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economic re p ort of the president transmitted to congress | march 2023 together with the annual report of the council of economic advisers economic re p ort of the president transmitted to congress | march 2023 together with the annual report of the council of economic advisers Contents Economic Report of the President. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 The Annual Report of the Council of Economic Advisers. . . . . . . . . . . . . 7 Chapter 1: Pursuing Growth-Enhancing Policies in Today’s Changing World. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Chapter 2: The Year in Review and the Years Ahead. . . . . . . . . . . . . . . . 51 Chapter 3: Confronting New Global Challenges with Strong International Economic Partnerships. . . . . . . . . . . . . . . . . . . 93 Chapter 4: Investing in Young Children’s Care and Education. . . . . . . 125 Chapter 5: Building Stronger Postsecondary Institutions.. . . . . . . . . . . 153 Chapter 6: Supply Challenges in U.S. Labor Markets. . . . . . . . . . . . . . 183 Chapter 7: Competition in the Digital Economy: New Technologies, Old Economics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 Chapter 8: Digital Assets: Relearning Economic Principles.. . . . . . . . . 237 Chapter 9: Opportunities for Better Managing Weather Risk in the Changing Climate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 References.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 Appendix A: Report to the President on the Activities of the Council of Economic Advisers during 2022. . . . . . . . . . . . . . . . . . 415 Appendix B: Statistical Tables Relating to Income, Employment, and Production....................................................................427 ____________ *For a detailed table of contents of the Council’s Report, see page 11. iii Economic Report of the President Economic Report of the President To the Congress of the United States: Our Nation has faced tremendous challenges in recent years. A deadly pandemic and unprovoked war in Ukraine have tested our economy unlike any time since the Great Depression. When I was sworn into office, COVID-19 was raging and our economy was reeling. Millions of workers were out of a job, through no fault of their own. Hundreds of thousands of businesses had closed, our supply chains were snarled, and many schools were still shuttered. Families across the country were feeling real pain. Today, two years later, 230 million Americans have been vaccinated, and COVID no longer controls our lives. We have created a record 12 million jobs, which constitute the strongest two years of job gains on record. Unemployment is at a more than 50-year low, with near-record lows for Black and Latino workers, and manufacturing jobs have recovered faster than in any business cycle since 1953. Growth is up, wages are up, and inflation is coming down. At the same time, a record 10 million Americans have applied to start small businesses—each of their applications an act of hope. More Americans have health insurance today than ever before in our history, and real household wealth is 10 percent above what it was before COVID. It is safe to say: Our economic plan is working, and American families are starting to have a little more breathing room. It is important to remember, however, that the economic anxiety so many have felt did not start with the pandemic. For decades, the backbone of America, the middle class, has been hollowed out. Too many American jobs have been shipped overseas. Unions have been weakened. Once-thriving cities and towns have become shadows of what they used to be, robbing people of hard-earned pride and self-worth. I ran for President to rebuild our economy from the bottom up and middle out, not from the top down—because when the middle class does well, the poor have a ladder up and the wealthy still do well. We all do well. And that is what we have been working for. This past year, we made critical investments to secure America’s future. Together, the Bipartisan Infrastructure Law, the CHIPS and Science Act, and the Inflation Reduction Act represent the biggest public investments in our history—expected to draw more than $3.5 trillion in public and private funding for infrastructure, the digital economy, and clean energy over the next decade. First, the Bipartisan Infrastructure Law is an investment in America and our competitiveness. You cannot be the number one economy in the Economic Report of the President | 3 world unless you have the best infrastructure in the world. That is why this once-in-a-generation law is finally rebuilding our roads, bridges, railroads, ports, airports, and more—to keep our people safe, our goods moving, and our economy growing. Families across the Nation will have safe drinking water and high-speed Internet. A network of electric vehicle charging stations will allow more of us to drive cleaner cars. To date, we have funded over 20,000 construction projects all across the country, creating tens of thousands of well-paying new jobs. Americans everywhere can take pride in seeing shovels in the ground. Second, the CHIPS and Science Act, which I signed in August, will make sure that America once again leads the world in developing and manufacturing the semiconductors that power everything from cars to refrigerators to smartphones. The United States invented these chips; it is time to again manufacture them at home, and to make sure our economy never again relies so heavily on foreign chipmakers. Private companies have already announced more than $300 billion in new investments in American manufacturing in the last two years, many of them thanks to this law, creating tens of thousands more jobs of the future in every corner of the country. Third, the Inflation Reduction Act, also enacted last August, takes on powerful special interests to cut costs for working families. It lowers health care and prescription drug costs—for example, capping insulin at $35 a month for seniors on Medicare, and capping drug costs at $2,000 a year for seniors with Medicare Part D starting in 2025. It extends the Affordable Care Act’s subsidies, saving families an average of $800 a year. It also makes the Nation’s most significant investment ever in combating the existential threat of climate change, investing in everything from climate-smart agriculture to more resilient electric grids. It builds a new clean energy economy, creating thousands of green jobs in communities too often left behind, while also lowering home energy bills for families. Meanwhile, my Administration has taken wide-ranging executive actions to help level the playing field and promote competition. From easing the burden of crippling student debt, to providing relief to families at the gas pump, to cracking down on unfair junk fees, we are building an economy that gives everyone a fair shot and a little more breathing room. Throughout, we have shown that we can invest in our future and be fiscally responsible at the same time. We are helping to pay for these historic programs by finally making the wealthy and corporations pay their fair share, without raising taxes on anyone making under $400,000 a year. And we cut the deficit by $1.7 trillion during my first two years in office, the largest reduction in history, with more to come. I have often said that a job is about more than a paycheck; it is about dignity and respect. This is why we are not only investing in record job growth; we are also providing historic support for workers and unions at a 4 | Economic Report of the President time of big shifts in our workforce. We plan to ban noncompete agreements for 30 million workers who have been unfairly locked in their jobs, giving them the right to be paid what they are worth. We have boosted pay and labor protections for Federal contractors, we have pushed to extend these same protections to all workers, and we have passed laws to ensure safe and fair workplaces, including for pregnant and nursing workers and workers who face sexual assault and harassment on the job. We are investing in job-training programs and registered apprenticeships, which give so many people a ladder up to well-paying jobs on which they can raise a family without a college degree. Now, it is time to finish the job. We have much more to do to build an economy that benefits everyone—from cracking down on the deadly fentanyl epidemic and investing in mental health care and recovery, to fighting for childcare and paid family leave for millions of working families struggling to care for their loved ones, so no one ever again has to choose between the paycheck they need and the family they love. Our Nation is at an inflection point that will determine our future for decades to come. But today, because of the choices and investments we have made, jobs are coming back, pride is coming back, and the United States of America is better positioned to lead than any other country on Earth. Our blue-collar blueprint to rebuild America is proving that democracy can deliver, building an economy that is fairer and stronger and leaves no one behind. The White House March 2023 Economic Report of the President | 5 The Annual Report of the Council of Economic Advisers Letter of Transmittal Council of Economic Advisers Washington, March 20, 2023 Mr. President: The Council of Economic Advisers herewith submits its 2023 Annual Report in accordance with the Employment Act of 1946, as amended by the Full Employment and Balanced Growth Act of 1978. Sincerely yours, Cecilia Elena Rouse Chair Jared Bernstein Member Heather Boushey Member 9 Contents Chapter 1: Pursuing Growth-Enhancing Policies in Today’s Changing World. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Investing in Production Drives Economic Growth. . . . . . . . . . . . . . 23 The United States’ Economic Growth Over Time.. . . . . . . . . . . . . . 25 The Inputs to U.S. Economic Growth Over Time. . . . . . . . . . . . 29 U.S. Economic Growth in Context. . . . . . . . . . . . . . . . . . . . . . . 34 Sustaining Economic Growth in Today’s Changing World.. . . . . . . Investing in Human Capital and the Labor Supply: Implications of More Women Participating in the Labor Force. . . . . . . . . . . . Investing in Physical Capital: Adapting to the Increasing Effects of Climate Change.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Investing in the Economy’s Productivity: The New World of Digital Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 35 41 44 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Chapter 2: The Year in Review and the Years Ahead. . . . . . . . . . . . . . . . 51 The Year in Review: The Continuing Recovery. . . . . . . . . . . . . . . . 53 Output in 2022: A Return to Near Its Trend. . . . . . . . . . . . . . . . 54 The Historic Strength of Labor Markets in 2022. . . . . . . . . . . . 59 The Cooling of Financial Markets in 2022.. . . . . . . . . . . . . . . . 61 Inflation in 2022.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Factors That Had an Impact on Inflation in 2021–22. . . . . . . . 67 The Forecast for the Years Ahead .. . . . . . . . . . . . . . . . . . . . . . . . . . 84 The Near Term . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 The Long Term . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 Chapter 3: Confronting New Global Challenges with Strong International Economic Partnerships. . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 11 The United States’ International Trade and Investment in 2022. . . . 94 Pandemic-Related and Macroeconomic Trends Have Shaped Record Goods Imports. . . . . . . . . . . . . . . . . . . . . . . . . . 98 Geopolitical Shocks and Global Demand Have Shaped Record Goods Exports.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 International Trade in Services and Digital Trade Have Been Resilient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 Continued Growth for Foreign Direct Investment Despite Elevated Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 Global Economic Relations Are at a Turning Point. . . . . . . . . . . . Imperatives of Economic Partnerships in the Changing Global Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Resilience during Global Supply Shocks. . . . . . . . . . . . . . . . . Responding to Geopolitical Challenges. . . . . . . . . . . . . . . . . . Promoting Opportunity and Managing Risks in Digital Trade. 110 112 113 117 119 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 Chapter 4: Investing in Young Children’s Care and Education. . . . . . . 125 The Effectiveness of Early Childhood Investments. . . . . . . . . . . . 126 Benefits for Children and Society. . . . . . . . . . . . . . . . . . . . . . . 126 Defining Quality in ECE. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 Benefits for Working Parents.. . . . . . . . . . . . . . . . . . . . . . . . . . 129 Challenges in the Market for Early Care and Education.. . . . . . . . 133 Workforce Challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The High Costs of High-Quality Care. . . . . . . . . . . . . . . . . . . ECE Pricing and Price-Sensitive Consumers. . . . . . . . . . . . . . Business Model Fragility. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Participation in and Availability of ECE. . . . . . . . . . . . . . . . . 134 136 140 141 142 The Role of Subsidies in the Market for Care . . . . . . . . . . . . . . . . 146 International Comparisons. . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 Subsidies in the United States’ ECE Market. . . . . . . . . . . . . . . 147 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 Chapter 5: Building Stronger Postsecondary Institutions.. . . . . . . . . . . 153 The U.S. Postsecondary Institutional Landscape.. . . . . . . . . . . . . . 155 12 | Annual Report of the Council of Economic Advisers Institutions Serve a Diverse Student Population.. . . . . . . . . . . Institutions Vary in Their Prices and Spending on Students.. . Institutions Vary in Their Student Outcomes. . . . . . . . . . . . . . Institutions Matter for Student Outcomes. . . . . . . . . . . . . . . . . 156 157 158 160 The Rationale for and Delivery of Public Postsecondary Investment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 The Economic Rationale for Public Sector Investment. . . . . . 163 How Public Funds Are Delivered: Student Aid and Institutional Support .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 The Imperfect Market for Postsecondary Institutions. . . . . . . . . . . 169 Geographic Constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 Informational and Behavioral Constraints. . . . . . . . . . . . . . . . 170 College Expansion Constraints. . . . . . . . . . . . . . . . . . . . . . . . . 171 Institution-Focused Policies That Promote Access to Postsecondary Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Supporting the Quality of Existing Colleges and Programs.. . 173 Institutional Accountability. . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 Addressing Geographic Barriers to Access. . . . . . . . . . . . . . . 178 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181 Chapter 6: Supply Challenges in U.S. Labor Markets. . . . . . . . . . . . . . 183 Labor Supply Fundamentals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184 Trends in U.S. Labor Market Participation. . . . . . . . . . . . . . . 185 Why Worry About Slower Labor Supply Growth?. . . . . . . . . . 186 Causes of U.S. Labor Supply Challenges. . . . . . . . . . . . . . . . . . . . 189 Demographic Trends. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Declining Labor Market Participation Among Men.. . . . . . . . Female Labor Force Participation: The United States Falls Behind. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The COVID-19 Pandemic’s Lingering Effects on the Labor Supply. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189 191 197 199 Options to Boost the U.S. Labor Supply. . . . . . . . . . . . . . . . . . . . . 201 Increasing Immigration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 Drawing More Adults into the Labor Market . . . . . . . . . . . . . 204 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 Contents | 13 Chapter 7: Competition in the Digital Economy: New Technologies, Old Economics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 The Benefits of Digital Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Reducing Search Costs .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 213 Increased Variety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 “Free” Products and Services. . . . . . . . . . . . . . . . . . . . . . . . . 215 How Is Competition Different in Digital Markets?.. . . . . . . . . . . . 217 Big Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Network Effects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Multi-Homing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . When Do Markets Tip?. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 220 223 223 The Role of Law and Regulation in the Digital Market. . . . . . . . . 225 Network Effects Create a Competitive Moat.. . . . . . . . . . . . . . The Challenge of Preserving Competition in Digital Markets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preventing the Extension of Dominance into Adjacent Markets.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Preventing the Misuse of Consumer Data. . . . . . . . . . . . . . . . Monitoring Pricing Algorithms and Collusion. . . . . . . . . . . . . 227 228 229 230 232 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Chapter 8: Digital Assets: Relearning Economic Principles.. . . . . . . . . 237 The Perceived Appeal of Crypto Assets. . . . . . . . . . . . . . . . . . . . . 239 Claim: Crypto Assets Could Be Investment Vehicles. . . . . . . . Claim: Cryptocurrencies Could Offer Money-like Functions without Relying on a Single Authority . . . . . . . . . . . . . . . . . . . Claim: Crypto Assets Could Enable Fast Digital Payments .. Claim: Crypto Assets Could Increase Financial Inclusion . . . Claim: Crypto Assets Could Improve the United States’ Current Financial Technology Infrastructure. . . . . . . . . . . . . . 244 244 244 245 246 The Reality of Crypto Assets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246 Crypto Assets Are Mostly Speculative Investment Vehicles. . . 246 Cryptocurrencies Generally Do Not Perform All the Functions of Money as Effectively as Sovereign Money, such as the U.S. Dollar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251 Stablecoins Can Be Subject to Run Risk. . . . . . . . . . . . . . . . . . 255 14 | Annual Report of the Council of Economic Advisers Crypto Assets Can Be Harmful to Consumers and Investors. . . There Have Been Limited Economic Benefits from DLT Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Risks of Financial Innovation. . . . . . . . . . . . . . . . . . . . . . Other Risks from Crypto Assets. . . . . . . . . . . . . . . . . . . . . . . . 256 258 263 264 Investing in the Nation’s Digital Financial Infrastructure. . . . . . . . 268 The FedNow Instant Payment System. . . . . . . . . . . . . . . . . . . . 268 Central Bank Digital Currencies. . . . . . . . . . . . . . . . . . . . . . . 270 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272 Chapter 9: Opportunities for Better Managing Weather Risk in the Changing Climate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 Economic Principles of Adaptation Policy and Planning. . . . . . . . 277 The Economic Costs and Financial Risks of Climate Change in the United States. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 The Costs of Climate Change for the United States’ Well-Being and Prosperity.. . . . . . . . . . . . . . . . . . . . . . . . . . . . 280 Climate Change and Financial Stability.. . . . . . . . . . . . . . . . . 284 The Federal Fiscal Implications of Physical Climate Risk. . . . . . . 288 Risk Assumption.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Climate-Exposed Assets.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The Provision of National Public Goods . . . . . . . . . . . . . . . . . The Programs of the Social Safety Net. . . . . . . . . . . . . . . . . . . 289 289 291 291 Market Failures and Distortions That Slow Adaptive Adjustments and Policy Responses.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292 Imperfect Information on Physical Climate Risks. . . . . . . . . . Information Asymmetries.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Externalities and Public Goods. . . . . . . . . . . . . . . . . . . . . . . . Credit Constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Moral Hazard. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293 293 294 295 296 Four Potential Pillars of the Federal Adaptation Strategy and Major Policy Opportunities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297 Producing and Disseminating Knowledge about Climate Risk.297 Long-Term Planning for the Climate Transition.. . . . . . . . . . . 298 Ensuring the Accurate Pricing of Climate Risk. . . . . . . . . . . . 300 Contents | 15 Protecting the Vulnerable. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 302 References.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 A. B. 1-1 1-2 1-3 1-4 1-5 1-6 1-7 1-8 1-9 1-10 1-11 1-12 1-13 1-14 1-15 1-16 1-17 1-18 1-19 2-1 16 | Appendixes Report to the President on the Activities of the Council of Economic Advisers during 2022................................................ 415 Statistical Tables Relating to Income, Employment, and Production................................................................................... 427 Figures Average Annual U.S. Real GDP Growth since 1790, by Decade and Contributor......................................................................26 Miles of U.S. Railroads, 1830–90......................................................27 U.S. Patents Issued, 1790–2000.........................................................28 Contributors to U.S. Real GDP Growth, 1930–50............................29 U.S. Secondary School Enrollment and Graduation Rates, 1890–1991...........................................................................................30 The U.S. Labor Force, 1800–2021....................................................31 U.S. Residences with Electricity, 1930–56........................................31 U.S. Capital Stock, 1925–2021..........................................................32 Total Factor Productivity Growth, 1953–2021..................................33 GDP per Capita for the United States, Argentina, and Singapore, 1800–2018........................................................................34 Woman’s Labor Force Participation Rate, 1970–2022.....................36 Percentage of Postsecondary Degrees Received by Women, 1980–2020...........................................................................................37 Percentage of Households with a Child under 18 That Have an Adult over Age 65 Years, 1989–2021...........................................38 Consumption of Nursing Home and Childcare Services and Women’s Labor Force Participation, 1960–2022..............................39 Share of Private Industry Workers with Access to Benefits.............39 Nonparticipation in the Labor Force, by Reason..............................41 Carbon Dioxide Levels Over Time....................................................42 Number of Billion-Dollar Natural Disasters in the United States, 1980–2022...................................................................43 Percentage of Adults Who Report Using the Internet Over Time.....45 The U.S. Economy, 2018–22.............................................................51 Annual Report of the Council of Economic Advisers 2-2 2-3 2-4 2-5 2-6 2-i 2-7 2-8 2-9 2-10 2-11 2-12 2-13 2-14 2-15 2-16 2-17 2-18 2-19 2-ii 2-iii 3-1 3-2 3-3 3-i 3-ii 3-iii 3-4 3-5 3-6 3-7 3-8 3-9 3-10 GDP and Trend GDP, 2012–22..........................................................54 The 2019–22 Period Compared with Previous Business Cycles.....55 Job Openings per Unemployed Person, 2000–2022.........................60 The Beveridge Curve at Two Intervals..............................................60 Stock Market and Bond Prices, 2019–22..........................................62 Types of Consumer Price Inflation, 2011–22....................................64 The Expectations-Augmented Phillips Curve at Two Intervals.......65 Decomposition of Inflation, 2019–22................................................66 Global Measures of Consumer Price Inflation..................................67 Supply Chain Pressures and Producer Inflation, 1990–2022...........72 Commodity Pressures and PCE Inflation, 2006–22..........................73 Employment Cost Index and Inflation, 2013–22..............................74 Nominal and Real Measures of the Policy Rate, 2016–22...............76 The Composition of the Federal Reserve’s Balance Sheet, 2007–22...............................................................................................77 The Fiscal Impulse and Inflation, 2012–24.......................................78 OMB’s Primary Deficit Forecast, 2017–33.......................................80 Excess Savings and Inflation, 2016–22.............................................81 Consumer Goods-Services Rotation, 2018–22.................................82 Actual and Expected Inflation, 2012–22...........................................83 The Evolution of the U.S. Population’s Age Composition...............89 Age–Labor Force Participation Rate Profiles in 2019......................89 Real U.S. Trade in Goods and Services, 2012–22............................94 U.S. Trade Balance, 2018–22.............................................................95 Real U.S. Services Trade, 2012–22...................................................95 Federal Reserve Board’s Real Broad Dollar Index, 2016–22..........97 Top Sources of U.S. Goods Imports, 2022........................................99 Top U.S. Goods Export Destinations, 2022......................................99 Real Imports of Consumer Goods, 2018–22...................................100 Real Imports of Capital Goods (Excluding Automobiles), 2018–22.............................................................................................101 Real Exports of Consumer Goods, 2018–22...................................102 U.S. Exports of Liquefied Natural Gas, 2021 and 2022.................103 U.S. Exports of Crude Oil, 2021 and 2022.....................................104 U.S. Trade in Potentially ICT-Enabled Services, 1999–2021.........105 Real U.S. Outward Foreign Direct Investment, by Destination, 2012–22........................................................................108 Contents | 17 3-11 3-iv 4-1 4-i 4-2 4-ii 4-iii 4-3 4-4 4-5 4-6 4-7 4-8 5-1 5-2 5-3 5-4 5-5 5-6 6-1 6-2 6-3 6-i 6-4 6-5 6-6 6-ii 7-1 18 | Real U.S. Inward Foreign Direct Investment, by Source, 2012–22.............................................................................................109 Real Russian Foreign Direct Investment Net Inflows, 2017–22.... 115 Return on Investment in Human Capital, by Age...........................128 Food Insecurity among Households with Young Children, 2000–2021.........................................................................................131 Labor Force Participation Over Time, by Maternal Status.............132 Percent Change in Maternal Employment.......................................135 Racial and Gender Breakdown of Employment..............................137 Formal ECE Consumption, by Income Level.................................138 Average Annual Expenses for Formal ECE as a Proportion of Income, by Income Level.................................................................139 Ratio of Young Children to Childcare Capacity in 2018................142 Ratio of Infants and Toddlers to Childcare Capacity in 2018........143 Excess Demand by Provider Type...................................................144 Reasons Households Face Difficulty Finding Care........................145 Distribution of Enrollment Across Institution Types, by Student Characteristics.....................................................................156 Variation in Per-Student Expenditures.............................................157 Variation in Undergraduate Student Outcomes...............................159 Average Public Tuition and Fees and Percentage of Students Receiving Public Financial Aid—Bachelor’s Degree Programs, 2019–20.............................................................................................166 Distance Between Home and College, 2004–16.............................169 Per-Student State and Local Funding for Public Higher Education, 1989–2019......................................................................171 U.S. Labor Force Participation Rate, 1948–2022...........................186 Percentage of Total Projected Population That Is Prime Age, 2026–50.............................................................................................190 Prime-Age versus Overall Labor Force Participation, 1990–2022.........................................................................................190 Sources of Annual Income for Prime-Age Workers by Sex and Labor Force Status, 2022..................................................................192 Prime-Age Male Labor Force Participation, by Race, 1976–2022.........................................................................................193 Prime-Age Female Labor Force Participation, 1984–2021............198 Prime-Age Female Labor Force Participation Rate, 1976–2022.....199 Percent Change in Teacher Employment, 2019–22........................203 Growth in Advertising Revenue by Digital Platform, 2002–21.....215 Annual Report of the Council of Economic Advisers 7-i 7-2 7-3 8-1 8-2 8-3 8-i 8-ii 8-4 8-5 9-1 9-2 9-3 How Data Brokers Aggregate Data from Government, Commercial, and Publicly Available Sources to Build In-Depth Profiles of Consumers.......................................................219 Network Effects Are Present in Many Markets—Not Just Online.........................................................................................221 Completed Acquisitions by Large Tech Firms................................229 A Taxonomy of Digital Assets and Central Bank Money..............239 Market Capitalization of Selected Crypto Assets, 2020–22...........240 Payment Types Used in the United States Over Time....................244 Examples of Hashed Output.............................................................247 Blockchain Blocks Linked by Hashed Values of Their Contents....250 Volatility of Crypto Assets versus Certain Traditional Assets, 2017–22.............................................................................................251 Nominal Cyber Insurance Prices Over Time...................................266 Small Changes in Climate Can Greatly Increase the Probability of Extreme Weather Events...........................................276 Count of Policies under U.S. Residual Property Insurance Market, 1990–2021, with Geographic Breakdown for 2021..........285 Governance of Climate Risk Is Complex and Multiscale..............297 Tables 2-1 2-2 2-3 2-4 2-5 5-1 5-2 8-1 8-2 Real GDP Growth and Its Components, 2022..................................57 Selected Legislative and Executive Actions in 2022........................79 Economic Projections, 2021–33.........................................................85 Evolution of Blue Chip Consensus Real GDP Forecast...................86 Supply Side Components of Forecasted Real Output Growth.........88 College Prices and Expenditures by Sector.....................................158 Student Outcomes by Sector............................................................160 Top Ten Crypto Derivative Platforms by Open Interest.................263 Ransomware and Downtime Costs by Country, 2020....................267 1-1 2-1 2-2 2-3 3-1 3-2 3-3 What Is an Aggregate Production Function?.....................................24 Measures of Consumer Price Inflation..............................................63 The Phillips Curve and Other Models of Inflation...........................68 Aging and Growth..............................................................................89 Effects of the Strengthening U.S. Dollar on the U.S. Economy......96 The United States’ Top Goods Trading Partners...............................99 Rising Digital Trade and U.S. Labor Markets.................................107 Boxes Contents | 19 3-4 3-5 3-6 3-7 4-1 4-2 4-3 4-4 4-5 5-1 5-2 5-3 5-4 5-5 6-1 6-2 6-3 6-4 6-5 6-6 6-7 7-1 7-2 7-3 7-4 7-5 8-1 8-2 8-3 8-4 9-1 9-2 9-3 20 | The United States’ New Approach to Economic Partnerships....... 112 Coordination Has Been Critical for the Success of the Sanctions Policy toward Russia....................................................... 115 The U.S.-EU Energy Partnership Diminishes Russia’s Leverage.. 118 U.S. Digital Trade Initiatives............................................................121 Nutrition Support in Early Childhood.............................................130 The American Rescue Plan and Support for Childcare..................135 Who Works in ECE?.........................................................................137 Federal ECE Investments.................................................................148 New Data and New Methods to Inform Investments in Children...149 The Private and Public Benefits of College....................................164 International Comparison of Income-Driven Student Loan Repayment.........................................................................................168 Policies Focused on Direct Institutional Support............................175 Gainful Employment and Other Accountability Regulations.........177 Supporting Workforce Training Quality..........................................179 Labor Supply Terminology...............................................................185 Work and Leisure in the United States and Europe........................187 Deaths of Despair in the United States............................................188 On What Income Do Jobless Men Live?.........................................192 The Missing Prime-Age Workers.....................................................200 A Critical Shortfall of Nurses and Physicians.................................202 Staffing Challenges in K-12 Education...........................................203 The Societal Implications of Digital Markets.................................216 Consumer Data as a Business Model..............................................219 Glossary for Describing Digital Markets.........................................222 International and Subnational Efforts at Regulatory Reform.........231 Artificial Intelligence and Digital Markets......................................232 What Are the Functions of Money?.................................................241 How Does Bitcoin Work?.................................................................247 Crypto Asset Mining as a Risk to the Environment........................253 Proposed Uses of Distributed Ledger Technology..........................259 Adaptation and Resilience Investments of the Biden-Harris Administration...................................................................................278 Climate Change Will Most Likely Interact with and Exacerbate Existing Inequalities......................................................281 Disaster Insurance in the Changing Climate: Challenges and Opportunities for Reform..........................................................286 Annual Report of the Council of Economic Advisers Chapter 1 Pursuing Growth-Enhancing Policies in Today’s Changing World Economists often tout the value of economic growth. They argue that as the size of the economic “pie”—the value of all goods and services produced in a year—increases, everyone can get a larger slice of it, making them better off. Of course, growth is not the only economic goal a society may prioritize. Many societies also have notions of fairness and justice such as lower poverty and inequality, so they are attentive to how the slices of the pie are shared. That said, sustained economic growth is an important priority for most societies and, over long swaths of history, is an indispensable driver of improvement in human well-being. Economic growth is constrained, however, when the size of the economy reaches what economists call “potential gross domestic product (GDP)” or “capacity.” An economy’s long-run capacity depends on such factors as a growing and skilled labor force, high-quality physical infrastructure, and the efficiency of the production process. Actions that affect any of these factors can either constrain or enhance the capacity of the economy over time. Investing in increased economic capacity enables the economy to accommodate more demand in the medium to long run, which can make it more resilient to economic shocks and minimize the risk of inflationary episodes. The core of the Biden-Harris Administration’s economic agenda is building a foundation for steady, sustainable, and shared growth by increasing economic capacity. Over the last 50 years, the United States’ social and economic context has changed, leading to both opportunities and challenges for increasing 21 economic capacity. This has been for a variety of reasons, but three important ones are worth noting. First, women have surpassed men in educational attainment, and they joined the labor force in historic numbers through the late 1990s, though there has been a slowdown in their labor force participation gains in recent years. Women’s increased participation—and thus an increase in the size of the labor force generally—helped drive economic growth in the second half of the 20th century. At the same time, a lack of public investment in care has challenged workers, especially working women, with caregiving responsibilities. Second, soaring carbon emissions in the second half of the 20th century have exacerbated global warming, and the resulting climate change will increasingly become a barrier to economic growth without effective adaptation. Interrelated with the climate crisis, the damage to ecosystems continues to accelerate, creating significant risks for businesses and the wider economy (World Economic Forum 2020). Third, computers have entered virtually all aspects of life and can now perform tasks that previously were thought not able to be automated. The Internet has changed how people find information, learn, do business, and communicate with one another. These changes have spurred growth and helped some industries weather economic shocks like the COVID-19 pandemic better than they otherwise would have. But they have also raised important issues about how established economic policy can and should adapt to the new digital world. To expand the potential growth of the U.S. economy, policymakers need to adjust how the Nation invests in response to these kinds of changes. This year’s Report highlights selected areas where the changing economic and social context calls for a new approach to increasing the capacity for economic growth. The Report discusses how the relevant context in these areas has changed, analyzes pressing current challenges to sustained economic growth, and highlights potential strategies to confront these challenges. 22 | Chapter 1 Investing in Production Drives Economic Growth The inputs to sustained economic growth can be understood through the lens of an aggregate production function, which is explained in box 1-1. According to such a function, an economy’s output depends on its stock of human and physical capital as well as on a productivity factor that summarizes how efficiently workers, machines, and other types of inputs are put to use. Thus, sustained output growth relies on continuing private and public investments in the economy’s workforce, physical capital, and productivity (Mankiw 2010). In general, well-functioning markets incentivize households and firms to make investments that expand the economy, even when these households and firms do not have the larger economy in mind as they make their individual decisions. For example, a high school graduate who anticipates enhanced career opportunities from attaining a higher degree will likely pursue a college education. A firm that wants to grow but is having trouble hiring may invest in making its workplace more attractive to potential employees or pursue a management strategy to improve the efficiency of its existing workforce. These decisions are made independently throughout the economy without considering their effect on aggregate production, yet they jointly increase total economic capacity and growth. Unlike the private sector, the public sector is designed to invest in the economy with explicit consideration of the aggregate context. This is reflected in the types of investments it should ideally make, many of which are aimed at markets’ overall efficient functioning. The public sector operates the basic institutions that enforce the rule of law and property rights and thus enable households and firms to engage in the complex market system. It is also tasked with promoting competition and preventing socially destructive profit-seeking behavior, ensuring the stability of the financial infrastructure that greases the economy, and representing the interests of the U.S. economy in negotiating terms of trade with the rest of the world. In addition, when the private sector underinvests, the public sector can step in to invest in human and physical capital. Private underinvestment occurs for various reasons but often entails some combination of coordination failure, externalities, and credit constraints. For example, although virtually all firms benefit directly or indirectly from having functional roads running across the United States, it would be nearly impossible for them to coordinate a plan of action to build them. Compared with what would be best for society, firms tend to underinvest in the use of clean energy because they do not bear the full burden of the cost of pollution (costs are externalized). And information asymmetries in private credit markets can make it difficult for some entrepreneurs to access funding for upfront investments Pursuing Growth-Enhancing Policies in Today’s Changing World | 23 Box 1-1. What Is an Aggregate Production Function? An “aggregate production function” summarizes the process whereby an economy transforms inputs into goods and services. Consider a tomato farmer. She needs the input of labor—workers—to run and maintain her farm; the input of human capital—the education, training, skills, health, and other valuable resources embodied in a person—to know how to plant, raise, and harvest the tomatoes; and the input of physical capital— raw materials like seeds, along with harvesting and hydration equipment. The farmer uses these inputs to produce tomatoes as her output. Some of these tomatoes will be sold to consumers at the grocery store or at farmers’ markets as final goods; others will themselves become inputs into different products like ketchup and pizza sauce. When all the individual production functions like this farmer’s are combined in an economy, the result is an aggregate production function, which gives the aggregate output of the entire economy resulting from all its inputs. Aggregate output increases when there are more workers with in-demand skills, when these workers work more hours, and when they have access to more and better facilities and equipment to help them effectively perform their jobs. Physical capital also captures the broader infrastructure of roads, bridges, and broadband that allows goods, services, and information to move throughout the economy; note that this infrastructure is often public. An economy also grows when it becomes more efficient at combining labor and capital to produce output—that is, when it can produce more output with the same amount of inputs. Economists call this “total factor productivity.” In the tomato farmer example, agricultural innovations such as better farm management techniques, including crop rotation, have enabled farmers to grow tomatoes more easily. When total factor productivity grows, output increases, even if an economy’s inputs—labor, human capital, and physical capital—are kept constant, because the economy becomes more efficient at using these inputs. Economists tend to discuss total factor productivity in shorthand as “technology”; but in reality, total factor productivity has many different drivers and constraints beyond what one might typically think of as technology. For instance, culture and norms can have a substantial impact on output (Guiso, Sapienza, and Zingales 2006). And corruption holds back economic growth not just by disincentivizing investment in human and physical capital, but also by decreasing the amount of economic growth that an economy sees from a given amount of human and physical capital (Mauro 1995). Summarizing aggregate production as a function of physical capital, human capital, and total factor productivity is a useful abstraction that provides a framework to understand differences in economic 24 | Chapter 1 activity across time and countries. For certain questions, it makes sense to extend the basic model. One increasingly relevant extension is explicitly to include natural capital—the stock of water, land, air, and renewable and nonrenewable resources—as a distinct production factor in addition to built or produced physical capital. In the example given above, factors such as soil quality and quantity, climate, irrigation water, and the populations of insect pollinators provide important contributions to the tomato farmer’s output production. As natural capital becomes an increasingly important driver of variation in economic activity due to climate change, more questions will benefit from a direct inclusion of natural capital. Indeed, the Biden-Harris Administration recently launched a multiyear effort to put nature on the nation’s balance sheet for the first time (White House 2023). in their businesses, even when those investments promise private and social returns in the future, resulting in credit constraints. The United States’ Economic Growth Over Time The last two centuries have seen remarkable gains in material well-being around the world, driven by rapid output growth. The United States is an excellent example of a country that has experienced this economic transformation. Estimates of historical output suggest that in 1800, the United States was not even among the world’s 10 largest economies (Groningen Growth and Development Centre, n.d.). But its rapid growth ever since—averaging about 3.5 percent a year between 1800 and 2021—has turned the United States into the world’s largest economy in nominal terms. Figure 1-1 decomposes U.S. economic growth since 1800. Total output can be mechanically separated into (1) labor supply (how many workers there are)—which in turn depends both on population size and labor force participation—and (2) how much average output these workers produce. Output per worker reflects the growth drivers other than labor supply mentioned above: human capital, physical capital, and total factor productivity. This decomposition of economic growth highlights how the relative importance of the American workforce’s size versus its productivity in driving growth has changed over time. Productivity-driven economic growth is ultimately what spurs sustained growth in output per capita, which better reflects how growth translates into improved living standards for individuals. In the first half of the 19th century, aggregate growth was driven mainly by the country’s increasing population and by its larger workforce. This Pursuing Growth-Enhancing Policies in Today’s Changing World | 25 Figure 1-1. Average Annual U.S. Real GDP Growth since 1790, by Decade and Contributor Percent average annual growth over the period 7 6 5 (Note: Decomposition not available in the 1790s) Post–Civil War boom World War II / postwar boom Rise in women's LFPR 4 Output per worker Reflects human capital, physical capital, and TFP Total growth 2 1 0 1800-2021 1950s 1960s 1970s 1920s 1930s 1940s 1890s 1900s 1910s 1850s 1860s 1870s 1880s 1820s 1830s 1840s Civil War / Emancipation 1790s 1800s 1810s –2 Population Aging of baby boomers 1980s 1990s 2000s 2010-21 –1 Reflects labor supply Labor force participation 3 Sources: Weiss 1999; Lebergott 1966; Bureau of Economic Analysis; Bureau of Labor Statistics; Census Bureau; CEA calculations. Note: LFPR = labor force participation rate; TFP = total factor productivity. implied comparatively more limited gains in material well-being for the average person. In contrast, aggregate growth in the 20th century was driven mostly by an increasingly productive workforce and thus translated more directly into higher individual output and incomes. Productivity-driven output growth in the United States has been the result of private and targeted public investment in the skills of its workforce, its equipment and infrastructure, and the technologies that enable its workers to most efficiently use their skills. Two decades in particular—the 1870s and 1940s—stand out for having the highest average growth in American history. These decades serve as case studies for some of the factors that drive economic expansion. Economic historians sometimes mark the 1870s as the start of a period of advancement called the “Second Industrial Revolution” (e.g., DeLong 2022). Some of the investments during this decade were made to repair the infrastructure that had been damaged or destroyed during the Civil War. But investment in the 1870s went well beyond replacement. In 1860—on the eve of the Civil War—there were about 31,000 miles of railroads in operation in the United States. By 1870, this had increased to 53,000 miles; and by 1880, to 98,000 miles (see figure 1-2). In 1869, Western Union operated about 105,000 miles of telegraph wires and handled just under 8 million messages annually. Ten years later, it had doubled the mileage of its wires and was handling more than three times the number of telegraph messages (Carter et al. 2006, series Dg 9 and Dg11). And this surge in investment in the 1870s was not limited to physical infrastructure but also extended 26 | Chapter 1 Figure 1-2. Miles of U.S. Railroads, 1830–90 Miles operated, thousands 180 160 140 120 100 80 60 40 20 0 1830 1835 1840 1845 1850 1855 1860 1865 1870 1875 1880 1885 1890 Source: Carter et al. 2006, series Df874. to ideas. The United States issued roughly 72,000 patents for inventions between 1860 and 1869; the next decade, it issued about 125,000 (see figure 1-3). Moreover, the Nation’s labor supply grew strongly during the 1870s: the U.S. labor force was about 35 percent larger in 1880 than it was in 1870, thanks both to natural population growth and immigration (Migration Policy Institute, n.d.). In comparison, the labor force grew by a total of 5 percent between 2011 and 2021. The expansion of physical capital and ideas, combined with the increasing labor force, corresponded with strong growth in the 1870s and beyond. Like the 1870s, the 1940s came on the heels of a catastrophe—in this case, the Great Depression. However, growth in the 1940s was less about an increase in the labor supply, given that the labor force grew at about half the rate it did in the 1870s. Instead, growth in the 1940s was driven by a combination of public investment and greater utilization of a labor force with a high unemployment rate at the start of the decade: although unemployment had fallen substantially from its 1932 peak of 22.9 percent, in 1940 it was still at an elevated rate of 9.5 percent (Carter et al. 2006, series Ba475).1 The United States’ entry into World War II accelerated this growth. The number of active-duty military personnel grew from just over 300,000 in 1939 to 12 million by the war’s end in 1945 (National World War II This series treats workers participating in Federal emergency New Deal programs like the Works Progress Administration and Civilian Conservation Corps as “employed”; official labor market statistics, which were still in their infancy at the time, classified these workers as “unemployed.” 1 Pursuing Growth-Enhancing Policies in Today’s Changing World | 27 Figure 1-3. U.S. Patents Issued, 1790–2000 Patents issued for inventions, thousands 180 160 140 120 100 80 60 40 20 0 1790 1805 1820 1835 1850 1865 1880 1895 1910 1925 1940 1955 1970 1985 2000 Source: Carter et al. 2006, series Cg30. Museum, n.d.). This mobilization, in combination with increased private hiring (driven itself in large part by wartime government orders) pushed the unemployment rate down to 1.2 percent by 1944 and expanded women’s labor force participation (Acemoglu, Autor, and Lyle 2004; Carter et al. 2006, series Ba475; National Archives, n.d.). Public investment in physical capital also skyrocketed. The Federal Government’s gross investment rose from $12 billion in 1940 to $270 billion in 1944 (inflation-adjusted).2 Growth in government consumption and investment was responsible for adding 10 percentage points to real GDP growth in 1941 and an astounding 28 percentage points in 1942 (see figure 1-4). The end of the war did not mean a return to the prewar economy of the 1930s that was characterized by high unemployment and depressed output. Demobilization led to a mild recession in 1945, as the United States began shifting away from the wartime economy, and the unemployment rate crept back up in the years after the war. But it did not return to its prewar levels of 9.5 percent and above (Carter et al. 2006, series Ba475). Even as Federal investment retreated in the years following World War II, private investment picked up. Between 1944 and 1950, real Federal defense investment fell by $257 billion (in 2021 dollars). But real private fixed investment rose by $224 billion over that same period, and real personal consumption was $444 billion higher. Indeed, increased private 2 This is measured in inflation-adjusted 2021 dollars. 28 | Chapter 1 Figure 1-4. Contributors to U.S. Real GDP Growth, 1930–50 Percentage points, annual real GDP growth 35 25 15 5 –5 –15 –25 –35 1930 1935 Personal consumption 1940 Private investment 1945 Net exports Government 1950 Total Source: Bureau of Economic Analysis. investment and consumption were even able to substantially offset the massive 29-percentage-point deduction to GDP growth from the postwar demobilization in 1946 (see figure 1-4). Government investments during World War II helped pave the way for private investments that sustained renewed economic growth throughout the latter half of the 20th century (Goodwin 2001). The Inputs to U.S. Economic Growth Over Time The economic growth of the United States over the last two centuries would not have happened without investments in the labor force, the physical capital stock, and total factor productivity. The previous pages of this section discussed what some of these investments looked like during two key rapid-growth decades: after the Civil War, and around World War II. This subsection steps back and considers each of these factors over a wider span of American history, highlighting key public and private investments for each one and discussing selected available measures of how they have evolved over time. The labor force. Over the past 200 years, both public and private actors have invested in the skills and size of the labor force. Consider the key input of education. For centuries, the United States has been a world leader in public education. Beginning in the 1700s, American communities began to establish publicly funded or free schools, along with land grants Pursuing Growth-Enhancing Policies in Today’s Changing World | 29 to support the creation and maintenance of schools (Kober and Rentner 2020). Over time, an array of private and nonprofit institutions—including private schools, universities, and vocational training programs—have also become integral to the U.S. educational landscape. Investments in education transformed the skills of American workers. In the first several decades of the 20th century, the United States underwent what is now termed the “high school movement” (see figure 1-5); between 1910 and 1940, the share of those age 18 years with a high school diploma (from a private or public institution) rose by over 40 percentage points (Goldin and Katz 2009). However, progress has at times been uneven; segregation and other forms of race and gender discrimination in the education system have presented barriers to educational attainment for women and people of color. The U.S. labor force has also grown—from roughly 1.5 million workers in 1800 to over 160 million today—with particularly rapid growth in the second half of the 20th century, although there has been a gradual decline in the growth rate since the 1980s (see figure 1-6). The physical capital stock. Investment has also focused on physical capital and productivity. This has included investments by the public sector, such as under the Rural Electrification Act of 1936, which provided loans to farmers and investors to expand electricity to rural communities, with remarkable results, as depicted in figure 1-7 (Sablik 2020). Later in the 20th century, the United States built out the Interstate Highway System, which is often described as one of the greatest public works projects in history Figure 1-5. U.S. Secondary School Enrollment and Graduation Rates, 1890–1991 Percent 100 90 80 70 60 50 40 30 20 10 0 1890 1900 1910 1920 1930 1940 Enrollment 1950 1960 1970 Graduation Sources: U.S. Department of Education; Goldin and Katz 2009; CEA calculations. Note: Graduation data were reported every 10 years before 1930; some years are missing after 1930. 30 | Chapter 1 1980 1990 (Capka 2006; Pfeiffer 2006). In parallel with the public sector’s investments, the private sector has invested in physical capital, such as by constructing Figure 1-6. The U.S. Labor Force, 1800–2021 Millions 180 160 140 120 100 80 60 40 20 0 1800 1820 1840 1860 1880 1900 1920 1940 1960 1980 2000 2020 Sources: Weiss 1999; Carter et al. 2006, series ba1-10; Census Bureau; Bureau of Labor Statistics; CEA calculations. Figure 1-7. U.S. Residences with Electricity, 1920–56 Percent 100 90 80 70 Rural Electrification Act 60 50 40 30 20 10 0 1920 1925 1930 1935 1940 Urban and rural nonfarm 1945 1950 1955 Farm Sources: U.S. Census Bureau 1975, table S 108-119; Sablik 2020. Note: Data on the percentage of dwelling units with electric service in urban and rural nonfarm settings are only available every five years. Pursuing Growth-Enhancing Policies in Today’s Changing World | 31 factories, farms, and office buildings—in 2021, gross private domestic investment exceeded $4 trillion (FRED 2022). Public and private investments have combined to facilitate the continued growth of the Nation’s capital stock over the past century, as highlighted in figure 1-8. (The capital stock includes physical capital, ranging from trucks to houses to software to roads.) Total factor productivity. As discussed above, total factor productivity is the most amorphous input into economic growth. It captures many aspects, from technological innovation to the quality of institutions that promote competition and the efficient allocation of scarce resources. Consequently, historical public and private investment in total factor productivity is multifaceted and not always straightforward to measure. For example, one paper estimates that between 20 and 40 percent of growth in aggregate output per person in the United States between 1960 and 2010 can be explained by improved talent allocation brought on by reduced discrimination and changing preferences among Black and white women and Black men (Hsieh et al. 2019). The Civil Rights Act of 1964 likely contributed to this reduced discrimination and thus, in addition to rectifying long-standing injustices, it functioned as an investment in the economy. But this type of investment is difficult to quantify, given that it is concurrent with broader social change. Figure 1-8. U.S. Capital Stock, 1925–2021 Trillions of 2021 dollars 90 80 70 60 50 40 30 20 10 0 1925 1931 1937 1943 1949 1955 1961 1967 1973 1979 1985 1991 1997 2003 2009 2015 2021 Sources: Bureau of Economic Analysis; CEA calculations. 32 | Chapter 1 Other forms of investment are more tangible, such as investments in research and development. The United States is consistently one of the top spenders on research and development (OECD 2022a). The majority of this spending comes from the private sector, but the public sector also plays an important role, especially in funding basic research (Burke, Okrent, and Hale 2022). The information technology revolution exemplifies the complementary roles of public and private investment in the economy. The government played an essential role in developing groundbreaking technologies, such as the Internet and the Global Positioning System. These early-stage investments were arguably too risky for any private firm to undertake (Mazzucato 2013). But it was the private sector that transformed these base technologies into the market-oriented ones that have shaped the way people work and live. Unlike the number of workers or the stock of physical capital, the return on these investments cannot be measured directly. However, economists can infer that total factor productivity is changing when total output changes more or less than would be expected based on observed changes in the labor force and the physical capital stock. That is, a greater-than-expected increase in total output suggests that total factor productivity has increased, whereas a smaller-than-expected increase suggests that it has decreased. Figure 1-9 shows total factor productivity growth in the United States since 1953. In the short term, productivity growth fluctuates considerably, Figure 1-9. Total Factor Productivity Growth, 1953–2021 Percent annual growth, five-year moving average 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 –0.5 –1.0 –1.5 1953 1958 1963 1968 1973 1978 1983 1988 1993 1998 2003 2008 2013 2018 Source: Bureau of Labor Statistics. Pursuing Growth-Enhancing Policies in Today’s Changing World | 33 partly because it can only be inferred from other measurements. Still, there are some notable trends over time. The postwar decades were marked by relatively high productivity growth, before a notable slowdown in the 1970s and through the mid-1990s. Productivity growth picked up again into the 2000s, but it fell during the Great Recession. The decade of slow productivity growth in the 2010s was common to most advanced economies, and it is still not fully understood (Dieppe 2020). U.S. Economic Growth in Context Although economic growth in the United States over the past 200 years has led to enormous gains in material well-being and life expectancy, and has given the United States an economic and political leadership role on the global stage, it cannot be taken for granted. For example, in the 1800s, the United States’ GDP per capita was about 70 percent larger than that of Argentina. But faster average U.S. GDP growth starting in the second half of the 20th century caused the two countries to diverge further, and today U.S. GDP per capita is about three times as large as Argentina’s (figure 1-10). Singapore, conversely, experienced slower growth for much of the 20th century before growing rapidly—more quickly than the United States—beginning in the 1960s; the country’s GDP per capita is now above that of the United States. Figure 1-10. GDP per Capita for the United States, Argentina, and Singapore, 1800–2018 Log scale, real GDP per capita, 2011 dollars 128,000 64,000 32,000 16,000 8,000 4,000 2,000 1,000 1800 1850 United States Source: Groningen Growth and Development Centre, n.d. Note: Missing values are interpolated. 34 | Chapter 1 1900 Argentina 1950 Singapore 2000 Economies have diverged widely in modern history, and though there have been various factors in this divergence, both public and private institutions—and economic policies—are a central part of the story. Indeed, Argentina and Singapore are telling case studies in this regard. The Argentine economy accelerated in the late 1800s thanks to immigration, exports, and foreign investment. However, productivity and economic growth in the country stagnated in the 20th century in the context of the Great Depression and political instability, beginning with the military coup in 1930 (Spruk 2019). Singapore’s economic growth, conversely, is generally considered an example of successful economic policy. The rapid rise of Singapore and the other “East Asian Tigers” has been attributed to a set of common, market-friendly economic policies that targeted macroeconomic stability, public infrastructure and education, and export orientation (World Bank 1993; Lee 2019). Sustaining Economic Growth in Today’s Changing World The preceding discussion highlighted the importance of past and continued private and public investments in the U.S. economy. Many of these historical investments remain relevant today. The Nation must continue to make investments to ensure access to high-quality education, from childhood through adulthood; to maintain its physical infrastructure; and to ensure that markets remain fair and competitive. However, these investments are not being made in a vacuum. They are influenced by changes in society and the economy that have an impact on the need for, and value of, different kinds of investments—in human and physical capital, as well as in total factor productivity. Sometimes the private sector adapts quickly and well to these changes; other times, the public sector needs to spur private investment and provide the necessary guardrails to protect individuals and the U.S. economic system. Investing in Human Capital and the Labor Supply: Implications of More Women Participating in the Labor Force Millions of American women entered the labor force in the latter half of the 20th century—with substantial implications for society, economic growth, and public policy. Between 1970 and 2000, U.S. women’s labor force participation rose from roughly 43 percent to 60 percent (figure 1-11). Although this overall trend masks important differences in levels of participation by dimensions such as race, age, income, and family status, virtually all groups of women saw large participation gains over these decades. This period has been termed the “Quiet Revolution” by the economist Claudia Goldin, who has identified turning points in the late 1960s and early Pursuing Growth-Enhancing Policies in Today’s Changing World | 35 Figure 1-11. Women’s Labor Force Participation Rate, 1970–2022 Percent 65 60 55 50 45 40 35 1970 1980 1990 2000 2010 2020 Source: Bureau of Labor Statistics. 1970s in the marriage age, college graduation rate, and extent of professional school enrollment for women—along with the gradual lifting of some discriminatory barriers for women, shifts in social norms for women’s family and career decisions, and factors accounting for women’s life satisfaction (Goldin 2006). Thus, Goldin attributes the increase in women’s labor force participation to many factors, such as reduced labor market discrimination against women and women’s increased choice in making reproductive decisions through the invention, dissemination, and legalization of the birth control pill. In addition to women’s labor force participation, women’s educational attainment increased drastically relative to men’s. Today, women earn the majority of bachelor’s, master’s, and doctoral degrees (figure 1-12). The economic consequences of these trends are significant. Using a methodology similar to the one used in the 2015 Economic Report of the President, updated CEA calculations indicate that the U.S. economy was almost 10 percent larger in 2019 than it would have been without the increase in women’s employment and hours worked from 1970 to 2019 (Council of Economic Advisers 2015). However, starting in about 2000, women’s labor force participation plateaued and began to decline. Men for their part have also seen a multidecade decline in participation, although they continue to participate at a higher rate than women. Removing barriers to women’s and men’s participation and educational attainment would ease labor constraints on firms 36 | Chapter 1 Figure 1-12. Percentage of Postsecondary Degrees Received by Women, 1980–2020 Percent 65 60 55 50 45 40 35 30 25 20 1980 1985 1990 1995 Bachelor’s 2000 Master’s 2005 2010 2015 2020 Doctoral Source: U.S. Department of Education, National Center for Education Statistics. Note: The dashed line indicates 50 percent. Doctoral degrees include all doctoral degrees, including M.D., J.D., and Ph.D. that want to open or expand, boost long-run economic growth, and increase prosperity. One factor affecting labor force participation, particularly for women, is household, community, and care responsibilities. Before entering the labor force in large numbers, women had long provided a large share of unpaid work in their homes and communities, including household maintenance tasks, raising children, caring for elder family members, and volunteering for community projects—work typically not captured in economic measures like GDP. Today, women working outside the home continue to disproportionately undertake these tasks; one recent study found that in heterosexual marriages, even when women’s wages are more than double those of their spouses, women do 44 percent more household work (Siminski and Yetsenga 2022). At the same time, in recent decades, the aging of the so-called baby boom generation (people born roughly between 1946 and 1964) and reduced fertility rates have increased the demand for senior care while constraining the supply of younger workers. The ratio of people age 65 and above to the number of people age 16 to 64, sometimes called the old-age dependency ratio, has more than doubled during the past seven decades. This has contributed to increased demand for care from adult children, creating the so-called sandwich generation of people who have care responsibilities for both older Pursuing Growth-Enhancing Policies in Today’s Changing World | 37 and younger family members (figure 1-13). In 2017 and 2018, more than 8 million parents of children under 18 also provided senior care, including nearly 5 million mothers (BLS 2019). Women’s shift into the labor force and demographic changes were associated with an increased demand for paid workers to provide what was previously unpaid labor, particularly caring for young children and older or disabled adults (figure 1-14). These care workers are often paid very low wages and are disproportionately women, particularly women of color. To meet care needs, in recent years, multiple States and cities have passed legislation to provide paid family and medical leave for workers (National Partnership 2022). Additionally, private firms have increasingly provided paid family and medical leave, remote work adjustments, and other benefits to help workers balance their care and work responsibilities (figure 1-15). However, the lack of a national paid family and medical leave program, of adequate affordable child care, and of Federal labor laws to guarantee flexibilities for workers with care responsibilities has limited the ability for caregivers, especially women, to remain in the labor force. The actions of the private sector have not been enough to meet the scale of the problem. Inequality in who has access to these benefits means that workers in the top 10 percent of the wage distribution are nearly eight times as likely as the lowest-paid workers to have access to paid family leave and more than four times as likely to have access to childcare through their work (BLS 2022a). Low-wage and hourly workers can particularly struggle to Figure 1-13. Percentage of Households with a Child under 18 That Have an Adult over Age 65 Years, 1989–2021 Percent 7 6 5 4 3 2 1 0 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 Sources: Current Population Survey; CEA calculations. 38 | Chapter 1 Figure 1-14. Consumption of Nursing Home and Childcare Services and Women’s Labor Force Participation, 1960–2022 Percentage of GDP Percent 65 1.2 1.0 60 0.8 55 0.6 50 0.4 45 0.2 40 0.0 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020 35 Consumption of nursing home and childcare services (left axis) Women’s labor force participation (right axis) Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations. Figure 1-15. Share of Private Industry Workers with Access to Benefits Percent 30 24 25 20 15 10 10 11 9 5 5 0 9 Paid family leave Childcare 2010 Flexible workplace 2022 Source: Bureau of Labor Statistics. manage work and care responsibilities due to limited workplace flexibilities, such as fair and predictable scheduling. Although data are limited, evidence suggests that the market and public sector underprovide both childcare and senior care. For example, the CEA’s analysis of the 2019 National Survey of Early Care and Education indicates that nearly three-quarters of childcare centers are experiencing excess demand (i.e., have a waiting list or reject children due to limited capacity). Pursuing Growth-Enhancing Policies in Today’s Changing World | 39 There are many reasons that the care industry has not been able to evolve to meet current needs. Among other issues, even as other service-providing industries have benefited from productivity-enhancing technological advances, the care industry has not; it still takes roughly as many people to watch 10 children today as it did 50 years ago, and the cost of childcare has risen in part due to increases in provider wages (although they are still quite low).3 Between 1990 and 2021, the price of childcare rose by 225 percent while the median household income rose by roughly 150 percent. In addition, because families face liquidity constraints, like the inability to borrow against future income, they are often unable to afford the childcare that best meets their needs. These challenges have a negative impact on labor force participation, particularly for women. In 2022, 14.6 percent of women between the age of 25 and 54 said that they were not in the labor force because they were caring for their home or family, representing roughly 60 percent of all the primeage women not working. While some women may prefer providing family care to participating in the labor force, research suggests that for others, the cost of care limits their choices. Studies indicate that government policies that reduce the costs of care can strengthen participation, particularly for women (Morrissey 2017; Shen 2021). But relative to its peers, the United States provides few policies that could help families meet these care needs, such as paid family and medical leave and childcare investments—an observation researchers often make when discussing trends in U.S. female labor force participation (Blau and Kahn 2013). Whereas in 1985, the participation rate among women age 25 to 54 in the United States exceeded the rate in Canada, the United Kingdom, Japan, Australia, and the European Union, in recent years, the United States has experienced lower women’s participation than Canada, the United Kingdom, Japan, Australia, and the European Union (OECD 2022b). Further, care responsibilities affect men’s participation in the economy along with women’s, particularly as gender norms evolve (figure 1-16). In 2022, 2 percent of men between the age of 25 and 54 said they did not work due to home or family responsibilities, up from 0.9 percent in 1995. Subsidizing care for families while simultaneously investing in the supply of childcare would likely increase the overall number of workers who are able to enter the workforce, and thus facilitate economic growth. The Baumol-Bowen cost disease is what economists call this tendency for wages and costs to rise in industries that see smaller productivity gains in response to increases in wages and costs from industries that have seen larger productivity gains (Maiello 2017). 3 40 | Chapter 1 Figure 1-16. Nonparticipation in the Labor Force, by Reason Percentage of prime-age people 25 20 15 10 5 0 Men, 1995 Disability Men, 2022 Retirements School enrollment Women, 1995 Home/family care Women, 2022 ʿʿSomething else’’ Sources: Current Population Survey; CEA calculations. Investing in Physical Capital: Adapting to the Increasing Effects of Climate Change Physical capital is the next important input for economic growth. The kinds and quantities of physical capital needed are in a constant state of flux. For much of human history, infrastructure was designed around animal power, such as horses. In the 19th century, infrastructure began shifting to railroads, while the 20th century saw rapid shifts toward infrastructure for automobiles and airplanes. Although much of the transportation infrastructure built in the 20th century remains useful today, the 21st century has seen massive investments in network infrastructure to allow for faster and more reliable communications and Internet access. At the same time, the large and growing effects of climate change pose a significant, broad-based risk for physical capital. Over the past century, the level of carbon dioxide (CO2) in the air has risen drastically (figure 1-17). In 2013, atmospheric CO2 concentration surpassed 400 parts per million for the first time in recorded history (Blunden 2014). In 2021, it averaged nearly 415 parts per million (Lan, Tans, and Thoning 2022). Climate models find that the increased level of greenhouse gases in the atmosphere is in turn responsible for rising sea levels, hotter weather, and more common and severe extreme weather events—trends that are predicted to continue even with an ambitious reduction of greenhouse gas emissions. Pursuing Growth-Enhancing Policies in Today’s Changing World | 41 Figure 1-17. Carbon Dioxide Levels Over Time Parts per million 440 2022 level 400 360 320 280 1950 level Highest historical COଶ level 240 200 160 –800 –700 –600 –500 –400 –300 –200 –100 0 Thousands of years relative to 1950 (1950 = 0) Source: Lüthi et al. 2008. Note: Data come from reconstructions from ice cores. The economic damage from climate change has already begun to accrue and have an impact on communities around the globe. Some of these types of damage emerge in human capital: beyond the effects on human health (e.g., Carleton et al. 2022), researchers have documented the effects of climate change on migration flows (Missirian and Schlenker 2017; Jessoe, Manning, and Taylor 2018), violent crime (Ranson 2014), labor productivity (Graff Zivin and Neidell 2012), and learning (Park et al. 2020; Park, Behrer, and Goodman 2021). However, this damage has also been observed in physical capital. In the United States, the damage from billion-dollar disasters (see figure 1-18) now averages roughly $120 billion a year (Smith 2023). Costs from rising extreme weather are being driven both by the changing climate and by rapid development in risky areas (Climate Central and Zillow 2018; Iglesias et al. 2021). Climate change has been found to affect crop yields and agricultural productivity, and increasingly frequent heat waves will likely exacerbate increasing strain on electrical grids (Woetzel et al. 2020; Auffhammer, Baylis, and Hausman 2017). Instability due to climate change is expected to cause new systemic risks for financial markets (Financial Stability Oversight Council 2021; Brunetti et al. 2021). In insurance markets, extreme weather events are driving higher payouts, which can raise premiums and reduce insurance availability (Lara 2019; Botzen, van den Bergh, and Bouwer 2010). Large disasters could cause insurance companies to fail altogether—as seen after Hurricane Andrew’s $15.5 billion in property damage in 1992, and along the Gulf 42 | Chapter 1 Figure 1-18. Number of Billion-Dollar Natural Disasters in the United States, 1980–2022 Number of billion-dollar natural disasters 25 20 15 10 5 0 1980 1985 Droughts 1990 Floods 1995 Tropical cyclones 2000 Severe storms 2005 2010 Freezes Wildfires 2015 2020 Winter storms Source: NCEI 2022. Note: Disaster costs are adjusted for inflation using the Consumer Price Index for All Urban Consumers. Coast after more recent hurricane strikes (Gelzinis and Steele 2019; Elliott 2022). Even if markets continue to adapt to past experiences, rising climate uncertainty may increase the risks of future failures as unanticipated costs rise for insurance companies. These effects of climate change have important consequences for physical capital and will likely require adaptation by institutions—from insurance and financial markets to construction firms, energy producers, and the government. To lessen the economic damage of climate change, these institutions, and many other actors throughout the global economy, will need to quickly transition away from fossil fuels and emit fewer greenhouse gases (known as “mitigation”). They will also need to protect physical capital from damage (“adaptation”), such as by building resilient infrastructure, employing nature-based solutions to improve resilience in the face of more frequent extreme weather events, and shifting new investments away from high-risk areas. The scale and timing of climate change have led many researchers to conclude that both mitigation and adaptation are necessary (IPCC 2014). Adaptation measures may range from actions on the individual level, like raising the foundations of houses to accommodate rising sea levels and changing agricultural cropping practices, to community-level actions, like building seawalls and expanding reservoirs’ capacities to deal with more variable rainfall. Climate change is also expected to alter the productivity and value of different forms of capital in complex ways. The existing infrastructure, Pursuing Growth-Enhancing Policies in Today’s Changing World | 43 which was designed for older climate conditions, may underperform in these new conditions. For instance, major hydropower dams in the Southwest may soon cease being able to produce electricity because of the decades-long drought (Ramirez 2022; Partlow 2022; Kao et al. 2022). In contrast, other forms of capital may become more valuable. Existing sea-walls and riverine flood defenses provide greater value in the face of the changing climate that is increasing coastal and inland flooding risks. Additionally, with the rise of clean energy technologies, some resources like the Sun and wind have acquired new value and have become important kinds of capital. For example, though societies have used windmills for centuries, wind has only become a widely used source of electric power in the last few decades as technological advances have met the energy needs in a changing climate. Climate change is already reducing—and will very likely continue to reduce—growth in GDP (Burke, Hsiang, and Miguel 2015; Newell, Prest, and Sexton 2021; Kalkuhl and Wenz 2020); this type of harm could be reduced with greater investment in adaptation. A recent summary of the literature from the Council of Economic Advisers and the Office of Management and Budget (2022) shows substantial variation in the estimates of the impact of global warming on U.S. GDP. For example, the Congressional Budget Office estimates that climate change will reduce the average annual GDP growth rate by 0.03 percentage point from 2020 to 2050 (Herrnstadt and Dinan 2020), which implies that the level of U.S. GDP would be just under 1 percent lower by 2050, whereas a study by the Bank of England (2021) finds that climate damage could reduce U.S. GDP by over 11 percent by 2050 in a worse-than-expected scenario. These estimates only capture a fraction of climate change costs, however, since many effects, such as increasing mortality risk and ecosystem disruption, are not fully reflected in market transactions or GDP estimates (Rennert et al. 2022; Bastien-Olvera and Moore 2021). By one estimate, more than half of global GDP is moderately or highly dependent on nature, which is being lost or dramatically altered by human activity (World Economic Forum 2020). Investing in the Economy’s Productivity: The New World of Digital Markets Rapid advances in information technology in recent decades have had a substantial impact on how Americans work and live. Computer and information technology occupations now account for 3 percent of all employment in the United States, and the Bureau of Labor Statistics (BLS) projects that the number of these jobs will increase by 15 percent over the next decade (BLS 2021, 2022b). Moreover, other occupations that are not explicitly computerrelated increasingly also rely on strong digital skills (Muro et al. 2017). 