.4
3,561.7
3,796.6
4,042.5
4,267.2
4,513.0
4,787.8
5,132.4
5,495.9
5,904.5
6,182.2
6,460.4
6,784.4
7,198.5
7,627.2
8,056.6
8,453.5
8,666.3
8,616.1
8,915.3
9,246.6
9,571.6
9,861.4
10,315.3
10,807.4
11,256.1
11,731.4
12,318.7
12,820.0
12,414.0
13,893.5
12,091.6
12,269.1
12,406.6
12,507.6
12,573.7
12,763.4
12,915.6
13,027.5
12,784.0
11,401.2
12,645.1
12,825.7
13,251.4
13,859.9
14,081.5
14,381.3

1 Includes other items not shown separately.
2 Food consists of food and beverages purchased for off-premises consumption; food services, which include purchased meals and beverages, are not

classified as food.
Source: Department of Commerce (Bureau of Economic Analysis).

370 |

Appendix B

Table B–11. Real personal consumption expenditures, 2002–2021
[Billions of chained (2012) dollars; quarterly data at seasonally adjusted annual rates]
Goods

Year or quarter

2002 ����������������������
2003 ����������������������
2004 ����������������������
2005 ����������������������
2006 ������������