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economic re p ort of the president transmitted to congress | april 2022 together with the annual report of the council of economic advisers economic re p ort of the president transmitted to congress | april 2022 together with the annual report of the council of economic advisers Contents Economic Report of the President............................................................. 1 The Annual Report of the Council of Economic Advisers*...................... 7 Chapter 1: The Public Sector’s Role in Economic Growth.................... 21 Chapter 2: The Year in Review and the Years Ahead............................. 45 Chapter 3: The U.S. Economy and the Global Pandemic....................... 97 Chapter 4: I nvesting in People: Education, Workforce Development, and Health............................................................................ 123 Chapter 5: B arriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination............................................ 157 Chapter 6: Building Resilient Supply Chains........................................ 191 Chapter 7: Accelerating and Smoothing the Clean Energy Transition. 221 References.............................................................................................. 251 Appendix A: Report to the President on the Activities of the Council of Economic Advisers during 2021...................................... 333 Appendix B: Statistical Tables Relating to Income, Employment, and Production........................................................................ 347 ____________ *For a detailed table of contents of the Council’s Report, see page 11. iii Economic Report of the President Economic Report of the President | 1 Economic Report of the President To the Congress of the United States: When I took office on January 20, 2021, I looked out at a Nation that was in the midst of the COVID-19 pandemic and experiencing a weak and uneven economic recovery. There were roughly 4 million workers who had been unemployed for more than 6 months. The Congressional Budget Office and private sector forecasters predicted a slow decrease in the unemployment rate throughout 2021. Our Nation needed an economic policy that was nimble enough to meet the significant and evolving challenges required to defeat a pandemic and recover from the severe economic disruptions it had caused. Recovery had to be swift and robust; it was not sufficient to return to where we had been, we also had to build toward a better future. Today, we look out at a markedly different America. Over 200 million Americans have been fully vaccinated and are now protected from the worst of COVID-19. Businesses have been able to resume activity. Schools and childcare centers are open again. Our Nation’s economic recovery has been strong, marked by dramatic increases in employment and GDP. Moreover, our progress has been achieved with a $360 billion decline in the Nation’s deficit in fiscal year (FY) 2021 and a historic $1.3 trillion projected decrease in FY22. This success was not preordained. It is the result of well-designed and well-administered policies. At the start of my Administration, the most important task was to free ourselves from the grip of a deadly virus. Last year, I signed into law the American Rescue Plan Act of 2021 (ARP), one of the most consequential economic rescue packages in American history. The ARP provided an insurance policy for businesses, workers, and families harmed by the virus. It prioritized resources to get and keep economic recovery on track: aid to State and local governments, checks in Americans’ pockets, support to allow schools to reopen safely, and a robust vaccination program. In addition to immediate assistance, the ARP provided scaffolding for long-term recovery—enabling workers and businesses to avoid many of the long-term, harmful effects that often follow an economic shock. We saw success across nearly every metric. At the end of 2021, our economy had created more than 6 million jobs, the largest number ever in 1 year, and we experienced the fastest drop on record for the unemployment rate. The United States saw the strongest economic growth since 1984, with GDP expanding by almost 6 percent. Poverty is projected to have reached historic Economic Report of the President | 3 lows, particularly for children. Real disposable income was up for the bottom half of the income distribution. With money in their pockets, Americans were poised to spend—and because the virus had depressed demand for travel, leisure, and other services, consumers largely turned to goods. This pent-up demand has added to backlogs, but my Administration has been working with industry to ease supply chain disruptions at every step in the process: the ports, the trains, and the trucks. As a result, store shelves are well stocked, and the muchpredicted holiday supply chain crisis did not occur. A pandemic-constrained economy, coupled with strong demand, has resulted in increasing prices. This trend is not unique to the United States; countries around the world are grappling with rising costs as the pandemic recedes and demand builds. Adding to this, the war in Ukraine has incited a supply shock that has increased energy and food prices around the globe. While we tackle these immediate challenges, we must also expand our productive capacity for the future. The pandemic exposed cracks in the United States economy that had been widening for years: decades of low and unequal economic growth that left Black and brown Americans and Tribal Nations disproportionately vulnerable; inadequate investment in research and infrastructure; increasing corporate consolidation and decreasing competition; a hollowed-out manufacturing sector; and a lack of support for America’s workers and middle-class families. We seek to build an economy that delivers stronger and more equitable growth for America’s families and workers. The Bipartisan Infrastructure Law (BIL) that I signed on November 15, 2021, provides a historic opportunity to build that economy. The BIL, which will create millions of new jobs, provides long-overdue investment in our Nation’s physical infrastructure—resources to modernize roads and bridges, ensure clean drinking water, deliver efficient and affordable broadband, and produce clean, reliable energy. These critical investments will be especially transformative in rural America, creating jobs and building wealth for these communities. This past year, I also signed several Executive Orders that improve the economy and increase the efficiency in Federal Government procurement. Examples include an Executive Order to promote competition so that firms cannot use concentrated market power to hurt workers or consumers, an Executive Order that establishes a $15 per hour minimum wage for workers on Federal contracts, and an Executive Order to address supply chain flaws. In my first year in office, I have also put forward whole-of-government approaches to combat climate change, strengthen worker organizing and empowerment, and pursue gender and racial equity. We must continue this important work. For decades, the United States has underinvested in our families, in our communities, in American 4 | Economic Report of the President businesses, and in our Nation. When we put resources toward children and families, and workers and United States businesses, we raise both the floor and the ceiling of the economy for all of us. We know, for example, that investments in education and training, particularly for young people, make economic sense. Universal access to highquality preschool is the norm in most other advanced economies. Providing all children with access to high-quality preschool will pay for itself down the road by producing benefits well into adulthood. Further, nearly half of new jobs created over the next decade are projected to require at least some postsecondary education or training at the entry level. Just as early-20th-Century universal elementary and secondary schooling helped create a highly skilled labor force, investments in higher education today can help workers fill the higher-paying jobs of tomorrow. The result will be broader and more robust economic growth. We have seen the economic consequences of our failure to put in place policies to help families balance work and family life. In 1999, labor force participation for people between the ages of 25 and 54 peaked at more than 84 percent. Since that time, it has never again reached that level. We know that workplace supports such as affordable, high-quality childcare and longterm care, as well as access to paid family and medical leave, can all lead to higher labor force participation. I have repeatedly said, and long maintained, that the middle class built this country, and unions built the middle class. Without unions, workers often lack bargaining power to secure higher wages, better working conditions, security for their families’ futures, and a voice in their workplaces. However, we have seen worker power diminish for nearly 70 years. That is why we must find ways to strengthen the United States labor force, always the backbone of the American economy, by finding ways that workers can gain strength by organizing. While we empower workers, we must also pay attention to costs families face, the ones that get discussed at the kitchen table and keep parents up at night: putting food on the table, caring for an aging parent, and ensuring their children are well cared for while they work. This past year, many American households were able to strengthen their own balance sheets, but we do not want to see cost increases erode the economic gains of 2021. From gas prices to groceries to housing costs, I will continue to use all the tools available to my Administration to address rising prices. The backdrop to all of this is a planet heating up at rates that we simply cannot sustain. The costs of climate change can be seen everywhere: damage from an increasing number of devastating storms and fires, droughts and flooding that hamper food production and make it more expensive, supply chain disruptions that slow down our economy, and illness produced by pollution. Economic Report of the President | 5 Last year alone, extreme weather and climate disasters cost our communities $145 billion and claimed hundreds of lives. Getting to net zero greenhouse gas emissions by 2050, while supporting American communities and workers and expanding new American industries, is a priority of my Administration. When I think of climate change, I think of jobs. Moreover, the war in Ukraine reinforces the fact that the United States must attain energy independence, which can happen by eliminating dependence on fossil fuels over the long term. As I said in my 2022 State of the Union Address, America has lived through 2 of the hardest years our Nation has ever faced. As I deliver this economic report, I am confident that we are building a historic recovery, and a better America. I came into office promising to not only find a way to repair the harms of the pandemic, but to turn the page on an economy that benefits only those at the top and rewards wealth over work. My Administration is committed to making critical investments in people, in innovative ideas, in 21st-Century physical infrastructure, and in combating climate change. We have laid the groundwork to build an economy from the bottom up and the middle out, ensuring growth that benefits all Americans. The White House April 2022 6 | Economic Report of the President The Annual Report of the Council of Economic Advisers Economic Report of the President | 7 Letter of Transmittal Council of Economic Advisers Washington, April 14, 2022 Mr. President: The Council of Economic Advisers herewith submits its 2022 Annual Report in accordance with the Employment Act of 1946, as amended by the Full Employment and Balanced Growth Act of 1978. Sincerely yours, Cecilia Elena Rouse Chair Jared Bernstein Member Heather Boushey Member Economic Report of the President | 9 Contents Chapter 1: The Public Sector’s Role in Economic Growth.................... 21 Before the Pandemic......................................................................... 25 Ensuring Macroeconomic Stability................................................... 29 Macroeconomic Stabilization during the Pandemic.................. 29 Addressing Market Failures.............................................................. 32 Market Failures during the Pandemic....................................... 33 Market Failures Beyond the Pandemic...................................... 35 Reducing Inequality.......................................................................... 38 Inequality Before and Beyond the Pandemic............................. 38 Inequality in the Pandemic......................................................... 41 Conclusion......................................................................................... 42 Chapter 2: The Year in Review and the Years Ahead............................. 45 Fiscal Policy in 2021......................................................................... 49 The Rise in Economic Uncertainty................................................... 53 Financial Markets...................................................................... 53 Consumer Sentiment................................................................... 56 The Economy during the Recession and Recovery: How Do This Recession and Recovery Differ from Others?.......................... Consumer Spending.................................................................... Business and Residential Investment......................................... Investment in Nonresidential Structures.................................... Investment in Equipment............................................................ Intellectual Property................................................................... Residential Investment................................................................ State and Local Purchases......................................................... Exports and Imports................................................................... 56 57 61 61 62 63 63 64 65 Global Supply Chain Disruptions..................................................... 66 Inventory Investment.................................................................. 68 Consumer Price Inflation........................................................... 69 Economic Report of the President | 11 Inflation Expectations................................................................. 70 The Labor Market............................................................................. Ways in Which the Labor Market Appeared Tight in 2021........ Ways in Which the Labor Market Appeared Loose in 2021...... Labor Supply and Labor Force Participation........................... The Historical Sluggishness of U.S. LFPR Recoveries.............. Caring for Family Members....................................................... The Unemployment Rate............................................................ Reconciling the Paradox............................................................ 71 72 79 80 86 86 87 88 The Forecast...................................................................................... Macroeconomic Forces during 2022......................................... The Forecast over the Long Term.............................................. The Supply Side of the Long-Term Forecast.............................. 89 90 92 94 Conclusion......................................................................................... 95 Chapter 3: The U.S. Economy and the Global Pandemic....................... 97 Recovery Amid Global Economic Challenges................................. 98 The Global Pandemic................................................................. 98 The United States’ Economic Recovery in the Global Context...................................................................................... 100 The Challenge of Inflation....................................................... 102 International Trade, the Economic Recovery, and Lingering COVID-19 Challenges.................................................................... The U.S. Trade Balance........................................................... International Trade in Goods................................................... International Trade in Services................................................ 104 107 111 113 Policies to Build an Equitable International Economy................... Broadening the Gains from Trade............................................ Leveling the International Economic Playing Field................ A Collaborative, Transparent Policymaking Process.............. 116 116 118 121 Conclusion....................................................................................... 122 Chapter 4: Investing in People: Education, Workforce Development, and Health.............................................................................................. 123 Human Capital Is Critical for Economic Growth and Individual Well-Being...................................................................................... 125 12 | Economic Report of the President Measuring the Stock of Human Capital.......................................... 128 Investing in Education and Skill Development.............................. Early Childhood Education and Care...................................... K-12 Education......................................................................... Postsecondary Human Capital Development.......................... 133 133 136 137 Investing in Health.......................................................................... 144 Deploying Human Capital.............................................................. Health....................................................................................... Family Support Policies........................................................... Employment Practices.............................................................. Occupational Licensing............................................................ Immigration.............................................................................. Incarceration............................................................................ Government Personnel Policies............................................... 148 148 150 151 152 154 154 156 Conclusion....................................................................................... 156 Chapter 5: Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination.............................................................. 157 Labor Market Inequality................................................................. 159 Racial, Ethnic, and Gender Wage Gaps.......................................... 161 Sources of Earnings Inequality....................................................... 168 A Lack of Competition in Labor and Product Markets............ 169 Racial and Gender Discrimination.......................................... 172 How Inequality Affects Economic Efficiency and Growth............ Monopsony Power Produces Inefficient Labor Market Outcomes.................................................................................. Discrimination Misallocates Talent and Suppresses Innovation................................................................................. Discrimination Reduces Incentives for Human Capital Investment................................................................................. Policies to Address Sources of Labor Market Inequality............... Promoting Competition............................................................ Unions and Labor Market Equity............................................ The Minimum Wage.................................................................. Full Employment and Tight Labor Markets............................. Care Economy Policies............................................................ 178 178 179 180 180 181 182 183 184 185 Economic Report of the President | 13 Progressive and Equitable Tax Policy..................................... 187 Conclusion....................................................................................... 189 Chapter 6: Building Resilient Supply Chains........................................ 191 21st-Century Supply Chains........................................................... Vertical Integration with Isolated Industries........................... Outsourcing with Isolated Industries....................................... Offshoring and Outsourcing with Isolated Industries.............. Outsourcing with a Central Node............................................ Arm’s-Length and Collaborative Relationships....................... Drivers of Change in Supply-Chain Structures....................... 192 193 194 194 195 196 199 Implications of Supply Chain Structures........................................ 204 Impact on Innovation............................................................... 204 Impact on the Macroeconomy.................................................. 207 The Rising Incidence of Supply-Chain-Related Disasters............. 210 Private Sector Incentives for Resilience......................................... Visibility.................................................................................... Redundancy.............................................................................. Agility....................................................................................... 210 211 211 212 Public Sector Strategies for Promoting Resilience......................... 214 Aggregating and Disseminating Information........................... 215 National Security...................................................................... 216 Indirect Supply Chain Policy.......................................................... 219 Conclusion....................................................................................... 220 Chapter 7: Accelerating and Smoothing the Clean Energy Transition............................................................................................... 221 Accelerating the Energy Transition................................................ 222 Global Efforts to Reduce Greenhouse Gas Emissions............. 224 Accelerating the Energy Transition in the United States......... 225 A Smooth Transition to Clean Energy............................................ 229 The First Challenge: Supporting Domestic Industries.................... 230 Strategies for Supporting Domestic Industries through the Energy Transition............................................................................ 236 14 | Economic Report of the President The Second Challenge: Supporting Communities That Rely on a Carbon-Intensive Economy..................................................... 240 The Geographic Concentration of Fossil-Fuel-Dependent Communities............................................................................. 240 The Inadequacy of Place-Neutral Policies.............................. 242 Strategies for Place-Based Policies......................................... 244 The Clean Energy Transition Provides Unique Opportunities to Implement Successful Place-Based Policies........................ 246 Discussion and Conclusions........................................................... 249 References.............................................................................................. 251 A. B. 1-1 1-2 1-3 1-4 1-5 1-6 1-7 1-8 2-1 2-2 2-3 2-4 2-i 2-5 2-6 2-7 2-8 2-9 Appendixes Report to the President on the Activities of the Council of Economic Advisers during 2021................................................ 333 Statistical Tables Relating to Income, Employment, and Production................................................................................... 347 Figures Women’s Labor Force Participation Rate, 25 to 54 Years........... 26 Men’s Labor Force Participation Rate, 25 to 54 Years................ 26 Growth Rates in Economic Expansions....................................... 27 Growth in Average Family Income, by Income Group................ 28 Infant Mortality............................................................................ 29 Gaps in Annual Earnings by Race, Ethnicity, and Gender.......... 39 Gaps in Average Hourly Earnings by Race, Ethnicity, and Gender.......................................................................................... 40 Poverty Rate by Racial Group...................................................... 42 Job Growth and Change in COVID-19 Deaths, September 2020–December 2021................................................................... 45 Daily COVID-19 Fatalities, February 2020–December 2021..... 46 Frequencies of Major SARS-CoV-2 Variants, 2021.................... 47 Level of Real GDP, 2021:Q4, versus Before the Pandemic........ 51 Federal Reserve Balance Sheet Composition, 2006–21.............. 52 The Standard & Poor’s 500 Index, 2006–21................................ 54 The U.S. Corporate Spread, 2006–21.......................................... 54 The CBOE’s VIX Index, 2006–21............................................... 55 University of Michigan Consumer Sentiment Index, 2006–21... 55 Total Spending on Goods: Cyclical Comparison......................... 57 Economic Report of the President | 15 2-10 2-11 2-12 2-13 2-14 2-15 2-16 2-17 2-18 2-19 2-20 2-21 2-22 2-23 2-24 2-25 2-26 2-27 2-28 2-29 2-30 2-31 2-32 2-33 2-34 2-35 2-36 2-37 2-38 2-39 2-40 2-41 16 | Total Spending on Services: Cyclical Comparison...................... 58 Personal Saving during the Pandemic Relative to Its Average Pace, 2008–21............................................................................... 58 Business Fixed Investment: Cyclical Comparison....................... 61 Structures Investment: Cyclical Comparison............................... 62 Equipment Investment: Cyclical Comparison.............................. 62 Intellectual Property Investment: Cyclical Comparison.............. 63 Residential Investment: Cyclical Comparison............................. 64 State and Local Purchases: Cyclical Comparison........................ 64 Exports: Cyclical Comparison...................................................... 65 Imports: Cyclical Comparison...................................................... 66 Forty-Foot Container Shipping Benchmark Rates by Route, 2020–21........................................................................................ 67 Cass Trucking Index..................................................................... 67 Air Cargo Rates by Route............................................................ 68 Inventory-to-Sales Ratio (Private Inventories to Final Sales), 1997–21........................................................................................ 68 Consumer Price Index (CPI) Inflation, 2007–21......................... 69 Components of Core CPI Inflation, Commodities versus Services, 2007–21......................................................................... 70 Job Openings per Unemployed Worker, 2000–2021................... 73 Median Hourly Wage Growth by Level of Education, 1998–21. 74 Median Hourly Wage Growth by Sex, 1998–21.......................... 75 Median Hourly Wage Growth by Workers Who Switch Industry/Occupation, 2004–21..................................................... 76 Median Hourly Wage Growth by Age, 1998–21......................... 77 Median Hourly Wage Growth by Race/Ethnicity, 1998–21........ 77 Median Hourly Wage Growth by Wage Quantile, 1998–21........ 78 Real Market Income Growth, 2020–21........................................ 78 Real Disposable Income Growth, 2019–21.................................. 79 Payroll Employment, 2020–22..................................................... 80 Employment Changes by Industry Sector, 2020 and 2021.......... 80 The Labor Force Participation Rate, 2020–22............................. 81 U.S. Prime-Age (25–54) LFPR, 2020–22.................................... 82 Prime-Age LFPRs during Past Recessions and Recoveries......... 82 Change in U.S. Rate of Nonparticipation in the Labor Force, February 2020–January 2022, by Reason for Nonparticipation.. 83 The Retirement Rate, 2010–22..................................................... 84 Economic Report of the President 2-42 2-43 2-44 2-45 2-ii 3-1 3-2 3-3 3-4 3-5 3-6 3-i 3-ii 3-7 3-iii 3-8 3-9 3-10 3-11 3-12 3-13 3-14 4-1 4-2 4-3 4-4 4-5 4-i 4-6 4-7 4-8 4-9 5-1 5-i 5-2 Retirement Flow Rates, 1998–22................................................. 84 Maternal LFPR versus the Same Calendar Month in 2019......... 87 The U.S. Unemployment Rate, 2020–22..................................... 88 Labor Supply and Demand, 2019–21........................................... 89 The Federal Fiscal Impetus by Quarter........................................ 91 International COVID Case Rates................................................. 99 International COVID Vaccination Rates.................................... 100 Real GDP by Country................................................................. 101 Discretionary Fiscal Response, 2020:Q1–2021:Q3................... 102 Consumer Price Level................................................................ 103 Recovery in Output and Inflation............................................... 104 Unemployment Rates................................................................. 105 International Employment.......................................................... 106 U.S. Trade Balance, 2001–21..................................................... 108 Trade in Petroleum Products...................................................... 109 Nominal Broad Dollar Index...................................................... 110 U.S. Trade in Goods................................................................... 111 Real Exports, Selected End-Use Categories.............................. 112 Real Imports, Selected End-Use Categories.............................. 113 Trade in Services........................................................................ 114 Trade in Travel Services............................................................. 115 Trade in Transportation Services................................................ 115 U.S. Gross Domestic Product per Person, 1870–2021.............. 126 Earnings Increase with Years of Schooling................................ 127 Average Years of Education by Age Group............................... 128 Life Expectancy, 1900–2019...................................................... 129 Percent Reporting Health as Fair or Poor, 1997–2018.............. 130 Share of COVID Deaths and Share of Population by Age........ 131 Degree or Certificate Completion Rates among Students Who First Enroll at a Public, Two-Year Institution............................ 142 Life Expectancy at Birth for U.S. Counties, 2010–19............... 147 Infant Mortality Rates by Race or Ethnicity, 2018.................... 148 Percent of U.S.-Born People Employed in the United States, by Age......................................................................................... 149 The Gap Between Productivity and Worker Compensation, 1948–2020.................................................................................. 159 Median and Average Wealth by Race and Ethnicity, 2019........ 160 Wage Gaps by Education, Race, and Ethnicity, 2021................ 162 Economic Report of the President | 17 5-3 5-4 5-ii 5-5 5-6 6-1 6-2 6-3 6-i 6-4 6-ii 7-1 7-2 7-3 7-i 7-4 7-5 7-6 7-ii 2-1 2-2 2-3 2-4 2-5 2-6 2-7 18 | Gender Wage Gap by Level of Education, 2021....................... 163 Wage Gaps by Gender, Race, and Ethnicity, 2021..................... 163 Average Household Income among Asian American, Native Hawaiian, and Pacific Islander Subgroups................................. 165 Mothers’ and Nonmothers’ Labor Force Participation Rates, 2021............................................................................................ 167 Average Income, Means-Tested Transfers, and Federal Taxes, 2018............................................................................................ 188 Common Types of Supply Chains.............................................. 193 Examples of Tier 1 and Tier 2 Supply Relationships................. 195 The Production Network Corresponding to U.S. Input-Output Data in 2002............................................................................... 197 Sources of the Components of a Hot Tub.................................. 199 Frequency of Billion-Dollar Natural Disasters by Type, United States............................................................................... 210 Domestic Business Ratios of Private Inventories to Final Sales............................................................................................ 213 Atmospheric CO2 Levels Across the Millennia to 2019............ 223 Global Carbon Dioxide Emissions Projections, 2025–40.......... 225 Representative Pathway to Meet Net Zero Emissions in the United States, 2005–50............................................................... 226 Changes in U.K. Greenhouse Gas Emissions and Real GDP since 1990................................................................................... 228 U.S. Fossil Fuel Consumption for Selected Years..................... 231 The United States’ and China’s Percentages of the Market Across Clean Energy Industries................................................. 235 Fossil Fuel Employment by County........................................... 241 Distressed Communities in the United States............................ 244 Tables Fiscal Support from Coronavirus Relief Laws in Fiscal Years 2020–23........................................................................................ 50 Historical Episodes of Fiscal Expansion since 1941................... 50 Consumer Spending Growth since the Beginning of the Pandemic...................................................................................... 59 Fixed Investment Components, 2019:Q4–2021:Q4..................... 60 Consumer Price Index Inflation Expectations.............................. 71 Economic Projections, 2020–32................................................... 92 Supply-Side Components of Actual and Potential Real Output Growth, 1953–2032...................................................................... 95 Economic Report of the President 7-1 7-i 1-1 1-2 2-1 2-2 2-3 2-4 3-1 3-2 3-3 4-1 4-2 4-3 5-1 5-2 6-1 6-2 6-3 6-4 6-5 7-1 7-2 7-3 7-4 7-5 Global Clean Energy Deployments in 2020 and 2030 Consistent with Net Zero Emissions by 2050............................ 227 Selected BIL Programs That Target Energy Communities........ 247 Boxes Unemployment Insurance during the Pandemic.......................... 31 Effective COVID-19 Vaccines as Public Goods.......................... 34 Historical Precedents for the COVID-19 Pandemic.................... 48 Monetary Policy in 2021.............................................................. 52 A Note on the Butterfly Figures................................................... 57 Fiscal Impetus by Quarter............................................................ 91 Lessons from Abroad for Labor Market Policy......................... 105 Trade in Oil and Petroleum Products......................................... 109 Greenhouse Gas Emissions and Trade....................................... 117 COVID and Health..................................................................... 131 COVID and Education............................................................... 138 Federal Investments in Lead Abatement and Rural Broadband. 145 Racial and Ethnic Wealth Gaps.................................................. 160 Improving Data Infrastructure for Equity Analysis................... 164 The Supply Chain of a Hot Tub................................................. 199 The Role of China in U.S. Supply Chains................................. 202 Outsourcing and Job Quality...................................................... 206 Low Inventories and Just-in-Time Production........................... 213 Policies to Improve the Functioning of Supply Chains............. 218 The United Kingdom’s Emissions Have Fallen Rapidly While Its Economy Has Grown............................................................ 228 The History of U.S. Government Support for Domestic Carbon-Intensive Energy Industries........................................... 232 Industrial Policy Successes and Failures.................................... 239 The Broader Issue of Distressed Local Economies................... 244 The Administration’s Actions on Place-Based Policies for Energy Communities.................................................................. 247 Economic Report of the President | 19 Chapter 1 The Public Sector’s Role in Economic Growth The U.S. economy is among the world’s strongest and most productive, but trends over the last several decades threaten to undermine its standing—and to diminish the living standards of most Americans. Since the 2001 recession, the United States has seen relatively weak economic growth, with income and wealth disparities at levels not seen in a century. Divisions along lines such as race, ethnicity, and gender persist. These economic challenges have many causes. A common theme among them is the retreat of the U.S. public sector from its complementary role vis-à-vis the private sector in economic growth. Over the last four decades, neglect of critical physical infrastructure, from ports to the power grid, has left the Nation with bottlenecks and vulnerabilities that restrict growth and make the economy less resilient to shocks and shifts. The United States has cast aside its history as a global leader in public funding for education—from the high school movement to the G.I. Bill—and now lags its peer countries in early childhood education and job training. Underinvestment has, in particular, diminished the pace of growth in U.S. economic capacity—that is, the maximum sustainable amount of goods and services our economy can produce when unemployment is low and other resources are being put to full use. This transformation of the U.S. public sector’s role did not occur by accident. It reflected an economic philosophy which maintained that private enterprise would thrive only if government got out of the way; otherwise, public sector investment would “crowd out” the activity of the private 21 sector. Put to the test, these predictions did not deliver. Proponents of this philosophy had ignored some of the economics discipline’s most celebrated ideas—ones revealing situations where the private sector cannot and will not substitute for the public sector. As a result, when the public sector stepped back, economic growth diminished and became less evenly shared. The private sector did not lose a rival; it lost a partner. During the pandemic, infrastructure problems created by underinvestment became crises. The absence of reliable broadband Internet, for example, made remote education a challenge for millions of children and families, setting them back (Auxier and Anderson 2020). The capacity constraints of U.S. ports and other aspects of freight infrastructure snarled supply chains, harming U.S. manufacturers (U.S. Department of Transportation 2022b). Yet underinvestment had constrained U.S. economic capacity before the pandemic, and it would have continued to do so if the pandemic had not exposed these vulnerabilities. When the public sector underinvested in people’s health and education, the private sector was left with a weaker foundation on which to build, hire, and invest. When the public sector underinvested in innovation and basic science, the private sector had fewer ideas and technologies that it could apply to products in such industries as clean energy and biomedicine. By building a large, healthy, and highly skilled workforce, and by fueling technological progress, public investments can expand the capacity of the U.S. economy— and thereby sustain the long-run advance of the American standard of living. The payoffs from public investment, however, are rarely immediate. Ideas take time to germinate into industries, as do children to mature into adults. This has two implications. First, the U.S. government must invest today if we are to benefit tomorrow, as the payoffs from investments take time to emerge. And if the government waits until the signs of underinvestment are fully revealed, it will have waited too long. There will be higher costs to replace infrastructure beyond repair, a more tumultuous transition to clean 22 | Chapter 1 energy, and a greater need for public assistance for adults instead of public investment in disadvantaged children. Second, the government’s role in increasing the aggregate capacity of our economy can be challenging and requires sustained effort. Building bridges, running research labs, enhancing the power grid, and educating children to become productive adults entail complex, long-term investments. They require patient, capable institutions that plan beyond budget horizons for the design and delivery of public services. When the public sector’s role is neglected, these investment aspects of the government’s capacity are likely to deteriorate the most. A core aim of the Biden-Harris Administration’s economic policy agenda is to restore the public sector as a partner in long-run growth, with a particular focus on the economy’s supply side—from physical infrastructure to the vitality of our workforce. This means, first, fixing what is broken in physical infrastructure. The Bipartisan Infrastructure Law, signed by President Biden in November 2021, makes a historic investment in transportation and utility systems—spending that will address decades of deferred maintenance of the infrastructure that keeps lights on, water clean, and people and goods flowing across the country. This law also upgrades infrastructure in several strategic areas—such as lead abatement, rural broadband, and electric vehicles. Such investments are important to make growth more robust, more widely shared, and more environmentally sustainable. However, restoring the public sector to its full role in promoting growth involves more than physical infrastructure investment. Long-run economic growth also depends on the growth of productive skills and abilities among workers—what economists call “human capital”—and the pace of technological progress (Romer 2019). These factors together determine the capacity of the U.S. economy. The U.S. government could also do much more to support growth through investments in workers, children, and families. For instance, though early childhood education is typically free or available at very low cost in other developed countries, it remains financially The Public Sector’s Role in Economic Growth | 23 burdensome for a large share of American children born into lower-income families (Boushey, Barrow, and Rinz 2021). Investments in early childhood education, like other public investments in human capital, would raise longrun productivity growth as children and students grow up to become workers (Cascio 2021). The fruits of economic growth must also be shared more broadly. Labor’s share of income, once famously stable, has declined to historic lows in the United States, and the distribution of labor income has become more skewed to the top earners since the 1970s (Congressional Budget Office 2021). Public investments in physical infrastructure and human capital also help ensure that economic growth is more broadly shared by making sure that people have access to economic opportunities. Two other ways to make growth more inclusive are tax policy and labor regulation. Some multinational corporations, for example, exploit the absence of effective international tax cooperation to shift where they report income and assets to tax havens, where tax rates are low and malleable. Establishing international standards and minimums can stop the global race to the bottom in corporate taxation, so that highly profitable companies pay for their fair share of the public investments and services they use. Stronger labor standards—such as a higher minimum wage, effective enforcement of wage-and-hour and occupational-safety regulations, and protections for workers’ right to organize—will also help to boost workers’ wages and working conditions. The Administration’s agenda could start to rebuild our economic capacity. According to an estimate by Moody’s Analytics, passing additional legislation based on the President’s policies, along with the Bipartisan Infrastructure Law and the American Rescue Plan, would lead to an economy that is about 1.5 percent larger in 2031 than it would have been without any of this legislation (Zandi and Yaros 2021). Economic projections from 24 | Chapter 1 the Administration’s Fiscal Year 2023 Budget find that passing it would raise the long-run annual growth rate by about 0.4 percentage point. This introductory chapter explains why a strong and effective public sector is not only smart economics but also critical to putting the United States back on the path of robust, inclusive economic growth. It begins with a brief portrait of the U.S. economy before the COVID-19 pandemic—which, due in part to a depleted public sector, struggled with disappointing growth in its productive capacity. Each section then considers one of three complementary roles of the public sector: (1) ensuring macroeconomic stability; (2) addressing areas where the private sector fails to deliver (market failures); and (3) reducing inequality. It first explains, on a conceptual level, why government has a role to play in each of these areas. Next, it describes how the U.S. government performed in this role during the pandemic. Finally, it discusses what role for government remains unfinished. Before the Pandemic How strong was the economy in the immediate years heading into the COVID-19 pandemic? By some measures of economic performance, it was stronger than it had been in many years. Unemployment was low, and stock and home prices were soaring. Yet that sunny account of the late 2010s ignores other weaknesses in the economic data, especially the warning signs coming from measures that serve as economists’ best proxies for long-run growth in U.S. economic capacity. Among these warning signs: U.S. labor force participation rates have dropped to some of the lowest in the developed world. Whereas in 1985, a larger share of prime-age American women participated in the labor force than their counterparts in Australia, Canada, the European Union, Japan, or the United Kingdom, U.S. female labor force participation has since been surpassed by all these countries or entities (figure 1-1). The decline in labor force participation among men is similarly staggering. In 1960, work among men age 25 to 54 years was nearly universal, with just 3 in 100 such men not working or looking for work (Krueger 2017). But, by 2019, nonparticipation among men of such ages had tripled, with more than 1 in 10 out of the labor force (figure 1-2). While this decline might have reflected changes in the gender division of household responsibilities, much of it appears unrelated to such shifts (White House 2016). The Public Sector’s Role in Economic Growth | 25 Figure 1-1. Women’s Labor Force Participation Rate, 25 to 54 Years Percent 90 85 Canada United Kingdom 80 75 United States 70 Australia 65 60 European Union 27 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 55 50 Japan Source: OECD (2021). Figure 1-2. Men’s Labor Force Participation Rate, 25 to 54 Years Percent 99 97 Japan 95 93 European Union 27 91 Canada 89 Australia United States 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 87 85 United Kingdom Source: OECD (2021). The weakness in both male and female rates of labor force participation has directly diminished the growth of the U.S. economy’s productive capacity. With a smaller labor force, U.S. firms can hire fewer workers domestically and thus can produce less in the United States than they would if participation rates were higher. The slow growth rates of output and productivity provide another grim perspective on U.S. economic performance before the pandemic. Comparing all U.S. economic expansions from start to end since 1950, there is none with a weaker average growth rate than the recovery from the Great Recession. Compared with the average for these expansions, growth in both 26 | Chapter 1 Figure 1-3. Growth Rates in Economic Expansions Percent growth (average annual rate) 6 5 Real output per capita Real output per hour 4 3 2 1 0 Q4:1949– Q2:1954– Q1:1958– Q4:1960– Q4:1970– Q1:1975– Q3:1980– Q4:1982– Q1:1991– Q3:2001– Q2:2009– Q3:1953 Q3:1957 Q1:1960 Q3:1969 Q2:1973 Q3:1979 Q3:1981 Q2:1990 Q4:2000 Q4:2007 Q4:2019 Sources: Bureau of Economic Analysis; CEA calculations. real output per capita and productivity (real output per hour) during the prepandemic expansion was less than half as fast (figure 1-3). Productivity growth provides an especially clear view on the slowdown in U.S. capacity growth, given that it adjusts for cyclical changes in unemployment and resource utilization. Economic growth has not only slowed; it has also become less broadly shared. From the end of World War II until the late 1970s, real incomes roughly doubled for families in the bottom fifth of the income distribution as well as families in the top 5 percent. Yet after the 1970s, the gains from growth became far more concentrated at the top. Since 1973, the real median income of households in the bottom fifth of the distribution has risen by less than 15 percent, compared with growth of more than 100 percent for families in the top 5 percent (figure 1-4). Furthermore, other data from the U.S. Federal Reserve and the World Inequality Database show that the share of net wealth held by the top 1 percent of households is at or near record highs (Federal Reserve 2021; World Inequality Database 2021). Signs of economic underperformance also appear in an array of other indicators. Over the last few decades, U.S. life expectancy at birth has slowly fallen behind that in other high-income countries (OECD 2021). It is now the lowest in the Group of Seven, with little net increase over the last decade. Furthermore, inequality and underinvestment in health are linked to infant mortality (Chen, Oster, and Williams 2016), which has also remained higher in the United States than in its peer countries since the 1980s (figure 1-5). Maternal mortality rates are also higher in the United States than in any other developed country (Declercq and Zephyrin 2020). Many analysts have also blamed economic stagnation for a surge in so-called deaths of despair related to alcohol, drugs, and suicide (Case and Deaton 2020). The Public Sector’s Role in Economic Growth | 27 Figure 1-4. Growth in Average Family Income, by Income Group Index (1979 level = 100) 250 Top 5 percent 200 80th - 95th percentile Fourth fifth Middle fifth Second fifth 150 100 Bottom fifth 0 1947 1950 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 2019 50 Sources: Census Bureau; CEA calculations. Note: Income is in dollars adjusted by the Consumer Price Index for all Urban Consumers, retroactive series, using current methods. To account for simmering discontent beneath a seemingly booming economy requires a more nuanced picture of the Nation’s economic health. The prepandemic economy was indeed at or approaching full employment for the first time in 20 years. But while the U.S. economy benefited from cyclical gains, the structural foundations for long-run inclusive growth were not being maintained. Accommodative macroeconomic policy could not substitute for everything else that the public sector should do as a partner of private enterprise. What is needed now is an effective partnership between the public and private sectors. The very existence of private business relies on functions that only the public sector can provide, ranging from an institutional legal framework to national security to reliable infrastructure. However, these basic government functions do not exhaust the complementary roles of the public sector in promoting economic growth through greater productive capacity, and in ensuring that well-being flourishes alongside growth. These functions are, in some ways, troublingly easy to neglect: The damages wrought by underinvestment accumulate slowly, and the task of public investment is inherently more demanding than a tax cut. But when these functions are neglected, government becomes less capable and less responsive to economic change. At the onset of the COVID-19 pandemic, for example, the lack of administrative infrastructure to channel support to businesses meant that the Paycheck Protection Program was far costlier and less well-targeted toward businesses most in need of rescue than similar programs in other high-income countries (Autor et al. 2022). The bill for public sector underinvestment eventually comes due in the form of less effective government. 28 | Chapter 1 Figure 1-5. Infant Mortality Deaths per 1,000 live births 35 30 25 Average of other Group of Seven countries 20 15 10 United States 0 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 5 Source: World Bank. Ensuring Macroeconomic Stability Although the COVID-19 pandemic has been the worst global outbreak of disease since the influenza pandemic of 1918, societies are also often hit by other aggregate shocks, including recessions and swings in prices of critical commodities such as oil and staple foods. These shocks are economy-wide, sudden, and—especially in the case of epidemics—at once rare, costly, and hard to forecast. As such, they may be difficult or impractical for individuals themselves to prepare for. An important function of government is to help insure society against such risks. For example, countercyclical monetary and fiscal policies are essential for boosting demand, output, and employment in depressed economies. Moreover, there are reasons to think that appropriate countercyclical policies raise living standards on average, instead of purely stabilizing the economy around its long-run growth path. When capacity is already being underused, as in a recession, the private sector faces weaker incentives to invest in more capacity, potentially limiting longer-run growth (DeLong and Summers 2012). Even if these so-called hysteresis effects are weak or absent, countercyclical policies may be able to raise the long-run level of output by reducing the amount of time spent below the economy’s capacity level, as in Milton Friedman’s famous “plucking” model of business cycles (Dupraz, Nakamura, and Steinsson 2021; Friedman et al. 1964). Macroeconomic Stabilization during the Pandemic At the onset of the pandemic, the loss of jobs and income threatened hardship for millions of families and bankruptcies for small businesses. The Public Sector’s Role in Economic Growth | 29 A massive public policy response likely prevented the pandemic’s public health crisis from creating a prolonged and spiraling economic one. The government provided the equivalent of an economy-wide insurance policy against the pandemic—with expanded unemployment insurance, support for temporarily shuttered businesses, aid to State and local governments, and Economic Impact Payments (EIPs, which were often referred to as “stimulus checks”). This response, as former Council of Economic Advisers Chair Christina Romer argued in a recent paper with David Romer, can be thought of as roughly enacting the “pandemic insurance” policy that families and businesses would have wanted to buy themselves, if such insurance had existed (Romer and Romer 2021). Although there has been a larger public focus on discretionary fiscal policies like EIPs, much of what the government “did” to prevent a catastrophic pandemic-induced economic crisis happened without Congress or the executive branch taking any affirmative action, through a set of policies known as “automatic stabilizers.” For instance, when workers are laid off, they can file for unemployment benefits and can typically collect up to 26 weeks of assistance as they search for work. Such spending eases those workers’ hardships and, when many workers lose their jobs at once (as in a recession), has a macroeconomic impact of preventing a cascading decline in income and spending (Kekre 2021). In crises, a program called Extended Benefits automatically adds weeks in certain states when the unemployment rate reaches certain metrics. As discussed in box 1-1, Congress did take important actions to make unemployment insurance (UI) more generous and more widely available during the pandemic, reflecting weaknesses in the current system, but some of the UI system would have been triggered without Congressional action. For instance, almost 25 percent of the increase in UI payments in 2020 relative to 2019 was due to “normal” UI programs (regular benefits and extended benefits). Though this increase may not have been enough to support workers during the pandemic, or even amid a normal recession, it does speak to the importance of ensuring that future policy includes robust “automatic stabilizers.” Monetary policies adopted by the U.S. Federal Reserve System also play a crucial role in macroeconomic stabilization. As reviewed in a recent paper by former Fed Vice Chair Richard Clarida and coauthors Burcu Duygan-Bump and Chiara Scotti (2021), the Fed’s efforts to halt and reverse the economic crisis sparked by the pandemic took several forms. First, the Fed implemented its conventional policy toolkit with unprecedented speed. It cut its benchmark nominal interest rate to zero, provided forward guidance that its zero-rate policy would remain until “the economy has weathered recent events and is on track to achieve its maximum employment and price stability goals,” and announced $700 billion in asset purchases of U.S. Treasuries and mortgage-backed securities. 30 | Chapter 1 Box 1-1. Unemployment Insurance during the Pandemic Unemployment insurance (UI) is an important component of the U.S. safety net, providing workers with income amid job loss that is out of their control. With UI, workers can continue to receive a portion of their wages and support their families as they search for new jobs. UI is also an automatic stabilizer (Kekre 2021). When the economy suffers a downturn, increased UI payments lift the economy, preventing a spiraling descent in consumption and output. Indeed, during the Great Recession in 2008, UI kept millions of Americans out of poverty while also saving millions of jobs (West et al. 2016). However, the COVID-19 pandemic, and the unprecedented job loss it precipitated, put the UI system to the test and exposed underlying weaknesses. The current UI system is fragmented—jointly funded by the Federal government and States, but primarily administered by States, which, within broad standards, set their own eligibility criteria, benefit levels, and benefit durations. And as the nature of work has evolved even before the pandemic began, UI has not kept up. For instance, workers who are self-employed, including independent contractors, are ineligible for UI. As the labor force has changed and grown tremendously in the past few decades, the UI taxable wage base has not grown with it (Vroman and Woodbury 2014). Expansions of UI enacted during the pandemic allowed the system to provide appropriate relief during a widespread national crisis, while strengthening the system’s ability to support workers and stabilize the economy. The Federal Pandemic Unemployment Compensation and Pandemic Emergency Unemployment Compensation programs set nationwide standards in benefit amounts and durations that accounted for the unprecedented labor market challenges the pandemic posed. Meanwhile, at its peak, the Pandemic Unemployment Assistance program made benefits available to nearly 15 million workers ineligible for traditional UI (Bivens and Banerjee 2021). The pandemic also highlighted a need for investment in UI systems and broader UI policy reforms (Bivens et al. 2021). In the summer of 2021, roughly 40 percent of workers receiving their first UI payment reported having to wait at least 3 weeks for it (U.S. Department of Labor 2022). Delay times in application processing and distribution of safety net polices can put financially vulnerable families in an even more precarious situation. Future economic downturns may require again extending UI benefits to currently excluded workers, suggesting a role for reforms that would incorporate them. In the subsequent weeks and months, the Fed established additional programs to safeguard liquidity in financial markets and to encourage banks to lend to small businesses and municipal governments, many of which The Public Sector’s Role in Economic Growth | 31 found themselves unable to borrow just when they most needed credit to survive. Finally, the Fed worked with banks to complete two rounds of stress tests focused on understanding the impact of the pandemic on banks’ capital positions, creating transparency that, as in the 2008 financial crisis, had the goal of raising investor confidence about the readiness of U.S. financial institutions to weather the crisis (Morgan, Peristiani, and Savino 2014). These policy actions helped to prevent not only another Great Depression but also another Great Recession. That is, through a response that was responsive to the scale and nature of the pandemic-induced crisis, the Fed’s actions helped to avert an even larger economic catastrophe and to fuel a postcrisis recovery that to date has been far stronger than after the 2008 financial crisis. The greatest challenges in years to come may arise with little warning. Just as the government buttressed the macroeconomy during the pandemic, so too must it be able to guide the economy through unanticipated shocks in the future. Social insurance programs that protect workers, families, and businesses from severe hardship play a central role in macroeconomic stabilization (McKay and Reis 2016). Unemployment insurance and the Supplemental Nutrition Assistance Program (SNAP) proved to be powerful countercyclical policy levers, shoring up household resources throughout the unforeseen demands of the pandemic (Rouse and Restrepo 2021). In the face of historic spikes in joblessness and hunger, some government aid was automatically assured. Recent updates to the Thrifty Food Plan will crucially reinforce the stabilizing power of SNAP in future recessions (Bauer 2021). Addressing Market Failures Although the private market adequately provides goods and services in many instances, there are textbook cases in which it does not. These situations constitute “market failures,” which occur when individual actors—such as households or businesses—do not achieve efficient outcomes on their own. Market failures are a pervasive feature of real-world markets. Left unaddressed, they inhibit the efficiency and capacity of the economy. One well-known example of a market failure is when the consequences of private decisions spill over onto people who were not party to those decisions, a phenomenon economists call “externalities.” The choices of industrial factories over how much to spend on equipment to reduce their emissions, for example, matter for everyone who breathes the air and drinks the water near these factories. And yet, when making such decisions, private firms have incentives to control emissions only to the extent that they affect their bottom line, likely emitting more than is desirable for society as a whole. Government involvement can improve outcomes through 32 | Chapter 1 policies that compel factories to account for this social damage in their decisionmaking. Even the need for macroeconomic stabilization can be characterized as a form of market failure that stems from price rigidities, incomplete insurance markets, and externalities from shocks to aggregate demand. Market failures can also arise when people are credit-constrained. When these credit constraints inhibit people’s ability to pay what something is worth, this inability to meet costs may incorrectly signal that the good or service has no long-term value. One notable example of this is childcare and education: just because families cannot meet the true costs of these services at this point in their lifecycle does not mean they are not valuable, hence motivating public involvement. Furthermore, efficient markets require buyers and sellers to be informed about the quality and prices of the goods and services traded. When participants are uninformed, markets struggle to yield mutually beneficial trades between buyers and sellers. For instance, in the market for health insurance, people buying it know more about their individual health status than the insurance companies, which causes these markets to provide inadequate coverage, out of fear that only unhealthy people will choose to buy adequate coverage. Finally, markets may not reach efficient outcomes when production and sales are highly concentrated in one or a handful of companies. A dominant position gives such companies an incentive to price their goods and services above their cost, to innovate less, and to take other anticompetitive actions to entrench their position and to extract monopoly rents from buyers. Market Failures during the Pandemic The pandemic has shown that people’s behaviors may accelerate or slow the spread of the virus. Testing, mask-wearing, social distancing, and vaccination all benefit more than just the people doing those things, producing beneficial health externalities for everyone with whom these people come in contact. Governments have taken several steps to encourage or require these pro-social behaviors during the pandemic, including the American Rescue Plan’s funding for the national vaccination campaign and free COVID-19 tests. The Federal Government, and many State and local governments, also mandated mask-wearing indoors to reduce COVID-19’s airborne spread. In addition, many State and local governments put in place temporary indoor capacity limits to encourage increased social distancing and implemented vaccine mandates for certain activities. The Federal Government has also funded the development and distribution of vaccines, given that vaccinations benefit many beyond vaccinated individuals themselves (see box 1-2). The Public Sector’s Role in Economic Growth | 33 Box 1-2. Effective COVID-19 Vaccines as Public Goods The life-saving impact of COVID-19 vaccines illustrates the importance of an important public good: basic scientific research. One consideration that makes such research a public good is that one use of knowledge—for example, to cure a given disease—does not take away from other potential applications of the same knowledge. In the case of COVID-19 vaccines, the central scientific breakthroughs were the result of decades of publicly financed research against other viral threats, including Ebola, MERS, Human Papillomavirus, and Human Immunodeficiency Virus (Harris 2021). The Biomedical Research and Development Authority, for example, was a key funder of research on messenger RNA, the vaccine platform eventually used in the Moderna and Pfizer vaccines. Public investment was also crucial in the final step of developing the COVID-19 vaccine: Richard G. Frank, Leslie Dach, and Nicole Lurie conclude, reviewing a variety of estimates, that the U.S. government invested between $18 and $23 billion in COVID-19 vaccine research and development and spent about $12 billion more on advance purchases of the vaccines. The United States also spent $20 billion on the vaccination campaign, according to analyses from the Kaiser Family Foundation (Kates 2021) and the U.S. Federal Emergency Management Agency (2021). Researchers have estimated that, without a vaccination program, there would have been approximately 1.1 million additional deaths and up to 10.3 million additional hospitalizations in the United States from December 2020 through November 2021 (Galvani, Moghadas, and Schneider 2021). Calculating the cost per life saved suggests that public spending on vaccines was remarkably cost-effective. In particular, assuming the COVID-19 vaccines would not have emerged without public investment, the cost of this investment was between $45,000 and $50,000 per American life saved. By comparison, some U.S. government agencies typically consider spending to be cost-effective if it costs around $11 million per life saved—indicating that half of a cent of spending on COVID-19 vaccines saved as many lives as $1 of spending on other U.S. policies (U.S. Department of Health and Human Services 2021; U.S. Department of Transportation 2021). Such thresholds, referred to as the “value of a statistical life,” are widely used to evaluate life-saving regulatory policies, from car safety to power-plant emissions (Viscusi 2018). Even these estimates, however, greatly understate the true cost-effectiveness of vaccine spending, as they do not account for the millions of lives saved abroad, those saved after November 2021, and those yet to be saved by COVID-19 vaccines, nor the avoided costs of hospitalizations, illnesses, and work absences. Taken together, these considerations suggest that public investments in COVID-19 vaccines were likely the single most cost-effective policy response to the pandemic. 