44 | Chapter 1 The spread of computer use is not limited to the workplace. In 1984, 8.2 percent of households had a computer at home; by 2021, this share had climbed to 95 percent (File 2013; U.S. Census Bureau 2021). More recently, information technology has been applied to traditionally analog devices— telephones, cars, watches, and the like—in what is sometimes referred to as the “Internet of Things” (Armstrong 2022). Figure 1-19 shows how rapidly Internet use has expanded in recent decades, with almost all adults using the Internet in 2021 compared with just over half in 2000. New technology is changing the way people interact with each other, both in markets and socially. Online sales now account for 14.8 percent of total retail sales, more than doubling their share over the last decade (U.S. Census Bureau 2023). Most job seekers now look for jobs online (Hernandez 2017). And people increasingly connect with each other on digital social media platforms to exchange ideas and information. The largest such platform, Facebook, counted 2.96 billion monthly active users as of December 2022 (U.S. Securities and Exchange Commission 2023). Through the lens of the aggregate production function, the contribution of recent technological advances to economic growth has been twofold. First, it has increased the physical capital stock of the American economy. The fall in the cost of computing power and the more recent rise of machine learning and artificial intelligence have led to a proliferation of computers in workspaces and robots in factories, which are helping workers specialize Figure 1-19. Percentage of Adults Who Report Using the Internet Over Time Percent 100 90 80 70 60 50 40 30 20 10 0 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 Source: Pew Research. Pursuing Growth-Enhancing Policies in Today’s Changing World | 45 in tasks for which they have the greatest comparative advantage, like bigpicture strategizing, designing products, and interacting with consumers. Second, at least in theory, recent technological advances have increased total factor productivity by enabling new production processes and making the allocation of resources more efficient. Indeed, increased investments in computers and software arguably played a substantial role in the fast productivity growth of the late 1990s and early 2000s (Weller 2002). The economic benefits of broadband Internet access, for example, have been widely accepted. One study comparing countries that belonged to the Organization for Economic Cooperation and Development between 1996 and 2007 found that a 10-percentage-point increase in broadband penetration increased per capita economic growth by 0.9 to 1.5 percentage points (Czernich et al. 2011). In the United States specifically, one study of the expansion of broadband access between 1999 and 2007 estimates that ubiquitous broadband access within a county would increase that county’s employment rate by 1.8 percentage points compared with no broadband access (Atasoy 2013). And when the COVID-19 pandemic prevented many Americans from participating in in-person work and school, new online technologies enabled people to continue learning and working. The practical importance of the productivity effect of more recent developments in machine learning and artificial intelligence remains a topic of debate, however, especially because the last decade saw the slowest productivity growth in the post–World War II era (according to CEA calculations using BLS data). One viewpoint is that recent innovations in technology have been more incremental and not as groundbreaking as previous technological changes (Gordon 2016). Other scholars, in contrast, have argued that traditional output measures fail to capture the full value of these new innovations and that their productivity gains will materialize in time (Brynjolfsson and Petropoulos 2021). In addition to the direct effects of those technological advances on output growth, policymakers are paying increased attention to the more indirect ways that these advances are affecting the structure of the U.S. economy. For example, blockchain technology has fueled the rise of financially innovative digital assets that have proven to be highly volatile and subject to fraud (White House 2022). The Internet and other new technologies have allowed for the provision of digital services, increasing the ability of people to perform and access services remotely, which affects trade, given that technological advances make it easier for countries to import and export services than in the past. Additionally, technological advances have raised distributional concerns, both in terms of access and their usage. Black, Hispanic, and lower-income Americans are less likely to have home access to a computer and broadband and the opportunities that those technologies provide (Pew Research Center 2021b, 2021c). And artificial intelligence 46 | Chapter 1 has been argued to deepen racial and economic inequities by perpetuating discrimination in areas such as housing, the criminal justice system, or mortgage lending (ACLU 2021). Addressing these areas of concern often draws on the traditional tools of policymakers in new contexts. For example, policymakers have focused on the high level of market concentration in the digital economy. Economic theory has long seen market power and monopolization as threats to productivity and output growth. The digital economy—broadly capturing the platforms that facilitate the online exchange of goods and information—is characterized by high levels of concentration, where markets are often dominated by a small set of firms (Digital Competition Expert Panel 2019). This concentration can be the result of the economic fundamentals of these platforms, whose scale can produce value for participants. Inherently, many of these markets exhibit some form of network externality. For example, buyers and sellers on an e-commerce platform are generally better off when more sellers and buyers are on the same platform. The economics underlying the susceptibility of digital markets to concentration is not new. But the scale of digital markets is amplified by the fact that they typically allow for a virtually unlimited number of participants without congestion. This implies that the “winners” in digital markets—the small set of firms that dominate the market—end up being larger and are of significantly greater importance for the overall economy. Because it is notoriously hard to define markets and there are many ways to measure concentration, it is difficult to precisely quantify the degree of concentration in digital markets. Nevertheless, big tech firms such as Amazon, Alphabet, and Meta have provided some of the most widely used services in recent years and generally have few direct competitors that come close to their size. From a policy perspective, these advances pose new challenges. The degree of concentration in digital markets raises long-standing concerns about whether dominant players in these markets leverage their market power to stifle competition and innovation. But unlike in some traditional markets, much of the value of digital companies comes from network effects—so antitrust actions may face greater challenges in preserving value for consumers while addressing problems associated with concentration. A world where digital technologies make services increasingly easy to trade requires adjustments to international trade policy. And digital assets require updating at least some regulations. In the future, the Internet and digital markets—and further innovation—will have the potential to drive continued increases in productivity. However, careful policymaking to address both the new and old challenges presented by these technologies will be necessary to ensure that productivity and output gains remain strong. Pursuing Growth-Enhancing Policies in Today’s Changing World | 47 Conclusion This year’s Report sheds light on these and other changes in the United States’ economic and social systems and how they challenge established economic thinking and policymaking. Chapter 2 summarizes the Nation’s economy during the past year, characterizing how the continuing recovery from the COVID-19 pandemic and the impact of Russia’s invasion of Ukraine have shaped the economy, and how sustained demand imbalances, supply chain delays, and pandemic policies have affected growth, inflation, and unemployment. It also presents the macroeconomic forecast underpinning the Biden-Harris Administration’s Fiscal Year 2024 Budget. Chapter 3 describes trends in international trade and investment in 2022 and characterizes how shifts over past decades in global interconnectedness have led to new challenges and opportunities for the United States. There is a need to balance the considerable benefits of globalization through economic linkages with the risks for economic and national security that international economic interconnectedness can entail. Working in concert with U.S. allies and partners can enable the Nation to effectively address shared challenges and take advantage of new opportunities in the changing global environment. Chapters 4, 5, and 6 point to shortcomings in, respectively, the supply of care, the supply of higher education, and the supply of labor—and highlight their significance for economic prosperity. Chapter 4 illustrates the significance of early childhood care and education for economic wellbeing and prosperity, focusing on the effects of childcare on children and families as well as the broader societal benefits. The chapter characterizes gaps in access and availability, and it details how challenges in the childcare industry, including the high cost of providing care, prevent the market from delivering childcare of an optimal quantity or quality. The chapter explains how policies that address these challenges by supporting families accessing care and providers supplying care can have substantial, long-run economic benefits. Chapter 5 highlights the importance of higher education in this context, with a particular focus on the role that postsecondary institutions play in creating the skilled workforce. The chapter notes that various features of the higher education market suggest that promising institution-focused policies and programs could meaningfully improve student outcomes and ensure that all students have access to a college degree of value. The recovery from the COVID-19 global pandemic has highlighted the importance of the labor supply for the economy. Chapter 6 shows that current labor supply shortfalls in the United States are not merely a lingering effect of the pandemic but are also due to population aging and long-run 48 | Chapter 1 declines in labor force participation. Policies to draw more adults into the labor force will be needed, without which the labor supply is likely to be constrained for the foreseeable future. Chapter 7 describes the significance of digital markets in the modern U.S. economy and the tension for this market environment’s regulators between promoting competition and enabling economies of scale. Digital markets have grown rapidly, and high levels of consolidation suggest that the government has a role in protecting consumers and promoting innovation through antitrust action. The importance of network effects means that regulatory interventions in the digital economy have nuanced effects. Chapter 8 explores recent developments in digital assets, along with their opportunities and risks. Although advocates often claim that digital assets, particularly crypto assets, are a revolutionary innovation, the design of these assets frequently reflects an ignorance of basic economic principles that have been learned in economics and finance over centuries, and this inadequate design is often detrimental to consumers and investors. Finally, chapter 9 describes the physical risks that the changing climate poses for U.S. economic production, the well-being of U.S. communities, and the fiscal position of the Federal Government, as well as opportunities to manage and reduce these risks. International and domestic climate policy has historically focused on policies to reduce greenhouse gas emissions, which are critical for mitigating the worst effects of climate change. However, the effects of climate change are already being felt across the United States and, even with ambitious emission reductions, will continue to increase until net global emissions fall to zero. Policies that enable households, businesses, and communities to plan for the changing climate and to manage evolving weather risks are an important complement to emission reductions in reducing the costs of climate change. Chapter 9 thus describes the economic foundations of these adaptation policies and outlines four pillars that could inform the Federal adaptation strategy. Pursuing Growth-Enhancing Policies in Today’s Changing World | 49 Chapter 2 The Year in Review and the Years Ahead The U.S. economy in 2022 continued to navigate an unprecedented global pandemic, and weathered an additional price shock to energy and food caused by Russia’s unprovoked invasion of Ukraine. Despite these and other challenges, the economy remained resilient with moderate output growth, strong employment growth, and inflation that peaked and then started to moderate late in the year (figure 2-1). In the face of supply constraints and changes in the composition of demand, the primary goal of fiscal and monetary policy in 2022 was to restore balance to supply and demand, fight inflation, and return the economy to a path of stable, steady growth. Russia’s invasion of Ukraine in February created acute supply constraints to energy, food, and other commodities that raised inflation globally. In Figure 2-1. The U.S. Economy, 2018–22 A. Real GDP B. Unemployment rate 2021 dollars (trillions) Percent 24.5 24.0 23.5 23.0 1 14 1 1 1 22.0 0 20.5 20.0 2018 2019 2020 2021 2022 0 10 8 1 6 4 4 3 2 0 0 1 0 2018 2019 2020 2021 2022 0 1 0 0 0 0 0 1 0 0 2 1 1 1 1 1 1 5 1 0 0 6 1 1 21.0 4-quarter percent change 1 12 1 22.5 21.5 C. Core PCE inflation 0 2018 2019 2020 2021 2022 0 Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations. Note: Nominal GDP was converted to 2021 dollars using the GDP Price Index. PCE = Personal Consumption Expenditures Price Index. Core PCE inflation excludes volatile food and energy inflation. All values are seasonally adjusted. 51 addition, in the first half of the year, the COVID-19 virus continued to weigh on economies across the world—in the same ways, if to a different extent, as it had in 2021 (Chetty et al. 2022)—especially when its Omicron variant caused cases and fatalities to surge in the United States and abroad. Due to pandemic-related disruptions, global supply chains were stressed. To support the U.S. economy, the Federal Reserve kept the target range for the Federal Funds Rate near zero until March. Although the majority of direct household relief funds from the CARES Act, the American Rescue Plan, and related legislation had been dispersed by the end of 2021, many of these funds had not been spent by households, and Americans entered 2022 with historically elevated savings. Recessions can leave lasting scars, but thanks to the fiscal and monetary support provided in 2020 and 2021, the United States’ real gross domestic product (GDP) in 2022 was close to what it had been forecasted before the pandemic (CBO 2019) to be in 2022. After muted growth for much of the previous two years, growth in real consumer spending on services was particularly strong during the four quarters of 2022, as spending patterns started to return to normal. By most measures, the labor market was extraordinarily tight in 2022, creating some of the most favorable conditions for job seekers in decades. As this chapter shows, the government’s comprehensive response to the pandemic helped achieve the solid positive outcomes of 2022. At the same time, the combination and interaction of numerous factors exacerbated the elevated inflation. Although it is difficult to determine the relative importance of each factor, the pandemic, and responses to it, had substantial effects on both the supply and demand sides of the economy. Specific factors of note include pandemic-induced supply disruptions, shifts in consumer demand, the accumulation of excess savings, and stimulative fiscal and monetary support throughout 2020 and 2021. 52 | Chapter 2 In 2022, monetary policy turned to fighting inflation and fiscal policy focused on strategies to complement that fight, while also working to guide the economy to stable and steady growth, in 2022 and in the future. Even before the year began, government spending and deficits fell closer to prepandemic trends. In March, the Federal Reserve began to reverse its asset purchase program and started what became a swift series of interest rate hikes; stock markets and residential investment declined quickly. President Biden authorized a drawdown of the Strategic Petroleum Reserve to lower gasoline prices after Russia’s invasion of Ukraine. In July and August, major pieces of legislation were passed to boost the economy’s long-term supply side. Some measures of labor market tightness and inflation began to moderate, with inflation showing an easing at the end of the year. The fight against inflation is expected to continue into 2023, resulting in a near-term outlook of below-trend GDP growth, a modestly rising unemployment rate, and falling inflation. This chapter begins with a review of the economy in 2022, first examining the recovery of GDP and its subcomponents, and then summarizing the conditions of labor markets and financial markets. Next, the chapter describes inflation in 2022, discussing possible causes along with the government’s response. Finally, the chapter presents the forecast underpinning the President’s Fiscal Year 2024 Budget and summaries of the near-term and long-term outlooks. The Year in Review: The Continuing Recovery This section summarizes the U.S. economy in 2022. By many measures, the economy had recovered from the recession induced by the COVID-19pandemic by the end of 2022; by a few measures, the economy had not. For example, real GDP was near the level it would have been if it had continued to grow at its average 2010–19 pace from its prepandemic peak in 2019:Q4. The unemployment rate was near its prepandemic low for most of the year, and other labor market indicators showed more tightness than they had in 2019:Q4. On average, wages adjusted for inflation declined over the year, though they saw growth in the second half. The stock market started the The Year in Review and the Years Ahead | 53 year at a record high, but fell over the year, partly due to rising inflation and tighter monetary policy. By most measures, and especially compared with recoveries from previous recessions, the economy in 2022 was healthy. Output in 2022: A Return to Near Its Trend Real GDP grew by 0.9 percent during the four quarters of 2022, a deceleration from its 5.7 percent pace during 2021. After a rapid decline in 2020 and a large bounce-back in 2021, the level of GDP in 2022 was roughly at its prepandemic trend. But GDP growth in 2022 was uneven, negative in the first half and positive in the second half. Some components increased and others contracted, reflecting the ongoing adjustment back to “normal” and away from the atypical spending and investment patterns seen over the past three years. As shown in figure 2-2, real GDP in 2022 had rebounded to a level that was at or above a log-linear trend extrapolated from preceding years of GDP growth, an important achievement. In some previous economic cycles, including the recovery from the Great Recession of 2007–9, the economy took much longer to return to its extrapolated trend, meaning that workers and consumers suffered negative consequences for a longer period. (See figure 2-3, panel H, for a comparison of this recovery with other recoveries.) The longer-run trend level of GDP is a simple estimate of what is sometimes called potential GDP, which is a measure of what the economy can produce at full capacity at a particular point in time. Recessions can cause output to Figure 2-2. GDP and Trend GDP, 2012–22 2021 dollars (trillions, log scale) 25 24 23 22 21 20 19 2012 2013 2014 2015 2016 GDP trend: 2002:Q1–2019:Q4 2017 2018 2019 2020 GDP trend: 2010:Q1–2019:Q4 2021 2022 Actual GDP Sources: Bureau of Economic Analysis; CEA calculations. Note: GDP trend lines were calculated by regressing the log of real GDP on time for the specified intervals, and plotting predicted values from that regression. Nominal GDP was converted to 2021 dollars using the GDP Price Index. All values are seasonally adjusted. 54 | Chapter 2 Figure 2-3. The 2019–22 Period Compared with Previous Business Cycles Index = 100 at business-cycle peak; 2019–22 cycle peak is 2019:Q4 B. Total spending on services A. Total spending on goods 120 115 110 105 100 95 90 85 80 –4 –3 –2 –1 0 120 Businesscycle peak 110 100 90 Quarters from businesscycle peak → 1 2 3 4 5 6 7 8 9 10 11 12 80 –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12 C. Business fixed investment D. Structures investment 120 120 110 110 100 100 90 90 80 80 70 70 60 –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12 E. Exports 60 –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12 F. Imports 140 130 120 110 100 90 80 70 –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12 140 130 120 110 100 90 80 70 G. State and local purchases H. Gross domestic product (GDP) –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12 130 130 120 120 110 110 100 100 90 90 80 –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12 80 Minimum of previous cycles –4 –3 –2 –1 0 1 2 3 4 5 6 7 8 9 10 11 12 Maximum of previous cycles 2019–22 cycle Sources: Bureau of Economic Analayis; CEA calculations. Note: Panels A and B include spending on goods and services by consumers, businesses, government, and as part of international trade, as defined in table 1.2.6 in the “National Income and Product Accounts.” Panel D includes business, residential, and government structures investment, also from table 1.2.6. All values are seasonally adjusted. run below its trend, which may be followed by faster growth that returns the level of output toward its trend. Growth can also be so fast that the level of output rises above its trend, a situation that may lead to high inflation as aggregate demand outstrips the capacity of the economy to produce the desired level of goods and services; this is often referred to as an overheated economy. Usually, high inflation provokes a policy response—for example, The Year in Review and the Years Ahead | 55 an interest rate hike by the Federal Reserve—that cools the economy and returns output to its trend.1 Estimating the trend of GDP is not straightforward. Figure 2-2 plots two log-linear trend lines estimated over different intervals. The longer estimation interval suggests that the United States’ output was above its trend in 2022, while the shorter one suggests that output was below it. Many other measures suggest the economy was running above its trend in 2022, including signals of tight labor markets, the elevated inflation rate and the growth of consumption without corresponding growth in investment or imports. Further, given the turmoil associated with the pandemic—lower labor force participation, demand shifts for specific skilled labor categories, and population movement—and the elevated inflation rate, there is ample reason to expect that the productive capacity of the economy was temporarily below its usual position in 2022. The position of the economy matters for the interpretation of growth in 2022, and has implications for the nearterm economic outlook. If GDP was above trend, the slowdown of growth in 2022, influenced by the Federal Reserve’s rate hikes, would mean the economy was returning to its trend, and may also presage continued slow growth in the near term. To illustrate the strength of the economic recovery in 2021 and 2022 relative to previous recoveries, figure 2-3 consists of eight “butterfly charts” that plot the evolution of various components of real GDP before and after the 12 post–World War II business-cycle peaks in the United States, as determined by the National Bureau of Economic Research. To construct these charts, each highlighted component of GDP was normalized to equal 100 in the quarter at the peak of each business cycle. The orange lines in the figure show the maximum paths of each component during the 11 business cycles before the current cycle; the light blue lines show the minimum paths; and the gray areas show the range of historical variation. The dark blue lines plot the postpandemic recession recovery. If, to the right of the green vertical line, a dark blue line is closer to an orange line than to a light blue line, this means that, relative to previous recessions, the recovery was stronger for that component. As can be seen in panel A of figure 2-3, the cumulative growth of real spending on all goods since the previous business cycle peak in 2019:Q4 through 2022 was at the top of historical experience. Conversely, in panel B of figure 2-3, real spending on all services was far below the range of historical experience at the end 2021, and growth through 2022 was only enough for it to recover to the lower historical bound by the end of 2022. As shown in panels C and D of figure 2-3, though real business fixed investment remained at the middle of its historical range, real investment in residential While higher GDP is generally beneficial, high inflation poses costs to the economy. It is these costs that the policy responses seek to avoid. 1 56 | Chapter 2 Table 2-1. Real GDP Growth and Its Components, 2022 Component Q4/Q4 Growth (percent) (1) Contribution to Q4/Q4 GDP Growth (percentage points) (2) Contribution to the Deviation of 2022:Q4 GDP from Its Trend (percentage points) (3) Total 0.9 0.9 –1.1 Consumer spending Goods Durables Motor vehicles and parts Nondurables Services 1.8 –0.9 0.5 –1.5 –1.7 3.2 1.2 –0.2 0.0 0.0 –0.3 1.4 0.5 0.9 0.3 –0.3 0.5 –0.4 Investment Business fixed investment Nonresidential equipment Nonresidential structures Intellectual property Housing investment Change in private inventories –4.0 4.3 4.0 –3.3 8.5 –19.0 – –0.7 0.6 0.2 –0.1 0.4 –0.9 –0.4 –2.3 –1.4 –0.8 –0.9 0.4 –1.1 – Net exports Exports Imports – 5.2 1.8 0.3 0.6 –0.3 – –1.1 –0.3 Government Federal Defense Nondefense State and local 0.8 0.1 –0.2 0.5 1.3 0.1 0.0 0.0 0.0 0.1 1.3 1.2 0.6 0.5 0.2 Sources: Bureau of Economic Analysis; CEA calculations. Note: Column 2 lists the contribution of each component to the annual rate of growth of real GDP. These may not precisely sum to totals because of approximations to the formulas used in the National Income and Product Accounts. Column 3 shows that that GDP was 1.1 percent below prepandemic trend in 2022:Q4 and how much each component of GDP contributed, negatively or positively, to this deviation from trend. It was calculated by regressing the log of each real GDP component on time from 2010 to 2019, calculating the percent difference of the 2022:Q4 level predicted by that regression from the actual 2022:Q4 level of each component, and multiplying by the importance of that component to overall GDP (the average of the 2019:Q4 and 2022:Q4 ratios of that nominal component of GDP to total nominal GDP). and other structures fell in 2022, and its recovery has remained near the bottom of its historical range. Table 2-1 breaks down real GDP growth into its subcomponents. The first column lists the four-quarter growth rate for each component over 2022. The second column lists the contributions of each category to overall real GDP growth over those quarters. Contributions can be negative or positive. For example, because real exports grew 5.2 percent during the four quarters of the year and constituted about 11.7 percent of GDP, its contribution to real GDP growth was 0.6 percentage point. The first row of the third column compares the 2022:Q4 level of real GDP with what it would have been if it had followed its 2010–19 log-linear trend (the light blue line in figure 2-2); all other rows show the approximate contribution of that real GDP The Year in Review and the Years Ahead | 57 component to this deviation. The major sectors that grew noticeably faster than overall GDP in 2022 include consumer spending on services, equipment investment, intellectual property investment, and exports. Imports also experienced relatively fast growth, but these reduce GDP. Expenditure categories that grew slower than overall GDP include consumer spending on goods, nonresidential and residential investment, Federal Government purchases, and inventory investment. State and local expenditures grew, but only slowly. Consumer spending. The nominal goods-to-services consumer spending ratio—which had been in a long-term decline—increased during 2020 and 2021, reaching its highest level since 2006. Real consumer spending on services fell sharply when the pandemic hit, as in-person activities such as dining out and traveling became more difficult. In contrast, real goods spending, after initially falling during the first two pandemic quarters, rebounded and spiked above its prepandemic level, as people stuck at home spent a larger share of their total real consumption on goods like furniture, appliances, and sporting equipment and a smaller share on services. During 2022, the goods-to-services spending ratio started to normalize; real goods spending fell 0.9 percent during the four quarters of 2022, while real consumer services spending grew 3.2 percent. Even so, this ratio remained well above prepandemic norms. Overall, real consumer spending grew modestly during the four quarters of 2022, at a 1.8 percent annual rate, with all of that growth accounted for by services. Investment. Real business fixed investment increased 4.3 percent during the four quarters of 2022, continuing its steady recovery from its pandemic-induced low. Investment growth was particularly strong in intellectual property, as it has been for the last decade. But investment by businesses in structures fell 3.3 percent during the four quarters of the year, with declines in investment in commercial and health care structures and power and communication structures. Investment increased in manufacturing and petroleum and natural gas mining structures. Increases in business fixed investment were offset by declines in fixed investment in residential and other structures, as the housing market cooled due to the rise in mortgage rates associated with the Federal Reserve’s tightening cycle. Both business fixed investment and fixed investment in residential and other structures were below their prepandemic trends. Overall, spending on structures was near the lower end of the business-cycle range, as shown in panel D of figure 2-3. Some of the slowing GDP growth in 2022—which followed strong growth in 2021—was accounted for by inventory investment. The overall real inventory-to-sales ratio shrank to the lowest on record in 2021:Q2, as firms fought supply chain bottlenecks and then began to rapidly recover, with inventory investment at high levels in 2021:Q4 and 2022:Q1. The 58 | Chapter 2 stock of real inventories continued to grow strongly in 2022, but because inventory investment was lower in 2022:Q2 and 2022:Q3 than in 2022:Q1, inventory investment subtracted from real GDP growth in those quarters and over the four quarters of the year. Government spending. The Federal Government’s real purchases (expenditures and gross investment) edged up slightly, by 0.1 percent, during the four quarters of 2022. Most of the surge in Federal spending that had supported households, businesses, and State and local governments in 2020 and 2021 consisted of transfers and subsidies that are not directly part of GDP; while these transfers and subsidies fell, purchases were little changed. Defense expenditures and gross investment barely changed during the four quarters of the year, while nondefense purchases edged up. State and local government purchases increased slowly, by 1.3 percent, during the four quarters of the year. Relative to the average cyclical response, State and local purchases were near the lower end of the business-cycle range, as shown in panel G of figure 2-3. Imports and exports. Finally, real exports grew faster than overall GDP during the four quarters of 2022, growing by 5.2 percent at an annual rate, reflecting the continued reopening of the world economy. Although real imports grew more slowly than real exports during the four quarters of the year, at 1.8 percent, that import pace exceeded the growth of real GDP by 0.9 percentage point. Due to the stronger growth in real exports relative to imports, real net exports partially recovered from their pandemic-induced decline in 2022, contributing 0.3 percentage point to overall real GDP growth. (See chapter 3 of this Report for an in-depth discussion of international trade and investment in 2022.) The Historic Strength of Labor Markets in 2022 Labor markets were very tight in 2022, as the strong economy led firms to continue to hire workers after pandemic-induced layoffs and hiring pauses. At the end of the year, the unemployment rate was 3.5 percent, matching the lowest rate—tied with September 2019 and prepandemic 2020—since 1969. Other labor market measures also showed a historically high degree of tightness, including the ratio of job openings per unemployed person, shown in figure 2-4, and the quit rate, considered by some to be the best measure of labor market tightness (Furman and Powell 2021), which reached at least a 20-year high at the end of 2021 and remained elevated through 2022. Figure 2-4 shows the ratio of total job openings divided by the total number of unemployed people. During recessions, this measure tends to fall, as firms slow hiring, reduce job openings, and lay off workers, and it plummeted in 2020. By April 2022, however, the measure had climbed to the highest level on record, indicating that the labor market was unusually The Year in Review and the Years Ahead | 59 Figure 2-4. Job Openings per Unemployed Person, 2000–2022 Ratio 2.5 2.0 1.5 1.0 0.5 0.0 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 Sources: Bureau of Labor Stastistics; CEA calculations. Note: All values are seasaonally adjusted. Figure 2-5. The Beveridge Curve at Two Intervals Vacancy rate (percent) 7 6 December 2022 5 4 3 2 1 2 4 6 8 10 Unemployment rate (percent) July 2009–February 2020 12 14 16 April 2020–December 2022 Sources: Bureau of Labor Statistics, CEA calculations. Note: All values are seasonally adjusted. tight. In the second half of the year, job openings decreased and the number of unemployed persons increased slightly. Figure 2-5 shows another view of the labor market: the Beveridge curve, the relationship between the unemployment rate and the percentage of job openings relative to labor demand, known as the “vacancy rate.”2 The 2 Labor demand equals job openings plus employment. 60 | Chapter 2 Beveridge curve during the pandemic-recession recovery, represented by the dark blue dots, shifted up and out, possibly due to increased pandemicrelated difficulties in hiring and retaining workers. All the months of 2022 are located in the upper-left-hand corner of the figure, where vacancy rates are high and unemployment rates are low, indicating that labor markets were tight and that labor demand was high relative to labor supply. Economists disagree about how much of this labor market tightness was due to a shortage in the supply of workers versus an excess demand for workers. On the demand side, the high aggregate demand described later in this chapter led to an increased demand for workers by businesses. There are a range of potential supply-side factors, which are discussed in chapter 6 of this Report. The Cooling of Financial Markets in 2022 The stock market recovered quickly from large declines during the COVID19 pandemic, reaching a new peak at the end of 2021. In early 2022, as inflation rose and the Federal Reserve began hiking the Federal Funds Rate to cool off the economy, stock prices declined. The losses in 2022 reversed only part of the gains made during the previous two years (figure 2-6). Along with stock prices, bond prices also fell.3 The price of 10-Year Treasury Notes, which moves inversely to the yield, began the year near historical highs but ended the year quite a bit lower, likely due in part to upward revisions in market expectations for the future path of inflation and associated revisions in market participants’ expectations for the path of the Federal Funds Rate.4 From near the beginning of the COVID-19 pandemic to the end of 2022, the correlation between changes in stock prices and long-term bond prices was reversed from its previous sign. From 2000 until the beginning of the COVID-19 pandemic in 2020, the correlation between changes in stock prices and bond prices was generally negative (Rankin and Idil 2014). During this 20-year period, the Federal Reserve lowered the Federal Funds Rate, increasing bond prices. These increases were primarily in response to negative aggregate demand shocks, which drove down stock prices, as during a typical recession. As shown in figure 2-6, the pandemic-induced recession fit this pattern in early 2020: stock prices fell and bond prices rose. In contrast, in 2022 inflation led the Federal Reserve to raise the Federal Funds Rate, causing both stock and bond prices to decline. This relationship can be seen in Bond prices, rather than bond yields, are discussed here in order to simplify the comparison with stock prices. The spot price of the 10-Year Treasury Note is calculated from the market yield, assuming no coupons. 4 A complete description of the drivers of changes in the interest rate on 10-Year Treasury Notes is beyond the scope of this chapter; see Stigum and Crescenzi (2007). 3 The Year in Review and the Years Ahead | 61 Figure 2-6. Stock Market and Bond Prices, 2019–22 Index; January 2019 = 100 Index; January 2019 = 100 215 125 195 120 115 175 110 155 105 135 100 115 95 95 90 75 2019 2020 2021 S&P 500 index (left axis) 2022 85 10-year Treasury Note price index (right axis) Sources: Federal Reserve System; Standard & Poor’s (S&P). Note: The prices of 0 coupon 10-Year Treasury Notes are shown relative to January 2019. figure 2-6, starting slightly before the tightening cycle began, possibly due to markets anticipating monetary actions. The change in the sign of this correlation after the start of the pandemic suggests that negative supply shocks were important for U.S. financial markets in 2022; these shocks moved the price level higher and output lower—thus hurting stock prices—and led to increasing interest rates, thus hurting bond prices. Inflation in 2022 Beyond the developments summarized above in discussing output growth, the historically strong U.S. labor market, and financial markets, the rise of inflation in 2021 and its continued elevation through 2022, exacerbated by Russia’s invasion of Ukraine, were important aspects of 2022’s overall economic picture. For most of the 2010–19 period, the rate of inflation was below the Federal Reserve’s long-term 2 percent target. Then the COVID19 pandemic hit the United States in early 2020. Prices fell briefly in the spring of 2020, when the pandemic initially struck, interrupting many forms of economic activity; but prices, and the economy, quickly recovered. Inflation began to climb in 2021. Although, at the end of 2021, many forecasters predicted that inflation would quickly fall, inflation instead persisted in 2022.5 The year 2022 was one of historically elevated inflation, but it was also a year that saw many actions taken to bring that elevated inflation E.g., the 2022:Q1 annualized CPI inflation rate predicted by the December 2021 Blue Chip consensus was 3.3 percent, close to the Federal Reserve’s target and much lower than the actual quarterly inflation rate of 9.2 percent. 5 62 | Chapter 2 Box 2-1. Measures of Consumer Price Inflation Inflation can be challenging to precisely define and measure. This box describes what inflation is not and what it is, how the government measures inflation, and what information key inflation measures provide. Defining inflation. Inflation can be tricky to talk about. First, inflation is the rate of change of the price level, not the level of prices. High inflation means that prices are rising rapidly, not that prices are high. Second, increases in the prices of specific goods and services do not always reflect inflation. Due to changes in relative demand and supply, prices for specific goods and services rise and fall relative to each other all the time. For example, during the COVID-19 pandemic, demand for television sets rose, and their prices increased. Concurrently, demand for airline tickets fell, along with their prices. Price indices—such as the Consumer Price Index (CPI) and the Personal Consumption Expenditures (PCE) Price Index, which are discussed below—aggregate prices in the economy in an attempt to measure the price level. Inflation is a positive rate of change in the price level. Measuring inflation. Measuring the price level, and therefore inflation, is a difficult task. This chapter frequently references two measures that approximate the level of prices faced by consumers: the CPI, produced by the Bureau of Labor Statistics (BLS); and the PCE Price Index, produced by the Bureau of Economic Analysis (BEA). (The main text refers exclusively to the CPI-U, which follows the market basket of urban consumers. The description “urban” refers to anyone not living in extremely rural areas, and covers about 90 percent of the U.S. population. The BLS also supports several other versions of the CPI. The CPI-W follows the market basket of wage earners; the CPI-E follows the market basket of the elderly; and the chain CPI follows the same consumers as the CPI-U, but it aggregates with a formula that allows for more substitution.) The CPI measures the prices of a fixed basket of consumer goods and services (BLS 2020). The basket, which was updated every two years from 2002 to 2022 and will be updated every year in the future, approximates the average consumption of a household as surveyed in the annual Consumer Expenditure Survey. The assumption of a fixed consumption basket makes comparing the prices of the same goods and services across time relatively easy, but it can misrepresent the rate of price changes households actually face (or experience) if households change what they consume when prices change. For instance, if the price of oranges falls relative to the price of apples, consumers will usually buy more oranges and fewer apples. The PCE Price Index, in contrast to the CPI, uses a formula that allows for such substitution. Further, while the CPI focuses on out-of-pocket expenditures, the PCE Price Index captures a wider range of consumer costs—including, for example, employer-provided health insurance. Largely because the PCE Price Index allows for more substitution (but also due to other differences), The Year in Review and the Years Ahead | 63 the 12-month change in the PCE Price Index has averaged 35 basis points less than the corresponding change in the CPI for the last 20 years. Headline inflation versus core inflation. Economists and policymakers focus on price indices that exclude goods and services with volatile prices, such as food and energy, in order to get a better sense of persistent movements in inflation (Gordon 1975). Food and energy prices are erratic largely because they are influenced by weather and international commodity markets, and therefore can move independently from the other goods and services whose prices are determined domestically to a greater extent. The core CPI and the core PCE Price Index exclude food and energy, whereas the corresponding headline CPI and headline PCE Price Index include food and energy. Of course, because consumers buy food and energy, headline inflation measures better reflect the costs consumers actually face. Monthly versus yearly inflation. Each month, the BLS and BEA update the CPI and the PCE Price Index, respectively, and the monthover-month percent change in each price level. They also report 12-month percent changes, which are substantially less volatile because they accumulate month-over-month percent changes over 12 months. Measures of annualized 3-month or 6-month inflation—the 3-month or 6-month percent change mathematically adjusted to be comparable to 12-month, or yearly, rates—can also be calculated from the raw price indices. These measures are less volatile than monthly inflation but are more timely than yearly inflation. Figure 2-i plots annualized 6-month inflation for four price indices: the headline CPI, the core CPI, the headline PCE Price Index, and the core PCE Price Index. All four inflation indices began to increase in 2021 but turned downward in the second half of 2022. Figure 2-i. Types of Consumer Price Inflation, 2011–22 Percentage change, 6-month annualized 12 10 8 6 4 2 0 –2 –4 2011 2012 2013 2014 Headline CPI 2015 2016 2017 Headline PCE 2018 2019 Core CPI Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations. Note: All values seasonally adjusted. 64 | Chapter 2 2020 2021 Core PCE 2022 Figure 2-7. The Expectations-Augmented Phillips Curve at Two Intervals 3-month annualized core CPI inflation (percent), controlling for expected inflation (see note) 7 6 5 2022:Q4 4 3 2 1 0 2020:Q4 3 4 5 6 7 8 Unemployment rate (percent) 2009:Q1–2019:Q4 9 10 11 2020:Q4–2022:Q4 Sources: Bureau of Labor Statistics; Federal Reserve Bank of Philidelphia. Note: CPI = Consumer Price Index. The y axis shows a measure of the actual rate of inflation minus the difference betwen the expected rate of inflation and the long-term rate of inflation, or π - (E[π] - π*), where π = core CPI inflation, E[π] = 1-year lagged median 1-year ahead core CPI inflation expectations from the Federal Reserve Bank of Philidelphia’s Survey of Professional Forecasters, and π* = the long-term (post-2000) average of core CPI inflation, 2.3 percent. Actual CPI inflation values are seasonally adjusted. down. As discussed in box 2-1, there are many ways to measure inflation. One of the most common, the 12-month rate of change in the headline Consumer Price Index (CPI), peaked at 9.1 percent in June 2022—a pace not seen since 1981. The fight against inflation has not been an easy one, but progress has been made as of December 2022, when the 12-month rate of change in the headline CPI inflation was 2.6 percentage points lower than in June. The unexpected nature of the inflation in 2021 and 2022 is exemplified by figure 2-7. The figure shows an estimate of the Phillips curve, the relationship between inflation, unemployment, and inflation expectations from 2009 until the last prepandemic quarter in 2019:Q4 (dark blue dots), and during the economic recovery from 2020:Q4 through 2022:Q4 (light blue dots). The light blue dots are substantially above the dark blue dots, indicating that inflation moved more strongly with unemployment during the economic recovery than in the previous economic expansion. Investigating why inflation responded so strongly, and the fiscal and monetary responses to it, occupies much of the rest of this chapter. (Also see box 2-2.) Measures of inflation can be approximately decomposed into contributions from subcategories of goods and services. Figure 2-8 plots the decomposition of annualized three-month headline CPI inflation into five categories: food; energy; core goods, which exclude food and energy goods; shelter, which includes rent and “owners’ equivalent rent,” and core services The Year in Review and the Years Ahead | 65 Figure 2-8. Decompostion of Inflation, 2019–22 Contributions to quarterly headline CPI inflation, percentage points, quarterly annualized rate 12 10 8 6 4 2 0 –2 –4 –6 2019:Q1 2019:Q3 Food 2020:Q1 Energy 2020:Q3 Core goods 2021:Q1 2021:Q3 2022:Q1 Core services less shelter 2022:Q3 Shelter Source: Bureau of Labor Statistics. Note: All values seasonally adjusted. less shelter, which also excludes food and energy services. The figure shows that inflation during 2022 in the United States was broad-based, with each of the subcategories contributing substantially to overall inflation. The timing of these contributions differs and tells an interesting story. In early 2021, the contribution of core goods inflation to overall inflation rose as consumer purchases rotated from services to goods during the pandemic, when supply chains snarled and productive capacity could not rise fast enough to match the rise in demand. As consumer behavior and supply chains both normalized in 2022, monthly core goods inflation declined and actually turned negative in late 2022. The contribution of food and energy inflation rose in 2021, and continued in 2022. Russia’s invasion of Ukraine in February 2022 increased pressure on global oil and agricultural commodity markets. Partly as a consequence, the contribution of food and energy to inflation rose both domestically and globally. Inflation in core services, which was the primary contributor to overall inflation in the decade before the pandemic, was only slightly above its prepandemic pace in 2021 but increased sharply in 2022. The decomposition shown in figure 2-8 is informative, but it is only an accounting exercise: it does not explain the underlying economic factors that led one category to move relative to another. If one category “contributed” more than another in a certain quarter, it means that prices in that category were increasing relative to prices in the other category, not necessarily that price increases in that category were the underlying cause of inflation. For example, it is possible for headline CPI inflation to be 0.0 percent, with core 66 | Chapter 2 Figure 2-9. Global Measures of Consumer Price Inflation 12-month percent change in national consumer price indices 12 10 8 6 4 2 0 –2 2018 2019 U.S. 2020 U.K. 2021 EU 2022 Canada Japan Sources: U.S. Bureau of Labor Statistics; U.K. Office for National Statistics; Eurostat; Japanese Ministry of Internal Affairs and Communications; Ministry of Statistics and Programme Implementation of India; Statistics Canada, CEA calculations. Note: Measures are of headline consumer price inflation less owner-occupied housing (sometimes called the Harmonized Index of Consumer Prices). goods inflation contributing negative 2.0 percent and core services inflation contributing positive 2.0 percent. The difference in goods and services inflation would mean that services prices were increasing relative to goods prices, not that either was causing inflation. In the next subsection, possible causes of U.S. inflation in 2022 are examined in detail. High inflation in 2022 was not just a U.S. phenomenon, as shown in figure 2-9. In 2021, after years of relative stability, inflation began to climb across a number of countries. In the second half of 2022, inflation in the EU and the United Kingdom was higher than in the United States, partially reflecting the EU countries’ and the United Kingdom’s greater exposure to the war in Ukraine, and specifically the war’s effect on energy prices. Inflation in some other countries, such as Japan, remained relatively low, though well above its prepandemic norm. Factors That Had an Impact on Inflation in 2021–22 As discussed in box 2-2, the root causes of inflation are imperfectly understood, and economists use many theoretical frameworks to model and study it. Because the most common framework used to analyze inflation is aggregate supply and demand, this subsection first discusses what are generally thought of as “supply” factors and then examines what are generally thought of as “demand” factors. The role of expectations, a common theme in many inflation frameworks, is also discussed. Fiscal and monetary actions are both The Year in Review and the Years Ahead | 67 Box 2-2. The Phillips Curve and Other Models of Inflation Economists have spent much time and effort trying to explain and predict inflation, using a variety of methods and approaches. This box explains one common model, the Phillips curve; describes its recent history; and discusses each of its components—inflation; economic tightness, or “slack”; inflation expectations; and other factors—before briefly discussing theories of inflation that do not depend on a Phillips curve–type relationship. The term “Phillips curve” is used to refer both to the empirical relationship between forms of inflation and measures of economic tightness or slack, used in the macroeconomic model developed by Klein and Goldberger (1955) and noted by Phillips (1958) (with regard to wage inflation and unemployment), and to the theoretical relationship between the two. Today, policymakers and forecasters often refer to the “expectations-augmented Phillips curve,” which recognizes that inflation expectations can influence inflation independently from measures of economic tightness or slack. As shown in figure 2-7 in the main text, the empirical relationship between the unemployment rate, one measure of tightness, and Core CPI inflation can change drastically, even when controlling for inflation expectations. The Phillips curve appeared to have become “flat” in about 2000, as discussed in the 2016 edition of the Economic Report of the President (CEA 2016). More precisely, the coefficient on the unemployment rate was near zero (hence, the adjective flat). This flatness during the 2009–19 business-cycle expansion is shown by the dark blue dots in figure 2-7 and the accompanying flat dark blue dashed line. Elevated unemployment rates failed to lower inflation during the first half of this cycle, while the low unemployment rates during the second half of that cycle failed to increase inflation. Viewed from the end of 2022, the Phillips curve has substantially changed, as the decline in the unemployment rate to near historic lows in 2022 coincided with the first major increase in U.S. inflation since the 1980s, as shown by the light blue dots in figure 2-7 and accompanying steeply sloped light blue dashed line. The increase in inflation during 2021 and 2022 was much larger than the consensus economic forecast, perhaps because most forecasters had come to believe in a flat Phillips curve anchored by stable inflation expectations (Federal Reserve Bank of Philadelphia 2020). One of the important questions facing the economy in 2023 is whether the Phillips curve will remain steeply sloped as inflation continues to cool. If the Phillips curve remains steep, this implies that inflation may fall without much of an increase in the unemployment rate. A Phillips curve that returns to near its prepandemic slope would imply 68 | Chapter 2 that inflation may fall, but with a larger increase in the unemployment rate than in the second half of 2022. Measures of inflation in the Phillips curve. As described in box 2-1, measures of inflation that include food and energy prices are volatile for reasons that have little to do with the domestic economy. Thus, core inflation measures, which exclude food and energy, fit better and are preferred for forecasting applications. Some practitioners use estimates of a deeper, more persistent, underlying inflation rate—as described or suggested by Ascari and Sbordone (2014), Yellen (2015), and Rudd (2020)—in order to enhance the fit and predictive power of the Phillips curve. Figure 2-7 uses annualized 3-month core CPI inflation. (Simple estimates of this underlying inflation rate involve a menagerie of methods and measures, as discussed by Detmeister 2011. These measures include averaging across months of inflation data, using the inflation rate on specific categories of spending, such as the median CPI, from the Federal Reserve Bank of Cleveland 2023; and trimming categories that see the most and least inflation when calculating the inflation rate, such as the Trimmed-Mean PCE from the Federal Reserve Bank of Dallas, n.d., among others.) Measures of economic tightness or slack in the Phillips curve. Choosing an appropriate measure of economic tightness or slack is a difficult conceptual issue. “Slack” refers to the intensity of resource utilization in the economy (Yellen 2015). Figure 2-2 shows one possible measure of slack: the difference between real GDP and a longer-run trend of real GDP. The situation at the end of 2022, when real GDP was higher than its trend, indicates that resource utilization was higher than normal, which may have fed through to inflationary pressures via increased costs to firms to produce a unit of output (Boehm and Pandalai-Nayar 2022). Another commonly used measure of slack is the deviation of the unemployment rate from the natural rate of unemployment, the rate of unemployment that would exist when the economy is stable in the longterm and not disrupted by shocks. Estimating the natural rate of unemployment, which is by nature unobservable, is a difficult task. (Many practitioners estimate the natural rate of unemployment together with the Phillips curve. But to have separate measurement power, that natural rate estimate would need to come from a method external to estimation of the Phillips curve itself, as was done by Michaillat and Saez 2022.) For simplicity, figure 2-7 uses the unemployment rate alone, without an external estimate of the natural rate. Inflation expectations in the Phillips curve. The expectationsaugmented Phillips Curve includes inflation expectations because many theories of inflation suggest that expectations may in some cases be self-fulfilling—in other words, if people believe that inflation will rise, inflation will rise; and if people believe that inflation will fall, it will The Year in Review and the Years Ahead | 69 fall. Empirically, expectations are important to explaining the decline in inflation since the 1970s, and its stability in the 2010s (Blanchard et al. 2015). The exact link between inflation expectations and actual inflation is still debated (Rudd 2021; Bernanke 2007, 2022; Werning 2022). Figure 2-7 uses projections of core CPI inflation from the Survey of Professional Forecasters. Given the importance of inflation expectations, managing expectations is an important aspect of managing inflation. Inflation expectations are said to be “anchored” when they do not change much, even when the economic environment changes. Though many believe that the Federal Reserve had an implicit inflation target at which it wanted to anchor inflation starting in the 1990s or earlier, it was only in 2012 that the Federal Reserve announced an explicit longer-run target of 2 percent annual PCE Price Index inflation (Federal Reserve 2012). In 2020, the Federal Reserve revised its “Statement of Longer-Run Goals and Monetary Policy Strategy” to indicate that it would conduct policy in a way that seeks to anchor inflation expectations at 2 percent and results in inflation that averages 2 percent over time (Federal Reserve 2020). As can be seen below in the text, even though inflation in 2021 and 2022 rose well above 2 percent, measures of long-run inflation expectations remained relatively stable, lending support to the idea that the Federal Reserve had successfully anchored inflation expectations. Other factors. While Phillips curves are often parsimonious models of inflation, factors other than expectations and slack may be used to help empirically estimate the curve and control for other influences. Yellen (2015) highlights the importance of changes in imported goods prices, which are an input to many production processes and can proxy for exchange rate dynamics. In a similar vein, below the text highlights a measure of supply chain pressures and its relation to a producer-side measure of inflation. The price of energy may also be included, although pass-through from energy prices to measures of core or underlying inflation has diminished in recent years (Clark and Terry 2010). Alternative models of inflation. The Phillips curve is one of most common frameworks that economists use to understand inflation, but it is far from the only one. For example, when economists talk about how supply and demand affect inflation, they are usually referring to the Keynesian Aggregate Demand and Aggregate Supply (AD-AS) model, which evolved from attempts by John Hicks to formalize the ideas of John Maynard Keynes in the 1930s (Hicks 1937; Keynes 1936). The Phillips curve is often considered to be part of Keynesian theory because, due to the link between employment and real output, something similar can be implied from the AD-AS model. Keynesian theory can be understood as one explanation for the connection between inflation and slack observed in the empirical Phillips curve. New Keynesian theory, which 70 | Chapter 2 is a modern, mathematically formal development of Keynesian theory, offers a related explanation (Galí 2015). The standard New Keynesian Phillips curve relates inflation to the theory’s measure of slack and features a larger role for expectations than most Keynesian models. Monetarism is both a theory that describes a group of formal mathematical models and also a set of less formal ideas. As a theory, it is most associated with Milton Friedman, who famously said, “Inflation is always and everywhere a monetary phenomenon, in the sense that it is and can be produced only by a more rapid increase in the quantity of money than in output” (Friedman 1970). Monetarist models emphasize inflation as a consequence of the growth of the quantity of money compared with the level and growth of output, rather than a connection between inflation and slack. Finally, a number of models of inflation emphasize the importance of government debt. One of the best-known of these models, the Fiscal Theory of the Price Level (FTPL), argues that increases in government debt that are not backed by credible promises of repayment via increases in future tax revenue or reductions in future spending lead to inflation (Cochrane 2023). Proponents and critics of the FTPL disagree over the direction of causality in this relationship, and the implicit assumptions that such causality implies (Bassetto 2008). usually considered to be demand factors in the near term; because they are both especially important, they are discussed separately.6 Over the last two years, many hypotheses about the causes of the current inflation situation have been proposed by academics, journalists, and politicians. The goal of this subsection includes reviewing prevalent propositions, not to argue for a single hypothesis or set of hypotheses. The possible causes discussed here likely played some role in the level and elevated nature of inflation in 2022—and the pandemic was a large exacerbating cause to each. Interactions between causes likely worsened inflationary pressures. Frequently cited hypotheses include the shock to energy, food, and other commodity prices associated with Russia’s invasion of Ukraine; pandemic-related supply chain issues; the extension of zero interest rate monetary policy and accompanying quantitative easing; household transfers In the medium to long terms, both monetary and fiscal actions can influence supply. For example, low interest rates can spur long-term investment. Government spending can build infrastructure— e.g., Donaldson and Hornbeck (2016)—and support research and development—e.g., Gross and Sampat (2020), as discussed in the paragraphs about legislative and executive actions in the text below. In general, these supply-side factors take longer to impact the economy than do demand-side effects of monetary and fiscal actions. 6 The Year in Review and the Years Ahead | 71 Figure 2-10. Supply Chain Pressures and Producer Inflation, 1990–2022 24-month change, index points 12-month percent change 500 10 400 8 300 6 200 4 100 2 0 0 –100 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 -2 24-month change in cumulative ISM supplier delivery index (left axis) 12-month PPI core finished goods inflation (right axis) Sources: Bureau of Labor Statistics; Institute of Supply Management (ISM). Note: PPI = Producer Price Index. The dark line is equal to Σ(Si - 50) with i = 0,23, where Si = the ISM supplier deliveries index, which is equal to 50 if the number of manufacturers that report lengthening delivery times is equal to the number of manufacturers that report shortening delivery times. Longer lags include more information, and the 24-month changes fit with recent data on the change in PPI inflation. legislated as part of the CARES Act, the American Rescue Plan, and related legislation; and households’ accumulation of “excess savings.” The impact of supply factors on inflation. As described in the 2022 edition of the Economic Report of the President, the COVID-19 pandemic introduced challenges to the labor force and constraints on the supply of goods and services (CEA 2022). In mid-2022, these disruptions finally began to ease. As shown in figure 2-10, increases in supply chain pressures were strongly correlated with rises in goods inflation in 2022. The measure of supply chain pressures in the figure is derived from an Institute for Supply Management (ISM) survey, in which supply managers are asked whether delivery times for their raw materials are shorter, the same, or longer than the preceding month. Because the resulting ISM measure captures monthly changes in delivery times, these responses must be cumulated over time to make an index of the level of delivery times.7 In figure 2-10, the change The ISM supplier deliveries index is calculated by subtracting the percentage of supply managers saying that delivery times are longer from the percentage of supply managers saying that delivery times are shorter, dividing by 2, and adding 50. To construct this index of delivery time levels, 50 is subtracted from the ISM and the index is cumulated over 24 months. The ISM delivery index indicates only the one-month change in delivery lags, so cumulating more months includes more information. Cumulating over the preceding 24-month period fits the recent data on the change in PPI inflation. 7 72 | Chapter 2 Figure 2-11. Commodity Pressures and PCE Inflation, 2006–22 70 12-month percent change 7 50 5 30 3 10 1 –10 –1 –30 –3 Russian invasion begins –50 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 –5 12-month percent change Retail gasoline price index (left axis) PCE Price Index inflation (right axis) FAO food price index Sources: Bureau of Economic Analysis; U.S. Energy Information Administration; Bloomberg Agriculture Spot Index. Note: FAO = Food Agriculture Organization of the United Nations. Data are displayed on two axes because commodity and gasoline prices are much more volatile than inflation. The PCE Price Index is seasonally adjusted. in this measure of delivery times, over an appropriate interval, is plotted against the change in the core Producer Price Index (PPI) for finished goods. The PPI measure reflects prices charged by manufacturers. The relatively high correlation between the change in delivery times and core PPI finished goods inflation since 1990 suggests that supply chain issues have a significant impact on finished goods inflation. According to the ISM survey, suppliers’ delivery times started lengthening substantially shortly after the start of the COVID-19 pandemic, and most supply managers were reporting lengthening delivery times until September 2022. Delivery times shortened during the final three months of the year, but were still elevated at the end of 2022. Another measure of supply chain stress, the Global Supply Chain Pressure Index, produced by the Federal Reserve Bank of New York, also increased notably in 2020–21, but fell for most of 2022. Collectively, these measures indicate that supply chain delays stopped getting worse and even began to unsnarl toward the end of the year. Still, overall inflation remained high, indicating that the drivers of inflation had broadened, including to the service economy (Powell 2022a). Figure 2-11 shows that commodity prices, as represented by gas and food price inflation, started rising in 2021. These commodities are traded on international markets, and their prices influence inflation globally. Then, in February 2022, Russia invaded Ukraine. The resulting chaos, both directly and indirectly, led food prices to quickly jump higher, and gasoline and natural gas prices soon followed. As commodity suppliers adapted to the The Year in Review and the Years Ahead | 73 Figure 2-12. Employment Cost Index and Inflation, 2013–22 12-month percent change 6 5 4 3 2 1 0 2013 2014 2015 2016 2017 PCE Price Index 2018 2019 2020 2021 2022 Employment Cost Index (ECI) Sources: Bureau of Labor Statistics; Bureau of Economic Analysis; CEA calculations. Note: The PCE Price Index is seasonally adjusted, but the ECI is not. disruption caused by the war, commodity prices fell. Since commodities are a basic input to most production processes—and consumers directly purchase some commodities such as food, gasoline, and natural gas directly—higher commodity prices can quickly feed into overall inflation. Russia’s status as a major oil exporter led to a spike in many energy prices, and the price of regular gasoline in the United States peaked at $5.02 a gallon in June. But by the end of the year, the price of regular gasoline had fallen to $3.20 a gallon, partly due to the Biden-Harris Administration’s decision to draw down the Strategic Petroleum Reserve, which is further discussed below. As the economy continued to recover from the recession in 2020 and consumer demand for goods and services increased, demand for workers to produce these goods and services also increased. Illustrated by the ratio of vacancies to unemployment shown in figure 2-4, the demand for workers relative to their supply has been high during much of the recovery from the pandemic-related recession. If firms are having difficulty hiring workers, then the relative price of workers—that is, hourly compensation—should increase. Figure 2-12 displays the Employment Cost Index, a measure of hourly compensation that adjusts for changes in the composition of the workforce, showing that inflation in 2022 was accompanied by rising wages. But rising wages can be both a cause and a consequence of inflation (Jordà et al. 2022). The BLS’s measure of real average wages, or wages relative to the overall price level, declined overall in 2022, falling in the first half of 2022 before rising in the second half. Some parts of the labor income distribution saw better real wage outcomes than others, with outcomes positive in the lowest quartile (Federal Reserve Bank of Atlanta, n.d.). 74 | Chapter 2 Although there were fears during 2022 of a “wage-price spiral”— where workers expecting increased inflation would demand higher wages, which would lead to higher realized inflation, and then workers would demand even higher wages, and so on—those fears lessened toward the end of 2022, as inflation and wage growth showed broad slowdowns. Notably, as shown below with the University of Michigan’s survey results (see figure 2-19), consumers’ short-term inflation expectations remained well below actual inflation throughout the year, and longer-term expectations remained anchored. Some have pointed to another factor that may have influenced the reaction of prices and thus inflation to the COVID-19 shock: increased market concentration in U.S. industries. More U.S. industries have become dominated by a few, large firms over the last 20 years. There is some evidence that these firms increase prices in response to cost increases more than firms without market power would have done in the past (Bräuning, Fillat, and Joaquim 2022). However, the link between market power and pricing when subject to shocks like the pandemic is not clear (Syverson 2019). Measuring market power is a difficult task, and measuring the prices firms charge above the cost of their inputs, their “markup,” isolated from the effects of the increased demand and constrained supply of 2022, is even more fraught. The impact of monetary factors on inflation. By controlling short-term interest rates, and through them, longer-term interest rates, the Federal Reserve is able to influence when consumers and businesses spend money versus save money, thereby affecting aggregate demand. In both traditional Keynesian and New Keynesian aggregate supply-and-demand frameworks (see box 2-2), higher interest rates lead to decreases in real output and inflation, all else being equal (Miranda-Agrippino and Ricco 2021). Figure 2-13 shows that the Federal Reserve kept the Federal Funds Rate close to zero from April 2020 until it began to raise the Federal Funds Rate in response to rising inflation in March 2022. By the end of 2022, the Federal Reserve had increased the Federal Funds Rate to a range between 4.25 and 4.50 percent. The rapid increase in the Federal Funds Rate was an attempt to bring demand into better alignment with supply and cool inflation. It is important to note that the Federal Funds Rate alone is not enough to judge the stance of monetary policy. The Federal Funds Rate is a nominal rate, so its effect on the real economy depends on inflation. The real Federal Funds Rate is approximated in figure 2-13 by subtracting short-term expectations of consumer inflation.8 Another perspective on the stance of monetary policy is the real rate relative to r*, the long-term real rate consistent with the economy growing at its long-term trend. Though it is hard to estimate, there is evidence that Exactly which measure of inflation is appropriate to use to deflate the nominal Federal Funds Rate is outside the scope of this chapter. 8 The Year in Review and the Years Ahead | 75 Figure 2-13. Nominal and Real Measures of the Policy Rate, 2016–22 Percent 5 4 3 2 1 0 –1 –2 –3 –4 2016 2017 2018 Federal Funds Rate 2019 2020 Real Federal Funds Rate 2021 2022 Real Federal Funds Rate – r* Sources: Survey of Professional Forecasters (SPF); Federal Reserve System; CEA calculations. Note: The bright blue line subtracts 1-year-ahead expected inflation from the SPF from the dark blue line. The orange line subtracts the median estimate of the appropriate long-term Federal Funds Rate from the Federal Reserve quarterly Summary of Economic Projections (SEP) from the bright blue line. r* declined during recent decades (Powell 2018). Because of this decline in r*, and depending on inflation expectations, low Federal Funds Rates may not be as stimulative as they were in the past (Jordà and Taylor 2019). The Federal Open Market Committee (FOMC), in its December 2022 “Summary of Economic Projections” (Federal Reserve 2022a), suggested that long-run r*, calculated by subtracting the longer-run inflation rate (2.0 percent) from the longer-run Federal Funds Rate (2.5 percent), was 0.5 percent. The difference between the real Federal Funds Rate and r*, shown by the orange line in figure 2-13, is a plausible measure of the stance of monetary policy. At the end of 2022, the stance of monetary policy, as measured by both the real Federal Funds Rate and the real Federal Funds Rate minus r*, was above 0 percent, indicating a restrictive monetary policy. An additional factor in judging the stance of monetary policy is the Federal Reserve’s balance sheet. In 2020, following the playbook used during the 2007–8 financial crisis, the Federal Reserve announced additional measures to support the economy, including emergency lending and asset purchase programs, sometimes known as “quantitative easing.” Figure 2-14 shows that assets held by the Federal Reserve—the sum of Treasuries, mortgage-backed securities, and all others—grew to $8.2 trillion by the end of 2021—more than double their size before the COVID-19 pandemic. The increase in the size of the Federal Reserve’s balance sheet contributed to a substantial increase in measures of the money supply. As discussed in box 2-2, in 2020, a monetarist would have predicted that the substantial 76 | Chapter 2 Figure 2-14. The Composition of the Federal Reserve’s Balance Sheet, 2007–22 2021 dollars (trillions) 12-month percent change 6 9 8 5 7 4 6 5 3 4 2 3 2 1 1 0 2007 2008 2009 2010 2011 2012 Treasuries (left axis) All other (left axis) 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 0 2023 Mortgage-backed securities (left axis) Core PCE inflation (right axis) Sources: Bureau of Economic Analysis; Federal Reserve Bank of St. Louis; CEA calculations. Note: Excludes unamortized premiums and discounts on securities held outright. Nominal dollars were converted to 2021 dollars using the PCE Price Index. The PCE Price Index is seasonally adjusted. increase in “money” at a time when real output was shrinking would lead to inflation. In 2021 and 2022, with some lag, they would have been right. But 10 years ago, they would have been wrong. When the Federal Reserve more than quadrupled its balance sheet in the five years after the 2007–9 financial crisis, inflation did not rise by much, and it quickly returned to a stable rate below 2 percent. There are important differences: the 2007–9 recession was longer and deeper; households and firms had worse balance sheets; the unique, pandemic-related supply-side challenges were not present; and the fiscal response to the crisis was smaller (Guerrieri et al. 2021). Nevertheless, the drastically different result in 2007–8 makes it hard to draw a straight line between the Federal Reserve’s balance sheet actions in 2020–22 and inflation (Crawley and Gagnon 2022). The impact of fiscal factors on inflation. Extraordinary monetary policy in 2020 and 2021 was accompanied by expansive fiscal policy. In 2020, the pandemic prompted an increase of slightly more than 10 percentage points in the Federal Government’s outlays relative to GDP, the largest such increase since the increase of nearly 20 percentage points when the United States entered World War II. Much of this increased spending was distributed in economic impact payments made directly to households. Support was also provided via large temporary expansions of unemployment benefits and funds offered to small businesses to maintain payrolls and extend operations. The Year in Review and the Years Ahead | 77 Figure 2-15. The Fiscal Impulse and Inflation, 2012–24 4-quarter percent change Percentage-point contribution to real GDP 6 15 5 10 4 3 5 2 0 1 0 –5 2012 2013 2014 2015 2016 2017 2018 Taxes and benefits programs (left axis) Federal spending on goods and services (left axis) 2019 2020 2021 2022 2023 2024 State and local spending (left axis) Core PCE inflation (right axis) Sources: Hutchins Center at Brookings Institution; Bureau of Economic Analysis. Note: All values are seasonally adjusted. Aggregate supply-and-demand frameworks predict that, all else being equal, increases in government outlays will increase output and inflation. Estimates of the “fiscal multiplier,” or the ratio of the change in total real output to an expansionary fiscal policy action, vary considerably, with different estimates suggesting that government spending increases total output by more, or by less, than the government spending itself (Ramey 2019). Empirical estimates of the impact of government spending on inflation are mixed; a recent meta-analysis found that increases in government spending, offset by tighter monetary policy, often tend to be deflationary rather than inflationary (Jørgensen and Ravn 2022). Figure 2-15 plots the Hutchins Center’s Fiscal Impact Measure (FIM), which uses information on the Federal Government’s spending on goods and services, State and local government spending on goods and services, and taxes and benefit programs to approximate the contribution of fiscal policy to total real GDP growth each quarter (Belz, Sheiner, and Campbell 2022). A positive fiscal impulse means that the contribution of fiscal policy to real GDP is larger than it was the quarter before. Figure 2-15 shows that the FIM spiked in 2020:Q2, mainly due to an expansion of transfer programs, and was positive for two of the next three quarters, but was a significant drag throughout 2022 and is projected to remain negative in 2023 and 2024, using projections for fiscal policy by the Congressional Budget Office in its current services baseline. Table 2-2 highlights legislative and executive actions that cannot be easily characterized as “fiscal policy”—and hence are outside the scope of 78 | Chapter 2 Table 2-2. Selected Legislative and Executive Actions in 2022 Date Action Goal April to October Release of 180 million barrels of crude oil from the Strategic Petroleum Reserve Increase the supply of gasoline to lower its price, and the prices of other goods May Additional funding for domestic fertilizer production and technical assistance in agriculture, and expansion of eligibility for double-cropping insurance Encourage farmers to expand production, lowering and stabilizing food prices May Housing Supply Action Plan Increase the supply of available homes to lower housing costs June Ocean Shipping Reform Act Lower shipping costs and improve supply chains by fostering compeition July President Biden announces a series of actions that incentivize solar adoption and energy efficiency upgrades Lower demand for fossil fuels and lower energy prices August IRA promotes clean energy adoption, authorizes Medicare to negotiate drug prices, and caps annual out-of-pocket prescription costs at $2,000 Increase the supply of clean energy to lower the price; reduce prices and lower markups in the pharmecutical industry October Executive Order on Promoting Competition in the American Economy Lower fees and hidden costs and increase consumer and small business bargaining power Note: IRA = Inflation Reduction Act. This table only captures some of the many actions taken in 2022. the FIM—which by most economic definitions is primarily concerned with the levels of government revenue and spending and the path of deficits. The actions can be roughly divided into two categories. First, there are measures to promote competition in 2022 and in the future, such as the Ocean Shipping Reform Act, President Biden’s Executive Order on Promoting Competition in the American Economy, and the Inflation Reduction Act (IRA).9 Second, there are measures meant to either directly or indirectly expand the supply of particular goods or services, such as the President’s decision to tap into the Strategic Petroleum Reserve to reduce gasoline prices, and executive actions in May intended to help increase agricultural production and add to the stock of affordable housing. The actions listed in table 2-2 have likely lowered costs for specific goods or services, many of which are key inputs to other industries, and increased the future supply of many products. The long-term impact of these plans should be disinflationary. Figure 2-16 shows the Federal Government’s historic primary deficits, or total revenues minus total spending not including interest payments on outstanding debt, and those deficits projected for the next 10 years by the Office of Management and Budget (OMB), which uses the economic Procompetitive IRA measures include provisions that granted Medicare greater bargaining power in prescription drug cost negotiations with pharmaceutical companies. The IRA’s clean energy provisions will boost supply in targeted industries in the long term. 9 The Year in Review and the Years Ahead | 79 Figure 2-16. OMB’s Primary Deficit Forecast, 2017–33 Percentage of annual fiscal year GDP 0 –2 –4 –6 –8 –10 –12 –14 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 Actual Projected (OMB) Sources: Office of Management and Budget (OMB); CEA calculations. assumptions from the Administration forecast presented in the next section. The winding down of spending under the CARES Act, the American Rescue Plan, and related legislation, combined with higher tax revenue due to the recovery in GDP, led to a smaller deficit in 2022 as a share of GDP than in 2020 and 2021, or the 3 years after the 2007–8 financial crisis; but the deficit was higher than the post–World War II prepandemic average. One of the intentions of the reforms to the tax code made during the Biden-Harris Administration—including an increase in the corporate minimum tax, an increase in the Internal Revenue Service’s funding to help it bring in uncollected taxes and close loopholes, and a new excise tax on stock buybacks— is to reduce future deficits (Gleckman and Holtzblatt 2022; Congressional Research Service 2022). In an op-ed on May 30, 2022, President Biden said that he expected the reduction in the Federal deficit in 2022 to help ease price pressures (Biden 2022). Some theories suggest that lower deficits (or higher surpluses) over time can ease inflationary pressures (see box 2-2). Empirical estimates of the impact of government deficits on inflation do not provide consistent answers (Catão and Terrones 2005; Banerjee et al. 2022). Nevertheless, the global coincidence of unprecedented, deficit-funded fiscal actions begun in 2020, and the highest rate of inflation in 40 years has convinced some economists that the two are related (Bordo and Levy 2021). In 2020 and 2021, partially due to pandemic-era fiscal measures, and pandemic-related constraints on in-person spending, consumer income exceeded consumer spending by substantially more than it usually does, 80 | Chapter 2 Figure 2-17. Excess Savings and Inflation, 2016–22 3-month annualized percent change 2021 dollars (trillions) 1.4 6 1.2 5 1.0 4 $2.7 trillion in excess savings accumulated from 2020:Q1 to 2021:Q4 0.8 $0.6 trillion in excess savings drawn down from 2022:Q1 to 2022:Q4 0.6 0.4 2 1 0.2 0.0 3 2016 2017 2018 Actual savings (left axis) Core PCE inflation (right axis) 2019 2020 2021 2022 0 Savings at 2010–19 average rate (left axis) Sources: Bureau of Economic Analysis; CEA calculations. Note: The average saving rate from 2010 to 2019 was 7.3 percent. Nominal dollars were converted to 2021 dollars using the PCE Price Index. All values are seasonally adjusted. leading to a surplus of savings beyond what would have occurred if the saving rate (i.e., saving as a share of disposable income) had remained at prepandemic levels. The buildup of excess savings was due to the increased precautionary savings and pandemic-related constraints on spending that led consumers to spend less and save more than usual (Bilbiie et al. 2021) paired with the direct payments and income support program expansions included in the CARES Act, the American Rescue Plan, and related legislation. Figure 2-17 plots one measure of excess savings; the dark blue line represents the deviation of actual saving from what it would have been under the average quarterly saving rate from 2010 to 2019 (7.3 percent); and the green shaded area between the dark blue line and the light blue line is the excess savings in the quarter. By the end of 2021, the amount of cumulative excess savings peaked at about $2.7 trillion, or more than two months of usual prepandemic consumer spending. Given the excess savings, households had the potential to spend more than they normally would without incurring debt, even after the withdrawal of some fiscal recovery programs. In an aggregate supply-and-demand framework, if households spend their excess savings, the spending will increase aggregate demand, exacerbating inflation when supply is constrained (Aladangady et al. 2022). Excess savings, as shown in figure 2-17, were drawn down by about $0.6 trillion in 2022, and consumer spending rose, counteracting the aggregate demand effect of the negative fiscal impulse shown in figure 2-15. If the drawdown of excess savings, together The Year in Review and the Years Ahead | 81 with current income, boosted aggregate demand, it could have contributed to high inflation in 2021 and 2022. Additional demand factors affecting inflation. The pandemic and recovery, supported by funds provided by the CARES Act, the American Rescue Plan, and related legislation, also generated large and unusual shifts in consumer demand—most importantly, away from in-person services and toward distancing-friendly goods, and then back again, as shown in panels B and C of figure 2-18. In April 2021, possibly driven by this unusual spending on goods, inflation in the price of goods over the preceding 12 months, as measured by the PCE Price Index, was higher than inflation in the price of services for the first time in nearly a decade, as shown in panels D through F of figure 2-18. In the second half of 2022, goods inflation settled some, but the consumer demand rotation back to services caused services inflation to increase. Correspondingly, the ratio of the consumption of real goods to that of real services also rose, and then fell back somewhat toward prepandemic levels, but remained elevated. Because consumer spending makes up nearly 70 percent of GDP, it is informative to look at consumer spending on its own, as a measure of where the economy in 2022 was relative to its trend, as shown in figure 2-2 above. Figure 2-18, panel B, shows that goods consumption remained above its Figure 2-18. Consumer Goods-Services Rotation, 2018–22 A. Total spending B. Spending on goods C. Spending on services 17.0 16.5 16.0 PCE 15.5 15.0 14.5 Trend 14.0 13.5 13.0 12.5 2018 2019 2020 2021 2022 6.0 5.8 5.5 PCE on 5.3 goods 5.0 4.8 Trend 4.5 4.3 4.0 2018 2019 2020 2021 2022 11.5 2021 dollars in trillions 2021 dollars in trillions 2021 dollars in trillions 11.0 Trend 10.5 10.0 9.5 PCE on services 9.0 8.5 8.0 2018 2019 2020 2021 2022 D. Overall prices E. Goods prices F. Services prices 115 115 115 111 111 Index; 2021 = 100 107 Index; 2021 = 100 PCE Price Index 107 103 103 99 Trend 95 2018 2019 2020 2021 2022 Goods PCE Price Index PCE Price Index Index; 2021 = 100 111 107 103 PCE Price Index Services PCE Price Index 99 99 95 2018 2019 2020 2021 2022 95 2018 2019 2020 2021 2022 Sources: Bureau of Economic Analysis; CEA calculations. Note: PCE = Personal Consumption Expenditures. Trend lines were calculated by regressing each respective series on time from 2015 to 2019. All values are seasonally adjusted. 82 | Chapter 2 trend through 2022. Services consumption—as shown in figure 2-18, panel C—recovering from the obstacles to in-person services during the pandemic and seeing a rapid rise in prices, remained below its trend. Overall, as shown in panel A of figure 2-18, consumer spending was near its trend. Business fixed investment, as broken out in figure 2-3—which is necessary to add to domestic productive capacity—did not see the same rapid increase as consumption. This disconnect between above trend goods consumption and the lack of increased production, whether due to supply constraints on production or slow investment, means that domestic supply was not able to provide the level of goods and services demanded. As supply chain disruptions made it challenging to address this imbalance through increased imports, inflation rose as goods prices increased (Guerrieri et al. 2021). The impact of inflation expectations. Expectations play an important role in the major frameworks that economists use to analyze inflation, as described in box 2-2. Some economists think that higher expectations of future inflation can be self-fulfilling, making efforts to fight inflation more difficult or painful. If businesses, consumers, and financial market participants expect inflation to be high, they will behave in ways consistent with this expectation and that may bring about actual higher inflation. For example, workers with high inflation expectations may demand higher wages, and businesses with high inflation expectations may price goods higher. The back and forth between these effects can lead to further increases in inflation. In 2022, long-term inflation expectations stayed near their historical levels, and short-term expectations moved with actual inflation, pointing Figure 2-19. Actual and Expected Inflation, 2012–22 12-month percent change 10 9 7 6 5 4 3 1 0 -1 2012 2013 2014 2015 1-year expected inflation 2016 2017 2018 2019 5–10 year expected inflation 2020 2021 2022 12-month headline CPI inflation Sources: University of Michigan; Bureau of Economic Analysis; CEA calculations. The Year in Review and the Years Ahead | 83 to inflation expectations that were dependent on actual inflation rather than being driven independently in a way that could lead to further inflation. When inflation began to rise in 2021, long-term inflation expectations had been steady for decades, and even as inflation started to climb, these expectations remained low. Figure 2-19 plots two of the most commonly tracked measures of inflation expectations: the median expected annual price change over the next 12 months, from the University of Michigan’s monthly survey of households; and the median expected average annual price change over the next five to 10 years, from the same survey. Although both measures increased during 2022, they did not increase by nearly as much as realized inflation. Long-term inflation expectations (5–10 year expected inflation, the light blue line) in particular were reassuringly stable, indicating that although elevated inflation was expected in the short run, it was not expected to last. As discussed in box 2-2, this stability was taken as evidence that inflation expectations were anchored. Still, toward the end of 2022, some economists worried that the modest increases in long-run inflation expectations, and the possibility of sustained increases in expectations, would make it harder to bring inflation down (Powell 2022b). The Forecast for the Years Ahead The Biden-Harris Administration finalized the latest version of its official economic forecast on November 28, 2022. This forecast provides the Administration’s estimated projections of key economic variables over the next 11 years, from 2023 to 2033, and also includes its forecast for 2022. During the interval between when this forecast was finalized and the publication of this Report, more 2022 data have become available, so that the official forecast discussed in this chapter differs from those published more recently. This overall forecast is a critical input to the President’s Fiscal Year 2024 Budget, because it is an input into the budget projections of many Federal agencies, and to projections of tax revenues. The forecast development also provides insight into what challenges lie ahead and where the economy might need additional investment and support. COVID-19 continues to generate forecasting uncertainty. Although U.S. COVID-19 fatalities surged to 1,700 a day in 2022:Q1 due to the new Omicron variant, they declined to 500 per day in April and then the fourweek moving average fluctuated in the range of 300 to 500 per day for the rest of the year—held down by vaccinations, increasing immunity, and new treatments. Further COVID-19 declines or future surges pose upside and downside risks for the forecast. The potential for future supply chain disruptions due to COVID-19 surges abroad or wartime disruptions provide further risks; the Russian invasion of Ukraine is another source of uncertainty. 84 | Chapter 2 Table 2-3. Economic Projections, 2021–33 Percent Change (Q4 to Q4) Inflation Measures Level (percent) Unemployment Rate Interest Rates Real GDP GDP Price Index CPI Annual Q4 3-Month T-Bills 10-Year T-Notes Actual 2021 2022 5.7 0.9 6.1 6.4 6.7 7.1 5.4 3.6 4.2 3.6 0.0 2.0 1.4 3.0 Forecast 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 0.2 0.4 2.1 2.4 2.0 2.0 2.0 2.1 2.2 2.2 2.2 2.2 6.6 2.8 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 7.6 3.0 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 3.7 4.3 4.6 4.4 4.3 4.2 4.1 4.0 3.9 3.8 3.8 3.8 3.8 4.6 4.5 4.4 4.3 4.2 4.1 4.0 3.8 3.8 3.8 3.8 2.0 4.9 3.8 3.0 2.5 2.3 2.2 2.3 2.4 2.4 2.5 2.5 3.0 3.8 3.6 3.5 3.4 3.4 3.4 3.4 3.4 3.4 3.4 3.4 Year Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; Department of the Treasury; Office of Management and Budget; CEA calculations. Note: These forecasts are based on data available as of November 28, 2022; actual data for 2022 arrived later. The interest rate on 3-month (91-day) Treasury Bills is measured on a secondary-market discount basis. Averaging these risks, the Administration presents a central forecast; table 2-3 summarizes its key aspects. The Near Term For this Report’s near-term forecast, two questions were paramount. First, does real GDP currently exceed its short- or long-run potential level? And second, how soon will inflation return to the Federal Reserve’s 2 percent target, and how will this return influence output and employment? The Administration forecast largely followed the consensus of Blue Chip forecasters by revising its GDP forecast downward. Over the six months between March and October 2022, the Blue Chip consensus economic forecast was revised to show substantially lower real GDP growth and higher inflation during the two years 2022 and 2023 (see table 2-4). This combination of revisions suggests that the consensus—implicitly—recognized that demand had exceeded available supply during 2022; the consensus panel did not make any offsetting upward revisions during the subsequent two years. The lack of a bounce-back in the consensus forecast for real GDP growth in The Year in Review and the Years Ahead | 85 Table 2-4. Evolution of the Blue Chip Consensus Real GDP Forecast 2022 Percent Growth, Annual Average to Annual Average 2023 2024 2025 2026 Real GDP March 2022 October 2022 Revision 3.5 1.6 –1.9 2.5 0.2 –2.3 2.1 1.5 –0.6 2.0 2.1 0.1 2.0 2.1 0.1 CPI March 2022 October 2022 Revision 6.2 8.0 1.8 2.6 3.9 1.3 2.3 2.4 0.1 2.2 2.2 0.0 2.2 2.2 0.0 Source: Blue Chip Economic Indicators. Note: The Blue Chip panel revises its long-term forecast in March and October, with growth rates that are annual average to annual average. 2024 and 2025 may reflect that the constraints on supply during 2022 partly reflected long-term factors. Between October and December 2022, inflation in 2022 came in lower and real GDP growth during 2022 came in stronger than the Administration had predicted as of November. In light of the new data available since the forecast was finalized, a forecast assembled today would differ from that finalized in November. The forecast given in table 2-3 predicted slow (0.4 percent) real GDP growth for the four quarters of 2023 because GDP growth may need to be less than trend growth to alleviate the current tight labor market. The Blue Chip consensus panel also predicted that 2023 real GDP growth would be slow over the four quarters of the year.10 The second question, how soon will inflation return to levels consistent with the Federal Reserve’s target, depends on the success of monetary and fiscal policy, and the legislative and executive actions discussed above. As a consequence of the FOMC’s decision to raise the target Federal Funds Rate from close to 0 percent in February 2022 to between 4.25 and 4.50 percent in December, other short-term rates also increased, including the yield on 91-day Treasury Bills, which rose 4.2 percentage points during the 12 months of the year to 4.3 percent by the end of the year. Though nominal interest rates on long-term securities also rose, they did not increase by as much as short-term rates, perhaps reflecting market confidence that inflation will recede over the next 10 years. As of November 2022, the Administration predicted that interest rates would continue to increase during 2023, but would then begin to decline in 2024. The Administration further predicted that inflation would fall quickly in 2023 from its 2022 pace as supply chains unsnarled, and would return to rates consistent with the Federal Reserve’s In October, the Blue Chip panel predicted that Q4-to-Q4 real GDP growth would be 0.4 percent, which was lowered to –0.1 percent in the December survey. 10 86 | Chapter 2 long-term targets by 2024 (see, e.g., the FOMC’s December 14, 2022, statement: Federal Reserve 2022b). Consistent with slow GDP growth, in November 2022 the Administration expected the unemployment rate would edge up in 2023, averaging 4.3 percent but peaking at 4.6 percent in 2023:Q4. The combination of this rising unemployment, slow GDP growth, a falling vacancy rate, the effects of expected fiscal policies and executive actions, and continued confidence in the Federal Reserve’s commitment to its 2 percent target rate was expected to lower the rate of CPI inflation to 3.0 percent during 2023, and to 2.3 percent during 2024. As mentioned in box 2-1 above, CPI inflation tends to outpace the PCE Price Index; hence, a 2.3 percent CPI inflation rate is consistent with the Federal Reserve’s target of a 2 percent PCE Price Index inflation rate. Another measure of inflation, the price index for GDP, was expected to fall from a forecasted 6.6 percent rate during 2022 to 2.1 percent during 2024. Post–World War II history suggests that bringing down inflation, via monetary policy or otherwise, will likely lower employment growth and output growth. Recognizing this relationship, in November the Administration expected that unemployment would increase during the four quarters of 2023, before starting to decline in 2024. From its expected 4.6 percent peak in 2023:Q4, the unemployment rate was expected to edge lower to 4.5 percent by the end of 2024, eventually falling—in 2030—to the long-term rate of 3.8 percent that the Administration considers to be consistent with stable inflation. The Administration’s near-term forecasts for real GDP growth in 2023–24, near-term inflation, the unemployment rate, and interest rates were roughly consistent with the forecast of the Blue Chip Economic Indicators (the consensus), and that of the FOMC as of November 2022.11 The Long Term In contrast to the near-term outlook, the Administration’s long-term forecast for real GDP growth exceeded the October 2022 Blue Chip consensus longterm forecast by an average of 0.2 percentage point a year during the nine years 2025–33. The Administration believed that potential real GDP growth in the long run would be modestly higher because of the expected effect of the President’s proposed economic policies, assuming that they are enacted, including a range of programs to enhance human capital formation, provide childcare, and reform immigration policy. In addition, the Administration recognized that the downward pressure on labor force participation from the The Congressional Budget Office’s forecast is absent from this list because its latest 2022 forecast (during the interval that the Administration forecast was in play) was finalized on March 2, 2022, before the release of much data on GDP and inflation, and was therefore out of date. 11 The Year in Review and the Years Ahead | 87 Table 2-5. Supply-Side Components of Forecasted Real Output Growth Percentage-Point Contribution to Annual Real Output Growth 1953:Q2– 2019:Q4 1990:Q3– 2001:Q1 2001:Q1– 2007:Q4 2007:Q4– 2019:Q4 2019:Q4– 2033:Q4 (1) (2) (3) (4) (5) 1 Population 1.4 1.2 1.1 1.0 0.7 2 Labor force participation rate 0.1 0.1 –0.3 –0.4 –0.2 Component 3 Employed share of labor force 0.0 0.1 0.1 0.1 0.0 4 Average weekly hours –0.2 –0.1 –0.2 –0.1 0.0 5 Output per hour 2.0 2.4 2.4 1.4 1.6 6 Output per worker differential –0.3 –0.3 –0.6 –0.4 –0.2 7 Sum: Real GDO 3.0 3.5 2.4 1.7 1.9 Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; Department of the Treasury; Office of Management and Budget; CEA calculations. Note: These forecasts are based on data available as of November 28, 2022. Total may not add up due to rounding. 1953:Q2, 1990:Q3, 2001:Q1, 2007:Q4, and 2019:Q4 are all quarterly business-cycle peaks. Population, labor force, and household employment have been adjusted for discontinuities in the population series. Detailed row defintions: (1) civilian noninstutional population, 16 + (4) nonfarm business average weekly hours (5) nonfarm business output per hour; output is measured as the average of income- and product-side measures (6) difference between output-per-worker growth in the economy as a whole and in the nonfarm business sector (7) gross domestic output (GDO) is the average of GDP and gross domestic income (GDI). retirement of baby boom cohorts is likely to wane during the last five years of the budget window (2028–33), as discussed in box 2-3. Although the circumstances surrounding this year’s near-term forecast were unique to 2022, the key issues affecting the long-term forecast are less tied to recent events. These issues can be described most clearly in terms of the supply-side components of GDP, which, although erratic in the short run, have more understandable long-term movements. The first set of key issues has to do with the long-term labor supply. As discussed in chapter 6 of this Report, the U.S. population is aging. The first row of table 2-5 shows that the Administration’s forecast expected that the civilian, noninstitutional population age 16 years and above would grow by an average of 0.7 percent at an annual rate from 2019 to 2033, below the average 1.0 percent annual growth rate from 2007 to 2019.12 Much of this expected growth will likely come from immigration.13 The labor force participation rate was projected to continue its decline, reflecting the aging of the baby boom cohorts into retirement. This downward pressure on the The civilian, noninstitutional population excludes individuals who are incarcerated or are living in mental health facilities or homes for seniors, or who are on active duty in the Armed Forces. Projected growth rates come from demographers at the Social Security Administration. Table 2-5 shows projected growth rates for the 15 years since the business cycle peak in 2019:Q4. The choice of this long period to discuss these supply-side components is because many of these components move sharply for business-cycle reasons (workweek and productivity), and others have large erratic components in the short run (labor force participation rate and the productivity differential). 13 Also see Social Security Administration (2022a). 12 88 | Chapter 2 Box 2-3. Aging and Growth The United States, like most advanced countries, is going through a demographic transition, and this will have a large impact on a variety of economic variables for years to come. In figure 2-ii, the blue line plots the age distribution of the United States in 2011, the bars show the current age distribution, and the orange line plots the expected age distribution in 2033. Although the U.S. population is still growing, the center of mass of the age distribution is shifting to the right—that is, to older ages. Of particular note is the baby boom cohort, whose members were between 58 and 76 years of age in 2022. Most baby boomers are now Figure 2-ii. The Evolution of the U.S. Population’s Age Composition Millions of people 5.0 Baby boom cohort 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 Years of age 2022 2011 2033 Source: Social Security Administration (2022b). Note: The U.S. Social Security population differs slightly from the U.S. civilian noninstitutional population. Figure 2-iii. Age–Labor Force Participation Rate Profiles in 2019 Percent 100 90 80 70 60 50 40 30 20 10 0 16 20 24 28 32 36 40 44 Male Age–LFPR profile 48 52 56 60 64 68 72 76 80 Female Age–LFPR profile Sources: Bureau of Economic Analysis; CEA calculations. Note: LFPR = labor force participation rate. The Year in Review and the Years Ahead | 89 at or above the age of retirement. As they age, the baby boomers will continue to push out the right tail of the distribution. Most people retire when they are between the ages of 62, the earliest age of eligibility under Social Security, and 70, as can be seen from the sharp decline in participation for those ages shown in the age– participation rate profiles given in figure 2-iii. Using the Social Security Administration’s projections for the age distribution through 2033, together with these age–participation profiles, overall participation is projected to drop about 0.4 percent (or about 0.2 percentage point) a year for the next five years. But during the last five years of the forecasted interval, this downward pressure on the overall labor force participation rate will be reduced to about 0.2 percent a year, because most of the baby boom cohort will have already retired. Using the identity shown in table 2-5, the less negative growth in the participation growth rate is expected to have a positive impact on GDP growth. participation rate was projected to wane after 2028, however, as discussed in box 2-3. The workweek (row 4 of table 2-5) was projected to stabilize after a long historical period of decline attributable to the entry of women, who, on average, have shorter workweeks than men, and to the declining share of manufacturing in total employment. In the Administration’s forecast, the employed share of the labor force was projected to remain close to its level at the 2019 business-cycle peak, and therefore made no net contribution over the forecast interval. Productivity growth (measured as output per hour) was projected to grow 1.6 percent a year over the 15-year interval, somewhat more slowly than its 2.0 percent long-term average but faster than the 1.4 percent growth rate during the 2007–19 business cycle. Finally, the output per worker differential, which is the difference between the output per person for the economy as a whole and the output per person in the nonfarm business sector, was expected to be negative, because of the national income accounting convention that productivity does not grow in the government or household sectors. Because productivity growth is assumed to be zero for these sectors of the economy, while productivity growth was forecasted to be positive in the nonfarm business sector, the differential was necessarily negative. That said, this differential was projected to be less negative than the historical average because of the projected declining share of government in total output. The long-term forecast of the inflation rate was based on the assumption that the Federal Reserve will succeed in hitting its target of 2 percent for inflation, as measured by the PCE Price Index. Forecasts for future interest rates were informed by the FOMC’s near-term forecast of the Federal Funds 90 | Chapter 2 Rate. Projections for the yield on 10-year Treasury Notes lie between the Blue Chip consensus forecast and the implicit forecast provided by forward rates derived from the market prices of U.S. Treasury securities. Conclusion The forces that have buffeted the U.S. economy since the beginning of the COVID-19 pandemic only began to calm in 2022. The United States found itself in an enviable position among advanced economies, with substantial growth during 2021 and positive growth in 2022, a low unemployment rate, and lower inflation than some other countries. Moreover, inflation pressures abated from their mid-year highs by the end of 2022, both in terms of headline and, more importantly for the future, core inflation. The U.S. economy has, by some economic measures, such as the record low unemployment rate and the return of output to—or even above—the trend, fully recovered from the COVID-19-induced recession. As discussed in this chapter, the rise in inflation during this period appears to have been driven partly by the intersection of constrained supply and strong demand. These dynamics reflected the effects of the pandemic on consumer demand and supply chains, along with the strong fiscal and monetary support that was necessary to offset the unique and powerful negative shock caused by COVID-19. Though these fiscal and monetary interventions contributed to the strong demand that played a role in the ensuing inflationary pressures, they also set the stage for the historically strong 2021–22 labor market and supported smoothly functioning financial markets. At the same time, these interventions helped avoid the deep and lasting hardships that otherwise would likely have beset millions of American households. In this uncertain environment, as President Biden said at the time, the risk of doing too little exceeded the risk of doing too much (White House 2021). Overall, the recovery from the pandemic-induced recession progressed far enough in 2022 that the U.S. economy is well situated to weather the anticipated below-trend growth over the near term. The speed and strength of the pandemic recovery testifies to the power of fiscal and monetary policy to fight even the largest negative shocks. The government is united in working toward sustainable growth, low inflation, and inclusive prosperity. The Year in Review and the Years Ahead | 91 Chapter 3 Confronting New Global Challenges with Strong International Economic Partnerships In 2022, the global economy continued to face challenges as the economic shocks associated with the COVID-19 pandemic persisted into their third year. In addition, Russia’s unprovoked invasion of Ukraine disrupted global commodity markets and caused businesses and governments to reevaluate key trade and investment linkages. Nevertheless, in the United States, persistently strong global economic ties contributed to the continuing recovery of manufacturing output, strong consumption, deepening business investment (BEA 2023a), and resilience to shocks. They also provided strategic room to counter geopolitical aggression. The global economic shocks of the past three years have highlighted the need for policies that balance the benefits of these economic ties with the risks to economic and national security that they can entail. The policy response to external challenges, along with the pursuit of greener and more inclusive economic growth at home, will transform the international economic linkages that manifest through global markets for goods, services, and data. Strong partnerships between governments are essential to effectively address these challenges. This chapter begins by describing how the global economic events of 2022 were reflected in the United States’ robust international trade and investment flows. It then examines how ongoing COVID-19 disruptions, more recent geopolitical tensions, and the expansion of the digital economy have affected global economic policymaking priorities. It closes by underscoring 93 the critical role of international partnerships between the United States and its allies and partners in ensuring the effectiveness of their collective response to these shared challenges. The United States’ International Trade and Investment in 2022 As the headline-grabbing supply chain challenges associated with the persistence of COVID-19 retreated, and despite Russia’s invasion of Ukraine, U.S. international trade and investment reached record highs in 2022. Trade in goods and services (exports plus imports) increased by 8 percent compared with 2021 in real, inflation-adjusted terms, surpassing the record set in 2019 (figure 3-1) and reflecting robust imports and exports of goods, despite headwinds from slowing global growth and the strong U.S. dollar (BEA 2023a). Record imports were driven by a surge in the first quarter of 2022, which retreated in the second half of the year. Although they declined from their first-quarter high, they remained strong in historical terms. In contrast, exports increased relatively steadily to the third quarter, with a shallow fourth-quarter decline. These distinct paths are reflected in the sharp increase and subsequent narrowing of the trade deficit (exports minus imports) in 2022 (figure 3-2). The trade deficit shot to 4.5 percent of gross domestic product (GDP) in the first quarter of 2022—the largest since the Figure 3-1. Real U.S. Trade in Goods and Services, 2012–22 Trillions of chained 2021 dollars, quarterly, seasonally adjusted at annual rates 4 3 2 1 0 –1 –2 2012:Q1 2013:Q1 2014:Q1 2015:Q1 2016:Q1 2017:Q1 2018:Q1 2019:Q1 2020:Q1 2021:Q1 2022:Q1 Balance Exports Goods exports Sources: Bureau of Economic Analysis; CEA calculations. 94 | Chapter 3 Imports Goods imports Figure 3-2. U.S. Trade Balance, 2018–22 Percentage of GDP, quarterly 2 1 0 –1 –2 –3 –4 –5 –6 2018:Q1 2018:Q3 2019:Q1 2019:Q3 2020:Q1 Goods 2020:Q3 2021:Q1 Services 2021:Q3 2022:Q1 2022:Q3 Total Source: Bureau of Economic Analysis. Figure 3-3. Real U.S. Services Trade, 2012–22 Billions of chained 2021 dollars, quarterly, seasonally adjusted at annual rates 1,800 1,600 1,400 1,200 1,000 800 600 400 200 0 2012:Q1 2013:Q1 2014:Q1 2015:Q1 2016:Q1 2017:Q1 2018:Q1 2019:Q1 2020:Q1 2021:Q1 2022:Q1 Other services exports Travel and transportation exports Other services imports Travel and transportation imports Sources: Bureau of Economic Analysis; CEA calculations. third quarter of 2008. The deficit then declined as imports fell from their peak, reaching 3.2 percent of GDP in the fourth quarter. Over the past 20 years, the U.S. goods trade deficit has been partially offset by a surplus in services trade. That is, U.S. exports of services have consistently exceeded imports of services. However, services surpluses have been depressed since the abrupt halt in international movements at the onset of the COVID-19 pandemic as exports of travel and transportation services Confronting New Global Challenges with Strong International Economic Partnerships | 95 have recovered more slowly than imports.1 In 2022, real travel and transportation services exports had only reached 67 percent of their 2019 level, whereas imports were at 89 percent (figure 3-3). In 2022, stronger growth in travel services imports (spending by U.S. travelers abroad) compared with exports (spending by foreign visitors to the United States) was likely driven in part by the dollar’s strength (box 3-1). For transportation services, the differences in recovery paths were compositional: U.S. transportation services exports are typically dominated by passenger air services, so fewer foreign visitors due to COVID-19 suppressed these exports. While the plurality of U.S. transportation imports are also typically passenger air services, a large share are maritime freight services. Since most shipping companies are foreign-owned, record goods imports pushed these services imports higher (BEA 2023b). In official U.S. data on services trade, this category is named “transport” rather than “transportation.” 1 Box 3-1. Effects of the Strengthening U.S. Dollar on the U.S. Economy In 2022, the U.S. dollar strengthened against the currencies of its main trading partners, particularly other advanced economies. The Federal Reserve’s broad, real exchange rate index increased by 10.7 percent between January 2022 and its peak in October 2022, falling back at the end of 2022 to realize a 5.4 percent year-over-year increase in December 2022 (figure 3-i). The dollar’s rise was driven by strong U.S. growth and rising interest rate differentials, as well as by the appeal of U.S. assets as safe haven investments as Russia’s invasion of Ukraine stoked global uncertainty. The weakening of the dollar at the end of the year reflects the Federal Reserve’s signal that the pace of rate hikes would slow and signs of relatively strong economic conditions in other advanced economies. Dollar exchange rates have an important influence on trade patterns because they determine the price of U.S. goods and services in the national currencies of the Nation’s trading partners. When the dollar is strong, it takes more foreign currency to purchase dollar-denominated goods and services. At the same time, it reduces the dollar cost that U.S. buyers pay for imported goods and services denominated in foreign currency, effectively making them cheaper. All else being equal, these changes in relative prices encourage U.S. buyers to substitute away from goods and services produced in the United States and toward foreignproduced goods and services (i.e., imports), deepening the U.S. trade deficit. 96 | Chapter 3 Figure 3-i. Federal Reserve Board’s Real Broad Dollar Index, 2016–22 Index: 2021 = 100, through December 2022 120 115 Strengthening U.S. dollar 110 105 100 95 90 Jan-2016 Sep-2016 May-2017 Jan-2018 Sep-2018 May-2019 Jan-2020 Sep-2020 May-2021 Jan-2022 Sep-2022 Broad Advanced foreign economies Emerging market economies Sources: Federal Reserve Board; CEA calculations. Note: Advanced foreign economies include Australia, Canada, the euro area, Japan, Sweden, Switerland, and the United Kingdom. Emerging market economies include Argentina, Brazil, Chile, China, Colombia, Hong Kong, India, Indonesia, Israel, South Korea, Malaysia, Mexico, the Philippines, Russia, Saudi Arabia, Singapore, Taiwan, Thailand, and Vietnam. In 2022, the dollar’s strength was only one of many strong currents shaping trade patterns. As such, it is difficult to distinguish its effects from other forces. However, as an example, it is likely that the strength of the dollar contributed to the comparatively stronger rebound in imports relative to exports of travel services, as depicted in figure 3-3. This is because when the dollar is strong, as explained above, it has more value in foreign currency terms, making travel budgets go further and thus incentivizing increased spending on hotels, restaurants, and other goods and services by Americans abroad. The opposite effect makes travel in the United States more expensive for foreign visitors. The strong dollar also likely dampened U.S. exports of agricultural commodities like soybeans, cotton, and corn in 2022 (Jiang et al. 2022). Indeed, exports in the Bureau of Economic Analysis’ (BEA’s) broad end-use category of food, feed, and beverages, which includes these agricultural products, fell to its lowest level in real terms since 2015, another period of the dollar’s strengthening. (The BEA classifies traded goods in six broad end-use categories: consumer goods; foods, feed, and beverages; industrial supplies and materials; capital goods; automotive vehicles, etc.; and other goods.) Because agricultural commodities tend to have relatively few intrinsic differences across countries of origin, it is particularly attractive for buyers to substitute away from U.S. varieties when a strong dollar increases their relative prices. Indeed, research suggests that exchange rates are a particularly relevant factor for buyers of less-differentiated commodities, and U.S. agricultural exports tend to decline in periods of real dollar strength (Cooke et al. 2016; Mattoo et al. 2017). Confronting New Global Challenges with Strong International Economic Partnerships | 97 The strong real exports of manufactured goods in 2022 seemingly conflict with the deterioration of U.S. currency competitiveness. (These exports are defined as goods exports under the North American Industry Classification System, chapters 31–33; U.S. Census Bureau 2023b; BLS 2023.) However, this may be explained in part by two offsetting forces. First, the dollar’s strength lowers the dollar costs of imported inputs and capital equipment priced in foreign currencies, thus increasing the cost-competitiveness of U.S. manufacturers that rely on these imports (Goldberg and Crockett 1998). Second, in 2022 U.S. manufacturers’ loss of currency competitiveness was likely offset by a deterioration of cost-competitiveness in other countries that were more exposed to rising input costs from energy price hikes. A strong dollar can also lower the dollar price of imported consumer goods, dampening inflationary pressures. In practice, however, the dollar’s impact on movements in U.S. consumer price inflation has historically been limited, due to the relatively low pass-through of exchange rate movements to U.S. import prices (Gopinath and Itskhoki 2021; Gopinath, Itskhoki, and Rigobon 2010). Moreover, imported goods constitute a relatively small share of the basket of goods used to calculate common measures of inflation—representing only 12.6 percent of the Consumer Price Index by one estimate (Borusyak and Jaravel 2021)—so declines in prices of imported goods are unlikely to have a substantial impact on measured inflation in a given period. Pandemic-Related and Macroeconomic Trends Have Shaped Record Goods Imports Strong demand growth and the unwinding of the pandemic-era supply chain pressures that mounted throughout 2021 underpinned the dramatic increase in goods imports in the first quarter of 2022 (for the top U.S. import partners, see box 3-2). Along with the strengthening dollar, these forces sustained elevated imports through the rest of the year. To illustrate how this pattern unfolded in record imports in the broad end-use category of consumer goods, figure 3-4 splits this category in two. The household goods series depicts trends in real imports of goods most closely associated with household consumption, such as apparel and footwear, cellphones, furniture and household appliances. The other consumption goods series reflects trends in real imports of goods like pharmaceuticals, artwork, and gem diamonds that are less associated with everyday household expenditures.2 The CEA is grateful to the International Trade Programs team in the Economic Indicators Division of the U.S. Census Bureau for suggesting this division. 2 98 | Chapter 3 Box 3-2. The United States’ Top Goods Trading Partners Although research suggests that the product composition of goods trade has shifted in recent years, the United States’ top trading partners have largely remained the same (Bown 2022a). The top U.S. export destinations and import sources are still China and the European Union—the two largest economies outside the United States—as well as the United States’ North American neighbors, Mexico and Canada. Together, these four economies are responsible for over half of U.S. trade (figures 3-ii and 3-iii). Figure 3-ii. Top Sources of U.S. Goods Imports, 2022 Share of nominal imports European Union, 17.0% Rest of the world, 38.8% China, 16.7% Mexico, 14.0% Canada, 13.5% Source: U.S. Census Bureau. Figure 3-iii. Top U.S. Goods Export Destinations, 2022 Share of nominal exports European Union, 16.9% Rest of the world, 42.7% China, 7.4% Mexico, 15.7% Canada, 17.2% Source: U.S. Census Bureau. Confronting New Global Challenges with Strong International Economic Partnerships | 99 Figure 3-4. Real Imports of Consumer Goods, 2018–22 Billions of 2021 dollars 90 80 70 60 50 40 30 20 10 0 Jan-2018 Jul-2018 Jan-2019 Jul-2019 Household goods Jan-2020 Jul-2020 Jan-2021 Other consumption goods Jul-2021 Jan-2022 Jul-2022 All consumer goods Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations. Note: Consumer goods exclude automobiles and parts. Household goods include apparel, footwear, and other household goods; furniture and other household goods; household appliances; cell phones and other household goods; and toys, games, and sporting goods. Real series have been adjusted with the Bureau of Labor Statistics' import price indices. Figure 3-4 reveals that the first-quarter import surge was largely driven by household goods, reflecting the pandemic-induced shift in consumption expenditures to goods and away from services (see chapter 2 of this Report). This shift disproportionately increased import demand throughout this period, in part simply because goods are more import-intensive than services. Compounding this, the persistence of remote work and diminished leisure spending outside the home increased demand for goods like computers and home improvement products that are particularly import-intensive in the United States (Chetty et al. 2022; Higgins and Klitgaard 2021; IMF 2022a). In the first quarter, easing of port congestion—in addition to high inventory investment by businesses responding to global market uncertainty after months of COVID-19-related supply chain snarls and the impending threat of Russia’s invasion of Ukraine—further boosted imports. Imports of household goods decreased from their first-quarter peak as consumption expenditures began to shift back to services, supply chain backlogs were cleared, and inventory rebuilding continued (see chapter 2). However, they remained well-above prepandemic levels throughout the first half of the year. In the second half of the year, household goods imports declined even more significantly as rising interest rates began to dampen consumer demand. Real imports of capital goods also set a record in 2022, exceeding the previous record set in 2021 by 10 percent. Together with robust imports of industrial supplies and materials—fuels, metals, and other key industrial 100 | Chapter 3 Figure 3-5. Real Imports of Capital Goods (Excluding Automobiles), 2018–22 Billions of chained 2021 dollars 75 70 65 60 55 50 45 Jan-2018 Jul-2018 Jan-2019 Jul-2019 Jan-2020 Jul-2020 Jan-2021 Jul-2021 Jan-2022 Jul-2022 Sources: Census Bureau; CEA calculations. inputs—these imports supported a strong rebound in domestic output in 2022 (see chapter 2). Like household goods, capital goods imports surged in the first quarter with relief from pandemic-era port congestion (figure 3-5). Unlike household goods, capital goods imports remained substantially above prepandemic levels as imports of various types of electrical equipment, industrial machinery, transportation equipment, and information and communications technology equipment—including semiconductors—benefited from a combination of easing supply constraints and strong business demand. Geopolitical Shocks and Global Demand Have Shaped Record Goods Exports Real exports of goods surpassed their prepandemic heights of 2019 by 2.6 percent in 2022 (see box 3-2 for the top U.S. export partners). Increased demand for U.S. energy exports was a key driver, as many countries—particularly in Europe—looked to replace Russia as a source of crude oil and natural gas supplies. U.S. exports in the broad end-use category of industrial supplies and materials—which includes energy goods—hit a record high in 2022, as did exports of consumer goods. In contrast to consumer goods imports depicted in figure 3-4, the increase in real consumer goods exports was driven by pharmaceutical goods (figure 3-6). Shocks from Russia’s invasion of Ukraine had a significant impact on global commodity markets in 2022 that echoed in U.S. exports. In contrast to other traded goods, commodities like oil—as well as many metals, minerals, and agricultural products are relatively standardized across source countries, Confronting New Global Challenges with Strong International Economic Partnerships | 101 Figure 3-6. Real Exports of Consumer Goods, 2018–22 Billions of 2021 dollars 25 20 15 10 5 0 Jan-2018 Jul-2018 Jan-2019 Household goods Jul-2019 Jan-2020 Jul-2020 Other consumption goods Jan-2021 Jul-2021 Pharmaceutical goods Jan-2022 Jul-2022 All consumer goods Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; CEA calculations. Note: Consumer goods exclude automobiles and parts. Household goods include apparel, footwear, and household goods; furniture and household goods; household appliances; cell phones and other household goods; and toys, games, and sporting goods. Real series adjusted with BLS import price indices. allowing buyers to substitute across source countries fairly easily. Because of this, their price in any given country is largely determined by global market movements. As such, although Russia and Ukraine are relatively small trading partners for the United States—representing only 0.5 percent of U.S. exports and 1.1 percent of U.S. imports in 2021—because they are major producers and exporters of key commodities, disruptions of their exports influence the prices U.S. consumers must pay for food and fuel, and also overall inflation (see chapter 2). In addition, since the United States is an exporter of some commodities also exported by Russia and Ukraine, notably energy and agricultural products, disruptions to supplies or changes in the pattern of exports from these countries can affect U.S. exports as well (IEA 2022a). Initially, Russia’s invasion largely cut off Ukraine—a major exporter of food commodities, especially wheat, corn, and vegetable oil—from global markets, threatening global food security. The loss of Ukraine’s export supply, along with the reluctance of global buyers to engage with Russian exporters on the exports of grains and oil seeds and Russia’s own export restrictions on fertilizer and other agricultural products, resulted in contractions along key supply lines for food staples and agricultural inputs like fuel and fertilizer, sending prices soaring in the immediate aftermath of the invasion (Glauber and Laborde 2022). Prices retreated as allied nations successfully collaborated to mitigate disruptions. Nevertheless, the uncertainty associated with Russia’s domestic actions and aggression toward Ukraine—including the destruction of infrastructure used to store and export food commodities, and the naval blockade of Ukraine’s Black Sea 102 | Chapter 3 Figure 3-7. U.S. Exports of Liquefied Natural Gas, 2021 and 2022 Millions of cubic meters 4.5 3.5 North America European Economic Area (including EU) and U.K. 47.8 China 117.4 21.9 5.1 Other Asia-Pacific 24.7 Rest of the world 39.1 31.1 52.5 165.9 Total 0 20 40 60 80 2021 100 120 140 160 181.9 180 200 2022 Sources: U.S. Census Bureau, accessed with Trade Data Monitor; CEA calculations. Note: Liquefied natural gas is covered by HS271111. Other Asia-Pacific includes Australia, Japan, South Korea, New Zealand, and all 10 nations that belong to the Association of Southeast Asian Nations: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam. trade route—continued to exacerbate elevated prices. This led U.S. exports of food, feed, and beverages to exceed their 2021 record by 10 percent in nominal terms, even as they fell to their lowest level since 2015 in real terms (Foggo and Mainardi 2022; U.S. Census Bureau 2023b; Yale School of Public Health 2022). Real exports of these products were ultimately depressed by the strong dollar, weakening global demand and other productspecific factors, including adverse weather conditions. Disruptions from Russia’s invasion of Ukraine had a more significant real impact on U.S. exports of energy goods, notably liquefied natural gas (LNG) and crude oil. The quantity of U.S. exports of LNG and crude oil rose substantially over 2021’s already-high levels. For LNG, U.S. exports also shifted dramatically to European countries as Russia restricted its oncedominant supply of natural gas via pipeline (figure 3-7). Crude oil exports expanded more broadly across destinations, with the notable exception of a decrease in exports to China (figure 3-8). Although this figure only captures a single year rather than a trend, research suggests that reductions in China’s energy imports from the United States in 2022 likely represented a shift to imports from other sources, including Russia, along with a drop in demand due to slower Chinese economic growth (Bown 2022b). International Trade in Services and Digital Trade Have Been Resilient Through the end of 2022, U.S. trade in services other than travel and transportation was remarkably stable and resilient amid the continued disruption of the COVID-19 pandemic and rising geopolitical tensions (see figure 3-3). In part, this is because digital technology enables adaptations that allow Confronting New Global Challenges with Strong International Economic Partnerships | 103 Figure 3-8. U.S. Exports of Crude Oil, 2021 and 2022 Millions of barrels 183.4 200.4 North America European Economic Area (including EU) and U.K. 400.0 95.4 China 176.0 231.2 Other Asia-Pacific Rest of the world 552.5 326.5 264.9 60.3 1,175.0 Total 0 200 400 600 2021 800 1000 1200 1,315.6 1400 2022 Sources: U.S. Census Bureau, accessed with Trade Data Monitor; CEA calculations. Note: Liquefied natural gas is covered by HS271111. Other Asia-Pacific includes Australia, Japan, South Korea, New Zealand, and all 10 nations that belong to the Association of Southeast Asian Nations: Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, the Philippines, Singapore, Thailand, and Vietnam. many traded services to be remotely provided. Further, many countries have made efforts to reduce obstacles to digital trade, including by promoting access to and efficiency of electronic payments (Klapper and Miller 2021). Just as remote work minimized pandemic-related disruptions in many domestic industries that specialize in information, digital technologies allowed movements of service providers to be converted into movements of data and thus minimized interruptions of international trade in these industries (Brynjolfsson et al. 2020; Dingel and Neiman 2020; Espitia et al. 2021; Pei, de Vries, and Zhang 2021). Furthermore, limitations on mobility increased demand for other traded digital services as more household consumption as well as work moved online. In fact, the pandemic likely accelerated the trend of rising digital trade flows. Though there is no standardized definition of digital trade, it can be conceptualized as including three general types of transactions. The first is traditional e-commerce, whereby the Internet facilitates a purchase that is delivered offline. The second is digitally provided services, which are provided and consumed online. This category includes a wide array of services that are increasingly part of everyday life, including digital media like streaming music and videos; digital platforms that connect individuals to make transactions; the services embedded in the Internet of Things, like “smart” household appliances and connected medical devices; and the cloud computing services relied on for business operations. The third category includes data, which are a basic element of many cross-border transactions but can also be deployed by companies as part of their operations or sold to other businesses to target advertisements, improve manufacturing 104 | Chapter 3 Figure 3-9. U.S. Trade in Potentially ICT-Enabled Services, 1999–2021 Billions of 2021 dollars 700 600 500 400 300 200 100 0 1999 2001 2003 2005 2007 2009 Imports of potentially ICT-enabled services Imports of all other services 2011 2013 2015 2017 2019 2021 Exports of potentially ICT-enabled services Exports of all other services Sources: Bureau of Economic Analysis; CEA calculations. Note: ICT = information and communications technology. Price indices for exports and imports of potentially ICT-enabled services are calculated as the average of price indices for their components (insurance services; financial services; charges for the use of intellectual property; telecommunications, computer, and information services; other business services; and personal, cultural, and recreational services), weighted by the category's nominal share. The nominal series is then converted with this price index. operations, and power machine learning for artificial intelligence (AI) tools, among many other uses (Meltzer 2019; OECD 2023a; Staiger 2021a; Wharton 2019). Although digital trade cannot be precisely measured using current data sources, the evidence suggests that there have been dramatic increases during the last two decades. Cross-border data flows that underpin digital trade transactions are estimated to have increased by a compound annual growth rate of 45 percent between 2010 and 2019 and by about 40 percent between 2019 and 2021 (Birshan et al. 2022). In comparison, flows of goods and services grew at a compound annual growth rate of about 3 and 4 percent, respectively, between 2010 and 2019 (BEA 2023c). Estimates suggest that e-commerce transactions grew at an average annual rate of 14.5 percent between 2010 and 2019 and by 30.3 percent between 2019 and 2021. E-commerce transactions made up an average of 14.5 percent of retail sales by value in 2022, up from 4.5 percent in 2010 and 10.5 percent in 2019 (U.S. Census Bureau 2022). Likewise, the subset of traded services that the BEA defines as “potentially ICT-enabled” (i.e., information and communications technology–enabled) has grown dramatically over time (figure 3-9).3 Real exports Potentially ICT-enabled services trade includes the categories of services trade for which digital technologies are thought to play the most prominent role. These include ICT services themselves, as well as insurance services, financial services, and charges for the use of intellectual property, including royalties and licenses. 3 Confronting New Global Challenges with Strong International Economic Partnerships | 105 and imports of potentially ICT-enabled services grew at an average annual rate of 7.0 and 8.5 percent, respectively, between 1999 and 2021. This was much faster than real exports and imports of all other services, which grew at average annual rates of 0.5 and –1.1 percent, respectively, during the same period. Unlike traditional trade in goods and services, for many digital trade transactions, there is no physical movement of a good or a person across a border. Rather, the transaction is fully realized by data flows. In great contrast to physical exchanges, the direct, marginal cost to move data across borders is nearly zero. Moreover, the cost difference in procuring an identical digitally delivered service from nearby versus from far away is also close to zero (Goldfarb and Tucker 2019). Absent a sharp increase in regulatory hurdles, digital trade is thus poised for further dramatic increases as digital technology continues to improve, as the Internet of Things continues to spread, and as robotics and artificial information technologies are further developed (Baldwin 2022). At present, U.S. trade in potentially ICT-enabled services is concentrated among advanced economies (BEA 2022). However, as digital technology develops, and as the infrastructure that enables Internet use improves, there will be more opportunities to draw on workers and consumers from around the world to provide and demand a wide range of digital services. This is likely to propel substantial increases in digital trade with emerging markets (Baldwin 2022) and provide benefits to U.S. consumers, workers, and businesses. However, increased competition from service providers abroad will also likely have negative effects on some American businesses and workers (box 3-3). Continued Growth for Foreign Direct Investment Despite Elevated Uncertainty Global foreign direct investment (FDI) flows exhibited strong growth during the first three quarters of 2022; total real global FDI flows grew by 9 percent during the first three quarters of 2022 compared with the same period in 2021; global FDI flows in the first quarter of 2022 reached their second highest level in the past five years, increasing by more than 15 percent yearover-year and by over 40 percent compared with the prior quarter (BEA 2023d; OECD 2023b).4 Global FDI as a share of world GDP reached about 2 percent of GDP in the first half of 2022, a continued recovery from the sharp contraction in international investment during the onset of the COVID-19 pandemic. Though FDI can sometimes pose risks (e.g., to national security in limited cases), research has found that inward and outward FDI can be Real FDI flows are calculated as the average of global FDI inflows and outflows in dollars, deflated by the U.S. Personal Consumption Expenditures Price Index (chain type). 4 106 | Chapter 3 Box 3-3. Rising Digital Trade and U.S. Labor Markets Advances in digital technology that facilitate the remote production and provision of goods and services will create significant opportunities and challenges for U.S. workers (Amiti and Wei 2006; Eppinger 2019; Grossman and Rossi-Hansberg 2008). U.S. workers have a comparative advantage in many tradable services and some sophisticated goods industries due to skill level and education. Access to a larger global market will allow these industries to expand, increasing demand for these skills, which may lift wages and provide opportunities for employment for a portion of the workforce. However, other workers in services industries that compete directly with digitally enabled imports (e.g., a worker for a traditional big box retailer competing with a foreign e-commerce company) may face lower wages and job loss. Importantly, research suggests that these losses may disproportionately affect individuals who are more economically vulnerable, exacerbating economic inequality within the United States (Oldenski 2011). In particular, Baldwin (2022) argues that an expansion of digital services trade may have particularly negative effects on U.S. workers providing intermediate services (e.g., administrative assistants, graphic designers, travel agents, and information technology help staff), who will face rising competition from lowwage counterparts in developing countries. Labor provisions are a core feature of the Biden-Harris Administration’s work with U.S. partners on digital trade and featured in the United States’ discussions with the EU in the U.S.-EU Trade and Labor Dialogue under the U.S.-EU Trade and Technology Council (DOL 2022), as well as in Pillar 1 of the Indo-Pacific Economic Framework for Prosperity (USTR 2022b). These provisions aim to ensure that trade policy supports fair competition for U.S. workers in the digital economy, raising the standard for workers abroad rather than facilitating competition on the basis of low labor standards. Research on previous labor market shocks—notably the so-called China Shock, whereby increased import competition in certain manufacturing sectors led to concentrated and persistent job losses in some communities—has revealed that the costs for many workers to adjust after a change in the demand for their labor can be very high (Autor, Dorn, and Hanson 2013, 2016, 2021; Eriksson et al. 2021). This suggests that there is an essential role for complementary domestic policies to equip U.S. workers who are exposed to increased competition through digital trade with the resources to adapt (CEA 2022, chap. 3; Clausing 2019). Confronting New Global Challenges with Strong International Economic Partnerships | 107 Figure 3-10. Real U.S. Outward Foreign Direct Investment, by Destination, 2012–22 Billions of 2021 dollars, quarterly 70 60 50 40 30 20 10 0 –10 –20 2012:Q1 2013:Q1 2014:Q1 2015:Q1 2016:Q1 2017:Q1 Group of Seven (excluding United States) 2018:Q1 2019:Q1 2020:Q1 2021:Q1 2022:Q1 Large emerging markets: Brazil, China, India Sources: Bureau of Economic Analysis; CEA calculations. Note: Data are net financial transactions (without current cost adjustment) on a directional basis, in this case those that relate to outward investment (U.S. direct investment abroad). Nominal series converted to 2021 dollars using U.S. Personal Consumption Expenditures Price Index. Data through 2022:Q3. the source of significant contributions to economic growth and increased resilience to shocks (Alfaro 2016; OECD 2020a). Although FDI flows are not directly subject to the same types of physical disruptions as international trade (i.e., the ability to carry out financial transactions is not affected by issues like port closures or physical distance), they are similarly responsive to changes in global economic conditions. Elevated uncertainty about global economic conditions and changes in the economic policy environment can reduce or reverse investment flows (Choi, Furceri, and Yoon 2020; Gulen and Ion 2016; Julio and Yook 2016). Businesses may decide to delay or suspend investment decisions when uncertainty is high and when investors find it difficult to determine when conditions are likely to normalize. Following the strong flows in the first quarter of 2022, elevated global inflation and tightening global financial conditions, as well as the compounding effects of Russia’s invasion of Ukraine, resulted in individuals, companies, and governments moderating global FDI flows in the second and third quarters of 2022 (although they still grew 5 percent compared with the second- and third-quarter flows in 2021) (OECD 2023b). Focusing on the United States, in the first half of 2022, the country was both the largest recipient and largest source of FDI globally (OECD 2022a). FDI flows into and out of the United States are largely flowing from or to advanced economies (e.g., the Group of Seven), especially in comparison 108 | Chapter 3 Figure 3-11. Real U.S. Inward Foreign Direct Investment, by Source, 2012— 22 Billions of 2021 dollars, quarterly 150 100 50 0 –50 –100 –150 2012:Q1 2013:Q1 2014:Q1 2015:Q1 2016:Q1 Group of Seven (excluding U.S.) 2017:Q1 2018:Q1 2019:Q1 2020:Q1 2021:Q1 2022:Q1 Large emerging markets: Brazil, China, India Sources: Bureau of Economic Analysis; CEA calculations. Note: Data are net financial transactions (without current cost adjustment) on a directional basis, in this case those that relate to inward investment (foreign direct investment in the U.S.). Nominal series converted to 2021 dollars using U.S. Personal Consumption Expenditures Price Index. Data through 2022:Q3. with FDI to and from large emerging market countries (figures 3-10 and 3-11). Along these lines, the United States, its allies, and its partners are taking measures to deepen investments in the critical industries in one another’s economies as a way of reducing dependencies on other countries that have had an outsized role in these industries, notably China. For instance, the United States, its allies, and its partners are coordinating to increase their collective capacity to produce semiconductors (Shivakumar, Wessner, and Howell 2022). As part of the United States’ CHIPS and Science Act, the State Department will manage the International Technology Security and Innovation Fund, which will promote the development of complementary, secure supply chain investments in key partners to strengthen and support the U.S. semiconductor industry (U.S. Department of State 2022a). Similarly, coordinated efforts to catalyze infrastructure investment in emerging and developing countries through the Partnership for Global Infrastructure and Investment—particularly to support the digital economy and the green energy transition—will help reduce uncertainty, strengthen secure supply chains, create new opportunities for businesses and workers, and boost overall economic growth (White House 2022a). The increased policy clarity resulting from these types of commitments and the shared experience of supply constraints during the pandemic may further catalyze mutual investment, thereby deepening the United States’ investment relationship with its key allies and partners. Confronting New Global Challenges with Strong International Economic Partnerships | 109 Global Economic Relations Are at a Turning Point Since World War II, a central focus of the international economic policies of the United States, its allies, and its partners has been reducing barriers to trade and investment in pursuit of greater economic prosperity (Irwin 2022a; CRS 2023). These policies have led to an expanded and strengthened web of integrated economic relationships in the form of global supply chains, and they have supported flows of goods and services across borders that have substantially increased national incomes around the world (CEA 2022, chap. 6; Irwin 2022b; World Bank 2020). However, disruptions of these flows during the global COVID-19 pandemic hit critical nodes of supply chains and hindered production worldwide, amplifying constraints on the supply of certain essential goods to businesses and households (Espitia, Rocha, and Ruta 2022). In addition, Russia’s invasion of Ukraine made it imperative for the United States, its allies, and its partners to sever economic relations with Russia that could facilitate its military aggression. The resulting economic sanctions, the reluctance of some international businesses to maintain even permitted economic relationships with Russia, and Russia’s retaliatory export restrictions made the risk of undiversified supply chains even more apparent. They also underscored the power of economic integration as a tool of foreign policy (Yellen 2022a; Lagarde 2022). Alongside these shocks, increased competition from imports over time has also hurt the employment and earnings outcomes for some groups of workers (box 3-3). Long-standing concerns about the associated role of international trade in rising income inequality within the United States (Autor et al. 2014; Chetverikov, Larsen, and Palmer 2016)—along with concerns about the climate crisis, through the greenhouse gas emissions embedded in the consumption of tradable goods and services within the United States—have led to calls to reassess and update the approach to trade policy in the United States and elsewhere (CEA 2022, chap. 3; Tai 2021a, 2021b; WTO 2022). Although market incentives and current trade rules do not always align production and trade flows with broader social, political, environmental, or national security objectives, international trade and investment can be powerful sources of economic gains. Empirical research has demonstrated that in addition to supporting lower costs for businesses and consumers (de Loecker et al. 2016; Jaravel and Sager 2019), and jobs and higher wages for workers in export industries (Feenstra et al. 2019; National Security Council 2022; Riker 2015; U.S. Department of Commerce 2021), trade and investment facilitate the flow of knowledge across borders, spurring productivity gains and innovation (Goldberg et al. 2010; Keller and Yeaple 2009). Beyond the United States’ borders, trade and investment with the United States provides opportunities for many developing countries to fight 110 | Chapter 3 potentially destabilizing poverty (Irwin 2022b) and can be a foundation for closer relationships with the United States in other domains (Chivvis and Kapstein 2022). Moreover, as leaders have emphasized, global economic integration is also part of a strategy to promote economic resilience and security (Georgieva, Gopinath, and Pazarbasioglu 2022; Lagarde 2022; Yellen 2022a). Extensive research has found that under a broad set of conditions, businesses are more resilient during supply disruptions when they are able to draw on a geographically diverse set of sources rather than a concentrated source of supply for inputs. Put simply, geographically distributed supplies can act as a “pressure valve” for supply challenges during periods of idiosyncratic supply disruptions (Bonadio et al. 2021; Eppinger et al. 2021; Caselli et al. 2020; D’Aguanno et al. 2021; Espitia, Rocha, and Ruta 2022; Grossman et al. 2021). Although the opportunity to trade does not automatically deliver geographic diversity in sourcing, it does enable it. Similarly, global markets can serve as a backstop for demand, providing alternative markets for businesses when domestic demand is low (Caselli et al. 2020; Lagarde 2022). As such, it is in the interest of the United States to pursue approaches to lower trade costs within greener, fairer, and more secure trade and investment partnerships. The United States, its allies, and its partners have thus reached a turning point in international economic policy, whereby it is necessary to reckon with a broad mandate: On one hand, it is desirable to maintain the benefits associated with international trade and investment and to facilitate the growth of these benefits in the digital sphere. On the other hand, the focus of trade policy needs to expand beyond reducing barriers. Decisionmakers need to ensure that policy supports increased resilience to global supply shocks; limits the ability of adversarial powers to weaponize economic integration to the United States’ detriment; preserves fair competition in the presence of large, nonmarket economies; and minimizes exposure to cybersecurity and regulatory risks, while facilitating digital trade flows. Trade policy can also advance other objectives that interact with international markets, such as fighting climate change, promoting workers’ rights and labor standards both at home and abroad, and expanding the benefits of trade to underserved communities (Meltzer and Kerry 2019; USTR 2022e). The mandate to balance these priorities exists both at the level of individual policy measures and for aggregate U.S. policy, making coordination across agencies within the U.S. government and between U.S. partners increasingly important. The approach the United States takes to international economic policy in this challenging environment sends a signal to businesses, consumers, and governments around the world about U.S. priorities. As such, it forms a key element of U.S. foreign policy. Confronting New Global Challenges with Strong International Economic Partnerships | 111 Imperatives of Economic Partnerships in the Changing Global Environment Confronting systemic vulnerabilities that have become more prominent over the past two years while preserving the benefits of international economic integration to the maximum extent will require close collaboration between the United States, its allies, and its partners. This subsection explores three critical policy objectives where cross-border trade and investment play an essential role in promoting economic well-being and for which there is a need to calibrate trade policy to meet current challenges: (1) building more resilient supply chains, (2) responding to adversarial or unfavorable political and economic policies abroad, and (3) safely advancing digital trade. Although the scope of this chapter is limited to these three areas, the United States and its allies and partners also face a broader mandate to update and strengthen the rules, norms, and institutions that underpin international business and economic relations in the twenty-first century environment. This includes facing sociopolitical challenges and combating climate change (CEA 2022, chap. 3). Existing institutions and frameworks for global dialogue and collaboration remain important as incubators for solutions to complex and evolving challenges (Staiger 2021b). Today’s challenges also provide a critical opportunity for the United States to play a global leadership role, working with its allies and partners to chart a modern course for a greener, more inclusive, more resilient, and more secure global economy (box 3-4). Box 3-4. The United States’ New Approach to Economic Partnerships The Biden-Harris Administration is pursuing deeper commercial ties through economic partnerships that address vulnerabilities to external shocks while making international trade and investment greener and fairer. The Indo-Pacific Economic Framework for Prosperity, a flagship effort, consists of four pillars. The trade pillar seeks to craft high-standard, inclusive, free, fair, and open trade commitments. The supply chain pillar seeks to establish commitments for supply chain transparency, diversity, and coordination. The clean economy pillar seeks cooperation on clean energy, decarbonization, and infrastructure. And the fair economy pillar seeks economic frameworks to enforce tax, antibribery, and anticorruption systems. Commitments within each of the four pillars will be designed to enhance the benefits for workers in the United States and around the world (White House 2022b). 112 | Chapter 3 Resilience during Global Supply Shocks In the early days of the COVID-19 pandemic, disruptions in the supply of manufacturing inputs like semiconductors, consumer products like bicycles, and medical supplies and equipment made Americans acutely aware of the importance of “supply chain resilience”—that is, the ability of businesses and public services to continue to provide goods and services when a source of supply or distribution is suddenly unavailable. The past three years have demonstrated how shortfalls of inputs or equipment in one industry can disrupt production and distribution in linked industries, slowing overall economic output (Cerdeiro and Komaromi 2020). Furthermore, Americans have witnessed how supply disruptions can even put public health and safety at risk. This experience has motivated both firms and governments to take steps to build resilience. Falling barriers to trade—induced by both policy and technological change—have enabled businesses to reach around the world to source the inputs and equipment that ultimately come together to produce the goods purchased by consumers and public service providers at lower cost, greater variety, and higher quality (Baldwin and Freeman 2022; de Loecker et al. 2016; Fan, Li, and Yeaple 2015; Krugman 1980). However, the inputs and equipment themselves are often also an amalgam of raw materials extracted in one country, processed in another, and combined with more materials in a third country. As a consequence of this global production process, firms and governments often have only a limited visibility of the critical nodes of supply chains, which limits their ability to evaluate or reduce their exposure to rising geopolitical tensions, climate-related disasters, and other risks. As such, researchers have emphasized that government support for initiatives to increase the visibility of supply chains, or to enhance supply chain transparency, can reduce the information costs of broader steps to increase resilience (CEA 2022, chap. 6; National Academies 2022). Because stages of production take place globally, engaging with partner governments to collect and share information can make efforts to map and monitor supply chains more complete. Such collaboration can alert governments to potentially fruitful avenues to mitigate destabilizing supply dependencies and, because sharing information highlights cross-country interlinkages, it can catalyze coordinated responses during crises. Indeed, experts have argued that a sustained commitment by countries to share information and coordinate policies affecting the supply of critical health-related goods and services will be essential in preparing for future public health crises (Bown 2022c; National Academies 2022). Partnerships to increase supply chain transparency can also reduce the costs of gathering information to satisfy climate and other policies, such as those that aim to eliminate trade in products made with forced labor, like the Uyghur Forced Labor Confronting New Global Challenges with Strong International Economic Partnerships | 113 Prevention Act in the United States (Baldwin and Freeman 2022). More generally, greater supply chain transparency gives both firms and consumers the information they need to “vote with their wallets” by choosing to buy from producers and vendors whose practices are consistent with their own values (Mollenkopf, Peinkofer, and Chu 2022). In this way, transparency can leverage market forces to reward and advance greener, more inclusive, and more secure business practices. The Biden-Harris Administration has initiated several ongoing dialogues on supply chains that focus on sharing information, designing early warning systems for supply chain disruptions, developing technical standards, and facilitating private investment. These discussions have been held through the U.S.-EU Trade and Technology Council, the Quad Critical and Emerging Technologies Working Group, the Minerals Security Partnership, and the Indo-Pacific Economic Framework for Prosperity. The United States also conducts regular bilateral dialogues on supply chains with a number of countries, including Canada, Mexico, the United Kingdom, Japan, and South Korea. These and other partnerships can further contribute to maximizing the benefits of government incentives to increase productive capacity for critical goods and materials—that is, traded goods that are essential building blocks for economically and intrinsically important goods and services, such as medical and energy supplies and core technologies (Baldwin and Freeman 2022; Miroudot 2021; IMF 2022a; OECD 2020b; White House 2021a). Cross-country coordination can reduce the risk that competing government subsidies lead to unproductive excess capacity or an oversupply that blunts incentives for further innovation. Likewise, since support from foreign governments can impose economic distortions on domestic competitors, frameworks for allies and partners to resolve differences can help to limit those distortions and avoid costly retaliatory measures (Bown and Hillman 2019; Staiger 2021b; Sykes 2015). Finally, partnerships to encourage cooperation and communication about industry standards for traded goods and services can enhance the ability for trade to contribute to supply chain resilience. Though there are legitimate reasons for countries to have differing approaches to regulations and standards affecting product design and distribution, fragmentation of entire supply chains because of regulatory differences can decrease resilience. For example, divergent industry standards may make digital systems less interoperable or standard manufacturing inputs less substitutable across production systems, making it more costly to find alternative sources in the event of a supply disruption. As such, forums to develop internationally recognized product standards as well as those that facilitate information sharing on domestic regulatory measures play a critical role in facilitating the ability of trade and investment to promote resilience. 114 | Chapter 3 Box 3-5. Coordination Has Been Critical for the Success of the Sanctions Policy toward Russia Russia’s invasion of Ukraine on February 24, 2022 started the largest land conflict in Europe since at least the conflicts in the Balkans in the 1990s. The scale and brutality of this conflict marked an abrupt departure from the post–World War II—and in particular the post– Cold War—rules-based global political and economic order (National Security Council 2022). The coordinated response by the coalition of the United States and more than 30 allied and partner nations to impose costs on Russia and address the associated threats to the global economy highlights how pooling resources and acting in coordination to achieve a policy goal is often more effective than a unilateral approach (Aslund and Snegovaya 2021; Berner, Cecchetti, and Schoenholtz 2022). To date, the coalition’s sanctions against Russia have targeted key aspects of the Russian economy. Extensive financial sanctions have restricted capital flows into Russia, depriving it of revenues necessary to continue funding its war. For example, the United States has prohibited U.S. persons from making new investments in Russia, and the United States and its allies and partners have sanctioned major Russian financial institutions and taken action to remove major Russian banks from the SWIFT financial messaging system (CRS 2022a). Beginning in early 2022 and continuing through the year, foreign direct investment into Russia fell sharply (figure 3-iv), highlighting the scope and strength of coordinated financial sanctions and the private sector’s responses to Russian aggression (OECD 2022b). The coalition’s member countries have also imposed extensive export controls and have revoked Russia’s normal trade relations status, Figure 3-iv. Real Russian Foreign Direct Investment Net Inflows, 2017— 22 Billions of 2021 dollars, quarterly 15 10 5 0 –5 –10 –15 –20 2017:Q1 2017:Q3 2018:Q1 2018:Q3 2019:Q1 2019:Q3 2020:Q1 2020:Q3 2021:Q1 2021:Q3 2022:Q1 2022:Q3 Sources: Bureau of Economic Analysis; Organization for Economic Cooperation and Development. Note: Nominal series converted to 2021 dollars using U.S. Personal Consumption Expenditures Price Index. Data through 2022:Q3. Confronting New Global Challenges with Strong International Economic Partnerships | 115 thereby increasing tariffs on imports from Russia and thus the cost of doing business with Russia (U.S. Department of Commerce 2022; Tai 2022). The United States’ coordination with its allies and partners on export controls has hampered Russia’s ability to backfill its imports of military or dual-use items (U.S. Department of State 2022b). Sanctions and export controls contributed to a sharp overall drop in Russia’s imports and a shift in Russia’s energy exports away from Europe, both of which researchers have characterized as key factors harming the Russian economy (Demertzis et al. 2022). The International Monetary Fund estimates that the Russian economy contracted by 3.4 percent in 2022 (IMF 2022b). In addition, some analysts estimate that Russia’s economy will continue to suffer significantly in the medium to long runs. For example, some predictions suggest that the Russian economy will not return to its prewar level of real GDP for five years or more (Economist Intelligence Unit 2022). Importantly, recognizing the potential for negative spillovers to the global economy from financial and trade sanctions, the United States and its allies and partners have coordinated to relieve global market stress, including by ensuring trade channels remained open in selected commodities exported by Russia and Ukraine (IMF 2022b; OECD 2022c). This meant going after Russian energy in a measured way, by coordinating with partners and allies to allow energy transactions to continue while also designing price caps on seaborne Russian oil and petroleum products to limit Russia’s revenue and ensure a stable global supply of energy (U.S. Department of the Treasury 2022a, 2022b). (The price cap on seaborne oil entered into force in December 2022. The price cap on petroleum products entered into force in February 2023.) In addition, the United States has carved out agricultural commodities, fertilizer, and medical supplies from sanctions and issued extensive public guidance to ensure these authorizations are well understood (U.S. Department of the Treasury 2022c). The United States has also worked with the United Nations to find a pathway for Ukrainian wheat to reenter global markets: through the Black Sea Grain Initiative, more than 11.1 million metric tons of grains and other foodstuffs left Ukrainian ports between July 22, 2022, when the program took effect, and November 17, 2022 (United Nations 2022). The global market’s spillovers from Russia’s war against Ukraine illustrate the broader themes of this chapter: the past year has been marked by profound new and lingering disruptions of global commerce. Nevertheless, global markets remain relatively robust, and economic coordination—a key element of the post–World War II era—between the United States and its allies and its partner countries has been critical. Without coordination in 2022, there was a nontrivial risk that divergent sanctions policies could have increased confusion and uncertainty in 116 | Chapter 3 markets to the detriment of the global economy, notably global price stability. A lack of coordination also could have lessened the impact on the Russian economy of these sanctions. In coming years, continued coordination between the United States and its allies and partners will remain important for crafting effective policies to respond to these kinds of disruptions and to mitigate the economic and political uncertainty that may arise as a result of rising geopolitical tensions (Georgieva, Gopinath, and Pazarbasioglu 2022). Responding to Geopolitical Challenges In today’s environment of rising geopolitical tensions and geostrategic competition, the United States’ economic strength is one of its most profound sources of global power and influence. This strength is greatly enhanced by the collective economic strength that it can wield, along with its allies and partners that share its support for a free, open, prosperous, and secure world (National Security Council 2022). Coordination between the United States and its allies and partners can enhance the ability of aligned countries to provide shared security against and resilience in facing adversarial actions by, for example, enabling a network of alternative sourcing and market opportunities. In a recent example, the United States and its allies and partners have been able to impose significant economic costs on Russia in response to its invasion of Ukraine in February 2022 (box 3-5). By coordinating their actions, U.S. allies and partners have been able to limit Russia’s access to goods and services necessary to pursue its illegal war. Indeed, research has shown more broadly how coordinated economic actions more effectively limit a targeted country’s ability to evade economic consequences than do unilateral measures (Bapat and Morgan 2009; Drury 1998; Peksen 2019). Equally, economic partnerships can mitigate the economic consequences of adversarial actions targeted at the United States, its allies, and its partners. Just as concentrated dependencies on foreign adversaries can create vulnerabilities, diversified linkages with allies and partners can lessen them. Strong, diverse, and reliable economic linkages between trusted partners give businesses alternative markets to which they can shift their sourcing and sales if necessary, mitigating the impact of adversarial actions (Harrell, Rosenberg, and Saravalle 2018). For example, Russia has sought to weaponize Europe’s dependence on its supply of natural gas in an attempt to weaken Europe’s resolve to support Ukraine and to continue imposing costs on Russia in retaliation for its aggression. However, trade partnerships with the United States and other allies and partners have ensured that Europe has Confronting New Global Challenges with Strong International Economic Partnerships | 117 Box 3-6. The U.S.-EU Energy Partnership Diminishes Russia’s Leverage One of Russia’s biggest sources of economic leverage has been its dominance as a supplier of energy via its natural gas pipelines to Europe. Historically, Russia supplied Europe with roughly one-third of its gas (Corbeau 2022). Since the start of its invasion of Ukraine, Russia has cut pipeline deliveries of natural gas to Europe by more than half, and it may stop flows entirely in 2023 (IEA 2022b). However, the EU was able to replace some Russian gas with imported liquefied natural gas, including from the United States, thus weakening Russia’s ability to impose economic damage by restricting supplies of this critical source of energy for European households and industry. Economists estimate that natural gas shortages in Europe could have caused a contraction in some European economies of up to 6 percent if the global LNG market had been unable to respond (Flanagan et al. 2022). The ability of the United States to contribute to easing natural gas shortfalls has thus been critical. Since Russia’s invasion of Ukraine, the United States and the European Union have strengthened their cooperation on energy security. Through the Joint Task Force on Energy Security, the United States has made commitments to supply LNG to Europe through 2023 (White House 2022c). Through this partnership, the United States and the EU have agreed to address short-term energy supply issues with LNG while minimizing greenhouse gas emissions from LNG through measures to increase energy efficiency, reduce demand for gas, and regulate methane emissions. The task force has also led to additional commitments to advance renewable energy by expediting renewable energy projects and accelerating the deployment of clean energy technologies (White House 2022d). The United States is also strengthening its bilateral partnerships with European countries to increase energy security, empower global decarbonization efforts, and achieve net-zero economies in hard-toabate energy sectors through clean nuclear energy technology. In 2022, the United States announced its support for the Front-End Engineering Design study to provide the basis for the deployment of a small modular reactor power plant in Romania (U.S. Department of State 2022c); support for a pilot of commercial-scale production of clean fuels from small modular reactors in Ukraine (U.S. Department of State 2022d); and technical assistance for the inaugural civil nuclear project in Poland (U.S. Department of Energy 2022). These investments will help reduce dependence on Russian energy in Eastern Europe in both the medium and long term. 118 | Chapter 3 had the ability to shift to alternative energy sources, limiting the damage of Russia’s coercive behavior on households, businesses, and workers (box 3-6). Promoting Opportunity and Managing Risks in Digital Trade As discussed earlier in this chapter, digital trade is poised to expand dramatically as work and consumption increasingly take place online; as the Internet of Things digitally connects more everyday objects; and as frontier technologies, for which masses of data are a fundamental input, such as AI, continue to develop. Digital trade may also provide solutions for some of the core challenges to global trade and investment discussed above. For example, with technologies like 3-D printing or other forms of so-called additive manufacturing, digital information flows can potentially facilitate the substitution of entire stages of manufacturing supply chains that currently involve the physical movement of goods, improving resilience in the presence of supply disruptions (Freund, Mulabdic, and Ruta 2022). Likewise, products in the growing “TradeTech” industry use advanced technologies, including AI, to enable supply chain transparency and traceability. These products could reduce the cost of ensuring that supply chains meet security, social, and environmental criteria that make trade safer, greener, and more equitable (Capri and Lehmacher 2021). However, digital trade also creates vulnerabilities that must be managed, especially given rising geopolitical tensions. Digital trade has two fundamental requirements. The first is the infrastructure and equipment that transmit, store, and process data flows, including the network of underwater fiber-optic cables that carry more than 95 percent of international data (Comini, Foster, and Srinivasan 2021; Morcos and Wall 2021; World Economic Forum 2020). The second is a regulatory environment that permits the flow of data across borders with appropriate safeguards. Absent guardrails, digital trade can introduce potentially critical risks to economic well-being and national security through both of these gateways (Meltzer 2020). The risks involved are manifest and nontrivial. Among the most salient are cybersecurity risks: The constant flow of large volumes of digital information creates an appealing target for the theft of data. This can allow competitors to capture intellectual property, including trade secrets, that threaten American businesses. It can result in unauthorized access to Americans’ sensitive personal information, violating their privacy and potentially enabling financial or other crimes. Digital technology can allow goods and services traders to falsify information, potentially facilitating the evasion of national laws, regulations, and standards. Digital systems can also be manipulated or disabled remotely, potentially compromising national Confronting New Global Challenges with Strong International Economic Partnerships | 119 defense and critical infrastructure (Meltzer 2020). Estimates suggest that the economic cost of security breaches of information and technology assets during 2020 were as high as 6 percent of global GDP (or about $6 trillion). Other studies suggest that the costs are disproportionately high for critical industries like health care, transportation, energy, and financial services (IBM 2022; UNCDF 2022). The expansion of the digital economy modifies existing markets and creates new ones, bringing new challenges to protecting consumers and workers and promoting competition. For example, the difficulties of verifying identity and quality online can compromise consumer protection laws and labor market protections (Goldfarb and Tucker 2019). Likewise, the importance of large userbases and quantities of data, and the ability of digitally enabled companies to attract suppliers of products and consumers from all over the world creates new market concentration dynamics and poses new challenges to regulators focused on competition policy (see chapter 8). Governments employ a variety of measures to address these challenges by regulating the movement, storage, and processing of data. Regulations affecting digital trade generally fall into a few categories. First, data flow restrictions—for example, limits on access to digital media—may be used to protect intellectual property rights or enhance security, among other objectives. Second, so-called data localization policies—government regulations that determine where and how data related to their citizens, government, and businesses are stored—may be used to enhance consumer privacy and facilitate regulation (Casalini and González 2019; CFR 2022). Such policies may also reflect domestic economic priorities to try to protect industry from international competition. These regulatory measures can mitigate some risks associated with digital trade, but they can also blunt the very benefits they are put in place to protect (Meltzer 2020). For example, data flow restrictions can hamper innovation, which benefits from sharing information and knowledge across borders (Valero 2016; White House 2022e). These restrictions can be particularly detrimental for the development and use of AI technologies, which rely on the availability of large data sets and are increasingly prominent in business and important for national security. The ability to aggregate, store, process, and transmit data across borders is similarly critical for the financial services sector and its development (Carr, French, and Lowery 2020). Similarly, data localization requirements can increase vulnerability to cyberthreats by concentrating data, thus making systems easier to target (Bauer et al. 2014). These requirements can also make integrated risk management, including monitoring and detecting fraud and cybersecurity risks, more difficult for global firms and institutions to conduct—particularly those in the financial services sector. Mismatches in equipment standards 120 | Chapter 3 and regulations can limit system interoperability and thus the resilience of digital systems. International cooperation to define the vulnerabilities associated with data flows and digital supply chains and the regulatory measures that diminish them can reduce the risks and enhance the economic benefits of digital trade (Ahmed 2019; Casalini et al. 2019; Huang, Madnick, and Johnson 2019; OECD 2015, 2022d). Efforts to enhance workers’ rights and increase consumer protections from cybercrime and fraud that crosses borders are integral to these efforts. Indeed, scholars, policymakers, and business leaders have all emphasized the importance of creating an international digital architecture that promotes trust in data flows (CFR 2022). To do so, governments must grapple with how to provide a regulatory system that is safe and secure without unnecessarily restricting the benefits of trade. Best practices in international trade suggest that regulations should be transparent, should be nondiscriminatory for like products and services, and should not be more Box 3-7. U.S. Digital Trade Initiatives Digital trade is an increasingly prominent element of various international working groups and agreements, reflecting its importance for inclusive economic growth and security and the challenges policymakers around the world face in developing appropriate and consistent regulatory approaches. Attesting to the focus that the Biden-Harris Administration has placed on ensuring that digital trade benefits people as workers and consumers, the United States has led efforts to foster trust in the digital economy, support innovation and competition, promote a resilient and secure digital infrastructure, ensure consumer protection and privacy, and address discrimination. It is pursing these efforts by cooperating in regional partnerships that include the Indo-Pacific Economic Framework for Prosperity, the World Trade Organization’s (WTO’s) Joint Statement Initiative on Electronic Commerce, the Americas Partnership for Economic Prosperity, and the U.S.-Central Asia Trade and Investment Framework Agreement, as well as bilateral engagements with the United Kingdom, Kenya, Taiwan, and other countries (CRS 2022b; USTR 2021, 2022a, 2022b, 2022c, 2022d, 2022e; White House 2022f). The United States has also actively participated in multilateral forums to exchange information on best practices and promote standards and frameworks for tackling the risks associated with digital trade. These include the WTO, the Asia-Pacific Economic Cooperation forum, the Organization for Economic Cooperation and Development, and the Group of Twenty and Group of Seven, which together cover a broad set of countries around the world (USTR 2022e). Confronting New Global Challenges with Strong International Economic Partnerships | 121 burdensome or restrictive than is necessary to achieve their goals, including enhanced security and economic resilience (Casalini and González 2019). In this regard, the Biden-Harris Administration is engaging with various forums to build this trusted system (box 3-7). These include working with partners and allies to promote an environment that fosters development of the global economy and facilitates robust cross-border data flows that are consistent with both privacy and security needs. However, given the rapid pace at which the digital economy is evolving and the variety of domestic regulatory objectives, negotiating every aspect of the digital regulation may not always be desirable or possible. In this context, frameworks to establish common principles and provide for regulatory transparency have tremendous value (Staiger 2021a). Conclusion The record-setting flows of trade and investment in 2022 demonstrate that the United States remains deeply connected with the global economy. However, disruptions such as those experienced during the COVID-19 pandemic and rising geopolitical tensions pose fundamental challenges to globally connected production systems. Though the shock from Russia’s invasion of Ukraine reverberated primarily in global commodity markets, it also increased the level of geopolitical uncertainty, which was already elevated after two years of pandemic-induced stress. The unprovoked invasion of Ukraine has exposed and intensified geopolitical rifts that, along with the experience of the pandemic-induced supply shock, have increased the perceived risk and uncertainty associated with global goods trade and some types of cross-border investments. These uncertainty effects may have longer-term effects on trade as governments adjust their international economic policies and businesses change their global sourcing patterns. Certainly, the economic links between Russia and the rest of the world, and global markets for the commodities in which Russia is a key player, will be transformed. Preserving the benefits from international trade and investment, while protecting national security, addressing the effects of climate change (Tai 2021a; USTR 2022e; White House 2021b), and promoting resilience and equity in a revitalized domestic economy demands new policy approaches to respond to both existing and emerging risks. Given the global nature of the challenges discussed in this chapter, the policy decisions that the United States, it allies, and its partners make now will reverberate in international trade and investment for some time. The importance of partnerships in the modern global economy cannot be overstated. Enhanced partnerships that feature commitments to share information and coordinate actions are essential to sustaining the economic dynamism and productivity delivered through global economic integration 122 | Chapter 3 in uncertain times (Yellen 2022b). Institutional arrangements must evolve to ease tensions between openness on one hand, and security and domestic imperatives on the other hand (Staiger 2021b). Effective coordination both across and within governments can help to ensure that the individual policies that sum to the aggregate of international economic policy reflect a deliberate, coordinated policy direction that responds to today’s challenges. Confronting New Global Challenges with Strong International Economic Partnerships | 123 Chapter 4 Investing in Young Children’s Care and Education Investments in the earliest years of a child’s life can generate substantial benefits, with returns over the child’s lifetime that often considerably exceed costs. In particular, a large body of evidence demonstrates that early care and education (ECE) programs can improve children’s short-term development and long-term well-being, producing benefits not only for them but for society as well. ECE programs also support parents’ employment, which has become increasingly important with the decline in households where a parent stays home to provide full-time childcare. Women’s labor market options have grown considerably over the past 50 years, increasing the opportunity costs of staying out of or reducing time in the workforce (Yellen 2020). Both men and women point to caregiving and family responsibilities as a major obstacle for their career advancement, with mothers particularly likely to report career interruptions and reduced engagement in the labor force (Parker 2015; Pew Research Center 2022). The challenge of balancing work and family looms largest for the parents of young children not yet enrolled in K-12 schooling, and has ramifications across parents’ careers. As a result of these trends, a market for nonparental ECE services has developed. Care for young children is wide-ranging—from informal care (paid or unpaid) by relatives, neighbors, or in-home caregivers to formal care in 125 home-based, center-based, or school settings.1 This decentralized patchwork of providers caring for children in homes, centers, and schools stands in contrast to, for example, the more structured system of public K-12 education in the United States. Though the ECE market is one upon which many families rely (NCES 2018), and despite ample evidence that ECE programs can both effectively facilitate children’s healthy development and support parents’ employment, the ECE market often does not function well. This chapter first presents evidence on the effects of ECE investments for children, their parents, and society. It then discusses ECE market challenges, including workforce turnover and low pay, the high costs of providing highquality care, price sensitivity among ECE customers, the fragility of the childcare business model, and the resulting underprovision of high-quality ECE relative to what would be socially optimal. The chapter closes with a discussion of the role of public subsidies in supporting a better-functioning ECE market. The Effectiveness of Early Childhood Investments Ample research documents the benefits that ECE investments can generate—both directly, for children who participate and working parents who rely on the care, and indirectly, through spillovers to their families and communities. This section summarizes and highlights the relevant evidence on ECE investments’ benefits for children and society, the role of ECE quality in improving outcomes, and the benefits of ECE for working parents. Benefits for Children and Society ECE investments support children’s healthy development and early learning starting at birth, which cascades into longer-term and broader benefits for them, their communities, and the economy. A large body of research points This chapter employs the term “ECE” to encompass childcare, preschool, and prekindergarten (pre-K) programming, because there is often a significant overlap in how programs are structured, funded, and delivered. Childcare typically refers to programs serving children from infancy through school age, while preschool and pre-K commonly refer to programs aimed at the year or two before formal school entry. As such, preschool programs typically serve children ages three and four years old and often operate on a school-day and school-year schedule. The terms “childcare,” “preschool,” and “pre-K” are used in the chapter when the policy, research, or data in question pertain to a specific segment of the broader ECE landscape. 