34 | Chapter 1 An important special case of externalities relates to “public goods”— goods and services, like national defense and some forms of infrastructure, that cannot be depleted by one person’s use and that benefit people whether or not they have paid for them. If left to the private sector to provide, public goods are undersupplied, as people can individually opt not to pay and to free ride on the willingness of others to pay. However, if everyone tries to free ride, there are no public goods to enjoy. Government spending on public goods can ensure that they are adequately provided and can thereby raise the economy’s productive capacity (see box 1-2). Emergency government assistance for small businesses during the pandemic can also be viewed as a policy response to market failures, as former Council of Economic Advisers Chair Joseph E. Stiglitz has argued (Stiglitz 2021). Many small businesses, for example, have insurance policies against “business interruption” to cover revenue losses due to fires, floods, or other disasters that are no fault of their own. These policies largely do not cover pandemics, which left the 41 percent of small businesses that temporarily closed in late April 2020 without coverage against revenue losses, putting them at risk of closing their doors forever (U.S. Census Bureau 2022). Grants and loans to small businesses, such as the Paycheck Protection Program and the Restaurant Revitalization Fund, addressed this lack of insurance coverage by directly providing a form of business interruption insurance. Market Failures Beyond the Pandemic Market failure is a unifying theme in making the case for public investment in infrastructure, child health and education, and clean energy. This subsection explores these areas of concern. Infrastructure. There is much evidence that the United States lags far behind its competitors in supplying the essential inputs to economic capacity. U.S. infrastructure provides several examples. The World Economic Forum’s Global Competitiveness Report found in 2019 that, out of 141 countries, the United States ranked 13th in quality of overall infrastructure, 17th in quality of road infrastructure, 23rd in electricity supply quality, and 30th in reliability of water supply (Schwab 2019). A separate ranking of global ports by the World Bank and IHS Markit found that no U.S. port made it into the top 50 globally, and just 4 are in the top 100. By comparison, of the top 10 ports, several are in China. The Federal Communications Commission (FCC 2018) has also ranked the United States 10th among developed countries for broadband speed and connectivity. In transporting goods and services, in connecting workers around the country and globe, in transforming technological progress into productivity gains, the United States is not at the frontier. The Public Sector’s Role in Economic Growth | 35 The public sector has an important role to play in building and maintaining the stock of physical infrastructure, which complements private capital investment. Though the private sector can adequately supply the economy with most physical capital—factories and offices, for instance— infrastructure projects, such as transportation systems, are far less suited to private development. Their construction often requires legal authority to use property to overcome holdups by individual landowners. Furthermore, some of the social benefits of these projects may stem from increases in innovation, economies of scale, and labor mobility—factors that private developers would not consider in their investment decisions, leading to underinvestment (Ramondo, Rodríguez-Clare, and Saborío-Rodríguez 2016; Perla, Tonetti, and Waugh 2021). The supply chain disruptions during 2021–22 have illustrated the critical importance of fast, efficient transportation for economic growth and have highlighted the cost to the United States when government does not invest adequately in transportation infrastructure. When these systems are strained, they may become bottlenecks for the rest of the economy, causing cascading shortages, delays, and price increases (Bernstein and Tedeschi 2021; Helper and Soltas 2021). In mid-December 2021, 71 percent of U.S. manufacturing small businesses reported delays with their domestic suppliers (U.S. Census Bureau 2022). Facing higher shipping costs, and unable to promise timely deliveries, these manufacturers have been put at risk of losing sales to international competitors and being forced to cut jobs and investment (Hummels and Schaur 2013; Clark, Dollar, and Micco 2004; Hornbeck and Rotemberg 2021). Children. Another large body of evidence documents how investments in children can have positive effects throughout the life cycle and on society at large (Almond, Currie, and Duque 2018). Education boosts workers’ productivity and wages in the long run, while reducing adult mortality and incarceration, thereby lifting the economy’s overall capacity (Card 1999; Oreopoulos and Salvanes 2011). Child health interventions, such as the provision of adequate nutrition, similarly have lasting effects on both medical and nonmedical aspects of well-being (Bailey et al. 2020). The returns to such educational and health investments have been shown for children of all ages, from newborns to young adults (Hendren and Sprung-Keyser 2020), suggesting broad benefits from investments in early education and childhood programs as well as in elementary and secondary schools. However, the private costs of childcare and health care are increasingly burdensome and must be paid upfront, even as the rewards are reaped in the future (Council of Economic Advisers and Office of Management and Budget 2021). Many of these benefits accrue in large part to society, rather than just to the family itself—such as through higher tax receipts, less crime, and lower spending on public assistance (Hendren and Sprung-Keyser 36 | Chapter 1 2020). Furthermore, the quality of childcare is often variable and difficult for parents to ascertain (Mocan 2007). These considerations point to the possibility that families are unable to invest in children relative to the long-run benefits of these investments for society as a whole. Government can help ensure that children receive high-quality education and care early in life through measures like direct public provision and subsidies. Despite strong evidence for the benefits of early education, only about half of three- and four-year-old Americans are enrolled in preschool, and children of lower-income families are much less likely to be enrolled in preschool than children of higher-income families (National Center for Education Statistics 2021; Cascio 2017). Improving pay for caregivers and instituting standards for care would raise quality across the country, which may also raise the long-term payoff from these programs by increasing their effectiveness (Banerjee, Gould, and Sawo 2021). The past decades of underinvestment in children mean that the United States is not well prepared for current and future demographic changes. The aging workforce and the resulting increase in the number of retired workers suggest that growth in human capital per worker, and by extension growth in productive capacity, will slow unless the United States reverses underinvestment in our future human capital, as we discuss in chapter 4. Climate change. Climate change caused by pollution presents another economic challenge. Each polluting activity contributes to global warming and environmental damage, but polluters do not individually bear the costs associated with their pollution. Already the economic damages from storms, floods, droughts, and wildfires have risen to over $100 billion per year in the United States (National Centers for Environmental Information 2022). The mirror image of this problem is underinvestment in clean energy, as private actors bear the upfront costs of transition investments but cannot themselves capture all the long-term social benefits. Government can correct these externalities by helping to ensure that the private costs of carbon and other greenhouse gas emissions, as well as the private benefits of clean energy, correspond to their long-term costs and benefits for society. Replacing subsidies for fossil fuels with subsidies for clean energy investments, such as electric vehicles, helps align these private and social incentives. Adapting the Nation’s energy systems for the future is not a task that can be achieved by individual households, businesses, or industries alone. Consider a consumer in North Dakota wishing to purchase an electric vehicle. According to the Department of Energy, North Dakota has a total of 138 public and private electric vehicle supply equipment ports (Alternative Fuels Data Center n.d.). That is one charging station per 510 square miles, which is equal to or beyond the distance that any electric vehicle currently sold in the United States can drive on one charge (Wallace and Irwin 2021). The Public Sector’s Role in Economic Growth | 37 Meanwhile, California has one charging station for each 4 square miles of land in the state (Alternative Fuels Data Center n.d.). A key challenge in electric vehicle infrastructure is coordination between vehicle buyers and charging-station suppliers: Neither wants to be the first to invest, creating a chicken-and-egg problem that delays the transition to electric vehicles (Li et al. 2017). This suggests a role for government in undertaking upfront investments in infrastructure, and thus allowing all Americans to take part in the energy transformation. Reducing Inequality Both economic efficiency and equity are important goals. But there is no guarantee that efficient economic outcomes are equitable ones. Governments have a role to play in ensuring that the benefits of economic growth are shared when they would otherwise go to a fortunate few—and in spreading the costs of economic dislocations, such as trade adjustment and technological change, when they would otherwise wreak concentrated harm on particular local economies and groups. Another important, if difficult, task for government lies in confronting the ongoing legacies of de jure discrimination that many minority groups face, from labor market disadvantages to residential segregation (Rothstein 2017). Inequality Before and Beyond the Pandemic The U.S. economy has long featured substantial inequalities in income, wealth, and other economic outcomes among individuals and families. These inequalities reflect variations in opportunities, earnings ability, preferences, bargaining power, and luck—along with structural divisions by race, ethnicity, class, gender, sexual orientation, and other markers of difference. Income inequality can be explained by two economic trends: the decline in labor’s share of national income, and rising earnings inequality among workers. From 2000 to 2019, labor’s share of income in the U.S. nonfarm business sector fell 6 percentage points, from 63 percent to 57 percent, according to Bureau of Labor Statistics data. In addition, labor earnings growth since the 1970s has been strongly tilted toward the best-off households (Congressional Budget Office 2021). Since the distribution of nonlabor income (i.e., payments to capital and business owners) is even more unequal than that of labor income, the decline in labor’s share and the increase in earnings inequality have both contributed to rising inequality in overall income. The fall in the labor share and the rise in earnings inequality reflect many contributing causes—among them, shifting relative supply and demand for skills, changes in public policies like top tax rates and antitrust enforcement, and changes in labor market institutions such as 38 | Chapter 1 Figure 1-6. Gaps in Annual Earnings by Race, Ethnicity, and Gender Percent 80 70 60 Female vs. male 50 40 Black vs. white 30 20 Hispanic vs. nonHispanic white 0 1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 10 Sources: Bureau of Labor Statistics; CEA calculations. unions (Furman 2016). Collectively, these economic shifts and institutional changes have undermined worker power, especially that of the most vulnerable workers, for the benefit of top earners and the owners of capital and businesses. At the same time, gaps by race and gender have been highly persistent. There has been strikingly little progress in closing gaps in hourly or annual earnings by race and ethnicity over the last 20 years, and progress in closing gender gaps has slowed over the same period (figures 1-6 and 1-7). While these economic disparities have proved persistent, policy action and legal efforts against discrimination have been important in driving the progress that did occur. Critically, the reduction in racial and ethnic inequality has been “episodic” rather than “continual,” reflecting identifiable shifts such as the Civil Rights Act of 1964, the Fair Labor Standards Act of 1966, and the tight labor market of the 1990s (Donohue and Heckman 1991; Derenoncourt and Montialoux 2021; Baker and Bernstein 2013). Improvements in school quality after the landmark U.S. Supreme Court decision in Brown v. Board of Education were another important contributor to the compression of racial and ethnic earnings gaps (Card and Krueger 1992). Research also suggests that past antidiscrimination policies not only benefited minorities but also expanded the overall capacity of the U.S. economy, as discrimination prevented the economy from making full use of the potential of all Americans. According to one analysis, between 20 and 40 percent of all U.S. economic growth from 1960 to 2010 can be explained by reductions in discriminatory barriers by sex and race (Hsieh et al. 2019). Although women and racial and ethnic minorities are now more able to enter The Public Sector’s Role in Economic Growth | 39 Figure 1-7. Gaps in Average Hourly Earnings by Race, Ethnicity, and Gender Percent 40 35 Female vs. male 30 25 20 Hispanic vs. white 15 10 2019 2017 2015 2013 2011 2009 2007 2005 2003 2001 1999 1997 1995 1991 1989 1987 1985 1983 1981 1979 0 1993 Black vs. white 5 Sources: Bureau of Labor Statistics; CEA calculations. high-earning occupations like law and medicine, occupational segregation remains an important contributor to income disparities by gender, race, and ethnicity (Cortes and Pan 2018; Weeden 2019). Overall, occupation and industry segregation account for about half of the gender pay gap as of 2011 (Blau and Kahn 2017). After the rapid advance of women in the workplace during the 1970s and 1980s (figures 1-6 and 1-7), progress in reducing gender disparities in the labor market has been slow in recent years. A key factor behind the remaining gender gaps, much recent research has argued, is how household responsibilities are typically divided within heterosexual couples, especially those with children. In the United States, women’s employment and earnings fall immediately upon the birth of their first child and remain 20 to 30 percent lower, even 10 years after childbirth. Worldwide, larger “child penalties” occur in countries and regions of countries with more traditional gender norms (Kleven 2021). Other research has suggested that the lack of fair and predictable work schedules may be a barrier to maternal labor force participation. Women are less willing to accept higher-paying jobs with longer commutes than men, likely because of their greater home and care responsibilities, and gender pay gaps are smaller in occupations that can accommodate flexible work hours (Barbanchon, Rathelot, and Roulet 2021; Goldin 2014). Though norms and a fundamental economic force—specialization in either paid or household work—push women and men to make different life choices, government could do more to accommodate caretakers, typically women, who want to manage both family and career, such as through paid leave and subsidized child care (Boushey 2016). 40 | Chapter 1 Inequality in the Pandemic The COVID-19 pandemic laid bare vast, alarming economic disparities. Many higher-earning workers, for example, continued in their jobs through telework, while 80 percent of job losses after the pandemic were concentrated in the lowest quarter of wage earners (Gould and Kandra 2021). Women bore the brunt of school and childcare closures by picking up additional care responsibilities, and labor supply among mothers of young children remained depressed even two years into the pandemic (Goldin 2021). Furthermore, analyses that have parsed U.S. economic data by race, sex, ethnicity, and education have found weaker pandemic recoveries in labor force participation among women with compounding sources of disadvantage, such as Hispanic and non-Hispanic Black mothers or mothers with less than a bachelor’s degree (Tüzemen 2021). The government’s pandemic response aimed to prevent its costs from falling heavily on specific groups of workers. Several programs provided targeted relief to pandemic-affected industries—such as air travel, hotels, and restaurants—as well as to their workers. In addition, the government patched several holes in the safety net that, if they had been left unaddressed, would have exposed millions of families to pandemic-related hardships (Wheaton, Giannarelli, and Dehry 2021). One of these patches was the expansion of unemployment insurance to cover “gig” workers and others who are typically ineligible for such benefits, such as the self-employed and people with limited work histories, through Pandemic Unemployment Assistance (see box 1-1). A second patch to the safety net was in housing policy: The government forbade banks and landlords from foreclosing upon or evicting families, and it provided relief with the Emergency Rental Assistance Program and Homeowner Assistance Fund. Third, school closures during the pandemic meant that the nearly 30 million children who received free or reduced-price school lunches before the pandemic needed other forms of nutrition support—a safety-net hole patched with the Pandemic Electronic Benefits Transfer program (Economic Research Service 2022). These safety net patches, along with other policies such as the expanded Child Tax Credit, helped to reduce poverty to its lowest level on record, despite the pandemic and recession. Official estimates for the year 2021 will not be released until late 2022, but in 2020, the poverty rate fell to 9.6 percent from 11.8 percent in 2019, according to the Supplemental Poverty Measure, which accounts for the resources that many low-income households receive from the government (Fox and Burns 2021). Declines in poverty were even larger for particular racial and ethnic groups, with the supplemental poverty rate among Black and Hispanic Americans falling by 3.7 and 4.9 percentage points, respectively (figure 1-8). The decline in the The Public Sector’s Role in Economic Growth | 41 Figure 1-8. Poverty Rate by Racial Group Percentage of population 30 Hispanic 25 Black 20 15 Asian 10 White, not Hispanic 5 0 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Source: Census Bureau. child poverty rate was equally dramatic, dropping by almost 3 percentage points and projected to fall even further in 2021 (Wheaton, Giannarelli, and Dehry 2021). The data illustrate the importance of public assistance in preventing pandemic hardships, because the poverty rate, as measured by the Official Poverty Measure—which does not reflect the increase in transfers— rose by a full percentage point to 11.4 percent in 2020 (Shrider et al. 2021). Conclusion Economists have long understood the myriad ways in which government action in the economy can promote growth and well-being, fulfilling the public sector’s role as a partner of the private sector. Ensuring macroeconomic stability, investing in public goods, addressing market failures, and reducing inequality are just some of the functions that markets cannot do alone—or do too little in the absence of government. When governments fulfill these roles, they are not interfering in the market or crowding out private enterprise; they are creating, protecting, and expanding markets and their potential to produce an inclusive and prosperous society. These complementary functions of government were on prime display during the COVID-19 pandemic. The health costs and risks of viral transmission meant that basic person-to-person interactions carried social implications, motivating a host of U.S. government policies to reduce these risks: physical distancing, subsidized testing, mask requirements, and public investment in vaccines and treatments for COVID-19. And just behind the public health crisis loomed a potential economic crisis, one that portended hardship for tens of millions of people who had lost jobs or income—a crisis that the U.S. government successfully alleviated with aggressive monetary 42 | Chapter 1 and fiscal responses that sustained aggregate demand and strengthened the safety net throughout the pandemic. The U.S. response to COVID-19 has been intentional in recognizing and undoing the pandemic’s unequal effects across our society—with progressive direct cash assistance, targeted support for workers in the industries most affected by the pandemic, and investments in broadband access and vaccine outreach to serve rural and other disadvantaged communities. The partnership between public and private sectors worked during the pandemic and has the potential to contribute to increased future economic growth. As the remaining chapters of this Report discuss, understanding the role of government is important in assessing economic policy options. A policy agenda to fulfill these roles can improve U.S. economic outcomes and expand U.S. productive capacity, both now and over generations to come. Chapter 2 provides an overview of the economy over the past year, focusing on how this recovery differs from past ones. The chapter discusses fiscal and monetary policy support, pandemic issues, inflation, and labor force participation. The macroeconomic forecast underpinning the Administration’s Budget is also presented. Addressing the pandemic-induced economic downturn has been a shared priority for countries around the world. Chapter 3 analyzes the U.S. economy in a global context, examining other countries’ paths toward recovery, inflation trends, and labor markets, as well as shifts in international trade and their impact on the U.S. trade deficit. The chapter then discusses principles for a U.S. international economic policy that promotes economic resilience and generates benefits that are shared broadly across American society. Human capital—or the knowledge, skills, health, and other valuable resources embodied in an individual—is a critical component of economic growth. However, the accumulation of human capital has slowed in recent years. For instance, life expectancy only rose by less than half a year in the decade before the pandemic, and the education levels of the current generation of young adults have grown only slightly compared with their parents’ generation. Chapter 4 discusses education, workforce development, and health (several of the major components of human capital), and explores public investments that would support the development of these forms of human capital, and policy changes that could allow human capital to be used more productively and expand U.S. economic capacity. Even when people develop strong human capital, countervailing forces can keep them from successfully utilizing it. For example, since the late 1990s, concentration has increased in about 75 percent of U.S. industries, and research shows that about 60 percent of U.S. labor markets are highly concentrated, likely reducing wages and the quality of working conditions (Grullon, Larkin, and Michaely 2019; Azar et al. 2019). Chapter 5 discusses The Public Sector’s Role in Economic Growth | 43 the forces that inhibit competition—and why it is critical for long-run growth to address monopsonies (a lack of competition among employers or other buyers of goods and services); monopolies; and racial, ethnic, and gender discrimination. In addition, chapter 5 examines how persistent inequality may reduce economic efficiency and capacity growth, particularly through its effects on labor market outcomes, talent allocation, innovation, and incentives for human capital investment. For decades, experts have warned that U.S. supply chains were fragile and thus vulnerable to shocks like extreme weather and global disturbances. However, it was not until the pandemic highlighted existing weaknesses that “supply chain” became a household term. Chapter 6 describes the evolution of the supply chain and discusses issues linked to firms’ increased reliance on outsourcing and offshoring. In critical industries, supply chain resilience has national security implications. In other industries, the complexity of supply chains can make it difficult for firms to coordinate their private planning and decisionmaking, suggesting a role for policies such as industry standards and information aggregation and dissemination. The chapter then provides examples of Administration proposals that would help to address these issues, strengthening supply chains’ resilience and innovation. Chapter 7 discusses climate risks and the global progress in mitigating these risks by transitioning to clean energy. Then it outlines the factors holding back the energy transition and policies that can cost-effectively accelerate the transition. The chapter explains the economic rationale underlying Federal climate policies to smooth the energy transition for U.S. domestic industries and vulnerable communities. Specifically, the chapter describes the opportunities and challenges of government interventions to support domestic clean industries and place-based policies for economic development in fossil-fuel-dependent communities. 44 | Chapter 1 Chapter 2 The Year in Review and the Years Ahead The COVID-19 pandemic was the dominant factor steering the U.S. economy in 2021, as it was in 2020. In early 2020, the paralyzing grip of the pandemic drove the deepest macroeconomic shock to the United States since the Great Depression; and, in 2021, more than a year after shutdowns and masking began, almost every driver of the economic ebbs and flows the United States experienced had stemmed directly or indirectly from this virus.1 The growth of payroll employment, for example, varied inversely with the rises and falls of the COVID-19 fatality rate (figure 2-1). Figure 2-1. Job Growth and Change in COVID-19 Deaths, September 2020– December 2021 Change in payroll employment (thousands) Month-to-month change in deaths (thousands) 1,000 120 Payroll growth (left) Change in COVID-19 deaths (right) 800 100 600 80 400 60 200 40 0 20 –200 Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2020 2020 2020 2020 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 2021 0 Sources: Johns Hopkins University; Bureau of Labor Statistics; Haver Analytics. Two broad and interweaving forces influenced COVID-19 dynamics in 2021. The first was continuing waves of infections; the second was continued progress on vaccinations.2 The official start of the pandemic in the United States was January 20, 2020, when the Centers for Disease Control and Prevention (CDC) confirmed the first U.S. coronavirus case in Washington State.3 By the end of 2021, deaths in the United States had accuFor historical quarterly U.S. output data, see Gordon (1986). See 91-DIVOC (2022). 3 David J. Spencer CDC Museum (2022). 1 2 45 Figure 2-2. Daily COVID-19 Fatalities, February 2020–December 2021 Seven-day moving average of COVID-19 fatalities 4,000 3,500 3,000 2,500 2,000 1,500 1,000 500 0 Sources: Our World in Data; CEA calculations. mulated to over 800,000,4 more than all the U.S. combat deaths combined in every war including the American Revolution.5 In early January 2021, at the height of the pandemic, measured cases spiked and fatalities averaged about 3,400 a day over seven days (figure 2-2). Cases and deaths fell markedly throughout the winter and spring, as over 1.5 million people were fully vaccinated each day on average. COVID-19’s more contagious Delta variant, however, emerged in June; and by August, Delta accounted for 90 percent of U.S. cases (figure 2-3), driving an increase in hospitalizations and deaths.6 The Delta wave may have been partially responsible for the temporary weakening of growth in real gross domestic product (GDP) in 2021:Q3. Later in the year, the even-more-contagious Omicron variant of COVID-19 displaced Delta. These variants served as sober reminders that the pandemic—and the economic devastation it has wrought—was not over. The second dynamic—the effort to vaccinate the population—began after the Food and Drug Administration (FDA) gave Emergency Use Authorization for the Pfizer/BioNTech vaccine on December 11, 2020; and, a week later, Moderna’s vaccine also got the go-ahead.7 Before taking office, President Biden set a goal of administering 100 million shots in his first 100 days in office and released a plan to accelerate the vaccination effort on his first full day in office, January 21, 2021.8 On March 11, President Biden instructed States to make vaccines available to all adults See 91-DIVOC (2022). Department of Veterans Affairs (2021). 6 CDC (2022a). 7 American Journal of Managed Care (2021). 8 White House (2021a). 4 5 46 | Chapter 2 Figure 2-3. Frequencies of Major SARS-CoV-2 Variants, 2021 Percent 100 Omicron variant Other 90 80 70 60 Alpha variant 50 Delta variant 40 30 20 10 0 Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Source: GISAID data via Nextrain.com, assembled by Hatfield et al., showing results of all sequence analyses in the United States, without regard for regional weighting. 18+ by May 1.9 Driven by Federal efforts to increase vaccine supply, that date was later pulled forward to April 19.10 The week ending April 12 saw 1.9 million new people each day become fully vaccinated—a pandemic record.11 On his 92nd day in office, April 21, President Biden announced that the United States had administered 200 million shots since he entered office, doubling his initial target of 100 million shots in 100 days and doing so eight days ahead of schedule.12 By midyear, 162 million people (49 percent of the population) had been fully vaccinated; by the end of the year, this figure had risen to 207 million (62 percent of the population).13 Among seniors, 78 percent of the population had been fully vaccinated by midyear, and 88 percent by year end.14 Progress continued on broad vaccination of Americans, with FDA authorization of the vaccine for children age 12 to 15 on May 10 and for children age 5 to 11 on October 29.15 In September, the Biden Administration announced vaccine requirements for Federal workers and contractors, as White House (2021b). Treisman (2021). 11 See 91-DIVOC (2022). 12 Naylor (2021). 13 See 91-DIVOC (2022). 14 This is from the CEA’s analysis of CDC data; see CDC 2022b. 15 See U.S. Food and Drug Administration (2021a, 2021b). 9 10 The Year in Review and the Years Ahead | 47 Box 2-1. Historical Precedents for the COVID-19 Pandemic After the 2008 global financial crisis, the recovery started out slowly, with job growth averaging only 173,000 a month during 2011—the first full year of the recovery. Yet the United States went on to experience steady economic growth, which evolved into the longest expansion in the country’s recorded history. The COVID-19 pandemic, however, upended society and halted economic activity, with devastating consequences for the well-being of countless Americans. COVID-19 was not the first time that the United States had to cope with a pandemic or a seismic shift in economic activity. The 1918 influenza pandemic—the most recent major pandemic to hit the United States—had a devastating impact in lives lost. However, it did not have an easily detectable impact on the macroeconomy. U.S. economic data at the time were far more limited than in 2021, and often were only available on an annual basis, making precise measurement of the pandemic shock difficult. Moreover, the substantial World War I effort likely compensated for any macroeconomic impact, according to Benmelech and Frydman (2020). Unlike World War I, World War II did not see a pandemic outbreak of similar magnitude. But the war and its aftermath offer an interesting parallel to the current COVID-19 experience. World War II involved dramatic wartime shifts in industrial production, followed by a rapid pivot back to regular economic activity after the peace. That shift in economic activity produced supply chain disruptions that very much resemble the disruptions witnessed in 2021. World War II shut down entire domestic industries or conscripted them for the war production apparatus. Not surprisingly, as a result of that shift in production capacity, supplies of regular products ran low or were exhausted entirely during the war. For instance, families had trouble buying cars and household appliances because they were not being produced. According to the Bureau of Labor Statistics, “[by] 1943, many durable goods, such as refrigerators and radios, were also dropped from the domain of the consumer price index as their stocks were exhausted” (BLS 2014). The lack of supplies put severe upward pressure on prices by the end of the war. In addition, the pent-up demand of consumers pushed up prices after World War II. During the war, widespread rationing limited household purchases. The government rationed foods such as sugar, coffee, meat, and cheese along with durable goods, including automobiles, tires, gasoline, and shoes. Personal savings increased substantially and were spent soon after the war ended. Between 1945 and 1949, the population of roughly 140 million Americans purchased 20 million refrigerators, 21.4 million cars, and 5.5 million stoves. The supply chain disruptions and pent-up demand that have occurred with the COVID-19 pandemic are similar—but less severe—to those that occurred after World War II. 48 | Chapter 2 well as a requirement for health care workers to get vaccinated.16 Workers at private businesses with 100 or more employees were required to either get vaccinated or be tested at least once a week.17 These requirements helped drive additional progress on the vaccination effort through the second half of 2021, with entities that implemented the requirements often seeing vaccination rates rise by 20 percentage points or more and compliance rates in the high 90 percent range.18 The United States also made major progress in the fight against COVID-19 in 2021 with new therapeutics, more and better testing, greater understanding of the disease, and an improved public health surveillance system. With increasing levels of immunity and more tools like tests and treatments available, the pandemic is likely to progress to one with lower mortality. That said, continued evolution of the virus is likely to require additional vigilance and investments to prepare for future variants. (See box 2-1.) The remainder of this chapter examines the COVID-19 recession and the emerging recovery through the lenses of fiscal policy, monetary policy, the rise in uncertainty, supply chain disruptions, and the expenditure components of GDP. The pandemic’s effects on the labor market are then assessed, both on the supply and demand sides. The forecast for the post-COVID-19 economy that underpins the President’s Fiscal Year 2023 Budget is presented. Finally, the chapter concludes with a look back at the convulsions of the past two years and makes an assessment for the years ahead. Fiscal Policy in 2021 The fiscal response to COVID-19 in 2020 was swift and massive, as exemplified by the bipartisan Coronavirus Aid, Relief, and Economic Security (CARES) Act, which was signed into law in March of that year. Fiscal support was strengthened even further in 2021. The major fiscal relief programs enacted during the pandemic are shown in table 2-1. One way to put the pandemic fiscal expansion into historical context is to look at past fiscal support. Table 2-2 identifies periods of fiscal support—that is, years when the primary (noninterest) deficit-to-GDP ratio was expanding. It then averages how much higher the primary deficit was during each of those years relative to the final year before the expansionary period. For example, during fiscal years 1941–43, the primary deficit was higher than in fiscal year 1940 by an average of 13 percent of GDP per year. Support during the two pandemic fiscal years has averaged 9.2 percent of White House (2021c). See U.S. Department of Labor (2021). 18 White House (2021d). 16 17 The Year in Review and the Years Ahead | 49 Table 2-1. Fiscal Support from Coronavirus Relief Laws in Fiscal Years 2020–23 % of nominal fiscal-year GDP 2020 2021 2022 2023 Date 4-Mar-2020 Coronavirus Preparedness and Response Supplemental Appropriations Act, 2020, H.R. 6074 Effect on Federal fiscal deficit 0.0 0.0 0.0 0.0 18-Mar-2020 Families First Coronavirus Response Act, Public Law 116127 Effect on Federal fiscal deficit 0.6 0.3 0.0 0.0 Coronavirus Aid, Relief, and Economic Security (CARES) Act, Public Law 116-136 Effect on Federal fiscal deficit 7.7 2.0 –0.5 –0.6 Paycheck Protection Program and Health Care Enhancement Act, H.R.266 Effect on Federal fiscal deficit 2.1 0.2 0.0 0.0 Coronavirus Response and Relief Supplemental Appropriationsa Effect on Federal fiscal deficit 0.0 3.3 0.3 0.1 American Rescue Plan, HR 1319 Effect on Federal fiscal deficit 27-Mar-2020 21-Apr-2020 27-Dec-2020 6-Mar-2021 Total increase in the deficit 0.0 5.2 2.2 0.4 10.4 11.0 2.0 0.0 Source: Cost estimates are from the Congressional Budget Office. Note: The nominal fiscal-year GDP is from the Administration’s economic forecast. aDivisions M and N of the Consolidated Appropriations Act 2021, Public Law 116-260, enacted on December 27, 2020. Table 2-2. Historical Episodes of Fiscal Expansion since 1941 Average Annual Support (percentage of GDP) Period Episode of Fiscal Expansion 1941–43 World War II mobilization 2020–21 COVID-19 pandemic 9.2 2008–9 Great Recession 5.5 1949–50 1949 Recession / Korean War 4.9 2001–4 2001 Recession and aftermath 4.7 13.0 Sources: Office of Management and Budget; CEA calculations. Note: This table shows the average annual increase in the primary deficit-to-GDP ratio, relative to the final year before the expansion (it includes both new and expanded programs). GDP per year higher than in 2019, making it the period with the largest support since the end of World War II. Fiscal support in 2021 began early. In the first weeks of January 2021, most households received a $600 economic impact payment for each adult through the Consolidated Appropriations Act of 2021 (H.R. 133), which was enacted in late December 2020. The legislation’s $900 billion in COVID-19 50 | Chapter 2 Figure 2-4. Level of Real GDP, 2021:Q4, versus Before the Pandemic Percentage of 2019:Q4 level +3.2 +0.9 +0.7 +0.1 U.S. actual France U.S., ex. ARP* Canada U.K. –0.4 Japan –0.4 Italy –0.3 Germany –1.1 U.S., ex. all fiscal response** –2.1 Sources: OECD; BEA; CBO; Department of the Treasury; CEA calculations. * CEA calculations using actual ARP spendout and CBO pandemic multipliers. ** CEA ARP calculations plus CBO calculations of GDP effects of 2020 fiscal policy response and Federal Reserve credit facilities. relief also reinstituted $300 per week in supplemental pandemic unemployment benefits, which the jobless began to see in January and which was key to making their families whole as the labor market recovered. Also in January, small businesses got an extension and expansion of the Paycheck Protection Program, giving many of them access to additional funds to maintain payroll and extend operations. Beginning in March, Americans received additional fiscal pandemic support in the $1.9 trillion American Rescue Plan (ARP). The ARP funded the vaccination rollout and continued to fund the COVID-19 response, both directly and by aiding States in their responses. Households received $1,400-per-person (including children) economic impact payments soon after enactment. Families with children started receiving monthly payments from the expanded Child Tax Credit in July. These were the first refundable tax credits to be automatically delivered this way; the payments maxed out at $250 per child age 6–17 per month and $300 per child under 6 per month. Because this credit was fully refundable, low-income families were, for the first time, eligible for the full amount. Supplemental pandemic jobless benefits were extended through early September, though some States chose to end these benefits beginning in July. Aid to States’ education efforts were designed to address educational challenges that arose during the pandemic, such as school closings and staffing issues. Also, the Emergency Rental Assistance program assisted households that were unable to pay rent or utilities. The upshot: the Federal fiscal response had a sizable effect on the economic recovery in 2021. The U.S. economy ended 2021 3.1 percent larger in inflation-adjusted terms than its prepandemic level, the fastest recovery The Year in Review and the Years Ahead | 51 Box 2-2. Monetary Policy in 2021 In response to the sudden COVID-19 pandemic upheaval in March 2020, the Federal Reserve and other central banks around the world slashed interest rates and stepped into their role as lenders of last resort. In addition to lowering the cost of borrowing through traditional bank channels, the Federal Reserve created “emergency lending facilities” under Section 13(3) of the Federal Reserve Act to support certain segments of the financial markets. In 2008, the Federal Reserve established six emergency lending facilities over the span of nine months. In 2020, by contrast, the Federal Reserve launched 13 emergency lending facilities in just two months, some of which were direct real economy support programs, not solely financial sector support programs. In early 2021, the emergency lending facilities funded by the CARES Act closed down. However, given the severity of the pandemic’s economic impact, the Federal Reserve did not stop its asset purchases of U.S. Treasury securities and mortgage-backed securities. The Federal Reserve’s balance sheet was $4.1 trillion in February 2020 (figure 2-i). Within three months, that shot up to $7.1 trillion and continued to grow at a rapid pace. From the end of 2020 through the end of 2021, the Federal Reserve’s holdings of U.S. Treasuries increased from $4.69 trillion to $5.65 trillion, and its holdings of mortgage-backed securities increased from $2.04 trillion to $2.62 trillion. The Fed’s overall balance sheet grew to $8.7 trillion by the end of 2021—more than double its size before the pandemic. Of note, in November 2021, the Federal Open Market Committee (FOMC) voted to gradually reduce, or “taper,” its ongoing purchases of Treasury and mortgage-backed securities. The FOMC planned to reduce the $120-billion-a-month net asset purchase pace by $15 billion per Figure 2-i. Federal Reserve Balance Sheet Composition, 2006–21 All other Mortgage-backed securities Treasuries Source: Federal Reserve Bank of Saint Louis. Note: Excludes eliminations from consolidation. 52 | Chapter 2 month beginning in late November until purchases reached $0, though the FOMC also noted it was “prepared to adjust the pace of purchases if warranted by changes in the economic outlook.” As of the end of 2021, the Federal funds rate target remained at 0 to ¼ percent. among Group of Seven nations (see figure 2-4). The CEA finds that the ARP likely contributed at least 2½ points to this growth, using various data on ARP spendout as well as demand and output multipliers from the Congressional Budget Office (CBO).19 Previously published CBO analyses of the 2020 fiscal relief packages, including the emergency Federal Reserve credit facilities, suggest that together these pre-ARP packages accounted for another 2.8 percentage points of real GDP growth during the pandemic.20 This extensive fiscal relief and monetary stimulus accomplished many critical goals—disseminating vaccines, restoring jobs, advancing the recovery, and reducing poverty. With the achievement of full employment, and with inflation rising as discussed in greater detail below, the Federal Reserve reduced its asset purchases and signaled an intent to start raising interest rates in 2022 (box 2-2). The Rise in Economic Uncertainty This section examines the rise in economic uncertainty, in the context of the COVID-19 pandemic. It explores, in turn, financial markets and consumer sentiment. Financial Markets Financial markets have fully recovered since the onset of the COVID-19 pandemic, supported by strong fiscal and monetary policy interventions. With respect to equities, the Standard & Poor’s 500 Index was 26.9 percent higher at the end of 2021 compared with the end of 2020; and it was 47.5 Based on data from OMB, the Department of the Treasury, BEA, and others, the CEA estimates that roughly half of available ARP funds were spent out over the course of calendar year 2021. The CEA applied the output multipliers from Seliski et al. (2020) to these spendout estimates. The CEA chose to use the midpoints of the CBO multipliers under social-distancing assumptions, which were lower than multipliers without social distancing, leading to the result that real GDP growth was 2½ percentage points faster than it would have been otherwise during the four quarters of 2021, due to the ARP. If fiscal policy was in actuality more effective than the CEA assumes—e.g., because social distancing was less binding over 2021 than in 2020—then the ARP would explain a larger share of 2021 GDP growth than is accounted for here. 20 Pre-ARP fiscal impact estimates are from Seliski et al. (2020) and the Congressional Budget Office (2021). At the time of this chapter’s finalization, the second estimate of 2021:Q4 GDP was the latest available. 19 The Year in Review and the Years Ahead | 53 Figure 2-5. The Standard & Poor’s 500 Index, 2006–21 Index level: Jan. 2017 = 100 400 350 300 250 200 150 100 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 0 2006 50 Source: Haver Analytics. Note: The red line denotes the start of the pandemic. Figure 2-6. The U.S. Corporate Spread, 2006–21 Percentage points 7 6 5 4 3 2 1 0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Source: Federal Reserve Economic Data from the Federal Reserve Bank of Saint Louis. Note: This series is a proxy of U.S. corporations’ borrowing costs, as measured by the Intercontinental Exchange Bank of America U.S. Corporate Index Option-Adjusted Spread. The index tracks the performance of dollar-denominated, investment-grade-rated corporate debt publicly issued in the U.S. domestic market. The red line denotes the start of the pandemic. percent higher at the end of 2021 compared with the end of 2019, before the pandemic (figure 2-5). The credit market has similarly recovered. Consider, for instance, the U.S. corporate credit spread, a proxy for corporate borrowing costs. In March 2020, this spread peaked at over 400 basis points (figure 2-6). (The higher the spread, the worse the borrowing conditions for U.S. corporations.) After the rapid government and central bank interventions, the spread fell dramatically and continued to fall through 2021. The spread averaged 54 | Chapter 2 Figure 2-7. The CBOE’s VIX Index: 2006–21 VIX level (index value) 90 80 70 60 50 40 30 20 10 0 Source: Haver Analytics. Note: This series is the Chicago Board Options Exchange’s Volatility Index (CBOE VIX), which measures market expectation of near-term volatility conveyed by stock index option prices. The red line denotes the start of the pandemic. Figure 2-8. University of Michigan Consumer Sentiment Index, 2006–21 Index level: 1966:Q1 = 100 110 100 90 80 70 60 50 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Sources: Haver Analytics; CEA calculations. Note: The red line denotes start of the pandemic. approximately 94 basis points in 2021, compared with 156 basis points in 2020 and 124 basis points in 2019. However, financial market volatility remained above pre-COVID-19 levels. Figure 2-7 shows a time series of the VIX, which measures the market’s perception of its own riskiness as valued in options markets. In March 2020, the VIX spiked to levels not seen since the 2008 global financial crisis. In the 21 months since then, including the 12 months of 2021, The Year in Review and the Years Ahead | 55 the measure has generally been on a downward trajectory. As of the end of 2021, however, it still remained higher than its prepandemic levels—at about 21 in December 2021, versus its 2019 average of 15—likely due to uncertainty with respect to the future path of the pandemic. Consumer Sentiment Consumers’ perceptions of the U.S. economy became highly pessimistic at the onset of the COVID-19 pandemic. According to the University of Michigan’s Consumer Sentiment Index, sentiment fell to its lowest levels since 2011.21 After a bounce-back in late 2020 and early 2021, consumer sentiment peaked in 2021:Q2, before declining in the second half of the year (figure 2-8). This decline in sentiment coincided with the onset of the Delta and Omicron waves, along with a rise in measured inflation. The Economy during the Recession and Recovery: How Do This Recession and Recovery Differ from Others? The 2020 U.S. recession was shorter than those in the past, and the recovery, based on several metrics, has been stronger. From February through April 2020, consumer spending fell faster and deeper than in any recession after World War II. However, the recovery has been faster than any other, and it differs in important ways, as is demonstrated in figures 2-9 to 2-19. For example, while the goods-consuming sector swiftly and completely recovered in 2020, the services-consuming sector has recovered only part of its loss, with some subsectors remaining far below their prepandemic peaks. As of the end of 2021, real goods consumption was almost 14 percent above its prepandemic peak at the end of 2019, the fastest goods recovery of any post-World War II recession, as seen in figure 2-9 (see box 2-3 for an explanation of this “butterfly” figure and the 10 subsequent similar ones).22 In contrast, services spending recovered as slowly as any prior post– World War II recession, as shown in figure 2-10. From peak to trough, services spending fell more steeply than ever before, and more steeply than purchases of goods, from peak to trough. And although services spending rebounded swiftly, the level of spending eight quarters after the peak remained below what was experienced during any previous business cycle. The low spending on services likely reflected social distancing Another often-cited survey is the Conference Board’s Consumer Confidence Index. Consumer confidence similarly showed a drop at the onset of the COVID-19 pandemic, followed by a bounceback in late 2020 and early 2021. 22 The National Bureau of Economic Research’s business cycle chronology names February 2020 and April 2020 as the monthly peak and trough of the 2020 recession, but in its quarterly chronology, the peak occurred in 2019:Q4, and the trough occurred in 2020:Q2. See National Bureau of Economic Research (2022). 21 56 | Chapter 2 Box 2-3. A Note on the Butterfly Figures The butterfly figures—figures 2-9 through 2-19—show how spending on goods (or services or construction) compares with that in previous business cycles. After indexing at 100 at each of the 12 post–World War II business-cycle peaks, the orange line in these figures is the maximum of the 11 previous business cycles; the blue line is the minimum of these business cycles; and the gray area shows the range of historical variation. The goods GDP concept comes from the National Income and Product Accounts’ (NIPA) table 1.2.6 and aggregates spending on goods within all GDP components (consumption, investment, government, exports, and imports). Spending on goods GDP in NIPA table 1.2.6 differs from the goods-producing sector in the GDP-by-industry accounts. For example, the value added from automobile retailing is part of goods GDP in NIPA table 1.2.6 but is part of the service-producing sector in the GDP-by-industry accounts. Figure 2-9. Total Spending on Goods: Cyclical Comparison Index = 100 at business-cycle peak 120 115 Business-cycle peak 110 2020–21 cycle through 2021:Q4 Maximum of previous cycles 105 100 95 90 Minimum of previous cycles 85 80 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Quarters from business-cycle peak → Source: BEA, NIPA table 1.2.6, “Real GDP by Major Type of Product.” and consumers’ avoidance of businesses and situations that involve faceto-face interactions, such as theater, medical, and personal services. Consumer Spending In 2021, consumer spending on goods increased rapidly, while consumer spending on services had not yet regained its peak, as shown in table 2-3. The Year in Review and the Years Ahead | 57 Figure 2-10. Total Spending on Services: Cyclical Comparison Index = 100 at business-cycle peak 120 Business-cycle peak 115 Maximum of previous cycles 110 105 100 Minimum of previous cycles 95 90 2020–21 cycle through 2021:Q4 85 80 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Quarters from business-cycle peak → Source: BEA, NIPA table 1.2.6, “Real GDP by Major Type of Product.” Figure 2-11. Personal Saving during the Pandemic Relative to Its Average Pace, 2008–21 During 2020–21, excess saving implies an accumulation of $1.7 trillion excess savings. Dollars (trillion, annual rate) 6 5 Actual quarterly saving 4 3 2021:Q1 2021:Q4 2020:Q1 2019:Q1 2018:Q1 2017:Q1 2016:Q1 2014:Q1 2013:Q1 Quarterly saving deviation relative to average 2012:Q1 2011:Q1 2010:Q1 0 2008:Q1 1 2009:Q1 Quarterly saving at last business cycle’s average saving rate 2015:Q1 2 Sources: Data from Haver Analytics; CEA calculations. Note: Quarterly saving at last cycle’s average saving rate is defined as disposable personal income times the average saving rate from 2008 to 2019 (6.8 percent). Because real consumer spending data are available monthly, the table shows real growth rates during the 22 months from the monthly (and prepandemic) business-cycle peak in February 2020 through December 2021. Overall, real consumer spending grew 1.6 percent at an annual rate during the 22-month 58 | Chapter 2 Table 2-3. Consumer Spending Growth since the Beginning of the Pandemic Type of Good or Service February 2020 to December 2021 % change, Annual Rate (1) 1.6 Contributiona (2) 1.6 Goods Motor vehicles and parts Durables, ex. motor vehicles Nondurables 6.5 2.2 10.1 5.8 2.10 0.09 0.76 1.22 Services Housing and utilities Health care Transportation Recreation –0.5 1.2 –0.7 –5.3 –0.36 0.21 –0.11 –0.17 –0.27 Total –7.2 Food services Accommodations Financial 0.9 –4.4 3.0 –2.0 –3.2 Otherb NPISHc 0.06 –0.05 0.24 –0.16 –0.10 Source: Bureau of Economic Analysis, NIPA tables 2.3.5U and 2.3.6U. a Contribution to the annual rate of growth of real consumer spending. These contributions may not precisely sum to totals and subtotals because of approximations to the Fisher index formulas used in the National Income and Product Accounts. b Other services include communication, education, professional and other services; personal pare and clothing services; social services and religious activities; household maintenance; and net foreign travel. c NPISH = net consumption of nonprofit institutions serving households. pandemic period, which was slightly lower than the roughly 2 percent annual rate of trend GDP growth. Real consumer spending on goods grew at a 6.5 percent annual rate during those 22 months, far in excess of the pace at which consumer spending growth could be maintained in the long run. This rapid growth came even as motor vehicle sales were constrained by a worldwide chip shortage, holding the growth rate down to 2.2 percent. Excluding motor vehicles, consumer durables spending grew at a rapid 10.1 percent annual rate, while nondurables grew at a 5.8 percent rate. In contrast, consumer services spending fell at a 0.5 percent annual rate during those 22 months, as shown in table 2-3. The consumer-spending categories with notable declines include health care (–0.7 percent), transportation (–5.3 percent), recreation (–7.2 percent), and accommodation services (–4.4 percent). Declines were also substantial among some of the categories within the “other services category” (not shown in table 2-3), including educational services (–2.4 percent), professional services (–1.8 percent), and The Year in Review and the Years Ahead | 59 Table 2-4. Fixed Investment Components, 2019:Q4–2021:Q4 Investment Component Nonresidential Nonresidential equipment Information processing equipment Industrial equipment Transportation equipment Other equipment Nonresidential structures Office Health care Multimerchandise shopping Food and beverage establishments Warehouses Other commercial buildings Manufacturing structures Power/communication facilities Mining exploration/shafts/wells Other nonres. structures Intellectual property Software Research and development Entertainment/literary/artistic originals Residential Annual Growth Rate 1.3 3.0 12.8 7.7 –15.7 2.3 –11.9 –11.9 –6.3 –20.4 –19.5 3.2 –14.0 –7.3 –16.1 –9.0 –16.5 7.1 10.2 5.4 2.0 6.7 Source: Bureau of Economic Analysis, NIPA tables 1.5.6, 5.4.6U, and 5.5.6U. personal care and clothing services (–16.0 percent). The spending categories that remained below their prepandemic levels were those that require faceto-face interaction. Income exceeded what consumers spent during 2020–21, with the excess partly due (on the spending side) to the constrained services sector and partly due (on the income side) to income support programs under the CARES Act and the American Rescue Plan Act. Figure 2-11 shows actual quarterly saving (in trillions of dollars) relative to the saving that would have taken place if the saving rate had remained flat at its average during the 2008–19 business cycle (6.8 percent). The blue shading in this figure represents the deviation from average quarterly saving. By the end of 2021, the stock of “excess” savings during the pandemic interval accumulated to $2.7 trillion, or enough to sustain household outlays for 1.9 months. 60 | Chapter 2 Figure 2-12. Business Fixed Investment: Cyclical Comparison Index = 100 at business-cycle peak 120 Business-cycle peak 115 110 2020–21 cycle through 2021:Q4 105 Maximum of previous cycles 100 95 90 85 80 Minimum of previous cycles -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Quarters from business-cycle peak → Source: BEA, NIPA table 1.1.6. Business and Residential Investment Real business fixed (nonresidential) investment edged up at a 1.3 percent annual rate from 2019:Q4 to 2021:Q4 (table 2-4). In comparison with the previous 11 post–World War II business cycles, overall business investment was stronger than the average cycle, but still within the previous range, as shown by figure 2-12. Investment in Nonresidential Structures Investment in nonresidential structures—which made up 3.1 percent of GDP in 2019—fell at an 11.9 percent annual rate (table 2-4) during the two years 2020–21 and was tracking near the lower end of preceding cycles at the end of 2021, as shown in figure 2-13. Sizable declines occurred in the construction of office buildings (possibly reflecting the transition to remote work). Construction also fell in those sectors that had been hurt by the general reluctance to engage in face-to-face transactions: health care facilities, shopping centers, and food and beverage establishments. Construction of manufacturing, power, and mining structures also fell. Most of these declines occurred during the four quarters of 2020, but overall nonresidential structures investment continued to decline slowly during 2021, with the major exception of petroleum and natural gas well drilling, which grew 40 percent, recovering from much of its year-earlier decline. The Year in Review and the Years Ahead | 61 Figure 2-13. Structures Investment: Cyclical Comparison Index = 100 at business-cycle peak 120 Business-cycle peak 115 110 105 Maximum of previous cycles 100 95 90 85 80 2020–21 cycle through 2021:Q4 75 70 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 Minimum of previous cycles 3 4 5 6 7 8 5 6 7 8 Quarters from business-cycle peak → Source: BEA, NIPA table 1.1.6. Figure 2-14. Equipment Investment: Cyclical Comparison Index = 100 at business-cycle peak 120 Business-cycle peak 115 110 2020–21 cycle through 2021:Q4 Maximum of previous cycles 105 100 95 90 85 80 75 70 Minimum of previous cycles -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 Quarters from business-cycle peak → Source: BEA, NIPA table 1.1.6. Investment in Equipment In contrast to structures investment, investment in equipment (which made up 5.8 percent of GDP in 2019) grew at a 3.0 percent annual rate during the eight quarters through 2021:Q4, which was as fast as during any preceding business cycle (figure 2-14). During these two years, double-digit growth 62 | Chapter 2 Figure 2-15. Intellectual Property Investment: Cyclical Comparison Index = 100 at business-cycle peak 120 2020–21 cycle through 2021:Q4 Business-cycle peak 110 Maximum of previous cycles 100 Minimum of previous cycles 90 80 70 60 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Quarters from business-cycle peak → Source: BEA, NIPA table 1.1.6. occurred in information-processing equipment, while industrial equipment investment grew at a 7.7 percent annual rate. In contrast, investment in transportation equipment fell sharply, likely due to the chip shortage that plagued motor vehicle manufacturing during 2021. Intellectual Property Investment in intellectual property, which made up 6.3 percent of GDP in 2019, grew 7.1 percent from 2019:Q4 to 2021:Q4, in the top half of the range experienced during the preceding cycles (figure 2-15). The subsectors of intellectual property diverged substantially: software investment skyrocketed, at a 10.2 percent annual rate; research and development rose at a 5.4 percent rate; and the category “entertainment, literary, and artistic originals” recovered from its early losses, and edged up slightly. Residential Investment Residential investment, which made up 3.8 percent of GDP in 2019, grew at a 6.7 percent annual rate from 2019:Q4 to 2021:Q4, which places it in the top half of the historical record of this volatile sector (figure 2-16). Growth was strong during the four quarters of 2020 (15.9 percent), but starts and construction of single-family and multifamily homes appear to have been restrained by supply constraints in 2021, which limited the pace of growth in those construction components to more moderate gains. Manufactured homes grew in both years, while dormitory construction fell sharply in both years. The Year in Review and the Years Ahead | 63 Figure 2-16. Residential Investment: Cyclical Comparison Index = 100 at business-cycle peak 150 Maximum of previous cycles 140 Business-cycle peak 130 2020–21 cycle through 2021:Q4 120 110 100 90 80 70 60 Minimum of previous cycles -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Quarters from business-cycle peak → Source: BEA, NIPA table 1.1.6. Figure 2-17. State and Local Purchases: Cyclical Comparison Index = 100 at business-cycle peak 140 Business-cycle peak 130 120 Maximum of previous cycles 110 2020–21 cycle through 2021:Q4 100 Minimum of previous cycles 90 80 70 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Quarters from business-cycle peak → Source: BEA, NIPA table 1.1.6. State and Local Purchases State and local purchases (in real dollars) increased only slightly (0.4 percent, at an annual rate) from 2019:Q4 to 2021:Q4 (figure 2-17), about 3 percentage points per year less than the average historical recovery experience 64 | Chapter 2 Figure 2-18. Exports: Cyclical Comparison Index = 100 at business-cycle peak 150 Business-cycle peak 140 130 Maximum of previous cycles 120 110 100 2020–21 cycle through 2021:Q4 90 Minimum of previous cycles 80 70 60 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Quarters from business-cycle peak → Source: BEA, NIPA table 1.1.6. but only a bit less than during the preceding eight quarters through 2019:Q4. Because tax collections increased faster than nominal GDP and because of Federal grants-in-aid authorized during the pandemic-era spending programs listed in table 2-1, the increase in overall State and local receipts exceeded the increase in spending (including not only purchases, but also transfers and subsidies). As a result, the overall State and local fiscal position was positive (with net lending at $3.1 billion) in 2020 and likely will be positive again in 2021 (based on the first three quarters).23 These would be the first positive annual fiscal positions for the State and local sector since 1946. These positive fiscal positions are consistent with the suggestion that some of the ARP funds were not yet fully dispersed as of 2021:Q3. Exports and Imports Exports have fallen at a 3 percent annual rate during the eight pandemic quarters, which places them at the lower end of the post–World War II business-cycle experience (figure 2-18). As discussed in chapter 3 of this Report, U.S. exports faced weak demand from abroad due to the severity of the economic effects of the pandemic and slower recovery in major U.S. trading partners as well as surging domestic demand for exportable goods. Imports grew solidly in the upper half of the business-cycle record measured relative to the average business-cycle experience or the median At the time of this chapter’s finalization, NIPA data on State and local revenues went through 2021:Q3. 23 The Year in Review and the Years Ahead | 65 Figure 2-19. Imports: Cyclical Comparison Index = 100 at business-cycle peak 150 Business-cycle peak 140 130 120 2020–21 cycle through 2021:Q4 Maximum of previous cycles 110 100 90 80 Minimum of previous cycles 70 60 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 Quarters from business-cycle peak → Source: BEA, NIPA table 1.1.6. one (figure 2-19). The recovery in output was driven by an exceptionally strong domestic demand for goods; in some sectors, imports contributed to meeting that demand when supply constraints meant that domestic production could not. Because faster domestic growth pulls in more imports, the strength of imports relative to exports reflected faster growth in the United States compared with our trading partners. It also meant that the net exports were increasingly negative and subtracted from real GDP growth. Global Supply Chain Disruptions The COVID-19 pandemic threw global supply chains into disarray. Many of the problems that surfaced had their roots in growing U.S. reliance on products assembled globally and transported, as discussed in chapter 6 on supply chains. Delays for ships waiting to offload at the Port of Los Angeles lengthened though the second half of 2021. Shipping costs increased substantially in the supply chain, from trucking to air cargo, as shown in figures 2-20, 2-21, and 2-22. Supply chain bottlenecks were evident for motor vehicles, because a shortage of computer chips kept automakers from increasing production to meet demand. Data also suggest that shortages of other inputs held back business activity in other sectors in 2021. For example, homebuilders surveyed by the National Association of Homebuilders reported shortages of key materials 66 | Chapter 2 Figure 2-20. Forty-Foot Container Shipping Benchmark Rates by Route, 2020–21 Route Rate $18,000 Composite Shanghai to LA $16,000 Shanghai to Rotterdam $14,000 Hong Kong to LA $12,000 Shanghai to NYC $10,000 Rotterdam to Shanghai LA to Shanghai $8,000 Rotterdam to NYC Shanghai to Genoa $6,000 $4,000 Dec 2021 Nov 2021 Oct 2021 Sep 2021 Jul 2021 Aug 2021 Jun 2021 Apr 2021 May 2021 Mar 2021 Jan 2021 Feb 2021 Dec 2020 Oct 2020 Nov 2020 Sep 2020 Aug 2020 Jul 2020 Jun 2020 Apr 2020 May 2020 Mar 2020 Jan 2020 $0 Feb 2020 $2,000 Source: Data from Bloomberg. Note: “Rate” refers to the benchmark rate for freight for a given shipping lane for a forty-foot container. Figure 2-21. Cass Trucking Index Index level: Feb. 2020 = 100 120 115 110 105 100 Dec 2021 Nov 2021 Oct 2021 Sep 2021 Aug 2021 Jul 2021 Jun 2021 May 2021 Apr 2021 Mar 2021 Feb 2021 Jan 2021 Dec 2020 Nov 2020 Oct 2020 Sep 2020 Aug 2020 Jul 2020 Jun 2020 May 2020 Apr 2020 Mar 2020 Jan 2020 90 Feb 2020 95 Source: Data from Bloomberg. such as framing lumber, wallboard, and roofing.24 Homebuilders responded to these shortages in part by delaying new construction, which was reflected in the slowdown of permanent-site residential investment to 4.0 percent during the four quarters of 2021 from its 16.0 percent increase in 2020. 