1 126 | Chapter 4 to ECE experiences as influential for children’s short-term outcomes, such as school readiness and early social-emotional and cognitive skill development, as well as long-term outcomes like educational attainment, executive function, employment, and earnings (Deming 2009; Duncan and Magnuson 2013; Heckman and Kautz 2014; Weiland and Yoshikawa 2013). These long-term, positive effects have been demonstrated in studies of childcare, Head Start, and other model preschool programs (Bailey, Sun, and Timpe 2021; Campbell et al. 2014; Gray-Lobe, Pathak, and Walters 2023; Heckman et al. 2010; Herbst 2017).2 Some studies of short- and medium-term program effects find that improvements in test scores fade out over time. However, when these studies also track long-term outcomes, they find substantial improvements in life chances, despite the short-run evaporation of test score gains (Chetty et al. 2011; Deming 2009). Moreover, there are documented complementarities between ECE investments and subsequent school investments (Johnson and Jackson 2019). Recent evidence captures the intra- and intergenerational spillovers of ECE exposure to siblings and the children of those exposed to the ECE program (Barr and Gibbs 2022; García et al. 2021; García, Heckman, and Ronda 2021). When deployed well, ECE investments can advance both economic efficiency and equity. The return on these investments manifests not only as improved individual life chances but also as societal benefits, in the form of greater productivity and economic growth; less individual reliance on government transfers; and fewer bad outcomes that are costly for society, such as poor health, high school dropout, and crime (Heckman and Masterov 2007). Figure 4-1 presents a stylized depiction of the return on investments at various stages of life, with examples of programs in each period. The figure shows how the Heckman curve, as it is known, maps the economic argument that $1 invested earlier in life can yield a greater return than $1 invested later (Heckman 2008). In other words, the efficacy of human capital investments likely declines with age, a conclusion that aligns with the science on a child’s developing brain and its malleability during the infant and toddler years (Knudsen et al. 2006; Shonkoff and Phillips 2000). According to this argument, policies and programs targeted at the earliest years of life have the greatest potential to generate large individual and societal returns, followed by investments in the preschool years, when children are three to five years of age. Research that estimates returns on human capital investments over a wide range of ages is generally consistent with the Heckman curve. In comprehensive assessments of the long-run benefits of specific early childhood programs, researchers estimate a $7 to $12 return on every $1 invested in Head Start is the federally funded preschool program for children from low-income families; it began in 1965 as part of the War on Poverty (ECLKC 2022). 2 Investing in Young Children’s Care and Education | 127 Figure 4-1. Return on Investment in Human Capital, by Age Early years programs Rate of return Preschool programs K-12 schooling Job training 0–3 years 4–5 years Middle childhood, early adolescence Late adolescence, early adulthood Age at time of investment Source: Adapted from Heckman (2008). Perry Preschool (Heckman et al. 2010), and even higher rates of return for the Carolina Abecedarian Project and the Carolina Approach to Responsive Education programs (García et al. 2020). Defining Quality in ECE Although there is solid evidence that ECE investments can be effective in the long run, less empirical evidence speaks directly to the features of ECE that matter for improving children’s outcomes. This research gap is due in part to limited data on inputs to, and outputs from, ECE programming, and in part to ECE’s multifaceted aims. In addition, the quality of the ECE experience is measured relative to the possible alternative settings where children could spend their time, and these settings vary widely. That said, there are some aspects that research suggests are important dimensions of high-quality ECE. While parents’ definitions of “good” ECE settings are likely subjective and include family-specific preferences for location, linguistic and cultural match, hours of operation, and program type, there have been efforts—across the United States’ mixed delivery system—to define and measure program quality objectively.3 Beyond the core safety and security requirements, systematic efforts to boost ECE quality include the Head Start Program Performance Standards and States’ quality rating and improvement systems A mixed delivery system provides care through home-based, community-based, and school-based settings, and can involve funding and accountability from Federal, State, and local sources in addition to families paying directly for ECE services. 3 128 | Chapter 4 (Office of Child Care 2011). These systems rely on various components, depending on the State—including licensure, lead teachers’ educational attainment, child–caregiver ratios, and other measures—and most States directly incentivize providers achieving higher levels of quality, as defined in the State’s system. States make information on program quality publicly available. Though research has not definitively established links between quality rating and improvement systems and measurable child outcomes (Cannon et al. 2017), the evidence suggests that assignment to a low rating does lead programs to improve on the measured dimensions and influences parents’ choices (Bassok, Dee, and Latham 2019). An important, measurable dimension of ECE quality is the nature of relationships and interactions between ECE staff and children in the care setting. Evidence suggests that stable, attached child–caregiver relationships in children’s earliest years provide a critical foundation for their subsequent healthy development (Hatfield et al. 2016; Pianta 1997; Sabol and Pianta 2012). Indeed, research points to the importance of the caregiver’s focused attention, which means that having more early childhood educators, or educators who have been trained in how to productively engage with children, could generate economy-wide, long-term productivity gains (Blau and Currie 2006). Relatedly, research suggests that ECE staff turnover is associated with children’s weaker language and social skill development (Caven et al. 2021). Childcare workers experiencing economic stress have a more difficult time fully engaging with children and offering a high-quality learning experience (Schlieber and Mclean 2020). Evidence also indicates that improvements in compensation and working conditions can significantly reduce turnover and are associated with better care and improved child outcomes (Bassok et al. 2021b; Grunewald, Nunn, and Palmer 2022; King et al. 2016). Some comprehensive, model programs have generated large returns, but they are made up of a package of components—including home visits, parenting programs, and health and nutrition offerings—which makes it difficult to isolate the impact of specific features of these programs in that evidence base. Notably, ECE settings provide early academic skill building and educational inputs alongside other types of support for children’s healthy development, including play-based and social activities, and physical and mental health and nutrition services. Box 4-1 explains the role of nutrition support in young children’s development. Benefits for Working Parents In addition to benefits for children, ECE programming can be important for families because it allows parents to participate in the labor market while raising their children. In 2021, 62 percent of families with children under six Investing in Young Children’s Care and Education | 129 Box 4-1. Nutrition Support in Early Childhood ECE settings often provide services and support beyond the classroom, including programs for parents, health services, and access to food. For example, from its inception in 1965, the Head Start program was designed to be a comprehensive early childhood development program, with an emphasis on health and nutrition components (Vinovskis 2005). Among young children, the Child and Adult Care Food Program (CACFP) provides funding for healthy meals and snacks for children in Head Start and other ECE programs. Research suggests that funding and standard-setting programs like the CACFP are associated with improvements in child nutrition offerings and reductions in households’ food insecurity (Heflin, Arteaga, and Gable 2015; Korenman et al. 2013; Ritchie et al. 2012, 2015). Researchers have established that increased access to healthier food—provided to children through nutrition assistance for their families or through meals while in childcare or at school—lead to improved health, cognitive functioning, and long-term well-being. Evidence from the introduction of both the Food Stamp Program—now the Supplemental Nutrition Assistance Program (SNAP)—and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) across the United States suggests that both programs have improved children’s early life health outcomes (Almond, Hoynes, and Schanzenbach 2011; Hoynes, Page, and Stevens 2011) and—if provided before age five—long-term economic outcomes (Bailey et al. 2020; Hoynes, Schanzenbach, and Almond 2016). Young children can also interact with the National School Lunch Program, School Breakfast Program, and Special Milk Program offered in participating childcare, preschool, and pre-K settings. Several studies indicate that school meals can improve nutrition and health outcomes (Gundersen et al. 2012; Bhattacharya, Currie, and Haider 2006). Although there are a few exceptions (e.g., Schanzenbach and Zaki 2014), many studies conclude that higher participation in these meal programs leads to increases in academic achievement and educational attainment (Imberman and Kugler 2014; Frisvold 2015; Hinrichs 2010). These findings are consistent with clinical evidence that nutrition is important for cognitive performance (Alaimo, Olson, and Frongillo 2001; Wesnes et al. 2003). Food assistance programs are an important tool to reduce food hardship for many Americans, particularly in times of economic distress. Though food insecurity had been trending down in the decade preceding the COVID-19 pandemic, most dramatically for Hispanic and Latino households (figure 4-i), nonwhite children experienced setbacks in food security in 2020 and 2021. Additionally, gaps that predate the COVID19 pandemic remain between Black and Hispanic or Latino children and 130 | Chapter 4 Figure 4-i. Food Insecurity among Households with Young Children, 2000–2021 Percentage of households with children under six reporting low or very low food security 25 20 15 10 5 0 2000 2002 2004 2006 2008 Hispanic or Latino Non-Hispanic Black 2010 2012 2014 2016 2018 2020 Non-Hispanic Asian American or Pacific Islander Non-Hispanic white Sources: Current Population Survey; CEA calculations. white and Asian American or Pacific Islander children (U.S. Census Bureau 2021a). As of 2021, the rate of food insecurity was higher for households with children, for households with children under age six, and particularly for single-woman-headed households than it was for households overall (USDA 2022a). As households with children face food insecurity, ECE settings and schools will continue to serve as an important source of nutritional assistance for children. Beginning in 2024, as part of ongoing efforts to advance families’ food security, children who receive free or reducedprice school meals will have access to a permanent food assistance program to address the summer gap in access to nutrition support (USDA 2022b). years had all parents in the household working (BLS 2022). In large part, this stems from a rise, over the past half century, in maternal labor force participation. From the mid-1970s to the mid-1990s, participation rates for both prime-age women and prime-age mothers grew by about 20 percentage points. Since then, both rates have plateaued and have even experienced periods of decline, although they remain higher today than in the mid-1970s (figure 4-2). Investing in Young Children’s Care and Education | 131 Figure 4-2. Labor Force Participation Over Time, by Maternal Status Percentage of prime-age women in the labor force 80 75 70 65 60 55 50 1976 1980 1984 1988 1992 1996 Overall 2000 2004 2008 2012 2016 2020 Mothers Sources: Current Population Survey; CEA calculations. As noted in chapter 1 of this Report, the rise in maternal labor force participation occurred in tandem with a rise in paid ECE and senior care. Time use data suggest that, among mothers of young children, more highly educated mothers in particular have reduced time spent on care of their children concurrent with the rise in maternal employment (Flood et al. 2022). While increased formal ECE was likely partially the result of maternal participation, research indicates that ECE also enables it (Herbst 2022; Morrissey 2017). Specifically, research examining ECE availability, expansion, and subsidization finds that ECE has large, positive effects on maternal employment (Blau and Tekin 2007; Gelbach 2002; Herbst 2017). Several studies of programs in other countries—specifically Canada, Germany, and Norway—also confirm the responsiveness of mothers’ employment to ECE expansions (Baker, Gruber, and Milligan 2008; Bauernschuster and Schlotter 2015; Finseraas, Hardoy, and Schøne 2016; Lefebrve and Merrigan 2008). Evidence from across ECE contexts, including childcare subsidy receipt and the introduction of public preschool and kindergarten programs, suggests that certain mothers’ employment is most affected. Those mothers who respond to program introduction and availability by working more are those whose youngest child is eligible for the program, and those who are relatively disadvantaged (i.e., single mothers and those with lower levels of education) (Cascio 2009; Cascio and Schanzenbach 2013; Fitzpatrick 2010, 2012; Gelbach 2002; Tekin 2005, 2007). Research on the Head Start program similarly documents that program access improved employment and earnings outcomes for single mothers (Wikle and Wilson 2022). 132 | Chapter 4 In addition to increasing parents’ likelihood of working at all, policies that expand access to ECE can boost their productivity in the workplace by allowing them to get additional education or job training and increasing the likelihood they will work full time (Davis et al. 2018; Herbst and Tekin 2011). These effects may have been especially important in the context of the COVID-19 pandemic, which, according to survey evidence, made parents, and mothers in particular, likely to reduce their work hours or productivity even while remaining in their jobs (Pew Research Center 2022). Increased access to ECE, such as through policies to expand availability and reduce costs, would likely enable more parents to work, which could bolster long-run economic growth and expand the economy’s productive capacity. However, as the next section describes, the market for ECE faces fundamental challenges, hampering families’ ability to secure ECE that meets their needs. Challenges in the Market for Early Care and Education Although, as noted above, investments in children can make a difference not only for the children themselves but also for their families and communities in ways that spill over to society, it is not at all clear the ECE market works for both providers and families. Important questions include: can families that need care access a well-functioning market to meet their needs? And, is the supply of ECE inefficiently low from society’s perspective? The evidence indicates that the care economy faces fundamental challenges in terms of both supply and demand, and thus there is an important opportunity for effective policies to improve the functioning of this market. On the supply side, a core concern is whether care businesses that invest in higher quality—such as through better staff compensation, professional development and coaching for early educators, and lower child–caregiver ratios—can recoup the increased costs while also charging rates that families can afford. On the demand side, families face liquidity constraints, given that they are more likely to be financially strapped when their children are young and the parents are in the early, and relatively unstable, years of their earnings trajectories (Davis and Sojourner 2021). That is, many families simply lack the resources to invest in high-quality care when it is needed and cannot borrow against future earnings to do so at competitive interest rates. Highquality care can consume a large fraction of families’ budgets, especially for low-income families (Landivar, Graf, and Rayo 2023; U.S. Department of the Treasury 2021). As such, many families are sensitive to the price of childcare and may respond by forgoing market-based care and instead relying on parental care or informal arrangements (Morrissey 2017). Investing in Young Children’s Care and Education | 133 Workforce Challenges Early care and education is a labor-intensive industry, and, as discussed above, a stable, qualified workforce is an essential ingredient in the provision of high-quality ECE services. According to the 2019 National Survey of Early Care and Education (NSECE), a nationally representative survey of childcare providers conducted before the pandemic, the average departure rate of caregivers in center-based care—the share of staff members who work directly with young children who left the focal program in the last 12 months—was 17 percent. Though this rate of turnover is comparable to the public teaching profession (16 percent), half of teaching departures are to another teaching position, whereas evidence suggests that many childcare providers leave the industry entirely (NCES 2016). Research in Louisiana suggests even higher turnover overall—finding that more than one-third of ECE educators depart annually, and that most turnover is a departure from the ECE profession (Bassok et al. 2021a). Evidence also demonstrates that turnover varies considerably across centers, with nearly 10 percent losing more than half their workforce in each year of the three-year study period (Doromal et al. 2022), a and higher turnover in centers paying low wages and those serving infants and toddlers (Caven et al. 2021). The 2019 NSECE also documents that the average longevity for a home-based ECE provider was relatively short, with about 46 percent of home-based providers having operated five or fewer years (NSECE 2019). Though these survey data predate the pandemic, the evidence suggests that periods of low unemployment in the broader economy are related to higher turnover in childcare employment (Brown and Herbst 2022). Thus, the tight labor market during the pandemic recovery period could exacerbate high workforce turnover and slow the recovery from pandemic-induced job losses in childcare (see box 4-2). Churn in the workforce prevents workers from gaining experience in the field and impedes staff continuity in ECE settings, potentially reducing the quality of care. The workforce challenges for ECE largely stem from workers’ low pay. As described below, this low pay results, in part, from the price sensitivity of consumers and the thin profit margins of care businesses. Childcare workers, who are described in more detail in box 4-3, make low wages relative to typical nonsupervisory workers. In the United States in December 2022, the typical production or nonsupervisory worker made, on average, $28.19 an hour, yet production or nonsupervisory childcare workers earned considerably less, $17.95 an hour (BLS 2023). According to one analysis, childcare workers earn 23 percent less on average than workers in other occupations with similar composition by age, education, and other demographic characteristics (Gould 2015). In particular, comparing the earnings of childcare, pre-K, kindergarten, and elementary school teachers illustrates 134 | Chapter 4 Box 4-2. The American Rescue Plan and Support for Childcare At the beginning of the COVID-19 pandemic, the childcare industry was severely affected. Between February 2020 and April 2020, childcare employment fell more than 35 percent. Recognizing the disruptions of the care infrastructure wrought by the pandemic, the American Rescue Plan (ARP) Act allocated funds to stabilize childcare, including $24 billion in funding for the new Child Care Stabilization Program. Data from the U.S. Department of Health and Human Services (2022; also see White House 2022) indicate that more than 200,000 childcare programs in the United States, with total capacity to serve as many as 9.5 million children, have received funding through these grants, intended to help the industry recover by providing grants to childcare programs to help cover operational costs such as wages and benefits, rent and utilities, and program materials and supplies. As of the fall of 2022, the most common uses of funds were personnel costs at centers, and rent and utilities at family childcare homes. These grants likely have had economic consequences that extend beyond their effects on childcare workers and providers. As described earlier in this chapter, access to childcare is an important input for parental employment, particularly for women (e.g., Morrissey 2017). This relationship between employment and access to childcare likely helps to Figure 4-ii. Percent Change in Maternal Employment Percent change in the 12-month rolling average of the employment–population ratio relative to January 2020 4 2 0 –2 –4 –6 –8 –10 2019 2020 Low ARP 2021 High ARP 2022 Sources: Current Population Survey; CEA calculations. Note: Data are restricted to mothers who have at least one child under six. “Low ARP” refers to employment among those living in the half of core-based statistical areas (CBSAs) with the lowest provider capacity covered by American Rescue Plan (ARP) funding per population. “High ARP” refers to employment among those living in the half of CBSAs with the highest provider capacity covered by ARP funding per population. Investing in Young Children’s Care and Education | 135 explain the significant, disproportionate drop that women experienced in their attachment to the labor force at the beginning of the COVID-19 pandemic. Whereas the overall employment–population ratio (i.e., the employment rate) fell by 16 percent between February 2020 and April 2020, the employment rate for women fell by 18 percent. Several studies indicate that during these months, employment for mothers of young children was particularly hard hit (Boesch et al. 2021; Collins et al. 2021; Heggeness 2020; Tüzemen 2021). The CEA’s analysis comparing maternal employment among those living in areas with relatively more provider capacity (as a share of population) supported by ARP funding to employment among those living in areas with less provider capacity supported by funding suggests that maternal employment has recovered more quickly in areas with greater capacity supported by stabilization grants (figure 4-ii). This analysis does not rule out other potential explanations of the differences in maternal employment across low- and high-ARP places, including underlying differences in community characteristics, but it points to an area for further research to better understand the effects of ARP childcare stabilization funds on the childcare industry and the parents who rely on it. the extent of low compensation among care workers. On average, childcare workers earn less than half, and preschool workers earn just over half, the average annual earnings of kindergarten and elementary school teachers (BLS 2021a). Childcare workers also rarely receive nonwage employee benefits; only 15 percent of these workers belong to an employer- or unionsponsored health insurance plan, compared with 58 percent of all workers (Gould 2015; BLS 2021b). In their labor-intensive industry, which typically has families’ payments as the sole source of revenue, childcare providers have limited options to cuts costs or raise revenue in order to pay higher wages. Low pay means that ECE workers are more likely to have an income below the Federal poverty line; 1 in 7 childcare workers lives in a family with an income below this threshold, compared with about 1 in 16 families overall (Gould 2015). In addition, 53 percent of childcare workers rely on a public assistance program, such as Medicaid or SNAP, compared with 21 percent of the United States’ workforce as a whole (Whitebook et al. 2018). The High Costs of High-Quality Care Given the importance of ensuring safe, secure, and high-quality ECE for infants and young children, and to make quality more visible to families, 136 | Chapter 4 Box 4-3. Who Works in ECE? Most childcare workers are women, and they are disproportionately women of color (Banerjee, Gould, and Sawo 2021). Figure 4-iii shows the breakdown of childcare employment by gender and race/ethnicity compared with the overall workforce. About 14 percent of childcare workers are Black and about 24 percent are Hispanic, higher than the share of Black and Hispanic workers in the overall workforce (6 and 8 percent, respectively). Additionally, historical norms that have devalued care work, typically performed by women, and labor market discrimination affecting women and people of color may exacerbate low pay. The current composition of the care workforce has legacies in slavery, when Black women acted as caregivers through coercion and force (Glenn 2012). Since the end of the Civil War, care workers have been shut out from workforce protections, such as those enacted under the New Deal (Burnham and Theodore 2012). Lawmakers continue to exclude many care workers from labor protections and benefits, including minimum wage laws, paid leave, retirement benefits, and overtime pay. The historical roots of the devaluation of care work, and the ongoing barriers to equal treatment that women and people of color face in the labor market, likely continue to affect the pay and working conditions for ECE workers today. Figure 4-iii. Racial and Gender Breakdown of Employment Percentage of employment 100 90 80 70 60 50 40 30 20 10 0 All workers Men Hispanic women White women AAPI women Childcare workers Black women Women of other race/ethnicity Source: Gould, Sawo, and Banerjee 2021. Note: AAPI = Asian American Pacific Islander. Investing in Young Children’s Care and Education | 137 there are rules and standards for formal (i.e., licensed and regulated) childcare providers. Some regulations vary by State, and others are Federal. For example, the Child and Adult Care Food Program’s Child Care Standards, which some childcare centers must meet to receive certain Federal reimbursements, require childcare centers to have at least one early childhood educator for every four children under age three (but at least six weeks of age), and one educator for every six children between age three and six. These quality regulations are critical for ensuring children’s safety and well-being, and insofar as they require higher staffing levels and childcare workers with in-demand skills, they necessarily increase providers’ costs of doing business. Additionally, though industries such as manufacturing have seen large technological advances leading to improvements in quality and labor productivity, these advances are less applicable to labor-intensive, servicebased industries such as ECE. Like those for many services, 60 to 80 percent of childcare business expenses are for labor (Workman 2018). Increasing wages in other industries that have higher labor productivity gains means that wages for care workers must also increase for care businesses to compete for workers, thus raising overall prices. As noted above, stable child–caregiver relationships are a key component of high-quality ECE, and one documented way to improve continuity in the ECE workforce is through competitive pay (Bassok et al. 2021b; Grunewald, Nunn, and Palmer 2022). Figure 4-3. Formal ECE Consumption, by Income Level Percent 70 60 60 49 50 40 30 37 34 29 20 1 2 21 17 13 11 10 0 37 3 Income quintile Share of households receiving care 4 5 Share of households paying for care Sources: 2019 National Survey of Early Care and Education; CEA calculations. Note: Early care and education (ECE) measures are limited to children under age 6 and to “formal” ECE, which includes paid individuals (with no prior relationship), center-based care, preschool, community-based care, and other organizational ECE on a regular and irregular basis. 138 | Chapter 4 High-quality ECE is fundamentally an expensive service, so it is not surprising that its use and costs vary considerably across the income distribution. Figure 4-3 shows formal ECE consumption by family income level, giving the proportions of both households receiving care and those paying for care. Both measures increase with income, and families across the income distribution are participating in some subsidized care, though much more pronouncedly, in the lowest income quintiles. Only about 15 percent of households with young children in the lowest quintile pay for ECE, while 53 percent of those in the highest quintile pay for ECE (NSECE 2020). Although low-income families more often qualify for subsidized services, those that pay for ECE devote a larger fraction of their income to ECE expenses than middle- and high-income families. Recently released data from the Department of Labor’s National Database of Childcare Prices document that prepandemic median childcare prices for one child account for between 8 and 19 percent of median family income in communities across the country, with even higher prices for infant care (Landivar, Graf, and Rayo 2023). For low-income families, that burden is even more pronounced. Figure 4-4 shows average annual ECE expenses as a share of income for all households with young children receiving formal ECE and for households that pay for ECE. For those paying for ECE, the share of income spent on ECE declines sharply by income level. The lowest-income families that pay for formal ECE spend one-third of their annual income on ECE, Figure 4-4. Average Annual Expenses for Formal ECE as a Proportion of Income, by Income Level Percent 35 33 30 24 25 20 18 15 10 11 9 8 8 10 8 6 5 0 1 2 3 Income quintile Among households receiving formal care 4 5 Among households paying for formal care Sources: 2019 National Survey of Early Care and Education; CEA calculations. Note: Early care and education (ECE) measures are limited to children under age 6 and to “formal” ECE, which includes paid individuals (with no prior relationship), center-based care, preschool, community-based care, and other organizational ECE on a regular and irregular basis. Investing in Young Children’s Care and Education | 139 compared with the highest-income families, which spend about 10 percent of their annual income on ECE. ECE Pricing and Price-Sensitive Consumers As noted above, businesses supplying care services face a pool of consumers with financial constraints that may limit their ability to afford the cost of high-quality care. In particular, low- and moderate-income families tend to be more likely than higher-income ones to curtail their purchases of these services if the price rises—by forgoing nonparental care altogether or by relying on informal, unpaid, or lower-quality ECE services. The budget constraints that families face in turn affect the supply of high-quality care. ECE providers serving families that are more sensitive to prices may be unable to afford costly quality improvements. In supplying care, providers choose their investment in quality at the point where their marginal revenues equal their marginal costs—that is, where an additional $1 invested is equal to an additional $1 earned. Providers serving low-income families have little economic ability to improve quality from a relatively low level; their clients may not be able to pay more for higher-quality care due to their budget constraints. In theory, with full information and accessible credit markets, parents may be willing to borrow against future earnings to access high-quality ECE that meets their and their young children’s needs. However, such credit is not generally available. As such, families must pay for childcare out of their current income, which may be particularly constrained when children are young and parents are in the low-earning stages of their careers (Davis and Sojourner 2021). Providers serving high-income families, conversely, can more easily charge higher prices to recoup the costs of their investments in quality. When these providers invest in better-quality care, their clients are generally able to pay higher prices, in large part because their family budget/income can accommodate it. This aspect of the ECE market gives rise to a stronger relationship between quality and total revenues among providers serving high-income families. ECE pricing is also complicated by parents who stay home full time and informal care providers. The most recently available evidence suggests that about one in five parents was a full-time, stay-at-home caregiver in 2016 (Livingston 2018). Further, some people who supply childcare services do so while also caring for their own children, altering their cost-benefit calculus (Porter et al. 2010). These care providers charge lower rates on average than larger, licensed providers, exerting downward pressure on prices in the broader ECE market and attracting families that cannot afford center-based care (National Women’s Law Center 2018). Parents may also shift to relatives, neighbors, or in-home providers on an ad hoc or more permanent basis 140 | Chapter 4 when market-based options fail to meet their needs. Evidence suggests that these informal ECE settings are often of lower quality than parental care and center-based care (Bassok et al. 2016; Flood et al. 2022). In 2018, 88 percent of childcare businesses were sole proprietors (i.e., with no employees other than themselves), and the average receipts per establishment were about $16,000 (U.S. Census Bureau 2018). Even under the unreasonable assumption that providers had no expenses, these receipts put the average sole proprietor at about the 20th percentile of the earnings distribution. Indeed, absent other resources, at these revenue levels, it would be difficult to sustain a family by running a childcare business. These data suggest that some providers may supply care at below-market rates, perhaps as supplementary income while providing care for their own children or family members, with altruistic motives, or because of limited employment options. Business Model Fragility As noted throughout this chapter, the ECE market is fundamentally challenged because it cannot provide high quality at prices families can afford. The ECE market has other fundamental characteristics that are factors in its business model, which are vulnerable to economic headwinds. Researchers have confirmed that childcare responds more strongly to negative economic shocks than other low-wage industries and takes longer to recover from recessions than the rest of economy (Brown and Herbst 2022). In sum, ECE is a highly fragmented industry, populated by small firms, often sole proprietorships, that pay low wages, have high labor turnover, and face low profit margins of less than 1 percent for most childcare providers (Carson and Mattingly 2020; Grunewald and Davies 2011; U.S. Department of the Treasury 2021). Liquidity challenges for childcare business owners explain why even a few weeks without revenue is often untenable for ECE providers, as became evident during the COVID-19 pandemic. According to the CEA’s analysis of November 2021 Small Business Pulse data, 82 percent of social assistance small businesses (which include childcare businesses) reported large or moderate negative effects of the pandemic on the business, compared with 66 percent of small businesses in general. In the same data, almost double the number of social assistance businesses (nearly 4 percent) reported temporarily or permanently closing, compared with all small businesses (2 percent). In 2021, social assistance businesses were also more likely to report that they anticipated needing financial support or additional capital in the next six months. The demographic composition of childcare providers may exacerbate the issue of limited access to capital. The owners of private childcare Investing in Young Children’s Care and Education | 141 Figure 4-5. Ratio of Young Children to Childcare Capacity in 2018 Ratio of children under 5 to childcare slots Sources: Center for American Progress (2020); CEA calculations. businesses are disproportionately women and people of color, aligning with the composition of the ECE workforce (as shown in box 4-2), and these providers may face more pronounced barriers in capital markets. Almost all childcare businesses—nearly 97 percent—are owned by women, while half are minority-owned (National Women’s Business Council 2020; Mueller 2020). Yet women and minorities tend to have fewer assets to get them through tough times. One study indicates that even after controlling for other differences, small business owners who are women or people of color have lower loan approval rates and pay higher interest on loans for their businesses (Asiedu, Freeman, and Nti-Addae 2012). Participation in and Availability of ECE Data on ECE participation represent the intersection of the supply of and the demand for ECE slots; participation requires both the availability of a slot, referring to its provision, along with take-up of the slot, which incorporates a family’s care preferences and needs. That is, for a family to access ECE in the United States, there must be an available slot that also meets the family’s needs in terms of cost, location, operating hours, and quality, among other factors. According to data on childcare capacity and population by county, more than half of Americans live in neighborhoods where the number of young children outpaces the availability of licensed childcare slots by three to one or more (Malik et al. 2018). Figure 4-5 maps the ratio of children under age five to licensed childcare slots across U.S. counties. The ratios of young children to childcare slots are larger when looking at infant and toddler care, as shown in figure 4-6 for counties in States with 142 | Chapter 4 Figure 4-6. Ratio of Infants and Toddlers to Childcare Capacity in 2018 Ratio of children 0–2 years of age to childcare slots Sources: Malik et al. (2018); CEA calculations. available data for 2018. In one analysis, researchers found that 80 percent of the counties for which they had data had at least three infants and toddlers for every childcare slot for children under three (Jessen-Howard, Malik, and Falgout 2020). Rural and low-income communities were more likely to have high child-to-capacity ratios—which could reflect lower demand for nonparental ECE in those areas—and Hispanic families were more likely to live in areas with high ratios (Malik et al. 2018). An undersupply of ECE slots may exacerbate a lack of participation in formal ECE. In 2019, 53 percent of children age three to six years who were not yet enrolled in elementary school were in a formal preschool setting outside the home (U.S. Census Bureau 2021b).4 Prepandemic data point to existing gaps by race, ethnicity, and family socioeconomic status. Hispanic children, in particular, have historically participated in formal care at lower rates, and Black children more likely to be in the care of relatives than other children (de Brey et al. 2019). Lower-income and disadvantaged families have used nonparental care at lower rates, though participation among families at the lowest end of the socioeconomic distribution resembles that of more advantaged families (de Brey et al. 2019; NCES 2022). Many argue that the differences by socioeconomic status and region of the country in child participation in ECE is due to a lack of availability of suitable slots. However, is it possible that what appears to be a lack of availability—more young children than capacity—could in fact be the result of lower demand due to parents’ preferences? Data sources, including surveys, In the October School Enrollment Supplement of the Current Population Survey, respondents are asked if the focal child attends “preschool” or “nursery school.” 4 Investing in Young Children’s Care and Education | 143 Figure 4-7. Excess Demand by Provider Type Percentage of providers experiencing excess demand 85 80 79 81 75 75 70 70 73 75 71 69 65 65 Overall 73 60 Child age group Community’s poverty density Rural Moderatedensity urban High-density urban High poverty Moderate poverty Low poverty Age 3–5 Age 0–3 50 Age 0–5 55 Community’s urbanicity Sources: 2019 National Survey of Early Care and Education; CEA calculations. Note: Excess demand is defined by whether providers have turned families away due to lack of capacity or had a waiting list in the past year. Line denotes the overall percentage of providers experiencing excess demand (73.4 percent). can help to identify whether observed participation rates fall short of families’ demand for ECE slots for their children. For example, to conduct the National Head Start Impact Study, researchers constructed a nationally representative sample of Head Start grantees. Because excess demand was a critical feature of the study design, grantees could only participate if they expected to be oversubscribed in the fall of 2002; 89 percent of Head Start grantees in the nationally representative sample were not serving all eligible children in the community who wanted Head Start (U.S. Department of Health and Human Services 2005). Analysis of prepandemic data found that there were 63 Head Start slots for every 100 income-eligible, preschool-age children who lived within 5 miles of a Head Start center (Ghertner and Schreier 2022). In addition to the Head Start program, other data sources similarly document excess demand for ECE providers’ limited slots. National Survey of Early Care and Education data for 2019 suggest that 73 percent of centerbased care providers experienced excess demand for childcare slots, in that they either rejected families because they were too full or maintained waiting lists. As presented in figure 4-7, excess demand varied by provider type. Providers serving only infants and toddlers and those serving all young children were more likely to report excess demand for their services (81 percent and 79 percent, respectively) than those serving only preschool-age children (65 percent). Though excess demand did not vary linearly with community poverty, the level of urbanicity was important, with providers in rural areas less likely to report excess demand. There is also a limited supply 144 | Chapter 4 Figure 4-8. Reasons Households Face Difficulty Finding Care Percentage of all households that face difficulty finding care reporting primary reason for difficulty 100 90 80 70 60 50 40 30 20 Mother’s employment Cost Child age group Lack of available slots Income relative to poverty threshold Quality Rural Urban Below Above Age 3–5 Age 0–2 Not in labor force Unemployed Employed 0 All 10 Location Location Sources: National Center for Education Statistics (2019); CEA calculations. Note: Households included are those that reported some or much difficulty finding the type of childcare or early childhood program they wanted for their child, or reported that they did not find the childcare program they wanted. Households are grouped by their response to the question “What was the main reason for the difficulty finding childcare or early childhood programs?” The four most common reasons are displayed, so the bars do not sum to 100. of childcare subsidies funded by the Federal Government and States; only 16 percent of the children who were eligible for oversubscribed subsidies in 2019 received them (Chien 2022). On the consumer side, households also report difficulty accessing care that meets their needs. In 2019, 76 percent of households that searched for care for their young children had difficulty finding care that met their needs (National Household Education Surveys Program 2019). Among this group, when respondents were asked the main reason for difficulty, the most common barrier was cost, followed by a lack of open slots (figure 4-8). Other significant barriers in the search for care included locational challenges and insufficient quality. Cost was a particularly pronounced concern among urban households and households with an income below the poverty threshold. A lack of available slots at the ECE providers they contacted was a more salient difficulty for households with working mothers, those above the poverty threshold, and those in rural areas. The disconnect between families’ reports of difficulty finding ECE and providers’ reports of a lower incidence of excess demand in rural areas perhaps suggests that the types of available care, ages served, or other program features offered in rural areas Investing in Young Children’s Care and Education | 145 do not meet families’ needs. Previous research also documents more pronounced search difficulty for Black and Hispanic households (NCES 2018). The undersupply of ECE warrants attention because of the documented effectiveness of investments in facilitating parents’ labor force attachment as well as in improving children’s short- and long-term outcomes, both of which are important for individual well-being and strong economic growth. The Role of Subsidies in the Market for Care The sizable social benefits of high-quality ECE and the challenges in the ECE market create an opportunity for policy innovation. Hendren and Sprung-Keyser (2020) document the returns on various investments across the life cycle using a metric called the marginal value of public funds, which includes any increased revenue and cost savings for the government, and find that investments in childhood health and education yield the largest returns. Though other public and private entities also spend money on ECE in the United States, increased Federal funding could help move the quality of care for young children closer to the socially optimal level (Davis and Sojourner 2021). Research indicates that improving ECE—and reaping the social and economic benefits of investing in children—requires (1) broadening access, and in particular, addressing disparities by race, ethnicity, and family socioeconomic status; (2) incentivizing supply building, including workforce support; and (3) ensuring quality. International Comparisons Many countries around the world subsidize ECE (Olivetti and Petrongolo 2017). Whereas among all countries that belong to the Organization for Economic Cooperation and Development (OECD), national governments spend an average of 0.74 percent of their gross domestic product on ECE, the United States spends only 0.33 percent (OECD 2021). As discussed in chapter 6 of this Report, women’s labor force participation in the United States has stagnated and fallen behind participation rates in many other comparable countries. Researchers have advanced the relative lack of family-friendly policies in the United States as one potential explanation for why U.S. women’s labor force participation has failed to increase at the same rates as its peer countries (Blau and Kahn 2013). Among OECD countries, the United States has one of the lowest ECE participation rates among children age three to five years, at 66 percent (OECD, n.d.). This rate was essentially unchanged between 2015 and 2020, measured before the COVID-19 pandemic. Notably, several OECD countries have universal or near-universal ECE participation rates among children age three to five. This group includes Ireland, which experienced 146 | Chapter 4 a large increase, from 79 percent in 2015 to universal participation in 2020 (OECD, n.d.), concurrent with major reforms of and national investment in ECE, including improved compensation for early educators (Moloney 2021). Though the United States stands out among advanced economies for its relatively low amount of spending on preschool-age children (age three to five), as measured by spending per child served or as a proportion of gross domestic product, public spending on ECE for the youngest children from infancy to age two is particularly low (OECD 2021). Many other countries, particularly the Nordic ones, spend the most on ECE for infants and toddlers and continue to invest heavily in the years before school entry. The United States’ ECE landscape is different from those of many other OECD countries, and it is also importantly embedded in a different policy context that has implications for the functioning of ECE programs. Of the OECD countries with available data, more than 70 percent have a centralized authority for ECE, with oversight for the system that serves children from birth or age one through primary school entry, unlike the United States; many also have established a right to at least one year of ECE enrollment before age five (OECD 2019). In addition, parental leave policies in many other countries alleviate pressure on the ECE infrastructure for providing infant care, which is the costliest to provide and the least agile in accommodating fluctuations in enrollment, due in part to smaller group sizes and child-to-adult ratios (Landivar, Graf, and Rayo 2023; OECD 2011; Office of Child Care, n.d.). All OECD countries, with the exception of the United States, offer nationwide paid maternity leave (OECD 2016). Many also offer paid paternity leave after the birth of a child, and 23 OECD countries provide paid parental leave that allows parents to share caregiving responsibilities in that time period (OECD 2016). Subsidies in the United States’ ECE Market Subsidizing the United States’ ECE infrastructure more robustly could make it possible for care providers to invest in high-quality services, including adequately compensating workers, at a price that families can afford. Box 4-4 outlines the major Federal investments in ECE. Two recent working papers find that a combination of subsidies targeting low-income families coupled with provider-side investments is the most effective means to expand enrollment in high-quality ECE (Bodéré 2023; Borowsky et al. 2022). Subsidies tied to the cost of providing high-quality care allow providers to invest in costly quality improvements, and adjusting the price childcare consumers pay based on their income makes it easier for families to fit high-quality care into their budgets. Investing in Young Children’s Care and Education | 147 Box 4-4. Federal ECE Investments Currently, the Federal Government invests in ECE through several channels, some of which direct funding toward private and public organizations to provide free or subsidized services, while others provide financial resources directly to families for spending on ECE services. Head Start is the federally funded program, operated by public agencies, private nonprofit and for-profit organizations, Tribal governments, and school systems, providing free ECE for preschool-aged children from low-income families (ECLKC 2022). The Early Head Start program serves pregnant women and infants and toddlers from low-income households through home visitation and center-based services (ECLKC 2019). The Preschool Development Grant–Birth through Five also invests in ECE, with the goal of supporting systemic enhancements in strategic planning, family engagement, workforce development, and quality improvement across all ECE programs, including but not limited to States’ preschool programs (OESE 2023; Office of Early Childhood Development 2022). The Child Care and Development Fund (CCDF), authorized by the Child Care and Development Block Grant (CCDBG) Act, provides funding to States, territories, and Tribal governments to invest in ECE programs as well as directly to low-income families pursing work, education, or training opportunities to spend on childcare (Office of Child Care 2022). Some ECE benefits operate through the tax code: the now fully refundable Child and Dependent Care Credit, a tax credit that supports working families with childcare expenses (IRS 2022), and the EmployerProvided Child Care Credit, which provides tax credits to employers with qualified childcare expenditures, including operating on-site childcare facilities or contracting with childcare providers to offer services to their employees (Smith, McHenry, and Mullaly 2021). Recent childcare policy proposals would encourage States to build the supply of high-quality ECE and expand access to it through, in part, incentives for providers to increase investments in quality. In addition, these proposals include subsidies targeting low- and middle-income households. Both these features would allow providers to recoup the cost of additional quality investments, counteracting market frictions that lead to underinvesting in quality, as discussed above. Investing in quality will require both process improvements and better job quality for care workers to attract and retain people with the appropriate 148 | Chapter 4 Box 4-5. New Data and New Methods to Inform Investments in Children Understanding the current lay of the land in ECE and how to effectively invest in children requires continued innovation in data infrastructure and research methods. There are no systematically collected measures of ECE programming, inputs, and outcomes across the mixed delivery system, and information on ECE enrollment from household surveys lags real time considerably. Misalignment in the timing and incidence of costs and benefits creates challenges for public investments in children; the time horizon over which such investments realize their returns is long, and most budget scoring calculations fail to account for long-term benefits. Timely, responsive data collection. Issues with the limited data on ECE provision and participation, and the timeliness of its availability existed before the COVID-19 pandemic, but real-time data collection became increasingly important during the pandemic for assessing which programs or elements of programs were achieving their intended goals (Cajner et al. 2022). Two surveys that emerged in response have been widely used in analysis: the Household Pulse Survey and the School Pulse Panel. In theory, these ongoing surveys have much potential to both inform policy and support research, but the Household Pulse Survey has issues with representativeness and low response rates (Bradley et al. 2021). Redesign and incentives could address these problems, and gathering data on households and schools over time holds promise for use in future research and policymaking. Unlocking and expanding the potential of existing data sources is likely to be more cost-effective than collecting new data. Administrative data, for example, often contain rich information on children’s and families’ interactions with services. The ability to link administrative data over time and across sources could facilitate many fruitful research pursuits to inform policy and practice (Bigelow et al. 2021). Measuring long-run effects. Several new methods capitalize on the documented relationships between short-term metrics and longer-term outcomes of interest to project or estimate the long-run and broader impact of interventions. One recent paper documents this evolution in economic research on the effects of the U.S. social safety net on children, as causal methods have evolved, sufficient time has elapsed, and data availability has improved (Aizer, Hoynes, and Lleras-Muney 2022). New and reinvigorated approaches to capturing long-term effects include lifecycle benefit forecasts (García et al. 2020), surrogate indices (Athey et al. 2019), and the framework of the marginal value of public funds (Hendren and Sprung-Keyser 2020). Ongoing innovation in this space demonstrates that there is interest in, and urgency to, more quickly measuring the broad and full impact of programs. This need is particularly pressing when assessing programs and policies that affect children. Investing in Young Children’s Care and Education | 149 skills. Evidence indicates that labor supply in ECE settings responds to higher wages, which suggests that as ECE jobs become higher quality, more qualified people will remain in care jobs and seek to be hired by care businesses (Blau 1993; Borowsky et al. 2022; Mocan 2007). Therefore, the supply and quality of ECE would increase, helping to counteract the longstanding undersupply of high-quality care. As discussed in box 4-4, the Federal Government currently invests in ECE programming through multiple avenues, and many States have proceeded with efforts to increase availability and lower costs, often using Federal funding from the ARP. A number of States—including Connecticut, Delaware, Georgia, Maine, Maryland, Oregon, Pennsylvania, and Vermont—are offering one-time bonuses for care workers or are permanently subsidizing pay increases (Child Care Aware 2022). In Texas, for example, lawmakers increased reimbursements to providers serving infants and toddlers from low-income households, and have required childcare programs receiving public subsidies to participate in the Texas Rising Star quality rating and improvement system (Goldstein 2022). The Federal Government could also play a significant role in improving data infrastructure that supports effective policymaking for ECE through better real-time information on availability and participation, and by building evidence. Box 4-5 discusses some of the new developments on this front, and avenues for improvement. Conclusion Early care and education programs play an important dual role for families: (1) contributing to young children’s development of cognitive and socialemotional skills; and (2) supporting parents’ engagement with the labor market. Both these channels also generate substantial benefits for society. Ensuring that all children have access to high-quality ECE requires investing both in families’ ability to access programs and in the provision of these programs—including supporting workforce improvements and smart capacity expansion. Such investments in ECE can yield significant long-run benefits not only for the affected children themselves but also for society at large. Although the COVID-19 pandemic exacerbated many gaps in the Nation’s ECE infrastructure, many of these challenges—and particularly disparities by race/ethnicity and family income—existed before the pandemic. There are critical issues in the market for ECE programs and services, and the Nation’s economy often fails to support care businesses, care workers, and the families in need of their services. These challenges ultimately lead to low pay for workers and exacerbate the undersupply of high-quality, affordable, and accessible care for families. However, these problems could be mitigated with policy improvements. 150 | Chapter 4 Carefully designed government policies would address frictions in the ECE market—including workforce challenges and low pay, the high costs of high-quality care provision, families’ price sensitivity, and the fragility of the ECE business model—thereby making childcare more affordable while improving pay for workers and ensuring investments in quality. The government could thus foster a better-functioning ECE market by funding subsidies for childcare providers, including incentives to improve the quality of care and higher caregiver pay, alongside subsidies and publicly provided ECE programming for families. Together, these policies would address both the supply and demand sides of the ECE market, ensuring that providers are willing and able to provide the high-quality, affordable care needed by families and society. Investing in Young Children’s Care and Education | 151 Chapter 5 Building Stronger Postsecondary Institutions The United States’ postsecondary education system is, in many ways, the envy of the world. Relative to most other international systems, U.S. postsecondary institutions are more numerous, diverse, and decentralized, as well as more likely to offer opportunities for exploration, transfer, and reentry. Students are likely to benefit from having flexibility to find the program that fits them best. The high number of institutions relative to those of peer countries may help spur innovation among competing institutions to be responsive to the needs of students. These features help explain why the United States is the top destination for international college students: over 1 million international students were enrolled in U.S. colleges in 2020—more than triple the number in 1980—and now account for one-fifth of all crosscountry student migration in postsecondary education (Bound et al. 2021; Institute of International Education 2020). Nonetheless, as the demand for highly educated workers has increased over the past half century, earning a valuable postsecondary credential has remained a challenge for many Americans. The United States no longer leads the world in postsecondary attainment, and large gaps by income and race have widened over the past several decades. This has consequences both for individuals, who miss out on the personal benefits of postsecondary education, and for society, which forgoes the increased civic participation, lower reliance on public benefits, increased tax revenues, higher economic growth, and other benefits that such education brings. Though college continues to be a good investment on average, increased student debt burdens relative to a generation ago mean this investment includes risks that some 153 students can be left worse off if their education does not yield the labor market benefits they expect. Federal and State support for postsecondary education has long included direct funding for institutions, but in recent decades the primary form of support has shifted to financial aid for students. These efforts have been essential to help offset rising tuition and fees, which before accounting for financial aid, have roughly tripled in real terms since 1980 and have increased even more at public four-year institutions (Ma and Pender 2022a). Yet policies aimed at institutions and the programs they offer—to build capacity, to support colleges in serving students well, and to hold them accountable when they do not—are a critical complement to policies lowering financial barriers to attendance. Federal policy can influence the quality of postsecondary options with both carrots, such as Federal support to help institutions improve student outcomes, and sticks, such as policies to hold institutions accountable for the economic value they provide. Where there are geographic barriers to access, institution-oriented policies can help facilitate the equitable expansion of high-value programs and deter the expansion of lower-value ones. Before considering institution-oriented policies, this chapter first describes the landscape of U.S. postsecondary education, documenting the extraordinary variation across such institutions, and summarizing evidence that institutions and their programs are themselves a critical determinant of student success. The chapter next explains the rationale for Federal investment in postsecondary education, and places the U.S. model of postsecondary education finance in historical and international contexts. The decentralized “high tuition, high aid” model currently used in the United States has some advantages but also generates economic risks for students who fail to graduate or whose education does not pay off in the labor market. Imperfections in the market for postsecondary education limit the potential of the market alone to drive improvements. Such imperfections include geographic constraints, 154 | Chapter 5 informational and behavioral constraints, and production constraints that limit institutions’ ability to react quickly to fluctuating demand. The chapter documents one source of production constraints: State appropriations for public postsecondary institutions tend to fall during economic recessions, precisely when demand for enrollment in such institutions tends to rise. The rest of the chapter considers how Federal policy can help support postsecondary institutions, reviewing a range of options to improve or maintain the quality of such institutions, to hold institutions accountable for student outcomes, and to reduce geographic barriers to access. The institution-oriented policy efforts described in this chapter have the potential to improve the landscape of postsecondary options. Throughout the discussion, the chapter highlights actions that the Biden-Harris Administration has already undertaken to improve the postsecondary institutional landscape, with the ultimate goal of ensuring that all students have access to a college education of value. The U.S. Postsecondary Institutional Landscape The degree of heterogeneity in the institutional landscape is a distinctive feature of the U.S. postsecondary system. Colleges in the public, private nonprofit, and for-profit sectors offer a different mix of programs, enroll a different composition of students, and are financed in different ways. Four-year institutions offer bachelor’s degrees in fields that can vary substantially in their connection to specific occupations. Community colleges offer a range of credentials, including academic associate degrees (e.g., for students who intend to transfer to a four-year program); occupational associate degrees; short-term certificates intended to help students access the labor market quickly; and, increasingly, bachelor’s degrees. Historically Black Colleges and Universities and Tribal Colleges and Universities have an additional mission: to serve communities that have historically been excluded from postsecondary education. In addition, institutions vary in the extent to which their students graduate and succeed in the labor market. This institutional landscape is both a driver and consequence of how the United States supports higher education and has implications for how its quality can be improved. Building Stronger Postsecondary Institutions | 155 Figure 5-1. Distribution of Enrollment Across Institution Types, by Student Characteristics Percentage enrolled in each institution Total enrollment Age 24 and under (66.2%) Age 25 and over (33.8%) White (48.1%) Hispanic or Latino (18.9%) Black or African American (12.0%) Asian (6.9%) American Indian or Alaskan Native (0.6%) Native Hawaiian or Other Pacific Islander (0.2%) 0 10 Private not–for–profit, 4–year or above 20 30 Public, 4–year or above 40 50 Public, 2–year 60 70 Private for–profit, any level 80 90 Other institutions Source: National Center for Education Statistics, Integrated Postsecondary Education Data System Fall Enrollment component, 2021 provisional data. Note: The category “other institutions” encompasses public institutions and private not-for-profits of less than 2 years, as well as private not-for-profits of 2 years. Reported in parentheses under each category is the percentage of total fall enrollment in all institutions captured by the given population. Percentages do not add up to 100 due to rounding and the omission of students with two or more races, students of unknown race/ethnicity, and students who are nonresident aliens. Institutions Serve a Diverse Student Population U.S. college students vary in age and residential status. Only about 13 percent of undergraduates both started college before age 20 and live on the campus of a residential four-year college (NCES 2022a, 2022b, 2022c). Nearly 30 percent of enrolled students started their programs at age 20 or above (NCES 2022a). Among enrolled students younger than 20, about 40 percent attend two-year (or less) institutions, and only about half the remaining students attending four-year institutions live on campus (NCES 2022d, 2022e). Over one-third of enrolled undergraduates are 25 or older, a proportion that rises to nearly 44 percent for community colleges and to nearly 62 percent for the for-profit sector (NCES 2022f). Undergraduates are, on average, fairly diverse with respect to income and race, and institutions vary substantially in the extent to which they enroll different types of students. In any given year, nearly one-third of undergraduates receive Pell Grants, a proxy for a low family income (NCES 2020a). Institutions vary substantially in the extent to which they serve low-income students, with about 16 percent of campuses having fewer than one-fourth of students receiving Pell Grants and about 22 percent having more than three-fourths receiving them.1 Overall, low-income students are relatively similarly represented across the two- and four-year sectors and are overrepresented in the for-profit sector, where about 53 percent of undergraduate students receive Pell Grants (NCES 2020b, 2020c). CEA calculations, using data from the College Scorecard. These College Scorecard data were the most recent available publicly, as of September 2022, which for most measures reflect the 2020–21 academic year. 1 156 | Chapter 5 100 Nearly two-fifths of undergraduates self-identify as Black, Hispanic, Asian, American Indian or Alaskan Native, or Native Hawaiian or Pacific Islander. Most such student groups are more heavily represented in community colleges and the for-profit sector. As figure 5-1 shows, students who self-identify as Black, Hispanic, American Indian or Alaskan Native, or Native Hawaiian or Pacific Islander are substantially more likely than white students to attend for-profit institutions. For-profit institutions also disproportionately attract those age 25 or above. Institutions Vary in Their Prices and Spending on Students Postsecondary institutions vary not only in the students they serve but also in the prices they charge and the amount they spend on student instruction. As table 5-1 shows, the average institution has an undergraduate sticker price of roughly $13,000 in tuition and fees per year; has a total cost of attendance of roughly $25,000 after housing, food, books, and other expenses are included; and has a net cost of nearly $15,000 after grant aid has been accounted for. Such prices vary tremendously by sector. Private nonprofit and for-profit colleges have undergraduate net costs of over $20,000 a year, while public four- and two-year institutions cost roughly $14,000 and $7,000 for undergraduates, respectively, after accounting for grants. Institutions also vary widely in the amount of resources they have available and spend on student instruction—the clearest measure available Building Stronger Postsecondary Institutions | 157 Table 5-1. College Prices and Expenditures by Sector All Institutions Private Not-for-Profit, 4-Year Public, 4-Year Public, 2-Year Private For-Profit, Any Level Tuition and fees $12,602 $34,235 $9,149 $3,338 $14,913 Total cost of attendance $25,235 $49,401 $22,529 $13,170 $26,204 Net cost of attendance $14,762 $26,045 $13,812 $7,101 $20,400 Instructional expenditures per FTE student $9,633 $14,071 $10,617 $6,292 $3,691 Measure Sources: College Scorecard; CEA calculations. Note: FTE = full-time equivalent. College prices and expenditures are per academic year, for full-time enrollment. of an institution’s financial investment in learning.2 Figure 5-2 shows that this resource distribution is highly skewed, with 70 percent of institutions spending less than $10,000 per full-time-equivalent student each year and 9 percent spending more than $50,000. Such spending can buy smaller class sizes, higher-quality instructors, better academic support services, and other resources that may contribute to student success. As table 5-1 shows, there is clear variation across sectors in these spending patterns. Across most sectors, higher prices tend to translate into higher spending on students. Private, nonprofit, four-year colleges spend about $14,100 a year on instruction; public four-year colleges spend about $10,600; and public two-year colleges spend about $6,300. The exception to the pattern are for-profit colleges, where students pay relatively high net prices but receive the lowest instructional spending of any sector (about $3,700). Institutions Vary in Their Student Outcomes Student outcomes, such as degree completion rates, also vary substantially by postsecondary institution. A relatively high fraction of U.S. undergraduate students who start college do not complete their degrees (Bound, Lovenheim, and Turner 2010; Bowen, Chingos, and McPherson 2009). Though recent research suggests that graduation rates have increased somewhat since 1990, fewer than 60 percent of undergraduates seeking a bachelor’s degree complete such a degree within six years of entry (Denning et al. 2022). As panel A of figure 5-3 shows, the average college student attends an institution where about 55 percent of undergraduate students complete their degree within 150 percent of the time expected (i.e., three years for two-year colleges and six years for four-year colleges). This average masks substantial variation, as nearly one-tenth of colleges have undergraduate completion rates under 25 percent and over one-third have completion rates above 75 percent. The instructional spending measure, available in the Integrated Postsecondary Data System (https:// nces.ed.gov/ipeds/), measures total spending across both undergraduates and graduate students, so care should be taken when comparing such figures with undergraduate costs of attendance. 2 158 | Chapter 5 Figure 5-3. Variation in Undergraduate Student Outcomes A. Variation in Completion Rate B. Variation in Students’ Earnings Sources: College Scorecard; CEA calculations. Note: The orange line in Panel A denotes the average fraction of students completing their undergraduate education within 150% of the expected time. The orange line in Panel B denotes the average fraction of former students earning more than a high school graduate. Not all noncompletion is problematic. The U.S. postsecondary system allows many students to explore college, even when they are uncertain about the experience. A recent pre-COVID-19-pandemic, large-scale survey of Americans who had attended college but had not completed their degree reveals a range of self-reported reasons for this noncompletion (Gallup 2019). Some students’ reasons for leaving suggest that better institutional practices or financial aid policies might have helped them complete their degrees, while other reported reasons indicate that some students leave after learning that college was not a good fit for them. Such exploration can be Building Stronger Postsecondary Institutions | 159 Table 5-2. Student Outcomes by Sector (percent) Measure Degree completion rate Proportion of students out-earning typical high school graduate All Institutions Private Not-for-Profit, 4-Year Public, 4-Year Public, 2-Year Private For-Profit, Any Level 50 63 56 29 47 70 77 75 59 59 Sources: College Scorecard; CEA calculations. costly for students attending high-priced institutions. This suggests a policy role for balancing the benefits of exploration with the need to protect students from making poor investments of time and money. Variation in postcollege earnings by institution is also striking. One such measure, available in the College Scorecard, is the percentage of a given college’s Federal-aid-receiving undergraduate students who, 10 years after entry, earn at least as much as a typical worker whose highest level of education completed is high school.3 Comparing earnings with those of workers who are high school graduates provides a rough proxy for whether a college’s enrollees have better economic outcomes than if they had not enrolled in college at all. The average college student attends an institution where about 60 percent of undergraduate Federal aid recipients out-earn a typical high school graduate. Yet, as shown in panel B of figure 5-3, at 19 percent of colleges, fewer than half of such students out-earn high school graduates 10 years later. Relatedly, colleges also vary widely in the extent to which their students experience upward economic mobility, measured by the fraction of students entering from the bottom quintile of the income distribution who later reach the top quintile (Chetty et al. 2017). Degree completion rates and postcollege earnings indicate striking variation across college sectors, as shown in table 5-2. Four-year institutions tend to have higher completion rates and earnings than two-year institutions. Students at community colleges have similar earnings outcomes to students at for-profit colleges, even though community colleges are substantially less expensive for students to attend.4 Institutions Matter for Student Outcomes The extent to which variation in student outcomes is driven by institutions themselves is challenging to measure. Some outcome differences are due The College Scorecard (https://collegescorecard.ed.gov/) is a website created by the U.S. Department of Education that gives students, families, and other interested parties information about the cost and value of nearly all higher education institutions. The earnings measure discussed here comes from a national average of the earnings of all those age 25 to 34 who indicated that high school completion was their highest level of education, were working, and were not enrolled in school during the measurement year. This threshold is about $31,000 in 2020 dollars. 4 The for-profit figures cited here cover all for-profit institutions. 3 160 | Chapter 5 to differences in students across institutions. A large and growing body of literature documents that some portion of undergraduate student outcome differences across institutions is causally attributable to the institutions themselves: colleges appear to vary widely in their effects on students. A given institution’s effects do not appear to be inherent, but depend in part upon available resources and how those resources are spent; better-resourced institutions and those that spend more per student on instruction generally produce better outcomes, including higher graduation rates and labor market earnings (Lovenheim and Smith 2022). As the evidence suggests, students of all kinds appear to benefit from attending the college with the best track record available to them, with the worst colleges leaving the typical student worse off than they would have been if they had not attended college at all. Within the four-year college sector, researchers have found that students are more likely to graduate and have higher earnings when they attend colleges with more resources, academically stronger peers, and better historical student outcomes such as graduation rates and earnings. Such patterns hold even when comparing otherwise similar students who enroll in different colleges (Long 2008; Smith 2013; Mountjoy and Hickman 2021; Cohodes and Goodman 2014). Evidence from States including Texas and California suggests that gaining access to well-resourced flagship institutions increases graduation rates and earnings, including for those whose access comes as a result of “Top Percent” guaranteed admissions policies (Hoekstra 2009; Andrews, Li, and Lovenheim 2016; Bleemer 2021; Black, Denning, and Rothstein 2020). Public four-year colleges have been documented to have substantially positive effects on students. For example, research in a variety of States shows that students whose academic background gives them access to less selective public four-year institutions are more likely to graduate and have higher earnings than those lacking such access (Zimmerman 2014; Goodman, Hurwitz, and Smith 2017; Smith, Goodman, and Hurwitz 2020; Kozakowski 2023). Consistent with observable resource differences across sectors, enrollment in four-year colleges generally improves student outcomes even more for those choosing between two- and four-year options. Comparing otherwise similar students who differ only in their proximity to two- and four-year options suggests that four-year college enrollment increases the rate of degree completion and may increase earnings (Rouse 1995; Mountjoy 2022). On average, community colleges have been shown to generate positive effects on students and substantially better outcomes than the for-profit colleges that enroll similar student populations (Cellini and Turner 2019; Armona and Cao 2022). Researchers have found that enrolling and completing an associate degree at a two-year college generally improves outcomes relative to not enrolling or completing one at all (Belfield and Bailey 2017; Building Stronger Postsecondary Institutions | 161 Mountjoy 2022). Further, they document that, relative to those who start but do not complete their two-year degrees, graduates of community colleges see substantial increases in their annual income five to nine years after college entry (Jepsen, Troske, and Coomes 2014; Bahr et al. 2015; Liu, Belfield, and Trimble 2015; Bahr 2016; Bettinger and Soliz 2016; Xu, Jaggars, and Fletcher 2016; Dadgar and Trimble 2015; Belfield 2015). The return from a two-year degree is even higher in subsequent years after entry and during economic recessions (Minaya and Scott-Clayton 2022). Some high-demand community college programs, such as nursing, raise students’ earnings so much that expanding the number of available slots in such programs would more than pay for itself via increased tax revenues returned to State and local governments (Grosz 2020). For-profit colleges have been found to generate particularly poor outcomes for their enrollees. Advocates of for-profit colleges have argued that such poor outcomes are due to the disadvantages with which their students start (Cellini and Koedel 2017). Differences in student composition have not, however, been enough to explain the large differences in outcomes between for-profits and other institution types (Deming, Goldin, and Katz 2012; Scott-Clayton 2018a). Community colleges appear to improve earnings more than for-profit colleges, even when accounting for variations in student characteristics (Cellini and Turner 2019; Armona and Cao 2022). Numerous studies comparing the earnings of the same for-profit students before and after enrollment find that such students see little or no earnings increase relative to those who do not attend college or to those enrolled in public colleges (Cellini and Turner 2019; Cellini and Koedel 2017). Résumé audit studies similarly suggest that for-profit degree holders receive employer callbacks less often than otherwise-identical degree holders from public colleges and no more often than those with no college education at all (Darolia et al. 2015; Deming et al. 2016). Enrollment in for-profit colleges increases debt and worsens labor market outcomes relative to other two- and four-year options (Armona, Chakrabarti, and Lovenheim 2022). Nearly two-fifths of for-profit college chains have negative returns for Federal aid recipients compared with returns from simply gaining experience in the labor market (Armona and Cao 2022). The Rationale for and Delivery of Public Postsecondary Investment To assess strategies to promote access and improve quality in the postsecondary sector, it is useful to understand the economic rationale for public sector involvement, and to consider the possible forms such involvement can take. 162 | Chapter 5 The Economic Rationale for Public Sector Investment A key motivation for promoting college is its value as an economic investment for both students and society. For example, though students express many reasons for pursuing postsecondary education, including personal exploration and growth, getting a better job tops the list (Fishman 2015; Stolzenberg et al. 2020). From a societal perspective, expanding educational access has been associated with economic growth, much like when the United States in the 20th century led the world in the transition to mass secondary—high school—education. This expansion also helped to dampen inequality (Goldin and Katz 2008). However, in recent decades, as the demand for highly educated workers has continued to increase, the United States has faced significant challenges in the transition to broad-based postsecondary education and training (Goldin and Katz 2008; Neelakantan and Romero 2017). Although postsecondary enrollment has increased substantially since 1980, the pace has slowed since 2000 (Ma, Pender, and Welch 2019). The United States is no longer a global leader in college degree attainment for adults age 25 to 34 years, and the 43 percent completion rate among those entering associate degree programs in the United States is among the lowest of all countries belonging to the Organization for Economic Cooperation and Development (OECD) that reported their equivalent of this statistic (NCES 2021; OECD 2022). Failing to navigate the transition to broader postsecondary education would represent a missed opportunity, given the substantial private and societal benefits of college (described in box 5-1). Of particular concern is the large and growing gap in bachelor’s degree attainment between highand low-income families, which is wider for cohorts born in the 1980s than for those born in the 1960s (Chetty et al. 2020; Bailey and Dynarski 2011). Racial disparities in college attainment have also grown over a similar time span, particularly among women (Emmons and Ricketts 2017; Ma, Pender, and Welch 2019). Finally, despite the substantial private and public benefits, many students cannot afford to attend a postsecondary institution without financial assistance. The problem is that the benefits of college accrue over the course of a lifetime, while the bill must be paid in advance. Typically, individuals solve this problem by borrowing to pay for an investment up front, such as when purchasing a car or home. However, private lenders typically do not provide loans unsecured by collateral (e.g., those for a car or a house that can be repossessed) because a college education cannot be returned or resold if the individual fails to make interest payments and defaults on the loan (Barr 2004). The existence of such “credit constraints” provides an important rationale for the public sector providing loans to students at subsidized interest rates. Building Stronger Postsecondary Institutions | 163 Box 5-1. The Private and Public Benefits of College The earnings premium for those with a college education is well documented (Barrow and Malamud 2015). Less well known is that the benefits of college accrue to a broad range of students at a broad range of schools. Students with relatively low grades and test scores who enroll in four-year institutions derive significant earnings benefits from college attendance (Zimmerman 2014; Ost, Pan, and Webber 2018; Smith, Goodman, and Hurwitz 2020), as do the 35 percent of students who enroll in open-access community colleges rather than not enrolling at all (Kane and Rouse 1995; Mountjoy 2022; NCES 2022g). Associate degrees—and even some shorter-course credentials in high-demand occupational fields—yield substantial returns in many fields, including for older students and displaced workers (Grosz 2020; Jacobson, LaLonde, and Sullivan 2005; Jepsen, Troske, and Coomes 2014). Postsecondary education also serves the public good. College attainment leads to increased civic participation, lower rates of involvement in the criminal justice system and reliance on public benefits, increased tax revenues, higher economic growth, and improved health in the next generation (Dee 2003; Lochner and Moretti 2004; Lochner 2011; Oreopoulos and Salvanes 2011; Hout 2012; Ma, Pender, and Welch 2019; Aghion et al. 2009; Currie and Moretti 2003). Reducing racial disparities in college attainment is particularly urgent, given that underrepresentation in highly credentialed professions can adversely affect the treatment and outcomes of historically excluded groups. For example, recent evidence indicates that students benefit from exposure to instructors of the same race (Fairlie, Hoffman, and Oreopoulos 2014; Gershenson, Hansen, and Lindsay 2021; Gershenson et al. 2022; Lusher, Campbell, and Carrell 2018), and that Black patients benefit from access to Black physicians (Alsan, Garrick, and Graziani 2019; Greenwood et al. 2020). Finally, postsecondary education and training serve as a form of social insurance, increasing workers’ resilience during economic shifts and mitigating the negative consequences of recessions (Hyman 2018; Barr and Turner 2015; Minaya and Scott-Clayton 2022; Barnes et al. 2021). How Public Funds Are Delivered: Student Aid and Institutional Support Public funding to promote college access and completion can be delivered directly to institutions, to support programming and keep prices below cost, or directly to students, who then use financial aid to help pay tuition and other costs at the institution of their choice. In primary and secondary 164 | Chapter 5 education, government support is delivered primarily through institutions, in the public provision of free schools, and with supplemental supports such as free meals delivered through those schools. Many countries follow this model, not only for primary and secondary education but also for postsecondary education, with direct institutional support for predominantly public institutions helping to keep tuition prices low (Marcucci 2013). Along these lines, at least 20 States currently offer statewide free community college programs (Mishory 2018; Education Commission of the States 2022), and the Biden-Harris Administration has developed a framework for a nationwide free community college program (White House 2021). The Federal Government’s earliest investments in postsecondary education also focused primarily on expanding capacity and keeping prices low at public institutions, which even today enroll over three-quarters of U.S. undergraduates (IPEDS 2020). The foundations of many of today’s State colleges and universities can be traced to direct institutional support—such as the Federal Morrill Land Grant Act of 1862, which granted each State 30,000 acres of public land to establish public postsecondary institutions; the Second Morrill Act of 1890, which directed Federal funds to newly designated Historically Black Colleges and Universities; and the subsequent significant push by States to establish and expand two-year colleges in the 1960s (Cohen, Brawer, and Kisker 2013). State and local direct appropriations for public institutions, which help keep tuition prices below the full cost of provision, remained the largest source of government support for postsecondary education in the United States through the end of the 20th century (Dynarski, Page, and Scott-Clayton 2022). The Higher Education Act of 1965, which established the foundations of today’s Federal student aid programs (including the precursor to Pell Grants), marked a significant shift from institution-focused to studentfocused assistance (Fountain 2017; Leslie and Johnson 1974). Delivering support via student aid may conserve public resources by targeting subsidies to those students who are most in need, support institutional quality by bringing in additional resources from those who can afford to pay, and promote competition and choice by enabling students to use their aid at the institutions they judge as highest value (Barr 2004). State and local direct appropriations for institutions have fallen from nearly two-thirds of support for undergraduates in 1990–91 to just under one-third in the academic year 2018–19 (Dynarski, Page, and Scott-Clayton 2022). Student aid is now the primary mode of support in the United States, providing $174 billion in grants, loans, and other direct support for undergraduates in 2021–22, with Federal sources accounting for about half this total, and loans accounting for about half of Federal student aid (Dynarski, Page, and Scott-Clayton 2022; Ma and Pender 2022b). The result, as shown in figure 5-4, is that only England exceeds the United States in the level of both tuition and student Building Stronger Postsecondary Institutions | 165 Figure 5-4. Average Public Tuition and Fees and Percentage of Students Receiving Public Financial Aid—Bachelor’s Degree Programs, 2019–20 Average public tuition and fees (listed prices in U.S. dollars converted using purchasing power parity) 14,000 England 12,000 10,000 United States Chile 8,000 6,000 Australia 4,000 2,000 0 Austria 0 10 New Zealand Italy Spain France Germany 20 30 40 50 60 70 80 90 100 Percentage of students receiving public financial aid Source: Organization for Economic Cooperation and Development (2021, tables C5.1 and C5.2). Note: Data refer to the academic year 2019-20 and are based on a special survey administered by the OECD in 2021. aid (OECD 2021). The contemporary “high-tuition, high-aid” model of U.S. postsecondary finance is thus distinctive in both international and historical contexts. In line with this high-tuition, high-aid model, inflation-adjusted published tuition and fees before accounting for financial aid (“sticker prices”) have since 1980 nearly tripled in the public two-year sector, more than tripled in the private not-for-profit four-year sector, and nearly quadrupled in the public four-year sector, though such prices have stabilized over the past decade (Ma and Pender 2022a, 2022b). At the same time, net prices in the United States—tuition and fees minus grants and scholarships—have increased much more slowly than published prices, and have actually remained flat or declined over the past decade (Ma and Pender 2022b). The Biden-Harris Administration has taken a number of steps to continue to improve college affordability and help student loan borrowers, including providing a $900 increase to grant aid for low-income students through the Pell Grant program over the last two years, streamlining and improving student loan repayment, and pursuing debt relief through the HEROES Act. The model of financing students rather than institutions has helped fuel a system of postsecondary education that is diverse and decentralized, with more opportunities for exploration, transfer, and reentry (Labaree 2017; Goldin and Katz 2008), because students decide where their subsidized dollars will be spent. In contrast, many other countries deliver funding primarily to public institutions that students can attend free (Marcucci 2013) but that 166 | Chapter 5 often offer fewer spots in a more constrained set of programs. The diversity and flexibility of the U.S. system may help explain why the United States is the top destination for international students (Institute of International Education 2020). In recent decades, some countries that formerly provided fully free public institutions have shifted to the U.S. model as a way to maintain affordability while expanding postsecondary capacity and improving quality (Marcucci 2013; Murphy, Scott-Clayton, and Wyness 2019). Still, the U.S. model of postsecondary education finance is not without challenges. Over the longer term, the dramatic increases in sticker prices have made it more difficult for today’s students and families to pay for college relative to a generation ago. In 2022–23, the maximum Pell Grant, the largest source of grant aid in the United States, only covered 30 percent of published tuition, fees, room, and board at the typical public, four-year institution, down from 50 percent in 1988–89 and nearly 80 percent in 1975 (Ma and Pender 2022b; Baum, Payea, and Steele 2009; Protopsaltis and Parrott 2017). Despite the availability of aid, research shows that tuition prices still influence students’ persistence and degree completion, even after initial enrollment (Acton 2021). Prospective students—particularly those who would be first in their families to attend college—may not even know that financial aid exists and thus may be dissuaded by the sticker price alone (Levine, Ma, and Russell 2022). Research has shown that the process of applying for financial aid is itself a barrier to access (Bettinger et al. 2012), and that students are more likely to apply when aid is guaranteed in advance (Dynarski et al. 2021). Further, research indicates many students are reluctant to borrow (Boatman, Evans, and Soliz 2017). This may reduce the effectiveness of loans relative to grants of the same size. Further, the decision to invest in college entails risks—in particular, the risk that the earnings students gain will be less than the cost they pay for their education. The breadth, flexibility, and multiple entry and exit points in the U.S. system also mean more risks of making mistakes and falling off track (Labaree 2017; Goldin and Katz 2008; Scott-Clayton 2012). Fewer than two-thirds of students who enroll in college finish any degree within six years (National Student Clearinghouse Research Center 2022a). Even among those who graduate with at least a four-year degree, roughly one in five male college graduates and about one in seven female college graduates has earnings no higher than the typical worker with only a high school diploma (Ma, Pender, and Welch 2019). Because many students rely on debt to finance a portion of their education, some students who attend college may end up worse off, even though the expected return is high on average. Nearly one-third of students who take on debt do not receive a degree (Miller 2017). More than one in four borrowers experience a student loan default within 12 years of college Building Stronger Postsecondary Institutions | 167 Box 5-2. International Comparison of IncomeDriven Student Loan Repayment Like the United States, postsecondary education systems in Australia and England also combine high tuition with high financial support for students. In contrast to the United States, students in England and Australia can fully defer tuition payments until after college and then repay via income-driven repayment (IDR). Under IDR, student loan repayments are capped at a fixed percentage of income, mitigating the risk that college enrollment leads to incomes too low to repay such debts. Research from the United States finds that IDR enrollment reduces borrowers’ risk of delinquency and default (Mueller and Yannelis 2019; Herbst 2023). IDR plans vary substantially across these countries in two important ways. First, U.S. undergraduate loans, though capped below most students’ cost of attendance, are available for a wider variety of programs, including short-term credentials and those at thousands of for-profit institutions (U.S. Department of Education 2023a; Ma and Pender 2022a). Both England and Australia allow undergraduates to borrow the full amount of public tuition but restrict the institutions eligible for IDR. England directs IDR primarily to public university students, and Australia originally restricted IDR to four-year colleges, only in 2009 expanding eligibility to vocational programs (Barr et al. 2019; Student Loans Company 2022). Second, IDR is the only loan repayment option in England and Australia, with automated enrollment and payments income-adjusted and collected automatically through the tax authority. In contrast, borrowers in the United States need to opt into IDR and annually update their own income (Barr et al. 2019). Only about one-third of U.S. student borrowers in 2022 were enrolled in such a plan (CEA calculations, based on Federal student loan portfolio data by repayment plan, from the U.S. Department of Education 2022a). The Biden-Harris Administration has proposed reforms of IDR to reduce monthly and lifetime payments, especially for low- and middle-income borrowers, and to eliminate barriers that prevent borrowers from accessing IDR. entry, including nearly half of Black student loan borrowers (Scott-Clayton 2018b). One tool for mitigating repayment risk in a high-tuition, high-aid model are income-driven repayment plans—but as box 5-2 discusses, the implementation and use of these plans differ substantially by country. Finally, the global experience suggests that countries expanding student aid to for-profit institutions face challenges in regulating quality to address poor student outcomes in this sector (Usher 2019; Salto 2019). 168 | Chapter 5 Although for-profit higher education is not unique to the United States, it is unusual in terms of both its size in the United States and its integration into the student aid system, including access not only to student loan dollars but also to nonrepayable grant aid (Kinser 2016; Levy 2019). For-profit colleges in the United States account for 12 percent of Federal student aid dollars and 30 percent of student loan defaults, even though they enroll only 8 percent of students (Century Foundation 2021). The Imperfect Market for Postsecondary Institutions In an idealized market, the United States’ approach of providing portable financial aid to support consumer choice might be sufficient to ensure that high-quality choices actually exist. At least in theory, this approach should promote institutional quality by weeding out low-quality institutions or prompting them to improve and encouraging better ones to expand (Barr 2004; Fountain 2017). The postsecondary education market, however, is too imperfect for institutional improvements to emerge simply from students voting with their feet (Leslie and Johnson 1974). Institutions may, for example, be able to attract students regardless of their program quality. Three main types of constraints—geographic constraints, informational and behavioral constraints, and constraints on colleges’ ability to expand quickly—diminish the power of market forces to promote productive innovation, improve quality, and drive down prices through student choice and Figure 5-5. Distance Between Home and College, 2004–16 Percentage of undergraduates 50 45 40 35 30 25 20 15 10 5 0 2004 Within 10 miles 2008 2012 Between 11 and 20 miles Between 21 and 50 miles 2016 Over 50 miles Source: National Center for Education Statistics (National Postsecondary Student Aid Study, 2016 undergraduates). Building Stronger Postsecondary Institutions | 169 competition alone. The resulting institutional landscape offers variety, but it is not always clear which aspects of this variety benefit students. Geographic Constraints The first main types of constraints, again, are geographic. First, as figure 5-5 shows, most students attend college close to home, limiting the scope for choice and competition. About 60 percent of U.S. undergraduates attend a college within 20 miles of their home, and the fraction attending within just 10 miles of home grew from nearly 39 percent in 2004 to 44 percent in 2016 (NCES 2022h). These proportions are substantially higher for students of color and low-income students (NCES 2022i, 2022j). These geographic constraints make college markets “thin,” as many students do not have a substantial number of options to choose from if they want or need to stay close to home (Hillman 2016; Blagg and Chingos 2016). The median commuting zone has just two colleges of any type or level. About 23 percent of those age 18 to 44 years live in a commuting zone with at most one public four-year college; about 27 percent live in a commuting zone with at most one public two-year college. Students with no options nearby must incur the high cost of relocating or lengthy commuting if they wish to attend college. Those with limited choices nearby may enroll in a program that is a poor fit for their goals (Klasik, Blagg, and Pekor 2018). Online programs provide an alternative but many generate poor student outcomes, as further discussed below. Informational and Behavioral Constraints Even for students with options close to home, informational and behavioral constraints can complicate decisionmaking. The United States’ college landscape is particularly complex, with 63 percent more bachelor’s-degreefocused institutions per capita than Canada, 71 percent more than the United Kingdom, and 67 percent more than Australia (World Higher Education Database, n.d.). The American community college, serving multiple missions including both transfer and terminal associate degrees, is a distinctive type of institution that only recently has begun to develop in other countries (Redden 2010). The United States also has a large for-profit college sector, adding to an already large and varied set of options students face. College is not a simple consumer good, but an “experience good” for which students may not have well-formed preferences in advance. The decision to enroll in college is made infrequently in one’s lifetime, limiting the opportunity to learn from previous decisions. Colleges differ along numerous dimensions, including both content and quality, which may be difficult to observe in advance. Benefits are uncertain and accrue over long time horizons, making it difficult for students to compare options. Even the 170 | Chapter 5 best-prepared students may oversimplify or avoid decisions when choices are complex, information is limited, and preferences are not well established (Hoxby and Avery 2013; Beshears et al. 2008; Lavecchia, Liu, and Oreopoulos 2016; Ross et al. 2013). Comparing financial aid offers can be particularly opaque. A recent report from the Government Accountability Office (GAO) found that 41 percent of colleges in a nationally representative survey did not provide information on net prices in their financial aid offers, and an additional 50 percent understated net prices by omitting some costs or including loans that must be repaid (GAO 2022). This complexity also affects students after college as they attempt to navigate student loan repayments (Turner 2021). Finally, many prospective college students are relatively young and inexperienced financial decisionmakers, which increases both their susceptibility to marketing campaigns and the likelihood of decision mistakes (Beshears et al. 2008; Agarwal et al. 2009). Indeed, reports have found that some for-profit colleges take advantage of this by outspending their public counterparts 20-to-1 on advertising (Cellini and Chaudhary 2020) and using dubious claims about future employment prospects to recruit students (McMillan-Cottom 2017; GAO 2010). College Expansion Constraints In a simpler market, increased demand for the best products can induce successful producers to expand and new producers to enter the market. The substantial fixed costs and labor-intensive model of traditional postsecondary Figure 5-6. Per-Student State and Local Funding for Public Higher Education, 1989–2019 2019 dollars 10,000 9,500 9,000 8,500 8,000 7,500 7,000 6,500 6,000 5,500 5,000 1989 1994 1999 2004 2009 2014 2019 Source: College Board Trends in College Pricing 2021b, as compiled by Ma and Pender (2021). Note: Shaded areas indicate recessions. Building Stronger Postsecondary Institutions | 171 education, however, constrain institutions’ ability to quickly respond to increased demand without diluting students’ experience, as discussed below. As figure 5-6 shows, per-student State and local funding is procyclical, falling during times of economic contraction. Demand for postsecondary education is, however, countercyclical, as students tend to seek skill training when employment opportunities are worse since the opportunity cost of enrollment is lower when jobs are scarce (Barr and Turner 2015). The combination of public funding’s procyclicality and demand’s countercyclicality means that per-student funding shrinks precisely when enrolling in a postsecondary program makes the most economic sense (Ma and Pender 2022b; Kane et al. 2005). This pattern leads to both higher tuition and lower resources provided per student during recessions, which has been documented to harm students’ outcomes (Chakrabarti, Gorton, and Lovenheim 2020; Bound, Lovenheim, and Turner 2010; Bound and Turner 2007; Deming and Walters 2017). Community college enrollments are particularly sensitive to economic conditions, partly because they are open-access institutions to which unemployed or underemployed adults often turn for midcareer training. Community college enrollments rise by about 1 to 3 percent overall for every increase of 1 percentage point in the local unemployment rate, with greater responsiveness among those age 25 and above (Hillman and Orians 2013; Betts and McFarland 1995). The only exception to this pattern has been the weak labor market early in the COVID-19 pandemic, when community college enrollment declined in part because instruction, particularly in fields requiring hands-on training, was disrupted by pandemic conditions (Schanzenbach and Turner 2022). At the same time, students are more likely to enroll in for-profit institutions when funding for local public institutions decreases (Cellini 2009; Goodman and Henriques Volz 2020). Although causal research does not establish the mechanisms underlying this result, when resources per student fall, four-year colleges may not be able to expand enrollment to meet demand. Community colleges do not typically have enrollment caps, but when public institutions have fewer resources per student, students may have more difficulty registering for the courses they want at the times they want, or they may be discouraged by staffing constraints that affect their ability to navigate registration, financial aid, and other aspects of enrollment. In contrast, for-profit institutions can cut costs and expand more quickly than traditional institutions by offering more highly standardized curricula, a more limited range of programs, fewer in-person courses, and lower-paid instructors (Deming, Goldin, and Katz 2012). A large portion of for-profit programs are fully online, making them particularly attractive to students lacking alternatives close to home (NCES 2019). The heavy concentration of online programs in the for-profit sector may partly explain why 172 | Chapter 5 for-profit enrollment declined much less than community college enrollment in the first year of the pandemic, when demand for remote learning increased substantially (National Student Clearinghouse 2022b). Institution-Focused Policies That Promote Access to Postsecondary Value Research has shown that the quality of institutions matters for student outcomes. Thus, policies aimed at institutions—to build capacity, to support colleges in serving students well, and to hold them accountable when they do not—are critical to ensuring that all students have access to an education of value. Federal policy can influence the quality of postsecondary options by supporting evidence-informed strategies to expand supply and improve outcomes at public institutions, while holding all institutions accountable for the value they provide and protecting students from the worst options. Supporting the Quality of Existing Colleges and Programs As more attention has been given to increasing college completion—not just enrollment—the base of evidence has grown for promising programs and policies (e.g., see the recent review by Dynarski et al. 2022). This subsection considers the potential benefits of expanding specific institutional programs with a track record of success, as well as the potential benefits of more flexible institutional support. Enhanced guidance and advising. Personalized guidance, coaching, and/or mentoring have been shown to help college students overcome both academic and nonacademic challenges. Several randomized-control studies find that such services can help students persist and complete their degrees at higher rates (Dynarski et al. 2022). Bettinger and Baker (2014) find that four-year college students randomly assigned to receive access to individualized student coaching from outside professionals were more likely to persist and graduate. Oreopoulos and Petronijevic (2018) find that oneto-one coaching by upper-year undergraduate mentors improved students’ academic performance, while less intense text and email “nudge” interventions did not. Randomized studies have found positive effects of related interventions for community college students as well (Linkow et al. 2017, 2019; Evans et al. 2020). Comprehensive programs. Comprehensive programs that provide multifaceted financial, academic, and nonacademic supports have shown particularly dramatic effects. Of these, the best known is the City University of New York’s (CUNY’s) Accelerated Study in Associate Program (ASAP). In addition to waiving tuition and fees, ASAP provides textbook vouchers, free transportation, a dedicated one-to-one adviser, and enhanced tutoring Building Stronger Postsecondary Institutions | 173 and career counseling. Students are required to enroll full time. A randomized evaluation found that the program nearly doubled associate degree completion rates three years after entry (40 vs. 22 percent), with large effects on completion persisting after six years (Scrivener et al. 2015; Weiss et al. 2019). ASAP has since been successfully replicated in Ohio (Sommo et al. 2018; Miller et al. 2020), and CUNY is piloting a version of the program at several of its four-year campuses (CUNY, n.d.). The version implemented in Ohio, though less expensive per student than the original CUNY model, cost 42 percent more per student than business as usual—but because the program dramatically increased completion rates, it lowered the average cost per graduate (Miller et al. 2020). CUNY’s ASAP was estimated to raise degree completion rates enough to more than cover its costs, so that enrolling 1,000 more students was estimated to provide taxpayers with fiscal benefits of $46 million in 2010 dollars (Levin and Garcia 2013). Direct institutional support. All the programs described above require resources. Research indicates that per-student institutional resources are an important driver of college persistence and completion (Bound and Turner 2007; Bound, Lovenheim, and Turner 2012; Webber and Ehrenberg 2010; Cohodes and Goodman 2014; Deming and Walters 2017). This echoes findings that resources matter for student outcomes in the K-12 context, particularly for low-income students (Jackson, Johnson, and Persico 2015; Hyman 2017). How resources are spent matters but, because the optimal use of funds may vary from context to context, general funding support—with appropriate guardrails—may give institutions the flexibility they need to optimize. Box 5-3 describes some of the Biden-Harris Administration’s efforts on this front. Various scholars have offered proposals for what a more regular program of Federal institutional support for postsecondary education could look like. Existing Federal support for K-12 schools provides one model: Federal grants have long been provided to districts, schools, and States through Title I of the Elementary and Secondary Education Act (Skinner and Cooper 2020). Hiler and Whistle (2018), for example, propose a version of Title I funding for postsecondary education that could be based on the number and proportion of Pell Grant recipients enrolled. Federal grants that match State spending—which have proven effective in increasing State spending on other programs, such as Medicaid (Kane et al. 2005)—could reduce the risk that Federal dollars simply crowd out State investment in public colleges (Deming 2017). Some scholars have suggested that aid be targeted to community colleges, the sector with the greatest need and potential (Goolsbee, Hubbard, and Ganz 2019). 174 | Chapter 5 Box 5-3. Policies Focused on Direct Institutional Support The Biden-Harris Administration has made direct institutional support a priority. The College Completion Fund for Postsecondary Student Success (funded by the Consolidated Appropriations Act of 2022, and following similar proposals in the American Families Plan and the President’s Budget Request) in 2022 provided $5 million in competitive grants to postsecondary institutions to support “data-driven and evidence-based reforms that encourage postsecondary retention, transfer, and completion” (U.S. Department of Education 2022b). These funds were targeted to institutions that disproportionately serve students of color and low-income students, with priority given to community colleges. Congress provided an additional $45 million for the program for Fiscal Year 2023 (U.S. Department of Education 2023b). During the COVID-19 pandemic, direct support for institutions was a key aspect of Federal support for postsecondary education. The American Rescue Plan Act of 2021 provided nearly $40 billion in institutional support via the Higher Education Emergency Relief Fund (HEERF). HEERF, initially established by the Coronavirus Aid, Relief, and Economic Security (CARES) Act of 2020, required institutions to spend half the funds on emergency student aid and the other half on “any costs associated with significant changes to the delivery of instruction due to the coronavirus” (U.S. Department of Education 2020, 2022c). About 90 percent of HEERF-participating institutions reported the program helped them keep students enrolled who might have otherwise dropped out (U.S. Department of Education 2023c). Evidence on similar programs during the Great Recession suggests that they help public research institutions maintain or increase their expenditures on both research and instruction (Dinerstein et al. 2014). Many states have used funding from the American Rescue Plan to expand or strengthen colleges and job training programs, seeing these as core strategies to build back from the pandemic (U.S. Department of the Treasury 2022). Institutional Accountability Accountability policies are wide-ranging, and they include (1) strict accountability policies that cut off institutions from Federal or State aid completely if they fail to meet certain minimum standards; (2) performancebased funding, whereby financial assistance is at least partly conditioned on institutional performance; and (3) policies that increase transparency and rely on the market to self-regulate. Evidence regarding each option is discussed in turn. Building Stronger Postsecondary Institutions | 175 Strict accountability. A variety of past and current Federal regulations suggest potentially promising results from holding postsecondary institutions accountable for program-level student outcomes as a condition of eligibility for Federal financial aid for students. For-profit colleges are most affected by such regulations, in part because of the legal authority granted to regulators under Federal law and partly because of their poor observed student outcomes (Cellini and Koedel 2017). Cutting off aid from programs that leave students unable to repay their loan debt is at least partly effective in discouraging enrollment in such programs (Darolia 2013). Cellini, Darolia, and Turner (2020) further show that when for-profit colleges experience large drops in annual enrollment due to policy sanctions, most such students shift to community colleges, whose loan default outcomes are substantially better. Kelchen and Liu (2022) demonstrate that having debtto-earnings ratios in excess of prescribed limits made poor-performing colleges and programs more likely to close, even though the regulations were rescinded by the Trump Administration before any sanctions were actually applied. Box 5-4 describes these regulations further. Performance-based funding. Currently, roughly 30 States have implemented policies that partly tie higher education appropriations to outcomes such as graduation rates. Known as “performance-based funding,” this strategy is an attempt to improve institutional accountability. A review of the evidence shows, however, little sign of such measures inducing institutional improvements in student outcomes such as degree completion (Ortagus et al. 2020). Researchers have also found that performance-based funding can incentivize behavior counterproductive to increasing the available quality of college opportunities, with some public, four-year institutions boosting their outcomes by decreasing admission rates and reducing enrollment of underrepresented students of color (Ortagus et al. 2020; Birdsall 2018). Some States have modified their plans to include additional incentives for improving measures related to equity, with potentially promising evidence that such equity-focused modifications can improve enrollments of low-income students and students of color (Gándara and Rutherford 2018). Increasing the transparency of student outcomes. Increasing the transparency of student outcomes can potentially make college quality more salient, both to prospective students and to institutions themselves, increasing the competitive pressure to improve. Research indicates, for example, that improving students’ information on labor market outcomes can influence their major choice (Baker et al. 2018; Wiswall and Zafar 2015). In addition to the College Scorecard, several States have their own databases of earnings data, organized by institution or industry. For example, the Salary Surfer (salarysurfer.cccco.edu), a collection of earnings data from the California Community College System, provides average salary information two years before, two years after, and five years after graduation, by industry 176 | Chapter 5 Box 5-4. Gainful Employment and Other Accountability Regulations Since its 1965 enactment, the Higher Education Act (HEA) has defined the types of institutions and programs that are eligible to participate in Federal financial aid programs. Under current law, educational programs must lead to a degree at a nonprofit or public institution or must prepare students for “gainful employment in a recognized occupation” in order to be eligible for financial aid under Title IV of the HEA (U.S. Department of Education 2022d). The “gainful employment” requirement was not, however, defined in regulations for the first few decades of the HEA. In 2014, the Obama-Biden Administration finalized regulations defining gainful employment as requiring aid-eligible certificate and degree programs to meet a specific debt-to-earnings ratio for graduates. The Department of Education estimated that 840,000 students’ programs would not meet this standard, nearly all at for-profit institutions (U.S. Department of Education 2014). The gainful employment regulation was rescinded under the Trump Administration. The Biden-Harris Administration is in the process of reinstating a new such rule to ensure that Federal funds are not directed to programs that do not lead to gainful employment (U.S. Department of Education 2022e). The Administration has already taken other actions designed to increase the accountability of postsecondary institutions and programs to students and taxpayers. The Department of Education has solicited public comment on the development of an annual watch list identifying programs with the lowest financial value and announced plans to request improvement plans from the institutions that offer such programs. The Department of Education also reestablished the enforcement unit in the Office of Federal Student Aid to hold institutions accountable, and withdrew authorization from the accreditor ACICS, which oversaw for-profit institutions involved in some of the worst outcomes for students. The Administration also closed a long-standing loophole that encouraged for-profit institutions to aggressively target and recruit veterans and their families. Research indicates that such institutions lower veterans’ earnings (Barr et al. 2021) and use this additional revenue stream to raise tuition rather than improve quality (Baird et al. 2022). Recent regulations enacted by the Biden-Harris Administration will ensure that private for-profit colleges derive at least 10 percent of their revenue from nonFederal sources, including veterans’ benefits, as required under changes made by Congress to the 90/10 rule in the American Rescue Plan Act. Building Stronger Postsecondary Institutions | 177 and subfield. The U.S. Census Bureau has released the Post-Secondary Employment Outcomes data product since 2017, providing earnings data by institution and degree program up to 10 years after graduation, and showing the flows of graduates from various degree programs into employment in various industries. Evidence from the release of the College Scorecard and earlier efforts at transparency suggests, however, that transparency on its own may have a limited short-term impact on student application and behavior. Publishing annual lists of institutions with the highest levels or changes in costs for students did not appear to affect those institutions’ prices or enrollments, at least in the short run (Baker 2020). For the first time, in 2015, the Scorecard made widely available average graduation rates and earnings of students enrolling at thousands of colleges nationwide. Research indicates that this release had limited effects on college search and application behavior, with effects concentrated among more advantaged students (Huntington-Klein 2016; Hurwitz and Smith 2017; Meyer and Rosinger 2019). The longerterm effects of the College Scorecard and other transparency efforts may, however, be more meaningful than the short-term effects, as information takes time to reach students, families, school counselors, and other decisionmakers. More research is needed to isolate such longer-term effects of data transparency on college quality. Addressing Geographic Barriers to Access Additional policy efforts may be needed to more directly address geographic constraints on access. Though the COVID-19 pandemic has led to increased awareness of the feasibility of remote learning at scale, it has also shown its limitations. This subsection discusses the evidence on the effectiveness of online education as well as other, more promising alternatives to provide more students with access to high-quality college experiences on the campus of their local high school. For older returning students, box 5-5 provides additional information on local workforce training interventions that have demonstrated promise in improving outcomes. Online programs. Some have suggested expanding online options to reduce geographic barriers to access, but research findings suggest caution about this approach. In some settings, such as four-year colleges, there are examples of students doing equally well across both online and in-person formats (Figlio, Rush, and Yin 2013; Bowen et al. 2014), as well as in blended learning approaches combining online and in-person components (Bowen et al. 2014; Alpert, Couch, and Harmon 2016). Other research finds, however, that courses taught through online formats often lead to worse learning outcomes than their in-person counterparts (Joyce et al. 2015; Alpert, Couch, and Harmon 2016; Krieg and Henson 2016). Research 178 | Chapter 5 Box 5-5. Supporting Workforce Training Quality Community colleges are the primary providers of education and training targeted at labor market needs, with Pell Grants now the largest source of funding for workforce training for low-income Americans (Ma and Pender 2022b; Holzer 2008). Postsecondary institutions that provide such training may, however, be slow to respond to sectoral shifts and changes in employers’ specific skill needs (Katz et al. 2022). The traditional academic schedule and program offerings may not always fit the needs of nontraditional students, such as workers displaced midcareer. Though displaced workers are eligible for Federal student aid, they may not be aware of their eligibility or how to use it (Barr and Turner 2018). Federal training resources specifically developed to serve such workers are funded at much lower levels—through the Workforce Investment and Opportunity Act—and with generally positive but somewhat mixed evidence of effectiveness (Rothstein et al. 2022; Holzer 2021). In this context, sectoral employment and training programs have shown promise. These programs typically involve partnerships between employers or industry associations, training providers (often community colleges), workforce boards, and intermediary organizations such as unions and local nonprofits (Holzer 2015). These programs may improve alignment between training programs and workforce needs, and the wraparound services they often provide may increase students’ likelihood of completion. Research on sectoral employment programs, many located at community colleges, concluded that such programs lead to substantial earnings gains for participants (Katz et al. 2022). Additional positive evidence comes from the Trade Adjustment Act Community College and Career Training (TAACCCT) Initiative, which provided $1.9 billion in grants to postsecondary institutions to enhance their workforce training capacity in the wake of the Great Recession (U.S. Department of Labor 2022a). A meta-analysis of quasi-experimental studies of TAACCCT programs concluded that the investments had overall positive effects on program completion as well as labor market outcomes (Blume et al. 2019). Registered Apprenticeships provide another promising pathway to well-paying jobs. Registered Apprenticeships are formally approved by the U.S. Department of Labor or a State Apprenticeship Agency and are vetted to ensure that they align with industry needs in high-demand fields (U.S. Department of Labor 2022b). In this “earn and learn” model, workers get paid while training under the supervision of a mentor, typically for two to four years, and simultaneously receive supplementary classroom instruction. One study found that, for every $1.00 invested, employers receive $1.44 in direct and indirect benefits in the years during and after training an apprentice (Kuehn et al. 2022). Research on the causal impact of such programs is limited, though descriptive research Building Stronger Postsecondary Institutions | 179 indicates workers experience strong wage growth in the year after finishing the program (Walton, Gardiner, and Barnow 2022). The BidenHarris Administration has expanded Registered Apprenticeships through additional funding and efforts like the launch of the Apprenticeship Ambassador Initiative (White House 2022). during the COVID-19 pandemic found that college students performed worse in courses that shifted or later remained online (Bird, Castleman, and Lohner 2022; Kofoed et al. 2021). Online coursework appears to work least well for less academically prepared students, in both the for-profit sector (Bettinger et al. 2017; Bird, Castleman, and Lohner 2022) and the community college sector (Xu and Jaggars 2013). Many existing postsecondary options that are fully online do not appear to improve students’ employment opportunities or earnings relative to no postsecondary education (Deming et al. 2016; Hoxby 2018). Online delivery of coursework may not even be less costly than in-person education (Hemelt and Stange 2020). This evidence suggests that online education is unlikely to fully address geographic barriers to high-quality programs. Leveraging local institutions. New, high-value college opportunities could also be created closer to home and earlier in students’ lives. Local community colleges are increasingly offering bachelor’s degrees, which may reduce the distance some students need to travel for such programs. As of 2022, about 15 percent of community colleges offered at least one bachelor’s degree program (Love 2022). Evidence is not yet available regarding the impact of such programs. “Dual enrollment” similarly brings college coursework closer by allowing high school students to take college-level courses, often delivered at the high school itself to minimize travel (Marken, Gray, and Lewis 2013). Enrollment in such coursework has grown rapidly in the last two decades (An and Taylor 2019). The limited evidence suggests that dual enrollment improves postsecondary trajectories. For instance, early exposure to dual-credit advanced algebra coursework increases the rigor of high school mathematics coursework taken and raises four-year college enrollment rates (Hemelt, Schwartz, and Dynarski 2020). Early college high schools are a more intensive version of dual enrollment, in which high schools form partnerships with local colleges to offer students the opportunity to earn an associate degree or equivalent amounts of college credit at little cost to their families (Webb 2014). Students admitted into early college high schools are more likely to earn college degrees and earn them faster than similar students denied admission (Edmunds et al. 2020; Song et al. 2021). Expansion of dual enrollment and early college 180 | Chapter 5 opportunities should be done with equity considerations featured prominently. Dual enrollment students are more likely to be white, high income, and high achieving than the typical student, likely because higher-income schools are more likely to offer dual enrollment and higher-income students at a given high school are more likely to enroll in such coursework (An and Taylor 2019). Promoting equitable access will require proactive planning and outreach by policymakers and educators. Conclusion The diversity and flexibility of the United States’ system of postsecondary education are among its greatest assets, and part of what makes it unique globally. These features also introduce complexity and risks for prospective students. In a simpler marketplace, choice and competition might be sufficient to promote quality improvements and to drive bad options out of business. But investing in postsecondary education is not like buying groceries or even a car. Most students are limited geographically, leaving them with a narrow set of options, and the choices they do have may be hard to fully evaluate and compare in advance. Further, public institutions are often constrained in their ability to meet demand, while less-constrained private, for-profit institutions have poor track records with respect to student outcomes. Students today rely more heavily on student loans to pay for college than did their counterparts a generation ago, increasing the risk of financial hardship for those who attend college but leave without gaining valuable skills. An examination of institution-oriented policy options reveals three major themes. First, a variety of institutional programs—many of them pioneered by community colleges—have demonstrated great potential for improving student outcomes. Many of these promising programs require additional resources to expand, and the Federal Government can both invest directly in postsecondary institutions and encourage States to increase their own investments. Second, discouraging the proliferation of low-quality postsecondary options is important in limiting the potential for students to make enrollment choices with low or negative returns. Finally, policymakers should continue exploring ways to address geographic barriers to college access through programs such as dual enrollment, early college, and community college baccalaureate degrees. Robust Federal and State efforts to improve the affordability of college have made progress in recent decades in expanding access to college. Yet, as this chapter has documented, making a good educational investment requires attention to both price and quality. Institution-oriented policies can help the U.S. postsecondary system build on its strengths, and ensure that all Building Stronger Postsecondary Institutions | 181 students who aspire to college have access to options that are both affordable and of high quality. 182 | Chapter 5 Chapter 6 Supply Challenges in U.S. Labor Markets Despite the enormous disruption of the COVID-19 pandemic, U.S. labor markets remained tight throughout 2022. For much of the last year, there were two job openings for every unemployed person—an unprecedented gap between labor demand and supply that has shifted the balance of power between workers and businesses. The resulting surge in hiring empowered many workers to change jobs and careers, with many job switchers experiencing substantial wage gains. Workplace organizing increased, with union representation petitions rising sharply in 2022, as workers sought to use their increased leverage to negotiate better working conditions. It is tempting to assume that today’s hiring challenges are primarily due to the lingering effects of the pandemic. However, tight labor markets predated the pandemic, and demographic trends indicate that labor supply challenges are likely to remain, even as the pandemic recedes. The baby boom generation is aging out of the labor force, and there are not enough younger workers to replace them. This tightening of the supply of labor due to population aging is a principal cause of current hiring challenges, constraining the economy’s capacity for growth by slowing the rate at which businesses can expand hiring. Unless efforts are undertaken to mitigate the impact of demographic change—by drawing more adults into the labor market and/or increasing immigration flows—the labor supply is likely to be constrained for the foreseeable future. This chapter examines both short- and long-run challenges for the U.S. labor supply. In the short run, some lingering effects of the pandemic remain, principally in the form of heightened labor market exits of older workers. 183 Immigration inflows, which were falling before the pandemic began, fell steeply when the U.S. borders closed and are just starting to recover. Chief among the long-run challenges are demographic trends, particularly population aging, but also falling labor force participation among prime-age adults. The chapter concludes with a description of several options to boost the U.S. labor supply; it also takes a closer look at labor markets experiencing especially acute labor shortfalls to see how macroeconomic forces are affecting specific industries. Labor Supply Fundamentals What determines the supply of labor? Simply put, individuals who are able to work decide whether to join the labor force, and if they do, how many hours they will work. Individuals can also decide to leave the labor force—to retire, to seek further schooling, or to care for small children. The aggregate labor supply of a nation is the sum of these individual choices. The aggregate labor supply is also a function of the size of the working-age population, which depends on fertility choices of earlier generations as well as immigration flows. In the simple framework used in economics textbooks to model the labor supply decision, employment is a choice between earning a wage and the alternative uses of that time (e.g., household tasks, childcare, leisure). Households also require a steady source of income to pay for necessary goods and services. This very simple model ignores many important variables that go into the labor supply decision, such as the psychological and social rewards of work and the nonmonetary aspects of particular jobs. Yet this simple model does allow economists to make useful predictions of how individual decisions are affected by more easily observed factors, such as changing wages and the availability of other household income. Chief among these inferences is that individuals who cannot meet essential consumption needs without working are highly likely to participate in the labor market. This suggests that individuals without a source of preexisting wealth or nonwage income are more likely to seek formal employment. A second implication is that individuals are more likely to enter the labor market when wages are high, when nonparticipation becomes more costly in lost earnings. This model also predicts that participation will fall when wages decline, for example, due to negative labor demand shocks. As discussed later in the chapter, many economists believe that declining 184 | Chapter 6 relative wages for non-college-educated workers are chiefly responsible for men’s declining participation in recent decades. Trends in U.S. Labor Market Participation The labor force participation rate (LFPR) is defined as the share of the population age 16 years and above who are working or who are actively seeking employment (BLS 2022). The labor force participation rate is an important measure of labor market potential and health (box 6-1). Nonparticipation in paid activity is not necessarily a source of concern—many nonparticipants are retirees, students, or parents with young children, many of whom do not desire formal employment. However, low participation rates can indicate an untapped potential labor supply, which includes individuals on the sidelines who would enter the labor market if attractive opportunities were available or obstacles to formal employment were removed. As shown in figure 6-1, labor force participation rose markedly in the second half of the last century, from 59.6 to 67.1 percent between 1968 and 2000. This growth in participation was due to the increased labor market activity of women—facilitated by changing societal norms, access to birth Box 6-1. Labor Supply Terminology Discussions about the labor supply can be confusing, given that the term “labor supply” is sometimes used to indicate the aggregate labor supply and at other times refers to labor force participation. In this chapter, we distinguish which term we are using as follows: • Labor supply typically refers to the aggregate labor supply, which is a function of the adult population (age 16 years and above), as well as the share of the adult population that participates in the labor market. However, analyses of the labor supply often take population trends as given and focus on the labor supply decisions of individuals, which are reflected in the labor force participation rate and the employment rate, as defined next here. • The labor force participation rate (LFPR) is the share of the noninstitutionalized adult population participating in the labor market; this includes people who are currently working or who are seeking employment. • The employment-to-population ratio, or the employment rate, is the share of the noninstitutionalized adult population that is employed. It is a participation measure similar to the LFPR but does not include unemployed people in the numerator. Supply Challenges in U.S. Labor Markets | 185 Figure 6-1. U.S. Labor Force Participation Rate, 1948–2022 Participation rate, in percent, age 16 years and above 90 80 70 60 50 40 30 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 2020 All Female Male Sources: Bureau of Labor Statistics; CEA calculations. Note: Data are seasonally adjusted. Rate is for age 16 years and above. control, and improved education and labor market opportunities—with large gains especially among married women (e.g., Blau and Kahn 2007; Goldin and Katz 2002; Black and Juhn 2000). The increase in women’s participation was more than enough to offset the decline in participation among men since the 1950s. As a result of more women entering the labor market and favorable demographic trends (the baby boom generation swelled the ranks of the workforce during this period), the U.S. labor supply grew steadily until 2000. Labor force participation in the United States began to decline after 2000 due to both supply and demand factors. The U.S. economy experienced two demand shocks during this period: the dot-com crash, which ended the economic expansion of the 1990s; and the global financial crisis, which began in 2007. Women’s participation growth also leveled off and began to decline after 2000. But the most significant factor pushing down participation in recent years has been the aging of the workforce, with the oldest baby boomers entering their retirement years at the beginning of the global financial crisis. Why Worry About Slower Labor Supply Growth? Declining labor force participation and slowing U.S. population growth mean that there is a dwindling supply of workers. A principal reason to be concerned about slower labor supply growth is that it implies slower economic growth (for further discussion, see chapter 1 of this Report). The 186 | Chapter 6 growth of economic output is determined by labor supply growth, capital investments, and productivity growth—all else being equal, if the labor supply’s growth slows, so too does economic growth. As labor market participation declines with age, an aging population also reduces the fraction of active workers in the population, thereby putting downward pressure on output per capita if not accompanied by capital investments or increases in productivity. Strong per capita economic growth was a primary driver of rising living standards over the last century; the aging population could have a negative impact on improvements in U.S. living standards in the future. Although demographic change is relatively easy to forecast, it is more difficult to account for changes in technology and productivity that may dampen the impact of the aging population on future economic growth. Cutler and others (1990) posit that labor scarcity could spur labor-saving technological innovation that would offset the impact of demographic change on output growth. Most studies have concluded that the relationship between Box 6-2. Work and Leisure in the United States and Europe John Maynard Keynes famously predicted that within two generations workers would work only 15 hours a week. For today’s Americans, that is simply not the case, despite living in a vastly richer country than Keynes experienced in the early 1900s. After decades of decline, the length of the typical American workweek began increasing in the 1970s, despite widespread expectations that productivity growth would lead to more leisure time for workers. Schor (1993) highlighted the plight of the “overworked American” in an influential book on work and leisure in the United States. She determined that a weakened labor movement and the erosion of workers’ power were largely responsible for setbacks in converting productivity gains into a shorter workweek for workers. Americans in the labor market now work longer hours, have less sick leave, and take fewer vacations than those in other wealthy nations. In 1960, hours of work and labor market participation rates were similar in the United States and Europe. But by 2000, there was a large gap in the work effort of the typical person in the United States compared with their counterparts in Europe. While the typical American works as many hours a year as they did in the 1970s, Europeans generally work much less, working fewer hours and weeks throughout the year. As suggested by Schor, differences in labor regulation and unionization appear to be the dominant factors explaining differences in annual hours worked per year between the United States and Europe (Alesina, Glaeser, and Sacerdote 2005). Supply Challenges in U.S. Labor Markets | 187 Box 6-3. Deaths of Despair in the United States For white Americans between age 45 and 54, average life expectancy is no longer increasing; in fact, it even declined for several years, a pattern not previously seen outside pandemics or wars. This occurred during a period when Black Americans saw gains in life expectancy, narrowing the enduring gap in outcomes between these two groups. Increases in mortality rates among whites are largely accounted for by higher rates of suicide, opioid overdoses, and alcohol abuse (Case and Deaton 2015). The term “deaths of despair” was coined by Case and Deaton (2020) in their influential book, which documents the impact of declining economic opportunity on the health and well-being of working-class society in the United States. They explain that these deaths of despair primarily affect white Americans without a four-year college degree, living in areas of the country that have a very low share of the workingage population employed. While economists usually frame employment as a choice between paid work and alternative uses of time, Case and Deaton’s (2020) work highlights the importance of good jobs in providing meaning, structure, and purpose to a community. They write: “Destroy work and, in the end, working-class life cannot survive. It is the loss of meaning, of dignity, of pride, and of self-respect that comes with the loss of marriage and of community that brings on despair, not just or even primarily the loss of money.” Their research shows how the diminished economic prospects of white working-class Americans constitute not only an economic crisis but also a public health crisis. Recent papers have challenged the notion that growing deaths of despair are a phenomenon unique to economically disadvantaged whites; deaths from suicide, drug use, and alcohol use have risen even more among Indigenous persons living in the United States (Friedman et al. 2023). Among Indigenous persons, local economic conditions have a heterogenous effect on deaths by suicide and drug use, suggesting that improvements in economic conditions alone may not be enough to reduce deaths of despair (Akee et al. 2022). an aging population and output is negative (e.g., Gagnon, Johannsen, and Lopez-Salido 2021; Maestas, Mullen, and Powell 2022; and Sheiner 2014). However, Acemoglu and Restrepo (2020) find that the impact on technological change dominates, and that an older workforce increases economic growth. Eggertsson, Lancastre, and Summers (2019) similarly find that population aging can increase growth due to increased national savings that accrue as populations age, driving down interest rates. However, as interest rates approach zero and cannot fall further, this mechanism is disrupted, and 188 | Chapter 6 they find that the impact of aging on growth becomes negative. Overall, the evidence suggests that aging populations slow per capita economic growth, but the economy’s potential to adapt to the tightening of labor supply in ways that spur productivity cannot be discounted. From an individual perspective, the fact that fewer people are engaged in paid labor market activity is not necessarily negative and could simply reflect personal choices. By way of example, John Maynard Keynes famously predicted in 1930 that technological progress would increase living standards so much that his grandchildren would work just 15 hours a week, devoting the rest of their time to leisure (Keynes 2010; orig. pub. 1930). As noted in box 6-2, this prediction did not come to pass for workers in the United States. But evidence suggests that many nonparticipants, if not otherwise engaged in schooling or caring for young children, are not happier than those who work. Prime-age men who are out of the labor force report low levels of emotional well-being, and derive little meaning from how they spend their time (Krueger 2017). They are also less likely to be married and much more likely to be living in poverty. Localities where employment rates have fallen the most have also seen a sharper rise in opioid deaths and suicides (see box 6-3), suggesting significant community stress in these areas. Causes of U.S. Labor Supply Challenges Slowing population growth and declining labor force participation are significant headwinds for U.S. labor supply. If these trends persist, and offsetting increases in productivity or capital intensity fail to materialize, there will not be enough workers to meet long-run demand. Because understanding the causes of current labor supply challenges is necessary to craft effective policy solutions, this section provides an overview of the dominant factors driving slower growth of U.S. labor supply since 2000. Demographic Trends Demographic trends are the principal cause of near-term U.S. labor supply challenges. Over the next decade, the share of the population in their prime working years (between age 25 and 54) will decline in the United States and in many other countries, as shown in figure 6-2. These demographic trends are the result of a sharp decline in fertility rates between 1960 and the late 1970s, with fertility remaining at or below replacement rates for the last several decades. Additionally, life expectancy in the United States has also not kept pace with that of other wealthy nations, and was decreasing for some groups even before the COVID-19 pandemic (see box 6-3). Due to low fertility rates, the vast majority of near-term working-age population Supply Challenges in U.S. Labor Markets | 189 Figure 6-2. Percentage of Total Projected Population That Is Prime Age, 2026–50 Percent 43 42 41 40 39 38 37 36 35 34 33 2026 2030 Canada 2034 China 2038 France 2042 Germany 2046 United States 2050 United Kingdom Sources: World Bank; United Nations Population Projections. Note: ‟Prime age” is 25–54 years. Figure 6-3. Prime-Age versus Overall Labor Force Participation, 1990–2022 Prime-age participation rate, age 25–54 (percent) Overall participation rate, age 16+ (percent) 85 68 84 67 83 66 82 65 81 64 80 63 79 62 78 61 77 1990 1994 1998 2002 Prime age (left axis) 2006 2010 2014 2018 2022 60 Overall (age 16+) (right axis) Source: Current Population Survey data. Note: ‟Prime age” is 25–54 years. growth will be accounted for by immigrants and their descendants born in the United States (Blau and Mackie 2017). The tightening of labor supply conditions due to these demographic trends was well under way before the pandemic, as can been seen in figure 6-3. Between 1990 and 2008, prime-age and overall labor force participation moved more or less in tandem. Starting in 2009, however, the two categories began to diverge, as baby boomers began to enter their early 190 | Chapter 6 retirement years. Increases in the participation rate among older workers and slack labor demand conditions in the years after the financial crisis dampened but did not completely offset the initial effect of retirements on the labor supply (Aaronson et al. 2014; Abraham and Kearney 2020). As labor demand recovered and labor markets tightened in the years preceding the COVID-19 pandemic, many workers left the sidelines and entered the labor market in response, easing concerns about the labor supply. Then the pandemic arrived, and COVID-19—a virus that is particularly dangerous for older members of the workforce—sped up many of the forces reshaping the labor market. In December 2022, the prime-age LFPR was just shy of its prepandemic peak, but overall participation had fallen by a full percentage point, due primarily to population aging and the increase in the propensity of older workers to retire during the pandemic. Declining Labor Market Participation Among Men In addition to population aging, another cause of slowing labor supply growth is the decline in participation among men, particularly those without a college degree. The participation rate for U.S. men in their prime-age working years peaked in the 1950s and has been falling in earnest since the mid-1960s. This decline is sharper than in other advanced economies. Prime-age men in the United States and the United Kingdom had similar rates of participation in the 1980s and 1990s, but the participation of men in the United States continued to fall after 2000, while the United Kingdom’s participation rates remained relatively flat. This trend has particularly significant implications for economic growth, because individuals are at their most productive in their prime-age working years. The underlying causes of declining male participation have been the subject of much scholarly interest but still remain an open debate. This very large body of research is summarized here to explain the potential causes of declining male participation and to illuminate potential policies to counteract the ongoing decline. Spousal income for heterosexual men. Increases in married women’s labor supply could reduce male labor force participation by reducing the cost of nonparticipation and increasing responsibilities at home, such as childrearing and senior care. However, the evidence suggests that this is not a significant factor driving down male participation. Men with working wives and men with children have had the smallest declines in participation among all men (Juhn and Potter 2006; CEA 2016). As discussed in more detail in box 6-4, nonparticipating men are more likely than other groups to rely on income from other family members, usually parents. It is plausible that changing trends in household formation, such as more adults living with their parents, will reduce male participation. However, the causality may go Supply Challenges in U.S. Labor Markets | 191 Box 6-4. On What Income Do Jobless Men Live? How do men between the age of 25 and 54 who do not work fund food, shelter, and other necessities? Figure 6-i documents sources of income for prime-age men who were not in the labor force in 2022. For comparison purposes, we also provide the composition of household income for men in the labor force and for women. A primary source of income for nonparticipating men is other household members, principally parents. In contrast, income provided by other household members accounts for only a small share of income for other groups. Spousal income available to nonparticipants is smaller than it is for men in the labor force, in part reflecting lower marriage rates. Government transfer income, particularly disability insurance, is a key source of income for some nonparticipating men, although, as can be seen in figure 6-i, it accounts for a relatively small share of income for nonparticipants as a group. Figure 6-i. Sources of Annual Income for Prime-Age Workers by Sex and Labor Force Status, 2022 Average annual income (thousands of dollars) 160 140 120 100 80 60 40 20 0 Male NILF Wage and salary Male labor force Spouse Female NILF Social insurance and government transfers Female labor force Other Other householder Sources: Bureau of Labor Statistics; CEA calculations. Note: NILF = not in the labor force. in the other direction: these trends themselves may be an outcome of higher housing costs and fewer labor market opportunities for young workers (Fry, Passel, and Cohn 2020; Matsudaira 2015). Disability insurance. Social Security Disability Insurance (SSDI) is another candidate for a supply-side explanation of declines in male labor force participation rates. SSDI receipts increased for several decades before peaking in 2010, after which their incidence fell (CBPP 2021). A substantial body of research indicates that the availability of SSDI benefits lowers participation for workers who are on the margin of eligibility (e.g., Bound 1989; Autor and Duggan 2003; Maestas, Mullen, and Strand 2013; and 192 | Chapter 6 Gelber, Moore, and Strand 2017). However, SSDI receipts do not appear to be an important determinant of declining male participation rates. From 1967 until 2014, prime-age male participation fell 7.5 percentage points, while the share of prime-age men who received SSDI benefits increased by 2 percentage points (CEA 2016). Moreover, this 2-percentage-point rise in the SSDI take-up rate among prime-age men should not be interpreted as having caused lower participation, given that many of the men receiving SSDI benefits would likely not have participated due to their disabilities. An analysis conducted by the CEA finds that, under reasonable assumptions, holding SSDI receipts constant for prime-age men during the 1967–2014 period would only have eliminated between 0.3 and 0.5 percentage point of the observed reduction in prime-age men’s participation (CEA 2016). Rising incarceration rates. As shown in figure 6-4, Black men have a lower labor force participation rate in the United States than Hispanic or white men, and participation among Black men has been falling more steeply than that of other groups. A steep rise in incarceration rates beginning in the 1980s is a potential culprit in the declining employment prospects of Black men, who face a much higher risk of incarceration than white men. Because standard labor market statistics exclude institutionalized populations, they understate the impact of rising incarceration rates on employment among Black men. Doleac (2016) shows that accounting for the incarcerated population lowered the employment rate (i.e., the percentage of the population Figure 6-4. Prime-Age Male Labor Force Participation, by Race, 1976–2022 Participation rate, age 25–54 (percent) 100 95 90 85 80 75 70 1976 1981 1986 1991 White 1996 2001 Black 2006 2011 2016 2021 Hispanic Sources: Bureau of Labor Statistics; CEA calculations. Note: ‟Prime age” is 25–54 years. Data are annual averages. Supply Challenges in U.S. Labor Markets | 193 that is employed) of Black men in 2014 by about 4 percentage points, with only a minimal impact on white men’s employment. Incarceration is also likely to have a negative impact on employment after release—an effect that would be reflected in official statistics. Formerly incarcerated people face a number of barriers to formal employment: limited labor market experiences while incarcerated, laws preventing them from being employed in certain jobs, and employer practices that discourage hiring those with criminal records. Résumé-audit studies have found that an applicant’s criminal record is a significant barrier to finding employment (Pager 2003). Mueller-Smith (2015) finds that for individuals with a previous formal labor market attachment, incarceration decreases the probability of subsequent employment, especially for those serving longer terms. Some recent papers using administrative data have not found strong evidence of significant scarring effects on postincarceration earnings and employment (Garin et al. 2022; Looney and Turner 2018). However, this empirical result is a consequence of poor labor market opportunities preceding incarceration; formerly incarcerated persons are disproportionately drawn from neighborhoods in extreme economic distress. A reasonable interpretation of these findings is that while incarceration likely does have an impact on future employment and earnings, many of the challenges that formerly incarcerated persons face in the labor market start long before their incarceration begins. Abraham and Kearney (2020) use estimates of the formerly incarcerated population and Mueller-Smith’s (2015) estimates of scarring effects to roughly calculate the impact of rising incarceration rates on declines in the overall employment rate. They estimate that rising incarceration accounted for a decline of 0.12 percentage point in the employment-to-population ratio between 1999 and 2018, a period when the ratio declined 3.8 percentage points. Though admittedly a rough estimate, their calculations suggest that rising incarceration accounts for a small part of observed declines in overall employment. However, rising incarceration disproportionally affects Black communities, so incarceration’s role in driving down participation among Black men is likely much larger. Geographic mismatches. Labor force participation varies dramatically across the United States, with striking gaps even among prime-age adults (Nunn, Parsons, and Shambaugh 2019). Migration flows within the United States were quite high in the mid-20th century. Those picking up stakes generally moved to where they could find work, and migration flows redirected population on net from low-income to high-income regions (Blanchard and Katz 1992). However, since 1980, internal migration has declined, and moves have become less likely to reallocate population to more prosperous parts of the country (Ganong and Shoag 2017). Reduced internal migration may be a result of increased housing costs and/or increased licensing costs 194 | Chapter 6 for certain occupations and industries (Hsieh and Moretti 2019; Johnson and Kleiner 2020). Declines in labor migration may have exacerbated declining labor force participation, with workers increasingly exiting the labor market instead of migrating in response to regional shocks (Dao, Furceri, and Loungani 2017). This change has been evident in manufacturing employment declines since 2000; unlike previous downturns, those who lost jobs have been less likely to migrate to other regions and more likely to exit the labor force (Autor, Dorn, and Hanson 2021; Charles, Hurst, and Schwartz 2018). However, some economists have argued that declining mobility is an appropriate response to improved information about distant labor markets reflecting rational expectations about potential employment success in those locations (Kaplan and Schulhofer-Wohl 2017) and to declines in urban premiums for non-college-educated workers (Autor 2019). The extent to which declining geographic mobility has exacerbated declining labor force participation remains an open question. The fact that out-migration responses to regional downturns have declined suggests that it does play a role, but the importance of this role cannot be fully ascertained with the evidence. However, the reduction in domestic migration to areas with better economic opportunities is well established in the literature. This indicates that policies designed to pull workers into the labor market will have limited success unless they can also improve job opportunities in regions where participation is currently low. Demand factors: import competition and technological change. The labor supply model outlined earlier in the chapter posits another theory for declining participation: wages. In the model, adverse demand shocks can have a negative impact on an individual’s decision to participate through wages alone. The steepest declines in participation since the 1970s have been among men without a four-year degree; these men have also experienced declines in wages throughout much of this period. It is reasonable to wonder, therefore, if declines in labor demand can account for declines in participation, particularly among men without a four-year degree. Why would demand for labor have fallen disproportionately for men without a college degree? Possible causes include globalization and technological change. These dual forces are thought to be key drivers of “job polarization”—a term used to describe the relative growth of high- and lowskill jobs and the disappearance of middle-skill opportunities (Acemoglu and Autor 2010; Autor and Dorn 2013). Middle-skill jobs generally include tasks that are most vulnerable to automation and offshoring—making it difficult to distinguish the relative impact of each factor on employment. Job losses during the two recessions preceding the COVID-19 pandemic were concentrated among middle-skill jobs, and workers who lost those jobs Supply Challenges in U.S. Labor Markets | 195 tended to exit the labor market rather than take lower-paying work (Foote and Ryan 2015). A number of papers have linked declines in U.S. manufacturing employment to increased import competition from China (e.g., Autor, Dorn, and Hanson 2013; Autor et al. 2014; Pierce and Schott 2016; and Acemoglu et al. 2015). Increased imports from China reduced demand for domestically produced manufacturing goods, which reduced demand for U.S. manufacturing workers, who are disproportionately less-educated men. More recent research by Bloom and others (2019) suggests that the negative impact of the “China shock” on U.S. manufacturing employment was sizable between 2000 and 2007 but has not exacerbated manufacturing declines in more recent years. However, increased import competition is not the only cause of manufacturing employment declines; automation has also increased. Looking specifically at the role of robots in employment declines, Acemoglu and Restrepo (2020) estimate that each robot displaces about 5.6 workers. Using this estimate, Abraham and Kearney (2020) tentatively conclude that increases in the stock of robots between 1999 and 2018 resulted in the loss of 1.1 million jobs during this period. Many economists believe that demand factors are the principal cause of declining male labor force participation. In their comprehensive review and meta-analysis of recent research on declining overall employment rates, Abraham and Kearney (2020, 636) state that “our review of the evidence leads us to conclude that, among the factors whose effects we are able to quantify, labor demand factors are the most important drivers of the secular decline in employment over the 1999 to 2018 period.” However, their estimates indicate that almost half the fall in employment rates during this period is unexplained after accounting for changes in demand. The CEA (2016) also concluded that low wages were the primary driver of male participation declines, with smaller roles for supply factors. A seeming puzzle with regard to the view that declining wages are driving participation declines is that real wages for lower-skilled men rebounded in the 1990s and the 2010s, periods when the participation of men continued to fall. One possibility, suggested by Wu (2022), is that it is not absolute but relative declines in wages that reduce participation. Widening inequality means that relative wages for men without a four-year degree have declined steadily for many decades, reducing their status, marriage prospects, and job satisfaction. Wu finds that changes in relative wages account for almost half the growth in labor force exits among noncollege men between 1980 and 2019. In a related paper, Binder and Bound (2019) argue that supply and demand factors are likely not additive but interactive, with negative demand shocks for noncollege men leading to less stable employment and lowering marriage rates, in turn leading to changes in household formation that reduce the male labor supply. Together, these papers suggest that demand factors 196 | Chapter 6 and rising inequality may together have a negative impact on participation by lowering the relative gain in status attainable through work. Summary of the evidence on the declining male LFPR. Despite significant scholarly interest in the decline of male labor force participation, questions remain. The evidence suggests that demand factors have played an important role, with globalization and automation reducing employment and wages, particularly for men without college degrees. However, a sizable participation gap remains unexplained by changes in labor demand. Supply factors, such as increasing incarceration and disability insurance, have also exacerbated the decline, although the impact of these factors has been small. An open question for research is how changes in gender roles and household formation—particularly declining marriage rates and more adults living with their parents—interact with demand factors and have exacerbated declines in participation. Despite the incomplete nature of the evidence on participation declines, the extensive research literature does point to policy measures that could boost participation among men. In particular—given the importance of demand factors and rising inequality in driving down participation—efforts to improve wages and working conditions for men without a four-year degree would likely draw more of them into the labor market. Several policy options to boost participation by addressing these factors are discussed in greater detail later in the chapter. Female Labor Force Participation: The United States Falls Behind Despite declining labor force participation among men, the U.S. labor supply grew for much of the last century, largely due to the growing participation of women. The many social and economic factors driving the growth of female participation in the last century are the subject of a large body of literature, a review of which is beyond the scope of this chapter. What is more relevant for current labor supply challenges is that the growth in women’s participation in the United States leveled off in the 1990s and began to decline. This stagnation did not occur in other advanced economies, where female participation continued to grow. As shown in figure 6-5, female participation levels and trends in the United States were similar to those in Canada and the United Kingdom until about 1995. Female participation continued to grow in these two countries after 1995, unlike in the United States. By 2015, U.S. female participation rates were below those for women in Japan, who were far less likely to work than women in the United States as recently as 2005. Most of literature on declining female participation since 2000 has focused on factors affecting the maternal labor supply. When discussing why U.S. female participation has fallen behind that of other countries, a frequent observation is that the United States lacks publicly provided childcare Supply Challenges in U.S. Labor Markets | 197 Figure 6-5. Prime-Age Female Labor Force Participation, 1984–2021 Participation rate, age 25–54, selected OECD countries (percent) 90 85 80 75 70 65 60 55 50 1984 1989 1994 Canada United Kingdom 1999 2004 France United States 2009 2014 2019 Japan OECD average Source: Organization for Economic Cooperation and Development. Note: ‟Prime age” is 25 to 54 years. and paid family and medical leave policies, which are common in most advanced economies. The United States spent only $2,600 on care and early education per child under 6 years of age in 2017, compared with the EU average of $5,500 (OECD 2019). In consequence, childcare in the United States consumes a significant portion of family budgets, with care costing up to one-third of the average earnings of a single mother (Ziliak 2014). Most of the empirical evidence indicates that publicly provided care options for young children boost the maternal labor supply (e.g., Gelbach 2002; Baker, Gruber, and Milligan 2008; and Haeck, Lefebvre, and Merrigan 2015), as discussed in the next section on policy options. Chapter 4 of this Report also discusses more broadly the social and economic benefits of greater public support for early care and education. However, there is no strong evidence tying childcare costs facing families to declines in female participation since 2000. Declines in participation among women are broad-based and are actually steepest among single women without children, whose participation declined by 7 percentage points between 1989 and 2016 (see figure 6-6). In many ways, female participation trends after 2000 resemble those of men in the United States, who also had declining participation relative to other advanced economies during this period. While the factors driving declining participation among men have been the subject of much research, much less attention has focused on declines in women’s participation—with a few notable exceptions, such as Black, Schanzenbach, and Breitwieser (2017) and Abraham and Kearney (2020). Although research has been understandably focused on the role of 198 | Chapter 6 Figure 6-6. Prime-Age Female Labor Force Participation Rate, 1976–2022 Participation rate, age 25–54 (percent) 90 85 80 75 70 65 60 55 50 45 1976 1981 1986 Single, no children 1991 1996 Married, no children 2001 2006 Single with children 2011 2016 2021 Married with children Sources: Bureau of Labor Statistics; CEA calculations. Note: ‟Prime age” is 25–54 years. Data are annual averages. care in boosting the maternal labor supply, the factors driving declining participation among women without children are also worthy of further investigation. The COVID-19 Pandemic’s Lingering Effects on the Labor Supply The forces constraining the supply of workers at the onset of the COVID-19 pandemic were amplified as countries attempted to mitigate the spread of the virus. Retirements among baby boomers, already fueling hiring challenges in many industries, spiked as older workers faced new and potentially severe health risks at work. Some employers encouraged workers to take early retirement in order to slash workforces in the face of reduced demand. Immigration bans and closed borders halted the flow of foreign-born workers who were critical for many industries, particularly food services and agriculture. The pandemic also disrupted the availability of childcare and in-person school, making it difficult for many parents to return to work. Prime-age participation has mostly rebounded from the pandemic (see box 6-5), and recent efforts to clear the backlog of applicants have returned immigration flows to prepandemic levels. But the lingering effects on the labor supply remain, principally lower participation rates among older workers. While retirements have increased in previous downturns (Gorodnichenko, Song, and Stolyarov 2013; Coile and Levine 2011), the pandemic-induced recession had a particularly severe impact. Quarterly retirement rates increased by 5 percentage points at the start of the pandemic, Supply Challenges in U.S. Labor Markets | 199 Box 6-5. The Missing Prime-Age Workers Early during the COVID-19 pandemic, declines in prime-age labor market participation were primarily due to pandemic-related disruptions. Layoffs, illness, and care responsibilities pushed many participants out of the labor market (Garcia and Cowan 2022; Goda and Soltas 2022; Cajner and others 2020). As businesses and schools reopened, and vaccines were rolled out, prime-age participation rebounded quickly (Forsythe, Kahn, Lange, and Wiczer 2022; Hansen, Sabia, and Schaller 2022). Individuals with disabilities actually increased their participation relative to prepandemic levels, likely due to increased telework and remote work practices adopted during the pandemic (Ne’eman and Maestas 2022). Yet despite tight labor markets and steadily rising wages, growth in prime-age participation slowed markedly in the latter half of 2022; at the end of the year, it remained 0.6 percentage point below the level in February 2020. As policymakers had hoped that continued prime-age participation growth would ease ongoing inflationary pressures, concern about these missing workers grew. But why prime-age participation remained below prepandemic levels three years after the start of the pandemic cannot be easily ascertained. One possibility is that the level of participation at the end of the last expansion was an exception and not the rule. The last expansion pulled many workers into the labor market, and the continued growth in participation between 2016 and 2019 surprised many economists. It may be that participation rates were well above the trend at the end of the long expansion that preceded the pandemic (Barkin 2022). Another possibility is that participation will keep growing as long as the current expansion continues. That participation decisions can respond to favorable economic conditions with a lag is suggested by Cajner, Coglianese, and Montes (2021), who find that participation declines after a negative shock can last up to four years, with nonparticipants who return to school or shift to care responsibilities during the downturn accounting for much of the lagged response. If households adapted to the pandemic in ways that can take a while to unwind, it may be that more prime-age workers will return to the labor market over time. a much larger spike in the retirement rate than occurred during the financial crisis (McEntarfer 2022). Unlike previous downturns, the pandemic-induced growth in retirements appears largely unrelated to local economic conditions, suggesting that the retirement surge may have been primarily driven by COVID-19 health concerns (Coile and Zhang 2022). A recent paper suggests that the increase in housing wealth during the pandemic may have 200 | Chapter 6 also played an important role, with more retirements among older workers in housing markets with stronger price growth (Favilukis and Li 2023). As of the end of 2022, the growth in the retired share of the U.S. population accounted for nearly all the shortfall in labor force participation relative to prepandemic levels. In a recent paper looking at the surge in retirements during the pandemic, Montes, Smith, and Dajon (2022) estimate that almost half the retirements since the start of the pandemic have been “excess” retirements that would likely have not occurred in absence of the pandemic. They find particularly sharp increases in retirements among workers over 65, among whites, and among those with a four-year degree. Given the advanced age of most excess retirees, it seems unlikely that a large proportion will return to the labor force. The authors also find that flows into retirement remained elevated almost three years after the start of the pandemic. They conclude that it may take some time for retirement behavior to return to prepandemic norms. Options to Boost the U.S. Labor Supply As detailed in this chapter, the United States faces long-run headwinds for its labor supply that may have an impact on future economic growth. This section discusses policy options for boosting the U.S. labor supply. Although the focus here is on broad-based measures, labor supply challenges are often unique to specific markets, and policy solutions to increase the labor supply in general may be insufficient to remedy supply issues in specific occupations and industries. To highlight this fact, this section also discusses the supply challenges facing two particular labor markets, medical care (box 6-6) and local public education (box 6-7) in greater detail. Increasing Immigration Increasing immigration to the United States is frequently cited as a way to mitigate the consequences of the aging population. Immigration increases potential output by increasing the size of the labor force; because new immigrants are typically working age, it also lessens the effect of the aging population on per capita economic growth. Immigrants also make other important contributions to the U.S. economy. For example, given that they often have fewer long-standing family and social ties, they are more mobile than workers born in the country and are more responsive to local economic conditions (Basso and Peri 2020). Skilled immigrants have been found to boost innovation and technological change, which is an added contribution to overall economic growth (Bernstein et al. 2022; Hunt and Gauthier-Loiselle 2010). Overall, research also suggests that the effects of newly arrived immigrants on the wages and employment of the Supply Challenges in U.S. Labor Markets | 201 Box 6-6. A Critical Shortfall of Nurses and Physicians Demographic shifts that began before the COVID-19 pandemic had already started to manifest in a noticeable reduction in the supply of health care providers. Researchers have documented the effect of the aging of the U.S. population on the supply of nurses and physicians in certain specialties such as primary care and psychiatry, along with geographic misallocation affecting rural areas (Buerhous, Auerbach, and Staiger 2017; Petterson et al. 2012; Satiani et al. 2018; Ricketts 2005). Given that the youngest members of the baby boom generation are just reaching retirement age, the aging of the population will continue to affect the availability of health care providers for the foreseeable future by simultaneously reducing the supply of providers and increasing demand for health care. Compounding these predictable shifts, the pandemic exerted a historic shock on the health care workforce, exacerbating existing challenges. Unprecedented rises in the demand for health care overwhelmed providers. Many left their jobs to protect themselves and their families from catching the virus, to care for their young children and elderly parents, and to focus on their physical and emotional health and lessen burnout (Galvin 2021). These actions increased the burden for providers who remained in the system. Swift, short-term solutions were implemented to stabilize the supply of critical health care workers. Hospitals utilized travel nurses to fill short-term increases in demand (Gottlieb and Zenilman 2020). The wages of these traveling nurse positions were set significantly higher than those of other nurses, which resulted in many nurses being willing to move to areas of the country with the greatest short-term need. Through provider payment incentive modifications, access to telehealth services was expanded for many Americans. Many States also relaxed their scope-of-practice restrictions, allowing for greater utilization of nurse practitioners and physicians’ assistants (Volk et al. 2021). Although these short-term solutions helped during the crisis, improvements in the education and training of health care providers will be required to maintain an adequate supply and distribution of providers in the long run. There are currently too few nurse educators to meet the demand. There are also too few nursing clinical placement spots to provide clinical experience for nursing students. Poor working conditions in many hospitals led to high turnover in the nursing profession well before the pandemic; improvements in patient-to-nurse staffing ratios and management practices would reduce turnover and improve patient outcomes (Vahey et al. 2004; Aiken et al. 2022). For physicians, there is an insufficient number of residency slots. Increasing funding for medical residency programs will also likely be needed to boost the supply of physicians (GAO 2021). 