24 NAHB (2021). The Year in Review and the Years Ahead | 67 Figure 2-22. Air Cargo Rates by Route Dollars per kilogram 18 16 Shanghai to North America 14 12 Hong Kong to North America 10 8 6 4 Dec 2021 Nov 2021 Oct 2021 Sep 2021 Aug 2021 Jul 2021 Jun 2021 Apr 2021 Mar 2021 Jan 2021 Feb 2021 Dec 2020 Oct 2020 Nov 2020 Sep 2020 Aug 2020 Jul 2020 Jun 2020 May 2020 Apr 2020 Feb 2020 Mar 2020 Jan 2020 0 May 2021 Frankfurt to North America 2 Source: Data from Bloomberg. Figure 2-23. Inventory-to-Sales Ratio (Private Inventories to Final Sales), 1997–21 Months’ supply of inventory 3.1 3.0 2.9 2.8 2.7 2.6 2.5 2.4 2021:Q4 2.3 1997 2001 2005 2009 2013 2017 2021 Sources: Bureau of Economic Analysis (NIPA table 5.8.6); National Bureau of Economic Research. Inventory Investment These supply chain problems, together with increasing consumer demand for goods, led to declines in the stock of inventories during the first three quarters of 2021, before a partial rebuilding in the fourth quarter. The stock of inventories began 2021 at a low level, as stocks had been liquidated at a rapid rate during the first two quarters of the pandemic in 2020. With the rebound in real final sales, the inventory-to-sales ratio (real inventories to real final sales) fell from the 2019:Q4 ratio of 2.56 to 2.41 months’ supply 68 | Chapter 2 at the end of 2021:Q3, and the lowest on record, as shown in figure 2-23. Rebuilding these inventories beginning in 2021:Q4—and shifting from negative inventory investment in 2021:Q3—contributed 4.9 percentage points to the annual rate of real GDP growth in 2021:Q4. The accumulation of inventories in 2021:Q4 rebuilt roughly one-third of stocks that were drawn down during the preceding seven pandemic quarters. Consumer Price Inflation The concentrated demand for goods and the limited supply of these goods, along with supply chain delays, elevated consumer price inflation. Headline inflation—according to the Consumer Price Index (CPI)—rose to 7.0 percent during the 12 months of 2021, up from the prepandemic rate of 2.3 percent during the 12 months of 2019 (figure 2-24). Some of the increase in inflation occurred in the volatile food and energy components; excluding food and energy, however, the core CPI also rose substantially during 2021, to 5.5 percent, from its prepandemic rate of 2.3 percent. Within core inflation, most of the increase—since the pandemic began—has been in core goods, where inflation increased to 10.7 percent during the 12 months of 2021 from its 2019 prepandemic pace of 0.1 percent (figure 2-25). In contrast, core services increased only to 3.7 percent during the 12 months of 2021, up from a 3.0 percent rate. Supply chain disruptions also had a material impact on consumer goods prices, notably in the motor vehicles sector. Prices of motor vehicles (new, used, leased, and rental) increased 21 percent during the 12 months of 2021, and this increase accounted for 36 percent of 5.5 percent core Figure 2-24. Consumer Price Index (CPI) Inflation, 2007–21 Twelve-month inflation rate 8% 6% 4% Dec Headline CPI Core CPI 2% 0% –2% –4% Sources: Bureau of Labor Statistics; National Bureau of Economic Research. The Year in Review and the Years Ahead | 69 Figure 2-25. Components of Core CPI Inflation, Commodities versus Services, 2007–21 Twelve-month change 12% Core CPI goods 10% 8% 6% Core CPI services 4% 2% Dec 0% –2% Sources: Bureau of Labor Statistics; CEA calculations. Note: CPI = Consumer Price Index. CPI inflation in 2021, and also for 40 percent of its year-to-year increase. That the rise in inflation was concentrated in goods suggests that the goods economy was operating close to its potential output in 2021. Inflation Expectations Expectations about future inflation are important in macroeconomic theory because they potentially create “self-fulfilling” outcomes; that is, when households and firms believe inflation will be high in the future, they may either ask for higher wages or raise their prices today. Inflation expectations increased during 2021, but the magnitude of the increase differed according to whose expectations were being followed and the horizon over which expectations were surveyed. The increase in shortterm inflation expectations was substantial for consumers (2.2 percentage points, to 4.8 percent, measured at the median, see row 1 of table 2-5), but more moderate (0.6 percentage point) for professional forecasters (row 2). To understand how inflation expectations for consumers and professional forecasters are moving after the first year, the first year’s effect must be extracted from the longer-term average expectation. Measured this way, the increase in implicit long-term inflation expectations was relatively small in 2021, whether measured among consumers (row 5), professional forecasters (row 6), or agents in the market for Treasury Inflation Projected Securities (row 7). The relatively small increase in long-term inflation expectations— even for consumers—is roughly consistent with the idea that agents viewed the near-term increase in inflation as not permanent. The end-of-2021 expectations for CPI inflation were only slightly above what would be consistent 70 | Chapter 2 Table 2-5. Consumer Price Index Inflation Expectations Expectation Term 1 2 Short term (1-year ahead) Consumers (median) SPF 1 year 1 year 3 4 5 6 7 2019 Avg. Date of Survey Nov.–Dec. 2021 Increase 2.6 2.0 4.8 2.6 2.2 0.6 Long term (5–10 years, including year 1) Consumers (median) Next 5 to 10 years SPF Next 10 years 2.4 2.2 3.1 2.6 0.7 0.4 Long term (4–9 years) excluding year 1 Consumersa 4–9 years after year 1 2.4 2.8 0.4 2.2 1.8 2.5 2.4 0.3 0.6 SPFb TIPS 5/5 9 years after year 1 5 years, 5 years forward Sources: University of Michigan Surveys of Consumers; Philadelphia Federal Reserve Bank; Survey of Professional Forecasters; Treasury Inflation-Protected Securities (TIPS) are from Haver Analytics. aCalculated from rows 1 and 3. bCalculated from rows 2 and 4; SPF = Survey of Professional Forecasters. with the Federal Reserve’s 2 percent target for a similar price index (the Price Index for Personal Consumption Expenditures), which generally is below CPI inflation by 0.3 percentage point a year. The Labor Market The labor market story in 2021 was complex and, at times, seemingly contradictory. There were both historic successes and continuing challenges. Some of the data suggest extraordinary tightness in the labor market, while others indicate considerable remaining slack. The U.S. economy added more than 6 million jobs on net over 2021; yet the labor force still remained several million below the precrisis trend. The labor force participation rate (LFPR) for prime-age (25–54 years) workers rose at its fastest December-to-December pace since 1979, but the LFPR for workers 55 and older was little changed (though the reported 55+ LFPR rate increased in January 2022, due to statistical adjustments by the Bureau of Labor Statistics, BLS). Some metrics signaled that the labor market was tighter in 2021 than before the pandemic, such as high rates of job openings, quits, and wage growth. Other metrics were murkier: the unemployment rate fell markedly in 2021 but was still somewhat elevated relative to prepandemic levels, and the rate of prime-age employment and the LFPR were The Year in Review and the Years Ahead | 71 still lower than in February 2020, though they were rising briskly by the end of 2021. With the exception of some prior structural trends that continued throughout the year—most notably, the aging of the U.S. population— COVID-19 was the dominant driver in the labor market. Whether in the form of worker concern, weak demand for certain services, school closures, workers absent or out of the labor force due to illness, long COVID, or other mechanisms such as limited child care options, this virus was ultimately responsible for the bulk of the labor force weakness starting in February 2020. Ways in Which the Labor Market Appeared Tight in 2021 To illustrate how bifurcated the labor market was in 2021, imagine a simple (and more than a little far-fetched) thought experiment. Suppose a labor economist were frozen in 2019, thawed out in early 2022, and then immediately asked to assess the state of the labor market based solely on a handful of economic charts laid out before her. No doubt, after catching up with the events of the intervening years, she would be shocked at the magnitude of the declines that happened in early 2020. But as she then focused on the state of the economy in late 2021 and early 2022, what might she conclude? At the very least, she would notice several measures suggesting a very tight labor market—that is, one where labor demand was high relative to labor supply. Job openings and quits. Two such metrics come from the Job Openings and Labor Turnover Survey (JOLTS): job openings and quits. In December 2021, there were 11.4 million open vacancies in the United States, the highest in the history of the data going back to late 2000, and about 50 percent more than the prepandemic record of 7.6 million openings set in November 2018.25 Economists generally think of job openings as a measure of unmet labor demand from firms; higher openings often suggest higher demand among employers, although equilibrium job openings can shift over time due to a number of different factors, such as the marginal cost of posting a vacancy and changes in workers’ bargaining power.26 Even relative to the number of workers actively searching for a job, vacancies were elevated. On average, in December 2021, there were 1.81 job openings per unemployed person, the highest in the history of the JOLTS data and about 48 percent higher than just before the pandemic, in February 2020 (figure 2-26). A more permanent concept of unemployment can be seen by stripping out temporarily furloughed workers from the denominator—in 25 26 BLS (2022). On the latter, see, e.g., Figura and Ratner (2015). 72 | Chapter 2 Figure 2-26. Job Openings per Unemployed Worker, 2000–2021 Ratio of job openings per unemployed worker 2.0 Openings per unemployed 1.8 1.6 1.4 1.2 Openings per permanently uinemployed 1.0 0.8 0.6 0.4 2021 2020 2019 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2000 0.0 2001 0.2 Sources: BLS; CEA calculations. Note: The permanently unemployed are defined as the unemployed less the temporarily unemployed plus nonparticipants who want a job. principle, a company is not supposed to count a furloughed worker’s job as a job opening in JOLTS—and by adding workers who are out of the labor force but saying they want a job. This shifts the ratio to 1.02 openings per permanent unemployed worker, still a record, and 45 percent above where it was in February 2020. In December 2021, the number of voluntary quits stood at 4.4 million, about 3 percent of employment and second only to November 2021 as the highest since JOLTS data began to be gathered in late 2000. Economists generally view a voluntary quit as a sign of labor market confidence, given other Census data suggest that people who voluntarily quit their jobs typically do so with another job already lined up, or are confident they can find another one quickly.27 Wages. An increase in nominal wage growth can be a sign that labor demand is outpacing labor supply. Several different wage measures accelerated in 2021. Average hourly earnings, a measure of the average wage of all nonfarm payroll workers in the private sector, rose by 4.9 percent over the 12 months ending in December 2021, in nominal terms (i.e., without adjusting for inflation).28 That is the largest nominal wage growth in any Decemberto-December period since data on all private sector workers began being collected in 2006. Excluding managers and just looking at production and nonsupervisory workers—who constitute about 80 percent of all workers, and whose wage data stretch back to 1964—wages grew by 6.2 percent over For analyses of direct job-to-job transitions, see U.S. Bureau of the Census (2022b); and Fujita, Moscarini, and Postel-Vinay (2021). 28 BLS (2022). 27 The Year in Review and the Years Ahead | 73 Figure 2-27. Median Hourly Wage Growth by Level of Education, 1998–21 7 Percent 6 5 4 < HS 3 HS only 2 AA/ AS 1 BA/ BS+ 0 1998 2003 2008 2013 2018 Sources: CPS; CEA calculations. Note: Values are Kalman smoothed monthly values. HS = high school; AA/AS = associate degrees; BA/BS = bachelor’s degrees. the same 12 months.29 Before the pandemic, one needs to look all the way back to 1981 to find a single year when wage growth was so high. These and other data suggest that the pandemic has driven particularly strong wage growth for lower-wage workers, given that production and nonsupervisory workers typically earn less than managers. As explained below, however, overall nominal wage growth has not kept up with inflation. There are three concerns when examining growth in average nominal wages: composition effects, distributional differences, and inflation. Composition effects arise in average wage measures when shifts in who has a job skew the average wage. For example, in the immediate wake of the pandemic—the sharpest macroeconomic contraction in almost a century— average hourly earnings increased. But this increase was not a signal of labor market tightness or economic health. It occurred because pandemicrelated layoffs disproportionately hit lower-wage workers. As a result, the remaining workforce was distorted toward higher-wage workers, so the resulting average wage rose mechanically. The Employment Cost Index (ECI), which is released by the BLS, controls for many such compositional effects.30 It shows that nominal private sector wages rose 5 percent from December 2020 to December 2021, a bit higher than implied by average hourly earnings in that same period. This represents the largest nominal ECI growth since 1984. BLS (2022). The ECI measures changes in hourly compensation, fixing the industry and occupational composition of its sample to a base period to keep compositional shifts from affecting its results. 29 30 74 | Chapter 2 Figure 2-28. Median Hourly Wage Growth by Sex, 1998–21 Percent 6 5 4 3 Men 2 Women 1 1998 2003 2008 2013 2018 Sources: CPS; CEA calculations. Note: Values are Kalman smoothed monthly values. Average wages can also hide important distributional differences by, for example, education, race, and age. The average hourly earnings and ECI data do not provide demographic breakdowns, but the Current Population Survey (CPS) provides monthly data that can shed some light on how different groups saw their wages evolve. The CPS suggests that year-on-year wage growth was not even across different groups during the pandemic, and that some groups that are typically on the margins of the labor force saw stronger wage growth. Notably, low-wage workers experienced some of the fastest median wage growth during the pandemic (figure 2-32), and wage growth was been faster among workers with only a high school education or less than it was for those with college degrees (figure 2-27).31 Women saw faster growth during the pandemic than men, especially later on in 2021 (figure 2-28). Young workers under age 25 typically saw stronger wage growth than older workers; this The median wage growth is calculated in the CPS by comparing the same workers employed 12 months apart and noting the 50th-percentile change in hourly wages over the year for each worker. This method partially controls for compositional effects, since it is calculated from of identical workers 12 months apart. Because the sample of workers in the CPS changes each month, however, it is not a traditional panel of workers, which would better control for compositional effects over time. 31 The Year in Review and the Years Ahead | 75 Figure 2-29. Median Hourly Wage Growth for Workers Who Switch Industry/Occupation, 2004–21 Percent 6 5 4 3 Same industry/ occupation 2 Switched industry/ occupation 1 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 Sources: CPS; CEA calculations. Note: Values are Kalman smoothed monthly values. was true even before the pandemic, due, in part, to the mechanical percentage effect of lower starting wages (figure 2-30). But during the pandemic, youth wage growth further widened its lead over other age groups. Finally, wage growth has accelerated across different race and ethnicities in recent months (figure 2-31). There is also some evidence that labor market churn—workers leaving and entering jobs—was associated with stronger wage growth. While it is not possible in the CPS data to fully identify workers who voluntarily quit their jobs, it is feasible to look at workers who stayed employed but switched industries or occupations—which captures many voluntary quits as well as some workers who nonvoluntarily left their jobs and found new ones in different lines of work (figure 2-29). Adjusting for inflation is the final factor to consider. While nominal hourly wage growth increased in 2021, so did inflation. Real (inflationadjusted) average hourly earnings continued growing earlier in the pandemic but fell on a year-on-year basis in the aggregate toward the end of 2021.32 There are two important other trends of note. First, in some specific industries, like leisure and hospitality, nominal wage growth outpaced overall consumer inflation. The second is that even though average hourly wage growth fell short of inflation in 2021, average real income growth per 32 BLS (2022). 76 | Chapter 2 Figure 2-30. Median Hourly Wage Growth by Age, 1998–21 Percent 12 9 6 Age (years) 16–24 25–39 3 40–54 55+ 0 1998 2003 2008 2013 2018 Sources: CPS; CEA calculations. Note: Values are Kalman smoothed monthly values. Figure 2-31. Median Hourly Wage Growth by Race/Ethnicity, 1998–21 Percent 7 6 5 4 3 White non-Hispanic 2 Black non-Hispanic 1 Hispanic Other race/ethnicity 0 1998 2003 2008 2013 2018 Sources: CPS; CEA calculations. Note: Values are Kalman smoothed monthly values. The Year in Review and the Years Ahead | 77 Figure 2-32. Median Hourly Wage Growth by Wage Quantile, 1998–21 Percent 8 7 6 5 4 3 2 Lowest Second 1 Third Highest 0 1998 2003 2008 2013 2018 Sources: CPS; CEA calculations. Note: Values are Kalman smoothed monthly values. Figure 2-33. Real Market Income Growth, 2020–21 Percent change in average inflation-adjusted market income per person since the fourth quarter of 2020 12 10 Bottom 50% Next 40% Top 10% Total 8 6 4 2 0 2020:Q4 2021:Q1 2021:Q2 2021:Q3 2021:Q4 Source: Preliminary estimates by Blanchet, Saez, and Zucman (2022), via realtimeinequality.org. adult across all sources was still often positive for the year. Preliminary data from a recent analysis by Blanchet, Saez, and Zucman (2022) suggest that average real market incomes—incomes from labor and capital before the effects of taxes and government benefits—rose by 5.6 percent during 2021 overall, and by almost 11 percent for the bottom half of households (figure 2-33). Real disposable income—which includes the effects of taxes and government benefits, including the recent fiscal response—was 5 percent 78 | Chapter 2 Figure 2-34. Real Disposable Income Growth, 2019–21 Percent change in average inflation-adjusted disposable income per person since the fourth quarter of 2019 45 Bottom 50% Next 40% 40 Top 10% Total 35 30 25 20 15 10 5 0 –5 –10 2019:Q4 2020:Q1 2020:Q2 2020:Q3 2020:Q4 2021:Q1 2021:Q2 2021:Q3 2021:Q4 Source: Preliminary estimates by Blanchet, Saez, and Zucman (2022), via realtimeinequality.org. above prepandemic levels at the end of 2021, and 11 percent above for the bottom half of adults (figure 2-34). Ways in Which the Labor Market Appeared Loose in 2021 Our unfrozen economist would see much to suggest a tight labor market in 2021. But she would also quickly see several important measures suggesting a meaningful amount of room for further growth. Employment. First, while the economy added 6.7 million jobs between December 2020 and December 2021, employment was still 3.3 million below its prepandemic level (figure 2-35). It is even further away when measured against the prepandemic trend, which tries to estimate the pace of job growth that would have prevailed without the pandemic. In its final prepandemic economic projections from January 2020, the Congressional Budget Office (CBO) assumed that payroll employment would grow at an average pace of about 97,000 a month during 2020 and 2021;33 this implies that employment remained about 5.4 million below the trend at the end of 2021. Even if one adjusts the CBO’s prepandemic projections for the mortality and lower immigration rates seen during the pandemic, its adjusted January 2020 path grows by 53,000 a month, suggesting that current employment is about 4.5 million below the estimated trend. The pain of the pandemic did not spread evenly across industries (figure 2-36). The leisure and hospitality subsector, for example, lost nearly half its jobs between February and April 2020; in December 2021, its employment was 11 percent lower than before the pandemic. However, information, professional and business services, and transportation and warehousing had fully recovered beyond their prepandemic employment levels by the end of 2021. 33 CBO (2020). The Year in Review and the Years Ahead | 79 Figure 2-35. Payroll Employment, 2020–22 Millions 160 155 150 145 140 135 Actual CBO Jan 2020 projections 130 Jan 2022 Dec 2021 Nov 2021 Oct 2021 Sep 2021 Jul 2021 Aug 2021 Jun 2021 May 2021 Apr 2021 Mar 2021 Jan 2021 Feb 2021 Dec 2020 Oct 2020 Nov 2020 Sep 2020 Jul 2020 Aug 2020 Jun 2020 Apr 2020 May 2020 Feb 2020 Mar 2020 CBO Jan 2020 (adjusted for actual population) 125 Sources: BLS; CBO. Figure 2-36. Employment Changes by Industry Sector, 2020 and 2021 Net change, in millions, seasonally adjusted 9 Feb.–Apr. 2020 job losses Apr. 2020–Dec. 2021 job gains 8 (8.2) 7 6.3 6 5 4 (3.2) 3.5 3 (2.3) 2 1 0 –1 (1.1) 1.0 (1.4) (2.8) 2.2 (1.4) 1.1 (0.3) 0.3 (0.1) 0.0 Mining and logging 2.7 Construction Manufacturing Trade, transportation, and utilities Information 1.1 (0.3) 0.3 Financial activities (1.0) 0.2 Professional and business services Education and health services Leisure and hospitality Other services Government Sources: Bureau of Labor Statistics; CEA calculations. Note: Parentheses denote negative values. Labor Supply and Labor Force Participation When the U.S. economy “shut down” due to the COVID-19 pandemic in early 2020, not only did employment fall sharply and unemployment rise quickly, but the Nation’s labor force—the number of people either working or looking for work—also declined sharply. As figure 2-37 reveals, the labor 80 | Chapter 2 Figure 2-37. The Labor Force Participation Rate, 2020–22 Percentage of population 16+ years of age 64.0 Feb. 2020 level 63.5 Feb. 2020 LFPR, adjusted for sex and age 63.0 62.5 Actual 62.0 61.5 61.0 Jan 2022 Dec 2021 Oct 2021 Nov 2021 Sep 2021 Jul 2021 Aug 2021 Jun 2021 Apr 2021 May 2021 Mar 2021 Jan 2021 Feb 2021 Dec 2020 Oct 2020 Nov 2020 Sep 2020 Jul 2020 Aug 2020 Jun 2020 Apr 2020 May 2020 Feb 2020 60.0 Mar 2020 60.5 Sources: BLS; CEA calculations. Note: LFPR = labor force participation rate. force as a share of the population age 16 and older—called the labor force participation rate or LFPR, as mentioned above—fell by an unprecedented 3.2 percentage points in just two months. Since then, the LFPR has partially recovered, and it rose by 0.4 percentage point over the course of 2021 alone. In January 2022, the LFPR rose an additional 0.3 percentage point due to new population controls from the BLS, noted earlier in this chapter. Still, as of January 2022, it remains 1.1 percentage points below prepandemic levels.34 It is important to note that even before the pandemic, the aging U.S. labor force was putting downward pressure on the LFPR. Because people of different ages have different degrees of attachment to the job market, the age structure of the population is one determinant of the LFPR. In the years running up to the pandemic, the aging of the large baby boom cohort into retirement was cumulatively reducing the LFPR by about 25–30 basis points (i.e., hundredths of a percentage point) each year.35 Many other determinants were (and still are) also in play, including the strength of labor demand, immigration trends, education levels (more highly educated persons tend to have higher LFPRs), persistent labor market barriers to entry, inadequate care options, and racial and gender discrimination. Data in this section run through January 2022 rather than December 2021 due to the magnitude of the adjustment from the Census Bureau’s 2022 population controls. 35 From CEA calculations. 34 The Year in Review and the Years Ahead | 81 Figure 2-38. U.S. Prime-Age (25–54) LFPR, 2020–22 Percentage of population age 25–54 83.5 Feb 2020 Level 83.0 82.5 82.0 81.5 81.0 Actual 80.5 Jan 2022 Dec 2021 Oct 2021 Nov 2021 Sep 2021 Jul 2021 Aug 2021 Jun 2021 Apr 2021 May 2021 Mar 2021 Jan 2021 Feb 2021 Dec 2020 Oct 2020 Nov 2020 Sep 2020 Jul 2020 Aug 2020 Jun 2020 Apr 2020 May 2020 Feb 2020 Mar 2020 80.0 79.5 Sources: BLS; CEA calculations. Note: LFPR = labor force participation rate. Figure 2-39. Prime-Age LFPRs during Past Recessions and Recoveries Index: 100 = cycle peak 101.0 1990-Jul 100.5 100.0 2007-Dec 99.5 2001-Mar 99.0 98.5 2020-Feb 98.0 97.5 97.0 96.5 96.0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 Source: Data from Haver Analytics. Note: The date denotes a month out from the monthly business-cycle peak (index level = 100). But because of the exit of a large number of older workers (who are not replaced by the same number of younger workers), it is unlikely that the overall LFPR will revert back to its prepandemic peak (63.4 percent) in the near future, even as temporary factors abate. (See the blue dotted line showing adjustment for sex and age line in figure 2-37.) To put this in perspective, if every age group returned to its February 2020 rate of participation, the overall LFPR would have been 62.9 percent in December 2021 rather than the 63.4 percent prepandemic rate, due to the older profile of the American population today. 82 | Chapter 2 Figure 2-40. Change in U.S. Rate of Nonparticipation in the Labor Force, February 2020 – January 2022, by Reason for Nonparticipation Percentage points of population, seasonally adjusted 4.0 3.5 Total rise in nonparticiation 3.0 Demographic age/sex shifts 2.5 2.0 “Something else” 1.0 Home/family Care 0.5 Excess retirements Shadow labor force 0.0 Disability -0.5 School enrollment Dec 2021 Oct 2021 Aug 2021 Jun 2021 Apr 2021 Feb 2021 Dec 2020 Oct 2020 Aug 2020 Jun 2020 Apr 2020 Feb 2020 -1.0 DEMOGRAPHICALLY ADJUSTED 1.5 Sources: CPS; CEA calculations. A different way to adjust for aging is to omit both seniors and the young and to look solely at prime-age participation. As figure 2-38 shows, the prime-age LFPR gradually rose throughout 2021; at the same point in the last two cycles, the prime-age LFPR was still falling (figure 2-39). There is no single overriding factor explaining the change in the LFPR between February 2020 and early 2022; rather, a variety of explanations are at play. In January 2022, there were 3.2 million fewer workers in the labor force relative to the size of the labor force if the LFPR had remained at its prepandemic level. The information provided by respondents to the CPS can be used to break down why these 3.2 million workers said they were not looking for work (figure 2-40): • Aging of the population: 880,000, explains 28 percent of the actual LFPR decline (none of the adjusted decline). As noted above, the aging of the population and retirement of the baby boomers is an ongoing force putting downward pressure on the LFPR (see, e.g., Cooper et al. 2021). Other population shifts have occurred during the pandemic, including lower immigration and higher mortality due to COVID-19. If the age profile of the U.S. population looked as it did in February 2020, in January 2022 the LFPR would have been about 35 basis points higher. Most of the persons accounted for in this category take the form of permanent retirements, though it is possible that a small portion may eventually reenter the labor force. • “Excess” retirements: 1.0 million, explains 33 percent of the actual LFPR decline (46 percent of the adjusted decline). These are retirements The Year in Review and the Years Ahead | 83 Figure 2-41. The Retirement Rate, 2010–22 Percentage of 16+ population 20.0 19.5 19.0 18.5 2010–20 trend 18.0 17.5 17.0 16.5 16.0 15.5 15.0 2010 2011 2012 2013 2014 2015 2016 2017 2018 Sources: Data from the Integrated Public Use Microdata Series and CPS; CEA calculations. Note: Seasonally adjusted, with CEA calculations. 2019 2020 2021 2022 Figure 2-42. Retirement Flow Rates, 1998–22 Percent, year-on-year 12 10 Outflow trend prepandemic 8 Outflow rate Percent of retired 6 4 Inflow trend prepandemic Inflow rate Percent of nonretired 2 0 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 Sources: Data from the Integrated Public Use Microdata Series and CPS; CEA calculations. Note: Trends are calculated with a linear regression of flows on time trend, share of population. beyond what one would expect, given aging (figure 2-41). The CEA finds that this increase was driven not by an increase in the likelihood of older workers entering retirement but by the diminished likelihood of leaving retirement to reenter the workforce (figure 2-42). That is, in the prepandemic course of retirement flows, an average share of about 9 percent of retirees each year left retirement status and reentered the labor force or engaged in other activities. This share declined between February 2020 and early 2021, but then began recovering. If this rise in retirement exits continues, overall retirement rates would decline. 84 | Chapter 2 People who are not in the labor force but who say they want a job: 730,000, explains 23 percent of the actual decline (32 percent of the adjusted decline). Such workers, sometimes referred to as in the “shadow labor force,” are not actively looking for a job, and thus are definitionally not unemployed. Historically, they have higher labor force reentry rates than other nonparticipants. The rise in the shadow labor force during the pandemic over 2021 was roughly even by sex but has been most acute among Hispanics.36 • Family or home care: 600,000, explains 19 percent of the actual decline (26 percent of the adjusted decline). Below, this chapter further explores the extent to which childcare and elder care responsibilities held back the labor supply of these caretakers, who are disproportionately women and mothers. • Enrollment and disability: –580,000, explains –18 percent of the actual decline (–25 percent of the adjusted decline). Nonparticipation due to school enrollment and disability slightly declined after February 2020, meaning that fewer people were in school without a job or cited disability as a reason for not being in the labor force. Note that what is charted here is “disability” as measured in the CPS: whether a respondent who does not want a job believes that her disability is preventing her from looking for work. This is an entirely separate concept from participating in disability benefit programs, like Social Security Disability Insurance and Supplemental Security Income—though CPS disability is strongly correlated with participation in these programs, which has also declined during the pandemic and over the last year.37 • Something else: 560,000, explains 18 percent of the decline (25 percent of the adjusted decline). This category captures rises in nonparticipation not explicitly accounted for in CPS questions. In summary, about 61 percent of the 1.2-percentage-point shortfall in the LFPR through January 2022 was due to either aging or excess retirements, with the remainder roughly split between the shadow labor force and workers who were out of the labor force due to family or home care obligations. There were other factors that decreased the labor force via their effects on the population as a whole rather than on the LFPR. Such factors can exacerbate a reduced labor supply in certain industries. Two examples are COVID-19 mortality and immigration. The CEA estimates—based on the age, sex, and the state of COVID-19 deaths to date—that the labor force was about 250,000 smaller at the end of 2021 due to the direct effects of COVID19 mortality. The population in 2021 was also smaller due to a decrease in immigration from the pre-2019 trend; this fall in immigration resulted from a combination of the pandemic along with prepandemic policies. The • 36 37 CEA calculations, using CPS microdata. SSA (2022). The Year in Review and the Years Ahead | 85 CEA estimates that the labor force would have been about 550,000 larger in January 2022 if immigration had followed its pre-2019 trend. The Historical Sluggishness of U.S. LFPR Recoveries It is also worth noting that, in recent decades, the LFPR appears to have recovered more slowly than unemployment after recessions. Hobijn and Sahin (2021) highlight this pattern, decomposing the growth in the employment-to-population ratio into the part accounted for by falling unemployment and the part explained by rising LFPRs. In at least the last three business cycles, rising LFPRs lagged the falling unemployment rate, typically by many years. For example, applying this decomposition to the current period, employment-to-population ratios for prime-age workers were up 9 percentage points since jobs began recovering in May 2020. About one-fifth of this growth was due to the rising LFPR, with the rest due to the falling unemployment rate. This is actually a relatively large LFPR contribution compared with recent cycles. For example, if one investigates a comparable period after the global financial crisis and Great Recession in 2008, employment-to-population ratios barely changed, and the components due to the LFPR and the unemployment rate barely changed either. The CEA also examined the same pandemic-cycle decomposition by gender and race, finding that a rising LFPR explained 19 percent of the increased employment rate for men, and 22 percent for women. Black, Asian, and Hispanic employment rates were up 9, 10, and 12 percentage points, respectively; the rising LFPR explains 37 percent of the gain for Blacks, 30 percent for Asians, and 20 percent for Hispanics. Again, during the comparable period after the Great Recession, the LFPR had not rebounded for any subgroup during this time, and thus held back employment rates for all groups. In one sense, this difference between the pandemic recovery and that of the Great Recession is not too surprising. The GDP and unemployment—and, to some extent, job growth—all bounced back faster in 2021 compared with slower, and initially more “jobless,” recoveries after other recent downturns. Caring for Family Members Family members’ responsibility to care for their children or elderly parents can also be a barrier to labor market entry or reentry, and the pandemic exacerbated the role of this barrier at times for some caregivers. One way to examine the potential role this of this barrier during the pandemic is to compare the labor force participation of parents and nonparents, or, because women disproportionately provide such care, between mothers and women without children. Research by the CEA and others reveals that at times 86 | Chapter 2 Figure 2-43. Maternal LFPR versus the Same Calendar Month in 2019 Percentage points, 95% confidence intervals 4 3 2 1 0 –1 –2 –3 –4 –5 Jan 2020 Apr 2020 Jul 2020 Oct 2020 Jan 2021 Apr 2021 Jul 2021 Oct 2021 Jan 2022 Sources: BLS; CEA calculations. Note: LFPR = labor force participation rate. The graph shows mothers of young school-age (3–13) children versus otherwise similar women without children. The data include controls for age, sex, race/ethnicity, education, marital status, foreign-born status, State, and metro size. during the pandemic, mothers were significantly less likely than otherwise similar women without children to be in the labor force, especially during the declines of 2020 and 2021, at the beginnings of school years. The CEA finds that relative to patterns that prevailed in 2019, the maternal LFPR was 2.1 percentage points lower than that of otherwise similar women without children in October 2021, but that this difference shrank and became insignificant in November and December 2021 (figure 2-43). There is some evidence that this reversal was due to schools and childcare centers reopening. The Unemployment Rate Just before the pandemic, the unemployment rate stood at 3.5 percent. The official rate then peaked at 14.7 percent in April 2020, before beginning a steady decline. Over the 12 months of 2021, it declined 2.8 percentage points, the largest December–December fall on record. But the official unemployment rate is still somewhat higher than prepandemic levels, suggesting some amount of remaining slack in the labor market. Moreover, the decline in the LFPR over the course of the pandemic put mechanical downward pressure on the measured unemployment rate given that, holding employment constant, a lower LFPR lowers the measured unemployment rate. The extent to which the official unemployment rate understates slack depends crucially on the assumed underlying trend participation rate. Assume for a moment, illustratively, that the LFPR recovered all the way back to the level consistent with where it was in February 2020 in ageadjusted terms. This implies that the unemployment rate would have been The Year in Review and the Years Ahead | 87 Figure 2-44. The U.S. Unemployment Rate, 2020–22 Percentage of the labor force 25 20 15 Adjusted for misclassification and trend LFPR 10 Adjusted for misclassification Official 5 0 Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Jan 2020 2020 2020 2020 2020 2020 2021 2021 2021 2021 2021 2021 2022 Sources: BLS; CEA calculations. Note: LFPR = labor force participation rate. 5.5 percent in January 2022 rather than 4.0 percent, with an extra 1.5 percentage points of slack in the unemployment rate space (figure 2-44). But if one assumes the other extreme—that the LFPR will not rise any further than current levels—then the official unemployment rate will not understate labor market slack, at least due to participation. Reconciling the Paradox How, then, does the unfrozen economist imagined earlier in the chapter reconcile these facts? How did the labor market seem to recover fully while also being more than 5 million jobs short of the prepandemic trend? Like so many other economic dynamics during the pandemic, a large part of the answer is that the COVID-19 pandemic has created an extraordinary set of circumstances in the U.S. labor market. Labor supply—the number of workers with or wanting jobs—and labor demand—the number of jobs employers want to have filled—were still depressed at the end of 2021 in level terms relative to the prepandemic (figure 2-45). Labor force participation was lower by 1.5 percentage points overall, and, if one adjusts for aging, by 1 percentage point—representing 2.6 million people. Labor demand, in contrast, had almost recovered to its prepandemic level by end of 2021; and in January 2022, it had grown further to slightly exceed it. Without question, demand for labor has recovered more quickly than the supply of workers. This is not surprising; as discussed above, the LFPR typically lags the unemployment rate in recovering during U.S. business cycles. And whereas labor demand was once clearly the binding, limiting factor in this pandemic, by the end of 2021 supply had become the more 88 | Chapter 2 Figure 2-45. Labor Supply and Demand, 2019–21 Percentage of population 16+ Index: Feb. 2020 = 100 102 Aging trend 100 98 Labor supply (LF + NILF, want a job) 96 94 92 Labor demand (employment + vacancies) 90 88 Jan 2022 Nov 2021 Jul 2021 Sep 2021 Mar 2021 May 2021 Jan 2021 Nov 2020 Jul 2020 Sep 2020 May 2020 Jan 2020 Mar 2020 Nov 2019 Jul 2019 Sep 2019 May 2019 Jan 2019 84 Mar 2019 86 Sources: BLS; CEA calculations. Note: “LF + NILF” means those in the labor force plus those not in the labor force, binding component. This creates tightness in two ways. First, the level of tightness is high. Demand exceeds supply in the aggregate and in many industries. Second, momentum is high. Even in industries where demand still lagged supply at the end of 2021, demand often grew quickly over the last year, and this could have created labor market friction. The Forecast The Biden-Harris Administration finalized the economic forecast that underpins the President’s Budget on November 10, 2021. By the third quarter of 2021, real GDP had recovered to a level that was 1.4 percent above its prepandemic level. That third-quarter level was, however, still 1.5 percent short of a plausible counterfactual path of 2 percent annual growth. Consistent with that shortfall from the counterfactual, and consistent with the consensus of professional economic forecasters, the Administration believes that the economy has additional room to grow during the next two years because aggregate demand appears to have enough momentum to make this happen. The Administration’s November 2021 forecast expected real GDP to grow 5.1 percent during the four quarters of 2021, and slow to 3.8 percent during 2022. In comparison, the consensus of private professional forecasters—the latest available at that time, published in October 2021—projected The Year in Review and the Years Ahead | 89 5.5 percent real GDP growth during the four quarters of 2021 and a slowing to 3.5 percent growth in 2022. Macroeconomic Forces during 2022 As this chapter has stressed, the ongoing pandemic generates unusually high forecast uncertainty, which has been exacerbated by the Russian invasion of Ukraine in February 2022. Nevertheless, the Administration must still present a central forecast. Among the expected manifestations of a supply-side surge were, at the time of the budget forecast in November, the anticipated resolution of supply chain problems, the gradual increase in the willingness of workers to staff a wide range of service industries, and a rebound in the LFPR. The near-term prospects for demand growth depend on large but competing forces. On the positive side, the supply of excess savings—accumulated during a period of large Federal transfers with limited opportunity to spend those funds—will probably support continued growth of consumer spending. Customers are expected to return to consumer-facing businesses and those establishments that include crowds (bars, restaurants, theaters, etc.). On the negative side, fiscal policy is now turning sharply negative, reflecting the disappearance of the substantial Federal subsidies and transfers of the emergency pandemic programs (see figure 2-ii in box 2-4). The Administration forecasts above-trend growth during the four quarters of 2022 and 2023 (at 3.8 and 2.5 percent, respectively, as shown in table 2-6) reflecting the CEA’s view in November 2021 that these supply and demand positives from emergence out of the COVID-restrained economy outweigh the swing to negative fiscal impetus due to the sunsetting of the temporary pandemic fiscal support. (See box 2-4.) The Administration’s inflation forecast focuses on two of the many price indices produced by the U.S. statistical agencies: the CPI and the price index for GDP. The CPI is important because it measures prices faced directly by consumers and because versions of it are used to escalate Social Security benefits, Federal pensions, and the notches in the Federal tax code. Based on the November forecast, the CPI is expected to rise 2.9 percent during the four quarters of 2022, down from its 6.7 percent (actual) pace during the four quarters of 2021 (which had been forecasted to be 6.6 percent when the forecast was finalized, as shown in table 2-6). This forecasted 2022 rate was higher than the consensus forecast available at the time the Administration forecast was finalized. Based on the forecast, starting in 2023, CPI inflation is expected to fall to the 2.3 percent rate that is consistent with the Federal Reserve’s inflation target of 2.0 percent for a different (but closely related) price index, the Price Index for Personal Consumption Expenditures. 90 | Chapter 2 Box 2-4. Fiscal Impetus by Quarter Positive effects on demand can follow an increase in Federal Government purchases or transfers, or a temporary tax cut. But as spending programs end, or temporary tax cuts expire, the subsequent quarters will exhibit negative demand effects. At the end of 2021, the large fiscal supports enacted during fiscal years 2020 and 2021 (see table 2-1 above) have mostly ended, and this ending will depress economic demand during 2022. To estimate the growth effects of this stimulus, and the negative effects of their termination, the CEA built an estimation system modeled on the one maintained by the Brookings Institution, which itself was modeled on one suggested by Federal Reserve staff. (See Kovalski et al. 2021; Brookings Institution 2019; Cohen et al. 1999; and Cashin et al. 2017.) The quarterly growth effects—both positive and negative—are shown in figure 2-ii. As can be seen, the effects of fiscal policy on growth are negative for 2022. These negative fiscal policy effects may be offset by positive supply side shocks from the emergence out of the pandemic-restrained economy, despite the uncertainty caused by the invasion of Ukraine and possible future variants of COVID-19. Figure 2-ii. The Federal Fiscal Impetus by Quarter Contribution to real GDP growth, annual rate, percentage points 30 25 20 15 10 5 0 -5 -10 -15 2019 2020 2021 2022 2023 Source: CEA calculations. The price index for GDP measures the price of everything produced in the United States, and its measure of inflation differs from the CPI because—in addition to consumer prices—it includes the price of investment, government purchases, and exports, while import prices are excluded. When averaged over long intervals, GDP price-index inflation tends to run slightly lower than the CPI, partially due to a different indexing formula. In The Year in Review and the Years Ahead | 91 Table 2-6. Economic Projections, 2020–32 Nominal GDP Year 2020 (Actual) 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 Percent Change (Q4 to Q4) Consumer GDP Real Price Price GDP Index Index Unemployment Rate (percent) Level (calendar year) Interest Rate Interest Rate 10-Year 91-day Treasury Notes Treasury Bills (percent) (percent) –1.0 –2.3 1.5 1.2 8.1 0.4 0.9 10.1 6.3 4.6 4.1 4.0 4.0 4.0 4.1 4.3 4.4 4.3 4.3 5.1 3.8 2.5 2.1 2.0 2.0 2.0 2.1 2.2 2.3 2.3 2.3 4.8 2.4 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 2.0 6.6 2.9 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 5.4 3.9 3.6 3.7 3.8 3.8 3.8 3.8 3.8 3.8 3.8 3.8 0.0 0.2 0.9 1.6 1.9 2.1 2.2 2.3 2.3 2.3 2.3 2.3 1.5 2.1 2.5 2.7 2.8 3.0 3.1 3.1 3.2 3.2 3.2 3.3 Sources: Bureau of Economic Analysis; Bureau of Labor Statistics; Department of the Treasury; Office of Management and Budget; Council of Economic Advisers. Note: The forecast was based on data available as of November 10, 2020. The interest rate on 91-day T-bills is measured on a secondary-market discount basis. GDP = gross domestic product. the forecast, inflation—as measured by the price index for GDP—is projected to fall to 2.4 percent during the four quarters of 2022, from a projected 4.8 percent in 2021. When the forecast was finalized, the October unemployment rate of 4.6 percent was the latest datum. The Administration expected it to fall further, and thus to average 3.9 percent in 2022, and to fall to 3.7 percent by the end of 2022, and then to average 3.6 percent in 2023. Subsequently, the unemployment rate fell sharply further in November (4.2 percent) and to 3.9 percent in December. Even so, the 3.9 percent average for 2022 remains plausible. The Forecast over the Long Term As described above, real GDP growth was forecast to edge down year by year from 2021 to 2024 (2 percent), in large part because by the end of 2021, GDP had almost fully rebounded from the recession, so less room remained for growth. Along this path, the unemployment rate descends to 3.6 percent by 2023:Q4, slightly overshooting the forecast estimate of the unemployment rate consistent with stable inflation (3.8 percent). But the unemployment rate edges back up to 3.8 percent by the end of 2024. The consensus estimate (October 2021, the latest available when the forecast was finalized) for potential real GDP growth in the medium term 92 | Chapter 2 appears to be about 2 percent annually. That is, the Blue Chip consensus panel forecasts a 2.0 percent average annual rate of growth during the four years 2024–27 while the unemployment rate is approximately constant. The Administration believes that potential real GDP growth in the long run could be modestly higher because of a range of policies supported in the 2021 Bipartisan Infrastructure Law (BIL) and the President’s other proposed economic policies. These include increments to infrastructure investment from the BIL, and a range of programs to enhance human capital formation and labor force participation. Altogether, these policies could plausibly boost real GDP growth by 0.3 or 0.4 percentage point a year during the 10-year budget window (2022–32). In addition, real GDP growth is expected to increase during the last four years of the forecast interval 2029–32 because the change in the LFPR becomes less negative at that horizon. The retirement of the baby boom cohort (those born from 1946 to 1962), is currently subtracting about 0.4 percentage point per year from the growth rate of the LFPR, and this downward force is likely to continue for the next several years. However, after 2028, after the last of these baby boomers (those born in 1962) reaches the standard retirement age of 65–66, these retirements will diminish. The negative contribution to real GDP growth from the retirement of the baby boomers moderates from about –0.4 percentage point per year through 2027 to –0.3 percentage point per year in 2028–30, and to –0.2 percentage point in 2031–32. During the last six years of the forecast (2027–32), the Administration’s forecast grows faster than the Blue Chip consensus (1.9 percent per year) because of the possible combination of these two factors: the Blue Chip consensus may not completely incorporate the growth-promoting aspects of the President’s proposals, and the consensus does not appear to account for the diminishment of baby boom retirements. Interest rates are projected to slowly rise during the 11-year projection interval, following paths that are similar (but slightly steeper) than those projected in the Blue Chip consensus panel’s October 2021 long-term interest rate projection. The Administration focuses on two interest rates: the rate on 91-day Treasury Bills, and the yield on 10-year Treasury notes. These interest rate forecasts are key to projecting the cost of servicing the Federal debt. The Treasury Bill rate is projected to creep up from an average of 0.0 percent in 2021 to a 0.9 percent average in 2023, and eventually to 2.3 percent during the last five years of our projection interval (2028–32). In comparison, the Blue Chip consensus panel’s October 2021 forecast of the Treasury Bill rate plateaus at 2.1 percent. The Administration’s interest rate forecast is slightly higher than that of the consensus because the The Year in Review and the Years Ahead | 93 Administration also forecasts slightly higher real GDP growth during those years, and higher growth is likely to boost interest rates.38 The Supply Side of the Long-Term Forecast Real GDP is expected to grow at an average 2.2 percent annual rate during the 13-year interval through the Administration’s budget horizon in 2032. The six components of the supply-side identity that account for this growth are shown in table 2-7, both over the forecast interval as well as over relevant historical periods. Because the growth of these supply-side components over short intervals is erratic and has cyclical patterns, growth rates between business-cycle peaks are shown. For this reason, this table shows the growth rates of these supply-side components starting from the last business-cycle peak in 2019:Q4. The Administration’s forecast of growth of the working-age (16+) population comes from the latest Social Security Administration Trustees’ report. The 0.7 percent projected rate of growth (row 1, column 5 in table 2-7) is below the average growth rate during the 66 years through 2019 (row 1, column 1), and also below the growth rates in each of the three preceding business cycles (columns 2, 3, and 4). The LFPR is expected to decline further (row 2, column 5 in table 2-7) over the forecast window, due to the continuing retirement of the baby boom cohorts. But during the last five years of the projection interval, this decline will become less steep as the retirements of those baby boom cohorts near completion. In addition, the President’s proposed policies are expected to promote higher labor force participation rates than would otherwise be the case. The employed share of the labor force (row 3, column 5, in table 2-7, equal to 1 minus the unemployment rate) usually contributes little to GDP growth because the employment rates are similar among business-cycle peaks. The workweek in the nonfarm business sector (row 4, column 5) is projected to remain flat, after falling at a 0.2 percent annual rate during the 66-year interval shown in column 1. The workweek shortened during that interval because of generally declining employment in manufacturing (where workweeks are long) and the rise in the labor force participation of women (who generally entered the workforce with shorter workweeks than men). Looking ahead, the workweek is expected to stabilize at its 2019 level because female participation is expected to plateau while the workweek of women rises. Labor productivity (output per hour in the nonfarm business sector) is expected to grow at an average 1.8 percent annual rate, above the 1.4 38 Higher interest rates are expected with faster growth; see Council of Economic Advisers (2015). 94 | Chapter 2 Table 2-7. Supply-Side Components of Actual and Potential Real Output Growth, 1953–2032 Growth Rate (percentage points) Component 1953:Q2 to 2019:Q4 1990:Q3 to 2001:Q1 2001:Q1 to 2007:Q4 2007:Q4 to 2019:Q4 2019:Q4 to 2032:Q4 (1) (2) (3) (4) (5) 1 Civilian noninstitutional population age 16+ 1.4 1.2 1.1 1.0 2 Labor force participation rate 0.1 0.1 –0.3 –0.4 0.7 –0.2 3 Employed share of the labor force 0.1 0.1 0.1 0.0 4 Average weekly hours (nonfarm business) 0.0 –0.2 –0.2 –0.1 0.0 5 Output per hour (productivity, nonfarm business) Output per worker differential: GDO vs. nonfarma Sum: Actual real GDOb 2.0 –0.1 2.4 2.4 1.4 1.8 –0.3 3.0 –0.3 3.5 –0.6 2.4 –0.4 1.7 –0.1 2.2 0.1 –0.3 0.3 –0.4 6 7 Memo: 8 Ratio of nonfarm business employment to household employment 9 Ratio of real GDO to nonfarm business output 0.0 0.3 0.4 –0.3 –0.6 –0.2 Sources: Bureau of Labor Statistics; Bureau of Economic Analysis; Department of the Treasury; Office of Management and Budget; CEA calculations. aThe output-per-worker differential (row 6) is the difference between output-per-worker growth in the economy as a whole and output-per-worker growth in the nonfarm business sector, and it is also equal to row 8 + row 9. b Real GDO and real nonfarm business output are measured as the average of income- and product-side measures. Note: All contributions are in percentage points at an annual rate. The forecast is made from data available on November 10, 2021. Totals may not add up due to rounding. The quarters 1953:Q2, 1990:Q3, 2001:Q1, 2007:Q4, and 2019:Q4 are all quarterly business-cycle peaks. Gross domestic output (GDO) is the average of GDP and gross domestic income. Population, labor force, and household employment have been adjusted for discontinuities in the population series. percent average annual rate during the preceding business cycle but below the average 2 percent annual rate over the 66 years through 2019. Again, productivity growth is expected to be boosted by the BIL, as well as the human-capital-building aspects of the President’s other proposed policies. Both the workweek and productivity are measured in the nonfarm business sector, but the supply side identity adds up to GDP (which includes the farm, government, and household sectors in addition to the nonfarm sector), and the employment rate is measured (from the household survey) for the economy as a whole. As a result, a conversion factor is needed to translate from nonfarm business employment to total employment (row 8 of table 2-7) and also from nonfarm business to GDP (row 9). The sum of these two rows (row 6) is the difference between the growth rate of output per person in the economy as a whole and the growth rate of output per person in the nonfarm business sector. Because the National Income and Product Accounts assume that productivity does not grow in the government and household sectors, the nonfarm business is the sector where productivity grows. As a result, the row 6 is negative over any long interval. Conclusion The story of the U.S. economy in 2021 was again one where COVID-19 was in the driver’s seat. But it was also one where the United States made The Year in Review and the Years Ahead | 95 enormous strides at recovery and normalization throughout the year, thanks in large part to extraordinary fiscal and monetary policy support and a historic campaign to research and distribute vaccines. Pandemic-induced disruptions were still evident throughout the economy at the end of 2021. The Omicron variant caused a spike in cases, hospitalizations, and deaths. Consumers were still favoring goods more than they had before the pandemic, to the detriment of services. The strong demand for goods strained supply chains and put upward pressure on prices. And labor markets were not fully recovered, with such key measures as the unemployment rate, prime-age employment, and the prime-age labor force still weaker than in 2019. But the progress over 2021 was significant. The United States ended the year with an economy more than 3 percent larger in real terms than just before the pandemic—the fastest pandemic recovery among the Group of Seven countries. The unemployment rate fell by its fastest December-toDecember pace since modern data began to be collected after World War II, and the economy added 6.7 million jobs. Given the historic damage wrought by the pandemic in early 2020, such progress was not preordained. This pace of recovery raises hopes that, even while managing future COVID-19 variant risks and geopolitical upheavals, the United States will not just normalize but also emerge with a stronger, healthier, and more inclusive economy. 96 | Chapter 2 Chapter 3 The U.S. Economy and the Global Pandemic The COVID-19 pandemic has had repercussions for economies around the globe. Although the U.S. economy suffered one of the sharpest contractions in its history during 2020, the economic damage was even greater in many foreign countries. Bolstered by an early and rapid vaccine rollout as well as by strong fiscal support, the United States’ recovery has been robust, outpacing that of most of our major trading partners in 2021. Inflation emerged as a challenge for the United States and nearly all our major trading partners, as strong demand, skewed toward goods and away from services, interacted with the supply chain stresses described in detail in chapter 6 of this Report. As a result of the rapid U.S. recovery relative to the rest of the world, the U.S. trade deficit has widened. The strength of the U.S. recovery has led to increased imports, as goods have flowed in from abroad to satisfy resurgent demand from firms and consumers. Although exports have hit record highs, they have increased at a slower pace than imports because many of the countries that buy U.S. goods have not recovered as fast. At the same time, new waves of infection depressed international travel and weighed on the recovery of some services that are important for U.S. exports, such as tourism. The pandemic highlighted the need to tackle long-standing economic issues, including those resulting from global economic integration. Due to a lack of supportive public policy in the past, many American workers and communities have borne the costs of shifting production around the world but have not fully shared in its benefits, contributing to widening inequality. 