202 | Chapter 6 Box 6-7. Staffing Challenges in K-12 Education Between February 2020 and October 2022, employment in local education fell by almost 300,000 workers, about 3.5 percent of this workforce. According to survey results from a nationally representative sample of public schools, 53 percent indicated that they were entering the 2022–23 academic year understaffed. Among those schools, respondents reported that their highest need areas were special education teachers (65 percent) and transportation staff (59 percent) (U.S. Department of Education 2022). Declines in education employment since the COVID-19 pandemic have not been broad-based but in fact have been concentrated in lower-income communities, as shown in figure 6-ii. Understanding the cause of these shortfalls is complicated, as there is no single education labor market (Goldhaber et al. 2015). Additionally, the lack of adequate data makes it difficult to identify areas experiencing difficulties in hiring (Nguyen, Lam, and Bruno 2022). Staffing challenges have long plagued schools that serve high proportions of students living in poverty and who belong to minority groups, and hiring challenges are more pronounced in specialty teaching areas such as special education, English language learning, and high school science, technology, engineering, and mathematics (Boyd et al. 2005; Cowan et al. 2016; Murnane and Steele 2007). Qualified teachers are not evenly distributed across schools and students, with poor, Black, and Hispanic students being much more likely to experience novice teachers (James and Wyckoff 2022). Figure 6-ii. Percent Change in Teacher Employment, 2019–22 Change in the 12-month rolling average of employment relative to January 2020 2 0 –2 –4 –6 –8 –10 Jan-2019 Jul-2019 Jan-2020 Jul-2020 Lower income Jan-2021 Jul-2021 Jan-2022 Jul-2022 Higher income Sources: Current Population Survey; CEA calculations. Note: Teacher employment is limited to government employees and includes preschool and kindergarten teachers, elementary and middle school teachers, secondary school teachers, special education teachers, tutors, and other teachers and instructors. “Lower income” refers to employment among those living in the half of core-based statistical areas (CBSAs) with the highest share of households with total household income under $50,000. “Higher income” refers to employment among those living in the half of CBSAs with the lowest share of households with total income under $50,000. Supply Challenges in U.S. Labor Markets | 203 When properly diagnosed, policy remedies emerge for areas with particular teacher and school staffing needs. Addressing these localized challenges requires targeted efforts, such as incentives to serve in hard-to-staff schools and high-need areas, and innovation and flexibility about alternative pathways into the profession and licensure reciprocity (Dee and Goldhaber 2017). The keys to addressing teacher and staffing challenges are facilitating mobility into these local labor markets and encouraging retention in high-need positions once they are filled. Some of these measures, such as relaxing licensing requirements, have also contributed to meeting long-term goals such as creating a more diverse teacher workforce (Bacher-Hicks, Chi, and Orellana 2021). domestic population are quantitatively very small, and that the fiscal effects of immigration are generally positive. For example, as new immigrants tend to be working age, they pay taxes without incurring the fiscal costs of youth and early education (for a comprehensive review of the fiscal and economic impact of immigration, see Blau and Mackie 2017). There is also a potential pool of laborers already residing in the United States without legal authorization to work and/or a path to citizenship. Legal permanent residence would expand the employment opportunities for a significant portion of this population. As such, immigration reform that provides a path to citizenship for the estimated 11 million undocumented individuals would help to increase the labor supply (Migration Policy Institute 2022). Additional immigration reforms could include removing per-country caps on employment, expanding diversity lottery visas, and expanding the J-1 exchange visa program, which would bring additional faculty, scientists, and students to the United States for training and sharing knowledge and methods. Drawing More Adults into the Labor Market Labor force participation among working-age adults in the United States is falling, and it is now lower than that in other developed nations. A likely culprit is the lack of public sector support for workers and families in the United States relative to other wealthy countries. Policies directed at improving the labor market prospects of nonparticipants and removing obstacles to their employment could increase participation among prime-age adults. This subsection outlines several policy options for drawing more adults into the labor market. Improving care options. Public spending on childcare and senior care in the United States is very low relative to that in other advanced economies. 204 | Chapter 6 The United States is also one of the few countries that has no guaranteed paid family and medical leave. In the absence of public support, the economic burden of caring for family members falls chiefly on women, whose labor market participation and lifetime earnings fall in consequence. Given women’s low participation rates relative to men, policies that reduce their care burden are a promising avenue for increasing participation and reducing gender disparities. The preponderance of empirical evidence suggests that childcare and preschool programs have a positive impact on maternal labor force participation (e.g., Bauernschuster and Schlotter 2015; Morrissey 2016; and Wikle and Wilson 2022). Some of the evidence arises from research on a policy change in Quebec, which introduced highly subsidized universal childcare in the late 1990s. These subsidies led to a very large increase in the use of care and a sizable and long-lasting impact on mothers’ participation (Baker, Gruber, and Milligan 2008; Haeck, Lefebvre, and Merrigan 2015). Morrissey (2017) evaluates a range of studies and concludes that a 10 percent reduction in the cost of childcare would likely increase maternal participation in the range of 0.5 to 2.5 percent. Paid family and medical leave can also help workers stay connected to their jobs while addressing family-related needs, maintaining good job matches, and boosting long-term attachment to the labor market (Baum and Ruhm 2016; Anand, Dague, and Wagner 2021). Blau and Kahn (2013) estimate that nearly one-third of the gap in U.S. women’s participation relative to that in other developed countries can be explained by the relative lack of such family-friendly policies. Criminal justice reform and removing barriers to reentry. The United States incarcerates people at a higher rate than any other country in the world (World Prison Brief 2021). Reducing the punitiveness of the criminal justice system in the United States would reduce the large fiscal burden of its current system and the collateral damage of incarceration on affected communities. It would also increase the labor supply by reducing both the incarcerated population and the scarring effects of incarceration on employment. In recent decades, several States have experimented with criminal justice reforms aimed at reducing incarceration rates, with noteworthy success. For example, California introduced changes to sentencing for less serious offenses and lessened punitive measures for technical parole violations, changes that caused a sharp decline in incarceration (Lofstrom and Raphael 2013). A series of reforms in New York to divert drug offenders to treatment facilities and to relax mandatory minimum sentencing practices have also sharply reduced incarceration rates in that State (Greene and Mauer 2010). In both States, these periods of reduction in incarceration generally coincided with declining crime rates. High incarceration rates have also created a large population of formerly incarcerated individuals who face significant barriers to reentry. Supply Challenges in U.S. Labor Markets | 205 Removing barriers to employment for the formerly incarcerated would likely improve their employment prospects after release. One such reform, undertaken by several States in recent years, is to remove occupational licensing barriers for people with arrest and conviction records. In many States, a prior arrest or conviction, no matter how long ago, can prevent one from becoming a licensed barber or cosmetologist, a drug counselor, or a firefighter (Rodriguez and Avery 2016). Noting that information about prior arrests or incarceration decades after the event in question is of limited information value to employers, Piehl (2016) advocates reforms that would set a time limit on information about past convictions for employment background checks. Expanding the Earned Income Tax Credit. The Earned Income Tax Credit (EITC) is a large government program that raises the after-tax return to work for low- and moderate-income households, particularly those with dependent children. As one factor depressing labor market participation is stagnating wages for non–college educated workers, the EITC can create additional incentives for participation by increasing the returns to work. A large body of research has shown that the EITC increased the labor supply of low-income mothers, a group it principally targeted (e.g., Bastian 2020; Eissa and Liebman 1996; and Meyer and Rosenbaum 2001). However, the maximum credit for families with two or fewer children has remained flat in real terms for many decades (Hoynes, Rothstein, and Ruffini 2017). Increasing the generosity of the current credits and/or expanding the EITC to provide more incentives to low-wage workers without dependent children would likely boost participation. Regional economic development. Efforts to improve the economic performance of a particular region usually (but not always) target areas that have experienced downturns, with the intent of helping its residents. While not explicitly intended to increase participation, improving economic opportunities in declining areas would likely improve participation in areas with low employment rates by boosting local labor demand. Regional economic development strategies can take many forms. A common form is that of enterprise zones, which provide tax incentives and sometimes exemptions from regulations, with the intent of spurring business investment and growth. Evidence on the effectiveness of enterprise zones in improving employment opportunities is mixed, and uncertainty remains about which policies work, how they work, and for whom they work (e.g., Neumark and Kolko 2010; Neumark and Simpson 2015; and Ham et al. 2011). However, evidence on the effectiveness of regional economic development programs involving infrastructure expenditures and investments in higher education and research is more promising. Kline and Moretti (2014) find positive long-run effects of the Tennessee Valley Authority, which 206 | Chapter 6 administers an ambitious regional development plan, on manufacturing employment and income in the targeted region. The Economic Development Administration (EDA) also encourages economic development in regional clusters, with notable successes, including Milwaukee’s water cluster and St. Louis’s agricultural technology initiatives (Feldman 2022). An expansion of these efforts was included in the 2021 American Rescue Plan, which funded EDA’s Build Back Better Regional Challenge, ultimately awarding 21 coalitions the resources to develop emerging regional industry clusters. The recent CHIPS and Science Act also has an explicitly place-based approach to boosting innovation and commercial activity, and it authorizes the establishment of the new Regional Technology and Innovation Hub Program at EDA to reduce geographic disparities and promote the growth of technology clusters in underrepresented and promising regions. Other regional economic development strategies include workforce development programs that aim to train people in new and emerging industries and occupations. These programs provide skill upgrading and certification programs for displaced workers and others who would otherwise be unable to access (at reasonable costs) the training required for employment in these industries or occupations. Nonmonetary incentives and job quality. As noted previously in this chapter, the classic labor supply model frames work as an exchange of effort for monetary compensation. Implicit in this framing is the notion that money alone motivates participation in the labor market. But the evidence suggests that workers care about nonpecuniary aspects of work—such as the meaningfulness of the employer mission, social interaction, greater scheduling flexibilities, and self-direction (Cassar and Meier 2018; Nikolova and Cnossen 2020; Clark 2015). Preferences for nonpecuniary amenities vary across workers, with women and nonwhites tending to value job quality attributes more than white men (Katz, Congdon, and Shakesprere 2022). These preferences can shape lifetime earnings in significant ways. For example, Wiswall and Zafar (2017) find that a quarter of the early career wage gaps for women can be explained by a higher preference among women for jobs with greater flexibility and stability. Job attributes vary widely across the working population. A national survey of working conditions in the American workplace found that men without a college degree, women, and younger workers generally experience substantially worse working conditions. Specifically, they tend to have less control over their work schedule, experience more verbal abuse and harassment, and have greater exposure to safety hazards (Maestas et al. 2017). Data on preferences indicate that, unsurprisingly, workers prefer work to have more “good job” aspects and fewer “worse job” aspects. This evidence has led many to speculate that improvements in job quality that Supply Challenges in U.S. Labor Markets | 207 would improve worker welfare could also potentially increase labor market participation. Some aspects of job quality can be improved through policies such as mandated sick leave or changes to scheduling practices. But many job quality attributes will remain the outcome of business decisions made by employers. Improving workers’ bargaining power. As indicated previously in this chapter, stagnating wages and rising inequality are key drivers of declines in labor force participation. Though many of the factors responsible for demand declines are global, inequality has risen more in the United States than in other advanced economies. This is at least partially due to declining worker power, particularly declines in unionization (Grossman and Oberfield 2022; Stansbury and Summers 2020). Worker power enables workers to negotiate with employers for better pay, safe conditions, predictable working hours, and other aspects of the work environment. Unions have historically been an important force in increasing workers’ leverage. Despite the recent upswing in petitions for union elections, union density in the United States continues to decline, from about one-third of the private sector workforce in 1950 to just over 6 percent today. The consequences of unions’ decline for workers include lower wages (e.g., Card 1996), including for nonunionized workers in the same sector (Farber 2005). Union density may also be tied to trends in income inequality, with U.S. inequality rising as union density has fallen (Farber et al. 2021). In short, as unionization has fallen, workers’ incomes have stagnated relative to output growth. Globalization, technological change, and employer concentration are commonly cited as key factors driving declining unionization. However, many economists have pointed out that these factors do not fully explain why unionization in nontradable goods sectors has fallen at a similar rate, or why unionization is lower in the United States than in other Western countries (Levy and Temin 2007; Schmitt and Mitukiewicz 2012). More likely reasons for declining worker power are institutional changes within the United States—particularly the expansion of right-to-work States, greater employer opposition to organizing efforts, and decreased enforcement of labor laws. The Biden-Harris Administration supports the Protecting the Right to Organize Act, or PRO Act, which would help restore that stated policy of the National Labor Relations Act, to “encourag[e] the practice and procedure of collective bargaining and [protect] the exercise by workers of full freedom of association, self-organization, and designation of representatives of their own choosing, for the purpose of negotiating the terms and conditions of their employment or other mutual aid or protection” by making it easier for workers to unionize by preventing companies from holding mandatory antiunion meetings and by imposing penalties on employers that retaliate 208 | Chapter 6 against organizers (White House 2021). The Administration has also taken significant steps to improve workers’ leverage, appointing former union officials to the National Labor Relations Board, increasing funding to allow the Board to pursue its statutory remit, establishing a task force to promote labor organizing, and adding prevailing wage and apprenticeship requirements to the recent CHIPS and Science Act and Inflation Reduction Act. Conclusion The United States faces a large shortfall in its labor supply as it continues to recover from the COVID-19 pandemic. This shortfall is not merely a lingering effect of the pandemic but is also due to long-run demographic trends and declines in labor market participation by adults. Without increased immigration and/or efforts to draw more adults into the labor market, the labor supply is likely to be constrained for the foreseeable future. The shrinking share of adults in the workforce and the Nation’s aging population may have a negative impact on its living standards through slower economic growth. More proactive policies to increase the labor supply—such as higher public spending on childcare, increasing immigration, and improving workers’ bargaining power—are needed to counteract these demographic trends. Supply Challenges in U.S. Labor Markets | 209 Chapter 7 Competition in the Digital Economy: New Technologies, Old Economics Digital markets have become an integral part of Americans’ daily lives. Over 14 percent of retail shopping now happens digitally (U.S. Census Bureau 2022), and digital markets now account for more than $2 trillion in value (over 10 percent of gross domestic product) and employ 8 million workers in the American economy (Highfill and Surfield 2022). The economic forces operating in digital markets are not particularly new; however, when combined with the scale afforded by digital settings, the low costs of connecting with others, and the large amounts of data being collected, the economics of these markets lead to new implications for how these markets look, how they operate, how they make an impact on the economy and society, and how they should be regulated. Nearly all digital markets feature positive “network effects”—meaning that the value of a product or service increases as the number of users grows (i.e., as the “network” gets bigger)—so having fewer, larger service providers can benefit users. A social media website, for example, is of little value to its users if it has very few users; it is, in fact, more convenient to have all your friends accessible via the same website. In addition to network effects, digital settings enable a global scale and the unprecedented collection of data, which can all favor the rise of dominant firms. These forces can also act as barriers to entry, preventing new firms from challenging dominant ones. Healthy competition among many firms pushes companies to produce goods at their lowest possible cost, offer products and services at the best prices, provide better wages and working conditions, create new technologies, 211 and develop and sell new products that people want to buy. This, in turn, ensures that economic agents make the best use of society’s resources. In contrast, dominant firms with significant market power may use this power to increase prices, reduce quality, and lower output, making consumers and other market participants worse off. This is why regulations are necessary to ensure that the competitive process is protected and to maintain a level playing field for all market participants. This chapter reviews some of the potential economic benefits delivered by digital markets, such as lower search costs and increased variety. The chapter also explores other characteristics of digital markets that differentiate them from their offline counterparts, including the ability of firms to gather a huge variety and volume of data on users, potentially without their knowledge, either by running experiments or simply monitoring users’ behavior, and rapidly process these data to derive significant value. These data can be used to improve firms’ product offerings, which can benefit users, or for other purposes, such as personalized pricing, which may benefit firms but harm users. The chapter closes with a discussion of the regulation of digital markets. Regulators’ challenge is to deliver all the benefits of competition—such as innovation, privacy, and low prices—in a setting where economic factors may drive markets toward fewer competitors. As a result, regulators should seek to lower barriers to entry and also prevent a dominant firm from exploiting its power either in the same or a related market, or to engage in practices that harm consumers or other market participants in other ways. For regulators overseeing digital markets relative to offline ones, new areas of concern include the misuse of consumer data and collusion by pricing algorithms. Overall, digital markets present significant opportunities to benefit society if regulators, enforcers, and courts can adapt to the new digital landscape. 212 | Chapter 7 The Benefits of Digital Markets In this chapter, the term “digital markets” encompasses the interfaces that electronically bring together various agents for economic or social purposes. Although there is no unanimously accepted definition of digital markets or what goods and services they include, the chapter refers to these diverse interfaces—including app stores, operating systems, search engines, social media platforms, web browsers, and online marketplaces. Unlike many offline settings, where buyers and sellers typically transact directly with one another, most digital markets involve an intermediary that brings together different agents and facilitates their interactions. In addition, “marketplaces” include not only traditional marketplaces—where buyers sell tangible items to consumers, as would occur in offline markets—but also markets where different economic agents are being matched. For example, an online job search website would be classified as a “market,” as would a ride-sharing application on a mobile phone that connects drivers with riders. In many cases, users may value the additional convenience of having interactions facilitated digitally (Goldfarb and Tucker 2019). Digital markets have also provided other benefits to consumers by creating new forms of price competition and saving time from travel or searches for goods and services, among others. For example, one early study (Brynjolfsson and Smith 2000) found that Internet retailers’ prices were 9 to 16 percent lower and that they changed their prices by increments up to 100 times smaller as compared with traditional retailers, suggesting that they have lower costs for instituting price changes and that these savings are partially passed on to customers. However, other studies have produced more nuanced results, such as a more recent study (Cavallo 2017), which finds that online and offline prices are often identical among the largest firms. In e-commerce, digital markets allow for greatly increased product variety because there is much less of a physical inventory constraint when products are shipped directly to consumers. Digital markets also have benefits for businesses. They can potentially compete in markets that would otherwise be too costly to enter. The next sections further explore the value of these aspects of digital markets. Reducing Search Costs The seminal work of Stigler (1961) explores the value of lowering search costs. Digital markets theoretically enable perfect price comparisons across the universe of retailers of the same good at low cost, and also lower the acquisition costs of information. For example, digital marketplaces like eBay and Etsy are able to reduce search costs—such as the costs incurred to find a particular product or service, including the cost of the time spent looking—by bringing together and matching large numbers of buyers and Competition in the Digital Economy: New Technologies, Old Economics | 213 sellers that would otherwise spend a great deal of time searching for one another to transact a unique item. An early study in the digital era (Brown and Goolsbee 2002) found that the Internet led to lower prices for term life insurance. Other studies from the same period found that digital markets reduced prices for consumers, such as estimates of an average of 2 percent saved by customers of online car-buying referral services (Scott Morton, Zettelmeyer, and Silva-Risso 2001) and an average of 16 percent saved by consumers shopping for electronic products using an online price comparison tool (Baye, Morgan, and Scholten 2003). More recently, researchers have investigated the potential trade-offs between reducing search costs and increasing the potential for collusion; issues related to collusion are addressed later in the chapter. In theory, digital markets should be inherently more competitive, thanks to the low search costs and increased price transparency, all else being equal. However, one natural response by firms to combat this is to introduce obfuscation. Ellison and Ellison (2009) document that firms face a very high price sensitivity of consumers in online marketplaces that make price comparisons easy. As a result, sellers undertake price obfuscation behaviors, such as making product descriptions complicated so that comparisons are difficult, creating multiple versions of the same product, and attempting to “upsell” consumers who were drawn to an initial low price. Such behaviors have been documented in multiple government sources and findings, or engage in so-called drip pricing strategies (Blake et al. 2021; FTC 2017; CFPB 2022; White House 2016). Increased Variety Consumers have also benefited from increased access to variety in both products and services that has been enabled by digital markets. Brynjolfsson, Hu, and Smith (2003) estimate that the benefits to consumers attributable to increased product variety among online booksellers may be 7 and 10 times larger than those from increased competition and lower prices. Quan and Williams (2018) estimate that the value of the online footwear market is 5.8 percent greater than the traditional local retail market due to the increased variety available, and Gentzkow (2007) finds that a free online version of a newspaper in Washington was worth $0.35 per reader per day, or a total gain of about $52 million per year in 2021 dollars. One study also found the availability of online services meant that consumers in smaller, less densely populated places could be better connected to national markets, increasing their access to a larger variety of goods and services (Sinai and Waldfogel 2004). It is worth noting, however, that if a particular firm achieves dominance in a market, the variety offered becomes something that this firm can control. 214 | Chapter 7 “Free” Products and Services The set of products and services available in digital markets that appear to be “free” for consumers is large (e.g., Internet search engines, email, digital maps, music streaming, video streaming, price comparison tools, and online games). Research has shown that consumers value online tools like search engines and email services in thousands of dollars per year (Brynjolfsson et al. 2019). This phenomenon is not unique to digital markets; broadcast television and radio are free for those with a television set or radio, and some newspapers are offered for free. This apparently free access is often made possible by business models that depend on advertising revenue and collection of user data to subsidize consumer products and services. For example, figure 7-1 shows the exponential growth in advertising revenue for Google and Facebook, which enables them to offer a number of ad-supported products and services. A counterpoint to many “free” goods and services is that they could have negative externalities, meaning that there are external costs to society beyond the prices being paid for them. In other words, these products may not be free after all; instead, users are paying for them—for instance, by indirectly “selling” their data. The chapter elaborates on this dynamic in the next section. Given that so many products and services have zero monetary costs for consumers in digital markets and that these markets have become so large and pervasive, it is possible that U.S. current national accounts are missing Figure 7-1. Growth in Advertising Revenue by Digital Platform, 2002–21 Millions of 2021 dollars 250,000 200,000 150,000 100,000 50,000 - 2002 2004 2006 2008 2010 Google 2012 2014 2016 2018 2020 Facebook/Meta Sources: U.S. Securities and Exchange Commission, Bureau of Economic Analysis, and CEA calculations. Note: Google revenue includes advertising revenue across Google Search, YouTube, and Google Network members. Facebook/Meta revenue includes all advertising revenue. Nominal values are adjusted by the U.S. Personal Consumption Expenditures Price Index (chain). Competition in the Digital Economy: New Technologies, Old Economics | 215 much of the value that is created in these markets. One paper proposes a way to account for this with a new measure of gross domestic product, called “GDP-B” (Brynjolfsson et al. 2019). Many have also argued that some of the innovations in digital markets have had unintended or negative side effects on society more generally. Box 7-1 explores research on the broader societal implications of digital markets. Box 7-1. The Societal Implications of Digital Markets Many digital services serve not only economic purposes but also important social and political ones. As Americans spend more time online, these services are becoming an important conduit for learning and sharing information about contemporary events and social movements, both domestically (Suh, Vasi, and Chang 2017; DeLuca, Lawson, and Sun 2012; Carney 2016; Mundt, Ross, and Burnett 2018) and internationally (Gorodnichenko, Pham, and Talavera 2021; Aday et al. 2013). Online services, including social media platforms, also play an increasingly large role in political campaigns and advertising, as evidenced by the growing amount that politicians spend on digital advertising (Williams and Gulati 2017; Barrett 2021). This increase in the political information circulating online has influenced how Americans engage in politics. For instance, being exposed to online political information like social media advertisements has changed how people express their beliefs, including through their voting behavior (Beknazar-Yuzkashev and Stalinski 2022; DiGrazia et al. 2013). In addition, these effects often extend across networks of friends and social contacts (Bond et al. 2012; Jones et al. 2017). Social media platforms may exacerbate political polarization (Allcott et al. 2020). One study found that exposure to Twitter bots disseminating opposing views reinforced preexisting political positions (Bail et al. 2018). Levy (2021) conducted an experiment showing that social media algorithms limited exposure to news outlets with opposing views, increasing polarization. Conversely, other studies have suggested that the role of social media platforms in spurring political polarization is limited (Prior 2013; Fiorina and Abrams 2008; Boxell, Gentzkow, and Shapiro 2017). Racism, sexism, and discrimination also exist online, and in some cases, this can escalate to more hateful content and conduct. In an experiment conducted on eBay, Ayres, Banaji, and Jolls (2015) found evidence of racial discrimination, with Black sellers making less than white sellers, despite selling the same product: baseball cards. Similar results were found by Doleac and Stein (2013). Expanded broadband Internet access has also been associated with a rise in hate crimes (Chan, Ghose, and Seamans 2016), as has reliance on social media and support 216 | Chapter 7 for Islamophobic policies (Lajevardi, Oskooii, and Walker 2022). One particularly salient example involved Microsoft, which launched an artificial-intelligence-powered Twitter bot (automated online social media accounts are known as “bots”) named “Tay” in 2016 that was intended to learn as it interacted with users. The bot lasted one day before it was taken down for tweeting racist, misogynistic, and transphobic content (Victor 2016). A similar fate befell a South Korean chatbot after it began using homophobic slurs (McCurry 2021). Another concern involving online services is their ability to easily spread misleading or factually incorrect information. For example, one study found that fake news stories were widely circulated during the 2016 presidential election, with inaccurate stories favoring at least one of the two candidates being shared roughly 38 million times (Allcott and Gentzkow 2017). Bots were also found to play a role in spreading and amplifying misinformation during the COVID-19 pandemic (Himelein-Wachowiak et al. 2021; Xu and Sasahara 2022; Ayers et al. 2021), which became factors in COVID-19 vaccine hesitancy (Garett and Young 2021; Neely et al. 2022; Pierri et al. 2022). Finally, as social media plays a more central role in society, significant concerns have been raised about their effect on mental health, particularly among younger users. In 2021, the Surgeon General released a report titled “Protecting Youth Mental Health” (U.S. Surgeon General’s Advisory 2021) that specifically cited the dangers that arise when social media companies “[focus] on maximizing time spent, not time well spent.” The report called for additional research on the specific risks and harms presented by social media platforms. How Is Competition Different in Digital Markets? Economists are interested in encouraging competition because competition typically results in markets that deliver consumers and other market participants the best choices, highest quality, and lowest prices, among other benefits. When many firms are offering similar products to consumers, consumers will choose to buy at the lowest prices, which gives firms an incentive to lower their prices. It also gives an incentive for firms to improve the quality of the product they offer by innovating, as this may be a means to attract consumers. If instead there is a single firm offering a product, that firm is likely able to increase its price or diminish its quality without losing many of its customers, as their customers do not have any good alternatives. This is why economists typically view a market dominated by a few large firms as unlikely to be good for consumers or other market participants. Competition in the Digital Economy: New Technologies, Old Economics | 217 This section introduces the main characteristics of digital markets and discusses how they can lead to markets becoming dominated by only a few large firms. None of these characteristics are unique to digital markets; but, as argued later in this chapter, network effects in combination with vast amounts of data and the unlimited scale possible in digital settings can result in concentrated markets. Big Data In digital markets, huge amounts of data are generated as a by-product of activity. While a traditional retailer can observe what products you decided to purchase, digital retailers observe what you searched for, what you were shown, and what you ultimately decided to buy. Further, given that online retailers control search results and site layout for each individual separately, they are able to use these data to personalize your experience in a way that traditional retailers could never do. Because of this, users’ data can have increasing returns to scale and scope (Bergemann and Bonatti 2019) especially at smaller initial scales. The result is that data can serve as a barrier to entry for new firms that reduces competition. In addition, the flexibility of the digital setting makes the process of conducting experiments much easier by greatly lowering the cost and increasing the scale at which firms can run experiments (e.g., Dubé and Misra 2023). The data gathered from experiments can be used to further improve product quality and the user experience but may also be used to set prices, manipulate behavior, or to pursue price discrimination strategies that ultimately lead to consumers being worse off. This research raises important questions about how consumer data are gathered and used, how technology may lead to consumer harm in some settings, and whether this suggests a role for regulation. Related to the previous discussion of “free” products, users are often paying for services with their data as the “price” is the associated loss of privacy without further compensation. In fact, some products and services exist solely for the purpose of collecting valuable and sensitive user data. These data may be used in ways of which users are unaware; they may be used for targeted behavioral advertising, personalized pricing, or sold to firms known as “data brokers,” which aggregate user data from multiple sources to sell as a product. Box 7-2 explores the types of information collected and sold by data brokers. The existence of data brokers could be negative for consumers, if their data are used in inappropriate ways, or possibly positive for consumers, if data are a barrier to entry and data brokers enable more firms to enter the market. The Federal Trade Commission (FTC) called attention to the data broker industry as early as 2014 with a report calling for greater transparency (FTC 2014). 218 | Chapter 7 Box 7-2. Consumer Data as a Business Model At the nexus of big data are companies known as data brokers, which serve two primary functions: acquiring data and monetizing data (Crain 2018; Gu, Madio, and Reggiani 2021). These firms compile data from a variety of sources, including through public government records or through cooperative agreements, whereby a data broker and another entity like a retailer mutually share their records. Alternatively, brokers can purchase or license consumer data from retailers, banks, brokerages, and other data brokers (U.S. Senate 2013; FTC 2014). Using a combination of information gathered and inferences made based on these data, brokers assemble profiles and segments of consumers to predict how they might behave; for instance, their propensity to purchase certain products or services (FTC 2014; Mishra 2021). Although Americans may be aware that their data are being collected to be resold, theoretical and empirical studies have suggested that users might be unaware of the scale or degree to which they are being monitored (Crain 2018; Choi, Jeon, and Kim 2019; Acquisti, Taylor, and Wagman 2016). In fact, almost every American has had their data collected by one, and likely many, of the major brokers, given that multiple brokers have information on nearly every American. For example, by 2014, one broker, Acxiom, had more than 3,000 data points on nearly every U.S. consumer and information on 700 million people globally (FTC 2014). Others had information on 99.99 percent of all U.S. properties or payroll data from 1.4 million businesses (Sherman 2021). One data set used for marketing purposes had over 75,000 elements, Figure 7-i. How Data Brokers Aggregate Data from Government, Commercial, and Publicly Available Sources to Build In-Depth Profiles of Consumers Identifiers Sensitive information Demographics Name, address, geolocation, contact information Social Security number, driver’s license number, birthdate, birthdates of family members Religion, language, marital status, education level, veteran in household, foreign language household, single parent status Court and public record Financials Bankruptcies, criminal offenses, judgments, voter registration, party identification Owns stocks and bonds, investment interests, credit history, life insurance, net worth indicator, types of credit cards, tax return transcripts, holder of gold or platinum card General interests Apparel preferences, gambling, life events (e.g., expecting parent), magazine subscriptions, political leanings, preferred music/movie genres, membership club, activism Social media Purchase behavior Health Buying channel, holiday gifts, type of entertainment purchased, average days between orders, dollars spent, self-help book purchases, Internet shopper Over-the-counter drug purchases, tobacco usage, propensity to order prescriptions online, health/disability insurance, brand name preferences, allergy sufferer, corrective lenses users Previous purchases, “heavy Facebook user,” uploaded pictures, social media accounts, usage Home and neighborhood Type of housing, home equity, home loan amount and interest rate, move-in date, neighborhood crime rates, presence of home pool Sources: Data from FTC (2014); CEA compilation. Competition in the Digital Economy: New Technologies, Old Economics | 219 2/7/2023 including markers for whether someone was a whiskey drinker, had life insurance, enjoyed romance novels, or used yeast infection products (U.S. Senate 2013). In some cases, these data sets can also identify individuals as financially vulnerable. For example, some tags that might be associated with a profile include “rural and barely making it,” “tough start: young single parents,” and “zero mobility” (U.S. Senate 2013). Figure 7-i provides examples of the different types of data that a broker might collect on (or infer about) a single individual to build out a profile that it may sell to its clients. In August 2022, the FTC filed a lawsuit against one of these data brokers, Kochava Inc., for selling individuals’ precise geolocation and movement data, including “to and from sensitive locations . . . associated with medical care, reproductive health, religious worship, mental health,” and shelters for at-risk populations (FTC 2022). According to the lawsuit, Kochava claimed that on average, it was “observing more than 90 daily transactions per device.” The FTC alleged that Kochava’s clients who purchased the data would be able to identify or infer an individual’s identity (based on their nighttime location) as well as whether they visited sensitive locations, such as a reproductive health clinic, a place of worship, or a domestic violence shelter. Network Effects Network effects refer to any situation where the value of a product or service to an economic agent depends on the number of users (i.e., the size of the network) engaging with it. For example, the value of a messaging app depends on the number of users it has. Or the value of an e-commerce website for buyers depends on the number of sellers on the website, and vice versa. In many markets with network effects, the principal economic benefit comes from interactions between different types of participants (Rochet and Tirole 2003). Research has demonstrated the importance of network effects in many digital and traditional markets (Gandal 1994, 1995; Saloner and Shepard 1995; Rysman 2004); and with the proliferation of digital markets, network effects have become increasingly salient. A central feature of digital markets for determining competitive outcomes is the strength of network effects. Network effects can be categorized in two ways: direct and indirect. Direct network effects are benefits or costs derived from the total number of users that belong to the network, and the benefit or cost to a user increases with the number of other users. Take, for instance, a video-conferencing service. There is little incentive for users to join if there are few other users; but as the user base grows, the service becomes more and more appealing to 220 | Chapter 7 consumers. This is an example of a positive network effect, which is common to social media and instant messaging, among others. In contrast, congestion is a common form of negative network effect in telecommunications networks. Cellular data networks suffer from reduced speeds when a large number of users are accessing the network simultaneously, for instance. Indirect network effects occur when groups of different users interact and a given user benefits (or suffers) from having more users on the service from the other group(s). This situation exists for services such as e-commerce marketplaces, app stores, job-matching services, and food delivery services. For example, if a certain job-posting website has the most applicants looking for jobs, employers will find that site most appealing for posting jobs. Similarly, applicants will be more likely to look for openings on the website that has the most employers posting jobs on it. This creates a reinforcing cycle of more job applicants looking for jobs and more employers posting job openings. Another example would be marketplaces—either digital or brick-and-mortar—where more sellers attract more buyers, and vice versa. This dynamic is illustrated using a neighborhood farmers’ market in figure 7-2. A farmers’ market exhibits indirect network effects because the benefits for buyers and sellers increase with the number of agents of the other type present. As the farmers’ market attracts more sellers offering more varieties, the value of going to the market increases among potential buyers. And because more buyers are circulating in the market, the value of going to the market for potential sellers of additional goods increases. Of course, if the farmers’ market became too crowded, additional buyers and sellers would start to create negative congestion effects. Digital markets do not face this physical space constraint and therefore can continue to grow as more buyers and sellers enter the market. Figure 7-2. Network Effects Are Present in Many Markets—Not Just Online Sellers More buyers attract more sellers Farmers’ Market More sellers attract more buyers Buyers Sources: Eggs, honey, bread, and basket icons from Freepik via flaticon.com; face icons from Adobe Stock images. Competition in the Digital Economy: New Technologies, Old Economics | 221 2/7 Network effects have been considered a potential source of market power—the ability to raise prices without losing many customers—since before the rise of digital markets. In general, the presence of network effects constitutes a barrier to entry that raises the costs for new competitors to enter the market. If a new firm wanted to start a rival food delivery app to compete with an established firm, the new firm would be at a tremendous disadvantage because consumers and restaurants would likely see more value in the established firm’s network than in a start-up with a small network. Caillaud and Jullien (2003) describe how network effects create a chicken-and-egg problem that can hinder competition. In order for start-up competitors to attract buyers to a new e-commerce service and away from a Box 7-3. Glossary for Describing Digital Markets two-sided market network effects multi-homing tipping-point market 222 | Chapter 7 A two-sided market is a market where a firm enables interactions (i.e., acts as an intermediary or platform), bringing together two sets of parties (e.g., buyers and sellers) to transact and operate. For example, a ride-sharing service operates in a two-sided market by connecting riders and drivers. Network effects refer to phenomena where the value of a product or service increases or decreases as the number of users increases or decreases. For example, as more people sign up for a messaging service, it becomes a “better” service compared with a messaging service that has few users. Using more than one competing service provider is referred to as multi-homing. For example, users may switch between two different ride-sharing services to take advantage of different prices or a shorter waiting time. A tipping-point market is a “winner-takeall” market, where consumers flock to one or a few firms as opposed to patronizing many firms. For example, the market of social media platforms often “tips” in favor of dominant social media platforms with many users (as opposed to numerous platforms with few users). more established one, the competitor needs many sellers; but to attract sellers, they need many buyers. This dynamic can inhibit competition and can make a market susceptible to the phenomenon known as tipping. A tipping point is generally defined as a critical juncture beyond which a significant and potentially unstoppable change takes place. The application of tipping points to the economics of firms that bring together two different types of economic agents to intermediate their interaction—these markets are referred to as “two-sided markets”—goes back to Fudenberg and Ellison (2003), who identified the role of what we now label as network effects in creating the conditions for dominant firms to emerge. These markets often “tip” in favor of the leading firms, meaning that one or two firms drive out their competitors and dominate the market. Box 7-3 is a glossary of the terms used to describe digital markets. Multi-Homing Another pivotal factor of digital markets for determining competitive outcomes is the degree to which one type of user elects to use only one service among a group of competitors, which is referred to as “single-homing.” In other cases, users may be willing to use multiple competing services, or “multi-home,” such as when a consumer pulls up two different ride-sharing applications on their phone to compare prices. All else being equal, if users are willing to use multiple, competing services, then these services are less able to raise prices or set terms that are unfavorable to users because they are more willing to take their business elsewhere (Teh et al., forthcoming). When one side of the market multi-homes and the other single-homes, competition between services for users that only use one will be fierce (Armstrong 2006), because the service is the exclusive means by which the multi-homing side can reach those single-homing users, allowing higher prices to be charged on the multi-homing side (Jullien, Pavan, and Rysman 2021). Hence, users’ willingness to use multiple, competing services can limit market power, giving the service an incentive to hinder users from multi-homing (Scott Morton et al. 2021). This can be accomplished through the use of switching costs—that is, costs that users would incur if they tried to transfer their business to a competitor (Scott Morton et al. 2019). Firms can impose switching costs through exclusive contracts or agreements, loyalty programs, termination fees, or a lack of data portability. When Do Markets Tip? Tipping occurs more easily in digital markets than offline markets due to their combination of positive network effects, valuable data, and a potentially massive scale. Whether a market will tip depends, however, on the willingness of users to switch between different services for the provision Competition in the Digital Economy: New Technologies, Old Economics | 223 of goods and services (i.e., whether they multi-home). When positive network effects exist and consumers have a high propensity to use a single service, digital firms can often leverage network effects to entrench their market power. For instance, a social media platform may be incentivized to limit the ability of nonusers to connect and share content with users. For a consumer, this means that if he or she quits the platform, it would essentially sever the connections the user has made with other users of the application. This can keep the consumer locked in to a service, even if they have other concerns—for instance, regarding their privacy. Ultimately, as users are incentivized to join the largest network(s), the market can tip in favor of one or more dominant firms (Kades and Scott Morton 2020). Once a market has tipped in favor of a dominant firm, potential entrants that might want to offer innovative new features or charge lower fees would face a very uphill climb in establishing themselves. That is, the benefits of competition we would normally expect will not be realized. A dominant firm also has an incentive to acquire any potential entrant to prevent competition in the market. Dominant firms may further exploit their dominance in a market to give themselves an advantage in other markets, harming competition. Four factors are credited with preventing tipping in a two-sided market: product differentiation, multi-homing, interoperability, and congestion (Jullien, Pavan, and Rysman 2021). Product differentiation. If a competitor offers a higher-quality experience or other differentiated features beyond its role as an intermediary, it can draw enough customers who find these services valuable to enable it to survive. One example of how firms attempt to differentiate is to have superior recommendation algorithms so that they are better able to match consumers with products. Another is how firms make the process of transacting as simple as possible, thus requiring less effort on behalf of buyers and sellers. Multi-homing. When users of a service are willing to also use competing services, neither service has much market power over those users. Therefore, neither is likely to achieve dominance. Firms know this, and thus they actively engage in behavior that makes it more difficult for users to also use a competing service. Their tactics include things like having exclusive content, for example, among competing streaming services. If all videostreaming services offered the same content, consumers would likely choose the one with the lowest price; but once a streaming service has exclusive content that consumers demand, consumers will not be as willing to switch to other services. Another approach might be to have a loyalty program that makes users less willing to use other services. Interoperability. Making services “interoperable”—able to exchange data between themselves—weakens the network effects of either individual service. With interoperability, network effects no longer exist at the firm level; rather, they would aggregate at the market level (Kades and Scott 224 | Chapter 7 Morton 2020). Take the example of short-message/messaging service (SMS) text messaging. This clearly has a positive network effect, given that the value of SMS text messaging increases as more people have mobile phones that can send and receive these messages. This network effect is not firm-specific because the SMS text network is interoperable between cellular carriers and telephone operating systems. In contrast, an app like iMessage by Apple is only available on Apple devices and has no interoperability with Android messaging apps, so the network effect is firm-specific to Apple. By broadening network effects from only accruing at the firm level to covering the entire market, interoperability directly challenges the mechanism that can entrench the market power of dominant firms and spurs competition in the market. Open standards that allow interoperability between different firms’ products—for example, the universal serial bus standard—are one way to achieve network effects at the market level and encourage robust competition. Congestion. Finally, congestion—a negative network effect—tends to make the growth of some services beyond a certain size untenable due to the degradation of services as users are added to the network. In most digital markets, this is of less concern as the scale of most services is limitless before encountering congestion; however, as a social network grows, it may be subject to greater problems of fraud, cybersecurity attacks, and content moderation. Of these factors, firms operating in digital markets have the ability to control their degree of product differentiation and interoperability as well as to influence the tendency toward multi-homing (Athey and Scott Morton 2022). Regulators of these digital markets want to bring the benefits of competition to the economy and protect consumers either by acting to prevent markets from tipping in the first place or taking action in markets that have tipped. The Role of Law and Regulation in the Digital Market Economists often evaluate the benefits and costs of an action or innovation in terms of its value to society as a whole. When represented mathematically, this is called the “social welfare function.” This function includes the benefits and costs for consumers, producers, and the government as well as any benefits or costs for society stemming from inefficiency or externalities. These benefits and costs are not only measured in terms of prices and quantities for the economy’s goods and services but can also include effects on less tangible things like innovation, inequality, and well-being. All these concerns may inform the priorities of regulators and law enforcement in digital markets; this section focuses on the direct implications of the economic model underlying competition in digital markets. Competition in the Digital Economy: New Technologies, Old Economics | 225 U.S. antitrust laws seek to promote competition and protect market participants, including workers, consumers, sellers, and buyers from anticompetitive mergers and business practices. The enforcement of these laws is conducted by the U.S. Department of Justice (DOJ) and the FTC as well as by other Federal and State agencies. In addition, agencies such as the Federal Communications Commission and the FTC also have relevant regulatory (i.e., rulemaking) authority. The Biden-Harris Administration’s competition policy is overseen by the White House Competition Council, which was established by the President’s “Executive Order to Promote Competition in the American Economy,” which was issued on July 9, 2021 (White House 2021). The antitrust agencies monitor the conduct of firms, with a specific focus on mergers, monopolization, unfair methods of competition, and collusion. Before the 1980s, the antitrust agencies focused heavily on mergers and monopolization activity because firms that control a significant share of the market (or potentially all of it, in the case of a monopoly) generally have a greater ability to raise prices and reduce quantities or engage in other anticompetitive practices in an effort to maximize their profits. Though the focus of antitrust agencies shifted away from monopolization activity for a time, enforcement against monopolies has seen renewed attention in the past several years. The FTC also has authority to deter unfair or deceptive acts and privacy and data security degradations, which can intersect with competition oversight. A recent example of such practices is the $5 billion fine imposed on Facebook in 2019 for misleading consumers about their privacy on the platform (FTC 2019). The DOJ and FTC are also guided in their enforcement activities by a body of case law that has been developed over the last century. Much of this case law has focused on regulating mergers, particularly mergers between competitors selling the same or very similar products (“horizontal mergers”), with the aim of balancing the potential efficiency gains from the combination passed on to consumers against the risks posed by the loss of competition between the merging firms, such as higher prices or reduced innovation. As discussed above, digital markets, in combination with network effects, are predisposed to become highly concentrated and be controlled by a few large firms. Though concentration alone is neither procompetitive nor anticompetitive, highly concentrated markets are more susceptible to anticompetitive practices. Existing competition laws and regulations written before the emergence of digital markets may not have fully anticipated how these markets would function and may therefore be insufficient to ensure robust competition and protect consumers and other market participants. 226 | Chapter 7 Network Effects Create a Competitive Moat If network effects at the firm level are sufficiently strong, having larger firms may be better for customers. For example, as noted above, messaging services may be more useful when they have more users. Competition among many small, incompatible messaging services is unlikely to benefit consumers, given the fixed costs and returns to scale. And yet, left to its own devices, a dominant messaging service would likely raise prices above a competitive level, provide lower quality, potentially innovate less, or do all of the above. This would be seen as a market failure, which should be addressed via regulation, nationalization, or antitrust enforcement (Joskow and Rose 1989; Joskow 2007; Smiley and Greene 1983). Further, network effects have long been recognized as potentially becoming an “economic moat”—a protective barrier that guards a profitable business (the “castle”)—in that they lead to customers being locked in to certain products, making mass migration to a new product unlikely unless accompanied by a simultaneous technological advance somewhere else in the ecosystem (Bresnahan 2002). New entrants are less likely to be successful when facing an entrenched firm with network effects or the benefits of scale, eliminating some benefits of competition. The messaging service example is illustrative, in that a potential solution to bringing back the benefits of competition in the presence of network effects may be interoperability, although interoperability alone may not suffice to fully restore competition. Interoperability expands the benefits of network effects from the firm level to the market level. Requiring that competing services interoperate is one remedy that can dissolve some of the anticompetitive outcomes of network effects because all competitors would share the same network effect. Thus, interoperability would mean that both old and new services would need to compete on other dimensions like quality to keep users on their services. Another related tool is data portability, the idea that consumers can take, or “port,” their data to a different service. This reduces the switching cost created by network effects. For example, imagine that a user wants to switch from one music streaming service to another. One barrier for the consumer would be having to give up their playlists and liked songs. Data portability would allow the user to download and port these playlists to another streaming service, thereby reducing the barrier to switching. Both data portability and interoperability can make it more appealing for a potential entrant to introduce a competing service and increase the likelihood of new innovations being able to succeed. Competition in the Digital Economy: New Technologies, Old Economics | 227 The Challenge of Preserving Competition in Digital Markets Traditional competition policy analysis often focuses on estimating changes in prices to assess effects on consumers. However, this approach faces new challenges in digital markets arising from several sources—notably, the provision of free goods and services, and the cross-subsidization in markets with indirect network effects. For “free” goods with no monetary price, in a more competitive market, the true price could be negative (e.g., consumers could be paid to watch ads or fill out surveys with their personal data), or service could be better. As a result, demonstrating anticompetitive harm may require alternative measures rather than prices. Research into the effects of mergers in digital markets demonstrates heightened complexity in the expression of competitive effects. Chandra and Collard-Wexler (2009) empirically show that mergers of firms in twosided markets may not lead to higher prices on either side of the market in an application to the Canadian newspaper industry; and Song (2021) shows that mergers between firms in two-sided markets can lead to either higher or lower prices after the merger, but that even agents that experience higher prices may be better off due to increased network effects. Another study, of the merger of two platforms for pet-sitting services (Farronato, Fong, and Fradkin, forthcoming), found that on average consumers were not substantially better off with one platform than two competing ones because the network effects were not large enough to balance the losses due to higher prices and reduced variety after the acquired platform was shut down. In markets with indirect network effects, policies intended to increase competition may need to account for how an intervention on one side will affect the well-being and behavior on both sides of the market because pricing is linked to the costs and price sensitivity of users on both sides (Evans 2003; Wright 2004). These challenges are exacerbated by the scale of the task of protecting competition in digital markets. For example, large tech companies are highly acquisitive. Figure 7-3 shows that the volume and value of mergers and acquisitions among tech firms is large, a trend that has drawn the attention of antitrust authorities. Reviewing these acquisitions for anticompetitive harm requires significant resources due to the complexity of the markets, the sophistication of the firms, and the need to look beyond the impact on retail prices alone. Finally, digital markets can be highly dynamic, appearing and evolving rapidly. This can limit the ability of regulators to use current and historical data to analyze market behavior. In addition, it can be quite difficult for regulators to identify nascent competitors and potential entrants in assessing proposed mergers. Further, when antitrust authorities do identify such anticompetitive mergers (DOJ 2020), the lack of prices for the potential entrant 228 | Chapter 7 Figure 7-3. Completed Acquisitions by Large Tech Firms LinkedIn Microsoft Motorola Alphabet Beats Apple WhatsApp Meta Whole Foods Amazon 2009 2011 2013 2015 2017 2019 2021 2023 Sources: Bloomberg; Crunchbase; Mergr; Alphabet; Meta; Amazon; Google; Microsoft; TechCrunch. Note: The dark blue circle radius is proportional to the deal size, and the small black dots indicate acquisitions with no publicly available price. Deals include completed mergers -and-acquisitions deals by the specified firm as of the fall of 2022. or lack of significant market share for the nascent competitor are again problematic for traditional competition analysis, since anticompetitive harm has often been demonstrated using economic models showing that mergers would lead to higher prices. These challenges underscore the need for further research and approaches to evaluating competitive effects in complex digital environments. This is work the antitrust agencies are well positioned to do, in concert with academics and other stakeholders. Preventing the Extension of Dominance into Adjacent Markets Digital markets with network effects, big data, and a global scale have tended to coalesce on a small number of dominant firms. An obvious concern is that firms could exploit their dominance in one market to gain market power in or dominate adjacent markets. This type of conduct could be illegal under Section 2 of the Sherman Antitrust Act. Today, there are many examples of digital markets where a dominant firm also competes in an adjacent market: Google and Apple operate app stores, in which their own apps compete with other apps; Amazon operates an e-commerce marketplace, where its Amazon Basics brand competes directly with those from other firms; and Microsoft operates a video game marketplace, where they also compete as a video game developer. In these situations, one concern is that the dominant firm could have an unfair advantage for its competitive products, known as “self-preferencing.” For example, Apple was alleged to give its own apps higher priority when a person searched its app store (Mickle 2019). Competition in the Digital Economy: New Technologies, Old Economics | 229 If dominant firms exploit their dominance to give their own offerings an advantage, consumers may not get the full benefits of competition. One approach a regulator or legislature might take to improving the functioning of certain markets is to prohibit self-preferencing and similar practices. However, such a ban could be challenging to enforce, as a regulator would need to show that self-preferencing is intentionally built into a service instead of just occurring organically because, for example, the owner’s products have received better reviews. A related concern about marketplace operators that compete on their own marketplaces is the issue of how competitors’ data are used. Marketplace operators are able to gather extensive data on competitors’ products and customers, and they may have an incentive to use those data strategically, either in the design of their own competing products or in their pricing or promotional strategies. They could also intentionally limit what data from the site are available to competitors. Any of these actions would further put competing firms at a competitive disadvantage. A regulator may want to prohibit the use of competitors’ data or insist on the fair treatment of marketplace data for all firms in order to reset the competitive landscape, although enforcement of such a regulation could be a challenge requiring significant monitoring and oversight. The ability of a dominant firm to extend its dominance into adjacent markets is a threat to competition. Society may miss out on certain innovative products if entrepreneurs realize that their product may just get copied by a dominant marketplace operator and, therefore, decide against investing in developing it. In addition, the better product may not “win” on an uneven playing field. Regulators can address this market failure by clarifying who owns what rights to the data collected and leveling the playing field for all firms in online markets. An overview of some of the approaches that regulators are taking, both internationally and in the U.S., is presented in box 7-4. Preventing the Misuse of Consumer Data Assessing the competitive effects of data usage and policies can be difficult. Research suggests that when data can be used to reduce a firm’s exposure to risk, it can lead to increased innovation or efficiencies, potentially driving down prices (Eeckhout and Veldkamp 2022; Kirpalani and Philippon 2020; Competition Bureau Canada 2017). However, data can also become a barrier to entry that insulates firms from competition. Prüfer and Schottmüller (2022) show that under certain conditions, a data advantage can lead to market tipping. In addition, the ability of firms to collect massive amounts of data about individuals raises clear concerns about privacy and also about data protection, as leaks of massive data sets could expose individuals to 230 | Chapter 7 Box 7-4. International and Subnational Efforts at Regulatory Reform Numerous antitrust and consumer protection efforts are occurring both internationally and in the United States at the State level. For instance, the European Commission has proposed a pair of new laws focused on regulating digital markets—the Digital Markets Act (DMA) and the Digital Services Act (DSA) (Council of the European Union 2022). The DMA aims to promote competition by establishing rules about the types of conduct in which large “gatekeeper” firms can engage (European Parliament and European Commission 2022). In order to be designated a “gatekeeper,” in each of the last three financial years, a firm must have had at least 10,000 annual business users established in the European Union, 45 million monthly users established or located in the European Union, and €7.5 billion (about $7.4 billion in 2021 dollars) in annual revenue across the EU or a €75 billion market capitalization (about $74.4 billion in 2021 dollars). It must also provide the same “core platform” services—for example, web browsing, messaging, and social media—in at least three EU member states. To foster competition between firms and reduce barriers to entry, the DMA lays out requirements by which gatekeepers must abide. For example, gatekeepers must allow for data portability and must make messaging services interoperable. They must also be more transparent about their mergers and acquisitions and must allow users to uninstall predownloaded software on the gatekeeper’s operating system. At the same time, the DMA also restricts gatekeepers from engaging in certain business practices, like preferencing their own products over those of competitors on their platform (“self-preferencing”) or combining users’ personal data across the gatekeeper’s different core platform services. The DMA also prohibits gatekeeper firms from engaging in certain price-setting practices and creating operating terms that discriminate against certain businesses and app developers. For instance, the DMA makes it illegal for gatekeepers to make business users sign agreements to not offer better terms on other platforms (known as most-favored-nation clauses). These agreements have the potential to dampen competition, raise prices and fees, and reduce entry by competitors offering lower-priced alternatives (Boik and Corts 2016; Baker and Chevalier 2013; Wang and Wright, forthcoming). While the DMA primarily focuses on regulating the conduct of a few very large firms in an effort to promote competition, the DSA addresses the wider societal implications associated with digital markets and establishes regulations focused on filtering illegal content and protecting the fundamental rights of consumers online (European Parliament and Council of the European Union 2022). For example, the DSA requires that firms inform users about how and why advertisements are being targeted to them. It also bans firms from using personal data Competition in the Digital Economy: New Technologies, Old Economics | 231 to target advertisements if the firm is reasonably aware that the user is a minor. In addition, the DSA includes numerous other provisions, such as requiring online intermediaries to moderate illegal content (including hate speech), while giving regulators wide-ranging powers to request access to very large online platforms’ business practices and algorithms. In addition to new laws being passed abroad, certain States of the United States are also passing new regulations targeting digital markets, with a specific focus on consumers’ data rights. As of late 2022, five States—California, Colorado, Connecticut, Utah, and Virginia—had passed comprehensive State-level regulations on consumer data and privacy rights in digital markets (NCSL 2022; Connecticut 2022). For example, Connecticut passed a law in 2022 that gave consumers more control over how their data could be collected, used, or accessed (Connecticut 2022). Once the law takes effect, in July 2023, consumers will have the right to access, correct, and delete records of their personal data. Connecticut residents will also be able to opt out of having their personal data sold or used for targeted advertising. identity theft or other financial harm (Ichihashi 2020; Chapman and Bodoni 2022; O’Sullivan 2021). For all these concerns about the misuse of data and protection of privacy, a practical intervention is to regulate how data can be collected, used, shared, and stored. The authors of one study explore mediated data sharing to reduce the correlation between users’ data and thus to mitigate externalities that create excessive data sharing (Acemoglu et al. 2022). They propose sharing data with a third party that would transform their data to remove correlation with other users before sharing it with services requested by the user. Other policies that might impose fewer costs include “right-tobe-forgotten” provisions, which create time limits on data retention (Chiou and Tucker 2017). Monitoring Pricing Algorithms and Collusion Concerns have been raised that pricing algorithms could facilitate explicit price collusion by reducing uncertainty about consumer demand. O’Connor and Wilson (2021) suggest that this improved forecasting could either lead to lower prices and increased consumer benefits or enhance the ability of firms to support collusive arrangements. Other studies of retail gasoline markets have raised concerns about online price disclosure and experimentation facilitating the coordination of prices across firms (Luco 2019; Byrne and de Roos 2019). A simple example would be the use of posted prices 232 | Chapter 7 Box 7-5. Artificial Intelligence and Digital Markets A fundamental aspect of the operation of digital markets is using artificial intelligence (AI) to translate the data available to firms into actionable predictions, recommendations, and decisions (OECD 2019). Many of the features that make digital markets so appealing to users are powered by machine learning and other algorithmic tools (Brown 2021). Indeed, many of the key features of digital markets—efficient matching, low search costs, an unmatched variety of products, and personalization of prices—are made possible by a combination of data availability and the application of AI techniques like neural networks, natural language processing, or other forms of machine learning. Though the use of these algorithms can improve the experience of users and increase firms’ profitability, there are ongoing concerns that they will displace workers; introduce racial or other sorts of bias into these systems; make digital marketplaces even harder to regulate; and meaningfully impact individuals’ or communities’ rights, opportunities, or access to critical resources or services. For ride-sharing companies like Uber and Lyft, machine learning is the key to their ability to set prices to assure that there are enough drivers on the road to meet customer demand (Liu et al. 2022). AI also allows social media platforms to optimize their content. TikTok relies on its algorithm’s ability to use its wealth of data to select content that will keep users engaged longer (Smith 2021; Wall Street Journal 2021). Further, the ability of firms like Amazon to have the products that a customer is looking for in stock without having to maintain a surplus inventory is driven by AI-based predictions about demand at any given point in the future (Amazon 2021). All these features of digital markets are made possible because of the combination of data and algorithms. However, the reach of AI in digital markets raises concerns that there could be a wave of automation of jobs (Sisson 2022). Even in cases where AI augments existing labor, as with Uber’s algorithmic management of its drivers or Amazon’s of its warehouse workers, some workers report deep levels of frustration and resentment due to such concerns as the degree of surveillance and the lack of transparency about AI decisionmaking (Möhlmann and Henfridsson 2019). AI also has been shown to perpetuate and potentially exacerbate biases already present in society. There is a robust literature on this relationship, with findings of discrimination based on race alone found in algorithmic risk assessments in the health care space, facial recognition systems, and natural language processing (Obermeyer et al. 2019; Furl, Phillips, and O’Toole 2002; Caliskan 2021). Major players in the digital market have long struggled with these issues; for example, Amazon’s attempt to build an AI-based hiring program resulted in a system that taught itself to prioritize male candidates and penalize résumés that Competition in the Digital Economy: New Technologies, Old Economics | 233 mentioned women’s colleges and made other references to women (Dastin 2018). These biases can be both intentional, as when Facebook’s AI-based advertising made it possible for advertisers to exclude specific users based on their race, and unintentional, as when women were shown fewer career ads because the cost to advertise to women was higher online (Zang 2021; Lambrecht and Tucker 2019). Even when an algorithm itself does not increase bias, differential rates of utilization of the algorithm can deepen racial and gender disparities, as in the case of Airbnb’s Smart Pricing tool (Zhang et al. 2021). As governments around the world consider how best to regulate digital markets, they are confronting the fact that AI’s role in this market introduces levels of opacity and complexity that can hinder reasonable efforts at oversight (European Parliament 2022; Kroll 2021). Further, complexities emerge in assessing the intent of firms, which can be an important part of many regulatory systems (Chin 2019). Processes like algorithmic audits have been proposed as tools to overcome the “black box” features of AI that can create substantial information asymmetries between firms and regulators (Guszcza et al. 2018). These audits have received attention in areas related to hiring, and they are being actively considered both internationally and within the United States (Lee and Lai 2021; Engler 2021; Digital Regulation Cooperation Forum 2022). In 2022, the Biden-Harris Administration released the “Blueprint for an AI Bill of Rights” (White House 2022), which outlines five principles to guide AI system design that will protect the American public. online to institute price matching, enabling firms to potentially achieve a higher price than they could achieve if their rivals’ price was uncertain, as price-matching policies remove the incentive for competitors to lower prices. There is evidence that artificial-intelligence-based algorithms can potentially adapt to raise prices in a coordinated fashion, even if they have not been explicitly programmed to do so (Harrington 2018). This form of tacit collusion may be difficult to detect. In addition to the possibility of collusion through the use of algorithmic pricing, the use of automated software can support prices above competitive levels. This can intensify merger price effects in ways that are not accounted for in a traditional merger analysis and also generate greater price dispersion in the market (Brown and MacKay, forthcoming). In order to guard against the threats of tacit collusion and explicit price fixing enabled by pricing algorithms, antitrust authorities may require additional resources (i.e., computing, personnel, and financial resources). Box 7-5 explores other ways in which artificial intelligence affects the functioning of digital markets. 234 | Chapter 7 Conclusion Although the basic economics of digital markets are well understood, when combined with the effects of scale and the data collection potential of the digital world, they raise new concerns. Many digital markets have become dominated by a few firms or even one firm, and these dominant firms have incentives to protect their existing position, to extend their market power into other markets, and to exploit the huge amounts of data being gathered on their users. Governments must ensure that the benefits of competition—such as innovation, privacy, choice, and low prices—are realized while protecting market participants and promoting a fair and contestable playing field. Competition regulation and enforcement must adapt to the changes brought on by the digital revolution, given that harm to competition, market participants, workers, and consumers is now being manifested in novel ways. Creating digital markets that work for everyone would allow their full potential to be shared by all Americans. Competition in the Digital Economy: New Technologies, Old Economics | 235 Chapter 8 Digital Assets: Relearning Economic Principles Multiple financial crises have struck the United States during the last two centuries. Many of these crises have been caused by institutions that function like banks but are not registered or regulated as banks, so-called shadow banks. For example, the 1907 crisis—then called a “panic”—was mainly caused by trust companies, which were State-chartered entities that competed with banks for deposits. Because these trusts were not part of the central payments system, and thus processed only a small amount of payments, they did not hold a large amount of cash relative to deposits. To earn profits, they made as many loans as possible. After a series of events in October 1907 set off a rush for withdrawals, several trusts faced a run and were forced to suspend credit and liquidate assets, acting as a catalyst for a larger fire sale in financial markets. To save the financial system, J. P. Morgan, owner of the eponymous bank, and a small number of other financial leaders individually chose which banks to bail out (Moen and Tallman 2015). This helped government policymakers realize that when faced with a crisis, the financial system, as then constituted, would rely on a privileged group of individuals seeking to maximize their own profits rather than on institutions that had an obligation to protect the public’s interest. This realization helped lead to the creation of the Federal Reserve—the centralized entity that first aimed to serve as the lender of last resort and, over time, also obtained the exclusive power to issue U.S. dollar notes and manage the Nation’s monetary policy. Fast forward 100 years, and digital asset proponents are now aspiring to create a decentralized financial system without relying on governments 237 and their regulatory frameworks, which were shaped by important lessons learned from multiple previous crises, including the 1907 panic. Digital assets are electronic representations of value and operate as part of a complex and interconnected digital ecosystem. Crypto assets are a subset of digital assets that use cryptographic techniques and distributed ledger technology (DLT) but exclude central bank digital currencies (U.S. Department of the Treasury 2022a). DLTs rely on networks to store and process transactions. This chapter primarily examines crypto assets, whose proponents have been relearning the lessons from previous financial crises the hard way. In addition to the decentralized custody and control of money, it has been argued that crypto assets may provide other benefits, such as improving payment systems, increasing financial inclusion, and creating mechanisms for the distribution of intellectual property and financial value that bypass intermediaries that extract value from both the provider and recipient. Looking under the hood at these arguments, however, shows a more complicated picture. So far, crypto assets have brought none of these benefits. Meanwhile, the costs generated by several of their aspects—such as those for consumers, the physical environment, and the financial system—are not only substantial but are also being accrued in the present. Indeed, crypto assets to date do not appear to offer investments with any fundamental value, nor do they act as an effective alternative to fiat money, improve financial inclusion, or make payments more efficient; instead, their innovation has been mostly about creating artificial scarcity in order to support crypto assets’ prices—and many of them have no fundamental value. This raises the question of the role of regulation in protecting consumers, investors, and the rest of the financial system from panics, crashes, and fraud related to crypto assets. Even so, as companies and governments experiment with DLT, it is conceivable that some of their potential benefits may be realized in the future. 238 | Chapter 8 The Perceived Appeal of Crypto Assets This section reviews the potential benefits that crypto assets may offer, as often touted by their proponents, while the next section evaluates what they have actually achieved. To introduce the digital asset landscape, figure 8-1 illustrates certain types of digital assets. The label “cryptocurrency” is used in the industry to connote a crypto asset that is promoted to be an alternative payment instrument. “Stablecoin” is also an industry label for a form of crypto asset that is purportedly backed by a portfolio of underlying assets and claimed to have a stable exchange value with these assets. While some stablecoins mainly aim to become payment instruments, other stablecoins mainly aim to provide returns from investments. Regardless of the label used, a crypto asset may be, among other things, a security, a commodity, a derivative, or another type of financial product, depending on the facts and circumstances. Nonfungible tokens are the other primary type of crypto asset; they use DLT to track ownership of digital goods but are not a main focus of this chapter. The term “crypto asset” excludes digital currencies that may be issued by a central bank. Though central bank digital currencies might be designed to operate using DLT, there is no requirement for them to be on DLT, and a central bank digital currency does not necessarily involve using DLT (White House 2022a). Figure 8-1. A Taxonomy of Digital Assets and Central Bank Money Central bank money Digital assets Crypto assets CBDCs NFTs Cash Cryptocurrencies Stablecoins Sources: CEA analysis; Hoffman (2022). Note: NFTs = nonfungible tokens. Not drawn to scale. Cash represents currency as well as reserves. Regardless of the label used, a crypto asset may be, among other things, a security, a commodity, a derivative, or other financial product, depending on the facts and circumstances. Digital Assets: Relearning Economic Principles | 239 Crypto assets have gained substantial popularity in recent years— particularly since the beginning of the COVID-19 pandemic in 2020. As shown in figure 8-2, the estimated market values of selected crypto assets have increased significantly in recent years and reached a collective peak of nearly $3 trillion in November 2021. As of the end of December 2022, crypto assets collectively had a reported market value of a little under $1 trillion, due to a large downturn in prices over the year, and largely reflecting the failures of certain prominent crypto asset projects and firms. The development of crypto assets and their underlying distributed ledger technology have the potential to transform industries and business models. Recognizing both the potential opportunities and actual risks of crypto assets, in March 2022, President Biden signed Executive Order 14067, “Ensuring Responsible Development of Digital Assets” (White House 2022b), which tasked the Administration to study the effects of these novel assets. As a result, departments and agencies of the Federal Government have produced nine reports examining the implications of crypto assets for consumers, businesses, financial stability, national security, and the physical environment (White House 2022c). The first crypto asset, Bitcoin, was launched in 2009, shortly after the global financial crisis, as something of a repudiation of the existing financial intermediaries that caused the crisis (Nakamoto 2008). Bitcoin was Figure 8-2. Market Capitalization of Selected Crypto Assets, 2020–22 Trillions of dollars (nominal) 3.0 2.5 2.0 1.5 1.0 0.5 0.0 Jan-2020 Jun-2020 Nov-2020 Bitcoin Apr-2021 Ether BNB Sep-2021 Cardano Feb-2022 XRP Source: Coin Metrics, Inc; Federal Reserve Board of Governors Financial Stability Report. Note: Total market cap figures are subject to revision from Coin Metrics. 