97 Addressing these inadequacies requires policies that broaden the gains from trade while leveling the international economic playing field by countering unfair trade practices and putting in place a more equitable global tax system. Implementing such policy changes in a way that reduces uncertainty and engages with the United States’ trade and commercial partners can ensure that American consumers, workers, businesses, and investors benefit from global trade. The first section of this chapter places America’s economic experience during the pandemic in the global context by comparing it with that of our largest trading partners: the euro area, the United Kingdom, China, Canada, and Mexico. The next section examines how international trade has recovered from its sharp pandemic decline, discussing the causes of the widening U.S. trade deficit and the effects of supply chain bottlenecks internationally on traded inputs such as auto parts and capital goods. The last section discusses how the Biden-Harris Administration is reorienting U.S. international economic policy to mitigate rather than exacerbate economic inequality and to level the international economic playing field. Recovery Amid Global Economic Challenges Placing the U.S. recovery from the COVID-19 pandemic in the global context highlights how our robust fiscal support resulted in a faster return to a strong economy. The backdrop to this demand-driven recovery, however, was a tragic loss of human lives and higher inflation. The Global Pandemic The path of the global economy over the past year is best understood in the context of the coronavirus pandemic. The starkest measure of the pandemic’s effect is the number of deaths attributed to COVID-19. By the end of 2021, reported deaths due to the virus had exceeded 5 million people globally, including more than 827,000 in the United States (OWID 2021). The true global toll is probably much higher, because data collection challenges outside the United States suggest that many other countries may have substantially underreported deaths. For example, some estimates put the true death toll in India alone in excess of 4 million (Anand, Sandefur, 98 | Chapter 3 Figure 3-1. International COVID Case Rates Cases per million 2,500 2,000 1,500 1,000 500 0 Jan-20 Apr-20 United States Jul-20 Canada Oct-20 Jan-21 Euro area Apr-21 United Kingdom Jul-21 Mexico Oct-21 China Source: Our World in Data. and Subramanian 2021). With deaths measured as a share of the population, many of the hardest-hit countries have been middle-income countries in Latin America and Eastern Europe (Johns Hopkins 2022). Looking at total deaths can obscure the fact that different countries have been hit by waves of differing severity at different times. Which country is faring worst at any point in time has varied significantly. Official data show that the United States, the United Kingdom, and the euro area have all had the highest recorded cases per capita at some point in time (figure 3-1). Early in the pandemic, the United States led in per capita cases while the United Kingdom led in deaths. In the second half of 2021, the reverse was true. And the euro area reported the highest per capita cases in the spring of 2021. This variation demonstrates how nearly all major economies have been severely affected at some point during the pandemic. Progress and timeliness in vaccinating populations have also varied across countries. Both the United States and United Kingdom managed rapid vaccine rollouts that made them early leaders in the share of the population vaccinated (figure 3-2). Rollouts in Canada and the euro area accelerated dramatically in the summer of 2021, and vaccination rates in both places have since reached higher levels than in other major U.S. trading partners. During the second half of 2021, vaccination rates in many middle-income countries, such as Mexico, approached that of the United States, while rates in low-income developing countries (not shown) remain substantially lower (OWID 2021). The U.S. Economy and the Global Pandemic | 99 Figure 3-2. International COVID Vaccination Rates Percent fully vaccinated 100 80 60 40 20 0 Jan. 1, 2021 April 1, 2021 United States Canada July 1, 2021 Euro area Oct. 1, 2021 United Kingdom Mexico Source: Our World in Data. Note: “Fully vaccinated” is defined as having received all doses prescribed by the initial vaccination protocol. China does not report statistics on the share of its population that is fully vaccinated. The United States’ Economic Recovery in the Global Context The path of real gross domestic product (GDP) since the onset of the COVID-19 pandemic provides the most basic measure of the virus’s economic impact. The pandemic was accompanied by historic drops in output in almost all major economies. U.S. GDP fell by 8.9 percent in the second quarter of 2020 (figure 3-3), the largest single-quarter contraction in more than 70 years (BEA 2021c). Most other major economies fared even worse. The GDP of the United Kingdom in 2020:Q2 was 21.4 percent below its average in 2019 (ONS 2022). In the euro area, output fell by more than 12.4 percent (Eurostat 2022c). Closer to home, Canada’s GDP was down 12.4 percent, while Mexico’s GDP fell by 19 percent (Statistics Canada 2022; INEGI 2022). The U.S. recovery has outpaced that of all its major trading partners except China. By the second quarter of 2021, U.S. real GDP exceeded its prepandemic level, ahead of most other major economies. Output growth picked up in the euro area and Canada in the third quarter of 2021; but at the end of 2021, output in most major U.S. trading partners had only just reached its prepandemic level, while U.S output was 3 percent higher than before the pandemic (see figure 3-3). Though many effects of the pandemic are not captured by GDP, measured by this most basic indicator, the United States’ recovery remained farther along than those of nearly all its peers. The initial drop in real output in China was of a very similar magnitude to that of the United States (see figure 3-3), but the initial recovery was even faster. By the third quarter of 2020, China’s real GDP had not only exceeded its prepandemic level but was also above what would have 100 | Chapter 3 Figure 3-3. Real GDP by Country Index level: 2019:Q4 = 100 120 110 100 90 80 70 Jan-19 Apr-19 Jul-19 United States Oct-19 Jan-20 Canada Apr-20 Euro area Jul-20 Oct-20 Jan-21 United Kingdom Apr-21 Jul-21 Mexico Oct-21 China Sources: National data organizations. Note: Data are seasonally adjusted. been expected based on its prepandemic trend. The Chinese government did extend substantial support, primarily through infrastructure spending. However, exports have been a key driver of China’s recovery, climbing to more than 40 percent above their prepandemic level by the fourth quarter of 2021 (GACC 2021). As a result, the contribution of net exports to China’s real GDP growth reached nearly 30 percent in 2020, its highest level in more than 20 years (CNBS 2021a). In this way, China has benefited from the pandemic-induced pivot of global consumption away from services and toward goods, many of which are manufactured in China. Despite continuing support from strong demand for its exports, output growth in China slowed in the second half of 2021 as government support for the economy was withdrawn (CNBS 2021b). Future research by economists will fully assess what enabled some economies to weather the pandemic shock better or to bounce back more quickly. Based on what we know now, there are two areas of policy where the U.S. response stands out. The first is the speed of our vaccine rollout, discussed above. The fact that more than 40 percent of the U.S. population was fully vaccinated by May 2021, when vaccination rates in most European countries were still less than half that, gave our economic rebound an important head start. The other area where the United States stands apart is fiscal policy, suggesting that this also played a role in accelerating the recovery beyond those of most of our trading partners. U.S. Federal Government spending to directly support firms and workers, as well as State and local governments, The U.S. Economy and the Global Pandemic | 101 Figure 3-4. Discretionary Fiscal Response, 2020:Q1–2021:Q3 Percentage of 2020 GDP 30 25 20 15 10 5 0 Source: International Monetary Fund. was substantially larger than comparable efforts in other major economies (figure 3-4). As of the third quarter of 2021, the cumulative U.S. discretionary fiscal response (including not only additional spending but also revenue forgone due to discretionary tax cuts) exceeded 25 percent of GDP. By comparison, the U.K. response was under 20 percent of GDP, and average spending in the euro area was 12 percent of GDP. The scale here helped to ensure that, by the end of 2021, U.S. consumption had returned to its precrisis trend, while in the euro area, for example, consumption remained below its precrisis level (Boone 2021). The Challenge of Inflation Inflation has proved a serious challenge for many countries during the recovery. In the 12-month period ending December 2021, headline consumer price inflation in the euro area was 5.0 percent, well above its average of about 1 percent in the five years before the pandemic (Eurostat 2022a), as shown in figure 3-5. Canada and the United Kingdom have also seen substantially higher inflation than was the case before 2020. Inflation has also risen here; indeed, U.S. inflation has run higher than that of most of its major trading partners, although the gap narrowed in the second half of 2021. The fact that inflation has accelerated in so many countries underscores its common drivers. Pandemic-induced changes in behavior led to relatively more demand for goods than services. In many countries, the balance of consumption remained unusually tilted toward goods throughout 2021, so demand for goods grew substantially faster than would have been the case in a normal recovery (Bruce 2021; Boone 2022). As a result, the world’s economic recovery put stress on the already-vulnerable global 102 | Chapter 3 Figure 3-5. Consumer Price Level Index level: Dec. 2019 = 100 115 110 105 100 95 Jan-19 Apr-19 Jul-19 United States Oct-19 Jan-20 Canada Apr-20 Euro area Jul-20 Oct-20 Jan-21 United Kingdom Apr-21 Jul-21 Mexico Oct-21 China Sources: National data organizations. Note: Data are seasonally adjusted. supply chains for consumer goods, as discussed further in chapter 6 of this Report. This phenomenon of recovering demand for goods interacting with supply constraints can help to explain the relatively higher inflation in the United States, where the recovery was relatively stronger. Looking across countries, inflation was higher where the gap between the real GDP and its prepandemic level—a main measure of progress toward economic recovery—was smaller (figure 3-6). Rising prices for motor vehicles were a key driver of U.S. inflation, with prices of new cars nearly 12 percent higher at the end of 2021 than they were a year earlier. Prices of used cars jumped by almost 40 percent during the year (BLS 2022b). Though other countries also saw higher car prices, their rise was not as dramatic. Indeed, the CEA calculates that consumer prices, excluding those of new and used cars, rose by similar magnitudes in the euro area (4.7 percent), for example, as in the United States (5.1 percent). Globally, factors pushing up car prices included rebounding demand and a shortage of semiconductors (Gross, Miller, and Inagaki 2021). Car manufacturers both in the United States and abroad have faced production challenges due the semiconductor shortage, but during 2021, U.S. auto production outpaced that of many peers. At the end of 2021, U.S. auto production stood at just under 5 percent below its prepandemic level, ahead of the recovery of German, French, and Japanese production (Federal Reserve Board 2022; Eurostat 2022b; METI 2021). Thus, the greater rise in U.S. prices came in spite of a faster recovery in production. The fact that the rise in car prices has been larger here than abroad stems partly from the particularly resilient demand created by the U.S. recovery passing The U.S. Economy and the Global Pandemic | 103 Figure 3-6. Recovery in Output and Inflation Annualized CPI growth, Feb. 2020–Sep. 2021 8.0 Brazil 6.0 Senegal United States 4.0 Canada 2.0 Malaysia 0.0 –2.0 China 90 95 100 105 110 115 2021:Q3 real GDP (as % of 2019:Q4 GDP) Sources: National data organizations. Note: CPI = Consumer Price Index. Data are seasonally adjusted, except for Senegal’s CPI. through to the auto sector—real consumer spending on new motor vehicles rose 16 percent in 2021, a level reaching 18 percent above its prepandemic level (BEA 2022b). Though higher vehicle prices do pose challenges for American households and businesses, the strength of the recovery in the U.S. auto sector relative to other major auto-producing countries highlights the important benefits of the U.S. demand-driven recovery for workers and businesses. (See box 3-1.) International Trade, the Economic Recovery, and Lingering COVID-19 Challenges In 2021, international trade broadly recovered from the sharp decline that followed the onset of the COVID-19 pandemic, with U.S. exports and imports of goods exceeding prepandemic records. Import growth outpaced export growth, widening the U.S. trade deficit. Though trade in goods broadly recovered in 2021, supply bottlenecks slowed the recovery of both imports and exports of such products as automotive and capital goods that are at the heart of the global value chains that were disrupted by pandemicrelated challenges. In contrast, waves of COVID-19 infections have weighed down the recovery of cross-border trade in services. Although trade in services that are less reliant on personal contact followed a recovery pattern similar to 104 | Chapter 3 Box 3-1. Lessons from Abroad for Labor Market Policy By some measures, the U.S. labor market appears to have recovered rapidly. America’s unemployment rate jumped at the onset of the pandemic, but then fell steadily, and by the fourth quarter of 2021 was once again lower than in the euro area, Canada, or the United Kingdom (figure 3-i). However, though the number of people employed at the end of 2021 was above its prepandemic level in most of our trading partners, this is not true here (figure 3-ii). The reason: though labor force participation has increased significantly over the past year, relatively more people left the U.S. labor force early during the pandemic than in many other countries. The discretionary fiscal response in the United States was larger than that of most of our trading partners when considering the three major pieces of fiscal legislation passed over the course of the pandemic, and the government support associated with that response was delivered to individuals and households in a very different way. As discussed in chapter 2, pandemic support payments were generally received in the form of unemployment insurance or as direct payments. By contrast, governments in the euro area and the United Kingdom adopted or strengthened existing job retention programs, which subsidized employed workers’ incomes. (OECD 2020). These programs come in two forms: short-time work programs, in which the government pays employees for hours not worked; and wage subsidies, in which the government either subsidizes pay for hours the employee actually works or raises employees’ pay to a minimum level, regardless of time worked. These programs help explain why unemployment rates increased remarkably little in the euro area and the United Figure 3-i. Unemployment Rates Percent 16 14 12 10 8 6 4 2 0 Jan-19 May-19 United States Sep-19 Jan-20 Canada May-20 Euro area Sep-20 Jan-21 United Kingdom May-21 Mexico Sep-21 China Sources: National data organizations; OECD. Note: Data are seasonally adjusted, except China. The United States measures age 16 and above, Canada measures age 15+, and China measures urban area unemployment. Other metrics are total unemployment. The U.S. Economy and the Global Pandemic | 105 Figure 3-ii. International Employment Index level: 2019:Q4 = 100 105 100 95 90 85 80 Jan-19 Apr-19 Jul-19 Oct-19 Jan-20 Apr-20 Jul-20 Oct-20 Jan-21 Apr-21 Jul-21 Oct-21 United States Canada Euro area United Kingdom Mexico Sources: National data organizations. Note: Employment metrics vary slightly by source. The United States measures 16 years and above, Canada measures 15 years and above, and the United Kingdom measures a three-month rolling average for employment 16 years and above. The euro area and Mexico measure total employment. All data are seasonally adjusted, except for Mexico. Kingdom, both in absolute terms and relative to the change in the U.S. unemployment rate. By design, job retention programs ensured that many people working few or no hours remained on the payroll, receiving paychecks from their employer that were almost entirely government funded (OECD 2020). The difference between the U.S. approach and these job retention programs may seem semantic: workers were on the job dramatically less in the spring and summer of 2020, whether or not they were technically employed, and the magnitude of the drop was similar in the United States and other major economies. However, in the United States, workers were formally separated from their jobs and became unemployed (Boissay et al. 2021). Unemployed workers leave the labor force (meaning they stop looking for a job) at a rate almost 10 times greater than employed workers, who exit the labor force if they leave their job and do not try to find a new one (for details of what constitutes being in the labor force, see BLS 2014). Once they leave the labor force, workers tend to stay out (Hobijn and Şahin 2021). As the U.S. economy has recovered, unemployed workers have found jobs and the unemployment rate has fallen quickly. But unlike countries that adopted job retention programs, in the United States there are also more workers who are no longer in the labor force—meaning that they are neither working nor actively trying to find a job; and this slows the rebound in the number of people employed (BLS 2022a; CRS 2021). Since 2012, the United States has had a job retention program—the Short-Time Compensation Program—similar to efforts adopted else- 106 | Chapter 3 where during the pandemic. Twenty-six States, which are home to 70 percent of the U.S. labor force, have active versions of the Short-Time Compensation Program. However, participation in these local programs is very low, in part due to the associated administrative burdens (Von Wachter 2020). Viewed in light of the data on transitions in and out of the labor force discussed above, the trajectory of U.S. employment during 2021 suggests that reforms aimed at expanding participation in this program could ensure a speedier labor market recovery after future downturns. That said, in considering this policy option, a very important open question is how European-style job retention programs are affecting the reallocation of workers across types of jobs during the economic recovery. goods, others—particularly travel and transportation services1—continue to be impaired by the persistence of the virus. The sharp contraction of trade in travel services was a notable drag on the U.S. trade balance in 2021. Exports of these services in the form of foreign tourists, students, and business travelers are typically a significant contributor to the surplus in the U.S. trade balance in services. The U.S. Trade Balance The strong domestic demand for goods that has characterized the economic recovery in 2021 is reflected in the deepening deficit of the U.S. trade balance—defined as the difference between the total value of goods and services that U.S. residents buy from abroad and the value of all the U.S. goods and services sold abroad (BEA 2022a). At 4 percent of GDP, the 2021 trade deficit is the largest since 2008 (measured as a share of GDP) (figure 3-7). Deeper trade deficits in the United States over the past two decades have been correlated with economic growth because they reflect strong demand; 2021 was no exception (BEA 2022b). Over the past 20 years, the United States has typically maintained a deficit in goods trade that is partially offset by a surplus in services trade. The higher overall trade deficit in 2021 reflected a larger goods trade deficit and a smaller services trade surplus relative to recent years. In particular, the increase in the goods and services trade deficit from 2.8 percent of GDP in 2019 to 4.0 percent in 2021 reflects a 0.5-percentage-point reduction in the services surplus and a 0.7 percentage point increase in the goods trade deficit (figure 3-7). Although both developments can be traced to challenges stemming from COVID-19, the reasons for these outcomes are distinct. In official U.S. data on services trade, this category is named “transport” rather than “transportation.” 1 The U.S. Economy and the Global Pandemic | 107 Figure 3-7. U.S. Trade Balance, 2001–21 Percentage of GDP 2.0 1.0 0.0 –1.0 –2.0 –3.0 –4.0 –5.0 –6.0 –7.0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Services Goods Total Source: Bureau of Economic Analysis. The increases in consumption and investment expenditures that drove strong economic growth in 2021 entailed greater expenditures on both domestically produced and imported goods and services. American producers of goods, challenged by pandemic-induced labor and input supply obstacles, strained to keep pace with surging domestic demand for goods, which reduced the available supply for exports (Furman and Powell 2021). The dampening of growth in exports of U.S. goods was amplified by the fact that America’s fiscal policy response was larger than most other major economies (see figure 3-4). Though demand here exceeded its prepandemic trend, demand abroad lagged. As a result, American firms and consumers stepped up purchases of imported goods to a greater degree than their foreign counterparts, widening the U.S. trade deficit in goods (Milesi-Ferretti 2021). Also contributing to the widening goods trade deficit was the shift in the balance of trade in oil and petroleum products from surplus to deficit, which is discussed in box 3-2. Further, restrictions on foreign nationals entering the United States and rising costs of maritime freight transportation, a service that is primarily provided by foreign-owned firms, brought down the surplus in services trade (BEA 2022a). Macroeconomic developments here and abroad have contributed to the widening trade deficit through another channel: exchange rate movements. As the COVID-19 virus spread in early 2020, the U.S. dollar appreciated 9.7 percent from January to late March, reflecting the dollar’s status as a safe asset (figure 3-8). In times of heightened economic uncertainty, investors around the world purchase dollar assets, which they view as a reliable store of value (Jiang, Krishnamurthy, and Lustig 2021). From the end of March 2020 through the end of 2020, the dollar depreciated as global 108 | Chapter 3 Box 3-2. Trade in Oil and Petroleum Products The United States is the world’s largest oil producer, and both an important exporter and a major importer of petroleum products (EIA 2021a). These products constitute more than 10 percent of U.S. exports and about 7 percent of U.S. imports. Prices of oil and gas rose significantly during the first 10 months of 2021, with West Texas Intermediate Crude prices finishing the year more than 55 percent above its end-2020 level (EIA 2022) and global natural gas prices increasing almost sixfold between November 2019 and November 2021 (IMF 2021). Higher prices, along with rising volumes of imports and exports, meant that the dollar values of U.S. petroleum products exports were almost 50 percent above their 2020 level, while imports were up more than 75 percent (figure 3-iii). Foreign and domestic factors drove the rise in energy prices in 2021. In China, overall supply was constrained by ambitious government efforts to rein in the burning of coal while manufacturing establishments’ energy demand jumped as production surged (Riordan 2021). As a result, natural gas prices in Europe and Asia jumped due to the higher Chinese demand for natural gas as a substitute for coal. Also pushing up global energy prices was the OPEC+ (Organization of the Petroleum Exporting Countries Plus) group of oil producers’ reluctance to more rapidly expand oil production (Lawler, Ghaddar, and Astakhova 2021), which they had cut by 10 million barrels per day (about 10 percent of global production) in 2020 in response to the pandemic-induced drop in demand (EIA 2020). In the United States, weak investments in new energy sources during 2020 weighed on energy supply as the economy recovered in 2021 (IEA 2021). Additionally, bad weather, including an unusually cold winter in Texas and hurricanes Ida and Nicholas in the Figure 3-iii. Trade in Petroleum Products Dollars (billions) 25 20 15 10 5 0 Source: Census Bureau. Note: Data are seasonally adjusted. Balance Exports Oct-21 Jul-21 Apr-21 Jan-21 Oct-20 Jul-20 Apr-20 Jan-20 Oct-19 Jul-19 Apr-19 Jan-19 –5 –10 Imports The U.S. Economy and the Global Pandemic | 109 Gulf of Mexico, also affected America’s oil production (EIA 2021b, 2021c). On the demand side, the widespread availability of vaccines starting in the spring of 2021 meant the resumption of travel and some commuting, pushing up gasoline demand (EIA 2021d). Pandemic-induced shifts in the modes of transportation used for travel and commuting further boosted gasoline demand, as many opted to drive rather than use mass transit or travel by plane (Bair, Guerra Luz, and Bradham 2021). financial conditions began to normalize and the earlier flight to safety was reversed. That depreciation also reflected the very aggressive action of the Federal Reserve to support the U.S. economy by keeping interest rates low (Economist 2021). This benefits American businesses and households that borrow to purchase equipment or homes, but it makes U.S. financial assets less attractive to global investors. Lower foreign demand for U.S. assets, in turn, resulted in dollar depreciation from April through December 2020, as seen in figure 3-8. In 2021, the dollar resumed appreciating and ended the year up 3.6 percent against the currencies of its major trading partners, as measured by a Federal Reserve Board index (figure 3-8). Expectations were that the Federal Reserve would begin to tighten policy earlier than other central banks, and that contributed to the rise in the dollar’s value (Rovnick, Rennison, and Platt 2021). Such expectations reflected two aspects of America’s macroeconomic performance relative to our trading partners: the more rapid recovery in U.S. output, and the relatively larger rise in inflation. A strengthening Figure 3-8. Nominal Broad Dollar Index Index level: Jan. 2, 2020 = 100 110 ↑ Dollar appreciation 105 100 95 Jan-19 Jul-19 Source: Federal Reserve Board. 110 | Chapter 3 Jan-20 Jul-20 Jan-21 Jul-21 dollar tends to widen the trade deficit by making imported goods cheaper for American consumers, which boosts imports, and U.S. exports become more expensive for foreign buyers, depressing exports (Gruber, McCallum, and Vigfusson 2016). International Trade in Goods U.S. trade in goods rebounded relatively quickly after the sharp drop at the onset of the COVID-19 pandemic in 2020, and continued to rise through 2021. Both exports and imports of goods broke nominal records set in 2018. Goods imports breached record levels in real terms as well. This swift and robust rebound stands in sharp contrast to the stagnation in trade that followed the Great Recession, beginning in 2008 (figure 3-9). From the start of the Great Recession, goods exports did not recover from their precrisis peak for more than two years, and goods imports did not systematically rise above their precrisis peak for nearly 10 years. As discussed in the previous section, 2021 growth in imports generally outpaced that of exports. This has been true throughout the economic recovery. Even though goods imports had fully recovered in real terms to prepandemic levels by November 2020, U.S. exports did not achieve that feat until more than a year later, in October 2021 (Census Bureau 2022b). The faster recovery of imports relative to exports is a direct consequence of the broader macroeconomic context discussed earlier in this chapter. However, the effects of pandemic-related disruptions inhibited export recovery for some products more than others. Figure 3-9. U.S. Trade in Goods Dollars (billions) 300 250 Imports 200 150 Exports 100 50 0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 Source: Census Bureau. Note: Data are seasonally adjusted. The U.S. Economy and the Global Pandemic | 111 Figure 3-10. Real Exports, Selected End-Use Categories Index level: Feb. 2020 = 100 140 120 100 80 60 Capital goods Consumer goods Automotive goods Oct-21 Jul-21 Apr-21 Jan-21 Oct-20 Jul-20 Apr-20 Jan-20 Oct-19 Jul-19 Apr-19 20 Jan-19 40 Food, feed, and beverage goods Source: Census Bureau. Note: Data are seasonally adjusted. In real terms, U.S. exports of food, feed, and beverages were little affected and exceeded their February 2020 levels for most of the second half of that year. U.S. exports of consumer goods surpassed their prepandemic level in November 2020 (figure 3-10).2 By contrast, exports of capital goods did not exceed their prepandemic value until April 2021, and remained at about that level for the rest of the year. Exports of autos and parts were more than 10 percent below their prepandemic level for most of the year. The relatively swift rebound in exports of consumer goods highlights the global nature of the pandemic-induced switch from services to goods consumption. The softer performance of capital goods and auto exports reflects the flip side of the strong demand unleashed by the economic recovery. Supply challenges for critical inputs disrupted the global value chains that characterize production in the automotive and other capital goods industries, inhibiting their ability to meet surging domestic and foreign demand (see chapter 6 for a full discussion of supply chain challenges). The final goods produced and exported by American businesses in these industries are complex. Automotive exports often rely on semiconductors, the global supply of which was notably stressed in 2021 (McKinsey & Company 2021; Ewing and Boudette 2021). Civilian aircraft, engines, and parts represented the largest share of the decline in exports of capital goods relative to 2019, reflecting diminished demand by airlines after COVID-19 dramatically reduced air traffic (Census Bureau 2022a; Kuzmanovic and Rassineux n.d.). The BEA end-use category “food, feed, and beverages” consists of agricultural commodities, including those used for animal feed, as well as fish and shellfish, prepared foods, and alcoholic and nonalcoholic beverages. 2 112 | Chapter 3 Figure 3-11. Real Imports, Selected End-Use Categories Index level: Feb. 2020 = 100 160 140 120 100 80 60 40 Capital goods Consumer goods Automotive goods Oct-21 Jul-21 Apr-21 Jan-21 Oct-20 Jul-20 Apr-20 Jan-20 Oct-19 Jul-19 Apr-19 Jan-19 20 Food, feed, and beverage goods Source: Census Bureau. Note: Data are seasonally adjusted. The composition of U.S. imports growth in 2021 highlights the strength with which U.S. demand has recovered and the challenges economies around the world continue to face. U.S. goods imports dipped across the board during the initial months of the pandemic, but to a lesser extent than exports, and then rapidly exceeded their pre-COVID-19 level (figure 3-11). Consistent with the increased consumption of goods relative to services, imports of consumer goods showed a striking increase in 2021, rising to 16.6 percent above their 2019 level. Imports of capital goods, such as machinery used in factories, also rose notably in 2021, to 11.3 percent in real terms above their 2019 level, as domestic American firms expanded to satisfy booming U.S. demand. The trajectory of automotive imports illustrates the global nature of the supply chain stresses that emerged during 2021. Though automotive imports initially rebounded, they subsequently declined as global supply chains were disrupted (Ewing and Boudette 2021). Imports in this category were 9.6 percent below their 2019 level in 2021. This category includes both motor vehicles and parts, but the decline was entirely due to falling imports of finished vehicles, while parts were slightly above their 2019 level (Census Bureau 2022a). As discussed previously in this chapter, the recovery of the U.S. automotive sector outpaced that of other major auto-manufacturing countries in 2021. International Trade in Services In contrast to the relatively swift recovery of trade in goods, the exigencies of containing the spread of COVID-19 continue to suppress global demand The U.S. Economy and the Global Pandemic | 113 Figure 3-12. Trade in Services Dollars (billions) 80 70 60 50 40 30 20 Balance Exports Oct-21 Jul-21 Apr-21 Jan-21 Oct-20 Jul-20 Apr-20 Jan-20 Oct-19 Jul-19 Apr-19 0 Jan-19 10 Imports Source: Bureau of Economic Analysis. Note: Data are seasonally adjusted. for services. The overall decline in both exports and imports of services at the onset of the pandemic (figure 3-12) is primarily due to a steep drop in trade in travel services (figure 3-13). Total exports and imports of services other than travel and transportation services—which covers finance, insurance, maintenance, construction, information, personal and government services, intellectual property, and other services—exceeded their 2019 value in 2021. Figure 3-13 illustrates that neither imports nor exports of travel services have approached their prepandemic levels. However, while imports of travel services have increased relatively steadily since the pandemic first hit the United States, exports saw only a minimal increase until November 2021, when the Biden-Harris Administration eased travel restrictions that had prevented many foreign tourists, students, and business travelers from traveling to the United States, resuming revenue from travel exports (White House 2021c).3 By contrast, most other countries were open to U.S. travelers for much of 2021 (Schengen Visa Info 2021; Ponczuk 2021). Trade in transportation services has likewise been shaped by the exigencies of the pandemic and economic recovery. The dramatic increase in the deficit for the transportation services balance depicted in figure 3-14 directly reflects the challenges faced by shippers of goods in 2021. The rise in maritime freight services imports largely drove the increased value of imported transportation services (BEA 2022a). The skyrocketing cost of moving goods from abroad to the United States meant that U.S. importers paid dramatically more to shipping companies (Harper Petersen 2022). Exports of travel services include goods and services acquired by foreign visitors, including foreign students, while visiting the United States. Similarly, imports of travel services cover goods and services acquired by U.S. residents visiting foreign countries. 3 114 | Chapter 3 Figure 3-13. Trade in Travel Services Dollars (billions) 18 16 14 12 10 Exports Imports 8 6 4 2 Jan-21 Apr-21 Jul-21 Oct-21 Jan-21 Apr-21 Jul-21 Oct-21 Oct-20 Jul-20 Apr-20 Jan-20 Oct-19 Jul-19 Apr-19 Jan-19 0 -2 Source: Bureau of Economic Analysis. Note: Data are seasonally adjusted. Figure 3-14. Trade in Transportation Services Dollars (billions) 12 10 8 Imports 6 4 Exports 2 0 –2 Oct-20 Jul-20 Apr-20 Jan-20 Oct-19 Jul-19 Apr-19 –6 Jan-19 –4 Source: Bureau of Economic Analysis. Note: Data are seasonally adjusted. In official U.S. data on services trade, this category is named “transport” rather than “transportation.” Because nearly all major shipping firms are foreign-owned (Marine Digital 2021), these costs register as U.S. service imports. In contrast, U.S. exports of transportation services are dominated by passenger air transportation, which, like travel services, were suppressed by restrictions on foreign travel to the United States until the end of 2021 (BEA 2022a). Categories of services that saw a robust recovery included finance and insurance trade and other business services imports. Because they do not rely as heavily on in-person interaction, both imports and exports increased The U.S. Economy and the Global Pandemic | 115 year-on-year relative to their 2019 levels throughout the pandemic and recovery. Similarly, trade in intellectual property, telecommunications, and other business services recovered quickly and is now above 2019 levels. Policies to Build an Equitable International Economy U.S. participation in the global economy has yielded important benefits, including lower prices for consumers, lower costs for American manufacturing inputs, and access to a greater variety of products as well as larger markets for American-made goods and services. However, global economic integration has also increased the exposure of American businesses and their workforces to import competition, which has meant loss of livelihoods for some American workers, thus contributing to the troubling rise in inequality documented in chapter 1 of this Report (Clausing 2019; Autor, Dorn, and Hanson 2013, 2016, 2021). Other factors have also pushed up inequality, ranging from the declining progressivity of the tax system (Antràs, de Gortari, and Itskhoki 2017) to increased automation in manufacturing production (Moll, Rachel, and Restrepo 2021). Nonetheless, the effects of U.S. international trade and investment policies on American workers and communities, and thus on economic inequality, have also played a role. The COVID-19 pandemic provided an opportunity to refocus domestic and international policies to alleviate the disruptions that participation in the global economy can inflict on American workers and increase the opportunities that it can offer them. This means seeking a better balance between, on one hand, reducing costs for American businesses and lowering prices for consumer products and, on the other hand, ensuring that workers whose livelihoods are at risk from global competition are not disproportionately harmed. Ensuring that U.S. participation in the global economy supports the Biden-Harris Administration’s goal of a more equitable economy at home also requires policies that level the international economic playing field by improving labor standards abroad, confronting unfair practices by our trading partners, and making the international tax system fairer. Trade policy can also support another fundamental policy goal, the reduction in greenhouse gas emissions; box 3-3 describes how this can be accomplished. Broadening the Gains from Trade The uneven effects of the COVID-19 pandemic demonstrated inequalities within American society, showcasing how negative economic shocks can be disproportionately concentrated among individuals who are more economically vulnerable.4 Similarly, the job and income losses that have accompanied rising import competition have often fallen disproportionately 4 See, e.g., Mongey et al. 2021; Chetty et al. 2020; Liu and May 2020; and Hardy and Logan 2020. 116 | Chapter 3 Box 3-3. Greenhouse Gas Emissions and Trade As an example of how trade policy can support a broader set of goals, consider international trade policy oriented toward incentivizing the reduction of greenhouse gas emissions. Effectively combating climate change requires policies that reduce global emissions of greenhouse gases and increase resilience to the climate changes that have already happened. However, those very policies can put domestic production at a competitive disadvantage relative to production in countries with less stringent mitigation policies (Dechezlepretre and Sato 2017). Further, local policies that reduce emissions by producers in one country are ineffective—from a global perspective—if their primary effect is to shift emissions elsewhere. To create a level playing field in domestic markets with strong climate policies and ensure maximal decarbonization from those policies, scholars and policymakers have suggested introducing trade rules based on the carbon content of traded goods and services. Such a policy could, for example, impose a carbon fee on goods imported from countries with less ambitious climate policies that offsets the climate regulatory costs that producers face in the domestic market. Research suggests that these policies can help accelerate decarbonization globally and protect the domestic industry in the countries enacting them (Campbell, McDarris, and Pizer 2021). For example, the United States and European Union reached an agreement in late 2021 to negotiate a global arrangement for trade in steel and aluminum that takes the carbon intensity of these industries into account and that aims to drive industrial decarbonization around the globe (White House 2021b). These emissions-based trade policies need not favor any one mechanism for incentivizing decarbonization, recognizing that domestic mitigation policies can take many forms—from regulations to tax incentives to a carbon price. Instead, these trade tools can retain the flexibility for countries to enact a range of climate policy tools, as long as emissions are decreasing. As discussed in chapter 7 of this Report, policies that encourage domestic industries to shift toward clean energy could, for example, take the form of regulations, tax incentives, and other similar provisions. on low-skilled workers, exacerbating inequality (Clausing 2019). A large body of economic research focused on the effects of the dramatic increase in import competition from China in the early 2000s, the so-called China Shock, has demonstrated that, though the gains from international trade have been substantial, the costs have outweighed these gains for some U.S. communities. Increased import competition from China has had adverse effects on employment and incomes in labor markets that are more exposed The U.S. Economy and the Global Pandemic | 117 to competition from China, and these adverse effects have persisted long after the initial shock (Autor, Dorn, and Hanson 2013, 2016, 2021). In the future, U.S. policy should aim to mitigate and indeed reverse the effects that greater exposure to import competition has had on inequality in America. This requires rebalancing the objectives of trade policy to give greater weight to its impact on individuals and communities that are negatively affected. To effectively incorporate these interests, policymaking must become more inclusive, and thus must be informed not only by the views of American firms directly engaged in international trade and workers competing with imports but also by the views of affected communities and other stakeholders. In addition, economic scholarship has consistently called for complementary domestic policies to increase American workers’ competitiveness and address the disruptions experienced by those affected by negative trade shocks. Basic economic policies focused on workers would better equip them to adapt to changes in the economy, including those that are transmitted through international trade (Clausing 2019; Rodrik 1996; Hanson 2021; Dixit and Norman 1986). The investments in transportation infrastructure that have been made possible by the Bipartisan Infrastructure Law will make it easier for U.S. goods exports to reach markets overseas. Greater exports, in turn, promote economic growth and support well-paying jobs, especially for blue-collar workers (Riker 2015). Along with the other policy proposals to fortify America’s supply chains discussed in chapter 6 of this Report, these investments will also bolster U.S. competitiveness both at home and abroad, and more broadly distribute the gains from the country’s participation in the global economy. Looking ahead, the investments in human capital outlined in chapter 4 of this Report would equip American workers with skills and education that would enlarge their share in the benefits of international trade and investment. Leveling the International Economic Playing Field Key to broadening the gains from trade is ensuring that American workers are competing on a level playing field. Too often, the competitiveness of American workers and firms has been eroded by other countries’ inadequate labor standards and unfair trade policies and practices, and also by international tax competition.5 Economic analyses that ignore the negative effects of these practices provide only a narrow and potentially misleading view of the gains from trade and how they are distributed domestically and internationally. Labor standards. An important component of modern trade agreements between countries are provisions to improve labor conditions. These 5 Chapter 5 of this Report discusses the importance of fair competition in domestic markets. 118 | Chapter 3 are intended to ensure that workers are appropriately compensated and protected during their work, and that relative competitiveness is not driven by differences in labor standards between the countries. Twice in 2021, the United States invoked the rapid response mechanism included in the United States–Mexico–Canada Agreement to respond to allegations that workers in Mexico were being denied the rights of free association and collective bargaining. The first time was in response to corruption uncovered during a worker vote on a collective bargaining agreement at an automotive plant, which resulted in the United States and Mexico negotiating a plan to address the violations and provide for a free and fair vote on the agreement (USTR 2021b). The second responded to a petition filed by the AFL-CIO and others alleging violations during a union organizing campaign at an auto parts company (USTR 2021a). The resulting agreement with the company in question not only secured compensation for the adversely affected workers but also put in place mechanisms to protect workers’ rights. Labor standards are also crucial when some producers resort to practices that are not only unfair but also inhumane, in that they rely on forced labor. The International Labor Organization (ILO) estimates that 25 million individuals on any given day are subjected to forced labor (ILO 2017), and that this forced labor generates large profits for the firms involved (ILO 2014). Though some have argued that market forces on their own will drive coercive employers out of the labor market, recent theoretical modeling calls this result into question (Acemoglu and Wolitzky 2011). Indeed, the tragic persistence of forced labor suggests that policy actions are needed to combat the practice. To this end, Group of Seven leaders, including the United States, made combating forced labor a priority starting at their June 2021 meeting (Group of Seven 2021). After discussions of conditions in China’s Xinjiang Uyghur Autonomous Region (White House 2021a), the Group of Seven called for strengthened cooperation and collective efforts to eradicate the use of all forms of forced labor in global supply chains. Responding to unfair trade policies and practices. One of the most significant challenges for the United States’ ability to realize broadly distributed gains from trade is the direct and indirect support for targeted industries used by some foreign governments to promote their own domestic producers at the expense of other producers, including the United States. Foreign governments implement such policies using a variety of tools, including taxes, subsidies, preferential regulatory treatment for domestic enterprises, broad support for state-owned enterprises or other state-affiliated entities, and formal and informal restrictions on the ability of foreign enterprises to compete in the domestic market. At a minimum, these interventions create economic distortions that disadvantage foreign producers in the domestic market and often in third-country markets as well, diminishing the benefits of the commitments they have made under multilateral and preferential trade The U.S. Economy and the Global Pandemic | 119 agreements. In more egregious circumstances, they can concentrate market power in the country that uses them, stifling global competition, limiting innovation, and creating opportunities for economic coercion (Sykes 2003; Hart 2020; Autor et al. 2020; Bown 2022). Global markets for industries such as steel, aluminum, and solar panels bear the hallmarks of government policies designed to secure market power. Over time, China’s array of government support and policy directives, which experts have argued amount to sizable subsidies, have led China to become the dominant global supplier in each of these industries (Bown and Hillman 2019). Public statements of policy suggest that China is using continued, targeted government support for specific high-tech manufacturing industries aimed at promoting its dominance at the expense of its trading partners (CRS 2020; Creemers et al. 2021). Unchecked, the effects of China’s capture of these industries can be expected to give Chinese firms substantial market power, further concentrating crucial aspects of global manufacturing in a single country, at the expense of producers of competing goods in the United States (Bown and Hillman 2019). Such policies can also hinder the adoption of critical innovations, because the subsidies that facilitate market dominance are not necessarily directed toward the best technology available (Hart 2020). Importantly, the burdens associated with China’s system of targeted industrial policies fall not only on the United States but on all countries whose producers compete with China in global markets (McBride and Chatzky 2019). As such, efforts to counter the use of these policies are most effective when pursued collaboratively and in concert with U.S. allies and partners (Mattoo and Staiger 2020). Reform of the international corporate tax system. Leveling the playing field for American workers and businesses requires reform of the international corporate taxation system to curtail a race to the bottom in corporate taxation, whereby countries lower their tax rates to attract mobile multinational activities (Azemar et al. 2020). This practice distorts businesses’ decisionmaking, including production decisions, while also generating less tax revenue than could be obtained if countries engaged with one another cooperatively (Cobham and Jansky 2018). Large multinational firms have taken advantage of this tax competition among countries by shifting profits and economic activities to minimize their tax burdens (Guvenen et al. 2019). In 2021, world leaders reached a historic agreement that will address these challenges and stabilize the international tax system. The plan to reform international tax practices was agreed to by the overwhelming majority of the world’s economies—representing over 90 percent of world GDP. The agreement includes a global minimum tax of 15 percent that would apply to profits of multinational firms that have more than €750 million (about $822 million) in sales globally. It also includes provisions that would reallocate some taxing rights over certain residual profits of multinational 120 | Chapter 3 firms to the markets where products are consumed, regardless of whether these firms have a physical presence in these markets (OECD 2021). These reforms respond to concerns that businesses generate value from profits in certain jurisdictions while paying minimal taxes there. As such, the agreement addresses existing international tax tensions by incorporating commitments from several countries to withdraw digital services taxes that would have fallen disproportionately on multinationals headquartered in the United States (Giles 2021). The reforms would generate additional revenue that could help countries address the myriad challenges they face, including rising inequality. A Collaborative, Transparent Policymaking Process Reorienting policy to ensure that the United States’ participation in the global economy does not exacerbate rising inequality requires important changes, but experience shows that the benefits of such policy shifts are greater when they happen after consultation with our trading partners and through a process that is transparent for those affected. Through trade agreements and through entities such as the World Trade Organization, the United States has long cooperated with its trading partners to establish and enforce global trade rules (Bagwell, Bown, and Staiger 2016). In addition to providing reliable market access for U.S. exporters, such institutions limit the use of beggar-thy-neighbor policies, which advance one country’s targeted economic outcomes at the expense of those of other countries (Ossa 2014). An approach to addressing the flaws in current U.S. trade policy and in global trade rules that ignores the commitments the United States has made weakens these institutions and diminishes the benefits that they bring to American firms and workers. This is exemplified by the retaliatory measures taken by many of our trading partners in response to U.S. trade policy actions in 2018 and 2019 that they judged to be in violation of commitments made by the United States under the World Trade Organization’s rules (Mattoo and Staiger 2020). These retaliatory measures cost U.S. manufacturing jobs (Flaaen and Pierce 2019), exports (Morgan et al. 2022), incomes, and more broadly economic welfare in the period immediately after their imposition (Amiti, Redding, and Weinstein 2019; Cavallo et al. 2021). Fundamentally, the global system of trade rules benefits not only domestic producers directly engaged in international trade as importers or exporters but also buyers of goods and services for which prices are influenced by global markets. A large body of research has established that uncertainty negatively affects economic outcomes (Bloom 2014), and more recent work makes clear that this is also true of trade policy uncertainty (Caldara et al. 2020; Heise et al. 2021). Global trade rules limit uncertainty about future changes in tariffs or the imposition of other trade restrictions, The U.S. Economy and the Global Pandemic | 121 which can in turn foster investment and employment. Although changes to U.S. trade policy are needed, elevated uncertainty about how trade policy might alter prices and availability along global value chains pose a particular challenge in the wake of the COVID-19 pandemic’s supply chain disruption (Miroudot 2020). Making the necessary changes to U.S. international economic policy to ensure the benefits from trade are more broadly distributed and that competition takes place on a level playing field demands rethinking some of the existing rules and norms governing international economic relations. The practical difficulties of making changes within existing institutions creates a complex challenge for governments seeking to develop sustainable international economic policy. However, implementing changes noncooperatively could ultimately leave the United States worse off if its trading partners no longer feel constrained to respect their own commitments (Mattoo and Staiger 2020; Bown and Hillman 2019). Trade policy that is long on combative rhetoric and indifference to trade partners’ interests, but short on substance and consistency, puts American firms at a disadvantage. It dissuades our partners and allies from working with the United States to tackle common challenges. Importantly, it cannot deliver on creating jobs, reducing inequality, or promoting economic growth more generally. Since 2021, the Biden-Harris Administration has been renewing strong relationships with our trading partners, working to resolve outstanding trade issues and to establish cooperative frameworks to address emerging challenges. Conclusion Comparing the performance of the United States’ economy during 2021 with that of our trading partners demonstrates this country’s resilience at a time of daunting challenges. Supported by a strong fiscal response and a rapid vaccine rollout, the GDP of the United States exceeded its prepandemic level before those of other major advanced economies. However, as the recovery got under way, demand continued to tilt toward goods and away from services. This shift in global consumption patterns interacted with stressed supply chains to generate inflation in the United States and most of our major trading partners, although this effect was particularly pronounced here due to the relative strength of our recovery. The faster pace of the U.S. economic recovery has also resulted in a widening trade deficit. Openness to international commerce provides substantial benefits to the U.S. economy. However, these benefits have at times come at the cost of wider domestic inequality. We must engage with our partners and allies to make international economic engagement work for all Americans, by ensuring that the global rules are aligned with domestic objectives and values, and that these rules are rigorously enforced. 122 | Chapter 3 Chapter 4 Investing in People: Education, Workforce Development, and Health To increase productivity and growth, we must invest in the American people. U.S. investments in universal primary and secondary education in the early 20th century, combined with medical advances in such areas as vaccines and antibiotics, contributed to strong growth throughout most of that century (Goldin and Katz 2008; Goldin 2016). Life expectancy at birth increased by nearly 30 years between 1900 and 2000 in the United States (CDC 2017), and we developed a highly skilled labor force (Goldin and Katz 2008). These gains contributed to economic growth and rising living standards across the Nation. However, increases in educational attainment and life expectancy have slowed in recent decades, and the United States is now falling behind other peer countries. When society invests in people, the economy has more capacity to grow. In the first half of the 20th century, for example, the United States led the world in high school enrollment (Goldin and Katz 2008) and ranked among the top 10 in life expectancy.1 In contrast, by 2017, the country had slipped to 12th in the share of 25- to 34-year-olds having completed some postsecondary education and to 29th in life expectancy at birth among members of the Organization for Economic Cooperation and Development (OECD) and its partner countries.2 These slips in rank are not simply a matter of other CEA calculations, based on life expectancy data from Roser, Ortiz-Ospina, and Ritchie (2013). CEA calculations, based on tertiary education data from the OECD (2022a) and life expectancy data from the OECD (2022b). Tertiary education data are not available for India or China in 2017. 1 2 123 countries catching up, but rather of the United States falling further behind.3 This suggests that the United States may be underinvesting in people, potentially dampening economic progress. Further, there are widespread and long-standing disparities in the United States by race, ethnicity, and gender in measures of human capital investment and accumulation. For example, in 2019, 82 percent of Asian young adults immediately enrolled in college after high school completion, compared with 58 percent of Black recent high school graduates (de Brey et al. 2021, table 302.20); and in 2018, life expectancy at birth for a Hispanic infant was seven years longer than for a nonHispanic Black infant (Arias and Xu 2020). Inequitable access to relevant resources exacerbates the persistence of these issues. For more discussion of the structural nature of such racial and gender disparities, see chapter 5. Economists analyze investments in people in terms of the “human capital” they produce—a concept that captures the knowledge, skills, health, and other valuable resources embodied in a person. Just as investments in physical or financial capital can reap benefits, well-timed investments in people can generate payoffs to individuals, employers, and society. Education and job training are classic examples of inputs to human capital. Other crucial investments include mental and physical health and work experience. The contributions these investments in people make to economic well-being and growth depend on how effectively the human capital they produce is developed and deployed.4 This chapter focuses on what is known about key investments in human capital—education, workforce development, and health—as well as policies to ensure that individuals, society, and the economy can fully benefit from In 1992, the United States ranked second in the percentage of young adults with postsecondary education, 3 percentage points behind Canada. By 2019, just over 50 percent of U.S. young adults had completed some form of postsecondary school, roughly 13 percentage points behind Canada and more than 19 percentage points below number-one-ranked South Korea. Further, in 1975, the U.S. life expectancy at birth was within three years of the top-ranked OECD country (Iceland); but by 2019, U.S. life expectancy at birth was four years below Iceland and five and a half years below that of top-ranked Japan. 4 See Jacobs and Hipple (2018) for a discussion of inequality and intergenerational mobility with a similar frame. 3 124 | Chapter 4 these investments. The first section explains why human capital plays such an important role in economic growth. The second section discusses ways in which additional investments in education, workforce development, and health are needed to improve the development of human capital and reverse the course of the past 20 years. And the third and final section highlights several areas where changes to government policy or institutional and societal practices could help people deploy their human capital more productively. Human Capital Is Critical for Economic Growth and Individual Well-Being In thinking about how human capital affects individuals and the economy, researchers focus on both macroeconomic and microeconomic perspectives. From the macroeconomic perspective, human capital improvements are a key factor in generating economic growth; and, ultimately, long-run economic growth helps determine living standards. Generally, economists look for output to grow at least as fast as population growth to maintain living standards and to grow faster than population growth to improve them; thus, they often rewrite total output in terms of output per person. Figure 4-1 shows the time series of per capita U.S. gross domestic product (GDP)—the most popular measure of economic output—on a ratio scale from 1870 through 2021. The ratio scaling means that the slope of the fitted line (shown in orange dots) represents the average annual growth rate over the period. As shown in figure 4-1, the growth rate was remarkably stable over this time, despite large deviations from that trend during and after the Great Depression. Over the roughly 150-year period, per capita U.S. GDP grew at an average rate of 1.8 percent a year. In a simple model of the economy, output per person can be written in terms of four factors—the (physical) capital-output ratio, human capital per person, research intensity (idea generation), and the number of people in the economy. When Fernald and Jones (2014) decompose per capita GDP growth into growth from these four components over the 1950–2007 period, they estimate that 20 percent of growth came from increases in human capital, nearly 60 percent can be attributed to increases in research intensity, and the remaining roughly 20 percent was due to a growing population.5 From a microeconomic perspective, human capital accumulation is associated with various benefits to individuals, their families, and their communities. Although many benefits of human capital investment accrue Since capital and output grew at roughly the same rate, the capital-output ratio added 0 percent to per capita GDP growth over this period. 5 Investing in People: Education, Workforce Development, and Health | 125 Figure 4-1. U.S. Gross Domestic Product per Person, 1870–2021 Gross domestic product per capita (2012 dollars), ratio scale 64,000 32,000 16,000 8,000 4,000 2,000 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2020 Source: Updated and reproduced from Fernald and Jones (2014). Data for 1870 to 1929 are from Madison (2008). Data for 1929 to 2021 are from the Bureau of Economic Analysis. Note: Orange dots represent the fitted line. directly to individuals in the form of buying power or the ability to enjoy life, this chapter primarily focuses on individuals as workers in the economy and how human capital investments contribute to U.S. productivity and growth. The relationship between additional years of education and earnings is among the most extensively documented in economics. Figure 4-2 illustrates this relationship, reflecting that, on average, more highly educated workers both earn higher wages and enjoy higher employment rates. Researchers find positive returns to additional education at the elementary and secondary levels as well as the postsecondary level (Angrist and Krueger 1991; Card 1995; Kane and Rouse 1995; Ashenfelter and Rouse 1998; Card 1999; Zimmerman 2014). Additional years of education increase wages, on average, because education increases worker productivity in the labor market, which increases output growth. Similarly, work experience is associated with higher earnings as workers develop valuable skills through on-the-job training. Researchers find that more education also reduces adult mortality rates (Buckles et al. 2016) and incarceration rates (Lochner and Moretti 2004) and raises civic engagement (Milligan, Moretti, and Oreopolous 2004). Research also finds that maternal education has a positive effect on infant health 126 | Chapter 4 Figure 4-2. Earnings Increase with Years of Schooling Annual wage and salary income (2019 dollars) 80,000 70,000 Total Personal Income 60,000 $100,000 $90,000 50,000 $80,000 $70,000 40,000 $60,000 $50,000 30,000 $40,000 $30,000 20,000 $20,000 $10,000 10,000 $0 0 0 1 2 0 1 2 3 3 4 4 5 5 6 6 7 8 9 10 11 12 13 14 15 16 17 18 7 8 9 10 11 12 13 14 15 16 17 18 Years of Schooling Source: 2015–19, American Community Survey, 5-year sample. Note: The sample is limited to individuals 25 and above. (Currie and Moretti 2003). As such, investments in education can even raise the human capital of the next generation. Although health is prominent as one of human capital’s key elements (along with education, migration, labor market information, and job training) in the original formulation of human capital by Schultz (1962), fewer studies have explored health within this framework. Vaccines and public safety measures, through reductions in death and work-hampering disability, can increase the size and productivity of the workforce (Bleakley 2010; Hamory et al. 2021). Other health investments increase productivity by improving workers’ mental health or quality of daily living. At the macroeconomic level, cross-country regressions suggest that health is a robust predictor of economic growth, with a one-year increase in life expectancy predicting an increase in GDP per capita of about 2 to 4 percent (Sharma 2018; Bloom and Canning 2003). Investing in People: Education, Workforce Development, and Health | 127 Measuring the Stock of Human Capital Researchers would, ideally, like to study all forms of human capital but remain limited to those aspects that can be easily or consistently measured. For example, the World Bank’s index of the stock of human capital in different countries is constructed using measures of childhood survival and health in addition to quality-adjusted educational attainment, which are combined with estimates of how these dimensions affect productivity (Kraay 2018). Measures of human capital used to estimate potential GDP growth in the United States depend largely on educational attainment, work experience, and estimates of how both affect productivity. Educational attainment and years of work experience are only proxies for human capital. Notably, years of completed education are not adjusted for differences in quality and do not reflect job-training programs, such as apprenticeships, that operate outside a school-based setting. Further, any systematic change in human capital that is unmeasured, such as improvements in the quality of education or declines in health, can bias estimates of human capital and potential output growth. Rising U.S. educational attainment was a main driver of measured human capital growth over the second half of the 20th century (Aaronson and Sullivan 2001). However, though recent cohorts of Americans are the most educated ever, their average years of completed education only modestly exceed what the prior generation attained. Figure 4-3 displays average years of education over time for individuals age 25–34 years and age 55–64. In 1960, individuals in the 55–64 range had completed 9.2 years of education on average, while the younger group, 25–34, averaged 11.3 years of education, a difference of just over 2 years. By 1990 this gap had closed to Figure 4-3. Average Years of Education by Age Group Average years of education 16 14 12 10 8 6 4 2 0 1960 1970 1980 1990 Age 25–34 2000 2010 2019 Age 55–64 Sources: Census Bureau; CEA calculations. Data for 1960 and 1970 are from the 1 percent sample; data for 1980, 1990, and 2000 are from the 5 percent sample; and data for 2010 and 2019 are from the American Community Survey. Note: If educational attainment is nursery school to grade 4, the observation is coded as 4 years of education. If educational attainment is grade 5, 6, 7, or 8, the observation is coded as 8 years of education. Observations with 5+ years of college are coded as 17 years of education. 128 | Chapter 4 Figure 4-4. Life Expectancy, 1900-2019 Years 85 80 75 70 65 60 55 50 45 40 35 1900 1910 1920 1930 1940 Females, age 45 1950 1960 Males, age 45 1970 1980 Females at birth 1990 2000 2010 Males at birth Source: Our World in Data 1900-2014, Social Security Administration 1915-2019. just over 1 year, and by 2019 the gap was only half a year. When younger cohorts far exceed their elders in years of completed education, the average education level of the workforce increases rapidly. However, as that gap closes, retirees are replaced by new entrants with roughly the same level of education. This slowing in the growth of educational attainment corresponds to a slowing in the growth of human capital per worker, all else being equal. When measuring health as a contributor to human capital, life expectancy at birth is a common metric used in the United States and other developed countries. Figure 4-4 illustrates life expectancy at birth and at age 45 for both males and females between 1900 and 2019. In that period, life expectancy at birth rose about 30 years for both sexes. Most of the gains occurred between 1900 and 1955, largely due to reductions in infant and child mortality (Crimmins, Preston, and Cohen 2011). As a result, life expectancy at age 45 increased by a more modest 13 years for females and 10 years for males. In the decade before the COVID-19 pandemic, life expectancy at birth rose by less than half a year, compared with gains averaging about 4 years per decade between 1900 and 1950 and 1.7 years per decade between 1950 and 2010. The COVID-19 pandemic has directly destroyed human capital through death. The virus has also reduced and delayed investments in Investing in People: Education, Workforce Development, and Health | 129 Figure 4-5. Percent Reporting Health as Fair or Poor, 1997–2018 Percent 25 20 15 10 Age 25–44 Age 45–54 2018 2017 2016 2015 2014 2013 2012 2011 2010 2009 2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 0 1997 5 Age 54–65 Source: National Health Interview Survey. education, experience, and health. Of note, COVID-19 accounted for sizable reductions in estimates of provisional life expectancy between 2019 and 2020 (Arias et al. 2021), even though children who were born in 2020 are unlikely to experience the same conditions at older ages that led to the estimated decline. This is one example of why changes in life expectancy can be less meaningful as a reflection of health human capital than alternatives such as disease prevalence, work-limiting disabilities, or indices of activities of daily living. (For more on the effect of the COVID-19 pandemic on health human capital, see box 4-1.) That said, the plateauing of gains in life expectancy before the pandemic is consistent with evidence from other measures of health that are only available for more recent years. Self-reported health, for example, can be predictive of subsequent mortality, even after controlling for socioeconomic status and comorbidities (McGee et al. 1999). Figure 4-5 presents data from the National Health Interview Survey on the percentage of respondents by age group reporting that their self-assessed health status was either fair or poor between 1997 and 2018. For adults in all three age groups, the percentage rating their health as fair or poor has held steady or even increased over this period. These findings suggest that the growth in the stock of health human capital among working-age adults has slowed. Demographic change, driven by the current and upcoming retirements among the large baby boom generation, also has implications for growth in human capital per person. When baby boomers first started entering 130 | Chapter 4 Box 4-1. COVID and Health Through the end of 2021, there were over 820,000 reported deaths from COVID-19 in the United States (CDC 2022). One measure of excess deaths, which includes unreported pandemic deaths along with deaths from related causes, suggests the true COVID-19 death toll through 2021 might be 15 percent higher than reported (Giattino et al. 2020). About 75 percent of reported deaths from COVID-19 have occurred among those over age 65 (CDC 2021a). As shown in figure 4-i, deaths from COVID-19 are more concentrated among older people, especially those age 85 and above. However, deaths do not tell the whole story; communities of color saw higher rates of hospitalization and greater losses in life expectancy between 2019 and 2020, largely due to the effects of COVID-19 (CDC 2021c; Arias et al. 2021). Further, there were over 54 million reported COVID-19 infections through the end of 2021, and tens of thousands of patients were hospitalized with the virus during a typical week in 2021 (CDC 2022; Johns Hopkins University 2022). These consequences of the pandemic are causes for concern from a human capital perspective: COVID-19 can cause many health complications aside from death, and those complications may be occurring in people who have much of their working lives ahead of them. The effects of COVID-19 on health are not limited to those who become infected, however. Secondary consequences of the pandemic have created a series of health challenges. Primary among them has been an overall decline in mental health. More than half of women and a third of men reported worsening mental health after the beginning of the pandemic, with about a fifth saying the pandemic had a major impact Figure 4-i. Share of COVID Deaths and Share of Population by Age Share 30 Share of COVID deaths 25 Share of population 20 15 10 5 0 0–17 18–29 30–39 40–49 Years 50–64 65–74 75–84 85+ Source: Centers for Disease Control and Prevention. Note: Data are current as of January 18, 2022. Investing in People: Education, Workforce Development, and Health | 131 (Frederiksen et al. 2021). One study estimates the risk of depression among college students in spring of 2020 was 50 percent higher than prepandemic rates (Giuntella et al. 2021); another finds that the average share of adults reporting symptoms of anxiety disorder and/or depressive disorder were up nearly fourfold in January 2021 (Panchal et al. 2021). The problem has also been worse for groups that are already socially marginalized. Women with children, Hispanic and Black people, the unemployed, and essential workers were more likely to report mental health issues during the pandemic (Panchal et al. 2021). Declines in mental health during the pandemic exacerbated other negative outcomes. A late 2020 survey found that 15 percent of adults in the United States reported starting or increasing substance use as a way of dealing with the pandemic (Czeisler et al. 2021). In November 2021, the Centers for Disease Control and Prevention (CDC) estimated that there were 100,306 overdose deaths in the 12-month period ending in April 2021, up nearly 30 percent from the previous 12 months and the highest count on record (CDC 2021b). Domestic partner violence also increased globally by about a third in 2020 as compared with 2019 (Newman 2021). The pandemic also created difficulties in receiving medical care for other conditions. In the initial phases of the pandemic, 29 percent of adults reported forgoing medical care due to fears of catching COVID-19, while another 7 percent missed care due to COVID-related financial concerns (Anderson et al. 2021). This number was still about 10 percent in April 2021, with Black and Hispanic adults, those with low incomes, and people with chronic conditions the most likely to miss care (Gonzalez, Karpman, and Haley 2021). Another study finds that, among adults reporting that they missed or delayed health care due to the pandemic, one-third reported that doing so negatively affected their health, ability to work, or ability to perform other activities (Gonzalez et al. 2021). Declines were particularly acute in the use of mental health services, substance use treatment, primary care, childhood vaccinations, and dental visits (CMS 2021). Uses of many types of care had not fully rebounded as of mid-2021. Hospital admissions were still about 20 percent below the prepandemic trend in April, while health spending remained 7 percent below trend in June (Gallagher et al. 2021). Some of these changes may be due to longer-lasting responses to the pandemic by medical professionals. Two percent of physicians in one survey reported closing their practice due to COVID-19, while 32 percent cut back on staff (Physicians Foundation 2021). the labor market in the late 1960s, the average age of the labor force (and therefore the number of years of expected work experience) declined. This decline continued until the last of the baby boom generation entered the 132 | Chapter 4 labor force in the mid-1980s (Aaronson and Sullivan 2001), at which point the average age of the labor force began to increase, marking a positive effect on average human capital. As the baby boom generation retires, the U.S. labor force is losing a large population of highly experienced workers. Slowing growth in educational attainment and health improvements, combined with the retirement of baby boomers, results in slower overall growth in human capital per worker. These factors are reflected in economic forecasts of slower potential growth (see, e.g., Woodward 2013; Fernald 2016; and Fernald and Li 2019). There is scope for increasing human capital through targeted investments, and additional scope for increasing effective human capital through policies that help individuals deploy their human capital more efficiently. Such investments help bolster future economic growth. Investing in Education and Skill Development Long-term trends point to future cohorts having similar years of educational attainment but no more years of experience than current workers. This raises the question: how else can we develop more human capital during the time in life typically devoted to education? One promising strategy is working to close existing inequities between children in different circumstances—such as different racial or ethnic groups, urban and rural communities, and moreand less-advantaged economic backgrounds—through interventions starting with early childhood. Early Childhood Education and Care Although the terms used may differ based on the age of the children involved, all forms of care in early childhood present opportunities for important cognitive, social, and emotional development. Indeed, a National Academy of Sciences review notes that “virtually every aspect of early human development, from the brain’s evolving circuitry to the child’s capacity for empathy, is affected by the environments and experiences that are encountered in a cumulative fashion, beginning early in the prenatal period and extending throughout the early childhood years” (Shonkoff and Phillips 2000, 6). Both theoretical models and empirical evidence indicate that access to high-quality early childhood care and education improves human capital. Cunha and Heckman (2007) develop a model of human capital production in which early investments in human capital are complements to investments made later in life. In this model, early investments make later investments more productive; conversely, early investments only have limited productivity if not backed up by later investments. This theory, referred to as dynamic Investing in People: Education, Workforce Development, and Health | 133 complementarity, is an important basis for supporting investments in highquality early childhood care and education. Children from low-income families often begin kindergarten at an academic disadvantage. Though there are also disparities at entry by race and ethnicity, these differences are smaller than those by family income. Based on a nationally representative sample of children entering kindergarten in the fall of 2010, mathematics and reading skills for children from families in the bottom income quintile were, on average, more than 1 standard deviation below math and reading skills for children from families in the top income quintile;6 by the spring of fifth grade, these gaps were largely unchanged.7 These large differences in early skills are predictive of worse later outcomes in educational attainment and even arrest rates (Duncan and Magnuson 2011). As a result, expanding access to high-quality early care and education has long been viewed as having the potential to improve outcomes for children from low-income families. In the short run, many early childhood programs have been shown to increase student achievement, particularly for children from low-income families (Cascio 2015, forthcoming; Yoshikawa, Christina, and BrooksGunn 2016). These early test-score advantages often fade out in the medium term (e.g., see Puma et al. 2012; Durkin et al. 2022). In contrast, high-quality early childhood programs have a long track record of improving a broad array of longer-term outcomes ranging from educational attainment and earnings to criminal activity. For example, a study of the cohorts of children who benefited from heavily subsidized universal child care as a result of the Lanham Act during World War II finds that they were more likely to be employed, had higher earnings, and received less cash assistance during adulthood than the cohorts of children born just after those exposed to the Lanham Act funding (Herbst 2017). Similarly, another study finds that a large-scale expansion of subsidized childcare in Norway during the mid1970s had large positive effects on children’s educational attainment and labor force participation as adults and reduced their welfare dependency (Havnes and Mogstad 2011). Further, studies of Head Start, the program established as part of President Lyndon B. Johnson’s “War on Poverty” to boost services to low-income children and their families, find long-term benefits of these investments for several human capital and labor market outcomes (Ludwig and Miller 2007; Deming 2009; Bailey, Sun, and Timpe 2021). More recently, students who were randomly selected for preschool Researchers often measure differences in outcomes in standard deviation units in order to be comparable across different outcomes such as graduation rates and test scores. In a normal distribution, 68 percent of the observations are within 1 standard deviation of the mean, meaning that only 16 percent of all observations are more than 1 standard deviation below the mean. Thus, lowincome students scoring, on average, more than 1 standard deviation below high-income students is a large difference. 