240 | Chapter 8 Other Jul-2022 Dec-2022 Box 8-1. What Are the Functions of Money? In early history, bartering was a common way for people to exchange goods and services. Bartering, however, takes time, because individuals need to find another person who is willing to trade one physical good or service for another. A workaround for this was the invention of money; some of the earliest forms of money appeared in about 1200 BCE (Tikkanen, n.d.). Money’s key innovation was to facilitate trade between individuals by using an item that had a common representation of value that was widely agreed upon by members of society. That is, instead of having to take a goat everywhere and hoping to find someone who wanted the goat, money enabled individuals to carry something that everyone valued, such as polished beads, which could be exchanged for a wide variety of goods and services (Jordan 1997). The first money was in the form of things like seashells, beaver pelts, and even large stones (Tikkanen, n.d.; Hudson’s Bay Company History Foundation 2016; Goldstein and Kestenbaum 2010). Eventually, money took the form of “specie,” or coins such as gold and silver, which could be produced to a specific standard of weight (Velde 2012). While money like specie money was decidedly more convenient than carrying around a goat, it was still cumbersome to transport. To get around this, paper money was created, which was substantially easier to transport. To ensure that paper money still had financial value, it was “backed” by specie (Tikkanen, n.d.). That is, the paper money essentially served as a promissory note for specie sitting in a bank, and it could be freely redeemed. This system worked well, but it had a key vulnerability that became a common theme of many crises: banks could earn higher profits by issuing more paper currency than the amount of specie they held in their vault. For example, a bank could hold 50 gold coins, but could issue 100 units of a paper currency, each giving the holder the right to 1 gold coin. Then, if all holders of the currency demanded their money back at the same time, the bank would not have enough gold coins to meet the holders’ redemptions (Diamond and Dybvig 1983). This dynamic—referred to as a bank run—also has a long history, dating back to as early as the fourth century BCE (Flood 2012). Eventually, institutions and faith in currencies—particularly the U.S. dollar—became strong enough that specie was not needed to assuage investors’ concerns about what was “backing” the currency. This led to the creation and adoption of “fiat” currency, or currency issued by the government that is not redeemable for specie. Fiat currency’s value is largely a function of (1) the currency being the only instrument with which individuals can pay taxes; (2) the strength of the government’s institutions, such as the legal system and military; and (3) a shared social trust in the value of the money itself (Bank of England 2020). Digital Assets: Relearning Economic Principles | 241 Money, as defined in the Uniform Commercial Code and certain other specialized sources, is a medium of exchange currently authorized or adopted by a domestic or foreign government (U.S. Commercial Code, n.d.). In contrast, here the economic functions and common understanding of money are considered. For a type of money to actually be useful in the economic sense, there must be wide agreement about its value—either derived from assets backing it (e.g., the gold standard) or from things like institutions and social trust. Money serves three core functions: as a medium of exchange, as a unit of account, and as a store of value (U.S. Department of the Treasury 2022b). First, money can serve as a medium of exchange if it can be used widely to trade for goods and services. For example, the U.S. dollar can be used for purchasing anywhere in the country, and even in many places abroad. In contrast, for example, while cigarettes are often used inside prisons to trade for goods and services, they cannot be used to purchase groceries or buy plane tickets (Lankenau 2007). Second, money can be considered a unit of account if it acts as a benchmark upon which the values of different goods and services can be compared. For example, instead of estimating how many chickens it would take to trade for one cow, a person can instead simply express the value of chickens relative to cows through their respective monetary values—so if 1 chicken costs $10 and 1 cow costs $2,000, then a person can simply use their relative dollar values to conclude that 200 chickens are worth the same as 1 cow. Finally, money can be a store of value if its purchasing power does not fluctuate dramatically over short intervals of time. For example, the number of apples a $10 bill can buy does not vary much from one day to the next. This is one reason why very high levels of inflation—socalled hyperinflation—can create uncertainty in the purchasing power of money. “Sovereign money” is money issued by the governing authority of an independent country. Sovereign money can easily satisfy money’s functions to serve as a medium of exchange and as a store of value over time. This is because sovereign money is an information-insensitive asset; it is unlikely that one side of a transaction is acting based on private information about the value of sovereign money (Gorton and Zhang 2022). The more information-sensitive an asset is, the less likely it is to be a medium of exchange. For example, if there is a high possibility that someone is buying gold to protect themselves against losses from holding another asset, the gold seller may decide that it is better not to exchange gold for that asset. Sovereign money is also a liability of the central bank, meaning that its value is backed by the bank. The U.S. dollar is widely accepted as a medium of exchange, and it is also a store of value. Indeed, roughly half of all international trade is invoiced 242 | Chapter 8 in dollars (CRS 2022). This does not mean that all sovereign currencies have the features of money. For example, Zimbabwe’s currency lost its role as a store of value in 2007, when its annual inflation rate rose to over 66,000 percent (Siegel 2008). In Zimbabwe’s case, consumers and firms shifted toward the widespread use of other sovereign currencies, which effectively replaced Zimbabwe’s currency (Noko 2011). Bank deposits can also act as money. Banks offer deposit accounts to their customers, and these deposits are pegged one-for-one against sovereign currencies. The value of this private form of money is generally supported by a nexus of regulatory and supervisory requirements, such as capital and liquidity requirements, designed to protect the customer against a possible bank run. This account-based private money is linked to an individual person or entity. In contrast to sovereign currencies, there are limits on account-based money to circulate. For example, if Jeff writes a check to Greta to pay rent, Greta’s check from Jeff represents money that belongs to Jeff (i.e., the money is linked to his deposit account), and she can redeem it in exchange for circulating currency (cash). Although Greta is legally allowed to exchange Jeff’s check for gasoline, third-party checks are not widely accepted as a payment method. Hence, in reality, Greta first needs to cash the check and then purchase gasoline. designed as a purported peer-to-peer payment system that does not rely on intermediation by a “trusted authority” to keep track of transactions. Instead, Bitcoin uses cryptography to record transactions across an open (“permissionless”) network of computers.1 These transactions are recorded digitally on a “blockchain,” which uses cryptographic techniques to link transactions to each other in a manner that makes it challenging to edit or tamper with previous transactions. Because the Bitcoin blockchain is a public ledger, network participants can view and validate transactions as they happen in real time.2 The supply of bitcoins is capped to ensure that each unit retains value, since digital assets otherwise could be reproduced perfectly forever, and they would have no value if there were an unlimited supply. This “artificial scarcity” was one important feature of Bitcoin, and has been replicated by many new crypto assets introduced since Bitcoin. There are also “permissioned” DLTs, where all nodes have to be given permission to participate in the network. However, if the trust in the network is established by authentication, that runs counter to the purpose of the trustless system. 2 Formally, the network tracks the “unspent transaction output” from transactions for each account, which represents the transfer of specific units (e.g., like coins being transferred between individuals), or by how much available funds exceed withdrawals. 1 Digital Assets: Relearning Economic Principles | 243 Both the number of crypto assets and their combined market value have risen over time, reflecting their increasing popularity around the world. There are several possible benefits that proponents claim for this popularity of crypto assets. These claims are reviewed in the next subsections. Claim: Crypto Assets Could Be Investment Vehicles People invest in assets with the hope of making returns on their investments by accepting a certain level of risk of loss. For example, traditional investments such as equities and bonds offer a certain level of expected returns for their risk exposure. Similar to these traditional types of assets, it has been argued that crypto assets are also investment vehicles that offer an expected return for a given risk exposure. Hence, depending on the risk appetite of investors, one might invest in crypto assets with the hope of quickly making a large profit. Moreover, some have argued that crypto assets can serve as a hedge against inflation, hoping their value will keep pace with or rise more than the rate of inflation. Claim: Cryptocurrencies Could Offer Money-like Functions without Relying on a Single Authority One stated goal of cryptocurrencies has been to create a financial system that is “censorship resistant” and unable to be controlled by a government, instead distributing control among pseudonymous global actors that do not rely upon any trust in existing financial institutions. In particular, some cryptocurrencies aim to replace central authorities that issue money by instead relying on a distributed network, with benefits spread across the network that issues representation of value that can be minted and transacted without central authorities. For example, when implementing monetary policy, governments can profit from issuing money because the value of money is generally higher than the cost of issuing it (this is called “seigniorage”). In contrast, many cryptocurrencies aim to distribute the profit from issuing a cryptocurrency by rewarding participants that can verify a transaction through a consensus mechanism (Acemoglu 2021). In this process, participants can be rewarded with the new issuance of a cryptocurrency as well as transaction fees, earning them a profit for supporting the distributed network that maintains the cryptocurrency. This could be seen as a novel way to distribute the profits from issuing new assets. Box 8-1 discusses the functions of money. Claim: Crypto Assets Could Enable Fast Digital Payments In recent years, the usage of cash has declined dramatically as the usage of digital payments has increased substantially. Figure 8-3 demonstrates the trends in cash and check transactions against those in debit/credit payments, 244 | Chapter 8 Figure 8-3. Payment Types Used in the United States Over Time Percentage of payments in a typical month 70 60 50 40 30 20 10 0 2009 2010 2011 2012 2013 Cash and check 2014 2015 2016 Debit/credit card 2017 2018 2019 2020 Other Sources: Federal Reserve Bank of Atlanta; CEA calculations. which are forms of digital transactions. In the last decade, payments in cash and checks have declined dramatically, while digital payments have notably increased. As the demand for digital payments increases, it has been argued that stablecoins could be used as near-instant 24/7 payment instruments (Liao and Caramichael 2022). As of December 22, 2022, there were about 200 stablecoins, with an estimated market size of roughly $140 billion. The two crypto assets Tether and USD Coin alone accounted for roughly 80 percent of the total market of stablecoins.3 Since stablecoins try to be pegged to a reference asset such as the U.S. dollar (or another currency, or a basket of currencies), proponents argue that stablecoins could eliminate exchange risk when used as a settlement method. That is, if one stablecoin is always worth $1, then an individual using a stablecoin to buy or sell goods has the expectation that its nominal purchasing power will not change dramatically after their transaction. Stablecoins have been suggested as a possible way to simplify cross-border transactions and remittances. Claim: Crypto Assets Could Increase Financial Inclusion Some segments of the U.S. population are unbanked, meaning they do not own a bank account. Others are underbanked—that is, they own bank accounts but often use expensive nonbank financial services. Black households have disproportionately higher rates of being unbanked and underbanked (FDIC 2022). Crypto assets often are promoted as a tool for 3 Market capitalizations exhibit volatility. See, e.g., CoinMarketCap (2023). Digital Assets: Relearning Economic Principles | 245 reaching these populations to improve their access to financial services and build wealth to achieve upward mobility. For example, many crypto assets do not impose minimum account requirements or charge overdraft fees, in contrast to some traditional banking institutions. Unbanked individuals cite such attributes as primary reasons they do not have bank accounts (FDIC 2022). A recent report found that minority households are more likely to have invested in crypto assets than other households (Faverio and Massarat 2022). Claim: Crypto Assets Could Improve the United States’ Current Financial Technology Infrastructure The distributed ledger technology that underlies many crypto assets is based on a number of technological advances. It addresses the problem in certain circumstances of establishing trust and a consensus on the true history of transactions among a group of “mutually suspicious” parties. It is effectively a shared database whose contents can generally be trusted, even though it is operated by entities that generally do not have a reason to trust one another. For crypto assets, the database stores the set of transactions that have occurred among network participants. In addition, more recent developments in DLT have enabled new features and improved efficiency, such as “smart contracts,” which automatically trigger particular actions without the need for ongoing oversight. Box 8-2 further describes how Bitcoin and distributed ledgers work. The Reality of Crypto Assets This section investigates the claimed benefits reviewed earlier in the chapter and presents the risks and costs of crypto assets. Crypto Assets Are Mostly Speculative Investment Vehicles As shown in figure 8-4, compared with many other asset types, crypto assets are very volatile, and, hence, highly risky. Because they are very volatile, crypto assets can be used for speculation, an investment strategy that seeks to make a profit from short-run trading. One reason many crypto assets are highly volatile is that many of them do not have a fundamental value. For example, stocks are claims on the future profits of firms and debt is a claim on interest and principal payments. Even commodities such as gold and silver have fundamental values, because they can be used in jewelry and for special manufacturing purposes (Nogrady 2016). Conversely, unbacked crypto assets are traded without fundamental anchors, suggesting that their market prices only reflect speculative demand, or market sentiment, not claims on cash flow. Relatedly, the U.S. Department of Labor (2022) issued 246 | Chapter 8 Box 8-2. How Does Bitcoin Work? This box explains how Bitcoin functions, as it was the first crypto asset. Subsequent crypto assets have often incorporated key features from this design. Bitcoin relies on several innovations, including the novel use of a hash function, a well-established cryptographic technique. What is a hash function? A hash function, which is sometimes called a “one-way” algorithm or “trap-door” algorithm, uses a mathematical algorithm to take an input (e.g., a number, a string of letters) and produce an output that satisfies three requirements: (1) reproducibility—or running the algorithm on the same input always produces the same output; (2) irreversibility—or even knowing the algorithm, it is not possible to easily invert the output to recover what the input was; and (3) collision avoidance—or any unique input string must produce exactly one unique output. This is a “one-way” function, in that there is no efficient way to recover the input from just the output; the only way would be to hash every possible input to see if it matches the output. Figure 8-i gives examples of hashed output. The hash function is usually quick and has many applications. For example, most websites do not store a person’s actual password on their servers; instead, they store a hash of the password. That way, if there were ever a hack of their systems, the hackers would only have the hashed versions, which would not work as passwords and could not easily be used to determine passwords. When you log onto a website, its server hashes the password you enter and compares that with what is stored in its database and only lets you in if they match. Note that a change of the input as seemingly small as from “hello” to “Hello” usually creates a drastically different hash, and that a vastly different phrase produces a hash that is equally random. Two key participants in the Bitcoin space are users and miners. Users. Crypto assets generally require a user to have a “wallet.” A digital wallet is a software application, piece of hardware, or other device or service that stores a user’s public and private cryptographic Figure 8-i. Examples of Hashed Output Input Text Hashed Output (in hexadecimal using the SHA-256 algorithm) hello 2cf24dba5fb0a30e26e83b2ac5b9e29e1b161e5c1fa7425e73043362938b9824 Hello 185f8db32271fe25f561a6fc938b2e264306ec304eda518007d1764826381969 The quick brown fox jumps over the d7a8fbb307d7809469ca9abcb0082e4f8d5651e46d3cdb762d02d0bf37c9e592 lazy dog Source: CEA analysis. Digital Assets: Relearning Economic Principles | 247 keys, which allow users to interact with one or more blockchains and send and receive crypto assets. Users can have custodial wallets, which are provided and maintained by an intermediary or third-party provider, or non-custodial wallets, also known as unhosted wallets, for which users are responsible for their own wallets and private keys. For Bitcoin, wallets have an associated “private key,” typically a randomly generated string of digits, which can be hashed to derive a “public key.” The public key similarly can be used to generate the wallet’s address using a different, known hash function. Anyone can initiate a transfer to a wallet if they know its address. This is used as either the source or destination of transfers on the Bitcoin blockchain. However, to send crypto assets, one needs to know the private key for the wallet that is sending (Outten 2021). In particular, someone wanting to send crypto assets can construct the transaction, create a hash of it, and combine that with a private key to create a digital signature of the transaction. A useful analogy is that the public key is akin to your home address, while the private key is the physical key to your home. It is the difference between letting someone know where you live versus giving them access to your house. Any node of the network can then compare the hash of the digital signature with the public key, and with the hash of the transaction data, and determine if the transaction is valid. Nodes will reject any invalid transactions, so private keys are required to transfer crypto assets. From the perspective of the user, who typically uses a wallet app to manage this process, all that is needed is the knowledge of the addresses of the sending and receiving accounts, the private key if sending, the amount, and a fee. The fee incentivizes miners to include the user’s transaction in an upcoming block. A transaction with a high fee is more likely to be included in upcoming blocks than one with a low (or zero) fee. This means that transactions with low fees may takes days to be processed or may not be processed at all. Miners. The key part of the Bitcoin ecosystem that is different from physical currency is that there are no central, trusted arbiters of truth. Instead, the system operates by consensus among nodes of the network about what the truth is (i.e., the distribution of bitcoins across all wallets). This means, in theory, that governance of the cryptocurrency is arbitrated by network participants, not a central authority, although control in some blockchains is more centralized as there may be a significant concentration among network participants that effectively consolidates governance between a few parties. The Bitcoin blockchain uses what is called the SHA-256 algorithm (developed by the National Security Agency and the National Institute of Standards and Technology), which, for any text input, always produces a 64-digit (256-bit) hexadecimal output string (Brown 2002). The Bitcoin 248 | Chapter 8 blockchain and many other cryptocurrencies use a “proof-of-work” method to achieve a consensus among all the nodes of the network. Miners monitor the network and maintain a pool of transactions that are yet to be validated. In a proof-of-work network, the network’s miners are competing to be the ones to successfully mine the next block of transactions in the chain. The actual way this is accomplished is that the miner puts together a candidate block of transactions to include as well as a “block header,” or some metadata for the block (Rybarczyk 2020). These metadata include the hash of the last successfully mined block of the chain, the version of software used, and some technical parameters that are explained just below: the target difficulty, a digital signature unique to the block of transactions they are including (the “Merkle root”), and the “nonce.” They then take all the information in the block header, combine it into one string, and push it through the SHA-256 algorithm to get the hash of that information. Here is the competition aspect: the nonce field is a number that miners can choose arbitrarily. Their goal is to pick a nonce such that the resulting hash—a hexadecimal number—is less than the target—also a hexadecimal number—currently set by the blockchain. Given how the hashing process works, there is no way to do this efficiently; a miner must continue trying different numbers until they are successful. Since the nonce must be an 8-digit hexadecimal number, a little over 4 billion nonces can be tried. If no possibility is successful, the miner needs to get creative in how to try new hashes against the target, such as changing the set of transactions that are included in the block, which changes the Merkle root in the header, thus changing the proposed block’s hash. While finding a valid nonce and set of transactions requires a large amount of brute-force computing power, verifying that a proposed block is valid is trivial—nodes just need to compute the hash of the proposed block and compare it with the target—and this means that once a block is found to be valid and is broadcast across the network, a consensus can be quickly reached that it is a valid block. At that point, it is added to the chain, and competition commences on adding the next block of transactions, the next element of truth in the system. Miners receive two types of compensation for the work that they do: the fees that are included in the transactions they choose to put in a block; and the “miner reward,” defined by the blockchain’s protocol. For Bitcoin, the mining reward was initially 50 bitcoin for every mined block, but this has diminished due to a “halving rule.” This rule limits the total supply of bitcoins to 21 million over the lifetime of the coin and means that every four years, the payout for mining a new block falls in half. The reward was 6.25 bitcoin, as of December 31 2022; but, given prevailing prices, this was worth over $100,000 (Coindesk 2022). The “target” difficulty parameter is adjusted every two weeks to ensure that a Digital Assets: Relearning Economic Principles | 249 new coin is mined roughly every 10 minutes. As the number of resources dedicated to mining has increased, higher levels of difficulty have been required to keep pace. In the five years before October 2022, the number of attempts to mine a typical block of the Bitcoin chain increased by a factor of 19 (BTC 2022). Once the maximum supply of 21 million bitcoins is reached (which is projected to occur in about 2140), miners will only benefit from transaction fees (Timón 2016). Why does the blockchain mechanism “work”? Once the blockchain is running, suppose a bad actor wanted to modify the history of the blockchain by, for example, inserting a fraudulent transaction in an earlier block. In theory, this would not work, since any other node of the network could immediately verify that this block did not previously belong in the chain because no subsequent block would point to its (changed) hash as being its predecessor. So, a bad actor would need to recompute the entire chain, from the fraudulent block to the current one, 2/21/2023 with new hashes, which would require an inordinate amount of computing power. This highlights the origins of blockchain technology in ensuring trust among mutually suspicious groups (Chaum 1982). Figure 8-ii demonstrates how a blockchain is formed. Many other blockchains have a design similar to that of Bitcoin, although with different parameters and features, such as smart contracts. Ethereum, for example, allows more daily transactions than Bitcoin, is calibrated to have blocks added every 12 seconds, and recently switched its consensus protocol to be less energy-intensive (Etherscan 2022). An important criticism of crypto assets is their energy intensity. A more complete discussion of the technological options of blockchain design is beyond the scope of this chapter. Figure 8-ii. Blockchain Blocks Linked by Hashed Values of Their Contents Transactions: … … … Block #203 Bitcoin version: Previous block hash: Merkle root hash: Time Bits: Nonce: Transactions: … … … Hash of this block Block #202 Hash of this block Block #201 Bitcoin version: Previous block hash: Merkle root hash: Time Bits: Nonce: Bitcoin version: Previous block hash: Merkle root hash: Time Bits: Nonce: Transactions: … … … Source: CEA compilation. Note: “Bits” refer to the current difficulty level of mining a new coin. It is stored in an encoded manner, but a lower target implies a higher difficulty. As of October 2022, it required testing approximately 31 trillion nonces to mine a block of Bitcoin. 250 | Chapter 8 Figure 8-4. Volatility of Crypto Assets versus Certain Traditional Assets, 2017–22 Thirty-day rolling standard deviation of daily returns 0.12 0.10 0.08 0.06 0.04 Crypto assets High-yield bonds Real estate Foreign exchange U.S. equities Investment-grade bonds 12-01-2022 09-01-2022 06-01-2022 03-01-2022 12-01-2021 09-01-2021 06-01-2021 03-01-2021 12-01-2020 09-01-2020 06-01-2020 03-01-2020 12-01-2019 09-01-2019 06-01-2019 03-01-2019 12-01-2018 09-01-2018 06-01-2018 03-01-2018 12-01-2017 0.00 09-01-2017 0.02 Emerging market equities Commodities Sources: Bloomberg L.P.; CEA calculations. guidance to protect investors’ retirement plans with respect to this asset type. Recall that one of the purported benefits of crypto assets like Bitcoin was to hedge against inflation, meaning that their value does not erode as inflation increases. But as inflation increased globally in the second half of 2021 and in 2022, the prices of crypto assets collapsed, proving them to be, at best, an ineffective inflation hedge. Cryptocurrencies Generally Do Not Perform All the Functions of Money as Effectively as Sovereign Money, such as the U.S. Dollar As discussed in box 8-1, money serves three functions: as a unit of account, which means that it acts as a benchmark upon which the values of different goods and services can be compared; as a medium of exchange, which means that it can be used to trade goods and services; and as a store of value, which means that the amount of goods and services that a unit of the money can buy does not fluctuate dramatically over short intervals of time. Although cryptocurrencies currently serve each of these functions, they only do so in limited ways in the United States, so they do not serve, from an economic perspective, as an effective alternative to the U.S. dollar. For the first monetary function question, cryptocurrencies can serve as a unit of account, given that the relative values of goods and services can be expressed in cryptocurrency (e.g., a single chicken in commerce is worth roughly 0.0001 bitcoin). However, individuals would likely need to first convert bitcoins or other cryptocurrencies to dollars to understand relative values as cryptocurrencies are not as effective as the U.S. dollar as a medium Digital Assets: Relearning Economic Principles | 251 of exchange (discussed below). Thus, cryptocurrencies currently do not fully serve as units of account. The second question is whether cryptocurrencies can serve as a medium of exchange. The answer is that in the United States, they are not as effective a medium of exchange as the U.S. dollar. This is because they can be used to purchase other cryptocurrencies and to buy goods and services at a smaller number of firms relative to the U.S. dollar (Modderman 2022). The strength of the U.S. dollar is derived from several important factors, such as faith in government institutions and the legal system, but cryptocurrencies lack these factors. Third, cryptocurrencies currently experience substantial amounts of volatility, and thus are not stable stores of value. For example, the value of a bitcoin (relative to the U.S. dollar) increased by over 1,000 percent from March 2019 to March 2021, and then decreased by over 70 percent from November 2021 to October 2022. This volatility means that anyone who is using bitcoins to store their savings is subject to high-volatility risk in their purchasing power. As figure 8-4 shows, the volatility of cryptocurrencies outpaces those of many other financial asset types. Cryptocurrencies regularly exhibit a similar amount of volatility as U.S. equities experienced at the onset of the COVID-19 pandemic. There is also tension in an asset being promoted as both money and an investment vehicle. As money, the instrument should have a stable value, suggesting limited price volatility. But as a risky asset, it should experience price volatility, for which an investor would be compensated with a high expected return. Holding everything else constant, the riskier an asset is, the less likely it can effectively serve as money. In sum, in addition to generally being speculative assets, cryptocurrencies currently are not effective alternatives to sovereign money such as the U.S. dollar. As mentioned above, most cryptocurrencies do not have fundamental value, but that is not a requirement for them to function as money. In fact, sovereign money does not have a fundamental or intrinsic value (Berentsen and Schär 2018). Even so, sovereign money can easily satisfy money’s requirements, as discussed in box 8-2. The main reason for this is that the value of sovereign money is backed by a trusted institution—the central bank. One important feature of many cryptocurrencies is validating transactions through consensus mechanisms, which are a way to distribute profits from new issuance among participants such as cryptocurrency miners that verify the cryptocurrency transactions. (See box 8-3 for the impact of cryptocurrency mining on the physical environment.) Hence, the supply of cryptocurrency generally increases with the number of verified cryptocurrency transactions. In the case of a new issuance of sovereign money, monetary policy reasons play a major role, and the resulting profits from the new issuance of sovereign money accrue to governments. In advanced 252 | Chapter 8 Box 8-3. Crypto Asset Mining as a Risk to the Environment The growth of trading in crypto assets has necessitated a corresponding increase in the mining of crypto assets. As discussed in box 8-2, crypto asset “mining” (cryptomining for short) is a process by which highpowered computers perform calculations to verify transactions using distributed ledger technology for some kinds of crypto assets (White House 2022d). Cryptomining can be lucrative for successful miners, which are compensated with the crypto assets they are mining but which also consume large amounts of energy. According to recent estimates by Goldman Sachs, cryptomining accounted for more than 2 percent of U.S. power consumption as of early 2022. The amount of electricity used to mine bitcoins in the United States is similar to what is used to power all the country’s home computers or residential lighting (White House 2022d). A recent inquiry by Congress into the electricity consumption of cryptominers found that just seven of the largest cryptomining operations in the United States had a combined capacity of 1,045.3 megawatts as of February 2022, with plans to expand capacity significantly in the coming months and years. For comparison, these miners alone could use roughly as much power as all residential units in Houston, the Nation’s fourth-largest city (Tabuchi 2022). While comparing usage across different types of activities is difficult because not all activity is recorded on-chain, some have estimated that in 2021 mining a single bitcoin used roughly the same amount of electricity as nine years’ worth of the average American household’s consumption (Huang, O’Neill, and Tabuchi 2021). Bitcoin additionally uses more energy than several entire countries, such as Finland, Belgium, and Chile (University of Cambridge 2022). Globally, Bitcoin accounts for 0.42 percent of all electricity usage. This effectively means that Bitcoin is using the same amount of electricity as a medium-sized advanced economy. Not all cryptomining operations consume the same amounts of power. Energy-intensive consensus mechanisms, such as a proof-ofwork, use substantial amounts of power by encouraging machines in a network to race against each other to solve a mathematical puzzle. Bitcoin, which accounted for over of a third of all crypto assets’ value as of December 2022, is the most notable crypto asset that is mined using proof-of-work. Ethereum, conversely, switched in September 2022 from a proof-of-work consensus mechanism to a proof-of-stake consensus mechanism that selects specific miners to validate a transaction at a given point in time, thereby reducing electricity usage in exchange for reducing the security of the network and increasing the power of individual actors vis-à-vis the network’s intensity. There are benefits Digital Assets: Relearning Economic Principles | 253 and drawbacks from different consensus mechanisms, and they have different energy, transparency, and security attributes. Despite Ethereum’s switch to proof-of-stake, Bitcoin has not announced plans to make a similar change. Evidence suggests that cryptomining has substantial costs for local communities and has few, if any, attendant benefits. Cryptomining facilities produce substantial noise pollution, which has been compared to a “jet-like roar” (Williams 2022). Cryptomining facilities can also lead to increases in local air and water pollution (White House 2022d). Local cryptomining operations also push up community electricity prices, as increased electricity consumption forces generators to rely on more expensive energy sources and, in the case of communities with hydropower where cryptomining operations are often located, reduces electricity surpluses. For example, in the Mid-Columbia Basin of Washington State, an energy surplus produced by hydroelectric dams originally pushed down electricity prices for both residents and businesses. But after cryptomining facilities began placing additional demand on the energy grid, exports of energy surpluses decreased, substantially raising residential electricity prices (Samford and Domingo 2019). Continuously running an electricity grid at maximum capacity can cause grid equipment that was not designed for such high-intensity usage to degrade over time, increasing the risk of fire in vulnerable communities. In places like Texas, which expects to add 27 gigawatts of additional cryptomining demand in the next four years—equal to roughly 30 percent of the generation capacity of the entire Texas grid—cryptomining could increase the likelihood of power crises, where demand overwhelms the grid’s ability to provide sufficient generation (Calma 2022). Furthermore, the intensive nature of mining bitcoins requires frequently replacing machines, and as the old equipment becomes nonfunctional, it can become “e-waste,” which often contains toxic chemicals and heavy metals that can leach into soils if not properly disposed of (de Vries and Stoll 2021). Just as mining energy-usage comparisons are difficult, comparing e-waste across activities is imprecise, especially because old machines used to mine bitcoins may be temporarily retired but then used again if the price increases enough (White House 2022d). With that being said, some have estimated that it would take as much as 114,000 Visa transactions to generate the same amount of e-waste as a single bitcoin transaction. Alternatively, a single bitcoin transaction may generate more e-waste than 2.7 iPhones (Digiconomist 2022). 254 | Chapter 8 economies such as the United States, the profits from the issuance of sovereign currency benefit taxpayers by lowering tax needs, as central banks effectively return these profits as government revenue. Stablecoins Can Be Subject to Run Risk Some cryptocurrencies, specifically stablecoins, are promoted to have the potential to be fast digital payment instruments. A fundamental problem with stablecoins is one that has been known in the traditional banking sector for centuries: run risk (Humphrey 1975). If stablecoin holders wish to redeem their stablecoins for $1 each, this will require the stablecoin issuer to liquidate some of its reserves (Adams and Ibert 2022). Depending on how liquid these reserves are, and the state of broader financial conditions, this liquidation may lead to disruptions in the markets for the reserve assets and reduce the market value of the issuer’s remaining reserves because the sales of the reserve assets put further downward pressure on the prices of remaining reserves. If reserves are falling in value at the same time holders are seeking redemptions, then the issuer may receive less than $1 for each $1 placed in stablecoins, thereby causing the stablecoin issuer to become insolvent. In fact, money market funds, which have balance sheet characteristics that a number of stablecoins purport to have, faced runs during the 2008 financial crisis and at the onset of the COVID-19 pandemic in 2020 (Schmidt, Timmermann, and Wermers 2016; Anadu et al. 2021). Deposits in bank accounts can be used to make payments, and banks aim to maintain parity between deposits and dollars; that is, $1 deposited in a bank account can be withdrawn for $1 at a later point in time. One important distinction between stablecoins and bank deposits is that in the United States, bank deposits are subject to a comprehensive set of regulatory and supervisory requirements. In contrast, stablecoins are not subject to requirements designed to maintain this exchange rate. A different approach to maintaining a stablecoin that does not fully rely on holding reserves is the so-called algorithmic stablecoin of TerraUSD (and the closely linked Luna token), which had the stated objective to maintain its exchange rate peg with the U.S. dollar using an algorithm (Baughman et al 2022). The idea behind the Terra/Luna coins was that Terra (known as UST) was a stablecoin pegged to $1 and was maintained through arbitrage (Wong 2022). Theoretically, 1 UST could always be traded for $1 worth of Luna. If the value of Terra ever fell below $1, arbitrageurs could exchange 1 Terra for $1 worth of Luna, a different coin. In theory, this would allow the arbitrageur to make a gain, decrease the supply of Terra (the exchanged token was “burned”), and raise the value of Terra. If the value of Terra rose above $1, arbitrageurs could buy (“mint”) 1 UST in exchange for $1 of Luna, making a small gain but increasing the supply of Terra and pushing Digital Assets: Relearning Economic Principles | 255 down its value. This was meant to be the mechanism to keep the value of Terra at $1, although there was also a reserve of other cryptocurrencies kept to support the peg, but not enough to fully cover the market value of Terra. At one point, Terra was the world’s fourth-largest stablecoin, in part due to the fact that people who were willing to deposit UST on Anchor, a smart contract-lending protocol, which promised investors an annual interest rate of 19.5 percent on their investments (Briola et al. 2023). Eventually, a run occurred as a few major withdrawals in May 2022 knocked UST off its $1 peg, leading to a stampede out of Terra into Luna, depressing Luna’s value, and ultimately causing the total crash of the two cryptocurrencies. Another key risk of stablecoins for U.S. retail users is that redemption may be a secondary concern for liquidity on crypto asset trading platforms. As noted in the Financial Stability Oversight Council’s “Digital Asset Financial Stability Risks and Regulation Report,” U.S. retail customers cannot directly redeem the two largest stablecoins (Tether and USD Coin) for dollars (U.S. Department of the Treasury 2022a). Stablecoin holders that lack redemption rights may be unable to find willing counterparties to exit their stablecoin positions. Gorton and Zhang (2021) evaluate a number of solutions to the run risk of stablecoins. For example, they assert that if stablecoins are required to be fully backed by safe assets, they would risk attracting funds that would ordinarily go to banks, which make loans. This would have the potential to hurt credit availability for individuals and firms. In subsequent research, Gorton and Zhang (2022) argue that stablecoins could challenge the government’s monetary authority to have an exclusive monopoly on currency issuance and disrupt financial stability. Stablecoins currently have a few major impediments against becoming fast payment instruments. For one, stablecoins are too risky to satisfy this need at present. Additionally, as discussed below, general concerns about consumer and investor protections in the crypto asset space also apply to stablecoins (U.S. Department of the Treasury 2022a). Nevertheless, there is continuing experimentation in using distributed ledger technology for digital payment systems. While crypto assets are currently not payment or settlement technologies for the rest of the financial system, it is still possible that in the future, their underlying DLT could be adapted into a payment or settlement system for the broader financial system. Crypto Assets Can Be Harmful to Consumers and Investors For consumers and investors to use crypto assets to access financial services, the crypto asset industry must have sound consumer, investor, and market protections. However, many participants in the crypto asset industry are not acting in compliance with existing laws and regulations, and some 256 | Chapter 8 of the most common unlawful activities in the crypto asset industry are scams especially aimed at retail investors (U.S. Department of the Treasury 2022a). One of the principal areas where there is mass noncompliance is disclosure surrounding crypto assets that are securities. This lack of disclosure prevents investors from recognizing that most crypto assets have no fundamental value. For example, many fraudsters develop intricate and professional-looking websites that purport to offer investors an exciting, high-return investment opportunity. When a victim gives crypto assets to the criminal to invest, the criminal can simply abscond with the funds. Examples of this includes a matter in September 2021, when the U.S. Securities and Exchange Commission (SEC) filed an action against the platform BitConnect for allegedly committing $2 billion worth of fraud (SEC 2021a). In its action, the SEC alleged that BitConnect purported to offer investors a “lending” program using a “proprietary volatility software trading bot,” but instead simply took investors’ crypto assets and transferred them into digital wallets controlled by the criminals. To date, the SEC has filed charges alleging a number of fraudulent offerings and other types of misconduct involving crypto assets (SEC 2022). In May 2021, the Federal Trade Commission (FTC) released a post detailing the increase in scams involving crypto assets since October 2020 (Fletcher 2021). Between October 2020 and May 2021, more than 7,000 people reported losses from these scams, which totaled more than $80 million, with a median loss of $1,900. One particular type of scam identified by the FTC is “giveaway scams,” where promoters claim to instantly multiply a given number of crypto assets but instead appropriate the crypto assets upon receipt. According to the FTC, young people were most susceptible to this type of fraud; those between 20 and 39 years of age lose far more money to investment fraud than any other type, more than half of which was attributable to crypto assets. In November 2022, the Consumer Financial Protection Bureau (CFPB) released a bulletin summarizing the consumer complaints it had received about crypto assets (CFPB 2022). In a period of less than four years, from October 2018 to September 2022, the CFPB received more than 8,300 complaints related to crypto assets, with the majority received since 2020. In this period, roughly 40 percent of crypto asset complaints handled were primarily frauds and scams. Transactional issues with crypto assets and issues with assets not being available when promised made up about another 40 percent of complaints. Other risks identified in the CFPB’s bulletin included romance scams and “pig butchering,” difficulty obtaining restitution, and fraudulent transactions.4 Pig butchering refers to a practice where scammers develop close personal relationships with a victim in order to convince them to set up crypto asset accounts from which the scammers can steal. 4 Digital Assets: Relearning Economic Principles | 257 Furthermore, there can be conflicts of interest at crypto asset platforms. For example, some crypto asset platforms combine exchange, brokerage, market making, and clearing agency functions. This vertical integration of products and services has long been prohibited in traditional markets and leads to risks to customers. For instance, a platform that combines exchange and market making functions would have an incentive to trade ahead of its own customers, and would have less incentive to seek out best executions for its customers. FTX, one of the largest crypto asset platforms until 2022, reportedly transferred billions of dollars in customer accounts to its affiliated trading firm, Alameda Research (Goldstein et al. 2022). By borrowing against FTT, the native token of FTX, Alameda Research reportedly made risky bets and lost a large fraction of FTX customers’ funds (Tortorelli and Rooney 2022). In November 2022, FTX and its affiliates declared bankruptcy and the price of FTT posted massive losses; at this time it is unclear whether FTX customers and creditors will get their funds back (Ge Huang, Osipovich, and Kowsmann 2022). There Have Been Limited Economic Benefits from DLT Technology The ability of DLT to solve the difficult problem of ensuring that two parties that do not have a reason to trust each other can nonetheless transact securely is a notable achievement of computer science. This solution has led to excitement about DLT, with even some enthusiasm that this technology will change the way business is done (Iansiti and Lakhani 2017). DLT and blockchain technology are not necessarily suitable for all applications; some considerations have been proposed for successful blockchain technology applications (Yaga et al. 2018). See box 8-4 for the proposed DLT use cases. However, at its core, DLT is simply a database, and many proposed DLTbased projects do not actually employ decentralization (as discussed below). Some have sought to profit from the hyperbole of blockchain—it has become a common tactic for non-crypto-related businesses to announce a “pivot to blockchain” to generate interest in a product or enterprise (Griffith 2018). For example, in December 2017, a beverage maker named “Long Island Iced Tea” added “Blockchain” to its name—though changing nothing substantive about its business—and its stock shares tripled in value (Cheng 2017). Ultimately, three persons involved with the firm were charged with insider trading by the SEC, which alleged that these insiders used the “pivot to blockchain” tactic to increase the firm’s share prices before they sold their stakes in the firm (SEC 2021b). In addition, many prominent technologists have noted that distributed ledgers are either not particularly novel or useful or they are being used in applications where existing alternatives are far superior. For example, Bruce Schneier (2019), a cybersecurity expert, has called crypto assets “useless” 258 | Chapter 8 Box 8-4. Proposed Uses of Distributed Ledger Technology The excitement generated about DLT has drawn substantial investment capital and has prompted governments and firms outside the crypto asset industry to experiment with its underlying technological processes. In some cases, this excitement has led to large writedowns or failed projects. Here, we review three current cases and give examples of experimentation. Walmart Canada and supply chains. A commonly touted use for distributed ledger technology is supply chains, where a single, distributed ledger could improve traceability throughout a supply chain and reconcile records between a firm and its multiple suppliers (Laaper, n.d.). In 2021, Walmart Canada launched a blockchain that attempted to handle payment disputes between 70 third-party freight carriers. An article in the Harvard Business Review dubbed the experiment “a tremendous success,” noting that before the blockchain system, 70 percent of invoices were disputed, but after the rollout, that share dropped to less than 1 percent (Vitasek et al. 2022). Though seemingly impressive, the firm that partnered with Walmart Canada to develop the blockchain platform stated in a report describing the project that the platform ran on “more than 600 virtual machines (VMs) to securely store and manage data points from thousands of transactions per day” (Hyperledger Foundation, n.d.). This implies that each VM is, at a maximum, handling 17 transactions per day. For reference, a minimally configured AWS (Amazon Web Services) RDS (relational data store) database with two VMs configured with best practices could process thousands, if not tens of thousands, of transactions per second (Amazon 2017). Furthermore, a prominent technologist stated that it was not even obvious what functional role blockchain was playing in the system, and that the program was more akin to using an existing technology in an inefficient way (Orosz 2022). Helium and the decentralized Internet. Helium is a company that is attempting to build a peer-to-peer wireless network by allowing users to buy “hotspots”—small devices that can send data over long distances—that can, together, create a Wi-Fi network. When the company was founded, it did not intend to have crypto assets as a central part of its business model (Roose 2022). Instead, it attempted to use traditional economic incentives for those helping build the network by simply sharing some of the fees from network users to hotspot owners. In 2019, however, the company pivoted and attempted to make crypto assets central to its business model by creating an incentive system where users that purchased hotspots that cost roughly $500 (and thus contributed to the network) were rewarded with Helium crypto asset tokens. If the prices of tokens rose, then so, too, would the reward for Digital Assets: Relearning Economic Principles | 259 owning a hotspot, thus encouraging more users to build out the necessary network infrastructure. After this pivot, large venture capital firms like Andreessen Horowitz (also known as a16z) helped Helium raise hundreds of millions of dollars in equity (Seward 2021). Alameda Research (the failed hedge fund affiliated with FTX) was also a large investor in Helium. Despite the sizable funding and widespread interest, Helium came under scrutiny in July 2022, when its cofounder tweeted that the company had generated $2 million a month in fees from new users joining (buying hotspots), but only $6,500 (0.3 percent) of that was from users actually using the Internet service (Levine 2022). Furthermore, a Forbes investigation in September 2022 found that the executives of the firm gave themselves and their families a windfall in Helium tokens early in the company’s history that was not publicly disclosed (Emerson, Jeans, and Liu 2022). Also, in September 2022, Helium ended the use of its own blockchain, which purportedly incentivized broader provision of Internet access as a core feature (“proof of coverage”) and shifted its operations and coins to the Solana blockchain, the same technology on which many other speculative crypto assets are traded, calling into question whether this use could be distinguished from any other type of crypto asset (Yaffe-Bellany 2022). Although these pieces of news may present a significant headwind for Helium’s future, the Helium token nonetheless has a market value (as of December 22, 2022) of over $253 million (CoinMarketCap 2022). Nonfungible tokens and virtual real estate. Nonfungible tokens (NFTs) are digital assets that are not interchangeable. Each NFT is unique, with its ownership recorded on a distributed ledger. Ownership of an NFT can pass between two users by recording the transaction and transferring it on a blockchain. NFTs often contain a pointer to a digital object, such as an image file. As a famous example, in March 2021 Jack Dorsey, the cofounder and former CEO of Twitter, auctioned off an NFT of an image of his first tweet on Twitter from 2006, with the winning bid coming in at more than $2.9 million (Locke 2021). While anyone could create (“mint”) a new NFT of the same digital image (and the digital image can be easily reproduced), the original transaction is maintained on a blockchain, so it would not truly be the same (OpenSea 2022). This highlights the “artificial scarcity” view of crypto assets. Borri, Liu, and Tsyvnski (2022) studied the market for NFTs from 2018 to 2021 and created an index of NFT value based on the repeat sales method. They found the average NFT market return was 2.5 percent a week in this period, although with a weekly standard deviation of 19 percent. This highlights the volatility and variability of NFT returns. The market for NFTs cooled in 2022; the owner of Dorsey’s tweet listed 260 | Chapter 8 it for sale in April 2022 for $48 million, but the highest bid as of January 4, 2023, was about $82,000 (OpenSea 2022). NFTs can be a natural way to track ownership of virtual real estate. Several different “metaverses” have begun offering “land” in virtual worlds. Ownership of land translates into the title of a virtual property being recorded on a distributed ledger. What one does with their land depends on the platform—on Decentraland, a large metaverse platform, owners are free to develop their land as they see fit: they could open a store selling virtual goods, create a game app for visitors, build a gallery for their virtual art collection, or build a virtual “house” (Kamin 2021). Dowling (2022) studied the value of land in Decentraland and found that the daily values of the virtual land tokens between 2019 and 2021 changed with extreme volatility. As in the physical world, location matters—while the average transaction value for a property in the data set is $1,311, a firm paid $2.5 million for land in Decentraland’s Fashion District (Putzier 2021). Experimentation. The current uses discussed above have demonstrated only limited, if any, economic benefits so far. Even so, proponents still claim that this technology could find productive uses in the future as companies and governments continue experimenting with potential uses; however, they often use “permissioned” networks of machines that have been authenticated as a trusted member of the network (Oracle 2022). For example, it is possible that distributed ledger technologies can be used to improve the settlement and clearing processes of banks (Bech et al. 2020). In fact, as mentioned above, banks are experimenting with distributed ledger technology to improve the efficiency of trading, clearing, settlement, and custody (Yang 2022). In addition, the New York Innovation Center of the Federal Reserve Bank of New York (2022) is participating in an experiment with the notion of a regulated liability network, a conceptual financial market infrastructure that could enable transactions between regulated financial institutions potentially using DLT. and has noted that despite claims of being decentralized and trustless, blockchain-based applications are in practice neither; often, users access their crypto assets by going to a limited set of crypto asset platforms, and a small group of miners perform the majority of mining in most crypto assets, an activity that has costly implications for the physical environment, as discussed in box 8-3. When it comes to the “trustlessness” of blockchains, Schneier notes that a blockchain does not eliminate the need for trust but simply shifts trust away from individuals and institutions to a technology— along with all its features and bugs. Digital Assets: Relearning Economic Principles | 261 James Mickens, a leading computer scientist who studies distributed systems, has stated that in addition to not actually being decentralized and trustless, blockchains are often a very poor fit for their purported uses (Mickens 2018). This is primarily because the instant that the identity of a person or firm is needed (as is the case for supply chains, medical records, and land deeds), existing technologies can solve the same problem in a much more efficient way. For example, many of the cybersecurity benefits of an immutable, distributed blockchain can be replicated through existing features like tamper resistance (the ability to not change digital signatures at a later point in time) and nonrepudiation (a receipt of a sender of information’s identity that is delivered to both the sender and receiver of information, thus guaranteeing that both parties have processed the information) (World Bank, n.d.; NIST, n.d.). Proponents of blockchain technology claim that it will not only improve firms’ performance but also be the backbone of an entirely new Internet. Web3—the so-called new Internet—purports to retain all the privacy/networking benefits of the earliest versions of the Internet that existed roughly before 2000 (often called “web1,” which featured decentralized, community-governed open protocols), while keeping the high functionality of various features of web2 (the current version of the Internet) without the existing dependencies on large centralized firms like Google and Apple (Dixon 2021). However, Moxie Marlinspike (2022), the cryptographer and founder of the messaging app Signal, argues that the reason the current Internet features so much centralization is because it makes things easier, for two specific reasons. First, he argues that a decentralized Internet would require individuals and firms to host their own servers. However, centralized hosting of servers can be done much more cheaply and reliably by large entities and therefore benefits from economies of scale. Second, he notes that protocols—or the rules that Internet systems run on—are much more difficult to change than platforms. That is, centralized, non-open-source protocols can be managed by a single entity (as opposed to many), facilitating a wider variety of features that can change with much greater speed than if they were decentralized. Marlinspike also notes that web3 is already trending toward a centralized structure because of the ease and convenience that centralization brings, but in a much clunkier way than if traditional technology were being used. He specifically notes that “once a distributed ecosystem centralizes around a platform for convenience, it becomes the worst of both worlds: centralized control, but still distributed enough to become mired in time.” 262 | Chapter 8 Table 8-1. Top Ten Crypto Derivative Platforms by Open Interest Rank (1) 1 2 3 4 5 6 7 8 9 10 Exchange (2) BTCEX Binance BTCC Futures Deepcoin BingX Bitget Futures OKX Bybit MEXC Global Bitmart Futures 24-Hour Open Interest (Nominal $) (3) $8,314,364,513 $7,714,660,817 $5,103,831,418 $4,781,751,226 $4,334,560,170 $4,331,916,947 $3,586,501,924 $3,397,272,483 $3,228,041,626 $2,707,627,218 24-Hour Volume (Nominal $) (4) $7,180,531,116 $32,741,616,672 $7,968,963,153 $9,854,658,307 $5,165,147,675 $5,414,169,494 $8,449,781,644 $8,090,497,597 $2,263,323,835 $4,283,383,129 Source: CoinGecko. Data were collected on January 19, 2023. The Risks of Financial Innovation While the crypto assets ecosystem and its underlying technology introduce the potential for newfound efficiencies, efforts to challenge basic economic principles have frequently resulted in financial calamities. The economist Hyman Minsky hypothesized that financial crises often follow a similar cycle, whereby initially strong investments turn increasingly more speculative until a bubble bursts (Minsky 1992). Further, Minsky stated that this repeatedly happens because regulators are initially vigilant in the immediate aftermath of a crisis; but as time goes on, and the instrument of speculation changes, regulators take a less proscriptive approach to not harm “innovation” (Minsky 2008). According to Minsky, this relaxed regulatory environment invariably leads to another crisis. Indeed, other economists have argued that the most effective financial regulation has been introduced only after a crisis has occurred (Gorton 2012). Minsky’s theories became popular in the aftermath of the global financial crisis, when complicated financial products involving mortgages that exacerbated the crisis were initially hailed as innovative, and individuals discussing their risks were labeled “Luddites” by prominent commentators (Cassidy 2008; Wheatley 2013). Minsky’s writings, as they apply to past financial crises, may prove instructive for policymakers today. Fortunately, there has not yet been a systemic crisis caused by crypto assets, in part because they are not yet fully integrated with the rest of the financial system, giving policymakers time to Digital Assets: Relearning Economic Principles | 263 act appropriately. The risks presented by crypto assets stem from excessive speculation, high leverage, run risk, environmental harm from crypto asset mining, and fraudulent activities that harm retail investors and corporations. Because crypto assets appear to be here to stay, policymakers should consider these risks to avoid a “Minsky moment” caused by crypto assets. Other Risks from Crypto Assets Some risks that apply to crypto assets require further examination. Many of these risks are not unique to crypto assets; combined with innovative technology, they pose challenges for policymakers and regulators trying to minimize risks while encouraging responsible innovation. Leverage risks. Crypto asset derivative platforms—where investors can buy and sell financial derivatives linked to crypto assets—have seen substantial growth in the past two years (Damalas et al. 2022). Table 8-1 shows that the top 10 platforms for crypto asset derivatives, which account for roughly 76 percent of all volume in these derivatives, have over $47 billion in open interest and roughly $91 billion in daily trading as of January 18, 2023. According to one international regulator, one of the largest platforms, Binance, refuses to provide adequate and reliable information in response to regulatory requests (FCA 2021). Exchanges frequently tout the high amount of leverage they offer clients, stating that investors can take up to 100-to-1 leverage (debt-to-equity ratios) (Pechman 2021). These derivative platforms can create financial instability because positions with high leverage (debt-to-equity ratios) can amplify a shock to prices of crypto assets and lead to large losses and even defaults (U.S. Department of the Treasury 2022c). In particular, leverage leaves little room for prices to fall in a short amount of time, as steep price declines could induce brokers to issue large margin calls, thus forcing broader liquidation (Carapella et al. 2022). A relatively new application of DLT in financial markets where there is a relatively unknown amount of leverage is so-called decentralized finance (DeFi). DeFi attempts to offer financial products, such as loans, on the blockchain through the use of “smart contracts” (Carapella et al. 2022). The basic promise behind DeFi is to replace financial intermediaries, instead linking savers directly with borrowers (or buyers with sellers), allowing them to save on the spread that traditional intermediaries charge for creating the match with software. Though DeFi applications claim to help broaden access to credit by decreasing intermediation fees, they create serious risks to investors and cause at least two risks for the broader financial system: the use of significant leverage, and the performance of regulated functions without compliance with appropriate regulations. DeFi platforms acting as unregulated banks, broker-dealers, exchanges and other entities subject to 264 | Chapter 8 regulation should be operating in compliance with existing regulations and rules. DeFi lending platforms effectively receive funds from investors and use them to generate loans, promising interest to investors. This dynamic inherently causes run risks, where more investors try to redeem more of their funds than the platform can accommodate at a given time, thus causing the platform to either suspend convertibility or fail outright (Carapella et al. 2022). Furthermore, DeFi presents the opportunity for “synthetic leverage,” whereby investors can mask the true amount of leverage they are undertaking from the party from which they are borrowing (Tian 2021). If DeFi were limited to small, retail investors, the failure of a DeFi platform could still hurt these investors, but the shock could be relatively contained. Banking agencies issued a statement that expressed concerns with business models that are concentrated in crypto-asset-related activities or have concentrated exposures to the crypto asset sector (Federal Reserve Board 2023). Price volatility. Most crypto assets experience substantial price volatility. Holding such volatile assets could present challenges for large financial institutions if they were permitted to hold crypto assets, as the volatility would lead to constant changes on the asset side of their balance sheets. This volatility, in turn, could increase funding costs for banks and other financial institutions, thereby requiring banks—which fundamentally borrow so as to be able to lend—to increase the funding costs (interest rates) that they charge, leading to tighter credit conditions. Currently, this contagion risk is relatively muted, given that banks are limited in their ability to conduct crypto-related activities, such as acting as custodians of crypto assets (i.e., holding crypto assets for clients, not on their own balance sheets) (OCC 2020). Indeed, banking regulators such as the Federal Reserve have issued guidance requiring regulated financial institutions to inform their regulator before engaging in crypto-asset-related activity (Gibson and Belsky 2022). But other, less-regulated financial institutions, such as hedge funds, are increasingly investing in crypto assets. Such activity of lightly regulated or nonregulated entities can lead to “liquidity spirals,” as described by Brunnermeier and Pederson (2007). These spirals occur when a dramatic crash in the price of an asset—such as a crypto asset—leads a hedge fund to be margin-called, requiring the fund to sell off other positions to meet the margin call. If enough funds are exposed to the asset or assets with declining prices, then sell-offs could be broad enough to cause a deterioration in market liquidity. Illicit finance risks. Crypto assets are the standard form of payment extorted from victims of “ransomware,” whereby a malicious actor hacks an organization and demands payment to release control of the victim’s network and often to purportedly forgo leaking the victim’s stolen data. Crypto assets remove a critical friction in performing a ransomware hack. Because the attacker can demand that crypto assets be sent to a pseudonymous wallet Digital Assets: Relearning Economic Principles | 265 Figure 8-5. Nominal Cyber Insurance Prices Over Time Nominal index value: June 2014 =100 Annual change, in percent 450 135% 400 115% 350 95% 300 250 75% 200 55% 150 35% 100 15% 50 0 Jun-2014 Jun-2015 Jun-2016 Jun-2017 Jun-2018 Year-on-year % change (right axis) Jun-2019 Jun-2020 Jun-2021 Jun-2022 -5% Cyber insurance pricing index (left axis) Source: Howden Nova Analytics platform. instead of a bank account linked to a specific person, attackers can more easily launder or obfuscate payments made to them, in comparison with fiat currency (U.S. Department of Justice 2022). Importantly, like other financial assets, crypto assets can be misused for a range of illicit activities, including ransomware payments. Crypto assets have also been misused by human traffickers, by individuals exploiting children for sexual abuse, and by drug traffickers and scammers; to fund the activities of rogue regimes, such as the recent thefts by the Lazarus Group, which is affiliated with North Korea; and to finance terrorist activities (GAO 2021; U.S. Department of the Treasury 2022d). The other key illicit financing risks associated with crypto assets come from gaps in implementation of the international Anti-MoneyLaundering/Combating-the-Financing-of-Terrorism (AML/CFT) standards abroad; the use of anonymity‑enhancing technologies; in some cases the lack of covered financial institutions as intermediaries—and thus the absence of AML/CFT controls—in some crypto asset transactions; and service providers that are noncompliant with AML/CFT and other regulatory obligations, including compliance with sanctions obligations. With regard to the last, when crypto asset firms fail to register with the appropriate regulator, fail to establish sufficient AML/CFT controls, or do not comply with sanctions obligations, criminals are more likely to exploit their services successfully, including to circumvent U.S. and United Nations sanctions. Ransomware uses. As hacking to receive crypto assets becomes more widespread, more firms will attempt to insure themselves against these attacks by purchasing cyber insurance. However, the existence of such insurance may not eliminate the underlying problem, and instead may even 266 | Chapter 8 Table 8-2. Ransomware and Downtime Costs by Country, 2020 Country (1) Total Submissions (2) Minimum Cost ($, Nominal) (3) Estimated Costs ($, Nominal) (4) United States France Spain Italy Germany Canada United Kingdom Australia Austria New Zealand Total 15,672 4,476 4,088 3,835 3,747 3,236 2,718 2,072 819 265 40,928 5,123,606,318 1,452,222,393 1,332,008,900 1,255,260,122 1,214,481,832 1,058,505,964 878,155,444 678,541,158 268,888,310 86,448,688 13,348,119,130 20,494,425,272 5,808,889,571 5,328,035,599 5,021,040,489 4,857,927,329 4,234,023,855 3,512,621,775 2,714,164,633 1,075,553,242 345,794,755 53,392,476,519 Source: Emsisoft Malware Lab. create an incentive for hackers to attack insured firms and get paid by insurance. In fact, in an interview with The Record, a member of the Russian hacking group REvil was explicitly asked if they targeted organizations that have cyber insurance. The member responded: “Yes, this is one of the tastiest morsels. Especially to hack the insurers first—to get their customer base and work in a targeted way from there. And after you go through the list, then hit the insurer themselves” (Smilyanets 2021). One can observe evidence consistent with this vicious cycle from cyber insurance prices. The insurance brokerage Howden compiles a “Global Cyber Insurance Pricing Index,” which broadly measures premiums for cyber insurance (Howden 2023). As shown in figure 8-5, the cost of cyber insurance has increased more than 300 percent since July 2014. In addition to paying for ransom costs, companies affected by ransomware attacks typically are unable to maintain their business activity until they have made the payment. In its annual “State of Ransomware” report, the cybersecurity firm Emsisoft estimated the combined cost of ransom payments and business downtime to be $19.6 billion in the United States in 2020, and roughly $51 billion in total across the United States, France, Spain, Italy, Germany, Canada, the United Kingdom, Australia, Austria, and New Zealand (as shown in table 8-2) (Emsisoft Malware Lab 2021). It is crucial to note that the costs described here are direct costs. The indirect costs are likely higher. Instead of engaging in productive activities where firms have comparative advantages, they must divert resources to activities and products that help fend off attackers, such as buying cyber insurance and adding more personnel for information technology security. Thus, both welfare and economy-wide production decrease by a multiple of Digital Assets: Relearning Economic Principles | 267 the direct dollar costs of resources that firms are using to stop ransomware attacks. Investing in the Nation’s Digital Financial Infrastructure The growth of crypto assets has revealed a demand for a faster and more inclusive financial system with a real-time payment system and circulating digital money. Some have hoped that crypto assets could act as a form of decentralized money, making the U.S. payment systems faster, cheaper, safe, and more inclusive. This vision has not been realized. That said, there are still other ways near-term progress can be made on at least some of these goals. As a regulator of and participant in the Nation’s payment systems, the Federal Reserve has a historical role in maintaining these systems’ integrity (Federal Reserve Board, n.d.). For example, in the past decentralized payment systems were costly, in part, because some banks did not pay the full amount of a check from other banks—so-called nonpar collection or nonpar banking (Federal Reserve Board 1988). In some cases, this was done by levying a fee on checks deposited from other banks. Shortly after the establishment of the Federal Reserve System, it started providing payment services to banks, and over time it helped eliminate nonpar banking (Federal Reserve Bank of Minneapolis 1988). This section first discusses an upcoming improvement to U.S. payments, which will help many consumers and businesses make cheap, instant payments. It then discusses the possibility of introducing a central bank digital currency (CBDC), which is a digital form of money. While operating under the supervision of a trusted authority, both these mechanisms have the potential to realize many of the benefits that crypto asset developers have promised. The FedNow Instant Payment System In terms of overall value as of 2020, the largest retail payment system in the United States was the Automated Clearinghouse (ACH) (Federal Reserve Board 2022a). ACH provides an electronic means to exchange funds between banks and other depository institutions (Federal Reserve Bank of San Francisco, n.d.). Typical ACH payments include salaries, consumer and corporate bills, interest payments, dividends, and Social Security payments. Peer-to-peer payment platforms such as Venmo complete transfers that are in and out of their platforms by accessing ACH network services through a participant bank (Venmo, n.d.). The regional Federal Reserve banks and the Electronic Payment Network are the country’s two national ACH operators (Federal Reserve Board 2020). The prevalence of ACH offers many benefits; but a larger, more fast-paced economy is starting to arise. ACH 268 | Chapter 8 payments can be processed in same-day batches between banks, throughout the day, but a standard ACH transfer can take up to three business days for funds to be settled and available to end users. In addition, ACH settlements occur only on business days (Nacha 2021). Businesses and individuals alike are increasingly in need of faster payment systems. Advances in technology have created an opportunity for significant improvements in the way individuals and businesses make payments in today’s economy. In recent years, members of Congress, staff members of the Department of the Treasury, and other experts have called for the Federal Reserve to offer a faster payment system for both businesses and retail users (Warren 2019; Mnuchin and Phillips 2018; Klein 2019). As a result of the COVID-19 pandemic and increased consumer demand for e-commerce options, many businesses have also increased their efforts to offer quicker payment options (Rathjen 2022). In response, the Federal Reserve has prioritized designing and developing a faster payment system (Federal Register 2019).5 The Federal Reserve plans to launch this new system, which is called the FedNow Service, later in 2023 (Federal Reserve Board 2022b). Through financial institutions participating in FedNow, businesses and individuals will be able to send and receive payments conveniently, and recipients will have nearly instant access to funds, giving them greater flexibility to manage their money and make time-sensitive payments. This service will be operational 24 hours a day and 7 days a week. This uninterrupted processing of fund transfers is an important improvement over existing payment systems (Federal Reserve Board 2022b, 2022c, 2022d). This service is different from peer-to-peer services such as Venmo in many ways. For example, funds transferred via FedNow will be available more quickly than those that must first exit a peer-to-peer payment service and then enter the ACH bank transfer process, which can take time to settle. Beyond speed and convenience, near instant payments can yield real economic benefits for both individuals and businesses by allowing them to make time-sensitive payments whenever needed and providing them with more flexibility in managing their money. In particular, near instant payments under FedNow could bring significant benefits to vulnerable segments of the population. Slow payment systems can cost Americans billions of dollars. In addition to incurring bank overdraft fees, consumers can be forced to use high-cost alternatives like check cashers and payday lenders (Klein 2019). In 2019, it was estimated that a fast payment system such as FedNow could reduce these kinds of fees, generating savings of more than $7 billion a year for American households (Klein 2019). Because lowerincome individuals are more likely to be hurt by slow payment systems, Note that there is a private faster payment system, RTP, whose adoption has been low (Clearing House 2022). 5 Digital Assets: Relearning Economic Principles | 269 they could especially gain from these savings if FedNow is adopted widely. Using innovation productively and responsibly in this way could make banking services more inclusive. FedNow requires commitment and active engagement by the private sector to make it interoperable, which means connecting and communicating with other payment services (Federal Reserve Board 2022c). According to the Federal Reserve, interoperability is crucial for “payment messages [to be] routed or exchanged and settled such that the sender may initiate a payment that will seamlessly reach the receiver. With interoperability, an individual or business with a bank account would be able to send a payment to another individual or business without having to choose, understand, or even be aware of the path taken by the payment.” While noting that interoperability can take different forms, the Federal Reserve has maintained that it alone cannot fully establish the interoperability of FedNow; achieving this will require active partnership and collaboration with the financial industry (Federal Reserve Board 2022c). Some have suggested that near instant digital payment systems like FedNow may reduce the need for circulating digital money (NAFCU 2022). In this case, the benefits of circulating digital money after FedNow is launched may be minimal. In fact, Federal Reserve governor Michelle Bowman commented in August 2022 that “my expectation is that FedNow addresses the issues that some have raised about the need for a CBDC” (Bowman 2022). Conversely, FedNow is intended to mainly focus on domestic payments and may bring limited improvements to the cross-border payment system, at least initially. In addition, FedNow is not a digital asset, which can be used in settlements or provide transaction programmability, roles that circulating digital money could play in the globally integrated financial system. Central Bank Digital Currencies It is important to note that money can come both in a physical format (e.g., cash) and in a digital format (e.g., electronic bank accounts). Thus, a central bank’s digital currency is a liability of a central bank similar to cash, but it exists on a digital platform, where it can be exchanged and settled in real time. A CBDC system is made up of the CBDC itself, the public and private sector components that work alongside the CBDC, and the laws and regulations that apply to these digital assets (White House 2022a). A CBDC system can be set up in numerous different ways, such as a wholesale CBDC, which allows for access only by financial institutions (e.g., banks); and a retail CBDC, which allows for access by individuals. “That said, certain design features and questions related to the underlying infrastructure 270 | Chapter 8 of CBDC may blur these distinctions to some degree” (U.S. Department of the Treasury 2022e). As of January 5, 2023, 11 countries have launched CBDCs (Atlantic Council 2022). In addition, a number of foreign central banks, including the European Central Bank and the Bank of Japan, are exploring CBDCs; and some central banks, such as the People’s Bank of China, are piloting a retail CBDC (Gorton and Zhang 2022). While some countries have considered using DLT for their CBDC, it is worth noting that many of the pilot programs for CBDC systems are not built on DLT; instead, they rely on a trusted central authority—a country’s central bank—to operate key aspects of the CBDC system. This seems likely to be the case if a U.S. CBDC is introduced. A White House assessment of a potential U.S. CBDC system recently noted that “while a U.S. CBDC system could, in theory, be mostly ‘permissionless’ from a governance standpoint, this design choice introduces a large number of technical complexities and practical limitations that strongly suggest a permissionless approach does not make sense for a system that has at least one trusted entity (i.e., the central bank)” (White House 2022a). This is somewhat ironic, given that this is different from an oft-cited founding principle of crypto assets like Bitcoin, whose purported aim was to create decentralized money without any trusted central authority. A U.S. CBDC—a digital form of the U.S. dollar—would have the potential to offer significant benefits. It could enable a payment system that is more efficient, provide a foundation for further technological innovation, facilitate faster cross-border transactions, and be environmentally sustainable (White House 2022a). It could also promote financial inclusion and equity by enabling access for a broad range of consumers (Maniff 2020). A potential U.S. CBDC could also help support other policy goals. For example, a potential U.S. CBDC could help ensure that such payment systems are aligned with the principles of human rights, democratic values, and privacy (U.S. Department of the Treasury 2022e). There are also some risks from having a CBDC in the financial system. Similar to one-to-one backed stablecoins, CBDCs may also pose credit availability risks (U.S. Department of the Treasury 2022b). That is, a widely available CBDC could serve as a substitute for commercial bank deposits. Just as in the case of stablecoins that are fully backed by safe assets, this substitution effect could reduce the aggregate amount of deposits in the banking system, which could in turn increase bank funding expenses, and thus could reduce credit availability or raise credit costs for households and businesses. In addition, because central bank money is the safest form of money, a widely accessible CBDC would be particularly attractive to riskaverse users (and likely more so than a stablecoin), especially during times of stress in the financial system. The ability to quickly convert bank deposits into a CBDC could make systemic bank runs more likely or more severe Digital Assets: Relearning Economic Principles | 271 (Bank of Canada et al. 2021). In addition, CBDCs could cause operational risks. If the CBDC platform could not function due to a system failure or a cyberattack, it could erode investors’ confidence. Recognizing the potential benefits and risks from a U.S. CBDC, the Biden-Harris Administration has developed “Policy Objectives for a U.S. CBDC System,” which reflect the Federal Government’s priorities for a potential U.S. CBDC (White House 2022e). These objectives flesh out the goals outlined for a CBDC in the Executive Order. According to these objectives, the “U.S. CBDC system, if implemented, should protect consumers, promote economic growth, improve payment systems, provide interoperability with other platforms, advance financial inclusion, protect national security, respect human rights, and align with democratic values.” Conclusion Innovation in financial services brings both risks and opportunities for the broader economy. It can challenge business models and existing industries, but it cannot challenge basic economic principles, such as what makes an asset effective as money and the incentives that give rise to run risk. Although the underlying technologies are a clever solution for the problem of how to execute transactions without a trusted authority, crypto assets currently do not offer widespread economic benefits. They are largely speculative investment vehicles and are not an effective alternative to fiat currency. Also, they are too risky at present to function as payment instruments or to expand financial inclusion. Even so, it is possible that their underlying technology may still find productive uses in the future as companies and governments continue to experiment with DLT. In the meantime, some crypto assets appear to be here to stay, and they continue to cause risks for financial markets, investors, and consumers. Much of the activity in the crypto asset space is covered by existing regulations and regulators are expanding their capabilities to bring a large number of new entities under compliance (SEC 2022). Other parts of the crypto asset space require coordination by various agencies and deliberations about how to address the risks they pose (U.S. Department of the Treasury 2022a). Certain innovations, such as FedNow and a potential U.S. CBDC, could help bring the U.S. financial infrastructure into the digital era in a clear and simple way, without the risks or irrational exuberance brought by crypto assets. Hence, continued investments in the Nation’s financial infrastructure have the potential to offer significant benefits to consumers and businesses, but regulators must apply the lessons that civilization has learned, and thus rely on economic principles, in regulating crypto assets. 272 | Chapter 8 Chapter 9 Opportunities for Better Managing Weather Risk in the Changing Climate Global temperatures as high as those in recent years are unprecedented in the time span of human civilization and have likely not been seen in at least the last 125,000 years of Earth’s history (Gulev et al. 2021). Many nations, including the United States, are working ambitiously to limit the impact of climate change by reining in greenhouse gas emissions and harnessing the opportunities of the clean energy transition. However, given the time it takes to transform the global energy system and for the climate to respond, the climate will continue changing at least until global greenhouse gas emissions fall to zero. In the coming decades, more intense and frequent weather extremes and the uncertainty of the changing climate will present a range of economic and financial risks to the U.S. economy and will confront the Federal Government with related fiscal challenges. Physical climate risks can be managed by anticipating and planning for coming changes in climate, a process known as adaptation. Adaptation presents opportunities to lower climate change costs over the long-term while also building resilience to natural hazards and weather risks today. The design of climate adaptation policies must recognize that actors across the United States, including individuals and businesses and all levels of government, already face incentives to adapt to climate change. But they also face informational, financial, and legal constraints that may limit their ability to adapt. Targeting adaptation policies to alleviate these constraints and address related market failures should be most effective in supporting private action. 