7 CEA calculations, based on de Brey et al. (2021, tables 220.40 and 220.41). 6 134 | Chapter 4 slots in Boston were more likely than students who were randomized out of preschool access to take the SAT, graduate from high school, and enroll in college (Gray-Lobe, Pathak, and Walters 2021). The fact that high-quality early education and care programs have long-term effects on outcomes such as high school graduation and college enrollment suggests that they can generate long-run improvements in children’s human capital. Building noncognitive skills (sometimes called soft or social skills) is particularly relevant because of their importance in the current labor market. In this computer age, the tasks that prove difficult to automate are those that rely on personal interactions (Autor 2015). Deming (2017) finds that, between 1980 and 2010, occupations requiring social skills grew by nearly 12 percentage points; wages also grew more rapidly for these types of jobs. This evidence reinforces the role early childhood education can play in increasing human capital and in providing the skills necessary for a modern economy. However, access to high-quality early care and education differs by family income and race or ethnicity. For example, Hispanic and American Indian / Alaska Native populations are more likely to live in neighborhoods without adequate childcare availability, as are families in the lowest-income neighborhoods (Malik et al. 2018). In Georgia, Bassok and Galdo (2016) found that state preschool classrooms in low-income and high-minority communities were rated significantly lower in quality, even though Georgia is considered a national leader in high-quality early education and care. Children from low-income families are also less likely to be enrolled in preschool. In 2019, 42 percent of three- and four-year-old children living in households below the poverty threshold were enrolled in preschool, compared with 54 percent of those living in households at or above 185 percent of the poverty threshold (de Brey et al. 2021, table 202.20). Thus, greater access to public preschool programs may help close gaps in kindergarten readiness between lower- and higher-income children. However, results vary between universal preschool programs, which serve all children, and ones that are means-tested, which serve only children from families with low enough incomes to qualify. Cascio (forthcoming) finds that state-funded universal preschool programs generate large test score gains, particularly for children from low-income families. Indeed, Cascio estimates a cost/benefit ratio of $3.52 for universal preschool programs. Universal preschool for all three- and four-year-old children, combined with investments in childcare provisions, could help ensure equal access to high quality early education and care for all children. Investing in People: Education, Workforce Development, and Health | 135 K-12 Education Despite a long-standing debate on the question of whether increased school spending improves student outcomes, modern quasi-experimental research on the topic suggests that increased school spending has a positive causal effect on students’ future education and labor market outcomes (Card and Payne 2002; Jackson, Johnson, and Persico 2016; Hyman 2017; Lafortune, Rothstein, and Schanzenbach 2018). However, as in early childhood education and care, access to highquality K-12 schools differs by family income and race or ethnicity. Rouse and Barrow (2006) and Barrow and Schanzenbach (2012) find that though some resource measures may be quite similar or even somewhat higher in districts with greater shares of disadvantaged children, children from more advantaged backgrounds arguably attend higher-quality public elementary and secondary schools. For example, students from families of low socioeconomic status are more likely to have teachers with less than three years of experience and to attend schools with inadequate facilities or temporary buildings. Similarly, high-poverty schools are more likely to employ teachers who do not have a certificate or major in the field they teach. Additionally, differences in academic achievement by race and ethnicity widen between kindergarten entry and fifth grade, suggesting that there may be systematic differences in elementary school quality by student race and ethnicity.8 As such, policy interventions aimed at improving school quality for children from disadvantaged families and communities of color are likely to be important for increasing human capital growth. Although there is little consensus about effective education policies, several themes have emerged from the literature beyond the basic finding that resources matter. Barrow and Rouse (2007) review evidence on several inputs in K-12 education, including class size, teacher quality, time in school, and technology. Several studies find that class size matters, particularly for students in the early grades (Angrist and Lavy 1999; Krueger 1999; Krueger and Whitmore 2001), though class size reduction is expensive and implementation at scale can be a challenge (Bohrnstedt and Stecher 2002). Not surprisingly, researchers also find strong evidence that teachers matter (Aaronson, Barrow, and Sander 2007; Rivkin, Hanushek, and Kain 2005; Chetty, Friedman, and Rockoff 2014), and many school reform efforts in the early 2010s included the adoption of teacher performance rating systems that combined measures of teachers’ effects on student achievement (value added) and classroom observation (National Council on Teacher Quality 2017). Researchers find that these types of reforms can improve average teacher quality by leading the lowest-performing teachers to exit teaching at higher rates (Sartain and Steinberg 2016; Dee, James, and Wycoff 2021). 8 CEA calculations, based on de Brey et al. (2021, tables 220.40 and 220.41). 136 | Chapter 4 There is also some evidence that teacher performance evaluation can lead to improvements in teacher practice (Taylor and Tyler 2012). Instructional time has also been shown to have a positive effect on student achievement, through evidence that a longer school year can improve student outcomes (Pischke 2007), as can longer school days (Figlio, Holden, and Ozek 2018; Atteberry, Bassok, and Wong 2019). The evidence on accountability policies and technology is somewhat more mixed. Though accountability policies have been shown to cause schools to change instructional practices in meaningful ways, leading to increased test score performance (Rouse et al. 2013), in other settings test score improvements have been shown to come from gaming the system rather than from generating improvements in educational practices that benefit all students (e.g., see Neal and Schanzenbach 2010; Booher-Jennings 2005; and Hout and Elliott 2011). Finally, research on the use of technology in the classroom continues to find mixed results (Bulman and Fairlie 2016), even though the potential for computer-aided instruction to allow for more self-paced learning remains promising (Barrow, Markman, and Rouse 2009). The COVID-19 pandemic has disrupted instruction at all levels of education, with potentially serious consequences for students. For more discussion of this issue, see box 4-2. Postsecondary Human Capital Development The development of universal and compulsory primary and secondary education in the United States during the 20th century meant that, by 2019, more than 90 percent of adults age 25 years and above had completed at least high school (de Brey et al. 2021, table 104.10). After high school, Americans take many paths to further develop their human capital. Some enter the labor force directly and develop their skills through on-the-job training and experience. Others pursue apprenticeship opportunities, military service, or gap-year programs. The majority (66 percent in 2019), however, pursue further academic or vocational training—including certificate programs—at a college or university (de Brey et al. 2021, table 302.20). And over a lifetime, many workers find the need or desire to go back to school or enter a workforce training program to further their career or switch tracks entirely. Access to postsecondary education has expanded over time, such that two out of three recent high school graduates enrolled in a two-year or four-year college in 2019, up from one out of two in 1965 (de Brey et al. 2021, table 302.10). Community colleges, also known as two-year public colleges, are open enrollment and tend to cost less than programs at public and private nonprofit four-year colleges and private for-profit institutions. They also offer flexibility that allows working adults to attend college. As a result, community colleges enroll nearly one in three first-time degree- or Investing in People: Education, Workforce Development, and Health | 137 Box 4-2. COVID and Education The COVID-19 pandemic disrupted all levels of formal education in the United States, and exacerbated existing disparities in educational opportunities and outcomes (U.S. Department of Education 2021a). By the end of March 2020, leaders of 48 States, 4 territories, and the District of Columbia ordered or recommended building closures in K-12 schools for the remainder of the academic year, affecting over 50 million public school students (Decker et al. 2020). Many school districts, students, and families were not prepared for online learning, particularly in rural communities (Hampton et al. 2020), low-income communities, and communities of color. In 2019, over 10 percent of school-age children in families in the bottom income quartile did not have Internet access at home, and an additional 14 percent only had access through a smartphone (Irwin 2021). Before the pandemic, only 75 percent of Black and Hispanic children lived in a house with a computer, compared with 91 and 96 percent of white and Asian children, respectively (KewalRamani et al. 2018). Online school exacerbated barriers to good educational opportunities and outcomes, especially for children who lived in a home without a computer. The challenges of switching to remote education thwarted many students’ involvement in education. School districts saw attendance decline, and educators expressed concerns about adequate student engagement (Carminucci, Rickles, and Garet 2021; Chambers, Scala, and English 2020). The result has been higher rates of chronic absenteeism (Dorn et al. 2021), which has been shown to have negative effects on the absent students’ grades, graduation rates, and college success (Allensworth and Evans 2016). COVID-19 changed education in a way that also likely affected children’s development of noncognitive/social and emotional skills. Students could not interact with their classmates and teachers in the same way and, in many cases, they were cut off from services they accessed at school, such as physical and mental health services, or the support of a social worker. Further, many extracurricular activities were canceled or moved online, limiting social interactions with peers. By limiting these activities, COVID-19 may have disrupted development of students’ social and emotional skills, which are associated with future academic achievement (Blake et al. 2014). School districts, which were largely unprepared for the transition to remote learning, were forced to make changes that will likely have an impact on human capital accumulation. Though some teachers, schools, and districts were ultimately able to effectively transition to remote learning, others were unable to develop a plan to deliver classroom work in a form that would be most effective for students, particularly in the short run. Before the pandemic (2011–12), only about one-third of 138 | Chapter 4 teachers reported having training in the use of computers for instruction (Garcia and Weiss 2020). A nationally representative survey of school districts found that in the spring of 2020, 85 percent of school districts expected students to spend less than four hours daily on instructional activities during the pandemic (Rickles et al. 2020). The prepandemic daily instructional time average was five hours (U.S. Department of Education 2021a). These changes to formal K-12 school have resulted in academic learning loss. One study finds that by the end of the 2020–21 school year, students were, on average, five months behind in math and four months behind in reading (Dorn et al. 2021); another study estimates that by the fall of 2021, students were scoring below expected performance levels based on historical trends by 9 to 11 percentile points in math and 3 to 7 percentile points in reading (Lewis et al. 2021). In both studies, estimated learning losses were larger for historically marginalized students and those enrolled in high-poverty schools. The COVID-19 pandemic has also affected higher education. Fall enrollment in postsecondary institutions peaked in 2010 and had been declining at an average annual rate of 0.8 percent, primarily driven by declines in enrollment at sub-baccalaureate institutions and all levels of for-profit institutions (CEA calculations, based on de Brey et al. 2021, table 303.25). However, enrollment fell more precipitously with the pandemic, particularly at the sub-baccalaureate level. According to National Student Clearinghouse (2021) data, there was an approximately 8 percent drop in undergraduate enrollment between the fall of 2019 and the fall of 2021 (roughly 4 percent each year), with community colleges losing 14.8 percent of students over the two years. However, enrollments in graduate and professional certificate and degree programs rose, suggesting that, though fewer students were taking initial steps in higher education, more degree holders were returning for additional credentials. Many students enrolled in higher education during the pandemic have seen disruptions to the mode of instruction, which may affect their learning and ultimately their completion. Prepandemic research found that taking a course online instead of in person reduced student success in the course and mitigated progress in college (Bettinger et al. 2017). Research conducted at the U.S. Military Academy during the pandemic also found that students randomly assigned to online instruction performed worse than those randomly assigned to in-person instruction covering the same material (Kofoed et al. 2021). Adverse effects of online instruction were largest for students who were academically at risk. Learning loss associated with the pandemic is likely to lower the educational attainment of the future workforce by reducing the share of college-educated adults (Blagg 2021; Fuchs-Schündeln et al. 2020). Using estimates of the decline in the educational attainment of the Investing in People: Education, Workforce Development, and Health | 139 future workforce from Fuchs-Schündeln and others (2020), Fernald, Li, and Ochse (2021) estimate that the pandemic learning disruption will decrease average yearly output over the next 70 years by 0.23 percentage point, peaking at a gap of half a percentage point (just below $150 billion, inflation-adjusted) from 2045 to 2050. Similar estimates at the microeconomic level translate learning losses into lifetime earnings losses. Goldhaber, Kane, and McEachin (2021) use the decline in math achievement found by Lewis et al. (2021) to estimate that these losses, if permanent, equal $43,800 in lifetime earnings for each student, or over $2 trillion across the 50 million public school students currently enrolled in grades K to 12. In order to support educational equity and address these losses, the American Rescue Plan Act of 2021 included $122 billion in the Elementary and Secondary School Emergency Relief Fund to help schools safely reopen and address the academic, social, emotional, and mental health needs of their students (White House 2021a). The act further required States and districts to spend a combined minimum of 24 percent of the total funds on evidence-based practices to address lost instructional time and the coronavirus’s impact on underserved students. The funding has been used for such activities as implementing summer learning and enrichment programs and hiring nurses and counselors (U.S. Department of Education 2021b). certificate-seeking students.9 Importantly, research shows that community colleges increase the earnings of their students (Kane and Rouse 1995; Marcotte 2010; Jepsen, Troske, and Coomes 2014; Bahr et al. 2015; Minaya and Scott-Clayton 2022). However, college enrollment rates differ by family income and by race and ethnicity (as noted in the introduction). For example, in 2016, 83 percent of high school graduates from families in the top income quintile immediately enrolled in college after high school graduation, compared with only 67 percent of high school graduates from families in the bottom income quintile (Snyder, de Brey, and Dillow 2017, table 302.30). These differential enrollment rates suggest that some students may face more barriers than others in making the transition to college. Students who decide to continue their education at a college or university must first navigate complex application, enrollment, and financial aid processes. These hurdles can deter students from continuing to develop their skills through formal education, and those from more advantaged families are likely to have access to better information about how to enroll in higher education than students from less advantaged families. For example, 9 CEA calculations, based on two-year public institutions, given by de Brey et al. 2019, table 305.10. 140 | Chapter 4 students whose parents attended college are well situated to receive firsthand advice on navigating the college enrollment process and information on what to expect as a college student. In addition, many students and their families have struggled to complete the Free Application for Federal Student Aid (FAFSA), a financial aid application necessary to access Federal postsecondary student aid, including Pell Grants and Direct Loans (Bettinger et al. 2012). Unclear and/or incorrect expectations about the cost of attending selective four-year institutions may dissuade low-income students from applying and attending schools where they would qualify for aid (Hoxby and Turner 2015; Dynarski et al. 2018). The FAFSA Simplification Act of 2021 aims to make applying for aid easier and the award amount more transparent and predictable for students (Congressional Research Service 2022b). These changes, combined with more readily available information on the net price a student faces (as opposed to the overall “sticker price”), can help reduce barriers in the transition to college. Free community college is another proposal aimed at increasing access to postsecondary education. Although some of the increased enrollment may come from students who would have otherwise enrolled in a four-year college or a private two-year junior college, there is strong evidence that making community college tuition free may also increase enrollment among individuals who otherwise would not have enrolled at all (Carruthers and Fox 2016; Mountjoy 2019; Nguyen 2020). Despite the fact that community college tuition is effectively free for many low-income students due to the availability of Federal Pell Grants and other State and local grant aid (Ma and Pender 2021), a recent study in Michigan finds that students are particularly responsive to a clear, upfront offer of free tuition (Dynarski et al. 2018). In this study, low-income, high-achieving students were randomly selected to receive a promise of free tuition and fees if they applied and were admitted to the University of Michigan in Ann Arbor.10 Notably, the intervention did not change the probable costs for the students but rather guaranteed grant aid for which the students were likely already eligible.11 The likelihood of applying to the university more than doubled, and the researchers find that students in the treatment group were 4 percentage points more likely than the control group to attend any postsecondary institution. However, many students who do enroll in college still fail to complete any degree or certificate program (Chen et al. 2019), and completion rates at two-year public colleges are particularly low. Five years after enrolling, only 39 percent of first-time college students who started at a public two-year Randomization was at the school level, and parents and principals were also notified. The offer was unconditional, e.g., students were not required to fill out the FAFSA form, and the offer was guaranteed for four years. This was prominently stated in the mailing, but students were also encouraged to fill out the FAFSA because they would likely qualify for even more aid. 10 11 Investing in People: Education, Workforce Development, and Health | 141 Figure 4-6. Degree or Certificate Completion Rates among Students Who First Enroll at a Public, Two-Year Institution Percent 45 Women Hispanic or Latino White 40 Asian Men 35 Black or African American 30 25 20 15 10 5 0 . By gender By race or ethnicity Source: U.S. Department of Education (2019). institution in 2011–12, with the expectation of completing a four-year bachelor’s degree, had received any degree or certificate, compared with 68 percent of students who started that year at a public four-year institution.12 Further, as shown in figure 4-6, these completion rates differ by sex and race or ethnicity, ranging from 34 percent for men to 42 percent for women and 27 percent for Black or African American students to 41 percent for Asian students. Investments aimed at encouraging higher education institutions—and community colleges in particular—to adopt evidence-based strategies for improving student completion are important for increasing human capital accumulation, particularly for students from backgrounds historically marginalized in higher education. These supports include wraparound services, ranging from childcare and mental health services to faculty mentoring. Community college students often live complicated lives, which may be one reason why completion rates are relatively low. Research on initiatives such as the Accelerated Study in Associate Programs has shown that enhanced student services combined with additional financial supports can double graduation rates (Scrivener et al. 2015). Workforce development programs help create opportunities for displaced workers, new entrants, and current workers seeking higher-paying and more fulfilling work. Having workers with the right skills can raise labor productivity, which in turn increases economic growth. As Holzer (2021, 4) notes, “Workforce development policies, programs, and practices are critical to any effort to improve economic productivity, income mobility, and equity This computation was by PowerStats, from the National Center for Educational Statistics, using data from the U.S. Department of Education (2019). 12 142 | Chapter 4 among American workers.” Such programs can be important alternatives for those not pursuing more formal education after high school. For example, registered apprenticeship programs—including many that are cooperatively run by employers and labor organizations—offer opportunities for individuals to earn industry-recognized credentials through a combination of on-the-job paid training and classroom-based instruction. These programs have been shown to be effective at increasing workers’ earning potential. A study of apprenticeships in 10 States finds that individuals who completed their training earned an average of $240,037 more over their lifetime than nonparticipants.13 Further, the study’s conservative estimate of the net social benefits is $49,000 over the course of the apprentice’s career (Reed et al. 2012). That said, apprenticeships remain relatively rare. In a 2016 survey of adults focusing on participation in “work experience” programs—internships, externships, co-ops, practicums, and apprenticeships—a little over 20 percent reported having completed any type of work experience program, and only 3 percent reported having ever completed an apprenticeship program.14 Even among apprenticeships, which many think of as being noncollege-track work experiences, participation was highest among those with a bachelor’s degree or higher (5.4 percent) and was lowest among those with no postsecondary enrollment (1.0 percent). Other sector-focused training programs aimed at preparing disadvantaged workers for employment in high-demand occupations have also been shown to be successful. Examples of promising sector-focused training programs include the Wisconsin Regional Training Partnership, an association of unions and employers in Milwaukee concentrating on two- to eight-week training programs in construction, manufacturing, and health care (Maguire et al. 2010); Year Up, a year-long training program for young adults (18–24) focusing on employment in information technology and business and financial services; Project Quest, a one- to three-year program serving early- to mid-career adults (largely Hispanic women) targeting jobs in the health care sector; as well as programs evaluated under MDRC’s WorkAdvance program, which targeted employment in information technology, health care, manufacturing, and transportation (Katz et al. 2020). Katz and others (2020) review these and other programs and investigate the mechanisms whereby programs affect participant outcomes. Their findings indicate that sectoral training programs increase earnings by getting participants into higher-wage jobs and higher-earning occupations, rather than simply increasing employment. They also find that programs that produce the largest and most persistent earnings gains offer a combination of upfront screening of participants This finding controls for demographic differences at the time of enrollment. This computation was by PowerStats, from the National Center for Educational Statistics, using data from the U.S. Department of Education (2016). 13 14 Investing in People: Education, Workforce Development, and Health | 143 on basic skills and motivation, wraparound support services for participants, and strong connections to employers.15 Investing in Health A multitude of studies link early-life conditions to human capital accumulation, though many lack definitive explanations of the mechanisms driving these links (Almond and Currie 2011). As Mushkin (1962) highlights, health and education are interrelated in many ways. She notes that formal education is impossible if a child is unable to attend school and learn due to poor health. Lengthening life expectancy by improving health increases the return to education. The relationship between health and human capital development through school attendance is well documented. One early and important study finds that the eradication of hookworm in the southern U.S. States in the early 20th century increased school attendance, enrollment, and literacy. These changes resulted in higher income about 30 years later (Bleakley 2007). Investments in lead abatement have similar potential today. Other studies link poor childhood health and malnutrition to lower levels of educational attainment (Alderman, Hoddinott, and Kinsey 2006; Case, Fertig, and Paxson 2005; Haas, Glymour, and Berkman 2011). (For discussion of some of the recent Federal infrastructure investments with the potential to improve human capital, see box 4-3.) Even if children are able to attend school, physical and mental health problems can hinder educational progress. For example, children in the United States and Canada with symptoms of Attention Deficit Hyperactivity Disorder (ADHD)—the most-common chronic neurodevelopmental disorder in young children—performed less well than their siblings without ADHD symptoms on such school-related outcomes as test scores and grade promotion (Currie and Stabile 2006), suggesting that children with ADHD symptoms may accumulate less human capital. A relationship can also be drawn between health and the development of cognitive and noncognitive skills beyond the classroom. One recent study finds that childhood illnesses lead to poor financial management later in life (Luik 2016). Other studies show similar findings, noting that low income—and the poor early childhood health that comes along with it—is associated with lower socioemotional skills in later childhood (Fletcher and Wolfe 2016). That poor formation of noncognitive skills is associated with Minimum skill requirements applied to all participants before random assignment for treatment. As noted by Katz et al. (2020), whether these programs can provide a successful career pathway for individuals who do not meet the minimum skill requirements—high school diploma or General Educational Development certificate and testing at the 6th- to 10th-grade level in math and reading—remains an open question. 15 144 | Chapter 4 Box 4-3. Federal Investments in Lead Abatement and Rural Broadband Recent Federal legislation, including the Bipartisan Infrastructure Law (BIL), provides funding for lead abatement and rural broadband development, both of which would be expected to have positive effects on human capital development. In particular, the BIL invests $55 billion in clean drinking water (White House 2021b); this increases the size of the Clean Water State Revolving Fund (CWSRF) and the Drinking Water State Revolving Fund (DWSRF) to nearly six times their previous appropriation levels, with $15 billion for lead service line replacement as well as a combined $5 billion to address emerging contaminants (Congressional Research Service 2022a). Reducing lead exposure through abatement methods is one of the key provisions of the BIL (White House 2021b). The Centers for Disease Control and Prevention recognizes that there is no safe blood lead level in children and that lead service pipes can be one source of lead in a child’s environment (CDC n.d.). Situations like the Flint, Michigan, water crisis are a potent example of the nearly 10 million households that lack safe drinking water. Lead abatement also has important equity implications as Black children are at greater risk for elevated blood lead levels than white or Hispanic children, even after controlling for risk factors such as living in pre-1950s housing (Yeter, Banks, and Aschner 2020). Lower blood lead levels are associated with improved health, educational, and economic outcomes. Prenatal lead exposure has been linked to reduced gestational age, lower birth weight, and potential fetal loss (Schwartz 1992a; National Research Council 1993), and childhood exposure has been shown to increase adolescent impulsivity, anxiety, depression, and body mass index (Winter and Sampson 2017). Educationally, lower average blood lead level reduces the probability of suspension and detention among boys (Aizer and Currie 2019) and increases test scores (Aizer et al. 2018). Despite the potentially long-lasting nature of elevated blood lead levels, lead abatement interventions have shown promise in reversing many of the negative consequences of early childhood exposure, demonstrating the potential benefits of the BIL lead abatement funding even for somewhat older children (Billings and Schnepel 2017). In addition, since the beginning of the COVID-19 pandemic, investments investments in rural broadband connection have been included in the Consolidated Appropriations Act (CAA), the American Rescue Plan (ARP), and the BIL. While the BIL funds $65 billion in broadband investments for all States (White House 2021b), the other bills include programs aimed at digital equity. The Emergency Broadband Benefit ($3.2 billion, CAA), the Emergency Connectivity Fund ($7.2 billion, ARP), and the Capital Projects Fund ($10 billion, ARP) all provide exclusive funding for expanding and discounting broadband to address Investing in People: Education, Workforce Development, and Health | 145 education and health gaps. The ARP includes an additional nine provisions amounting to $388.1 billion in flexible funding that could apply to rural broadband, as well (Tomer and George 2021). The investments in broadband help address digital equity and geographic disparities in Internet access. According to FCC estimates, about $80 billion in investments are necessary for ubiquitous broadband access (FCC 2017). Given that population density is a major determinant of both service provision and lower prices (Ribiero Pereira 2016), these investments will likely be heavily concentrated in rural areas. The economic benefits of broadband access are well documented. A 10-percentage-point increase in broadband penetration has been found to increase per capita economic growth by 0.9–1.5 percentage points (Czernich et al. 2011). Counties gaining broadband access in the early 2000s were found to have an increase in employment rates by 1.8 percentage points (Atasoy 2013). The benefits of broadband access likely expand to health and education benefits as well. A prepandemic survey of community-based health centers found that, among those not using telehealth, those located in rural areas were much more likely to report broadband as a barrier to adoption (Lin et al. 2018). And survey data show that students in rural school districts with high-speed Internet at home had higher grades and standardized test scores than their peers without access (Hampton et al. 2020). The investments in broadband in rural communities will help spur economic growth and help provide more equitable services to those previously left behind by the digital divide. lower probabilities of employment in adulthood suggests the connection with human capital accumulation (Carneiro, Crawford, and Goodman 2007). Interactions between health, life expectancy, and decision-making also affect human capital development. As shown in figure 4-7, life expectancy varies dramatically across geographic areas. In 2010–15, life expectancy at birth for a person born in Mississippi was 74.9 years (and was less than 70 in some areas), while those born in Hawaii could expect to live for 82 years (Tejada-Vera et al. 2020). Similarly, life expectancy at birth in 2018 across the United States was 81.8 years for Hispanic people and only 74.7 for non-Hispanic Black people (Arias and Xu 2020). And the difference in life expectancy between the richest and poorest 1 percent of individuals was 14.6 years (Chetty et al. 2016). Some of these variations are driven by differences in infant mortality rates. As shown in figure 4-8, the infant mortality rate for non-Hispanic Black babies is more than double the rates for Hispanic, white, and Asian babies (Ely and Driscoll 2021). Reducing these 146 | Chapter 4 Figure 4-7. Life Expectancy at Birth for U.S. Counties, 2010–19 Sources: Centers for Disease Control and Prevention, National Center for Health Statistics; CEA calculations. geographic, racial, and socioeconomic differences could improve average life expectancy without requiring scientific or medical advances. Focusing on policies that improve health care access and equity could be one path toward improving human capital development. Becker (2007) notes that if an individual expects to live for fewer years, the return on investment in healthy decisions, such as exercising or avoiding addiction, is lower. In other words, he argues that it may not simply be that nonsmokers and people who exercise and eat well are healthier, but rather that the causality runs in the opposite direction. Namely, good health causes people to choose healthier habits. Expansion of public health insurance coverage could boost the development of human capital. Studies of health insurance coverage during childhood have found many positive benefits, including improvements in school performance. For example, one study finds that eligibility for Medicaid reduced the probability of children being below grade for age (Qureshi and Gangopadhyaya 2021). These early human capital effects can be longlasting; children with more years of Medicaid eligibility during childhood had higher college enrollment, delayed fertility, increased wages, and lower mortality as adults (Brown, Kowalski, and Lurie 2019). Policies focusing on maternal health by expanding coverage for pregnancy and postpartum care could also lessen inequalities in human capital development. Expansions in postpartum Medicaid coverage under the Investing in People: Education, Workforce Development, and Health | 147 Figure 4-8. Infant Mortality Rates by Race or Ethnicity, 2018 Rate per 1,000 live births 12 Black 10 Native Hawaiian or other Pacific Islander American Indian or Alaska Native 8 6 Hispanic White Asian 4 2 0 Source: Centers for Disease Control and Prevention. Affordable Care Act increased outpatient visits for mothers, likely improving health outcomes (Gordon et al. 2020). Adequate prenatal care can also create better health habits for mothers, with one study finding that firsttrimester prenatal care led to decreases in parental smoking and increases in well-child visits after birth (Reichman et al. 2010). Deploying Human Capital Deploying human capital effectively—putting a worker’s skills to more productive use—is an important component of economic output. Although the deployment of human capital is often straightforward, the real world can present workers with obstacles to making the most of the skills they have rigorously developed. Health problems of their own—or those of a child, parent, or other loved one—can prevent an individual from putting their human capital to work. Sometimes entire groups of workers are legally prevented from working or have their options significantly limited. Other times, as shown in chapter 5, illegal discrimination in the labor market can keep affected workers from realizing their full potential, as can anticompetitive practices that limit workers’ ability to change jobs. These obstacles create a smaller, less equitable economy and a less prosperous country. Health Better health allows people to deploy their existing human capital more effectively. In his canonical 1972 paper, Michael Grossman (1972) creates a model of “good health” that parallels other models of human capital. He assumes that such inputs as diet, exercise, and health care spending produce health stock, which provides a person with a time allocation of “healthy 148 | Chapter 4 * $!$ (+'*4( )/Ҋ +*+0' /$*)- /$* 4 " ѶршупѶршхпѶ Figure 4-9. Percent of U.S.-Born People Employed in the United States, by Age ршчпѶспппѶ ) спрш Percent 100% 90 80% 80 60% 40% 70 20% 60 0% 50 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 Age 40 30 Share employed, 1940 Share employed, 1960 Share employed, 1980 Share employed, 2000 Share employed, 2019 Share employed, 1940 10 Share employed, 1960 Share employed, 1980 Share employed, 2000 0 Share employed, 2019 20 18 22 26 30 34 38 42 46 50 54 58 62 66 70 74 78 Age (years) 1940 1960 1980 2000 2019 Sources: CEA calculations from the Decennial Census; Bureau of Labor Statistics, American Community Survey; Statista data. Note: This figure shows U.S.-born people employed in the United States as a percentage of all people born in the United States, by age. The denominator for the percentage in this figure includes people who have died or have emigrated from the United States, along with those living in the country. To estimate the total number of births for each age and year, we multiplied an estimate of the birth rate by population estimates for that year, interpolating where necessary. days.” People with more healthy days can readily take part in labor and leisure activities; however, those who are sick are more limited. Grossman’s model implicitly underlies research showing that health is crucial in the deployment of human capital. People in better health are more likely to enter and stay in the labor force. Krueger (2017) finds that 40 percent of men not in the labor force report that pain prevented them from doing jobs for which they were otherwise qualified. Further, adults with a serious mental illness are twice as likely to be out of the labor force as adults with no mental illness and are also less likely to be employed full time (Luciano and Meara 2014). Even for those who do enter the labor force, those with poorer health are often prevented from working a full number of days and hours. Multiple studies show that missed workdays due to mental and physical health problems, which result in significant payroll losses, are top causes of work absenteeism each year (Dewa et al. 2004; Luciano and Meara 2014; Currie 2008). Finally, being in good health allows workers to work more intensely on days when they are present, allowing them to more fully expend their human capital (Goldin 2016). Good health also facilitates deployment of human capital through longer life expectancy; those who live longer and are in better health can work for more years. One analysis shows that “working life expectancies” Investing in People: Education, Workforce Development, and Health | 149 have grown as healthy life expectancies and life expectancies have increased across Europe (Loichinger and Weber 2016). To illustrate how this has played out over time in the United States, figure 4-9 shows the likelihood that a U.S.-born person is alive and working, by age, from 1940 to 2019. At every adult age below 60, this likelihood has increased substantially since 1940. Gains in this age range were especially large between 1940 and 1980. Family Support Policies Family responsibilities can sometimes pose an obstacle to human capital deployment—a reality made all too clear during the COVID-19 pandemic. Short-term family priorities such as caring for a child or elderly relative may conflict with longer-term priorities, like maintaining a job or career that is necessary to support the family. Without external supports, people may be forced to make decisions that result in underutilization of their human capital. Evidence from settings ranging from recessions and mass layoffs (e.g., Jacobson, LaLonde, and Sullivan 1993; Sullivan and Von Wachter 2009; Oreopoulos, Wachter, and Heisz 2012; Yagan 2019; Stuart 2022; Rinz, forthcoming) to the birth of a child (Bertrand, Goldin, and Katz 2010; Angelov, Johansson, and Lindahl 2016; Goldin and Mitchell 2017; Kleven et al. 2019) to routine job separations (Fallick et al. 2021) indicates that spending time out of work can have persistent adverse effects on earnings and, more broadly, on accumulated human capital. Providing financial support to keep people connected to their jobs while they address family-related needs can facilitate their return to work. Studies of State programs that provide paid family and medical leave suggest that access to leave can increase mothers’ longer-term labor supply after the birth of a child (Baum and Ruhm 2016; Byker 2016; Jones and Wilcher 2020; Saad-Lessler 2020). Leave can also increase the likelihood that a mother returns to her prior employer after having a child (Baum and Ruhm 2016), which can be particularly beneficial when her job made good use of her human capital. Evidence suggests that paid leave may also produce labor supply benefits when used for other purposes, such as caring for a spouse with a work-limiting disability or chronic health condition (Anand, Dague, and Wagner 2021). Though evidence to date finds limited use of paid leave among fathers in U.S. programs (Baum and Ruhm 2016) and little role for paid paternity leave in mitigating gender earnings gaps that tend to emerge after the birth or adoption of a child (Andresen and Nix 2019), available research also suggests that these earnings gaps are driven largely by gender norms and preferences about the allocation of care responsibilities rather than biology (Andresen and Nix, forthcoming; Kleven, Landais, and Sogaard 2019). See chapter 5 for further discussion of paid leave and gender norms. 150 | Chapter 4 Paid leave helps with situations where family members want to take care of a new child or ill family member and also retain their job. In other cases, the family member may want to go to work but needs care for their child or disabled or infirm family member. Both childcare and care for the elderly and disabled can be prohibitively expensive. But research indicates that public childcare and preschool programs can help parents of young children, particularly mothers, remain engaged in the workforce. This evidence is based on State programs (Cascio and Schanzenbach 2013), Head Start (Wikle and Wilson 2021), the expansion of kindergarten access to slightly older children (Gelbach 2002; Cascio 2009), and historical experience with childcare provided in the United States from 1943 to 1946 under the Lanham Act (Herbst 2017), as well as various programs in other countries (Bauernschuster and Schlotter 2015; Finseraas, Hardoy, and Schøne 2017). Likewise, programs that provide care for elderly or disabled people can also increase their relatives’ ability to work. One recent study finds that for every 2.4 to 3 women whose parents gained access to formal home care as the result of Medicaid covering that service in some States, one additional daughter worked full time (Shen 2021). Employment Practices Working conditions can also influence how effectively human capital is deployed. Certain employment practices, sometimes called “high road” practices, for which labor unions have long been important advocates, support employees’ success on the job by meeting the needs they have in life. They can also increase workers’ productivity and reduce turnover, benefiting both workers and businesses. Higher compensation is an important element of these practices. One recent study based on general compensation policies at a large online retail company indicates that higher wages for warehouse and call center workers increased productivity more than dollar for dollar (Emanuel and Harrington 2020). Another study finds that minimum wage increases led to increased productivity and reduced termination rates among department store sales workers (Coviello, Deserranno, and Persico 2021). Other studies find that increases in compensation driven by changes in the minimum wage reduce separations more generally (Reich, Hall, and Jacobs 2004; Dube, Lester, and Reich 2016; Bassier, Dube, and Naidu, forthcoming). Compensation in the form of benefits like paid sick leave or the ability to work remotely can improve employee health and reduce workplace infection (DeRigne, Stoddard-Dare, and Quinn 2016; Pichler and Ziebarth 2017; Stearns and White 2018; Zhai et al. 2018) or allow them to work under conditions they find most conducive to success (Bloom et al. 2015; Choudhury, Foroughi, and Larson 2021). Investing in People: Education, Workforce Development, and Health | 151 Maintaining a safe and respectful workplace also allows workers to get the most from their human capital. Workplace injuries and illnesses reduce productivity by decreasing the quantity and/or effectiveness of time spent at work. A study of randomized inspections by California’s Division of Occupational Safety and Health suggests that attention to safety in high-injury industries can reduce injury rates and associated costs without reducing employment, sales, or business survival rates (Levine, Toffel, and Johnson 2012). Treating workers fairly and respectfully can also contribute to higher productivity. For example, one study indicates that the average worker would be willing to give up a substantial share of their wages to avoid having their employer set their schedule on short notice (Mas and Pallais 2017). Avoiding this practice can both improve workers’ well-being (Harknett, Schnieder, and Irwin 2021) and increase their productivity. For example, when Gap, Inc., experimentally implemented consistent, predictable scheduling practices at its stores in San Francisco and Chicago, productivity increased by about 5 percent (Kesavan et al. 2021). Skilled and experienced workers can be tapped to help businesses respond to changing economic conditions in ways that promote resilience and growth. When workers are invested in their jobs and unlikely to leave, managers can reorient business processes and adapt job content to get more from their employees. A wide variety of jobs could incorporate more satisfying tasks if, for example, workers were cross-trained in different types of work or allowed to make certain types of decisions. Setting up processes to reduce errors and eliminate waste can also ensure that employees are as productive as possible. Case studies indicate that, when implemented thoughtfully, these high-road approaches can succeed in sectors ranging from manufacturing (Helper 2009) to retail (Ton 2012). Because the adjustments are broad and largely depend on generating the desired response from workers to be worthwhile, a comprehensive implementation of high-road employment and managerial practices may be more effective than trying to change particular practices on a one-off basis. Occupational Licensing Occupational licensing policies are often introduced to ensure safe, highquality services from professionals, like dentists and electricians, whose safety and quality are difficult for consumers to ascertain themselves. These policies frequently establish minimum standards for workers’ human capital investments—such as by mandates to acquire specific credentials or to pursue continuing education. Kleiner and Soltas (2019) show that these standards induce workers who enter these occupations to invest more than they otherwise would, especially in occupation-specific forms of human capital such as vocational associate degrees and master’s degrees. 152 | Chapter 4 However, occupational licensing can make it more difficult for workers to enter fields or move to places where their human capital would be more productive by increasing the cost of mobility in terms of fees for obtaining a license or time to complete required training or other licensing requirements. Research finds that licensing requirements decrease employment and churn within an occupation (Blair and Chung 2019; Kleiner and Soltas 2019; Kleiner and Xu 2020). On the positive side, licensing increases wages and wage growth within licensed occupations (Kleiner and Krueger 2010, 2013; Gittleman, Klee, and Kleiner 2017; Kleiner and Soltas 2019; Kleiner and Xu 2020). One analysis suggests that the magnitude of the licensing wage premium is comparable to the premium associated with union membership (Kleiner and Krueger 2010). Though licensed workers may benefit from higher wages, other similarly skilled workers who lack the resources to acquire a license may be prevented from moving into jobs where they would be more productive and better paid. There is also evidence that occupational licensing reduces interstate migration (Johnson and Kleiner 2020), making it more difficult for workers to relocate and deploy their human capital where it would be most beneficial for them. This especially affects mobile populations such as military spouses, who are 10 times more likely to have moved across State lines in the last year than their civilian counterparts and experience persistently high unemployment due to relocations (U.S. Department of the Treasury and U.S. Department of Defense 2012). Although many occupations require licenses in some jurisdictions, relatively few require licenses in all jurisdictions (Council of Economic Advisers et al. 2015), suggesting that there is substantial scope to tailor occupational licensing to balance interests in quality, safety, and effective human capital deployment. In 2019, Current Population Survey data show that just under 20 percent of California’s labor force held a professional certification or State or industry license, the lowest share for any State; at the other extreme, in Wyoming, that share was just over 30 percent. In the average State that year, about 84 percent of workers with licenses needed them to do their jobs.16 In some cases, States have taken steps to reduce barriers associated with occupational licensing, such as creating reciprocity arrangements or interstate compacts related to licensing in certain occupations (National Conference of State Legislatures 2020). For example, during the COVID-19 pandemic, some States waived or modified requirements associated with telehealth to allow providers licensed in other States to serve their residents (Federation of State Medical Boards 2022). As more licensed occupations are deemed well-suited for remote work, further adoption of additional allowances will help reduce barriers for workers to deploy their human capital effectively. 16 CEA calculations, based on Current Population Survey data. Investing in People: Education, Workforce Development, and Health | 153 Immigration There are about 11 million undocumented immigrant residents of the United States, a group of people who are not able to fully deploy their human capital because they lack legal authorization to work or are authorized to work only temporarily. Research suggests that granting these immigrants permanent legal status would increase the productivity of their human capital. Unauthorized immigrants in the workforce experience a wage penalty relative to what native-born and authorized immigrant workers earn, even after controlling for educational attainment. The gap in wages can largely be explained by differences in the industrial and occupational composition of employment between unauthorized immigrants and other workers. This suggests that allowing these workers to move to different jobs that better utilize their skills could increase their productivity and wages (Rouse et al. 2021). Legal status would enable greater job mobility, a key channel through which workers find more productive job matches during their careers (Engbom 2022). Research also suggests that access to permanent legal status for undocumented immigrants could facilitate the development of additional human capital, because studies have found that legal status leads to increases in labor force attachment, education attainment, and other types of skill development (Gathmann and Keller 2018; Liscow and Woolston 2017; Cortes 2013). Increasing authorized immigration can also lead to more human capital being deployed in the United States, boosting growth without waiting for a new generation of workers to complete the entirety of their education. Immigrants supply labor to produce a wide variety of goods and services, from agricultural products to medical services. Immigrants also consume a wide variety of goods and services, and this demand creates opportunities for other workers to deploy their human capital productively. On top of this, research identifies innovation and entrepreneurship benefits associated with immigration, which make use of the human capital of both the innovator/ entrepreneur immigrants and the U.S. workers employed by their ventures (Hunt and Gauthier-Loiselle 2010; Fairlie and Lofstrom 2015). Incarceration A highly carceral criminal justice system as we have in the United States incapacitates a substantial amount of human capital; people cannot work to their full capacity while they are imprisoned. Even after they have served their time, the formerly incarcerated face barriers to being hired in jobs for which they may be fully qualified. About 1.4 million people were incarcerated in Federal or State prisons at the end of 2019, a population that is disproportionately male and nonwhite. About one-third were Black, and nearly another quarter were Hispanic (Carson 2020). Including people 154 | Chapter 4 incarcerated in local jails, who are typically incarcerated for shorter periods, would likely bring the total closer to 2 million.17 People who are incarcerated are generally not available to participate in the labor market, and they have very limited opportunities to put their human capital to use. This fact is sometimes overlooked because commonly used labor market indicators like the employment-population ratio and the labor force participation rate exclude people who are incarcerated. Producing employment-population ratio measures that include the incarcerated population reveals lower levels of human capital utilization, especially for Black men, and larger gaps between races. In December 2019, the white employment-population ratio was 61.2 percent, while the Black employment-population ratio was 59.3 percent. If people who were incarcerated in Federal or State prisons were included in these estimates, the Black ratio would fall by about 0.8 percentage point, to 58.5 percent, and the white ratio would fall by only about 0.1 percentage point, to 61.0 percent—increasing the difference between the two races to 2.5 percentage points. Including people incarcerated in local jails in this exercise would likely increase this gap further. Laws that limit post-incarceration employment opportunities create longer-term obstacles to effectively deploying human capital for the formerly incarcerated. Having been incarcerated renders workers ineligible for certain types of employment, licenses, or credentials, regardless of their qualifications. Federal, State, and territorial governments collectively apply over 40,000 restrictions and requirements to people who have been convicted of crimes, 72 percent of which affect the employment opportunities available to them (Umez and Gaines 2021). For example, some of the incarcerated people who helped fight wildfires in California in recent years found themselves ineligible to be hired as firefighters after being released from prison because they were not eligible to receive certification as emergency medical technicians (Romo 2020). Though California has since passed a law that attempts to address this, the law requires that formerly incarcerated people petition to have their convictions expunged, a process that can be burdensome (Smith 2021). Reducing incarceration and post-incarceration employment restrictions could mitigate the extent to which the criminal justice system limits the deployment of human capital, as could improving and increasing access to programs designed to help formerly incarcerated people return to work. A total of 734,500 people were incarcerated in local jails in 2019, and 549,000 people were incarcerated in local jails in 2020 (Minton and Zeng 2021, table 2). A total of 1,379,786 people were incarcerated in State or Federal corrections facilities in 2019, and 1,182,166 were incarcerated in 2020 (Minton and Zeng 2021, table 3) for total incarcerated populations of 2.1 million in 2019 and 1.7 million in 2020. 17 Investing in People: Education, Workforce Development, and Health | 155 Government Personnel Policies In certain fields, the government plays an important role in determining how human capital is managed and/or compensated. Decisions about how much Medicare and Medicaid pay for various medical procedures, for example, have a direct impact on physicians’ earnings (Gottlieb et al. 2020). The government’s role extends to other areas of health, such as home health care services. Nursing homes are one area where government payment policies have particular significance. In 2019, Medicaid accounted for around 29 percent of all spending on nursing care facilities and continuing care retirement communities, and Medicare covered another 22 percent (MACPAC 2021). Evidence suggests that the introduction of State Medicaid policies designed to increase wages in nursing homes was associated with increased staffing of certified nurse aides (Feng et al. 2010). Other evidence on wages in nursing homes also suggests that higher pay keeps workers in the industry. Ruffini (2021) finds that higher minimum wages increased retention among lowwage workers in nursing homes. She also finds that higher wages improved the quality of service provided by nursing homes, as reflected in reduced inspection violations; adverse, preventable health conditions; and mortality. This suggests that increasing compensation not only helps direct human capital toward an industry where it is needed but also induces workers to deploy their human capital more productively. Conclusion Increases in human capital accumulation contribute to faster economic growth and improved standards of living. Yet human capital accumulation has slowed over the past two decades, and the United States has fallen behind many other countries in both educational attainment and life expectancy. Further, many long-standing discrepancies remain in human capital accumulation and in deployment between individuals by income, race, and ethnicity. Thus, the Nation can benefit from investing more in education, workforce development, and health, and from exploring policies that can help individuals deploy existing human capital more effectively. These policies range from improving early childhood education and care to ensure that children get a strong start in life to lifting barriers to permit unauthorized immigrants and the formerly incarcerated to employ their human capital in its most effective form. Investments in people expand the productive capacity of the U.S. economy, boost living standards, and ensure that our workforce has the skills and education needed to compete in this dynamic world. 156 | Chapter 4 Chapter 5 Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination Markets function well when firms must compete for employees or customers. In competitive product markets, the right amounts of goods are produced to meet demand, with prices that accurately reflect value. In a well-operating labor market, workers are able to switch jobs, wages reflect productivity, and differences in earnings only reflect such factors as ability, effort, education, experience, and random chance. However, empirical economics research has documented the many ways in which this ideal does not reflect reality. Perfect competition does not describe most labor markets, for example, and not all workers are able to easily move through the labor force to obtain more satisfactory compensation. Two concrete examples are (1) the market power of employers, which allows for unfair hiring and compensation practices; and (2) discrimination, which has exacerbated persistent forms of inequality in earnings across racial and gender lines. Nearly 20 percent of U.S. workers report being bound by noncompete agreements, which limit an employee’s ability to join or start up a competing firm (Starr, Prescott, and Bishara 2021). Also, in general, employer market power is responsible for wages that are at least 15 percent lower than they would be in a perfectly competitive market (U.S. Department of the Treasury 2022). In addition, Federal government statistics show that, on average, Hispanic and Black employees earn less than 80 percent of what white employees earn (BLS 2021). Women earn, on average, roughly 83 percent of what men earn, and the disparities are 157 even greater for most nonwhite women (Department of Labor 2022a). These earnings differences remain even after accounting for such factors as educational attainment and experience (Blau and Kahn 2017; BorowczykMartins, Bradley, and Tarasonis 2017). Although many groups can be targeted by such discrimination—including those with disabilities; lesbian, gay, bisexual, transgender, and queer (LGBTQ+) people; and members of religious minorities—this chapter focuses on discrimination by race, ethnicity, and gender. Noncompetitive labor markets are not completely devoid of competitive forces, though they generally feature fewer job options, reducing the wellbeing of workers, and discriminatory barriers, resulting in a misallocation of talented workers. Broader costs for the overall economy include lower productivity and slower economic growth. New Deal labor reform laws sought to protect workers by establishing the right to bargain collectively, establishing a floor for wages, and providing protection from overwork, while the Civil Rights Act sought to break through discriminatory barriers across all kinds of economic activity, including in the labor market (Boone 2015). Emblematic of these laws’ success, Hsieh and others (2019) estimate that the removal of barriers to higher-income occupations for women and people of color accounted for 20 to 40 percent of growth in output from 1960 to 2010; this was driven by an improvement in the allocation of talented workers within the economy. Despite this progress, barriers to equality in the workplace remain today, in no small part due to the market power of employers. The opening section of this chapter provides a summary of current levels of inequality in wages, income, and wealth. The next sections document the forces that inhibit workers from being fully rewarded for their skills in labor markets—such as excessive wage-setting power by employers and racial and gender discrimination—and discuss how these forces impede economic growth. The final section discusses several policies, including legal measures designed to protect workers and members of disadvantaged groups and more general 158 | Chapter 5 economic policies with the potential to counteract the adverse effects of a lack of competition—thereby, reducing inequality as well as boosting economic growth. The chapter finishes with a discussion of tax reforms that can help to offset inequality that may remain even if barriers to healthy competition are removed. Labor Market Inequality Research reveals the significant scope of economic inequality—in wages, incomes, and wealth—in the United States (Gould 2019; Congressional Budget Office 2021; Piketty 2014; Wolff 2021). These inequities across demographic groups cannot be fully explained by differences in such characteristics as education or experience that provide an indication of their productivity, suggesting that people may not be equitably rewarded for their economic contributions. This section reviews current patterns of inequality, with a primary focus on wage inequality by race, ethnicity, and gender. For most households, earnings account for most of their income; thus, wage inequality translates to income inequality. Wealth inequality reflects earnings and income inequality—as well as disparities in access to capital, returns on those assets, and transmission of wealth across generations (see box 5-1). Figure 5-1 shows that, while net productivity has grown by nearly 62 percent over the past four decades, average hourly pay for the typical worker Figure 5-1. The Gap Between Productivity and Worker Compensation, 1948–2020 Cumulative change (index: 1979 = 100) 180 162 160 140 118 120 100 80 Net total economic productivity 60 Nonsupervisory compensation 40 20 0 1948 1958 1968 1978 1988 1998 2008 2018 Source: Economic Policy Institute, analysis of data from the Bureau of Labor Statistics and the Bureau of Economic Analysis. Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 159 Box 5-1. Racial and Ethnic Wealth Gaps Although differences in income across groups typically provide an account of inequality in resources on an annual basis, wealth disparities track how these income flows can contribute to divergences in accumulated resources across longer time periods and even over multiple generations. A household’s net worth, measured as the difference between its assets and its debts, has many components. For most American families, the largest single asset is their home; thus, the largest portion of net worth is often tied to the value of one’s home minus the mortgage or the other debts against it. Net worth also includes savings and retirement accounts, stocks and or other property, and inheritances and gifts from family members. Sources of debt also include credit card balances and loans for education, vehicles, or durable goods. In the United States, there are substantial racial wealth gaps, as shown in figure 5-i. In 2019, the net worth of the median white family was $199,498, almost eight times higher than that of the median Black family and five times higher than that of the median Hispanic family (Bhutta et al. 2020). The average net worth within each group is higher than the median, because the average incorporates information about the ultrawealthy, who account for a large proportion of overall wealth: The average white family has nearly seven times more wealth than the average Black family and almost six times more than the average Hispanic family. The causes of current wealth inequality are complex, as today’s net worth reflects the accumulation of differences in past income between racial groups, differences in savings rates for households with similar incomes, differences in the return to savings for households with similar savings rates, differences in transfers of wealth between generations, and the possibility of individual-level and/or structural discrimination at any Figure 5-i. Median and Average Wealth by Race and Ethnicity, 2019 Wealth (2021 dollars) $1,000,000 $1,042,323 Black Hispanic White $800,000 $600,000 $400,000 $199,498 $200,000 $25,544 $0 $151,038 $175,417 $38,263 Median net worth Average net worth Sources: 2019 Federal Reserve Board Survey of Consumer Finances; Haver analytics; CEA calculations. 160 | Chapter 5 of these stages. In this regard, civil and legal rights play an important role. For example, after Emancipation, the promise of land for Black freedmen in the South did not materialize, meaning that Black freedmen exited slavery without land they could farm and pass on to their children. This lack of land ownership has been documented to have affected asset accumulation (Miller 2020). The lack of access to assets continued throughout much of the 20th century, as Jim Crow policies and practices limited access and mobility for Black Americans. Further, systemic disinvestment and exclusion from federally subsidized homeownership opportunities in Black neighborhoods, collectively referred to as “redlining,” were associated with lower property values decades later (Aaronson, Hartley, and Mazumder 2021; Fishback et al. 2021). Moreover, Derenoncourt (2022) shows that the attempts of Black Americans to migrate to neighborhoods with greater opportunity were often met with “white flight” and disinvestment, limiting the potential for escape from segregated economic fortunes. Given the large role played by homeownership wealth on modern-day balance sheets, this history provides just one example of how racial wealth gaps are sustained over time. has increased by just under 18 percent (Economic Policy Institute 2021). The divergence between the two trends suggests that there may be forces suppressing the pay of workers relative to their productivity. Racial, Ethnic, and Gender Wage Gaps There are substantial differences in the wages paid to white women, and to Black, Hispanic, American Indian, and Alaska Native workers of any gender, relative to white men, and some differences remain even after accounting for differences in education, occupation, and experience. Focusing just on differences in educational levels, as shown in figure 5-2, reveals the basic pattern. In 2021, Black workers were paid less than white workers, on average at every education level, with the Black/white wage ratio ranging from 76 percent to 91 percent. Hispanic, American Indian, and Alaska Native workers were paid less than white workers at all but the lowest level of education (less than a high school degree). The patterns suggest that differences in earnings between these groups are driven by more than simply such differences as educational attainment and level of experience. The wage profile of Asian American, Native Hawaiian, and Pacific Islander workers (AANHPI, or “Asian” for short) is distinct from that of other nonwhite groups. Asian workers earn more than white workers, on average, at most education levels. However, the overall group average Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 161 Figure 5-2. Wage Gaps by Education, Race, and Ethnicity, 2021 Average hourly wages (2021 dollars) $13.60 $16.92 $16.30 $15.25 $15.00 $18.20 $19.50 $19.47 $19.77 $21.85 $19.65 $21.17 $21.61 $23.05 $24.34 Less than high school High school Some college Black American Indian / Alaska Native Hispanic AANHPI White $30.22 $37.18 $32.65 $42.01 $39.88 $39.67 $45.74 $45.00 College Advanced degree $49.65 $0 $10 $20 $30 $40 $50 $58.15 $60 $70 Sources: Economic Policy Institute; Current Population Survey extracts; CEA calculations. Note: AANHPI = Asian American, Native Hawaiian, and Pacific Islanders. masks a substantially higher within-race wage inequality among Asian people than that found within other groups. This can be captured by comparing the wage of the worker at the 90th percentile in earnings, including earnings among salaried workers, with the wage of the worker at the 10th percentile. In 2021, among Asian people, the worker at the 90th percentile earned $81 an hour, 6.4 times more than the worker in the 10th percentile, who made almost $13 an hour. Meanwhile, among the other racial and ethnic groups, the wage of the 90th percentile worker was only 3.5 to 4.8 times as large as that of the wage of the 10th percentile worker. The varied experiences of Asian workers are further demonstrated by comparisons across different ethnic subgroups within the larger group (see box 5-2). There are also earnings differences by gender: women are paid less, on average, than men. Although the wages of both men and women increase with education, figure 5-3 shows that the gender wage gap is even larger for those with more education. Among those with an advanced degree, the average wage for women is 70 percent of that for men. As laid out by Crenshaw (1989), examining inequality along one dimension of identity at a time may obscure the specific experiences that lay at the intersection of race and gender identities. Figure 5-4 therefore presents wages separately by race and gender. On average, Black women’s wages are 62 percent of white men’s wages, while Hispanic and American Indian / Alaska Native women’s wages are 59 and 62 percent of white men’s wages, respectively. The average wages of Asian women are higher than those of women in the other racial and ethnic groups, though still below those of white men. In addition, Asian women experience a larger within-race gender 162 | Chapter 5 Figure 5-3. Gender Wage Gap by Level of Education, 2021 Average hourly wages (2021 dollars) $13.30 Less than high school Women $16.98 Men $17.93 High school $22.55 $20.35 Some college $25.89 $32.49 Bachelor’s degree $44.29 $41.46 Advanced degree $58.82 $0 $10 $20 $30 $40 $50 $60 $70 Source: Economic Policy Institute, Current Population Survey extracts. Figure 5-4. Wage Gaps by Gender, Race, and Ethnicity, 2021 Average hourly wages (2021 dollars) $22.42 Black $24.73 Women Men $22.33 American Indian / Alaska Native $25.59 $21.21 Hispanic $24.49 $33.01 AANHPI $45.43 $27.68 White $35.91 $0 $10 $20 $30 $40 $50 Sources: Economic Policy Institute, Current Population Survey extracts; CEA calculations. Note: AANHPI = Asian American, Native Hawaiian, and Pacific Islanders. gap than women in any of the other racial and ethnic groups, earning 73 percent of the average wage of Asian men. It is important to note that, as seen in figure 5-4, the lower gender wage gap among Black, Hispanic, American Indian, and Alaska Native workers is partly due to the relatively low wages earned by men in these groups. Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 163 Box 5-2. Improving Data Infrastructure for Equity Analysis Understanding the mechanisms underlying the inequality discussed in this chapter involves gathering evidence, both quantitative and qualitative. Research plays an important role in uncovering these patterns, and shedding light on issues related to equity across different groups requires adequate information and data on the many dimensions of an individual’s identity. However, many barriers remain to collecting the information needed for such equity analysis. First, the existing set of questions typically asked on household surveys may not be detailed enough to capture certain important subpopulations. This may prevent the discovery of unique outcomes for important subgroups and can reduce the accuracy of equity analyses by lowering rates of self-identification among respondents who do not see themselves represented in the available categories (Census Bureau 2021). Members of Asian American, Native Hawaiian, and Pacific Islander racial/ethnic communities, for example, are commonly grouped together, masking the greater economic challenges faced by some subgroups within the broader category. This is demonstrated in figure 5-ii, which shows a great deal of variation in average income across subgroups of this population. In addition, survey respondents of Middle Eastern and North African origin generally do not have a satisfying option to select in the standard list of racial and ethnic categories, which may result in higher rates of nonresponse to these questions. Likewise, the concepts of sex and gender are often collapsed into binary categories that exclude a number of gender identities and expressions. Moreover, even when surveys do have questions that capture key aspects of identity, the survey sample size may be too small to be representative of certain groups in the population, and privacy concerns may require suppression of statistics for those groups to prevent tracing the information back to a specific respondent. For example, before February 2022, labor force statistics from the Current Population Survey for American Indian and Alaska Native respondents were not reported as a separate category, due to small sample sizes. Likewise, statistics on wealth and net worth from the Survey of Consumer Finances are released publicly for Black, white, and Hispanic respondents, separately, but not for Asian, Native Hawaiian, Pacific Islander, American Indian, or Alaska Native respondents (Bhutta et al. 2020). A second concern is that many key economic indicators are measured using administrative data; that is, data are collected for the purposes of implementing a program, and not necessarily with the primary purpose of facilitating general research analysis. In these cases, it may not be necessary to collect demographic information, and may be counterproductive or illegal to do so. For example, administrative tax 164 | Chapter 5 Figure 5-ii. Average Household Income among Asian American, Native Hawaiian, and Pacific Islander Subgroups AANHPI average U.S. average household income household income Asian Indian Taiwanese Malaysian Chinese Pakistani Filipino Sri Lankan Japanese Fijian South Korean Indonesian Other Asian Vietnamese Guamanian/Chamorro Thai Hawaiian Cambodian Laotian Samoan Hmong Tongan Bangladeshi Nepalese Bhutanese Mongolian Other Pacific Islander Burmese Other Micronesian $0 $50,000 $100,000 $150,000 $200,000 2021 dollars Sources: American Community Survey, 2017–19; Haver analytics; CEA calculations. Note: AANHPI = Asian American, Native Hawaiian, and Pacific Islanders. data have proven useful in analyses of income inequality by incorporating the incomes of the ultrarich, but the Internal Revenue Service does not collect many demographic characteristics on the 1040 tax return (Huang and Taylor 2019). Such demographic data are also not typically collected for other key programs that generate useful data for tracking economic outcomes, such as the Unemployment Insurance (Kuka and Stuart 2021) programs across different states, and the Supplemental Nutritional Assistance Program (Prell 2016). There are possible solutions to the issues outlined above, and some efforts are under way to facilitate equity analysis. The Biden-Harris Administration’s Executive Order on Advancing Racial Equity and Support for Underserved Communities Through the Federal Government established an Equitable Data Working Group, an interagency committee, to explore ways to make available data disaggregated by race, ethnicity, gender, and other key demographic variables (Nelson and Wardell 2021; White House 2021a). These include a comprehensive review of race, ethnicity, and gender-related questions on Federal Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 165 surveys, and exploration of the possibility of merging Federal datasets to append demographic information to administrative data. An example of the type of analysis possible is the ongoing collaboration between the U.S. Treasury and U.S. Census Bureau to merge individual-level data on race and ethnicity with tax data to study when members of different racial groups received their first Economic Impact Payment as a part of the 2020 CARES Act (Adeyemo and Batchelder 2021; U.S. Congress 2020). The Administration’s National Strategy on Gender Equity and Equality calls for the collection of gender-disaggregated data to better track outcomes such as gender gaps in the labor market and entrepreneurship, financial outcomes, including within households, and gender-based violence (White House 2021b). In another case, the U.S. Census Bureau’s Household Pulse Survey, designed to provide real-time tracking of outcomes during the COVID-19 pandemic, for the first time introduced separate questions about sexual orientation and gender identity on a Census Bureau survey in July 2021 (File and Lee 2021). In terms of data by income group, the 2022 Green Book included proposed funding to share data between the Treasury and Bureau of Economic Analysis (BEA), which would aid in the estimate of the distribution of income growth across different income percentiles (U.S Department of the Treasury 2021a, 101). BEA has explored prototype estimates of the distribution of personal income, which covers outcomes as recently as two years in the past; and recent developments, such as the Realtime Inequality project (Blanchet, Saez, and Zucman 2022), demonstrate the potential for even more timely estimates at a higher frequency from BEA (U.S. Bureau of Economic Analysis 2021). These wage gaps reflect the fact that women—particularly nonwhite women—and most nonwhite men are overrepresented among the low-wage workforce. For example, in 2021, nonwhite men made up 39 percent of all men in the workforce, but over half (51 percent) of low-wage men in the workforce. Likewise, nonwhite women made up 39 percent of all women in the workforce and 45 percent of low-wage women in the workforce. The gender pay gap has narrowed over time, partially as a result of women increasing their skills through educational attainment and greater labor market experience. Women are now better educated than men—being more likely than men to graduate from college and earn graduate degrees (National Center for Education Statistics 2022). The share of women in the labor force (either working or actively looking for work) nearly doubled from 1950 to 2000, from 33.8 percent to 59.9 percent (BLS 2022a). Boustan and Collins (2014) show that these historical trends have varied across racial groups: the labor force participation rate for Black women, for example, was 166 | Chapter 5 Figure 5-5. Mothers’ and Nonmothers’ Labor Force Participation Rates, 2021 Labor force participation rate (percent), prime-age women 90 Nonmothers 80 Mothers 70 60 50 40 30 20 10 0 White Black American Indian / Alaska Native AANHPI Hispanic Sources: 2021 Current Population Survey; CEA calculations. Note: AANHPI = Asian American, Native Hawaiian, and Pacific Islanders. 14 percent higher than that of white women in 1950, and the two rates did not converge until about 1990. However, the increase in women’s labor force participation has stalled since 2000, and the gap between the share of men and women in the labor force has remained fairly steady since that time in the United States, while such gaps continued to shrink across many other countries that belong to the Organization for Economic Cooperation and Development (OECD) (Blau and Kahn 2013). In 2019, before the COVID-19 pandemic, 58 percent of women and 69 percent of men were in the U.S. labor force. One general factor at play is parenthood; on average, prime-age (age 25 to 54 years) women with children have lower labor force participation rates than those without children, as shown in figure 5-5. However, there is variation in participation patterns across women of different racial and ethnic backgrounds, and the relationship between parenthood and participation does not hold for Black and American Indian women and for Alaska Native women, whose participation rates do not substantially differ by motherhood status. This differential pattern may in part be driven by a greater share of women in these groups being the breadwinners for their household (Institute for Women’s Policy Research 2016) and therefore less able to afford to exit the labor force. A number of studies have also documented a concentration of income among the richest households. This is the result of the wage inequality discussed above, including relatively high rates of compensation for executives (Mishel and Kandra 2021), and the fact that the highest-income households receive a disproportionately high share of capital income earned from assets Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 167 and savings. The most recent estimates show that, in 2021, the top 1 percent received 19.5 percent of pretax income, as compared with only 11.4 percent for the bottom 50 percent of the population (Blanchet, Saez, and Zucman 2022). Although there is some variation in such estimates due to differences in data and methods, various studies find that between 14 and 20 percent of income has been accrued by the top 1 percent of households in recent years (Piketty, Saez, and Zucman 2018; Auten and Splinter 2020; Internal Revenue Service 2021; Congressional Budget Office 2021). There is also considerable income inequality among households below the top 1 percent. For example, in 2018, U.S. households at the 90th percentile of the income distribution earned 12.6 times more than households at the 10th percentile (Horowitz, Igielnik, and Kochhar 2020), a ratio that is among the highest for OECD countries (OECD 2022). Sources of Earnings Inequality This section explores how earnings inequality can arise from noncompetitive market forces and discriminatory barriers. A robust and growing body of evidence shows that some degree of economic inequality stems from forces inconsistent with competitive markets. In a noncompetitive market, barriers emerge that prevent some individuals from realizing the gains from their productivity. This chapter focuses on two specific aspects of noncompetitive markets: the market power of employers, and discrimination. New empirical research provides evidence that many firms have some power to set wages, violating the core tenet of a competitive labor market (Card 2022), and allowing for persistent differences in outcomes across racial and gender lines. These are not the only sources of earnings inequality; nor does the presence of inequality necessarily imply that labor markets are not competitive. For example, even a random event such as a serious illness could have implications for an individual’s potential earnings. Earnings inequality can also appear within competitive markets due to differences in worker productivity. A worker’s skills and experience—that is, their human capital—affects their marginal productivity, as discussed more fully in chapter 4. A large body of research has focused on productivity-related explanations for inequality, examining the roles of technological change, innovation, and trade policy that have increased the productivity of some workers while replacing other workers whose jobs could be outsourced or automated (Autor, Levy, and Murnane 2003; Autor, Katz, and Kearney 2006; Acemoglu and Autor 2012; Autor 2010). Recent work has found evidence that import competition from China and other developed economies has had adverse effects on U.S. employment in manufacturing and per-capita income in more trade-exposed labor markets, particularly among workers with less 168 | Chapter 5 than a college degree (Autor, Dorn, and Hanson 2013, 2016; Hakobyan and McLaren 2016). Further, these adverse effects spill over to overall employment and persist long after the initial severe loss of manufacturing jobs (Autor, Dorn, and Hanson 2021). A Lack of Competition in Labor and Product Markets Noncompetitive markets can emerge under many conditions, such as when mergers result in dominant firms that can use their consolidated market power to charge higher prices, offer decreased quality, and block potential competitors from entering the market (Boushey and Knudsen 2021). A distinguishing feature of noncompetitive markets is the existence of “economic rents,” which are profits derived from prices that are higher than needed to cover the investment and production costs of goods. In a perfectly competitive market, neither workers nor firms earn such rents in the long run; if there are excess economic rents in a product market, for example, this would create an incentive for new firms to enter the market, which in turn would drive down prices and rents. A critical question in noncompetitive markets is how the economic rents are split between employer profits and employee wages. When firms use their market power to capture a greater share of economic rents, the outcome can be “suboptimal”; meaning that, from society’s point of view, workers are paid too little or firms charge too much for their products. Another implication of noncompetitive markets is that they provide an incentive for firms to do less, not more. If the firm has labor market power, theory says it will restrain hiring to maintain low wages, because adding more employees would mean paying higher wages to lure new applicants. Similarly, a firm with product market power will restrain production in order to charge higher prices than it would if it had competitors. This subsection explains how a lack of competition not only affects efficiency but also can exacerbate labor market inequality. Labor market monopsony. The classic form of a noncompetitive labor market is a monopsony. In the case of a pure monopsony, a concept first developed by Joan Robinson (1933), there is a single employer that uses its market power to set wages below what the competitive rate would be; that is, the firm has the power to set such wages. Robinson’s theoretical model of a single employer has been extended to incorporate the concept that an employer’s monopsony power can come from representing a larger share of the labor market, which limits the options of employees to push toward competitive wages. Employers may also derive monopsony power from situations where it is difficult for workers to switch jobs due to issues of commuting distance or workplace scheduling flexibility, which give employers greater power to set wages (Manning 2020a). Stelzner and Bahn (2021) argue that, because female and nonwhite workers may be more likely Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 169 to experience these difficulties, monopsony power can translate into greater gender and racial inequality. A number of studies focus on a direct measure of monopsony power by estimating a firm’s ability to adjust the wages it offers, as opposed to offering a market wage that a competitive market would demand. Using job applications data, Azar, Berry, and Marinescu (2019) find strong evidence of this monopsony power in many markets, and they conclude that workers’ productivity is 17 percent higher than the wage they receive. There is similar evidence of monopsony power even in online, on-demand labor markets where the costs of searching for and switching jobs should be relatively low (Dube et al. 2020). A meta-analysis of 53 studies concludes that, overall, the literature provides strong evidence for monopsony power among many employers, implying sizable markdowns in wages (Sokolova and Sorenson 2020). Importantly, two studies find that the degree of monopsony power is substantially larger in low-wage labor markets (Bassier, Dube, and Naidu 2021; Webber 2015). Moreover, research by Webber (2015, 2016) shows that the negative effect of a firm’s market power on wages is strongest in the lower half of the earning distribution and among female workers, suggesting that monopsony power amplifies both overall and gender wage inequality. One way that a firm can derive monopsony power is from providing a large share of the jobs available in a local labor market. Economic research has found a link between higher labor market concentration and lower wages (Azar, Marinescu, and Steinbaum 2019; Benmelech, Bergman, and Kim 2020, CEA 2016; Philippon 2019; Qiu and Sojourner 2019; Rinz 2020). Two recent studies find that wages are lower when concentration in local labor markets increases due to mergers and acquisitions (Arnold 2019; Benmelech, Bergman, and Kim 2020). A third study focuses on hospital mergers and finds that they decrease the wage growth of workers whose skills are specific to their industry (Prager and Schmitt 2021). Recent research has raised the question of whether employers are able to gain or maintain a greater share of the labor market through actions that may violate antitrust laws (Naidu, Posner, and Weyl 2018; Posner 2021). Monopsony power can also arise from practices that reduce the outside options of workers (Manning 2020b). One such practice is the use of noncompete agreements, which prohibit employees from joining or starting competing businesses, typically within a specified time frame or geographic boundary. Starr, Prescott, and Bishara (2021) find that almost 20 percent of U.S. workers were bound by a noncompete agreement in 2014, including 12 percent of workers with annual income less than $20,000. Such agreements are increasingly used by employers in low-wage industries, such as fast food chains and home health agencies (Quinton 2017). A recent study found that when Oregon initiated a ban on noncompete agreements, wages rose by 2 to 3 percent, with larger effects in occupations where noncompete agreements 170 | Chapter 5 were more common (Lipsitz and Starr 2021). Johnson, Lavetti, and Lipsitz (2021) examine this relationship in the national context, and find that greater enforcement of noncompete agreements reduces earnings, with stronger negative effects on the earnings of female and nonwhite workers. Some employer practices hamper worker mobility by impeding their ability to gain information about important characteristics of potential jobs, such as expected compensation and working conditions. For example, nondisclosure agreements (NDAs), which are often bundled with noncompete agreements in employment contracts, prevent an employee or former employee from disclosing information about employers. Though NDAs can be used to protect confidential business information, some are much more broadly applied and can reduce the ability of workers to share information about the work environment. Research suggests that overly broad NDAs can reduce the reporting of workplace harassment (Sockin, Sojourner, and Starr 2021). Workers may also lack information on the wages offered at other jobs, partly due to employer practices that promote pay secrecy. Research has shown that workers, especially those with low incomes, are unaware of potential higher-paying job options (Jäger et al. 2021), and that reducing pay secrecy could reduce the gender wage gap (Baker et al. 2021). Another practice that can reduce workers’ mobility are no-poach agreements, which are compacts made between employers agreeing to not hire workers from each other for a specified period of time. Employees may not even be aware that these agreements are in effect, and because nopoaching agreements between separate employers are illegal per se under antitrust laws, and therefore hard to discover, it is difficult to know how common they are. In a slightly different context, Krueger and Ashenfelter (2021) documented that in 2016 almost 60 percent of franchise agreements, including for some major fast-food chains, contained no-poaching clauses. The study also found that no-poaching clauses were more common for franchises in low-wage and high-turnover industries, though a number of fastfood franchises have already dropped them from their franchisee contracts in response to public pressure and legal challenges (Abrams 2018). Product market monopoly. Whereas a pure monopsony refers to a market with a single buyer, a pure monopoly refers to a market with a single seller. Accordingly, a firm gains greater monopoly power when the market in which it sells products is more concentrated—what is often referred to as an oligopolistic market—with just a handful of sellers. This allows the firm to charge higher prices and leads them to produce less than it would if it faced greater competition. In addition, Boushey and Knudsen (2021) cite growing evidence that market concentration has reduced innovation and economy-wide investment in the United States. Product market concentration may also contribute to economic inequality. This can occur when firms with market power are able to set Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 171 prices above what they would be in a competitive market. This pricing power harms consumers but improves the payoffs to shareholders, as explored in recent research (Gans et al. 2018; Philippon 2019). This phenomenon can exacerbate inequality, since consumers are spread across the income distribution, while the shareholders who benefit are more likely to be near the top of the income distribution. Research has also shown that higher levels of market concentration are associated with workers receiving a lower share of the income generated by economic output (Barkai 2020; Autor et al. 2020; Eggertsson, Robbins, and Wold 2021). Joining the two strands of the literature on market concentration, Qiu and Sojourner (2019) note how product and labor market concentration may interact. They use the example of a town with two nursing homes, which may be the only employers of nurses and the only providers of nursing care in the local market, giving them power in both the labor and product markets. They find that the negative effect of labor market concentration on wages is stronger in more concentrated product markets. Chapter 6 explores additional cases where varying levels of competition and market power at different points along the supply chain create similar dynamics, as discussed here in the context of labor market inequality. Racial and Gender Discrimination Racial and gender inequality can arise from discrimination that occurs both at the individual level and under broader, more structural conditions. This section explores the extensive evidence on how discrimination has exacerbated inequality, along with how such inequality can be sustained and worsened by employer market power. Not all differences in earnings by race, ethnicity, and gender are the result of a lack of competition or discrimination, because they can emerge in competitive labor markets due to differences in characteristics such as educational attainment that enhance a person’s work productivity. There are notable disparities in educational achievement by race and ethnicity. For example, while 35.8 percent of white, non-Hispanic people have earned a bachelor’s degree, the shares are lower for Black (21.6 percent), Hispanic (16.4 percent), Native Hawaiian and Pacific Islander (17.8 percent), and American Indian and Alaska Native (15.0 percent) people (McElrath and Martin 2021). Asian Americans have the highest educational attainment, with 54.3 percent earning a bachelor’s degree or higher. There is a large literature on the extent to which differences in productivity-related characteristics, known as “human capital,” can explain racial and gender earnings gaps. Residual gaps in wages and earnings by race, ethnicity, and gender remain even after accounting for differences in educational attainment and a wide range of other productivity-enhancing characteristics (Burnette 2017; 172 | Chapter 5 Kamara 2015; Borowczyk-Martins, Bradley, and Tarasonis 2017). For example, recent research finds that—even after accounting for factors such as education, occupation, work experience, and unionization status—40 to 60 percent of the gender wage gap remains unexplained (Blau and Kahn 2017; Foster et al. 2020). In fact, given that educational attainment of women is now higher, on average, than that of men, accounting for gender differences in education increases the unexplained portion of the gender wage gap. This unexplained portion is even larger for Black and Hispanic women, who face wage gaps that are greater than the sum of the gender wage gap and the racial wage gap. (Paul et al. 2018; Bahn and McGrew 2018). Moreover, while educational disparities can explain some of the differences in economic outcomes across racial and ethnic groups, these disparities can also result from discrimination that occurs before individuals enter the workforce. Individual-level discrimination. One leading explanation for “residual” inequality is individual-level discrimination in labor markets on the basis of race or gender. A large literature in the field of economics homes in on two leading models of discrimination in the labor market, (1) so-called taste-based discrimination (Becker 1971), where some employers individually have a distaste for hiring workers of a certain group; and (2) statistical discrimination (Phelps 1972; Arrow 1973), which occurs when employers that do not have full information about a potential worker’s skills use the average characteristics of their racial or gender group to make wage offers (for a review of theory and empirical evidence, see Guryan and Charles 2013). Regardless of intent, both forms of discrimination have disparate negative effects on the group against which the discrimination is occurring. These forms of discrimination in the labor market take place during individual transactions between workers and employers, and they are theoretically unlikely to persist in well-functioning markets. In the case of taste-based discrimination, differential treatment should decline as discriminatory employers are driven from the competitive market by those whose employment decisions reflect only the productive capacity of their workers. Meanwhile, statistical discrimination may potentially decline over time as employers gather more accurate information about workers (Altonji and Pierret 2001). However, Sarsons (2019) shows that this need not be the case, finding that after the death of a patient, female surgeons experience a greater drop in referrals from primary physicians than their male counterparts, which suggests that the same kind of information may be interpreted less favorably for women doctors as compared with men. Evidence on individual-level discrimination by race or gender has been found through the use of experimental methods such as résumé studies, where résumés with identical qualifications, but with different racial or gender identities, are sent to employers. Bertrand and Mullainathan (2004) Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 173 find that résumés with white-sounding names were called back at a 50 percent higher rate than those with Black-sounding names. Quillian and others (2017) conducted a meta-analysis of all such experimental studies of racial and ethnic discrimination, and find that white applicants got 36 percent more callbacks than Black applicants and 24 percent more callbacks than Latino applicants. The study also finds no change in the levels of discrimination against Black applicants between 1990 and 2015, but a modest decline in discrimination against Latino applicants. Related research focusing on discrimination against Hispanic and Latino workers in the housing market, which can reduce overall labor market mobility, finds that immigration and assimilation play an important role. An experimental study using email correspondence by Hanson and Santas (2014) finds that 6.9 percent of landlords discriminate against seemingly recent Hispanic immigrants, with little to no discrimination against applicants who appear assimilated, suggesting significant barriers to mobility for marginalized Hispanic and Latino people. Experimental studies also find individual-level labor market discrimination against women. Qualified women are less likely to be hired or promoted compared with men (for a case study of symphony orchestras, see Goldin and Rouse 2000), and the hiring discrepancy is particularly strong for positions where expected income is higher (Neumark et al. 1996). More recent résumé studies shed light on how gender discrimination is concentrated among particular firms and is stronger in certain industries (Kline, Rose, and Walters 2021), and find evidence that it can be particularly acute among employers in male-dominated professions (Hangartner, Kopp, and Siegenthaler 2021) and those seeking to fill jobs that require a major in science, technology, engineering, and/or mathematics (Kessler, Low, and Sullivan 2019). Beyond individual-level discrimination: structural racism. A growing body of research documents how theories of individual-level discrimination are incomplete, particularly in explaining the persistent gaps in outcomes between racial groups, because they do not adequately incorporate the legacy of historic forms of discrimination in the United States. For example, current Black/white gaps in economic outcomes can be partially explained by periods throughout U.S. history ranging from the era of chattel slavery, to Jim Crow regimes of segregation, to the present era of mass incarceration (Cook and Logan 2020). To establish a theory capable of explaining these persistent gaps, William Darity Jr. developed the subfield of “stratification economics” (Darity 2005; Darity, forthcoming; Chelwa, Hamilton, and Stewart, forthcoming), in which he argues that economic gaps have persisted because of the material incentive to maintain distinct group identities. With these group identities in place and entrenched within a hierarchy, theories such as Acemoglu and Wolitzky’s (2011) model of coercion can be used to show 174 | Chapter 5 how “structural” forms of racism can take hold in labor markets.1 Under this theory, employers have an economic incentive to coerce workers into undesirable, low-wage work arrangements that maximize profits, in the extreme using force or violence, or, under softer versions of coercion, weakening workers’ bargaining power by limiting their mobility and outside options. Naidu (2010) provides evidence of this, showing that enticement fines that prevented employers in the postbellum U.S. South from recruiting alreadyemployed agricultural workers reduced the labor market mobility and wages of Black sharecroppers. A second key insight regarding structural racism is that discrimination by a subset of actors can spill over to others in the same setting or market, or in other parts of the economy, generating more pervasive disparities. For example, discrimination in law enforcement and legal systems exacerbates disproportionate rates of incarceration across racial groups. Though there are 233 people in State or Federal prisons per every 100,000 white U.S. residents, Hispanic people have a 50 percent higher rate, at 351 per 100,000, American Indian and Alaska Native people have more than twice the rate, at 565 per 100,000, and Black people have nearly five times the rate, at 1,160 per 100,000. And though those who identify as Asian American alone have a much lower imprisonment rate, of 39 per 100,000, people identified as Native Hawaiian and Pacific Islander have a rate more than 12 times as high, at 497 per 100,000 (Carson 2021). In addition, there is substantial evidence of labor force discrimination against formerly incarcerated people, both due to concerns about recidivism and gaps in work experience, and also due to a general stigma above and beyond productivity-related factors (Agan and Starr 2018). This discrimination is at times codified in restrictions that keep them from working in certain sectors; a number of States deny occupational licenses to those with a prior arrest or conviction (Sibilla 2020). Chapter 4 provides further detail on some of the obstacles that limit the employment opportunities of formerly incarcerated people. Even if the barriers faced by the formerly incarcerated were not racially targeted by design, higher rates of incarceration for certain racial groups mean that these employment barriers disproportionately block members of these groups, resulting in a structural form of racial discrimination. In some cases, the long-run impact of historical racial discrimination can result in economic indicators that might naively be interpreted as evidence that discrimination has been overcome. Suzuki (1995) examines the improvement in economic outcomes for Japanese immigrants between 1920 and 1930, as measured by a greater share employed in “professional” For further discussion of this application, see the notes on structural economic racism by Acemoglu and Wolitzky (2011). 1 Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 175 and higher-paid occupations during this period.2 These patterns are cited by some as an example of exceptionalism among Asian American families, which continue to have some of the highest levels of earnings among different racial and ethnic groups. Suzuki (1995) challenges this common narrative, pointing out that during that 1920–30 period, nine States passed laws banning the purchase of farmland by Japanese immigrants, the Supreme Court deemed Japanese people ineligible for naturalization as they were neither white nor of African descent, and the U.S. government passed a law excluding Japanese immigrants. The author also shows that the laws were associated with a significant return of these immigrants to Japan, and that this outflow was disproportionately made up of those in lower-earning occupations. Thus, the apparent economic success story of Japanese immigrants may have actually been driven by highly discriminatory policies that resulted in selection bias among those who remained here. One of the most notable cases of historic economic stratification involves the widespread dispossession of land from indigenous people and nations during the expansion of U.S. territory that began in the late 1700s. Carlos, Feir, and Redish (2021) argue that though historians often highlight the key roles of abundant land, property rights, and the rule of law in U.S. economic development, these discussions erase the simultaneous erosion of these very same inputs and institutions for members of existing Native groups and entities. In addition to the direct types of harm caused by the often-violent process of relocation and geographic restriction, the centurieslong process helped give rise to adverse economic outcomes for present-day American Indians and Alaska Natives. As just one example, Akee (2020) studies the Nelson Act of 1889, which took collectively held property of the Minnesota Anishinabe reservations and allotted parcels to individual owners, allowing them to sell lands to non-Indian buyers (U.S. Congress 1889). While increased private ownership of land might be expected to support a more productive use of land, Akee (2020) finds, compared with reservations not affected by the allotment, a rapid reduction in land ownership, home ownership, and self-employed farming, along with an increase in renting and wage labor in the timber industry. These reductions in land and capital ownership likely resulted in lower wealth levels and poorer economic outcomes for subsequent Anishinabe generations. Gender-based occupational segregation and bias. Beyond employer discrimination in hiring and promotion, economists have also considered broader sources of gender inequality in the labor market, such as occupational segregation and employers’ assumptions about the division of labor in the household. Occupational segregation plays a major role in the gender wage gap. Research finds that differences in the types of occupation and Although income itself may be considered a better measure, it was not captured by the Census surveys used for this analysis. 2 176 | Chapter 5 industries in which men and women work are some of the largest contributors to the wage gap, accounting for one-third to one-half of the gap (Blau and Kahn 2017; Foster et al. 2020). There is also evidence that the gender wage gap is linked to the disproportionate rewards for long hours and weekend work in some occupations (Goldin 2014; Foster et al. 2020). Although occupational segregation by gender has been decreasing over time, progress has stalled in recent decades (del Río and Alonso-Villar 2015). In the years 2011–15, more than 40 percent of workers were in occupations in which more than three-fourths of workers were of one gender, with women more likely to be in low-paying occupations (Gould, Schieder, and Geier 2016). Women are more likely to enter occupations that entail caring for others. For example, 94 percent of workers in the childcare sector and 89 percent of workers in home health care are women; of those, Black, Hispanic, and Asian American / Pacific Islander women are overrepresented relative to their share in the overall workforce (Gould, Sawo, and Banerjee 2021). Average wages in these sectors are roughly half the average among workers overall. Furthermore, research has documented a wage penalty associated with certain caregiving occupations that persists after controlling for the education and skills required for these jobs (England, Budig, and Folbre 2002; Barron and West 2011; Pietrykowski 2017; Budig, Hodges, and England 2019; Folbre and Smith 2017). This “care penalty” means that even highly skilled care workers may be paid less than they would be in jobs that require similar qualifications but do not involve caregiving. Estimates of the care penalty vary across studies, but the most comprehensive recent study finds a 15 percent wage penalty for female childcare workers, nursing aides, and health aides (Budig, Hodges, and England 2019). The study also finds a 6 percent wage penalty among men in these fields, consistent with other studies that find that the wage penalties in these caregiving occupations are not confined to women. Recent research has found evidence that stereotypes about gender-specific skills and gender-specific roles can explain at least some of this occupational segregation (Bertrand 2020; Levanon, England, and Allison 2009; Pan 2015). The predominance of women in relatively low-paying occupations translates into greater gender wage inequality. Another source of gender inequality relates to the division of labor in the household, as well as employers’ assumptions about it. Though the increase in women’s labor force participation has been accompanied by a decrease in their average time spent on household labor (including housework and child care), research shows that women spend a higher fraction of their hours in unpaid family care and that men spend a higher fraction of their hours in paid work (Bianchi et al. 2012). In 2019, mothers spent almost double the amount of time as fathers caring for children in the household (BLS 2020). This is true regardless of a woman’s wages relative to those of her spouse, as Siminski and Yetsenga (2021) find even Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 177 at the extreme—where women’s wages are more than double those of their spouses—women do 44 percent more household work. A potential result of imbalances within the household is that mothers experience long-term wage penalties related to the reduction in labor supply and loss of work experience that occurs when a child is added to their household (Kleven et al. 2019). In addition to the direct effect of this period of labor force exit on mothers’ long-term earnings, experimental evidence shows that employers’ expectations of women’s greater childcare responsibilities can influence women’s labor market outcomes. A résumé study modeled on the research of Bertrand and Mullainathan (2004) found that prospective employers were almost twice as likely to call back women without children as they were women with children, while their callbacks of men were unaffected by fatherhood status (Correll, Benard, and Paik 2007). Petit (2007) similarly uses a résumé study to find significant hiring discrimination against young women for high-skill positions in the French finance industry, where time off for dependent care may be particularly penalized. How Inequality Affects Economic Efficiency and Growth Although part of the motivation for addressing imperfect competition in labor markets and discrimination is rooted in the spirit of fairness and justice, there is also an important case to be made that such measures can contribute to overall economic output and growth. When the policies that reduce inequality also serve to curtail costly rent seeking, economic efficiency and productivity are improved. Similarly, when the inequality stems from barriers that have kept some from fully taking part in the economy, removal of these barriers supports economic growth. Monopsony Power Produces Inefficient Labor Market Outcomes As explained above, firms with monopsony power in the labor market can set lower wages and employ fewer workers than they would under more competitive conditions, contributing to wage inequality. These inefficiently low levels of employment also directly hurt economic output.3 A recent study estimates that monopsony power in the U.S. economy reduces overall economic output by 13 percent (Naidu, Posner, and Weyl 2018). In addition, noncompete clauses and no-poach agreements, along with nondisclosure agreements and pay secrecy practices, can harm workers throughout the wage distribution. By reducing competition among employers and limiting workers’ mobility, these restrictive employment practices reduce economic In addition, lower levels of employment and lower wages mean that there are fewer workers and that these workers have less money to spend, thereby reducing consumer demand. This reduction in consumer demand will, in turn, create a drag on overall economic growth in the long term (Caldwell and Naidu 2020). 3 178 | Chapter 5 efficiency by preventing some workers from finding the job that best matches their qualifications. Discrimination Misallocates Talent and Suppresses Innovation A number of empirical studies argue that various forms of racial and gender discrimination can sideline talented workers, resulting in slower economic growth. For example, a recent study by Buckman and others (2021) estimates that if employment, education, and earnings were equalized across racial and ethnic groups over the period from 1990 to 2019, gross domestic product would have increased by $22.9 trillion. These gains emerge both by allowing current workers to fully realize their potential, and also by signaling a more reliable return to investments in skills among underrepresented racial groups, which yields growth in the future. Likewise, Hsieh and others (2019) show that increased access to high-income occupations for underrepresented groups, over the period from 1960 to 2021, accounted for 20 to 40 percent of growth in aggregate output. Bucknor and Barber (2016) estimate an $80 billion cost to gross domestic product due to lower levels of employment among those who are formerly incarcerated, which is in part driven by discrimination and disproportionately affects Black, Hispanic, American Indian, and Alaska Native communities. Finally, research by Cook (2014) finds that racist violence led to hundreds of fewer patents by African American inventors in the late 19th and early 20th centuries, and a study by Cook and Gerson (2019) shows how closing the gaps in patenting for women and underrepresented minorities can increase economic growth. As a concrete example, research shows that alleviating entrenched racism in the South was associated with greater regional economic growth. The brief period of increased Black political power in the South during Reconstruction saw increases in taxation and spending on public education (Logan 2020). Likewise, the Great Mississippi Flood of 1927, which forced the migration of Black workers to industrial cities and reduced the coercive powers of southern landowners, resulted in a greater reliance on capital investment and technology adoption (Hornbeck and Naidu 2014) in the region. Subsequent economic growth in these regions suggest that private gains from coercive labor practices had come at the expense of more socially valuable investment and efficient production. Most notably, Wright (2013) argues that the revolutionary changes brought about by the Civil Rights Movement led to improvements in access to jobs, education, and health care that yielded benefits not only for Black southerners but also for the entire southern economy, helping to partially undo decades of underdevelopment. Overall, the moments in history where entrenched racism in the South was partially dislodged have tended to be times where the region has best been able to catch up with the more industrialized northern economy. Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 179 Discrimination Reduces Incentives for Human Capital Investment Discrimination and monopsony power can also have large, long-term negative effects on economic growth if they reduce the extent to which the affected individuals invest in their education and skill development. A worker who expects to be paid a wage lower than their productivity, whether due to discrimination or an employer’s monopsony power, may have less incentive to engage in activities like training that could increase their productivity, compounding already-existing barriers to such training. For example, in one study, Latina high school students who anticipated future career barriers due to their immigration status were found more likely to plan to attend a two-year college than a four-year college (McWhirter, Ramos, and Medina 2013). The benefits of greater human capital development for economic growth are discussed in more detail in chapter 4. Policies to Address Sources of Labor Market Inequality Addressing inequality is important for ensuring that people are rewarded fairly for their efforts and contributions to productivity as well as for fostering stronger productivity and growth. Because this occurs in so many ways, there are no one-size-fits-all solutions. Instead, there are a number of specific policies designed to address the inequality that stems from noncompetitive and discriminatory market outcomes, as well as policies that address larger, structural problems. Core to addressing inequality is increased enforcement of current labor protection and antidiscrimination laws. The 1935 National Labor Relations Act (U.S. Congress 1935), which established the National Labor Relations Board; the 1938 Fair Labor Standards Act (U.S. Congress 1938), which led to the Wage and Hour Division at the Department of Labor; and the 1964 Civil Rights Act (U.S. Congress 1964), which established the Equal Employment and Opportunity Commission, are each important to ensuring that workers are treated fairly. More recent policies, such as the Americans with Disabilities Act of 1990 (U.S. Congress 1990) and the Family and Medical Leave Act of 1993 (U.S. Congress 1993), have focused on particular equity concerns. The proposed Equality Act, if passed, would prohibit additional forms of discrimination, including on the basis of sexual orientation and gender identity in settings beyond the realm of employment (U.S. Congress 2021e). Research on the effects of laws prohibiting discrimination against workers generally finds positive effects on outcomes for the intended beneficiaries (for studies of specific groups, see Collins 2003; Neumark and Stock 2006; and Neumark et al. 2019). These results also underscore the need to address workers’ misclassification, whereby workers who should be 180 | Chapter 5 classified as employees, and therefore receive coverage of the above laws, are instead treated as independent contractors. More general economic policies have the potential to further counteract the forces that underlie wage inequality and racial/gender discrimination. Though far from an exhaustive list, this section surveys several such policies. Promoting Competition Healthy market competition is fundamental to a well-functioning U.S. economy. Basic economic theory demonstrates that when firms must compete for customers, it generally leads to lower prices, higher-quality goods and services, greater variety, and more innovation. In 2021, President Biden signed the Executive Order on Promoting Competition in the American Economy, establishing a multiagency approach to push back on decades of decline in competition. The Executive Order not only calls on the traditional antitrust agencies—the Department of Justice (DOJ) and the Federal Trade Commission (FTC)—to enforce existing laws vigorously and to consider updating their merger guidelines; it also directs all agencies and departments to use their detailed knowledge and expertise to ensure that their work clearly supports competition in the markets they regulate (White House 2021c). This whole-of-government approach is designed to address the concern that antitrust agencies are limited both by resources and the current judicial interpretation of the antitrust laws. It also relies on the fact that Congress has delegated authority to police anticompetitive conduct and oversee mergers to many agencies—not just the DOJ and the FTC. The Executive Order therefore directs or encourages roughly a dozen agencies to engage in more than 70 specific actions that will remove barriers to entry and encourage more competition. Increased enforcement of antitrust laws would also alleviate labor market monopsony and therefore its negative effects on wages, equality, and race- and gender-based pay gaps (Marinescu and Posner 2019). Antitrust law has been used to combat no-poaching agreements, noncompete agreements, and related contractual restrictions on workers’ mobility. It can also be used to block mergers that would concentrate labor markets excessively and to penalize large employers that use illegal methods to obtain or maintain labor monopsonies. Though some of these uses of antitrust law have been rare until recently, the Executive Order on Promoting Competition calls for agencies to make greater use of antitrust law to promote competition in labor markets. For example, the DOJ and the FTC have begun the process for revising the merger guidelines, and have called for public comment on labor market implications (Federal Trade Commission 2022). Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 181 Unions and Labor Market Equity Unions can provide workers the increased leverage to bargain with their employer, serving as a counterweight to the power that employers have to set wages and working conditions. Numerous studies support this notion, including research showing that unions’ negotiating power increases wages (Card 1996; Chava, Danis, and Hsu 2020), and that union representation also increases worker satisfaction and job tenure (Freeman and Medoff 1984). Unions also give workers a voice, which can improve productivity (Cai and Wang 2020). In the presence of employer monopsony power, the compensation gains achieved by unions may shift economic rents from employers to employees, reducing inequality without significant efficiency costs. Consistent with this view, higher rates of unionization have been shown to mitigate the negative effect of monopsony on wages (Benmelech, Bergman, and Kim 2020; Qiu and Sojourner 2019; Prager and Schmitt 2021; Dodini, Salvanes, and Willen 2022), and there has been, historically, an inverse relationship between the degree of union membership and income inequality (Farber et al. 2021). Unions also have the potential to foster equitable pay and working conditions for people of different genders and racial and ethnic backgrounds. For example, higher rates of union membership among Black workers have led to increased wages; and, for Black women, have led to a substantial reduction in the gap in their wages relative to white women (Rosenfeld and Kleykamp 2012). Also, collective bargaining is associated with lower gender wage gaps among teachers (Biasi and Sarsons 2022). This has not always been the case in U.S. history: some unions have, in the past, supported exclusionary, anti-Asian immigration policies (Frymer and Grumbach 2020), and major unions have at times faced criticism for discriminatory practices against Black workers (Hill 1959) or limited representation of women among leadership roles (Ledwith 2012). Nonetheless, labor unions were important proponents of the Civil Rights Act of 1964 (Collier and Grumbach 2022), and later waves of unionization in the United States have been associated with greater representation for women in these organizations (Milkman 1990). In 2021, union membership was quite diverse; more than a third of unionized workers are Black, Hispanic, Asian, or members of another nonwhite group, and almost half are women (BLS 2022b). And among white workers, Frymer and Grumbach (2020) find that union membership leads to lower racial resentment and greater support for policies that benefit African Americans. Despite declining union membership since the 1960s, almost half of nonunionized workers report interest in joining a union if one were available at their workplace (Hertel-Fernandez 2020), suggesting that there is a valuable role for policy efforts that support the right to union organizing. To 182 | Chapter 5 support these efforts, President Biden signed Executive Order 14025, which established the Task Force on Worker Organizing and Empowerment (White House 2021d). The Task Force, charged with identifying how the executive branch could support worker power and collective bargaining, released 70 recommendations focusing on how the Federal government can serve as a model employer and support workers by sharing information and improving transparency when it comes to organizing rights (Harris and Walsh 2022). In addition to the executive branch’s efforts, key legislation related to worker empowerment includes the Protecting the Right to Organize (PRO) Act (U.S. Congress 2021a). The PRO Act aims to protect workers’ right to join a union by introducing penalties for companies that violate workers’ rights, expanding workers’ collective bargaining rights, and ensuring access to fair union elections. The Public Service Freedom to Negotiate Act (U.S. Congress 2021b) similarly provides support to workers in the public sector, while the National Domestic Workers’ Bill of Rights (U.S. Congress 2021c) proposes to expand coverage of labor protections to domestic workers, providing greater regulation of labor standards for a sector that is disproportionately home to women, workers of color, and immigrants. The Minimum Wage The Fair Labor Standards Act was first signed into law over 80 years ago, and subsequent amendments have extended coverage to a broader range of workers. In addition, 30 States and the District of Columbia currently have a minimum wage that is higher than the Federal minimum (Department of Labor 2022b), and 40 localities have adopted minimum wages above their State minimum wage (Economic Policy Institute 2022). Mandating a minimum wage can decrease inequality by ensuring that those with the least earnings potential receive at least a minimum level of compensation for each hour they work. The potential for minimum wages to—on net—make low-paid workers better off depends on several factors, including whether employers have to compete for workers. A minimum wage could cause employers in a perfectly competitive labor market to cut back on hiring workers at the higher hourly rate. However, when workers’ wages are low due to a lack of competition or discrimination, minimum wage legislation may not be distortionary because employers are setting wages lower than a worker’s productivity and hiring fewer workers than they would under more competitive conditions. Though debate continues on the employment effects of minimum wage laws (Neumark and Shirley 2021; Dube 2019; Cengiz et al. 2019; Card and Krueger 1994), recent empirical evidence indicates that they do not materially reduce employment in concentrated labor markets and may even increase employment as market concentration increases (Azar et al. 2019). This suggests that policies like the minimum wage can reduce Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 183 wage inequality without reducing employment or sacrificing economic output. The minimum wage has been shown to reduce inequality by increasing growth in earnings, with effects that persist over several years (Rinz and Voorheis 2018). When the Fair Labor Standards Act was amended in 1966 (U.S. Congress 1966) to extend Federal minimum wage coverage to some of the country’s lowest-paid sectors, wages increased and racial earnings gaps were reduced (Bailey, DiNardo, and Stuart 2020; Derenoncourt and Montialoux 2021). Derenoncourt and Montialoux (2021) estimate that the minimum wage law accounted for 20 percent of the reduction in the Black/ white earnings gap during the Civil Rights Era. Although legislation is required to increase the Federal minimum wage from its current level of $7.25 per hour, the Biden-Harris Administration’s Executive Order 14026 establishes a new hourly minimum wage of $15.00 for workers performing work on or in connection with covered Federal contracts (White House 2021e). In addition to directly lifting the wages of hundreds of thousands of contract workers, this Executive Order could have broader effects, as competitors in the same labor markets as Federal contractors may increase wages, too, as they seek to compete for workers (Derenoncourt et al. 2021). In addition, President Biden has endorsed several other adjustments to minimum wage policy, including raising the Federal minimum wage to $15 for all workers, indexing future increase to inflation, phasing out the lower minimum wage that applies to some workers who receive tips, and expanding coverage of the Federal minimum wage to teens and workers with disabilities, all of which are features of the proposed Raise the Wage Act of 2021 (U.S. Congress 2021d). Full Employment and Tight Labor Markets Although minimum wage legislation and support for unionization efforts can directly help to reduce overall wage inequality, fiscal and monetary policies to support full employment conditions can play a strong underlying role as well. Full employment—the lowest rate of unemployment possible without spurring inflation—can put workers in a position to demand pay increases in accordance with their productivity. This can both offset the market power of employers and limit their ability to engage in discriminatory practices. When the number of job openings relative to workers seeking jobs is high, there are improved outside options for all workers, which may be especially important for those subject to discrimination. For example, the American Rescue Plan, crafted both to address the COVID-19 pandemic and support the economy, contributed to much higher growth than anticipated, with over 6 million jobs added to the U.S. economy in 2021, the largest percentage rise during a calendar year since 1978. However, the world has learned 184 | Chapter 5 that expansionary fiscal policy can become challenging when the supply of goods and services is constrained, as has been the case during the pandemic. Research by Dahl and Knepper (2021) supports the idea that full employment can protect workers from discriminatory practices. They find that tighter labor markets and more generous unemployment insurance benefits, which allow job seekers greater ability to search for jobs, increase the reporting of sexual harassment by workers who may otherwise avoid reporting out of fear of retaliation. Beyond the substantial moral considerations, policies that support tighter labor markets and help limit gender discrimination in the workplace may also improve economic efficiency by allowing bad actors to be identified and held accountable, rewarding good employers, and ensuring better matches between employers and employees. Dahl and Knepper (2021) find similar evidence from discrimination claims that tighter labor markets reduce age-related discrimination. There is also evidence that tighter labor markets can reduce the gender wage gap, as shown by Biddle and Hamermesh (2013). In contrast, however, the authors find that Black/white gaps in wages are actually larger during tighter labor markets, though that may be partially due to the fact that more low-wage Black workers are able to enter the workforce when unemployment is low (Ashenfelter 1970; Freeman et al. 1973). Indeed, other research finds that the Black/white gap in unemployment tends to fall during tighter labor markets (Rodgers 2008; Hoynes, Miller, and Schaller 2012; Cajner et al. 2017). This smaller Black/white gap in unemployment during tight labor markets does not appear to operate through lower levels of racial discrimination in callbacks to job applicants, however. A number of résumé studies have shown that the gap in callbacks between these groups persists through periods of both high and low unemployment (Bertrand and Mullainathan 2004; Nunley et al. 2015; Quillian et al. 2017). Care Economy Policies The provision of affordable childcare and early childhood education in the United States has the potential to reduce gender wage inequality by helping to support the paid labor force participation of women in families with children and reducing care-related discrimination by employers. The pandemic highlighted the importance of the availability of care, as school and childcare closures exacerbated existing shortages in the availability of care (Carson and Mattingly 2020). Childcare and universal preschool can ease the trade-offs that families with children must make between care responsibilities and paid work. But many families find the prices for high-quality childcare and early childhood education on the private market unaffordable, and credit constraints may keep them from accessing needed childcare at a time in their lives when their earnings and savings are lowest Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 185 (U.S. Department of the Treasury 2021b). Subsidizing childcare and providing universal public preschool, therefore, can help many families access otherwise unaffordable options. In addition, there may be positive economic spillovers that parents do not completely factor in when deciding whether to purchase childcare or early childhood education. As discussed in chapter 4, high-quality childcare provides long-lasting benefits for children, especially those who are more economically disadvantaged (Herbst 2017), thereby benefiting the rest of society by fostering economic growth. Moreover, viable options for childcare and preschool, by providing parents with the option to remain in the paid workforce, can mitigate the motherhood penalty associated with a labor force exit and reduce the likelihood of employer discrimination related to expectations of childcare responsibilities that arise even for women without children. Much research on past childcare and preschool programs has found positive effects on maternal labor force participation and household income (Blau and Kahn 2013; Davis et al. 2018; Herbst 2017; Morrissey 2017; Bauernschuster and Schlotter 2015; Wikle and Wilson 2021). Olivetti and Petrongolo (2017) examine cross-country differences and find that the provision of early education and childcare are particularly beneficial to women’s employment and earnings. In contrast, Kleven and others (2021) find that the expansion of parental leave and subsidized childcare in Austria had no effect on gender inequality in the labor market. This suggests that the provision of generous family policies is necessary, but not always sufficient, to reduce motherhood penalties in the labor market. Whether or not they are sufficient to reduce motherhood penalties, generous family policies do allow parents to ensure that their children will receive high-quality care while they have the option to participate in the labor force. In addition, policies that support the care industry also have the potential to disrupt the “low road” equilibrium of low wages and difficult working conditions in this sector. Subsidies that bolster the wages of childcare workers, one of the lowest-paid occupations in the U.S. economy, can increase their earnings and expand employment. Moreover, given that the care sector is home to a disproportionate share of women—especially Black, Hispanic, and Asian American and Pacific Islander women—childcare subsidies can also directly reduce both gender and racial wage inequality. Another policy that could help families manage care responsibilities is the establishment of a national paid family and medical leave program, building on the 1993 Family and Medical Leave Act, which requires covered employers to provide employees with 12 weeks of unpaid leave to care for a new child, care for a seriously ill family member, or recover from the worker’s own serious illness. Paid family and medical leave programs have been enacted in nine U.S. States and the District of Columbia (Kaiser Family Foundation 2021). Paid leave used at the time of the birth of a child has been 186 | Chapter 5 shown to increase the mother’s attachment to the labor force (Byker 2016; Rossin-Slater, Ruhm, and Waldfogel 2013), which can potentially increase long-term earnings. Along with other policies that maintain their labor force participation, moderate lengths of parental leave can reduce motherhood wage penalties (Budig, Misra, and Boeckmann 2016). Paid leave may also produce labor supply benefits when used for other purposes, such as caring for a spouse with a work-limiting disability or a chronic health condition (Anand, Dague, and Wagner 2021). The structure of parental leave in the United States differs markedly from that of other countries, where parental leave is often tied to a child, and family members can choose who takes the leave. In contrast, leave in the United States is tied to the worker, and cannot be transferred between family members. This means that parents of a new child can maximize their combined parental leave by having more than one parent take it. This nontransferable leave has the potential to reduce care-based discrimination against women by creating an incentive for both men and women to use it. Research has shown that when other countries have introduced policies designed to increase fathers’ use of parental leave, the labor supply and earnings of mothers have increased, though the persistence of the effects has varied (Dunatchik and Ozcan 2020; Druedahl, Ejrnaes, and Jorgensen 2019). Such polices have also had positive health effects on mothers as well as long-lasting effects on the division of labor in the household (Patnaik 2019; Persson and Rossin-Slater 2019). Progressive and Equitable Tax Policy A progressive system of taxation, where higher-income households pay a greater share of their income in taxes, can play an important role in reducing inequality, including that which is driven by differences in skills and luck, or other forces that remain even when barriers to competition have been addressed. Figure 5-6 demonstrates how the combination of means-tested transfers and Federal income taxes increased incomes of the lowest quintile by 68 percent, and reduced incomes in the highest quintile by 24 percent. Using an alternative summary measure of income inequality, the Gini coefficient was reduced by 8 percent by taxes and transfers in 2018. And given that white women and Black, American Indian, Alaska Native, and Hispanic people of any gender are overrepresented in the low-wage workforce, progressive taxation can also reduce racial, ethnic, and gender inequality. Tax credits that provide direct transfers to middle- and lower-income households can support the goals of reducing inequality and enhancing equity. The Child Tax Credit has emerged as a key lever in this area. While this credit traditionally accrued to largely middle-income households, the American Rescue Plan Act temporarily increased the credit and made it fully Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 187 Figure 5-6. Average Income, Means-Tested Transfers, and Federal Taxes, 2018 Thousands of 2021 dollars $400 Highest quintile Income $350 before taxes and transfers $300 + - Meanstested transfers Federal taxes = Income after taxes and transfers $250 $200 $150 $100 Lowest quintile $50 $0 Income quintiles Sources: Congressional Budget Office; Haver analytics; CEA calculations. refundable in 2021, allowing all households at the lower end of the income distribution to receive the maximum credit, even if they had no tax liability. The most direct impact of these changes was to reduce poverty, especially for children in recipient households, with the greatest estimated reductions in poverty for Black and Latino children (Center on Poverty and Social Policy 2021). These credits also support investments in human capital, such as educational attainment, as discussed in chapter 4, and the associated longrun increases in employment, earnings, and longevity. A key challenge to progressivity is the preferential tax treatment of capital income—such as dividends generated from an investment or the gain in the value of stocks or other assets (Tax Policy Center 2020). Capital income is generally taxed at lower rates than wage and salary income, and the increase in the market value of stocks and many other assets is not taxed until the gain is “realized” when the asset is sold. Thus, these capital gains are allowed to accrue and compound for years before being taxed, and, if passed on at death without being sold, the gains in the value of the asset over the lifetime of the holder will escape taxation completely. Recent research shows that when capital income is instead counted as income in the year it accrues, the 400 wealthiest households pay between 6 and 12 percent of their income in taxes (Leiserson and Yagan 2021). This is a much lower rate than would be paid by households that had received all their earnings through labor income, and because capital income is concentrated among higher income households, these factors tend to exacerbate inequality in after-tax income. 188 | Chapter 5 In addition, households with significant capital income are more likely to get away with tax evasion. It is estimated that nearly 99 percent of income taxes on labor wages and salary are paid, while a much lower percentage of taxes owed are collected on the forms of income, such as short-term capital gains, that are more likely to be accrued by higher-income households. (U.S. Department of the Treasury 2021c; Internal Revenue Service 2019). Recent research suggests that highly sophisticated forms of tax evasion, including through offshore accounts and pass-through businesses, go undetected and account for nearly one-third of evasion (Guyton et al. 2021). Moreover, while audits by the Internal Revenue Service (IRS) have decreased in general in recent years, they have decreased more rapidly among higherincome earnings, skewing enforcement toward a group with lower rates of underpayment (Sarin 2021). One reason for a decline in audits among higher-income taxpayers is that audits among this group are costly—they have access to advanced forms of evasion—and the IRS has been underfunded during the last decade. Policies that achieve greater parity in tax rates on capital income relative to labor income, and greater funding for the IRS to enhance taxpayer compliance, can therefore improve the progressivity of the tax code. This includes taxing capital income at ordinary income tax rates and taxing the capital gains on assets transferred at death, both of which were proposed, with some progressive exclusions, as a part of the revenue policies in President Biden’s Fiscal Year 2022 budget (U.S. Department of the Treasury 2021a). On the tax compliance side, this budget also outlined a number of improvements to the IRS’s enforcement capability, including additional funding to help combat sophisticated forms of tax evasion, better information from third-party reporters on capital income, technological upgrades at the IRS, and improved regulation of paid tax preparers. This combination of policies would likely increase the effective tax rate faced by those with capital income, which, given the concentration of capital income among the richest households and the underrepresentation of marginalized groups among this category, would facilitate greater progressivity and racial and ethnic equity in the tax code. Conclusion This chapter has explored and defined the scope of forces that keep labor and product markets from being truly competitive, and that prevent individuals from reaching their full potential. These include a lack of competition in markets affecting a broad range of workers, and racial and gender discrimination more specifically. The costs of ignoring these structural forces are increased inequality and reduced economic growth and output. These societal and economic costs stem from inefficient labor market outcomes, Barriers to Economic Equality: The Role of Monopsony, Monopoly, and Discrimination | 189 misallocated talent, suppressed innovation, and reduced incentives for human capital investment. Government actions can curtail these forces by enforcing existing antidiscrimination laws and promoting competition in the economy—at large, and in labor markets in particular. Policies that establish a minimum wage or protect the rights of workers to join a union are examples of actions that counterbalance employers’ market power, while government support for the care economy can bolster wages and increase employment in that sector. These and other polices can begin relieving the historical burdens on disadvantaged groups of workers, helping to reduce inequality and bolster economic output and growth. 190 | Chapter 5 Chapter 6 Building Resilient Supply Chains The year 2021 was when supply chains—the networks of producers, transportation companies, and distribution centers that develop and move products and services—entered dinner table conversations. Though this term has certainly been part of the lexicon going back to the 1980s, and has been a part of doing business for centuries, COVID-19 highlighted supply chains’ vulnerabilities, which became front-page news. Supply chains have become more complex, interconnected, and global than they were in decades past. The share of world trade that crossed at least two borders increased from 37 percent in 1970 to nearly 50 percent in 2014 (World Bank 2020a, 2020b). This increasing segmentation of the production process has reduced prices in the United States, while also raising productivity and aggregate incomes in many of the low-income countries that are integral to global supply chains (World Bank 2020a). However, the globalization of production has also made supply chains more vulnerable to disruption. This fragility has been exacerbated as firms have removed excess capacity (e.g., extra inventory, or reserves of people with the time and skills to solve problems), making supply chains less resilient. That is, they have less ability to recover quickly from unexpected events. Thus, though modern supply chains have driven down consumer prices for many goods, they can also easily break (Brede and de Vries 2009; Baldwin and Freeman 2021; Miroudot 2020; de Sá et al. 2019; White House 2021a). Though it was not inevitable, movement toward this more fragile configuration has been happening for decades, as public and private policies have undermined firms’ incentives to invest in such capacity to ensure 191 resilience. The COVID-19 pandemic is not the first time that supply chains have been disrupted; the production and distribution of goods have been regularly snarled by natural disasters, cyberattacks, labor strikes, supplier bankruptcies, industrial accidents, and climate-induced weather emergencies (de Sá et al. 2019). The pandemic simply exposed just how complex and interconnected modern supply chains have become. These highly publicized disruptions and product shortages made the public painfully aware of the many steps involved in getting a product produced, transported, and placed on shelves or doorsteps. The first section of this chapter describes modern supply chains and explains their evolution, focusing on manufacturing. Supply chains are shaped by a complex network of relationships; these relationships affect not just the movement of supplies from place to place but also the incentives of lead firms and suppliers to invest in producing new products, in providing good jobs, and in achieving resilience. The second section describes how increasingly frequent disruptions of the economy suggest that supply chain fragility will continue to be a problem. The third section outlines the private sector’s incentives to become more resilient in the face of these challenges. Finally, the fourth section suggests vital roles for government in helping to shape supply chains and overcome market failures. 21st-Century Supply Chains Supply chains are the linkages in the production process that facilitate the transformation of raw materials into finished goods or services. A supply chain is made up of producers and logistics providers that move inputs from one stage to the next, and also of participants in the distribution channels for the finished product, including wholesalers, distributers, and retailers. This chapter primarily focuses on manufacturing supply chains that facilitate the production of physical products from unprocessed materials.1 Figure 6-1 depicts some of the ways supply chains are commonly organized. Even within the same industry, firms have different supply chain In addition to goods, services are also part of supply chains and often face some of the same issues that are discussed in this chapter. 1 192 | Chapter 6 Figure 6-1. Common Types of Supply Chains B C D Country B Country A A Source: Adapted from Cavalho (2014). Note: From left to right: A, vertical integration with isolated industries; B, outsourcing with isolated industries; C, outsourcing and offshoring with isolated industries; D, outsourcing with a central node (starshaped). Arrows denote flows of products, information, and the like between companies. configurations (Kamalahmadi and Parast 2016; Lund et al. 2020). This figure gives four stylized examples of how supply chain relationships could be formed: Vertical Integration with Isolated Industries Panel A of figure 6-1 illustrates a three-firm configuration, where each firm (shown by the dots in the figure) is self-sufficient—that is, completely vertically integrated. Thus each firm produces everything, starting from raw materials and ending with the finished product. In this configuration, supply chains are completely internal to a firm. A prototypical example of this is the automaker Ford’s River Rouge Plant, which in the 1930s included a steel mill, glass factory, power plant, rubber factory, foundries, machine shops, stamping plants, assembly lines, a cement plant, a paper mill, a leather Building Resilient Supply Chains | 193 plant, and a textile mill (Weber 2019). Ford also owned a rubber plantation in Brazil, coal mines in Kentucky and West Virginia, and railway cars to transport raw materials. This allowed Ford to maintain direct control over the entire manufacturing process. However, this complete vertical integration also made it difficult for Ford to cut costs during the sharp decrease in demand for cars during the Great Depression, as the automaker continued to bear the fixed costs of component production. In contrast, Chrysler, which was much less vertically integrated during this time period, did not need to bear these fixed capital and administrative costs; Chrysler’s suppliers did (Chandler 1962, 1992). A firm’s decision to vertically integrate depends in part on whether the costs of transacting in different markets outweighs the cost of managing these activities internally (Coase 1937). Outsourcing with Isolated Industries Panel B of figure 6-1 represents three industries, each with significant supply-chain relationships. Here, inputs travel “downstream,” where they are transformed into a final good. The lead firm typically designs products and directs production by multiple tiers of suppliers in many locations, but it does not own most of these suppliers. This is called outsourcing. Outsourcing allows the lead firm to contract with firms that may have lower production costs due to lower wages or other competitive advantages (see box 6-3 below). The chain includes direct suppliers of the lead firm (tier 1 suppliers), as well as suppliers to those suppliers (tier 2 suppliers), and so on—all the way back to the raw materials used to produce the good. A firm can have hundreds of tier 1 suppliers and thousands of tier 2 suppliers, as shown in figure 6-2 (Lund et al. 2020).2 Looking at the publicly disclosed lists of suppliers for 668 companies, the McKinsey Global Institute found that the number of direct suppliers was large and that the network of indirect suppliers was even larger, often numbering in the thousands (Lund et al. 2020). As discussed below, the degree of coordination between the firms, represented by the arrows in figure 6-1, can vary between two extremes: arm’s-length transactions and collaborative relationships. Offshoring and Outsourcing with Isolated Industries If lead firms choose suppliers across national boundaries, this is called offshoring, as shown in panel C of figure 6-1. Offshoring gives companies expanded scope to locate production in areas with lower wages, or that have other competitive advantages not available in their home country, such Note that, due to data limitations, the tier 2 suppliers in figure 6-2 may not be supplying inputs into the lead firms’ products; rather, they are suppliers of the tier 1 suppliers, which usually produce for more than one lead firm. 2 194 | Chapter 6 Figure 6-2. Examples of Tier 1 and Tier 2 Supply Relationships General Motors 18,000+ Airbus Apple 12,000+ 7,400+ 856 Nestlé 1,676 Publicly disclosed tier 1 suppliers 638 5,000+ 717 Tier 2 suppliers and below Source: Adapted from Lund et al. (2020), relying on the Bloomberg Supply Chain Database. as access to natural resources or better technology (Antràs 2020; World Bank 2020b). Competitive advantage may be the result of naturally occurring endowments or developed by government or private sector policies (Mazzucato 2016; Lee 1995). In the past, internationally traded goods were largely either raw materials, such as cotton, or finished goods, such as clothing. Since the early 1990s, there has been a large rise in trade of “intermediate goods” or components, such as fabric that has been cut but not sewn. In both panels B and C of figure 6-1, no connections exist between the blue industry and the parallel orange and black industries. In this diagram, nodes are industries with few overlapping suppliers, such as electronics and autos in the past. Outsourcing with a Central Node In contrast to the isolated industries depicted in panels B and C of figure 6-1, supplier firms usually sell to more than one lead firm and may sell to several different industries, as shown in panel D (Carvalho and Tahbaz-Salehi 2019; Carvalho 2014). One example is a star-shaped configuration, with one central node (the green node) that is used in production by all other nodes. Firms in this general-purpose industry supply a wide number of other industries and often also use inputs from the industries they supply (Carvalho 2014).3 These types of supplier relationships allow firms to take advantage of In practice, some suppliers, and even the central node of panel D, may be offshored as well as outsourced; for simplicity, this configuration is not depicted. 3 Building Resilient Supply Chains | 195 economies of scale, where per-unit costs decrease as the number of units produced increases and the supplier is able to sell to multiple firms. Firms’ decisions regarding the design of their supply chains lead to a complex web of connections. Aggregating firm-to-firm supply chain connections, industry A has supply chain connections to industry B when firms in industry A purchase inputs from firms in industry B. Though comprehensive data on firm-to-firm supply relationships are lacking for the United States, the network structure of the U.S. economy can be visualized at an industry level. This industry-level analysis can shed light on which industries supply inputs to many other industries and the structure of network connections between industries. These connections can amplify microeconomic disruptions. The U.S. economy is complex and interconnected, with several central hub industries that have connections to most other sectors. Using the most disaggregated, publicly available sectoral data—the Bureau of Economic Analysis’s (BEA) Input-Output Accounts Data—it is possible to see the supply chain connections between 417 different industry sectors, as depicted in figure 6-3. Each node is a sector, and the connections between them represent flows of inputs from one supplying sector to another. The network is sparsely connected; on average, each narrowly defined industry is connected to only 11 other industries (Carvalho 2014). However, a small number of hub industries are highly connected to many others in the network. Although most industry pairs are not directly linked, they are indirectly connected by a small number of steps through these hub industries (Carvalho 2014). The most-connected input supply sectors (the numbered nodes) in 2002 included real estate, electricity generation and distribution, iron and steel mills, depository and credit intermediation, petroleum refineries, and truck transportation (Carvalho 2014). The CEA’s analysis of the 2012 input-output tables shows that semiconductors have become a highly connected industry, while truck transportation has dropped from the top 10 list (Carvalho 2014; Bureau of Economic Analysis 2012). Other countries also have similar patterns of central hub industries, though the central industries may be different (Carvalho and Tahbaz-Salehi 2019; Fadinger, Ghiglino, and Teteryatnikova 2015; McNerney, Fath, and Silverberg 2013). Arm’s-Length and Collaborative Relationships The arrows in figure 6-1 represent connections between the nodes in the supply chain. The nature of these connections can vary between two extremes: arm’s-length transactions and collaborative relationships. In an arm’s-length transaction, a firm purchases a standard input from an unaffiliated firm, often choosing from a large set of possible sellers. In this case, the connection is very simple: the seller provides an off-the-shelf 196 | Chapter 6 Figure 6-3. The Production Network Corresponding to U.S. Input-Output Data in 2002 Source: Carvalho (2014). Copyright the American Economic Association; reproduced with permission of the Journal of Economic Perspectives. product to the buyer, which sends payment. If there is a problem with a supplier (e.g., the price is too high or a disaster causes it to be unable to produce), the buyer can easily find another supplier. Lead firms may benefit from these relationships because they are able to easily change suppliers, creating competition that requires suppliers to reduce their prices to win business. In collaborative relationships, firms in a supply chain communicate frequently about the product and production process; performance requirements (e.g., price, quality, specifications, and delivery schedule) are customized for a particular product, and are usually set by the lead firm (Gereffi 2020). In some instances, these are transactions between affiliates of a large company, while others involve a lead firm and financially independent suppliers. For instance, companies such as Nike do not own the facilities in which their products are manufactured; instead, they provide the design, product specifications, advertising, distribution, and coordination of the complex network of contractors that make the shoes (Gereffi and Korzeniewicz 1994). Building Resilient Supply Chains | 197 Suppliers in collaborative relationships provide these highly customized inputs on a repeated basis, usually without complete or easily enforceable contracts (Hart and Moore 1990). Both the buyer and supplier invest in capital, equipment, or knowledge that is useful only with a particular partner (Antràs 2020). These relationship-specific investments increase the cost of finding and switching to a new supplier, but often pay off in components that better fit the lead firm’s needs and in quicker responses to unexpected situations (Antràs 2020; Helper 1991; Gibbons and Henderson 2011). A large literature describes the potential benefits to lead firms of having collaborative relations with suppliers, such as reduced costs, defect rates, and lead times; and increased investment, responsiveness, innovation, and problem solving (Delbufalo 2012; Gibbons and Henderson 2011; Aoki and Wilhelm 2017). A key reason for the long-term profitability of firms such as Toyota and Honda is their investment in collaborative relationships with their suppliers (Aoki and Wilhelm 2017; Liker 2004; Lieberman, Helper, and Demeester 1999). The rise of these sticky buyer–seller relationships is a distinctive aspect of the recent rise in global value chains (World Bank 2020a). Understanding why some firms adopt collaborative relationships and others do not is an area of active research in many disciplines, including economics, management, and sociology (Bernstein 2015; Gil and Zanarone 2018; Schrank and Whitford 2009). Box 6-1 provides an example of how one firm currently combines domestic production, offshoring, vertical integration, and offshoring to make its products. However, there is no single optimal way to organize a supply chain. Even within the same industry, firms often choose different strategies. For example, on average, automakers producing in the United States have 4.7 suppliers for each product category, a financial stake in 22 percent of transactions, and relationships with suppliers that last 2.4 years. However, there are substantial differences among automakers in these practices. Japanese vehicle manufacturers engage more in collaborative outsourcing than do their U.S. counterparts; therefore, Japanese relationships with suppliers last 70 percent longer, and they have fewer than half as many suppliers for each part as do U.S. automakers. These differences persist even when automakers are selling similar products in the same market, and after controlling for component volume and mix (Helper and Munasib 2022). Automakers differ in their offshoring strategies as well. For example, in 2020, Ford had 24 percent more production offshore than did Stellantis.4 These data, from the American Automobile Labeling Act (AALA), do not allow the separation of U.S. and Canadian content (Center for Automotive Research 2020). 44 198 | Chapter 6 Box 6-1. The Supply Chain of a Hot Tub The M9 hot tub is made by Bullfrog Spas in Utah, where 500 workers assemble almost 1,850 parts from 7 countries and 14 states (see figure 6-i). The hot tub top shell starts as a flat acrylic sheet from Kentucky, which is then combined with a different type of plastic in Nevada and sprayed with an industrial chemical from Georgia. Parts of the frame shell of the hot tub are driven in by trucks from Idaho several times a week. Many of the electric motors come from China and are assembled into water pumps in Mexico and then driven to Utah. Additional material for exterior cabinets is transported from Shanghai on container ships through the ports of Long Beach or Oakland. Water-spraying jets are made in Guangzhou, China; are sent through the Panama Canal and Eastern ports to the supplier’s warehouse in Cleveland, Tennessee; and then are sent on to Utah. Once fully assembled, the finished hot tubs are placed on trucks or trains and delivered to retailer warehouses. This example illustrates both the extent of outsourcing, which increases the number of individual companies involved in the production of a single good, and the geographic distance traveled by each component, estimated to total nearly 900,000 miles, as well as the dependence on transportation and logistics this entails. Figure 6-i. Sources of the Components of a Hot Tub Herriman, Utah Component Frame Seat backs Acrylic sheet Urethane Water jets Touch screens Electric motors Cabinet pieces Source: Adapted from Hufford, Kim, and Levinson (2021). Drivers of Change in Supply-Chain Structures Global supply chains that involve offshoring, and often outsourcing, multiplied rapidly from 1990 to 2008, though their growth slowed after the 2008 Building Resilient Supply Chains | 199 global financial crisis (World Bank 2020a). Manufacturing firms also outsource services, including logistics, cleaning, and security. That is, workers providing these services are no longer direct employees of manufacturers; instead, they work for financially independent contractors. For example, in food, cleaning, security, and logistics services, the share of those working for such contractors in the United States rose from about 5 percent to about 30 percent between 1950 and 2015 (Dorn, Schmieder, and Spletzer 2018). Two key changes have increased the attractiveness of outsourcing and offshoring. The first change is increased access to foreign suppliers, making offshoring more cost-effective for firms, largely due to advances in information technology (IT) and reductions in trade barriers since the 1990s. Advances in IT allow firms to convey detailed information about product and process specifications across long distances, while improvements in transportation, such as containerization, allow goods to be moved more quickly and consistently (Grossman and Rossi-Hansberg 2006). These developments make it possible to segment the production process, keeping highly skilled functions, such as research and development and management, in more advanced economies, while moving others, such as production of components or assembly, to countries with lower wages (Gereffi 2020). Major trading nations have signed agreements that reduced barriers to trade, such as the 1994 North American Free Trade Agreement. These trade pacts contain strong protections for property rights of corporations, but far weaker protections for labor rights. This disparity increased the attractiveness to multinational firms of offshoring production to low-wage countries (Drake 2018). The result has been increased availability of cheaper goods for American consumers, but also significant pressure on wages and benefits that have often driven workers from the middle class (Hakobyan and McLaren 2016). Finally, widespread international government subsidization of manufacturing industries has lowered prices that lead firms pay for inputs, and has oriented many nations’ domestic industry toward global supply chain participation (Hauge 2020). For instance, in the past few decades, the Taiwan Industrial Technology Research Institute has facilitated relationships between young, domestic semiconductor manufacturers and multinational buyers. The institute helped organize two firms—the Taiwan Semiconductor Manufacturing Company and the United Microelectronics Corporation—and gave them intellectual property. By 2020, these two companies accounted for 60 percent of global semiconductor revenue (Lee 2021; Breznitz 2005). Taiwan and China have extensively subsidized their semiconductor industries, with subsidies often approaching nearly 30 percent of a company’s revenue, according to the U.S. Department of Defense (2022, 36). “Made in China 2025,” China’s 10-year plan to transform itself into a world leader in high-tech industries, promotes policies that increase Chinese 200 | Chapter 6 firms’ market share and builds globally competitive industries in key sectors without relying on foreign firms (Congressional Research Service 2020). (See box 6-2.) The second key change is the growing role of financial criteria and institutions in corporate decisionmaking. This “financialization” of the economy has encouraged outsourcing and offshoring because of savings in costs that are easily measurable. Firms increasingly tie executive compensation to such financial measures as earnings per share, stock prices, and return on equity. Before the 1970s, only 16 percent of the chief executive officers in Standard & Poor’s 500 companies had compensation based on such measures; by the 1990s, 47 percent did, and in 2021, the vast majority employed by large corporations did (Admati 2017). Such incentives have encouraged managers to focus more on these financial statement numbers than on less easily measurable metrics, such as resilience. However, financial metrics can be misleading. Although an outside or offshore supplier may offer a lower unit price, these savings may be eaten away by hidden costs, such as longer lead times, increased vulnerability to disruption, and reduced access to ideas for innovation due to linguistic and geographic distance (Gray, Helper, and Osborn 2020). Such hard-toestimate costs are often ignored, even though they may negate the estimated savings from outsourcing (Barthelemy 2001). These less easily measurable metrics are often characterized as “soft” information, which, in contrast to “hard” information, may require knowledge of the environment and/or personal relationships to collect and understand. Soft information includes operational measures that use physical units, such as defect rates or downtime, and involve such intangibles as the value of research and development or of employee training (Liberti and Peterson 2019; Edmans, Heinle, and Huang 2016). It is often difficult to convert soft information into dollars. For example, it is not easy to measure how much an investment in training will improve quality, and how much this improvement in quality will flow to the bottom line. Such investments are also hard for outsiders or those without experience with a given product to verify. Thus, the pursuit of favorable performance as measured by financial indicators may induce firms to act in ways that could be detrimental to long-term performance, essentially trading longer-term resilience and sustainability for nearer-term profitability (Edmans, Heinle, and Huang 2016). For firms increasingly driven by short-term investors’ demands, the temptation to ignore these costs has often been great. A survey of senior U.S. financial executives found a willingness to sacrifice long-term shareholder value to meet Wall Street earnings targets or smooth reported earnings. For example, when managers were asked if they would “accept a sacrifice in value . . . to avoid volatile earnings,” 78 percent said yes; 55 percent would “delay starting a new project, even if this entails a small sacrifice in value” Building Resilient Supply Chains | 201 Box 6-2. The Role of China in U.S. Supply Chains A significant factor in the recent evolution of global supply chains has been the rise of China, which is now the largest source of U.S. imports. China’s manufacturing began exploding in the 1990s, and its share of world manufacturing exports rose from 3.1 percent in 1991 to 17.6 percent in 2015, before dipping to 14.2 percent in 2018 (Autor, Dorn, and Hansen 2021). Initially, China specialized in simply assembling products from imported components and designs. For example, it is estimated that in 2010 China provided less than 2 percent of the value added of the Apple iPhone 4; the product was designed in the United States, and the components were made in places like Japan and South Korea; no Chinese suppliers contributed components (Linden, Kraemer, and Dedrick 2011). However, China learned quickly, and for the iPhone X in 2018, it contributed more than 25 percent of the value added, including assembly and high-value components such as the battery pack and touch screen (Linden, Kraemer, and Dedrick 2007; Xing 2019). China’s entry into global supply chains was facilitated not only by technological advance in transportation and communication but also by changes in institutions. Particularly important were the United States’ granting of Permanent Normal Trading Relations (PNTR) to China in 2000 and the admission of China to the World Trade Organization in 2001, steps that gave imports from China permanent access to the relatively low tariff rates reserved for members of the World Trade Organization. These steps did not require China to change its labor policies, which banned workers from joining independent trade unions, involved reprisals against workers who sought higher wages, and involved forced labor. These policies suppressed wages in China, increasing the competitiveness of firms, including multinational firms, that produced there. China’s competitiveness was also facilitated by large subsidies, and requirements that multinationals transfer technology to Chinese firms. As the Congressional Research Service concluded, China aims to advance its national development goals and future global economic position through industrial policies that seek global civilian and military leadership in advanced and emerging technologies. China’s policies feature a heavy government role in directing and funding Chinese firms to obtain foreign expertise and intellectual property in strategic industries, including aerospace, semiconductors, microelectronics, pharmaceuticals, and electric vehicles (Congressional Research Service 2020). Through these policies, and aided by U.S. companies pursuing asset-light strategies, China gained large degrees of market power in a variety of critical supply chains. For example, China has 97 percent global market share of the ingots and wafers used to make solar panels (U.S. Department of 202 | Chapter 6 Energy 2022). It also produces 73 percent of global lithium-ion batteries, which are the primary source of energy for electric cars (Henze 2022). These policies may have contributed to the decrease in extreme poverty in China, which fell by over 90 percent between 1990 and 2016 (the latest year for which data are available) (World Bank 2016; Goodman 2021). These policies had significant effects on U.S. consumer prices; one estimate is that prices for consumer tradables fell 0.19 percentage point annually between 2004 and 2015 due to trade with China (Bai and Stumpner 2019). However, these policies had negative effects on U.S. factories’ vitality and innovation; increased exposure to Chinese imports significantly reduced sales, profitability, and research-and-development expenditures at U.S. firms facing import competition (Autor et al. 2020). The China shock also had adverse effects on workers. In the decade from 2000 to 2010, one-third of U.S. manufacturing workers lost their jobs; at least a quarter of this effect was due to China’s accession to the World Trade Organization (Autor, Dorn, and Hanson 2016). Workers in affected industries saw rapid job loss after the United States granted PNTR status to China (Pierce and Schott 2016). Communities more exposed to Chinese imports had reduced earnings for low-wage workers, housing prices, and tax revenues; and larger increases in childhood and adult poverty, single-parenthood, and mortality related to drug and alcohol abuse, as well as greater uptake of government transfers (Pierce and Schott 2016; Autor, Dorn, and Hansen 2021). to avoid missing an earnings target (Graham, Harvey, and Rajgopal 2019, 8). Underlying this willingness is a view that stock market investors lack the information to properly value long-term investments (Asker, Farre-Mensa, and Ljungqvist 2015; Poterba and Summers 1995). This financialization of the economy has been an important driver of U.S. lead firms’ supply chain strategies. Outsourcing of production and other capital-intensive activities is prescribed by consulting firms promoting an “asset-light” strategy. These firms note that, all else held equal, a lower amount of capital makes a given amount of revenue yield a higher measured return on assets (Kachaner and Whybrew 2014); the importance of the “all else held equal” assumption is discounted. Offshoring to suppliers with a low quoted price is also attractive. Chinese subsidies and wage suppression have yielded very low unit costs for Chinese suppliers; often, the price from a Chinese manufacturer of a finished manufactured component has been less than the price of raw materials for a U.S. supplier (U.S. Department of Defense 2022, 27). The disadvantages of such purchasing strategies are hard Building Resilient Supply Chains | 203 to quantify; in a financialized environment, where many purchasing agents are rewarded exclusively for driving down quoted prices, these disadvantages have typically been assumed (without much evidence) to be small (Gray, Helper, and Osborn 2020). Implications of Supply Chain Structures This section examines the relationship between supply chains and innovation, and the role of supply chains in the business cycle. Both outsourcing and offshoring have significant effects on innovation, some positive and some negative. Impact on Innovation Outsourcing can lead to the development of highly specialized and innovative suppliers. Take the example of semiconductors. The particular trajectory of innovation in this industry has led to a production process with very large economies of scale; for instance, a new fabrication plant (fab) now costs at least $12 billion to build. Because of the significant overhead involved, the more semiconductor chips a fab produces, the lower the average cost of each chip. And, with more sales, a fab’s owner can invest more in research and development, enabling it to produce even more sophisticated chips (White House 2021; Jie, Yang, and Fitch 2021). In part for this reason, it has become more advantageous for firms to purchase semiconductors from a specialized semiconductor firm than to make them in house (Breznitz 2005).5 This semiconductor example illustrates how a buyer can obtain the benefits of suppliers’ innovation simply by buying the product; as semiconductors improved and their prices fell, manufacturers were able to dramatically increase the computing power of products ranging from refrigerators to computers. Though many firms buy generic semiconductors from distributors, innovation often results from the interaction between a buyer’s needs and a supplier’s capabilities (Batra et al. 2016; von Hippel 1988). Apple’s cutting-edge products often result from significant interaction between its designers and the producers of its semiconductors (Owen 2021; Jie, Yang, and Fitch 2021). Although collaborative relationships have many benefits, as described above, they may also have costs, particularly in lost flexibility (Levin 2002). To minimize the costs of switching suppliers, a lead firm may use arm’s-length relationships and design its production processes to enable it to outsource to firms with weak bargaining power. Though this flexibility has benefits for lead firms, it may cause their suppliers to invest less in both As discussed above, government subsidies for semiconductor manufacturers were also an important reason why firms reduced their vertical integration into this industry. 5 204 | Chapter 6 innovation and workers due to uncertainty about the continuing demand for their products, because these investments often have customer-specific elements (see box 6-3) (Baker, Gibbons, and Murphy 1995; Helper and Henderson 2014). The use of semiconductors in the auto industry illustrates this point. Although semiconductors became key to the operation of modern vehicles more than a decade ago, many automakers did not begin to communicate directly with semiconductor manufacturers until late 2021. Rather, they bought chips indirectly, through distributors or first-tier suppliers, and did not commit to purchases more than a few weeks out. Thus, although their product plans included more intensive use of semiconductors in future vehicles, automakers had not been credibly signaling this intention to manufacturers. Without this commitment, semiconductor manufacturers were unwilling to build new fabs for automotive-grade chips, since fabs must maintain very high capacity utilization to be profitable. Further, they did not devote resources to innovating on the dimensions important to automakers, such as reduced cost and increased reliability. In contrast, Apple has long paid to reserve capacity in advance at fabs, and has worked with semiconductor manufacturers and design firms to innovate on the dimensions important to them—speed and power (Burkacky, Lingemann, and Pototzky 2021; Ewing and Boudette 2021; Fogarty 2020; Lawrence and VerWey 2019).6 Innovation is affected by offshoring as well. In some cases, foreign purchases increase the ability of U.S. firms to innovate by allowing access to innovative technology developed abroad. For example, companies such as Apple, Qualcomm, and Advanced Micro Devices rely on semiconductor designs from a U.K. firm called Arm; firms such as Intel rely on the Dutch company ASML for its advanced lithography equipment (Associated Press 2022a). And some scholars have argued that offshoring of production increases U.S. firms’ innovation by allowing them to focus on high-value tasks. However, there is evidence suggesting that geographically separating production and innovation impedes innovation. Engineers overseeing production are exposed to the capabilities and problems of existing technology, helping them to generate new ideas both for improving processes and for applying a given technology to new markets. Losing this exposure reduces the opportunity to generate such innovative ideas. For example, when production of consumer electronics migrated to Asia in the 1980s, the United States lost the potential to later compete in the burgeoning market for follow-on products like flat-panel displays, LED lighting, and advanced batteries (Pisano and Shih 2012; Berger 2015; Fuchs and Kirchain 2010). As discussed below, U.S. automakers have recently announced significant changes in the way they purchase semiconductors. 6 Building Resilient Supply Chains | 205 Box 6-3. Outsourcing and Job Quality Overall, 43 percent of U.S. workers are in supply-chain industries, employed either at lead firms or their suppliers (Delgado and Mills 2020). The structure of supply chains has significant implications for job quality for these workers. As mentioned above, sometimes outsourcing is efficient. However, in other cases, lead firms use outsourcing to gain access to suppliers with weak bargaining power, adopting a strategy that David Weil has called “fissuring.” In these cases, supplier firms have little ability to compete except by aggressively holding down wages (Weil 2017). For example, firms that sell to a small number of buyers pay lower wages than do similar firms with more customers; this greater dependence on large buyers lowers suppliers’ wages and has accounted for 10 percent of wage stagnation in nonfinancial firms since the 1970s, according to one estimate (Wilmers 2018). Research suggests that jobs that are outsourced from lead firms to suppliers are often worse for most workers, for several reasons (Handwerker and Spletzer 2015; Goldschmidt and Schmieder 2017; Helper 2021). As summarized by Helper (2021), these reasons include: • Design for supplier interchangeability. Many lead firms structure their supply chains to make contractors easily replaceable. For instance, U.S. automakers in the past brought product design and complex subassemblies in house, making it possible to have contractors compete on making small, predesigned components under short-term contracts. This strategy lowered barriers to entry for suppliers, meaning that suppliers did not capture many rents (Helper and Henderson 2014). This style of production has led many lead firms in the apparel industry to employ long chains of anonymous subcontractors. Walmart Corporation, for example, was surprised when goods marked with its label were found in the aftermath of the horrific fire at the Rana Plaza complex, in which over 1,100 Bangladeshi apparel workers were killed due to subcontractors’ poor safety practices (White 2017). • Monitoring without accountability. Some lead firms specify in detail the actions to be taken by workers in their supply chains, even those who are not their employees (Davis-Blake and Broschak 2009). That is, lead firms can control workers without taking responsibility for paying them benefits. Tight monitoring from lead firms means that one of the few profit-making strategies available to subcontractors is to keep wages low. Sometimes these workers are misclassified as independent contractors, even though they lack the autonomy of running their own business. When firms misclassify workers in this way, “they offload labor costs and risks onto workers—for example, by avoiding unemployment insurance 206 | Chapter 6 taxes and workers’ compensation premiums—and make it difficult for workers to organize or join a union and bargain collectively for better wages and conditions” (U.S. Department of the Treasury 2022, i). • Low supplier capability. When lead firms maintain tight control over suppliers’ work methods, subcontractors’ ability to create or capture value is low. Even though investments might yield productivity improvements, contractors often do not make them because they lack the capability to do so or they would not capture much of the benefit due to fierce competition. As a result, subcontractors often cannot increase pay without risking bankruptcy. Suppliers to lead firms that adopt financialized metrics also have difficulty adopting management practices that have been shown to be effective. Fewer than half of second-tier auto suppliers have adopted practices such as quality circles, in which production employees gather regularly to explore ways to improve quality; one-third report that they do not consistently do preventive maintenance, and one-quarter employ no engineers. In contrast, suppliers that report a collaborative relationship with customers were more likely to adopt high-road policies such as cross-training of workers, and had higher productivity (Helper and Martins 2020). • Weak ecosystems. Not only do U.S. suppliers lack support from lead firms; they are isolated in other ways as well (Berger 2015). The reason: There are few institutions to help with innovation, training, or finance (Ezell and Atkinson 2011). In contrast, Germany’s Mittelstand, which are medium-sized firms, are the backbone of the German manufacturing sector due to the help they get from community banks, applied research institutes, training institutions, and unions (Berger 2015). Impact on the Macroeconomy The structure of production networks, as described in figure 6-1, has important effects on the macroeconomy. The location of supply relative to consumers, the degree of interconnection and substitutability among firms and industries, the geographic concentration of supply, and the amount of collaboration and trust between buyers and suppliers all affect the degree to which a shock to one firm or industry propagates through the entire economy. Distinct configurations of supply chain structures carry distinct exposure profiles. For example, offshoring, or openness to international trade, can reduce exposure to domestic shocks by broadening supply or hedging against concentrated disruption (Caselli et al. 2020; Miroudot 2020). Building Resilient Supply Chains | 207 However, the greater distance that imported inputs must travel increases risks associated with transportation. For example, 40 percent of U.S. containerized imports go through the ports of Los Angeles and Long Beach, where the rise in demand for goods induced by the COVID-19 pandemic caused significant delays (Karlamangla 2021). Even supply chains that had no production problems suffered from the shipping bottlenecks. In addition, risks to a supply chain can grow with more global connections, because a disruption in one country will affect suppliers in all other countries. For instance, Bonadio and others (2020) estimated that one-quarter of pandemicrelated gross domestic product declines across 64 countries were related to global supply chain shock transmission. When disasters occur with supply chains abroad, as with the 2011 earthquake in Japan, recovery takes longer than if the supply chain was local due to the longer lead time involved in shipping. Dependence on a single supplier or a single location also carries risk. This is true even if the suppliers are domestic; for example, a severe 2021 freeze in Texas led to months-long disruptions in U.S. and global supplies of plastics because of the concentration of petrochemical companies there (Wiseman and Krisher 2021). These risks can be greater in industries important to national security that are located abroad, because decisions about supply would be affected by the policies of another country, as discussed below. If firms within an industry share suppliers with skills that are hard to replace, the bankruptcy of a few such suppliers can also take down other suppliers, and even lead firms, with them. Fear of this “cascading bankruptcy” in 2008, when auto sales suddenly fell 45 percent, led the CEO of Ford, Alan Mulally (2008), to ask for a government rescue of his major competitors, noting that 90 percent of Ford’s suppliers were shared with other automakers. Auto suppliers have hard-to-replace skills that include the ability to maintain high quality standards (e.g., to control variation in the size of parts produced to no more than 1/1000th of an inch, thinner than the width of a human hair), consistently over millions of parts that sell for a few dollars each. If these firms fail, other firms cannot easily enter the market to replace them. Dependence on shared suppliers is not uncommon; the computer giants Dell and Lenovo have more than 70 percent commonality in their top 20 suppliers (Lund et al. 2020). In contrast, if each downstream firm were vertically integrated (i.e., produced its own inputs), some firms might be affected by a disruption, but it is likely others would still be able to produce. Some of these potential vulnerabilities carry offsetting benefits. For example, geographic clustering of suppliers is common, and often is efficient, because suppliers can share skilled labor, specialized inputs, and 208 | Chapter 6 innovative ideas (Marshall 1919; Delgado, Porter, and Stern 2015).7 In addition, repeated dealings and face-to-face contact build the trust required for collaborative supplier–buyer relationships (Bernstein 2015). As discussed above, close relationships among firms in the supply chain could speed recovery from disruptions (Baldwin and Freeman 2021; Alfaro and Chen 2018). That is, the reduced ability to seek new suppliers is often offset by suppliers’ increased incentive to pitch in to help others. If firms could quickly recover from supply disruptions, then the macroeconomy would not be affected as much by global supply chains’ increasing exposure to shocks and dependence on other firms (Carvalho and Tahbaz-Salehi 2019). However, in the absence of such collaboration, shocks to supplier– buyer relationships can have persistent effects on the macroeconomy, especially if networks are highly connected (e.g., star-shaped) and frequently use hard-to-substitute inputs (Carvalho 2014). For instance, Barrot and Sauvagnat (2016) found that if a supplier was hit by a major U.S. natural disaster between 1978 and 2013, its key customers (those accounting for more than 10 percent of the supplier’s sales) experienced an average drop of 2 to 3 percentage points in sales growth for one to two years afterward. If the disrupted supplier produced hard-to-substitute inputs, this disruption further propagated to suppliers that were not exposed to the original shock (Barrot and Sauvagnat 2016). Bigio and La’O (2020) estimate that the input-output structure of the U.S. economy amplified financial distortions by a factor of 2 during the 2008 global financial crisis.8 As the consulting firm McKinsey noted in 2011, “Many global supply chains are not equipped to cope with the world we are entering. Most were engineered, some brilliantly, to manage stable, high-volume production by capitalizing on labor-arbitrage opportunities available in China and other low-cost countries” (Malik, Niemeyer, and Ruwadi 2011, 1). These conditions are less prevalent now. As networks become more connected, and climate change worsens, the frequency and size of supply-chain-related disasters rises. For this reason and others, understanding how to promote quick recovery is increasingly important. It is also vital for companies to have the incentive to make sufficient investments in resiliency, even when they may not be able to monetize all the benefits of these expenditures because of spillovers to other parts of the networked system. Sometimes the clusters are near where natural resources required for production are or once were concentrated (e.g., steel in Cleveland). Other times “clusters” of suppliers develop near where an invention happened to occur (e.g., floor coverings in Dalton, Georgia; see Krugman 1991). 8 The authors compared the effects of the current star-shaped structure of the U.S. economy (panel D of figure 6-1) to what they would have been if the economy looked more like panel A (vertically integrated firms). 7 Building Resilient Supply Chains | 209 Figure 6-4. Frequency of Billion-Dollar Natural Disasters by Type, United States Number of disasters 25 20 15 10 Droughts Flooding Tropical cyclones Year Severe storms Freezes Wildfires 2020 2015 2010 2005 2000 1995 1990 1985 0 1980 5 Winter storms Sources: NCEI (2021, 2022). Note: Disaster costs are adjusted for inflation using the Consumer Price Index for All Urban Consumers. The Rising Incidence of Supply-Chain-Related Disasters Although the pandemic has been a particularly dramatic example of a supply-side disruption, the global frequency of natural disasters increased almost threefold between 1975–84 and 2005–14 (Vinod and López 2015), mostly due to increases in climate-related events (NCEI 2021). Lund and others (2020) found that supply chain shocks affecting global production lasting at least a month occur on average every 3.7 years. The magnitude of damage from these events is also growing; the number of billion-dollar disasters has risen from an average of 5 annually to 20 over the past 40 years (figure 6-4). The frequency of such events is likely to continue to rise in the future, according to the United Nations Intergovernmental Panel on Climate Change (IPCC 2022). Private Sector Incentives for Resilience As supply chains have increased in complexity, firms’ need for risk management has also grown (Baldwin and Freeman 2021). When unable to produce due to lack of inputs, firms lose revenue, providing some incentive to invest in resilience (Miroudot 2020). Practices include understanding the structure of their supply chains (visibility), investing in backup capacity (redundancy), and improving their ability to solve problems and substitute 210 | Chapter 6 between inputs (agility), as well as vertically integrating components of the production process (Christopher and Peck 2004; de Sá and de Souza Miguel 2019). However, these strategies, especially redundancy, increase costs (Baldwin and Freeman 2021). Thus, it is not cost-effective for firms to invest in completely avoiding all disasters. Instead, these practices are designed to reduce firms’ risks, such that the perceived expected value of additional revenue during a disruption compensates for the cost of minimizing production issues (Miroudot 2020; Baldwin and Freeman 2021). One consequence of a firm underinvesting in resiliency is that it increases the exposure of other firms in the networked system to the negative spillover effects of a disruption. This type of market failure is likely when the firm’s investment decisions consider only its private costs and benefits, and it is unable to monetize the spillover benefits of its investment decisions for the rest of the system. Under certain conditions, the private sector can achieve an efficient level of investment. For example, if parties can bargain without high transaction costs, an efficient market outcome may be achieved through private contracting, or through self-policing, cooperative arrangements (Bernstein 2015). However, these approaches are infeasible when there is a large number of entities and/or contingencies involved, because these raise the transaction costs of negotiating and enforcing contracts (Coase 1960). In this case, there is an important role for government to play, as discussed below. Visibility A first step toward achieving resilience is for firms to learn more about their suppliers’ production and inventory levels. This allows firms to monitor the capability of their supply chain to meet demand, even if the suppliers do not directly supply the lead firm. Visibility into supply chain relationships is necessary to identify vulnerabilities in supply chains, so that firms can properly plan for disruptive events (Fujimoto and Park 2014). Gaining this knowledge is not just a technical challenge but also depends on trust between buyer and supplier (MacDuffie, Heller, and Fujimoto 2021). One reason is that if a buyer learns that a supplier has a lot of extra production capacity, the buyer could push for a lower price. Beyond being able to identify suppliers, key metrics include “time to survive”—how long demand for a particular component could be met from inventory or another supplier if the regular supplier was unavailable—and suppliers’ “time to recover” in case of an emergency (Simchi-Levi 2020; Simchi-Levi and Simchi-Levi 2020). Redundancy Firms may also invest in developing relationships with additional suppliers. Finding alternative suppliers for an input is time-consuming, and suppliers Building Resilient Supply Chains | 211 must often go through quality verification. If firms proactively invest in building relationships with several suppliers, the lead firm has ready alternatives. Even if one supplier is unable to produce, another one can step in as a replacement. Firms can also hold additional inventory, particularly if suppliers’ lead time, or how long it takes to make their products, cannot be brought down below their time to recover from a shock (Michaelman 2007). For example, Toyota learned that its semiconductor suppliers’ lead time was four months, so the automaker has kept four months of inventory of these products (Shirouzu 2021). Though redundancy generally increases costs, it can also increase profits during periods of supply chain stress by allowing production to continue. However, holding inventory may not always be effective, given that the stored parts may not be the parts needed in a crisis (Sheffi 2022). (See box 6-4.) Agility Firms can invest in their and their workers’ ability to solve problems, thus enabling them to pivot quickly to alternative products or processes or react to abnormal situations (Baldwin and Freeman 2021; MacDuffie, Heller, and Fujimoto 2021; Helper 2021). The new process may be one that allows use of a different raw material to replace one that is unavailable, or it may be a product and process very different from what the firm has traditionally made. Another option is to increase the flexibility of their production process so that the firm can use a less specialized input. A variety of techniques promote such flexibility, including: • Reducing lead times, by identifying the critical path and working to speed it up (Ericksen 2021). • Investing in surge capacity, for example, by maintaining more generalpurpose equipment (such as 3D printers), and more generally trained workers. • Maintaining collaborative relationships between suppliers and customers, to identify problems early and provide incentives to fix them. • Building problem-solving capability, including for front-line workers (see the “high-road” discussion in chapter 4, on human capital). • Maintaining real options, or the ability to postpone decisionmaking until more information is available; for example, by producing domestically rather than enduring long shipping lead times (de Treville and Trigeorgis 2010). Agility may require upfront investment by firms in a supply chain, but over time may reduce costs and enhance efficiency. Investing in problemsolving capability that reduces lead time can improve performance in normal times as well as in emergencies. 