273 The Federal role in climate change adaptation is complicated by the complex, nested governance structure of relevant policy areas. Many important areas that are relevant to adaptation, from land use planning and zoning to the regulation of insurance markets, are governed at the State or local level. The Federal Government, however, has a strong interest in advancing reforms in these areas to address climate change because of its role in managing risks across the United States, from credit and insurance provision to disaster response and recovery to social safety net programs. The risks that climate change poses are multidimensional, regionally specific, and vary based on underlying socioeconomic vulnerabilities. Adaptation policies need to be targeted to particular settings and therefore will need to be both varied and complex. This chapter proposes four overarching objectives for structuring further Federal adaptation efforts: • Producing and disseminating knowledge about climate risk • Long-term planning for the climate transition • Ensuring accurate pricing of climate risks • Protecting the vulnerable. T he United States has joined nations around the world in acting to reduce greenhouse gas emissions. If fully implemented, national pledges may limit global warming to 3.6°F (2°C; Meinshausen et al. 2022), achieving a primary goal of the Paris Agreement (United Nations Climate Change, n.d.). The steeply falling costs of low-carbon technologies and the increasingly ambitious climate policies of many countries are bending the global emissions curve, rendering worst-case outcomes of 8–10°F of warming by the end of the century increasingly unlikely (Hausfather and Moore 2022). The United States has implemented major domestic legislation to achieve its own goals to reduce emissions to 50–52 percent below 2005 levels by 2030 and to reach net-zero emissions by 2050. In particular, the August 2022 Inflation Reduction Act and the November 2021 Bipartisan Infrastructure Law together make $430 billion in investments in American infrastructure, with a focus on the climate challenges facing our Nation (U.S. Department of Energy 2022). However, even given the ambitious action to rein in emissions that will be required to meet these national commitments, the climate will 274 | Chapter 9 continue changing for the foreseeable future, for two main reasons. First, it will take decades to completely transform the global energy system, which currently relies heavily on greenhouse-gas-generating fossil fuels. Second, the climate system will take years to respond to changes in emissions. At present, temperatures across the lower 48 States are about 1.8°F above their level in 1900 (Vose et al. 2017, 185); even if all nations meet their emission reduction goals, global warming will at least double by 2100. (Meinshausen et al. 2022). The effects of global warming are already apparent across the United States, in the form of more extreme heat and longer heat waves (National Oceanic and Atmospheric Administration, n.d.; Lipton et al. 2018; Gutiérrez et al. 2021, p. 2004); more extreme rainfall events and associated flooding (U.S. Environmental Protection Agency 2021; Davenport, Burke, and Diffenbaugh 2021); more frequent and intense droughts driving huge wildfires (Williams, Cook, and Smerdon 2022; Borunda 2021; Burke et al. 2021); and higher sea levels driving coastal flooding and worse storm surges (Hino et al. 2019; Marder 2020). Among the events that have been formally linked to climate change in recent years are the ongoing drought in California (Diffenbaugh, Swain, and Touma 2015); exceptionally dry conditions in the Southwest (Park Williams, Cook, and Smerdon 2022); extreme heat in the Pacific Northwest (Bercos-Hickey et al. 2022); flooding in the mid-Atlantic (Winter et al. 2020); major western wildfires (Yu et al. 2021); the damage caused by Hurricane Harvey, which inundated Houston in 2017 (Frame et al. 2020; World Weather Attribution 2017); and severe, rain-induced flooding in Louisiana in 2016 (van der Wiel et al. 2017). Fluctuating weather conditions have always presented challenges. The inherent variability of the atmospheric system means that specific weather conditions cannot be predicted—even in principle—beyond a 7- to 10-day predictability horizon. In a stable climate, however, the probability of different weather conditions can be accurately estimated. A stable climate allows actors to understand the risks of weather-dependent outcomes and to plan accordingly, in designing infrastructure, allocating investments, and adjusting daily routines and habits. For example, statistical methods currently used to design infrastructure assume a stable and unchanging climate, where the probability of extreme weather over the lifetime of the infrastructure remains constant at historical values (Milly et al. 2018). With the human-induced climate change of today, however, it is no longer possible to assume that the future will be like the past and to use unadjusted past experience as a guide for the future. Decisions made using only historical weather records will become increasingly inaccurate and costly as weather patterns change (Milly et al. 2018; Electric Power Research Institute 2022). Weather variability occurring in the context of the Opportunities for Better Managing Weather Risk in the Changing Climate | 275 changing climate will result in repeated experiences of historically unprecedented weather conditions (Fisher, Sippel, and Knutti 2021). Even small changes in average climate conditions can produce large changes in the probability of previously rare weather events. Social, financial, and infrastructural systems that manage these risks typically have certain tolerances, with steeply increasing costs when these thresholds are exceeded. For instance, construction is often designed to standards based on historical weather conditions, such as a 1-in-100-years rain event, and conditions exceeding these design thresholds can produce dangerous conditions that have high economic and social costs (American Society of Civil Engineers 2018, 239). A shifting climate can quickly render these standards obsolete. In the example shown in figure 9-1 using an illustrative climate distribution, the mean changes by just under 20 percent, but the probability of an extreme event (the area of the distribution to the right of the green line) increases by about 80 percent. Modern societies have been and continue to be ordered for a climate that no longer exists; therefore, the projected rapid changes in the climate system will pose major, evolving risks for economic, social, infrastructural, and governance systems in coming decades. Recognizing and planning for these risks—a process that is often called adaptation—can reduce costs, improve stability, and protect the most vulnerable people and communities. Because climate change touches many aspects of economic production and societal well-being, adaptation policies need to be equally broad, ranging Figure 9-1. Small Changes in Climate Can Greatly Increase the Probability of Extreme Weather Events Probability of occurrence Past climate System design standard New climate Weather metric (e.g., five-day cumulative rainfall) Source: CEA calculations. 276 | Chapter 9 from the provision of decision-relevant climate information to the regulation of insurance markets to improved building codes and zoning. Box 9-1 describes some of the ongoing investments being made by the Biden-Harris Administration to build resilience while reducing and managing the costs of a changing climate. Economic Principles of Adaptation Policy and Planning The economic principles that support adaptation planning for climate change are both different from, and more varied than, those underpinning the reduction of greenhouse gas emissions, typically referred to as mitigation. Mitigation is a classic example of a public good. The costs of emissions accrue to people all around the world and will last far into the future. Because the market prices of fossil fuels do not incorporate these large social costs, climate change can be understood as a global externality—what Nicholas Stern has termed “the greatest market failure the world has ever seen” (Stern 2006). Private markets have little incentive to provide emissions reduction in the absence of government action. Moreover, because the benefits of reducing emissions accrue globally and are not captured within the borders of a single country, nations must cooperate to address the climate challenge (Nordhaus 2019). Aligning the incentives of private actors and nations throughout the world to account for the climatic effects of fossil fuels requires coordinated action. In contrast, many adaptation decisions are private goods, in that both the costs and benefits are largely internalized by the decisionmaker (Mendelsohn 2000; Kahn 2021; Kolstad and Moore 2020). For adaptation, communities, households, and businesses all have their own motivations for responding to and planning for climate risks. Examples include a homeowner investing in reinforced windows to defend against stronger storms, a farmer choosing what crops to grow in response to changing drought conditions, and a business adjusting suppliers to reduce weather-related disruptions that are shifting due to climate change. Though there are public goods problems and other market failures related to adaptation (discussed in detail later in the chapter), they are varied, specific, localized, and very different from the global externality problem that characterizes mitigation. Indeed, there is already evidence that private actors are starting to consider emerging climate risks in their decisions. For example, recent research suggests that both the risks of sea-level rise and the productivity effects of extreme heat are reflected in property and agricultural land prices (Keys and Mulder 2020; Bernstein, Gustafson, and Lewis 2019; Baldauf, Garlappi, and Yannelis 2020; Severen, Costello, and Deschênes 2018). Climate risk premiums are also showing up in longer-term corporate and municipal bonds (Painter 2020; Acharya et al. 2022; Goldsmith-Pinkham, Gustafson, Opportunities for Better Managing Weather Risk in the Changing Climate | 277 Box 9-1. Adaptation and Resilience Investments of the Biden-Harris Administration The 2021 Bipartisan Infrastructure Law (BIL) and the 2022 Inflation Reduction Act (IRA) both contain a number of provisions to build resilience to natural disasters and adapt social and economic systems to reduce the costs of climate change. The Biden-Harris Administration is in the process of implementing these laws, making historic investments in climate change resilience. The BIL in particular provides $50 billion for adaptive investments such as support for energy-efficiency improvements in low-income households, grants to states and territories for resiliency projects and flood mitigation, dedicated funding to improve resiliency of transportation systems, and funding for wildfire defense and coastal adaptation (White House 2022g, 2022h; U.S. Department of Transportation 2022; U.S. Department of Commerce 2022). Resiliency provisions within the IRA include tax credits and rebates for improving home energy efficiency and funding for addressing droughts and improving water infrastructure (White House 2022i). Beyond the investments provided by the BIL and IRA, the United States is pursuing a multipronged adaptation strategy that includes: inter-agency coordination on climate extremes, building codes and climate-related financial risks; the provision of climate data tools such as the Climate Mapping for Resilience and Adaptation portal; and building resilience into Federal procurement (UN Framework Convention on Climate Change 2021; Climate Mapping for Resilience and Adaptation, n.d.). Principal agencies are now required to develop and implement Climate Adaptation and Resilience plans and report annually on implementation and progress under the plans (White House 2022j). Several programs target assistance to groups that may be particularly vulnerable such as low-income and Tribal communities (White House 2022g, 2022h). These historic investments are laying the foundation that will be required for building adaption and resilience in the United States. This chapter describes both the broad economic principles that underlie adaptation policy generally and that could support future work building on this foundation. For instance, spending on adaptation and resilience projects will be most effective when coupled with reforms to zoning, building codes and infrastructure standards (mostly governed at the State and local level; see figure 9-3), as well as clear communication of strategic priorities for the management of climate risks at all levels of government. 278 | Chapter 9 and Lewis 2021). There is also evidence that property market adjustments are concentrated in regions where more people report believing in climate change, which suggests continuing pricing frictions in areas where there is greater skepticism about climate change (Barrage and Furst 2019; Bernstein, Gustafson, and Lewis 2019; Baldauf, Garlappi, and Yannelis 2020; Severen, Costello, and Deschênes 2018). The importance of private incentives in shaping climate action has implications for the design of adaptation policy. First, effective adaptation policy will recognize that individuals, households, businesses, and communities will act in response to the shifting climate. But these actions will be defined by legal, informational, and financial constraints, along with the sometimes-distorted or perverse incentives that these actors face. Government action that targets constraints and market failures that impede adaptation should be most effective in supporting and enabling private action rather than crowding out actions that would have occurred anyway. Examples of these constraints and market failures, as well as policies that can address them, are discussed in detail later in the chapter. Second, government policies and programs already play a role in determining how the costs of weather-related hazards are distributed through society, a role that will likely grow in importance as climate change effects worsen. Individuals with more wealth and a higher income are better able to avoid, prepare for, and recover from weather-related shocks. This means that, in the absence of counteracting policies, climate change costs will likely be disproportionately borne by the poor and marginalized. Programs of public lending, insurance, grants, and welfare can be designed to reallocate some of these risks and thus reduce the regressive nature of climate change costs. However, the existing disaster response and social support system is composed of legacy programs that were designed in a pre–climate change era and thus may not be adequate for addressing climate change. Programs are distributed across multiple agencies and levels of government, often with burdensome applications or complex requirements, making their net distributive effect difficult to determine (Mach et al. 2019; Howell and Elliott 2019). In fact, there is evidence that postdisaster aid can exacerbate rather than mitigate preexisting inequalities (Billings, Gallagher, and Ricketts 2022). Comprehensive reevaluation and reform of the system composed of these interacting support programs, building on the ongoing adaptation work within the Biden-Harris Administration (box 9-1), will be required to ensure that the system is able to protect the vulnerable as climate change damage grows. The next sections first review new evidence on the economic costs and financial risks that climate change poses to the United States and to Federal finances and then outline the role that a Federal adaptation strategy could play in managing and reducing physical climate risk. Opportunities for Better Managing Weather Risk in the Changing Climate | 279 The Economic Costs and Financial Risks of Climate Change in the United States Shifting climate patterns are producing a wide array of risks affecting the well-being of American communities. Earlier research on climate change economics assumed that higher-income countries like the United States would be able to manage the effects of changing weather conditions relatively easily because of the small share of clearly exposed sectors of the economy, such as agriculture and forestry, and the assumption that adjusting to climate change would be straightforward (Mendelsohn, Nordhaus, and Shaw 1994; Nordhaus 1991, 930). However, this assessment needs to be reconsidered in light of new economic evidence and the increasing intensity and frequency of extreme weather events across the United States that can be formally attributed to climate change (Seneviratne et al. 2021). The Costs of Climate Change for the United States’ Well-Being and Prosperity Weather variability has a range of effects within the United States. For example, studies have shown that very hot temperatures have adverse effects—including increasing premature mortality and worsening of the health of newborn babies (Deschênes and Greenstone 2011; Deschênes, Greenstone, and Guryan 2009; Barreca and Schaller 2020); decreasing crop yields (Schlenker and Roberts 2009); adversely affecting mental health (Burke et al. 2018); lowering the labor supply in exposed industries (Graff Zivin and Neidell 2014); increasing violence (Mukherjee and Sanders 2021); and reducing students’ ability to learn (Park et al. 2020). These effects are not borne equally across geographic regions or economic sectors within the United States, and they are felt most acutely among disadvantaged groups with a high vulnerability to natural hazards (box 9-2). Climate models predict that extreme heat will become more frequent and intense as climate change continues (IPCC 2021). Today, in many parts of the United States, the heat wave season is nearly three times longer than in the 1960s; in the summer of 2022 alone, 400 U.S. locations broke their monthly temperature records (Lipton et al. 2018; Stevens and Samenow 2022; U.S. Global Change Research Program 2018, figure 1.2b). There is some evidence that people, businesses, and communities can adjust to hotter temperatures over time—for instance, by changing the timing of outdoor activities (Graff Zivin and Neidell 2014; Dundas and von Haefen 2020) or by using more energy for cooling (Auffhammer 2022; Deschênes and Greenstone 2011). But these adaptations are costly, do not eliminate climate change costs, and may reduce the quality of life (Deschênes 2022). 280 | Chapter 9 Box 9-2. Climate Change Will Most Likely Interact with and Exacerbate Existing Inequalities The effects of climate change are not evenly distributed across the U.S. population by income, race, or ethnicity. Lower-income communities have fewer resources to prepare for or respond to extreme weather events, leaving them more vulnerable to their effects. For instance, residents of lower-income communities are less likely to evacuate away from the path of hurricanes and tend to live in more vulnerable structures, leaving them at a higher risk of mortality or injury (Deng et al. 2021; Fothergill and Peek 2022). Low-income Americans are less able to adjust their activities to avoid exposure to wildfire-induced air pollution or to adjust air-conditioning in response to extreme heat (Burke et al. 2022; Cong et al. 2022). They are more likely to work in industries such as agriculture and construction that are highly exposed to dangerously high temperatures (U.S. Environmental Protection Agency 2021). Major natural disasters are more likely to lead to financial hardships, such as debt defaults and bankruptcies in low-income and minority communities (Billings, Gallagher, and Ricketts 2022; Jerch, Kahn, and Lin 2022). Injustices throughout U.S. history mean that these effects are also strongly differentiated by race. For instance, because of forced relocation away from their tribal homelands, many Native American people live on marginal lands that are highly susceptible to wildfires, extreme heat, and droughts (Farrell et al. 2021). Minority and low-income areas within cities, including formerly redlined neighborhoods, are substantially hotter than wealthier, whiter, and nonredlined areas (Hoffman, Shandas, and Pendleton 2020; Benz and Burney 2021); Black and Hispanic students are more likely to attend schools without air-conditioning (Park et al. 2020); and minority communities are more likely to be affected by expected increases in extreme heat and coastal flooding due to climate change (U.S. Environmental Protection Agency 2021; Wing et al. 2022). Wealth and assets allow households to avoid, prepare for, respond to, and recover from weather-related shocks, leaving minority populations that lack these assets more exposed to intensifying weather extremes. Minority populations, particularly African Americans, were barred from accessing avenues for wealth accumulation for centuries—for instance, through discriminatory home-lending practices that persisted through much of the 20th century—leading to stark disparities today in wealth and assets by race and ethnic group; the median wealth of white households is almost eight times that of Black households (Rothstein 2017; Derenoncourt et al. 2022; Cook 2014; Bhutta et al. 2020). Moreover, disadvantaged and racial minority communities have generally received less financial assistance after disasters than affluent white communities (National Advisory Council 2020), partly because some of this aid is tied to property ownership, from which minorities have Opportunities for Better Managing Weather Risk in the Changing Climate | 281 been historically excluded. The Biden-Harris Administration is working to address these inequities. The Federal Emergency Management Agency (FEMA) has committed to establishing an equitable and fair distribution of Federal aid and assistance and to increase access for underserved populations (U.S. Department of Homeland Security 2022). In response to past inequities, the U.S. Environmental Protection Agency recently created the Office of Environmental Justice and External Civil Rights, which seeks to coordinate and prioritize environmental justice within the agency and in its partnerships with other Federal agencies (U.S. Environmental Protection Agency, n.d.). More generally, the Biden-Harris Administration is working to direct 40 percent of the benefits of climate and clean energy investments to disadvantaged communities. Several programs in the IRA and BIL, most notably the $27 billion Greenhouse Gas Reduction Fund, also target those communities (U.S. Environmental Protection Agency 2022; White House, n.d.). Beyond extreme heat, climate change is associated with a host of other costly events. About one-third of the cost of major flood events since 1988, totaling $79 billion, has been attributed to climate change (Davenport, Burke, and Diffenbaugh 2021). The western United States is currently having the worst drought in at least 1,200 years, requiring costly cutbacks in water use and endangering the functioning of the Lake Meade and Lake Powell reservoirs (Wheeler et al. 2022; Park Williams, Cook, and Smerdon 2022; Borunda 2021). California and the Pacific Northwest have suffered devastating wildfires that blanketed cities under thick smoke, undermining decades of air quality gains driven by the Clean Air Act and forcing preemptive blackouts to avoid igniting wildfires, temporarily cutting off power to millions of customers (Burke et al. 2022; Childs et al. 2022; Goss et al. 2020; Chediak 2019). Hurricane Ian struck Florida in September 2022, causing a coastal storm surge of up to 18 feet and widespread inland flooding; it will end up being one of the costliest storms on record, with losses to residential and commercial property estimated at between $36 billion and $62 billion (CoreLogic 2022; Paquette and Kornfield 2022). Unprecedented extreme events are exposing the weaknesses of aging U.S. infrastructure, which was designed to operate in different climate conditions. Multiple infrastructural systems gradually built over many decades— including electricity grids, dams and irrigation systems, coastal and riverine defenses, roads and railways, and ports—will need to be quickly redesigned, retrofitted, or rebuilt to maintain their functionality in the changing climate. And this investment in climate resilience will need to be made while also addressing the estimated $2.6 trillion in deferred infrastructure investments 282 | Chapter 9 required over the next 10 years just to restore existing infrastructure to working order. These costs will likely be borne by cities, States, and the Federal government, along with specific infrastructure user groups such as electric utility customers or irrigation district members (American Society of Civil Engineers 2021). The complexity of global supply chains means that extreme weather events around the world can ripple through international trade networks, to affect American producers and consumers (Woetzel et al. 2020a). Pankratz and Schiller (2022) show how disruption from extreme heat and flooding in supplier locations is transmitted through supply chains to affect the revenues of customer firms. As climate change intensifies, the chances of major disruptive weather events occurring simultaneously in multiple regions will increase, causing larger and more systemic threats to supply chains. The summer of 2022 saw major heat and drought events in the United States, Europe, and China disrupt global production, which prevented the transporting of agricultural products along rivers and shut down electricity generation for car and electronics factories, and thus exacerbated supply chain challenges (Ahmedzade et al. 2022; Bradsher and Dong 2022; Plume 2022). Crop failures and other effects of climate change could also exacerbate volatile conditions in fragile nation-states, leading to instability and conflict—with spillover effects to the United States via migration and escalation of local conflicts into national security concerns (Missirian and Schlenker 2017; Benveniste, Oppenheimer, and Fleurbaey 2020; Mach et al. 2019; White House 2015a). Studies have suggested that both the conflict in Syria and the flows of migrants from Central America have been exacerbated by climate-related stressors (Ash and Obradovich 2019; Kelley et al. 2015; Lustgarten 2020). Recent massive flooding in Pakistan led to the inundation of one-third of the country by floodwaters, internal displacement of 33 million people, massive disruption of the agricultural sector, and a sovereign debt downgrade (Lu 2022; Fitch Ratings 2022). By shifting where nonhuman species live and how they interact with humans, climate change could even increase the risks of zoonotic disease spillovers (Carlson et al. 2022). There is some evidence that climate change could also affect macroeconomic growth. Empirical investigations of the causal relationship between temperature fluctuations and gross domestic product (GDP) generally find negative effects from hotter temperatures, particularly in poor and hot countries, with some indications that this heat depresses growth rates (Dell, Jones, and Olken 2012; Burke, Hsiang, and Miguel 2015; BastienOlvera, Granella, and Moore 2022). According to modeling and simulation studies of the macroeconomy, negative effects of climate change on growth greatly increase both the magnitude and uncertainty of aggregate climate costs (Moore and Diaz 2015; Newell, Prest, and Sexton 2021), but little is currently understood about the mechanisms connecting weather shocks and Opportunities for Better Managing Weather Risk in the Changing Climate | 283 long-term climate trends to macroeconomic variables. Plausible mechanisms include faster depreciation of the capital stock resulting from more intense extremes (Hallegatte, Hourcade, and Dumas 2007; Otto et al. 2023), effects to productivity growth (Ortiz-Bobea et al. 2021), or effects on the labor force that either depress the supply of labor or slow the accumulation of human capital via effects on learning (Graff Zivin and Neidell 2014; Park et al. 2020). Improving empirical evidence and modeling capabilities in this area should be a high priority for future research. Because climate change affects many aspects of well-being, including those not traded in traditional markets, the costs of climate change and losses of other natural capital are mismeasured by GDP (Coyle 2015; Brunetti et al. 2021; Svartzman et al. 2021; NGFS-INSPIRE 2022). For example, production that emits climate-change-causing greenhouse gases adds to GDP, and so too do expenditures to adapt to climate damage or reduce emissions. Meanwhile, many important services that nature provides, including reducing risks to health and protection from climate-driven extreme weather events, are not reflected in GDP or are misattributed. Examples include the role urban trees play in providing shade and lowering heat extremes or the value of intact wetlands in reducing coastal storm damage. A more complete accounting system than GDP that tracks national wealth—the stock of multiple forms of capital that produce flows of both market and nonmarket benefits—could provide clearer macroeconomic information on climate change than exclusive reliance on GDP (Agarwala and Coyle 2021; Dasgupta 2021). Including natural capital in measures of wealth would help track climate change costs and nature loss in ways that complement GDP and fill in important blind spots. This is why the Biden-Harris Administration has begun the process of rigorously measuring natural capital in a way that could inform a more complete picture of economic progress and climate change costs (White House 2022c). Climate Change and Financial Stability Climate change risks have historically been unpriced in private markets, but as these risks become increasingly apparent and investors become more cognizant of the threat, price adjustments for exposed assets would be expected. Property and long-lived physical infrastructure are particularly exposed, and risk becoming stranded as climate conditions shift beyond their design standard, thus causing investments to underperform or fail altogether. There is substantial evidence that current natural hazard risks are undercapitalized in property markets, a setting that could produce sudden price shocks in response to new information that reveals underlying risks to buyers (Bakkensen and Barrage 2022; Baldauf, Garlappi, and Yannelis 2020; Hino and Burke 2021; Gibson and Mullins 2020). 284 | Chapter 9 Certain financial instruments—such as insurance contracts, catastrophe bonds, and mortgages—that directly or indirectly price weather-related risks are also highly exposed to climate change. Rapid changes in asset prices or reassessments of the risks in response to a shifting climate could produce volatility and cascading instability in financial markets if not anticipated by regulators. Because of the interaction of long-lived investments and direct exposure to weather extremes, property insurance against catastrophic natural hazards is at the forefront of climate change risk exposure and is already showing signs of strain in several states (box 9-3). Governments have typically stepped in to provide coverage when private insurers pull out from particularly risky areas or hazards. More than 95 2/6/2023 percent of flood policies in the United States are insured through the Federal National Flood Insurance Program (NFIP), and the number of policies in State-run insurance plans has more than doubled since 1990 (Kousky et al. 2018). Figure 9-2 shows the long-run growth in State-run disaster insurance plans combined with a sharp rise following major hurricane strikes in 2004 and 2005, after which the largest insurance companies pulled out of Florida (Leefeldt 2022). Although the State was able to move policies off its rolls and back into the private market in the 2012–15 period, Florida’s public insurer, Florida Citizens, is once again the largest property insurer in Figure 9-2. Count of Policies under U.S. Residual Property Insurance Market, 1990–2021, with Geographic Breakdown for 2021 Policies in force, millions 3.5 Other, 12.0% 3.0 Massachusetts, 9.1% California, 10.4% 2.5 Texas, 12.4% 2.0 North Carolina, 19.5% 1.5 1.0 Florida, 36.6% 0.5 0.0 1990 1995 2000 2005 2010 2015 2020 Sources: Insurance Information Institute, n.d.; Citizens Property Insurance Corporation of Florida, n.d. Note: Data are linearly interpolated for the years 1991–94, 1996–98, and 2001–2. Policies from North Carolina are included only for 2011 (2.2 percent of total policies in that year) and later. Opportunities for Better Managing Weather Risk in the Changing Climate | 285 Box 9-3. Disaster Insurance in the Changing Climate: Challenges and Opportunities for Reform Even in the absence of climate change, weather-related extremes—such as flooding, hurricanes, and wildfires—pose particular challenges to the insurance industry. Losses due to natural disasters are highly concentrated in space and time, leaving insurers financially vulnerable to a major weather event (Wagner 2020). Moreover, the distribution of losses from these events is “thick-tailed,” a statistical property meaning that expected losses are heavily influenced by extremely rare events, the risks of which are difficult to quantify and price (Conte and Kelly 2018; Kousky 2022, 38–42). In the face of these challenges, insurers must limit exposure by either exiting from or limiting activity in certain markets or by purchasing reinsurance, which raises costs. For these reasons, even without climate change, natural disasters hover on the edge of insurability. Without reforms to improve the functioning of insurance markets and reduce the costs of extremes (for instance through zoning changes and building code improvements), climate change could well make many more hazards uninsurable as the frequency and intensity of extremes increase. Climate change increases uncertainty, particularly in the tails of the distribution that drive expected losses, and raises the risk of completely unprecedented events for which there is no historical analog (figure 9-1). In the absence of high-quality, trusted information on quickly evolving climate risks, ambiguity in how to price extreme weather risks could lead insurers to leave certain markets altogether. Major insurers have already stopped offering hurricane wind coverage along the Gulf Coast and are increasingly exiting high fire-risk areas in California (Sadasivam 2020; Elliott 2022; Schuppe 2022; Querolo and Sullivan 2019). Moreover, the increasing likelihood that multiple catastrophic events could occur concurrently could raise costs or limit the availability of reinsurance. A lack of access to insurance makes extreme events more costly because it slows economic recovery in affected communities and raises the probability of cascading financial hardships, such as mortgage defaults and debt delinquencies (Billings, Gallagher, and Ricketts 2022; Kousky 2019; Kousky, Palim, and Pan 2020; Otto et al. 2023). Therefore, reforms to address challenges in hazard insurance markets should be a high priority for adaptation policy. A major issue in the U.S. disaster insurance system is that although climate change presents a systemic threat that simultaneously raises the risk of multiple perils (wind, fires, and floods) across the United States, hazards are insured on a peril-by-peril basis and are regulated at the State level. Alternative models—such as those used in France, Spain, and New Zealand—instead create broad, diversified risk pools by mandating comprehensive, multiperil catastrophe insurance while also providing an 286 | Chapter 9 explicit public reinsurance backstop that limits the exposure of private insurers (Kousky 2022, 53–55). These kinds of reforms will likely be increasingly important to stabilize insurance markets and expand access in the face of climate change. the State after seeing a 48 percent growth in policies during 2022 (Florida Citizens 2023; Insurance Journal 2022). Other sharp growth in State insurance rolls in recent years has come from California, where wildfire risk has led private insurers to pull out and the number of policies on the State’s FAIR plan to increase by over 80 percent since 2018 (State of California 2018; Insurance Information Institute, n.d.–a). These programs strain government finances because premiums generally do not cover payouts, meaning that losses are covered with general tax revenue or debt issuance (Hartwig and Wilkinson 2016). Even with these implicit public subsidies that effectively transfer risks to general State and Federal taxpayers, the penetration of natural hazard insurance is low; for instance, only one-third of homes within FEMAdefined 100-year flood zones have flood insurance while fewer than 3 percent outside these flood zones have it, despite still being at risk of flooding (Evan et al. 2020). Increased risks of delinquencies and defaults after disasters can have subsequent implications for property and mortgage markets, particularly in the absence of insurance (Kousky 2019; Billings, Gallagher, and Ricketts 2022; Kousky, Palim, and Pan 2020). Sastry (2022) suggests that mortgage lenders respond to the availability of federally backed flood insurance by requiring higher down payments when public insurance is either limited or not required, changing the demographics of eligible home buyers. Climate-change-driven weather extremes can have subsequent effects on State and municipal finances through several pathways beyond public insurance plans. First, responding to climate change places additional burdens on local budgets, which could cause serious financial hardships, particularly in communities with smaller budgets. The need to either rebuild damaged infrastructure after disasters or upgrade existing infrastructure to prepare for climate change raises the cost burden for cities, States, and Tribes. Second, repeated occurrences of climate extremes can threaten the property tax base and cause a decrease in revenues. For instance, the McKinsey Global Institute has estimated that an extreme storm surge event in 2050 would cause damage equivalent to 10 percent of the total market value of properties in Miami–Dade County and as much as 30 percent in Lee County, which was recently inundated by 18 feet of storm surge Opportunities for Better Managing Weather Risk in the Changing Climate | 287 from Hurricane Ian (Woetzel et al. 2020b; Paquette and Kornfield 2022). Residential real estate construction has been an important driver of growth in some U.S. coastal communities, and declines in response to emerging climate risks could have serious implications for local economies, employment, and tax revenue (Brunetti et al. 2022). Several researchers have found evidence that the municipal bond market is beginning to account for these risks in its pricing of loans to municipalities. Bonds for cities and towns in areas exposed to sea-level rise carry a premium, with significantly larger effects on longer-maturing bonds, implying that investors expect either a decline in cash flow or increasing volatility in exposed cities (Painter 2020; Goldsmith-Pinkham, Gustafson, and Lewis 2021). Acharya and others (2022) find evidence for the pricing of extreme heat in municipal and corporate bonds, beginning in about 2013–15, and also larger effects on longer-term bonds. Higher borrowing costs strain municipal finances and make it even more challenging for cities to finance disaster reconstruction or adaptive infrastructure investments without either raising taxes or diverting resources from other public services. In areas with local declines in tax revenue and rising climate change costs, municipal bankruptcies may be increasingly likely. Jerch, Kahn, and Lin (2023) find evidence that hurricane strikes decrease tax revenues and raise the risk of municipal default over the following decade, with the largest effects being felt in disadvantaged communities. In addition to losses to creditors, bankruptcy costs are borne by current and future residents in the form of higher taxes and service fees (Chapman, Lu, and Timmerhoff 2020). The Federal Fiscal Implications of Physical Climate Risk Climate change affects the Federal fiscal outlook via numerous pathways. On the revenue side, it threatens economic output, leading to a lower tax base. One estimate by the White House’s Office of Management and Budget (White House 2022b) suggests that the Federal Government could see 7.1 percent lower annual tax revenue by 2100 as a result of the adverse effect of climate change on macroeconomic growth. Though some of this could be offset by increasing taxes on income or capital, Barrage (2020) points out that the distortionary effects of these revenue-raising mechanisms can be substantial, increasing climate change costs by up to about 30 percent. Ongoing research within the Biden-Harris Administration is expanding the capacity of the Federal Government to integrate the modeling of both the physical and transition risks of climate change into macroeconomic forecasting in order to better explain and plan for these effects (White House 2022f). On the expenditure side, many Federal operations are being affected by the changing climate. Though these effects are too extensive to enumerate in detail here, this section briefly reviews four primary pathways by which 288 | Chapter 9 the Federal Government is exposed to physical climate risk: risk assumption, the operation and financing of climate-exposed assets, the provision of national public goods, and social safety net programs. Risk Assumption By fully or partially assuming certain types of risk, the Federal Government is able to attract private investment and support production across broad sections of the economy. One of the most significant examples of this is the Federal role in housing: Through the government-sponsored enterprises (GSEs)—Fannie Mae and Freddie Mac, which are privately owned but federally chartered and are currently in conservatorship—and Federal agencies (e.g., the Department of Housing and Urban Development, Ginnie Mae, and the Department of Veterans Affairs), the Federal Government guarantees mortgages and securities backed by mortgages. Together, the GSEs and Ginnie Mae accounted for more than 65 percent ($7.7 trillion) of total outstanding mortgage debt in 2022 (Urban Institute 2022). The growing damage from hurricanes, storm surges, and wildfires has implications for defaults, recoveries, and other key cost drivers—and, by extension, for Federal loss exposure (Kousky, Palim, and Pan 2020; Rossi 2020; Woetzel et al. 2020b). There is evidence that private lenders are shifting climate-exposed loans into the GSEs, which may bear a substantial share of the increasing climate risk in the absence of policies that manage Federal exposure (Ouazad and Kahn 2022). In addition to its support for the housing finance system, the Federal Government also directly assumes risk through various insurance programs. Flooding is the most frequent and most costly natural disaster in the United States, and the Federal Government underwrites essentially all home flood insurance policies via the NFIP (Federal Emergency Management Agency 2010; Kousky et al. 2018; Federal Insurance and Mitigation Administration 2022). Climate change will increase costs from flooding due to both more intense rainfall and higher sea levels, which worsen flooding from storm surges and slow drainage in low-lying coastal areas. The NFIP is already at risk of financial insolvency; it has a debt to the Treasury of $18.1 billion, despite the fact that Congress canceled $17 billion of its debt in 2017 (Federal Emergency Management Agency, n.d.; Environmental Law Institute 2022, 702). Without fundamental reforms of the U.S. disaster insurance system and of the Federal Government’s role in managing these risks (box 9-3), these losses will continue to grow (White House 2022b). Climate-Exposed Assets The Federal Government owns and operates critical climate-sensitive infrastructure, most significantly dams, irrigation systems, and major flood Opportunities for Better Managing Weather Risk in the Changing Climate | 289 defenses such as river and coastal levees, along with buildings, military installations, and other physical assets that may be at risk from climate change (White House 2022b; U.S. Department of Defense 2021a). These assets were built over many decades at substantial cost and are now critical foundations for communities and regional economies across the United States. The Bureau of Reclamation (2022) is the Nation’s largest wholesale water supplier, operating 338 reservoirs and maintaining 487 dams that supply water to about 10 percent of U.S. residents, as well as supplying hydropower and water for agricultural irrigation. The functioning of some of these systems will be challenged by the changing hydroclimate, which is already bringing both more intense rainfall and more droughts, including the persistent megadrought currently hitting western States (Kao et al. 2022). The Central Valley Project, which supplies water to cities and farmers in California, has slashed its water deliveries to cities and has completely cut water to many farmers in 4 of the last 10 years (James 2022). The project’s long-term operation is further threatened by sea-level rise, which will increase the salinity of the California Delta and eventually render its water unfit for drinking and irrigation (State of California 2018; Fleenor et al. 2008). Water levels in Lake Meade and Lake Powell reservoirs are close to the critical threshold, below which they will cease to produce electricity (Wheeler et al. 2022). The costs of either maintaining water and power services from Federal facilities in the changing climate or decommissioning projects and finding alternative solutions for dependent communities has not been fully estimated. The U.S. Army Corps of Engineers (n.d.) is tasked with building certain public infrastructure projects that manage the risks of flooding, including riverine and coastal levees and flood control dams throughout the country. More intense rainfall events, higher sea levels, and stronger storms are expected to increase the costs of maintaining existing flood protection and expanding it to newly at-risk areas. The costs resulting from climate change for Federal flood control could be extremely high. Future expenditures will depend on high-level strategic decisions that have yet to be made regarding what role flood protection infrastructure will play in managing growing coastal and inland flood risks. For example, the Corps of Engineers has released a feasibility study for a plan to protect the New York metropolitan area from coastal storms. This plan—which includes storm surge barriers, floodwalls, levees, seawalls, and other measures—would cost upward of $52 billion, with 65 percent borne by the Federal Government (New York District 2022). 290 | Chapter 9 The Provision of National Public Goods A central function of the Federal Government is to provide national public goods, most critically national defense, which accounted for about 45 percent of Federal discretionary spending in 2021 (CBO 2022a). Climate change poses threats to U.S. national security, which raises questions about its implications for defense spending and the Federal budget (U.S. Government Accountability Office 2022; National Intelligence Council 2021). The Department of Defense has identified climate change as a factor in national security planning and as an urgent and growing threat to U.S. security (White House 2015b; Department of Defense 2021b). Climate change effects are expected to increase global tensions, as nations compete for scarcer resources—threatening health and human rights and triggering conflict and mass migration (White House 2022e). A second aspect of Federal public goods provision that is substantively affected by climate change is the stewardship of natural resources, public lands, and biodiversity. By altering the climatic environment to which ecosystems are adapted, climate change threatens to degrade ecosystem functioning and species survival (U.S. Global Change Research Program 2018). The additional costs of managing public lands in this rapidly shifting environment are not fully known. An example of costs that have been partially quantified are annual Federal wildland fire suppression expenditures, which, on average, have more than tripled since 1989, partly driven by intense, climate-change-driven droughts in the West (CBO 2022b). Moore and others (2022) estimate that direct spending on biodiversity conservation via the Endangered Species Act could increase by 75 percent (roughly $34 billion) by 2100, as unmitigated climate change pushes an estimated one in six species toward extinction (Urban 2015). The Programs of the Social Safety Net The various Federal programs known as the social safety net provide benefits and assistance to maintain a minimum level of well-being for the U.S. population. Climate change could increase the burden on these programs through a number of pathways, most notably health-related expenditures and assistance for disaster response and recovery. Federal health programs—namely, Medicare and Medicaid—represented 38 percent of total national health expenditures in 2021, or about $1.6 trillion (Centers for Medicare & Medicaid Services 2022). Several studies have estimated that health-related risks constitute the largest fraction of climate-change-related damage, with particularly severe effects on those over 65 years of age, who are much more likely to be treated through government programs (Rennert et al. 2022; Hsiang et al. 2017; Carleton et al. 2022). A White House study estimated annual Federal health care costs from Opportunities for Better Managing Weather Risk in the Changing Climate | 291 the effects of climate change on air quality, Valley Fever, southwestern dust, and wildfires by 2100 at between $835 million and $22 billion (White House 2022b). An additional unquantified, but likely much higher, cost would arise from hospitalizations resulting from extreme heat conditions, such as dehydration, renal failure, and stroke (Green et al. 2010; Wondmagegn et al. 2021). Federal programs play a critical role in supporting communities affected by natural disasters. The Congressional Budget Office (CBO) estimates that the expected annual loss from hurricane winds and stormrelated flooding is $56 billion, or 0.3 percent of 2019 GDP, with annual costs to the Federal budget of $18 billion through disaster assistance and NFIP claims (CBO 2019). However, Deryugina (2017) shows that disasters have far larger fiscal implications due to increases in social insurance payments—such as Medicaid, disability insurance, and income maintenance programs—which persist for 10 years after a storm hits. The Federal Government supports postdisaster response and recovery, not just through the immediate FEMA response but also through low-interest loans from the Small Business Administration and Community Development Block Grant Disaster Recovery funding from the Department of Housing and Urban Development to aid rebuilding disaster-affected communities (Howe et al. 2022, 700–704; U.S. Small Business Administration, n.d.). Market Failures and Distortions That Slow Adaptive Adjustments and Policy Responses As described above, shifting weather risks present incentives to private actors to adjust so as to reduce the negative effects of climate change and take advantage of any opportunities it offers. Indeed, there is evidence that these adjustments are already happening, in prices that seem to be starting to account for climate change risks (Keys and Mulder 2020; Bernstein, Gustafson, and Lewis 2019; Baldauf, Garlappi, and Yannelis 2020; Severen, Costello, and Deschênes 2018), as well as other evidence that households and local governments are altering practices in response to or in anticipation of the changing climate (Berrang-Ford et al. 2021). Private adaptive adjustments occurring in the United States are subject to market imperfections—along with informational, institutional, legal, and financial constraints—that could limit or slow adaptation. Public adaptation policy should target these barriers to enable faster and more effective private action. This section reviews major market failures, imperfections, and distortions that are relevant to managing physical climate risks and describes the policy tools that can address them. 292 | Chapter 9 Imperfect Information on Physical Climate Risks Adaptation to the changing climate requires incorporating information about how shifting weather patterns will alter the distribution of future weather risks. This is particularly important for decisions that are highly sensitive to climate change through long-lived investments or exposure to low-probability, high-consequence tail risks. A substantial body of literature indicates that individuals consistently underestimate or discount the probabilities of catastrophic events, a phenomenon that can lead to low rates of disaster insurance coverage and underinvestment in risk reduction (Wagner 2022; Bakkensen and Barrage 2022; Royal and Walls 2018). Information on climate risks that is of high quality and is trusted, decision-relevant, and widely disseminated is foundational for adaptation planning and is urgently needed. Yet it is now largely missing. Modeling tools used to understand the global climate system, termed general circulation models, are most accurate over large spatial scales and long time frames. Different, more fine-tuned tools are needed to make information on specific risks in particular places over short to medium time frames widely available to stakeholders making adaptation decisions (Fiedler et al. 2021; Pitman et al. 2022; American Society of Civil Engineers 2018, 7). Governments already support a global network of satellites, in situ observing systems, weather stations, modeling facilities, and technical workforces that produce public weather forecasts. Similarly, as climate change information becomes an essential complement of weather data, governments will need to play a role in funding the production of and access to this essential public good to support climate-informed decisionmaking by many actors. This includes not just developing the ability of climate science to provide better information at the spatial and temporal scale at which decisions are made but also supporting the training of highly skilled workers who can translate and disseminate this information to the public and private actors seeking to use it (Fiedler et al. 2021; Kopp 2021). The Biden-Harris Administration has begun this work through development of the Climate Mapping for Resilience and Adaptation tool (CMRA, n.d.). Information Asymmetries Information asymmetries occur when one party in a transaction has more information than another, which can lead to price distortions and market failure (Akerlof 1970). In the climate risk context, information asymmetries could arise when buyers and sellers have varying knowledge about an asset’s climate change exposure, such as a property’s propensity to flood or wildfire risk. In this regard, Keenan and Bradt (2020) and Ouazad and Kahn (2022) find evidence of information asymmetries operating in coastal mortgage markets, with lenders shifting risks of flooding-exposed properties Opportunities for Better Managing Weather Risk in the Changing Climate | 293 onto Fannie Mae and Freddie Mac or to other lenders that have less knowledge of local risk exposure. Mandatory disclosure laws are one tool governments can use to correct information asymmetries. These require parties to share particular types of information relevant to asset valuation. A proposed rule by the U.S. Securities and Exchange Commission (2022) would add climate-related risks to the required disclosures of publicly traded companies. Disclosure laws related to property transactions are governed at the State level and vary widely in the degree to which they require sellers to disclose climaterelated risks (particularly flooding) to buyers (Hino and Burke 2021; Natural Resources Defense Council, n.d.). Only a handful of States—notably, Louisiana and Texas—have strong flood disclosure regulations to protect buyers, while 16 have no disclosure requirements at all (Federal Emergency Management Agency 2022). Building codes and standards can be another way to protect buyers from information asymmetries in property markets. Details of building construction that determine how vulnerable a structure is to water, wind, and wildfire hazards can be highly technical and not easily understood by home buyers. This creates a market failure, if more resilient but also more expensive construction cannot command a premium because these qualities are not easily observable. By setting common minimum standards for all construction, governments can prevent a race to the bottom in building quality (White House 2022f). Adverse selection in insurance markets—where buyers know more about their risk than providers, leading more risky individuals to opt into insurance at a given price—is another form of information asymmetry. There is some evidence of adverse selection operating in U.S. disaster insurance markets, where those with a lower flood risk (e.g., from an elevated house) are less likely to purchase coverage (Wagner 2022; Bradt, Kousky, and Wing 2021). In the absence of corrective policies, adverse selection can lead to an unraveling of insurance markets as insurers raise rates to cover the higher risk, driving lower-risk individuals from the marketplace and further concentrating insured risks. This market failure historically has been addressed through purchase mandates that solve the adverse selection problem by requiring everyone to participate in insurance markets thereby pooling the risk. Insurance mandates can also improve welfare in settings without adverse selection but where individuals systematically underestimate their exposure to a catastrophic loss (Wagner 2022). Externalities and Public Goods Although many adaptation actions are private goods that result from individual households and firms weighing their own costs and benefits, important 294 | Chapter 9 public adaptation goods will also be underprovided without government action (Mendelsohn 2000). Examples include building infrastructure for coastal protection; expanding the tree cover for urban cooling and other nature-based forms of climate adaptation; basic research relevant to adaptation, such as developing improved crop varieties; and the protection of species or ecosystems threatened by the changing climate. But unlike greenhouse gas mitigation, which is a global public good, many public adaptation benefits are more local and therefore may be best provided by State, Tribal, and local governments. The Federal Government may still have a role to play in coordinating, supplying information, and reducing transaction costs. In addition to pure public goods, some private adaptations may involve externalities that require collective action by communities or higher levels of government. This is particularly the case for environmental goods and natural resource management, where preexisting inefficiencies related to the lack of established property rights can be exacerbated by climate change. Examples include the spillover effects of coastal protection onto neighbors, and the increasing drawing down of open-access aquifers to meet higher crop water requirements resulting from warmer temperatures (Beasley and Dundas 2021; Gopalakrishnan et al. 2017; Rosa et al. 2020). Threats to biodiversity are similarly characterized by market failures and problems of public good provision. Though habitat conversion, pollution, and invasive species are primary drivers of species endangerment, climate change further stresses threatened species and exacerbates these drivers (Tilman et al. 2017; Moore et al. 2022; Hashida et al. 2020). In these settings, reforms to more sustainably manage natural resources will have ancillary benefits in reducing climate damage and can be thought of as an important tool for managing climate risks. Credit Constraints A number of adaptive actions require upfront capital investments that will pay off gradually. Examples range from homeowners installing airconditioners to farmers adopting irrigation to manage increasing heat and droughts to major community projects to slow or prevent coastal erosion. If actors are unable to finance these investments at competitive interest rates, they will be underprovided relative to an optimal level, meaning that governments may play an important role in alleviating credit constraints to enable adaptation. Examples of relevant government programs include targeted subsidies for home efficiency upgrades such as certain provisions in the Bipartisan Infrastructure Law and the Inflation Reduction Act (box 9-1). This will be particularly compelling for populations that have been historically underserved by financial institutions and for financing adaptations Opportunities for Better Managing Weather Risk in the Changing Climate | 295 where there is a strong social interest in ensuring adaptation to alleviate climate-related risks. Credit constraints do not just apply to individuals and business but may also limit the ability of States and municipalities to fund major public adaptation projects. Most States require voter approval for general obligation bonds backed by future tax revenues or have other constitutional limits on long-term debt issuance, constraints that do not apply to the Federal Government (Kiewiet and Szakaty 1996). The costs of major infrastructure projects to manage growing climate risks may be beyond the fiscal reach of many local governments, particularly if climate risks are simultaneously jeopardizing future tax revenue. Low-cost loans or grants from the Federal Government may therefore be important sources of financing for municipalities and States seeking to make major investments to reduce climate risks, a recent example being pilot grants for voluntary relocation of Tribal communities made by the U.S. Department of the Interior (2022). Moral Hazard “Moral hazard” refers to a phenomenon in insurance markets whereby access to insurance lowers incentives for risk-reducing or risk-avoiding behavior, increasing overall hazard costs. Settings with pervasive moral hazard can see higher insurance premiums or an unraveling of private insurance markets. Other programs that shift the costs of hazards—whether through subsidized insurance, publicly provided protection, or loan guarantees—can, unless carefully structured, also create moral hazard distortions. Annan and Schlenker (2015) estimate that the moral hazard associated with subsidized crop insurance has reduced the incentives of farmers to adapt to extreme heat and has led to a higher sensitivity to heat in insured crops. Baylis and Boomhower (2023) find that the implicit subsidy from public wildland fire fighting can reach 20 percent of home values in low-density, wildfire-prone areas. Similar indirect subsidies for building in flood-prone areas have likely led to more people and property in these risky areas (Panjwani 2022). The moral hazard problem does not only apply to individuals; it can also apply to State and local governments. Many decisions relevant to reducing the costs of weather-related disasters—including zoning, building codes, and land-use management—are made at the State or local level (figure 9-3). State and local governments making these decisions see benefits in growth and tax revenues, but they are shielded from the full costs of risky development because of the Federal Government’s assumption of disaster risk through the NFIP and disaster relief programs. Several States have seen rapid development in areas exposed to coastal flooding by sea-level rise, with local governments permitting two or three times more construction in these risky areas than in safer regions (Climate Central and Zillow 2018). 296 | Chapter 9 Reforming Federal programs and private insurance contracts to incentivize or require risk-reducing activities or to place a higher share of costs on those undertaking the risk (e.g., through higher deductibles) can help mitigate issues of moral hazard (Kousky 2022, 38). Four Potential Pillars of the Federal Adaptation Strategy and Major Policy Opportunities Adaptation to climate change is characterized by complex governance across multiple scales, with relevant decisionmaking operating at the national, State, Tribal, and local levels (figure 9-3). Given the complex regulatory and planning processes relevant to managing climate risks and the local nature of many adaptation benefits, this nested governance structure may be appropriate (Dietz, Ostrom, and Stern 2003). Federal adaptation policy needs to be developed with an appreciation of the multilayered, complex regulatory systems that characterize adaptation-relevant policy areas. This final sec2/6/2023 tion outlines four broad, cross-cutting roles for further Federal adaptation efforts to support specific policymaking across these many issue areas, and it highlights major opportunities for action in each area. Producing and Disseminating Knowledge about Climate Risk As firms, local governments, and individuals increasingly seek to account for climate change in their planning and investments, actionable information will be a necessary input. From home buyers deciding where to move to local governments planning new stormwater drainage to businesses looking to disclose their climate risk exposure, actors across the economy will Local Trade Defense Immigration Public lands and natural resource management Health and safety Disaster aid and recovery Regulation of insurance markets Real estate disclosure Building codes State Land use planning and zoning Federal Infrastructure planning and regulation Figure 9-3. Governance of Climate Risk Is Complex and Multiscale Sources: Howe et al. (2002); CEA calculations. Opportunities for Better Managing Weather Risk in the Changing Climate | 297 require high-quality, trusted, and accessible climate impact information. This is essential “informational infrastructure”—technical information produced at high fixed costs but with broad applicability and value. Thanks to strong Federal support, the United States is a global leader in climate science. But the modeling tools used to understand the global climate system are not yet designed to deliver the decision-specific information needed by most stakeholders to manage climate risk (Fiedler et al. 2021; Pitman et al. 2022; ASCE 2018, 7). Major opportunity: invest in the Federal capacity for catastrophic climate risk modeling. The U.S. government has an opportunity to lead the world in developing a high-performance public capability for catastrophic climate risk modeling. Managing evolving climate risks will require new scientific approaches that combine insights from climate models with other tools, such as statistical modeling and detailed engineering data to produce decision-relevant climate information tailored to the needs of stakeholders across the United States (Pitman et al. 2022). Catastrophe modeling is used in the insurance industry to understand the risks of extreme events, but it is done by just a few companies, is expensive to access, and can be difficult to evaluate (ModEx, n.d.). Given both the growing role of the public sector in absorbing climate risk and the need for many actors—ranging from State and local governments to homeowners to general businesses—to understand their exposure, information that is publicly available, credible, and from trusted sources is urgently needed. The U.S. government has the opportunity to build on its existing foundation of excellence in climate modeling and Earth system sciences to develop this critical capacity. Long-Term Planning for the Climate Transition Long-term, forward-looking planning that anticipates coming climate change is necessary to avoid unnecessary losses and destabilizing effects. Neumann and others (2021) estimate that proactive adaptations that anticipate future climate can reduce the costs of climate change for the United States’ road, rail, and coastal infrastructure by a factor of between 3 and 6 by 2090, compared with purely reactive adaptation; Diaz (2016) finds a similar magnitude of savings for forward-looking adaptation for global coastal defenses. The Federal Government, in a number of its capacities, from the Social Security Administration to the management of National Parks, has a particular role in the long-term stewardship of U.S. assets. It regularly makes decisions that will have consequences for decades if not centuries into the future. Planning across all affected Federal agencies should recognize the effects of climate change that are already apparent and are expected to intensify well into the future. Exposure to climate hazards should be incorporated 298 | Chapter 9 into an agency’s enterprise risk management process. For example, the U.S. Department of Defense (2021b) is planning to integrate climate risk into all relevant hazard threat assessments. In addition, high-level strategic planning will be essential to identify critical risks that climate change poses for agencies’ missions, high-priority opportunities to address these risks, and the additional resources or legislative changes that may be required to realize these opportunities. Current and future climate change effects may necessitate difficult trade-offs involving many stakeholders with conflicting interests. Timely work to identify principles and priorities that will guide agencies will improve coordination, identify necessary reforms that may take a long time to implement, and ultimately lower costs and improve effectiveness, a motivation behind the ongoing agency climate adaptation planning process (box 9-1). Clarity at the Federal level regarding priorities for funding public adaptation efforts is important for driving action by State and local governments. Estimated costs just for protection from sea-level rise are large, and are likely far beyond the means of many coastal communities (Diaz 2016). Other costs of responding to droughts, wildfires, and inland floods will further strain government budgets. As described above, Federal loans and grants will be essential to alleviate credit constraints for State and local governments, but these resources are necessarily limited. Establishing clear funding priorities and resolving uncertainty over which protection costs the Federal Government will finance can assist State and local actors in their own planning for the climate transition. Major opportunity: use access to Federal funds to incentivize subnational adaptive reforms. Many policies critical to building long-term resilience to natural disasters are controlled at the State or local level (figure 9-3). However, Federal funds flowing through States both directly and indirectly finance long-lived, climate-exposed infrastructure and development projects (CBO 2018). If physical climate risks are not fully integrated into agency enterprise risk management, these investments risk underperforming and becoming stranded. Similarly, decisions to convert land subject to wildfires or flooding to developed uses implicates the Federal Government via its various risk-absorbing functions, such as mortgage guarantees, flood insurance, and disaster management and response. Reforms to zoning, building codes, insurance markets, and residential disclosures can all have major benefits in reducing the costs of disasters. Climate-relevant Federal investments can be tied to the enactment of adaptive reforms that will protect both affected communities and the Federal budget. For example, FEMA has proposed reforms of the NFIP to Congress that would, if enacted, condition participation in the program on the development of community-level flood disclosure requirements for property transactions (U.S. Department of Homeland Security 2022), while Opportunities for Better Managing Weather Risk in the Changing Climate | 299 provisions of the Bipartisan Infrastructure Law lower the non-Federal share of certain grants for transportation projects prioritized in a State’s Resilience Improvement Plan (U.S. Department of Transportation 2022). Ensuring the Accurate Pricing of Climate Risk Provided that actionable and credible information on climate risk is available, prices would be expected to adjust, sending accurate signals to actors to reallocate investment and production in response to and in anticipation of the changing climate. However, market failures arising from information asymmetries or misaligned incentives can distort these signals and require a policy response. One role of the Federal adaptation strategy should be to identify and correct these market failures to enable stronger market signals that would guide adaptive decisions over the longer term. Specific market failures relevant to adaptation and policy tools to address them were discussed in the previous section; they include information provision, disclosure requirements, building standards, and insurance purchase mandates. An important example is the recent reform of pricing in the NFIP, termed Risk Rating 2.0, which prices policies based on individualized flood risk assessments while continuing to provide discounts for investments by individuals or communities that lower flood costs (CRS 2022). Market mechanisms can play an important role in allocating resources efficiently and sending price signals to market actors on the scarcity of resources. In places where markets are missing or incomplete, climatechange-induced scarcity could exacerbate existing distortions—meaning that reforms to expand market access or establish property rights over common-pool resources could lower total costs, one example being the allocation of water use in California (Arellano-Gonzalez et al. 2021). Even in the absence of markets, using mechanisms such as auctions to allocate resources cost-effectively could be a useful strategy to manage scarcity under climate change (Hagerty and Leonard 2022). Major opportunity: develop quality and transparency standards for climate data and the modeling used in market transactions. An important part of supporting the integration of physical climate risks into market prices will be oversight of the quality of climate information being used. Pricing climate risk requires the use of specialized modeling tools, and evaluating the quality of this information is a technical and highly specialized skill. Use of proprietary models of natural hazard risk that cannot be evaluated by the expert community as an input into significant regulatory decisions has caused tensions in the past, particularly in the insurance industry (Xu, Webb, and Evans 2019). Developing minimum standards and reporting requirements for the climate data used to inform significant investment decisions could help build trust, ensure quality, and enable broader adoption. 300 | Chapter 9 This is particularly important when insurance contracts are leveraged into more complex financial instruments, such as catastrophe bonds and other insurance-linked securities, or when used as input into scenario exercises central banks are beginning to use to evaluate climate risks to the financial system (Insurance Information Institute, n.d.–b; Braun and Kousky 2021; U.S. Federal Reserve 2023; Financial Stability Board 2022). Oversight and evaluation of the Earth system models used for significant economic or regulatory decisionmaking could be important in preventing natural disasters from triggering more systemic failures across the financial system. Protecting the Vulnerable Climate change is expected to increase weather-related hazards for many Americans, but its effects will not likely be felt equally (see box 9-2 above). Low-income and disadvantaged communities are both more exposed to climate effects (e.g., through working in industries exposed to extreme heat, such as agriculture and construction) and lack assets that can be drawn on to smooth the costs of weather-related disasters. In the absence of policies addressing the needs of low-income and marginalized communities, preexisting vulnerabilities—such as inadequate health care, poor-quality or overcrowded housing, and food insecurity—will likely interact with climate change effects to worsen inequalities. Addressing these underlying vulnerabilities and developing policies targeted at disadvantaged populations should be a critical part of an effective U.S. adaptation strategy, building on the robust set of existing programs within the Biden-Harris Administration targeted at low-income and disadvantaged communities (boxes 9-1 and 9-2). As prices adjust to reflect changing climate risks, this could present challenges for low-income communities, for whom higher prices are particularly burdensome. For instance, low-income households are already less likely to have flood insurance and almost 9 percent of policyholders in FEMA-defined 100 year floodplains pay more than 5 percent of their income for flood insurance premiums and fees (U.S. Department of Homeland Security 2018). To the extent possible, rather than restricting price adjustments, which would blunt the incentive for private risk reduction and increase risks over the long term, policies should seek to address these adverse distributional effects via targeted lump sum transfers. More generally, policies that seek to accelerate income growth and increase access to wealth-building opportunities for the poorest Americans should be thought of as broadly adaptive. Although by no means the main goal of programs that may be primarily focused on educational opportunities, housing affordability, or poverty alleviation, by increasing resources available to the most vulnerable that can be drawn on to manage the effects of climate shocks, Opportunities for Better Managing Weather Risk in the Changing Climate | 301 they should lower overall vulnerability and decrease climate costs over the long term. Major opportunity: develop criteria for public adaptation funding that reflect the social value of investments. Criteria for the prioritization and evaluation of public adaptation projects that accurately reflect the social benefits of these investments should be developed. Historically, public investments in flood defenses or community risk reductions have used the value of protected property as a key metric in evaluating project benefits (McGee 2021). However, inequality in property values and ownership in the United States reflects decades of exclusion of racial minorities from homeownership and public investment (Rothstein 2017). Evaluating the benefits of climate protections solely using property values is unlikely to capture the full, multidimensional benefits of these projects and risks perpetuating these historical injustices and exacerbating differences in vulnerability (Martinich et al. 2013). Additional criteria that capture differential vulnerability and variations in the extent to which communities are able to self-insure and recover from damage could be developed to assess project outcomes. Major opportunity: reenvisioning social insurance under climate change. The increasing frequency and intensity of climate-change-related disasters, along with the disruptions and dislocations required to adjust to changing conditions, will likely challenge the policies and programs that spread risks and protect the vulnerable as never before. Understanding the burden this will place on traditional social insurance programs in the United States and identifying reforms to strengthen the social safety net is essential in this era of climate change. In addition, a process for reimagining the public role in catastrophe management and social solidarity in the face of rising risks is also urgently needed. The United States’ current approach— managing catastrophic perils in a piecemeal way with fiscally unstable public insurance programs—will only become more problematic as climate change continues. U.S. policymakers should seriously consider models from other countries, where governments act as backstop reinsurers, capping catastrophic losses in the private sector to crowd in private financing while also spreading the risk broadly through mandated natural disaster coverage (Kousky 2022, 53–56). Conclusion The United States is making historic investments that will transform the American energy system to address the challenge of climate change and meet President Biden’s goal of halving U.S. greenhouse gas emissions by 2030. These investments are central to the global effort to rein in emissions and limit the effects of climate change. 302 | Chapter 9 Even with the massive shift already under way in global energy production, models show the climate will continue changing for decades (IPCC 2021; Meinshausen et al. 2022). Shifting weather patterns will likely subject communities to unprecedented extremes making the management of weather risks and natural disasters increasingly difficult. A large body of literature, as well as the mounting costs of climate-change-associated extreme events, shows that the U.S. economy is sensitive to the effects of climate change. Without forward-looking adaptive planning that anticipates changing conditions, climate costs will very likely keep growing, compounding risks to infrastructural, social, economic, and financial systems across the United States. Managing the risks of climate change is a complex challenge across multiple policy areas, characterized by varying market failures and nested governance structures. However, the particular capacities, authorities, and interests of the Federal Government mean that it should play an essential role in leading adaptation policy development and structuring the responses of subnational and private actors to emerging climate risk. An effective Federal adaptation strategy includes producing and disseminating knowledge about climate risk, long-term planning for the climate transition, ensuring accurate pricing of climate risks, and protecting the vulnerable. Managing the risks and consequences of the warming planet, along with continued efforts to reduce greenhouse gas emissions, will allow the Nation to face the climate challenges of the 21st century. Opportunities for Better Managing Weather Risk in the Changing Climate | 303 References Chapter 1 Acemoglu, D., D. Autor, and D. 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Christidis, A. Hoell, M. Hoerling, and P. Stott, S1–S8. Washington: American Meteorological Society. https://doi.org/10.1175/BAMS-D-20-0154.1. References | 413 Appendix A Report to the President on the Activities of the Council of Economic Advisers during 2022 415 Letter of Transmittal Council of Economic Advisers Washington, December 31, 2022 Mr. President: The Council of Economic Advisers submits this report on its activities during calendar year 2022 in accordance with the requirements of Congress, as set forth by Section 10(d) of the Employment Act of 1946, as amended by the Full Employment and Balanced Growth Act of 1978. Sincerely yours, Cecilia Elena Rouse Chair Jared Bernstein Member Heather Boushey Member Activities of the Council of Economic Advisers during 2022 | 417 Council Members and Their Dates of Service Name Position Oath of office date Separation date Edwin G. Nourse Leon H. Keyserling Chairman Vice Chairman Acting Chairman Chairman Member Vice Chairman Member Member Chairman Member Member Member Chairman Member Member Member Member Chairman Member Member Member Chairman Member Member Member Chairman Member Member Member Chairman Member Member Chairman Member Member Member Member Chairman Member Member Chairman Member Member Member Member Chairman Member Member August 9, 1946 August 9, 1946 November 2, 1949 May 10, 1950 August 9, 1946 May 10, 1950 June 29, 1950 September 8, 1952 March 19, 1953 September 15, 1953 December 2, 1953 April 4, 1955 December 3, 1956 May 2, 1955 December 3, 1956 November 1, 1958 May 7, 1959 January 29, 1961 January 29, 1961 January 29, 1961 August 3, 1962 November 16, 1964 May 17, 1963 September 2, 1964 November 16, 1964 February 15, 1968 February 2, 1966 February 15, 1968 July 1, 1968 February 4, 1969 February 4, 1969 February 4, 1969 January 1, 1972 September 9, 1971 March 13, 1972 July 23, 1973 October 31, 1973 September 4, 1974 June 13, 1975 July 22, 1975 January 22, 1977 March 18, 1977 March 18, 1977 June 6, 1979 August 20, 1980 February 27, 1981 June 12, 1981 July 14, 1981 November 1, 1949 John D. Clark Roy Blough Robert C. Turner Arthur F. Burns Neil H. Jacoby Walter W. Stewart Raymond J. Saulnier Joseph S. Davis Paul W. McCracken Karl Brandt Henry C. Wallich Walter W. Heller James Tobin Kermit Gordon Gardner Ackley John P. Lewis Otto Eckstein Arthur M. Okun James S. Duesenberry Merton J. Peck Warren L. Smith Paul W. McCracken Hendrik S. Houthakker Herbert Stein Ezra Solomon Marina v.N. Whitman Gary L. Seevers William J. Fellner Alan Greenspan Paul W. MacAvoy Burton G. Malkiel Charles L. Schultze William D. Nordhaus Lyle E. Gramley George C. Eads Stephen M. Goldfeld Murray L. Weidenbaum William A. Niskanen Jerry L. Jordan 418 | Appendix A January 20, 1953 February 11, 1953 August 20, 1952 January 20, 1953 December 1, 1956 February 9, 1955 April 29, 1955 January 20, 1961 October 31, 1958 January 31, 1959 January 20, 1961 January 20, 1961 November 15, 1964 July 31, 1962 December 27, 1962 February 15, 1968 August 31, 1964 February 1, 1966 January 20, 1969 June 30, 1968 January 20, 1969 January 20, 1969 December 31, 1971 July 15, 1971 August 31, 1974 March 26, 1973 August 15, 1973 April 15, 1975 February 25, 1975 January 20, 1977 November 15, 1976 January 20, 1977 January 20, 1981 February 4, 1979 May 27, 1980 January 20, 1981 January 20, 1981 August 25, 1982 March 30, 1985 July 31, 1982 Council Members and Their Dates of Service Name Position Oath of office date Separation date Martin Feldstein William Poole Beryl W. Sprinkel Thomas Gale Moore Michael L. Mussa Michael J. Boskin John B. Taylor Richard L. Schmalensee David F. Bradford Paul Wonnacott Laura D’Andrea Tyson Alan S. Blinder Joseph E. Stiglitz Chairman Member Chairman Member Member Chairman Member Member Member Member Chair Member Member Chairman Member Member Chair Member Member Chairman Member Member Chairman Member Member Chairman Member Member Chairman Chairman Member Member Chairman Member Chair Member Chairman Member Member Member Chairman Member Chairman Member Member Member Member October 14, 1982 December 10, 1982 April 18, 1985 July 1, 1985 August 18, 1986 February 2, 1989 June 9, 1989 October 3, 1989 November 13, 1991 November 13, 1991 February 5, 1993 July 27, 1993 July 27, 1993 June 28, 1995 June 30, 1995 January 29, 1996 February 18, 1997 April 23, 1997 October 22, 1998 August 12, 1999 August 12, 1999 May 31, 2000 May 11, 2001 July 25, 2001 November 30, 2001 May 29, 2003 November 21, 2003 November 21, 2003 February 23, 2005 June 21, 2005 November 18, 2005 November 18, 2005 February 27, 2006 July 17, 2008 January 29, 2009 March 11, 2009 September 10, 2010 March 11, 2009 April 19, 2011 April 19, 2011 November 7, 2011 February 7, 2013 August 4, 2013 August 6, 2013 July 21, 2014 August 10, 2015 August 31, 2015 July 10, 1984 January 20, 1985 January 20, 1989 May 1, 1989 September 19, 1988 January 12, 1993 August 2, 1991 June 21, 1991 January 20, 1993 January 20, 1993 April 22, 1995 June 26, 1994 Martin N. Baily Alicia H. Munnell Janet L. Yellen Jeffrey A. Frankel Rebecca M. Blank Martin N. Baily Robert Z. Lawrence Kathryn L. Shaw R. Glenn Hubbard Mark B. McClellan Randall S. Kroszner N. Gregory Mankiw Kristin J. Forbes Harvey S. Rosen Ben S. Bernanke Katherine Baicker Matthew J. Slaughter Edward P. Lazear Donald B. Marron Christina D. Romer Austan D. Goolsbee Cecilia Elena Rouse Katharine G. Abraham Carl Shapiro Alan B. Krueger James H. Stock Jason Furman Betsey Stevenson Maurice Obstfeld Sandra E. Black Jay C. Shambaugh February 10, 1997 August 30, 1996 August 1, 1997 August 3, 1999 March 2, 1999 July 9, 1999 January 19, 2001 January 12, 2001 January 19, 2001 February 28, 2003 November 13, 2002 July 1, 2003 February 18, 2005 June 3, 2005 June 10, 2005 January 31, 2006 July 11, 2007 March 1, 2007 January 20, 2009 January 20, 2009 September 3, 2010 August 5, 2011 February 28, 2011 April 19, 2013 May 4, 2012 August 2, 2013 May 19, 2014 January 20, 2017 August 7, 2015 August 28, 2015 January 20, 2017 January 20, 2017 Activities of the Council of Economic Advisers during 2022 | 419 Council Members and Their Dates of Service Name Position Oath of office date Separation date Kevin A. Hassett Richard V. Burkhauser Tomas J. Philipson Chairman Member Member Acting Chairman Vice Chairman Member Acting Chairman Vice Chairman Chair Member Member September 13, 2017 September 28, 2017 August 31, 2017 July 1, 2019 July 24, 2019 May 22, 2019 June 23, 2020 June 23, 2020 March 2, 2021 January 20, 2021 January 20, 2021 June 30, 2019 May 18, 2019 Tyler B. Goodspeed Cecilia Elena Rouse Jared Bernstein Heather Boushey 420 | Appendix A June 22, 2020 January 6, 2021 Report to the President on the Activities of the Council of Economic Advisers during 2022 Established by the Employment Act of 1946, the Council of Economic Advisers is charged with advising the President on economic policy based on data, research, and evidence. The Council is composed of three members: a Chair, who is appointed by the President with the advice and consent of the Senate; and two members, who are appointed by the President. Along with a team of economists, they analyze and interpret economic developments and formulate and recommend economic policies that advance the interests of the American people. The Chair of the Council Cecilia Elena Rouse was confirmed by the Senate on March 2, 2021, as the 30th Chair of the Council of Economic Advisers. She is the first African American to hold this position. In this role, she serves as President Biden’s Chief Economist and a Member of the Cabinet. She is the Katzman-Ernst Professor in the Economics of Education and Professor of Economics and Public Affairs at Princeton University. From 2012 to 2021, Rouse was Dean of Princeton University’s School of Public and International Affairs. Rouse served as a Member of President Barack Obama’s Council of Economic Advisers from 2009 to 2011. She also worked at the National Economic Council in the Clinton Administration as a Special Assistant to the President from 1998 to 1999. Her academic research has focused on the economics of education, including the economic benefits of community college attendance and impact of student loan debt on postgraduation outcomes, as well as other issues in labor economics, such as discrimination. The Members of the Council Heather Boushey was appointed to the Council by the President on January 20, 2021. Before assuming this position, Boushey cofounded the Washington Center for Equitable Growth, where she was President and CEO from 2013 to 2020. She previously served as Chief Economist for Secretary Hillary Clinton’s 2016 transition team and as an economist at the Activities of the Council of Economic Advisers during 2022 | 421 Center for American Progress, the Joint Economic Committee of the U.S. Congress, the Center for Economic and Policy Research, and the Economic Policy Institute. Jared Bernstein was appointed to the Council by the President on January 20, 2021. Before this appointment, Bernstein spent 16 years in senior roles at the Economic Policy Institute, and worked at the Department of Labor. He was a Senior Fellow at the Center on Budget and Policy Priorities from 2011 to 2020. From 2009 to 2011, he was Chief Economist and Economic Adviser to then–Vice President Biden. Areas of Activity A central function of the Council is to advise the President on all economic issues and developments, including preparing almost-daily memos for the President, the Vice President, and White House senior staff on key economic data releases and policy issues. The Council works closely with officials at various government entities—including the National Economic Council, the Domestic Policy Council, the Office of Management and Budget, and Administrative Agencies—to engage in discussions on numerous policy matters. The Council, the Department of the Treasury, and the Office of Management and Budget are responsible for producing the economic forecasts that underlie the Administration’s Budget proposals. Finally, the Council is a leading participant in the Organization for Economic Cooperation and Development (OECD), historically chairing the Economic Policy Committee and participating in OECD working meetings. The Council produces economic analyses in a series of blogs and issue briefs. This past year, these included: • “The Employment Situation,” a monthly blog analyzing the employment situation that corresponds to the monthly Jobs Report (January– December 2022). • “Looking Back, Moving Forward: Year One of President Biden’s Economic Agenda,” a blog analyzing how government support during the pandemic helped boost personal income and spending, thus contributing to economic growth (January 2022). • “New Data Show that Economic Growth Was Broadly Shared in 2021,” a