212 | Chapter 6 Box 6-4. Low Inventories and Just-in-Time Production In addition to moving production across firm and national boundaries, companies have been holding less inventory of both final and intermediate goods. Figure 6-ii graphs the ratio of private inventories to final sales from 1947 to 2021, for establishments operating in the United States. It is clear that, over the past 30 years, this ratio has decreased. Holding extra inventory for production increases storage costs; the lower their inventory, the less working capital is needed and the lower the probability the firm gets stuck with inputs that may become obsolete or spoil. However, if supply is disrupted and the firm has a low ratio of inventory to final sales, it has less inventory to fall back on, perhaps requiring it to shut down production until its supplier can recover its ability to produce or another supplier can be found. As originally envisioned by Taichi Ohno at Toyota, just-in-time production combines low inventories with additional policies that offset the dangers discussed above, by speeding up the supply chain’s ability to recover from disruption. These policies include localizing production near consumers and increasing operational “agility,” as discussed above (Liker 2004; Handfield 2021). In contrast, many U.S. firms have combined reduced inventory with longer supply lines, often of 4 to 6 weeks (Buchholz 2020), and with workforce policies that limit their ability to respond to shocks, as discussed above. That is, low inventories by themselves do not necessarily lead to fragility; problems arise when low inventories are combined with low agility. Figure 6-ii. Domestic Business Ratios of Private Inventories to Final Sales 6.5 6.0 5.5 5.0 4.5 4.0 3.5 3.0 2017 2007 1997 1987 1977 1967 1957 2.0 1947 2.5 Sources: Bureau of Economic Analysis; FRED Database of the Saint Louis Federal Reserve Bank. Note: Shaded areas indicate U.S. recessions. Building Resilient Supply Chains | 213 Collaborative relationships with suppliers are key to agile supply chains. For example, in February 1997, a fire at Aisin Seiki, the sole source for proportioning valves used in all Toyota vehicles, could have halted all Toyota production for weeks. However, assembly plants were reopened after only two days through collaboration between Toyota and its suppliers; more than 200 firms set up alternative valve production. This collaboration was orchestrated with limited direction from Toyota, haggling over intellectual property, or worry about repayment for expenses incurred. Long previous relationships, implicit competition for future contracts, pressure to maintain relationships, and trust with the Toyota group promoted the effectiveness and speed of the collaboration (Nishiguchi and Beaudet 1998). Increasing domestic production may also make a firm more agile. Because proximity leads to reduced transportation time and increases the potential for better communication, domestic production helps firms develop build-to-order capability. Reduced lead time also allows decisionmakers to forecast for a smaller range of outcomes (de Treville and Trigeorgis 2010; MacDuffie, Heller, and Fujimoto 2021). Public Sector Strategies for Promoting Resilience The public sector can play an important role in promoting supply chain resilience, especially in helping to incentivize private sector decisions that align with broader geostrategic and economic priorities. A supply chain that crosses national boundaries means that production depends on the decisions and activities of other nations, adding uncertainty to supply. In addition, many aspects of supply chains have externalities; that is, decisions affect not only the direct decisionmakers but also other actors in the supply chain. In the presence of public goods, such as national security, government policy can improve national welfare. The government’s role in promoting robust supply chains is particularly important in two types of industries: those that provide inputs into many individual supply chains with large spillover effects, such as energy production, semiconductors, or transportation; and those that are important for national security, including climate and health security, where the assured supply provided by domestic production is especially valuable. Specifically, public sector interventions to build robust supply chains can address challenges related to aggregating and disseminating information, and to assuring that we have the products and goods essential to effective national security. Each is discussed here. 214 | Chapter 6 Aggregating and Disseminating Information The public sector can play a role in disseminating information that helps markets work more efficiently. As noted above, firms often share suppliers with other firms, making them dependent on these other firms’ actions. Because supply chain information can be a competitive advantage, firms may be unwilling to disclose certain data to other firms. For example, when there is a shortage of a product, such as personal protective equipment (PPE), individual hospitals are likely to overorder and hold more inventory because they want to ensure their supply. PPE suppliers in this situation do not know if they should increase capacity because they do not know if the new level of demand will continue or whether hospitals are accumulating inventory that will cause them to reduce the quantity they demand in the future. Yet hospitals would not be willing to share information about their true demand with suppliers because they could then be downgraded in priority or receive a smaller quantity of PPE. The government has the capability to strategically collect sensitive data and release aggregate information to market participants in ways that can improve market functioning. For instance, the U.S. Department of Health and Human Services has taken on an important role in providing an accurate demand signal for PPE. The department’s Supply Chain Control Tower receives near-daily data from distributors that represent more than 80 percent of the volume for the commodities it is tracking, along with supply status from 5,000 hospitals. This dashboard alleviates hospitals’ fear of shortages, so they do not need to incur extra costs of holding inventory. The dashboard also allows distributors to receive a truer demand signal by reducing excessive ordering that exacerbates supply constraints (U.S. Department of Health and Human Services 2022, 13). In cases such as these, the public sector is well positioned to collect, aggregate, and disseminate this information. The government also has a role to play in convening and coordinating private sector actors. For example, standards bodies, such as the National Institute for Standards and Technology in the Department of Commerce, have played a key role in developing standard interfaces, such as for USB ports, that allow many firms to easily participate in electronic supply chains, which promotes innovation and cost reduction. In addition, major innovations in decentralized supply chains can suffer from a chicken-and-egg problem, in that upstream firms will not supply something until they see a demand for it, but downstream firms will not invest in products requiring that input unless there is a ready supply. A past success in resolving this dilemma was the 1990s development of a semiconductor industry road map by Sematech, a public–private partnership. The group came together to agree on common equipment needs and innovation Building Resilient Supply Chains | 215 direction, and to fund such equipment. Sematech’s convening helped equipment manufacturers make products that were compatible with what chip designers were thinking, and, conversely, helped chip designers understand the directions where equipment makers might go. Over the seven years that Sematech received Federal funding, more than $1.65 in benefits was generated for each $1 in Federal spending (Link, Teece, and Finan 1996).9 During the chip shortage arising from the pandemic, in the fall of 2021 the Department of Commerce convened CEOs of leading companies, enabling automakers and chip leaders to meet each other for the first time, and to discuss supply chain bottlenecks and identify common solutions. One such meeting led to a partnership between Ford Motor Company and Global Foundries. This partnership will focus on increasing the production capacity for Ford’s existing product lines and on facilitating joint research on future chip technologies that will be critical to the next generation of vehicles. Ford’s CEO announced that the company will also act to give chip producers “more confidence in future production” by buying directly from them, rather than buying chips indirectly through other suppliers (Hicks 2021, 1). General Motors recently announced a similar partnership with seven semiconductor producers. Advances in supply chain management will be crucial for auto manufacturers in the next several years, given that new vehicles, especially electric vehicles, could lead to a doubling of semiconductor requirements (Colias and Foldy 2021). National Security Dependence on a foreign supplier in times of geopolitical conflict makes supply chains fragile, particularly for a good that has few close substitutes. Foreign control of a key resource is a valuable geopolitical bargaining chip (Sanger and Schmitt 2022). Currently, the United States is heavily dependent on foreign suppliers for semiconductors and batteries, which are key inputs for much military technology. In 2021, 92 percent of the world’s supply of advanced semiconductors came from one company, TSMC, in one location, Taiwan (Lee, Shirouzu, and Lague 2021). Similarly, key parts of battery supply chains are largely located in China, which refines 60 percent of the world’s lithium and 80 percent of the world’s cobalt—two core inputs to high-capacity batteries, without close substitutes. Access to these inputs critical for defense is more assured if the goods are produced domestically (White House 2021a). The rationale for this calculation is as follows: “The unweighted ratio [of benefits to costs] for fully burdened cost is 3.3. Of course, when Federal dollars are added to the cost basis, all of the ratios in table 4 are reduced in half”; Link, Teece, and Finan (1996, 748). So, 3.33/ 2 = 1.65; these are private benefits only; the paper did not estimate public benefits (hence “more than” $1.65). 9 216 | Chapter 6 Profit-maximizing firms do not take full account of this spillover benefit to domestic production. National defense is an example of a public good; it is both nonrival—that is, consumption does not diminish others’ ability to consume the good—and nonexcludable, which means that those that do not pay cannot be blocked from using the good. Because people can use public goods without paying for them, the private sector will undersupply these goods. For this reason, governments typically provide for national security. Having at least some domestic production of critical goods also means that, in the event of a natural disaster, U.S. firms are not dependent on the policy choices of other countries. China’s COVID-19 policies that locked down whole cities or ports for a small number of cases disrupted production for firms in countries with different policy approaches and different case counts (Kuttner 2022). Though the United States has underinvested in a variety of industries, moving toward 100 percent domestic production is not necessarily the best response to these risks, given that allies and partners may have a competitive advantage in some goods, and may allow diversification in case of domestic disruption (White House 2021a). In addition to inputs directly used in defense production, governments spend significant amounts of time and money to protect electricity and communication networks from supply chain disruptions. These sectors are hub industries; as such, they are part of the production process for almost all economic activity. To protect the power grid from cyberattacks, the Federal Energy Regulatory Commission mandates minimum cybersecurity standards for systems necessary for operating the electric transmission network, and the Department of Energy provides cybersecurity training and guidance (GAO 2021). Though power generation companies have an incentive to protect against shutdowns that would decrease their revenue, disruption of the power sector could cause economywide disruption far larger than the impact on electricity industry revenue. In these types of industries, where disruptions affect the ability of other industries to produce, particularly industries that are important to the Nation’s health and safety, the private sector does not internalize the full costs of disruption to society. Public sector intervention can be beneficial in these cases. In critical sectors, the public may be willing to pay a higher cost than would the private sector to avoid shortages. For example, the United States maintains large stocks of food and keeps defense capabilities ready even during peacetime, because the possibility of insufficient supply is so costly (Baldwin and Freeman 2021). In these cases, the public sector must intervene to reach the socially optimal level of resilience. Such intervention could include investments in U.S. manufacturing, using public procurement to stabilize demand for U.S. supply chains, and helping small business invest in upgraded capabilities (see box 6-5). Building Resilient Supply Chains | 217 Box 6-5. Policies to Improve the Functioning of Supply Chains The Biden-Harris Administration has been taking a number of steps to help improve the functioning of supply chains. The focus has been on strengthening critical supply chains, including those necessary to tackle the climate crisis. The Administration has taken steps such as the following: • Signed an executive order that directs agencies to fortify our Nation’s supply chains and industrial bases, including focusing attention on the supply chains of products critical to our economic and national security (White House 2021a; Executive Order 14017 2021). • Established the Supply Chain Disruptions Task Force to address the challenges arising from the pandemic-affected economic recovery (White House 2021c). • Directed seven Cabinet agencies to publish reports identifying key weaknesses in some of the Nation’s most crucial supply chains, and devised multiyear strategies to address these weaknesses (White House 2022b). • Enacted the Bipartisan Infrastructure Law, which is our Nation’s most significant investment ever in modernizing the transportation systems on which our supply chains depend (White House 2021d). • Enacted the American Rescue Plan, which, among other programs, authorized the $10 billion State Small Business Credit Initiative, which will catalyze more than $70 billion in lending and investment in small businesses—including small manufacturers—during the decade (White House 2021e). • Issued the new Buy American rule that increases required U.S. content in Federal procurement, and will create a new category of critical products that are eligible for enhanced price preferences to ensure that Federal spending supports American businesses (White House 2021f). • Proposed a new domestic financing initiative through the ExportImport Bank to strengthen U.S. manufacturing exports. In addition to their direct effects, policies such as these have the potential to catalyze private sector investments, consistent with the argument in chapter 1 that the public sector can be a partner of the private sector, rather than a rival. National security includes not only direct inputs into military security but also inputs critical to citizens’ health, climate, and economic security. As such, developing new supply chains is key to U.S. efforts to address climate change (see chapter 7 on climate). In general, private firms invest 218 | Chapter 6 too little in addressing climate change due to the fact they do not capture all of the benefits, providing a rationale for government intervention, as discussed in chapter 7. Decentralized supply chains face an additional issue in making these investments: coordination of demand and supply (Samford and Breznitz 2022). For example, firms will not invest in making components for electric vehicles unless they think there will be demand for them. Conversely, automakers will slow their investments in electric vehicles if they think components will be hard to obtain. The Biden-Harris Administration’s actions will help to overcome these chicken-and-egg problems that make it hard to establish new industries. For example, the Bipartisan Infrastructure Law invests billions of dollars in establishing mining and recycling programs for batteries. The White House has also convened automakers, unions, environmental groups, and suppliers to coordinate plans to make and sell electric cars and trucks that would use these batteries. The Administration learned from these meetings what level of electric vehicle penetration might be feasible, before publicly announcing the goal that 50 percent of U.S. light vehicle sales should be “zero emission vehicles” by 2030 (White House 2021b). The certainty provided by these actions has unlocked billions in private sector investments in battery production that will employ thousands of people in states like Tennessee and Michigan (Associated Press 2022b; Eggert 2022). Similarly, the Administration has announced the goal that solar energy will produce 45 percent of U.S. electricity by 2050, with tax credits targeted at each stage of the solar panel supply chain (Fears 2021). Indirect Supply Chain Policy Many other government policies have implications for the structure of modern supply chains. This section provides examples of economic policies that are broader than supply chains but nevertheless have implications for their structure. The price of shipping intermediate goods thousands of miles during the production process does not incorporate the social cost of emissions. Transportation contributes about 29 percent of all U.S. greenhouse gas emissions, which have been rising (EPA 2021). For example, international shipping currently accounts for about 3 percent of total global greenhouse gas emissions. If treated as a country, international shipping would have been the sixth-largest emitter of energy-related carbon dioxide in 2015— more than Germany (Chen, Fei, and Wan 2019; Gallucci 2021; IMO 2021; Olivier et al. 2016; Rose et al. 2021; Olner et al. 2017). Pricing in the true cost of moving goods—that is, to include greenhouse gas emissions—would incentivize firms to reduce their use of transportation services; for example, by producing closer to where their customers live, or investing in new lowcarbon fuels. (See chapter 7, on climate.) Building Resilient Supply Chains | 219 Trade policy also has enormous implications for the structure of supply chains. As discussed above, China’s entry into the World Trade Organization led to a significant increase in offshoring, which has reduced consumer prices but also has harmed U.S. innovation, employment, and wages for decades. The North American Free Trade Agreement (NAFTA) has been found to have had similar employment and wage effects, albeit on a smaller scale (Hakobyan and McLaren 2016), although a 2020 revision to NAFTA, the United States–Mexico–Canada Agreement, has somewhat addressed these issues. Newer emissions-based policies—like the global arrangement for steel and aluminum trade between the United States and the European Union—promise to further reshape supply chains by incentivizing production of lower-emissions goods. These newer policies offer the promise that global supply chains can be designed in a way that benefits people in rich and poor nations alike. Conclusion Because of outsourcing, offshoring, and insufficient investment in resilience, many supply chains have become complex and fragile, with central nodes that lack agility and have few substitutes. Some of this change has been driven by advances in technology, which have beneficial effects. For example, because more of today’s products are electronic, semiconductors have become a central node in the economy.10 However, this evolution has also been driven by shortsighted assumptions about cost reduction that have ignored important costs that are hard to turn into financial measures, or that spilled over to affect others. The validity of these assumptions is reduced in a world where disruptions have become more prevalent and firms are more tightly interconnected. The COVID-19 pandemic has made these issues salient to the general public, which has experienced frustrating waiting times for the delivery of goods ranging from personal protective equipment to appliances. Though supply chains have performed well in the aggregate, with over 20 percent more goods flowing through the economy in 2021 compared with pre-pandemic times, it is still important to address supply chain fragility, given that disruptions are likely to continue. As disruptions become more common, private firms are beginning to increase their resilience through visibility, redundancy, and agility. The Federal Government has acted, and will continue to act, to build resilience in critical supply chains—for example, by providing clear signals of demand and supply that are already transforming sectors critical for the Nation’s military, climate, and health security. Note that this change was also significantly promoted by U.S. government supply chain policy over many decades, as described in the text; see also Council of Economic Advisers (2021). 10 220 | Chapter 6 Chapter 7 Accelerating and Smoothing the Clean Energy Transition Responding to the severe risks of climate change ranks among the most important and difficult challenges facing the United States. Levels of heat-trapping carbon dioxide in the atmosphere are higher than they have been in millions of years, causing gradually increasing temperatures and sea levels and worsening the catastrophic consequences of hurricanes, wildfires, and other extreme events. Along with the governments of other major greenhouse-gas-emitting countries, the Biden-Harris Administration has declared the United States’ intention to rapidly reduce greenhouse gas emissions to avoid the worst consequences of climate change. Because three-quarters of human-caused U.S. greenhouse gas emissions come from burning fossil fuels for energy, the most important step in reducing emissions is to shift from carbon-intensive to clean sources of energy (U.S. Energy Information Administration 2021a)—in short, to pursue a clean energy transition. A large and robust economics literature shows how policies can accelerate this energy transition by encouraging cost-effective emissions reductions. Completing this transition by mid-century would constitute a transformation of the energy system at a pace without precedent, and mark a giant achievement in human history, given the scale of the avoided damage to current and future generations (Newell and Raimi 2018). President Joseph R. Biden has also committed to build a clean energy supply chain stamped “Made in America,” reflecting the considerable economic opportunities and associated challenges presented by the energy transition. One challenge is how to support America’s continued industrial strength 221 and energy security. Doing so will require government actions that enable U.S. firms to compete on a level playing field in emerging global industries, especially given the degree to which other countries are supporting their own domestic firms. Another challenge presented by the transition is how to best support the communities across the United States that depend on carbon-intensive industries for jobs and tax revenue. In the past, when American communities have faced employment losses due to economic shocks—such as recessions, trade with China, and automation—workers and their families largely have not moved to communities where jobs are more plentiful, raising the important policy question of how to help people in the places where they are. This chapter highlights what economics can tell us about effective policy strategies to accelerate and smooth the United States’ clean energy transition. The first section provides background on climate risks, global progress in mitigating these risks, and the policies that will accelerate the transition. The second section describes the opportunities and challenges of supporting those domestic industries and communities that are most affected by the transition. The chapter concludes by highlighting the interdependency between the strategies to accelerate and to smooth the transition. Accelerating the Energy Transition The widespread adoption of fossil fuel energy technologies powered the steamships and factories that made the Industrial Revolution possible, and has helped spur economic growth for over a century (U.S. Energy Information Administration 2011; Friedrich and Damassa 2014). The burning of fossil fuels has also led to the rise in human-made carbon dioxide (CO2) emissions, which is changing the composition of the atmosphere and, with it, environments around the globe. Over the 800,000 years before the 20th century, the atmospheric concentration of CO2 vacillated between 150 and 300 parts per million, creating a climate hospitable for the world’s development, as detailed in figure 7-1. In early 2022, CO2 concentration levels are well above 400 parts per million and are continuing to grow. Because CO2 is a heat-trapping greenhouse gas, rising levels in the atmosphere have led 222 | Chapter 7 Figure 7-1. Atmospheric CO2 Level Across the Millennia to 2019 CO2 level (parts per million) 450 Current level 400 350 1950 level 300 250 200 150 –800,000 –700,000 –600,000 –500,000 –400,000 –300,000 –200,000 –100,000 0 100,000 Years before today Source: NASA (2021). Note: CO2 = carbon dioxide. to increasing temperatures, higher sea levels, more acidic oceans, and more frequent and severe cases of extreme weather and climate events (Zickfield, Solomon, and Gilford 2017; Bijma et al. 2013; Stott 2016). Climate change poses considerable risks to the global economy. Climate-driven extreme events and biodiversity loss can result in cascading damage to such critical and interconnected systems as energy, public health, water, and food (Garcia et al. 2018; Porter et al. 2021). In the United States, estimated damage from storms, floods, wildfires, and other extreme weather events has grown to about $120 billion a year over the past five years (Smith 2021). Climate change disproportionately harms low-income and historically marginalized populations, because vulnerable individuals lack the resources to adequately prepare for or cope with extreme weather and climate events (U.S. Global Change Research Program 2018). Because the rapid increase in greenhouse gases in the atmosphere is an ongoing planetary experiment, future damage from climate change is difficult to forecast precisely, and empirical estimates cover only a subset of likely effects. A 2017 meta-analysis finds that an increase in global temperatures of 5.4 degrees Fahrenheit (3 degrees Celsius) over preindustrial levels—a threshold that could be surpassed later in this century absent strong policy interventions—could cause economic damage equivalent to 7 to 11 percent of global gross domestic product (GDP) (Howard and Sterner 2017). In addition, studies that estimate the economic effects of climate change often fail to account for important aspects of climate change’s impact on public health, including temperature-related mortality (Bressler 2021) and the deaths and sicknesses caused by local pollution from fossil-fuel-related emissions (Shindell et al. 2018; Scovronick et al. 2019). Accelerating and Smoothing the Clean Energy Transition | 223 Global Efforts to Reduce Greenhouse Gas Emissions Average global temperatures have already risen about 1 degree Celsius above preindustrial levels (NASA 2021). CO2 remains in the atmosphere for centuries, so our continued emissions will cause temperatures to continue to increase (Archer et al. 2009). We can slow the pace of temperature increases by reducing global emissions, but halting global warming requires achieving net zero CO2 emissions (Net Zero Climate 2022). Considerable momentum toward this goal is building worldwide. The world’s major countries committed in the 2015 Paris Agreement to keep global warming well below 2 degrees Celsius above preindustrial temperatures, which is likely to require net zero emissions at the global level between 2050 and 2070 (UNFCCC 2021). Many countries, including the United States, have coalesced around a goal of net zero emissions by 2050. President Biden has additionally committed the United States to halve its net greenhouse gas emissions by 2030 (using a 2005 baseline) (McCarthy and Kerry 2021). In the European Union, the United Kingdom, and Japan, mid-century net zero emissions targets are stipulated by law (European Commission 2021a; Climate Change Committee 2021; Jiji Press 2021). The world’s largest emitter of greenhouse gases—China—has committed to net zero emissions by 2060 (Myers 2020). Many of the world’s largest companies have also made pledges to cut emissions to net zero, including financial institutions responsible for over $130 trillion in assets (Glasgow Financial Alliance for Net Zero 2022). Global annual CO2 emissions have begun to level off after centuries of increasing, partially as a consequence of this momentum (Our World in Data 2020). A recent United Nations report declares that the peaking of annual global emissions by 2030 is within reach (UNFCCC 2021). The projections of future global CO2 emissions by the International Energy Agency (IEA), displayed in figure 7-2, also show annual global emissions peaking and then beginning to decline in the decades ahead. But to achieve the climate goals specified seven years ago in the Paris Agreement, the energy transition will need to accelerate markedly from current trends: a recent study estimates that without additional policy actions, there is less than a 10 percent probability that temperatures will stay below 2 degrees Celsius above preindustrial temperatures by 2100 (Ou et al. 2021). Figure 7-2 shows that in 2040, global emissions under currently announced or implemented policies are projected to be seven times higher than emissions under a scenario in which the world is on pace to achieve net zero emissions by mid-century (IEA 2021b). 224 | Chapter 7 Figure 7-2. Global Carbon Dioxide Emission Projections, 2025–40 Million metric tons 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 2025 2026 2027 2028 WEO 2014 2029 2030 2031 2032 WEO 2021 2033 2034 2035 2036 2037 2038 2039 2040 Sustainable development scenario Source: International Energy Agency (IEA 2014, 2021), World Energy Outlook (WEO). Note: The WEO 2014 and WEO 2021 scenarios reflect projections that assume existing policy frameworks and announced policy intentions. The IEA’s Sustainable Development Scenario outlines how the world can deliver on the three main energyrelated goals: achieving universal access to energy, reducing the severe health effects of air pollution, and tackling climate change. Accelerating the Energy Transition in the United States An effective response to climate change requires policy actions around the globe, starting here at home. The United States’ annual greenhouse gas emissions are surpassed only by those of China, and our cumulative emissions are larger than those of any other country (Ritchie and Roser 2020; Our World in Data 2020). Shifting from carbon-intensive to carbon-free energy systems is the major challenge to achieving net zero emissions in the United States (see figure 7-3). While reducing deforestation and other actions outside the energy sector are also critical to slowing climate change, the production and consumption of energy are responsible for about three-quarters of U.S. emissions (Ge, Friedrich, and Vigna 2020; Climate Watch 2021). Successfully transitioning the U.S. economy to clean energy necessitates a large shift in economic activity. Americans spend over $1 trillion annually on energy, or about 5 to 10 percent of U.S. GDP in recent decades (U.S. Energy Information Administration 2018). Natural gas- and coal-fired power plants produce the majority of U.S. electricity, while petroleum products are the dominant fuel to transport people and products. Houses and buildings are often heated with furnaces and boilers that burn natural gas and oil, and the products Americans buy, the food we eat, and the sidewalks we walk on have carbon embedded in their production processes (White House 2021a). In 2019, 83 percent of the country’s energy demand was satisfied by coal, oil, and natural gas, down from about 87 percent in 2000 (Ritchie and Roser 2020). Meeting domestic and global climate targets means substantially stepping up the pace of clean energy deployments over the next decades, Accelerating and Smoothing the Clean Energy Transition | 225 Figure 7-3. Representative Pathway to Meet Net Zero Emissions in the United States, 2005–50 Reductions in net emissions (gigatons of CO2-equivalent per year) 7 6 5 4 3 2 1 Energy transition 0 Total Energy efficiency Decarbonizing Transition to electricity low-carbon fuels Non-CO₂ reductions Land sink CO₂ removal CO₂ removal technologies Source: U.S. Long-Term Climate Strategy. Note: CO₂ = carbon dioxide. as shown by a recent IEA analysis that details a pathway to net zero emissions by 2050 (see table 7-1) (Bouckaert et al. 2021). Though the world is not decarbonizing at the pace of this IEA scenario, recent trends and expert forecasts do tell a story of an explosive growth of clean energy technologies. In the United States, wind turbine technicians and solar energy installers are two of the five fastest-growing occupations, and over 80 percent of new electricity generation capacity built here in the first three quarters of 2021 was wind or solar (U.S. Bureau of Labor Statistics 2021a; Shahan 2021). Although many details about the energy transition are impossible to know in advance, the road map to meeting the energy demands of a growing economy with clean energy has become much clearer in recent years. Dozens of “deep decarbonization” studies point to a similar recipe: produce electricity with carbon-free sources and shift energy uses to this carbon-free electricity and other low-carbon fuels (National Academies 2021). A rapid energy transition will not occur without the implementation of a host of policy measures. If market prices fail to account for the damage caused by emissions, then consumers and producers will continue buying and selling too many artificially inexpensive, carbon-intensive goods and services. Carefully designed policies can change this behavior by raising the relative price of carbon-intensive goods and services compared with cleaner alternatives, which provides a financial incentive to shift away from the carbon-intensive products (Serrano and Feldman 2012). Such carbon prices could be implemented directly via carbon taxes, indirectly through a cap on emissions and tradable permits, or through other similar policy tools. Government revenues from the carbon price can be used 226 | Chapter 7 Table 7-1. Global Clean Energy Deployments in 2020 and 2030 Consistent with Net Zero Emissions by 2050 Type of Clean Energy 2020 2030 Global wind installations 114 GW per year 390 GW per year Global solar energy installations 134 GW per year Electric vehicles 5% of global car sales 60% of global car sales Heat pump installations 180 million per year 600 million per year Captured carbon 40 mt per year 1670 mt per year Source: Bouckaert et al. (2021, tables 2.5, 2.6, 2.9). Note: GW = gigawatts; mt = metric tons. 630 GW per year to compensate consumers for increases in energy prices or to invest in other societal priorities. Carbon prices of some form exist at the national level in 45 countries, including those that have been successful at sustaining emissions reductions, such as the United Kingdom (see box 7-1) (World Bank 2021). Canada’s federal carbon price is scheduled to increase from 50 Canadian dollars per metric ton of CO2 in 2022 to 170 dollars in 2030 (Government of Canada 2021). However, many countries have failed to implement carbon prices at the scale and scope needed to achieve large emissions cuts (OECD 2021). In the United States, Federal-level carbon pricing proposals have stalled in Congress for over 30 years, including legislation that passed in the House of Representatives in 2009 but failed in the Senate (Center for Climate and Energy Solutions 2021). Even in the absence of these political challenges, carbon prices are just one of many policy measures needed to cost-effectively accelerate the energy transition. After all, in addition to the failure of market prices to account for the damages caused by emissions, various other barriers stand in the way of a rapid, equitable, and low-cost transition. Complementary policies can make it cheaper or easier to conserve energy or to shift away from carbon-intensive products. Policy measures are needed for situations in which consumers cannot or do not fully respond to price signals; for example, tenants are often responsible for paying utility bills but have no control over what landlords could do to effectively reduce energy consumption (Ryan et al. 2011). Welldesigned incentives and standards can encourage broader use of energyefficient products and other energy-conserving actions. Measures that foster innovation are also necessary to reduce the costs of the clean energy transition. Private firms are likely to underinvest in technological progress because the benefits of their investments in emerging technologies partially accrue to society writ large. In addition, new products struggle to compete on a level playing field with established products due to Accelerating and Smoothing the Clean Energy Transition | 227 Box 7-1. The United Kingdom’s Emissions Have Fallen Rapidly While Its Economy Has Grown The United Kingdom passed a major climate change law in 2008 and implemented a combination of emissions pricing, regulations, subsidies, and spending on clean energy (London School of Economics 2020). Its emissions fell by about 20 percent between 2009 and 2019, as shown in figure 7-i; the trends shown are not due to swapping domestic production of carbon-intensive products for imports (i.e., “offshoring” emissions); in fact, between 2009 and 2019, emissions from imported goods decreased by more than emissions from exported goods (Ritchie and Roser 2020). Figure 7-i. Changes in U.K. Greenhouse Gas Emissions and Real GDP since 1990 Percent change since 1990 80 Change in real GDP 60 40 20 0 –20 Change in emissions –40 –60 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 Sources: Climate Watch; U.K. Office for National Statistics; CEA calculations. Note: Real GDP is reported in chained 2019 pounds. Greenhouse gases are reported in megatons and use productionbased accounting a host of competitive disadvantages, which include access to capital and the difficulty of acquiring the talent, materials, and customer bases necessary to scale up production. Well-designed policies can help encourage investments at all stages of the innovation process, from research to demonstration projects to initial commercialization (Gundlach, Minsk, and Kaufman 2019). Finally, even with these policies in place, the widespread adoption of cost-effective clean energy solutions requires building the necessary public infrastructure and regulatory structures that enable them to compete with more established products. For example, regulators can require financial institutions to assess climate risks in their investments, and Federal agencies can set guidelines to ensure that emerging technologies, such as carbon capture and storage, are deployed effectively and equitably (White House 2021b; Council on Environmental Quality 2021). 228 | Chapter 7 More broadly, policies that accelerate the transition can be designed to prioritize equity. Currently, lower-income households are often disproportionately harmed by higher energy bills. Further, energy infrastructure investments have historically led to environmental degradation in marginalized communities. Policies can be designed to lessen rather than exacerbate these equity concerns; for example, the Biden Administration has committed to devoting a substantial portion of Federal investments in clean energy development to disadvantaged communities through the Justice40 Initiative (White House 2021c). In many places that have implemented carbon prices (e.g., Canada’s federal carbon pollution pricing system), the revenues are returned to lower-income households so that they receive more in government payments than they pay in higher prices of goods and services (Government of Canada 2022). A Smooth Transition to Clean Energy The need to shift to clean energy is paramount to lessen the severe threats of climate change. However, an equitable transition to a clean energy economy requires more than efforts to reduce emissions. This section highlights the need for public policies that support certain domestic industries and vulnerable communities in response to two key challenges posed by the energy transition. First, domestic clean energy industries will become increasingly important for the Nation’s security and global economic position. Currently, the United States’ energy industry is carbon-intensive and a source of economic productivity and stability (U.S. Environmental Protection Agency 2021). For example, our domestic production of natural gas helps to keep costs low for American consumers and firms (U.S. Energy Information Administration 2021b). However, as the global energy transition progresses, the innovation and production of clean technologies will grow in importance. Fortunately, the United States has the needed resources, institutions, and workforce to support globally competitive clean industries. However, other nations are rapidly ramping up investments in clean energy and support for their domestic industries. Without strong and sustained Federal Government support, U.S. firms that can supply a clean economy are likely to struggle to compete in global markets. The second portion of this section describes the challenges the energy transition poses to communities across the United States where jobs, income, and tax revenues depend on carbon-intensive industries, such as the production of fossil fuels or downstream products like automobiles. Fossil fuel-dependent communities across the country are already facing economic challenges, and the energy transition poses additional risks to communities that are not well prepared and supported (Interagency Working Group Accelerating and Smoothing the Clean Energy Transition | 229 2021). In the past, workers and their families largely have not moved to find jobs when faced with the loss of major employers in their communities. Strategies to support these groups of Americans through the energy transition therefore require policies that target fossil fuel-dependent local economies. Although economists largely agree on the policy recipe for accelerating the energy transition, no similar playbook exists on how to smooth the transition for U.S. firms and communities. In fact, economists have long pointed to the risks of government interventions that advantage certain industries or geographic regions over others. However, the economic literature highlights ways to minimize policy risks and capitalize on the economic opportunities of creating global-leading firms and revitalizing local economies. The First Challenge: Supporting Domestic Industries This subsection describes the need for policy measures that support domestic clean industries, and the opportunities and risks of government interventions that can enable U.S. firms to compete in global markets that are growing rapidly during this energy transition. The domestic energy sector is important to the U.S. economy. Energy production is an important component of U.S. economic strength and stability. The United States is the world’s largest producer of petroleum and natural gas, surpassing Saudi Arabia in petroleum production in 2018 and Russia in natural gas production in 2011 (U.S. Energy Information Administration 2019). Despite being the world’s largest consumer of oil and natural gas, American producers are also now large exporters of these fuels (U.S. Energy Information Administration 2021c). Net imports of petroleum products (about three-fourths of which come from crude oil) fell from about 10 million barrels a day in 2000 (roughly half of U.S. consumption) to below zero by 2019; meanwhile, net imports of natural gas fell from about 4 trillion cubic feet in 2000 to about -2 trillion cubic feet in 2019 (U.S. Energy Information Administration 2021b, 2021c). The United States is also the world’s largest exporter of refined petroleum products and liquefied national gas (Observatory of Economic Complexity; U.S. Energy Information Administration 2021d). The value of fuel exports as a fraction of the total value of merchandise exports increased from about 2 percent in 2000 to 13 percent in 2020, indicating that fuel exports alone account for about 1 percent of U.S. GDP (World Bank 2020) (figure 7-4). In addition to fossil fuels, American firms are large producers and exporters of many other energy- and carbon-intensive products, including chemicals and steel (DeCarlo 2017; U.S. International Trade Administration 2020; IEA 2022a). The carbon-intensive auto industry makes up 3 percent 230 | Chapter 7 Figure 7-4. U.S. Fossil Fuel Consumption for Selected Years Trillions of cubic feet Trillions of barrels per day A 25 B 20 40 35 30 15 25 20 10 15 5 10 5 0 0 Consumption C Production Imports Exports Net imports Production Consumption 2019 2016 2013 2010 2007 2004 2001 1998 1995 1992 1989 1986 1983 1980 1977 1974 1971 1968 1965 1962 1959 1956 1953 -5 1950 2019 2016 2013 2010 2007 2004 2001 1998 1995 1992 1989 1986 1983 1980 1977 1974 1971 1968 1965 1962 1959 1956 1953 1950 -5 Net imports Percent 16 14 12 10 8 6 4 2 2020 2017 2014 2011 2008 2005 2002 1999 1996 1993 1990 1987 1984 1981 1978 1975 1972 1969 1966 1963 1960 0 Sources: U.S. Energy Information Administration; World Bank. Note: Figure panels, from left to right: A, U.S. petroleum consumption, production, imports, exports, and net imports, 1950–2020; B, U.S. natural gas consumption, dry production, and net imports, 1950–2020; C, U.S. fuel exports as a share of merchandise exports, 1960–present. of GDP, more than any other manufacturing sector (American Automotive Policy Council 2020). Despite the harmful effects of the United States’ reliance on fossil fuels, the reality is that we currently benefit in certain ways from our domestic energy production. In the winter of 2021–22, Europe was immersed in an energy crisis, including historically high natural gas prices caused by a series of shocks that led to increased demand and constrained supply, due in part to the continent’s dependence on natural gas from Russia (Cohen 2021; Stapczynski 2021; Sabadus 2021). The United States is somewhat insulated from turmoil in natural gas markets abroad due to our domestic production and the lack of a fully integrated global market—natural gas prices in Europe rose to over 10 times higher than prices in the United States in December 2021 (Reed 2021). In contrast, the global oil market is highly integrated, with a group of countries that essentially set prices (Fattouh 2007) and a mixture of stateowned and private producers with widely varying costs of production (Wall Street Journal 2016). American consumers of oil are therefore vulnerable to geopolitical turmoil and the decisions of policymakers in petrostates. The uninterrupted availability of affordable energy is a national security concern for the United States (IEA 2022b). Ensuring the security of our energy supply will require policy measures that diversify our energy sources and supply chains, and that build resilience into the energy system as a buffer against future shocks (Yergin 2006). The energy transition is an economic opportunity, but policies are needed to help build strong domestic clean industries. American oil Accelerating and Smoothing the Clean Energy Transition | 231 Box 7-2. The History of U.S. Government Support for Domestic Carbon-Intensive Energy Industries As industry and consumers ramped up their use of fossil fuels in the early 20th century, experts became concerned that the country would run out of oil unless new oil fields were found and brought online (Olien and Olien 1993). In 1913, the Federal Government added the intangible drilling oil and gas deduction into the tax code, which allowed companies to deduct from their taxes most of the costs of drilling new wells, reducing the high up-front expenses that could discourage exploration (Center for a Responsible Federal Budget 2013). This deduction remains in place today; at $2.3 billion a year, it is the single largest production tax benefit for the fossil fuel industry (Roberts 2018). The U.S. government has periodically intervened in markets to ensure stable prices in the face of turmoil. For example, in 1930 in East Texas, an enormous new oil field known as the “Black Giant” was discovered by the oilman Dad Joiner (Loeterman 1992). Thousands of independent producers (known as wildcatters) flocked to the area, flooding the market with supply and driving the price of oil down to as low as $0.02 a barrel, well below the cost of production. Faced with a possible collapse of the oil industry, the Governors of Texas and Oklahoma declared martial law in 1931, halting production and stabilizing the price (Goodwyn 1996). President Franklin D. Roosevelt’s Secretary of the Interior, Harold Ickes, led an effort to work out quotas and regulations with producers in the area. Three decades later, the founders of OPEC would look to that system as their model (Loeterman 1992). In 1959, President Dwight D. Eisenhower imposed a quota system restricting oil imports that would remain in place until 1973 (Council on Foreign Relations 2021). The U.S. government has also intervened to help American companies access energy sources around the world. For example, in the 1940s and 1950s, the U.S. Department of State worked with U.S. oil companies to negotiate profit-sharing agreements with oil-producing nations, including Venezuela and Saudi Arabia, to be as favorable as was feasible to U.S. companies (Council on Foreign Relations 2021). In a 1950 agreement with Saudi Arabia, negotiators cut a deal in which oil companies increased the taxes they paid to Saudi Arabia while reducing the taxes they paid in the United States (Ross 1950). This agreement allowed money to flow to Saudi Arabia outside the formal Congressional approval process. When the Mossadeq government in Iran nationalized the Anglo-Iranian Oil Company, the U.S. and U.K. governments launched Operation Ajax, which helped overthrow Mossadeq in 1953 (Allen-Ebrahimian 2017). In the aftermath, the five major U.S. oil companies, along with British and French companies, were given access to Iranian oil fields as part of the Iranian Consortium Agreement 232 | Chapter 7 of 1954; the companies were also given control over production levels (Heiss 1994). Government support comes in the form of boosting energy infrastructure and supply chains as well. A notable example is the Federal Highway Act of 1956, which built the networks necessary for fossil fuels to dominate personal and freight transportation in the United States, while potentially crowding out lower-carbon alternatives such as rail. producers are also vulnerable to decisions made in petrostates. Though the United States is currently the world’s largest oil producer, if the world moves to rapidly limit carbon and therefore reduce oil demand, state-owned oil producers in countries like Saudi Arabia may increasingly find it in their interest to maintain their production levels by setting prices closer to production costs than they are now, at the expense of higher-cost producers that include U.S. firms (U.S. Energy Information Administration 2021f). This means that while global oil demand may decrease only gradually in the coming decades, the effect on the U.S. oil industry may be more abrupt. Indeed, two recent projections show the oil market shares of the members of the Organization of the Petroleum Exporting Countries (OPEC) increasing from roughly one-third in 2021 to about one-half or two-thirds by 2050 in a net zero scenario (Bouckert et al. 2021; Mercure, Salas, and Vercoulen 2021). At the same time, the rapid growth of the demand for carbon-free products globally creates massive—but possibly fleeting—opportunities for U.S. firms. A key question is how the economic productivity and energy security of the United States will be affected as countries transition to clean energy. Will U.S. firms be able to compete in emerging global carbon-free industries? If not, the energy transition could lead to our reliance on imports of the batteries, heat pumps, low-carbon steel, and other critical inputs to a clean energy economy. Consider the transition from internal combustion engine (ICE) vehicles to electric vehicles (EVs). Cars are a major source of greenhouse gas emissions, and President Biden has announced a goal to increase the share of new passenger vehicle sales that are EVs and other zero emissions vehicles from 2.4 percent in 2020 to 50 percent in 2030 (Bui, Slowik, and Lutsey 2021). There are nearly 1 million workers in the U.S. automotive industry, and over 3 million in the car dealer industry (U.S. Bureau of Labor Statistics 2021b). The motor vehicle and parts industry has an annual output of over $500 billion (U.S. Bureau of Economic Analysis 2022). Reducing harmful emissions from vehicles will entail the reduction in output and employment related to ICE vehicles, but enormous growth in EVs—the value of the global EV Accelerating and Smoothing the Clean Energy Transition | 233 market is expected to grow from $163 billion in 2020 to over $800 billion by 2030, according to one expert’s forecast (Jadhav and Mutreja 2020). Over the past century, the combination of automaker innovations, workers’ unions, and labor laws have made ICE vehicles a staple of middleclass families—and in the process creating good jobs, new methods of production, and a strong domestic automobile industry. The United States has the resources and capital required to rapidly scale up a domestic EV industry that can satisfy the growing and changing nature of transportation needs. But this will not occur at a pace consistent with our climate goals without a policy strategy that encourages the redirection of capital and workers across the auto industry supply chain. More broadly, the United States is well positioned to incubate leadingedge clean energy firms (Rodrik 2014; Cleary et al. 2018)—with a highly educated population (National Center for Education Statistics 2021) and institutions that have enabled global leaders in Silicon Valley, biotech, pharmaceuticals, and other industries. Further, a unique endowment of natural resources makes certain United States’ geographic regions ideally suited to become hubs of carbon-free energy production (National Academies 2021). However, U.S. firms will require support to compete in emerging global markets for clean products. The inability to capture the full societal benefits of innovation has led to insufficient private sector investments in emerging clean technologies, inhibiting the expansion of clean industries (Council of Economic Advisers 2021). For example, a first-of-its-kind demonstration facility for low-carbon cement production may provide large societal benefits but also have a cost and risk profile that the private sector is unwilling to take on without government support. Even after a new technology has been successfully developed and demonstrated, its producers often face additional barriers competing with more established technologies. Established firms receive a range of benefits from the existence of a mature industry with extensive supply chains, agglomeration effects (i.e., interactions between innovation and production), and networks of consumers, whereas chicken-and-egg problems hinder emerging technologies. For example, the uptake of EVs is slowed by a lack of a nationwide charging network, and a nationwide charging network has not been built because there are not enough EVs on the roads (Wei et al. 2021). The robust industrial policy strategies of other countries can also be an obstacle to emerging clean industries in the United States. In an efficient global market, each country would provide its domestic firms with only the support required to overcome the types of hurdles described above, which should enable the most productive firms worldwide to become market leaders. In reality, if the U.S. government fails to provide domestic firms with sufficient support, or if other governments overcompensate their own 234 | Chapter 7 Figure 7-5. The United States’ and China’s Percentages of the Market across Clean Technology Industries Percent 90 China United States 80 70 60 50 40 30 20 10 0 Solar module manufacturing (crystalline silicon) Battery cell manufacturing Battery cathode Passenger manufacturing electric vehicle sales Wind turbine manufacturing Lithium mining Lithium refining capacity Source: BloombergNEFѵ domestic firms, American firms may not be able to compete in global markets, regardless of their potential competitive advantages. The Chinese government has made a concerted and successful effort to build domestic industries that can supply a global clean energy economy (Liu and Urpelainen 2021). Therefore, Chinese firms dominate clean energy manufacturing worldwide. Chinese companies produce about 60 percent of the world’s wind turbines and about 80 percent of its solar module cells (see figure 7-5). In addition, China now produces over 80 percent of the world’s battery cells used to power EVs. Ceding such industries to China is not only a lost opportunity for U.S. firms but also a risk to U.S. consumers, given the potential for the monopolization of important supply chains (see also chapter 6). Building a domestic battery industry—as well as other components of the EV supply chain, such as key critical minerals—that can compete with firms in China and other countries is a key challenge for the U.S. economy over the next decade—and a major economic opportunity, given the growing global demand for EVs. China and Russia are also making large bets on nuclear energy, another source of clean energy with the potential to grow rapidly in a global energy transition (Berthélemy and Cameron 2021). A recent study by the International Atomic Energy Agency projects nuclear energy capacity could grow between 17 and 94 percent worldwide by 2030 (IAEA 2013). In contrast, the growth of nuclear energy has stalled in the United States due to concerns related to costs, safety, and waste, although the Bipartisan Infrastructure Law and other Biden-Harris Administration proposals include substantial incentives to support the domestic nuclear energy industry Accelerating and Smoothing the Clean Energy Transition | 235 (Bordoff 2022; U.S. Energy Information Administration 2021g). Ceding the global-leading positions in the nuclear industry to China and Russia, whose companies are now supplying reactor technologies to other parts of the world, would forgo not only economic opportunities for U.S. firms but also the potential for the U.S. government to influence nonproliferation efforts in other countries with nuclear energy facilities (Bordoff 2022). Our allies are developing industrial policy strategies as well. For example, the European Union is the world’s leader in subsidizing renewable electricity generation (Taylor 2020), and it recently introduced a new strategy to support domestic industries with increased access to financing, reduced regulatory burdens, and capacity building for the transition to sustainability and digitization (European Commission 2020). The EU has also provided substantial support to key emerging technologies such as batteries and clean hydrogen, positioning European clean energy firms to be the global leaders in potentially game-changing technologies (European Commission 2021b, 2022). Strategies for Supporting Domestic Industries through the Energy Transition The world’s most advanced economies, including the United States, have implemented policy measures with the aim of industrial development (Goodman 2020). For over a century, U.S. policymakers have provided support to the fossil fuel industry, recognizing that a strong domestic energy industry is important for economic competitiveness and national security (Johnson 2011). Yet government interventions are not without risk; after all, market forces can improve the economic efficiency of decisions. The challenge for policymakers, then, is to design a fulsome strategy that maximizes the economic opportunities of the clean energy transition while minimizing the risks. Although there is no established playbook for green industrial policy, economists have offered numerous general principles (Vogel 2021; Rodrik 2014; Mazzucato, Kattel, and Ryan-Collins 2019). First, the government should provide domestic industries with transparent, high-level goals. National governments can launch national missions to confront the largest challenges facing societies, including climate change (Mazzucato, Kattel, and Ryan-Collins 2019). For example, during the Space Race of the 1960s, funding for the U.S. National Aeronautics and Space Administration reached nearly 4.5 percent of Federal spending, which fueled domestic industries like computer chip production and spawned a new generation of engineers and scientists (Chatzky, Siripurapu, and Markovich 2021). In contrast to high-level missions, supporting specific companies or technologies over 236 | Chapter 7 others comes with demanding informational requirements on policymakers, and government actors do not have complete information on the potential benefits, costs, and risks of each investment (Schultze 1983). Instead, the government may (at least partially) let political considerations influence investment decisions, which raises the odds of wasteful government spending. Another recommendation is that government should focus support on technologies that are not fully mature—from research and development to demonstration projects to initial commercialization. Without government support, firms that produce emerging technologies often cannot compete with firms that produce mature technologies. Many of the largest industrial policy success stories have come from investing in innovative technologies that exhibit a wide range of potential (and often unforeseen) applications (Goodman 2020). In contrast, subsidies for fully mature technologies can cause long-term declines in allocative efficiency, largely by untethering prices and output allocations from underlying economic conditions (Kim, Lee, and Shin 2021). Importantly, it may not be possible or desirable to avoid supporting specific emerging clean energy technologies, despite the associated challenges noted above. Governments need to balance the potentially conflicting needs to foster collaborations with industry while avoiding its undue influence on the policy process. Successful public policies often require considerable interaction between government officials and industry stakeholders, so that the government officials understand the businesses and technologies on which public policies focus (Rodrik 2014). Such interactions naturally heighten the concerns of political capture—whereby government officials put their own interests and the interests of industry stakeholders who lobby them above the interests of their constituents—because policy decisions are made by political actors (Gregg 2020). Indeed, whenever subsidies and tariffs are on the table, moneyed interests will lobby for the adoption and retention of their preferred policies, making these policies difficult to eliminate when they become unnecessary or counterproductive. For example, fossil fuel subsidies were first paid in the 1910s, and agriculture subsidies were first paid in the 1930s (Center for a Responsible Federal Budget 2013; Comparative Food Politics n.d.); in both cases, the subsidies have lasted to the present day due in large part to interests that benefit from them. Approaches to balance the needs to collaborate with industry, while avoiding their undue influence, include government institutions with some degree of independence from the political process and restrictions on a revolving door between government service and industry. Another way to maximize the effectiveness of government interventions is to make the regulatory environment as certain as possible. Ensuring that the parameters and duration of government support are clear and Accelerating and Smoothing the Clean Energy Transition | 237 concrete will give firms confidence about future technological and market opportunities, catalyzing investment and innovation that would not otherwise occur. In contrast, uncertain regulatory environments are not conducive to attracting private sector investments. For example, the periodic expiration (or near-expiration) of the production tax credit for renewables in the United States has inhibited investments in wind and other clean energy technologies and thus has inhibited the growth of these emerging industries (Sivaram and Kaufman 2019). Finally, just as an investor may be wise to consider a diversified portfolio rather than a concentrated set of individual stocks, the government should invest in a broad portfolio of clean energy solutions (Rodrik 2014). An important role of government is to take on risks that the private sector will not bear; a diverse portfolio accommodates such risks, even in the presence of the inevitable failed investments. For example, the Department of Energy’s Loan Programs Office was established to provide financing for innovative energy projects in the United States, including access to debt capital that private lenders cannot or will not provide (U.S. Department of Energy, Loan Programs Office 2017). The program has funded a few companies that went bankrupt—most notably the solar producer Solyndra—but those bankruptcies have not prevented the formation of a highly successful overall portfolio of investments (Rodrik 2014). The program has propelled the growth of game-changing companies, including Tesla (U.S. Department of Energy, Loan Programs Office 2017). The Federal Government should be willing to lose money to achieve such benefits; but instead, the monetary losses from the Loan Program have been less than one-third of the interest paid to the government on the loans to date (U.S. Department of Energy, Loan Programs Office 2021). Following this playbook, President Biden has announced a goal for 50 percent of passenger vehicle sales by 2030 to be EVs, along with helping to build a domestic supply chain to support EV production (White House 2021d). Moreover, the Federal Government is investing in the infrastructure needed to entice consumers to purchase EVs; there are currently only about 5,000 of the fastest EV chargers in the United States for public use, and these chargers are clustered in a few regions, including in the Northeast and on the West Coast. The 2021 Bipartisan Infrastructure Law is investing billions of dollars in building a domestic supply chain for batteries and nationwide network of EV charging stations (White House 2021d, White House 2021e). Previous attempts to support domestic industries in global markets have mixed track records (see box 7-3). Many failed investments might have been avoided with better processes for strategically targeting industrial policy opportunities. Perhaps more important than avoiding failed investments is creating the conditions where failures are expected and accepted as a learning experience, including with data collection, information sharing, 238 | Chapter 7 Box 7-3. Industrial Policy Successes and Failures Governments worldwide have had many successes and failures supporting domestic industries. Perhaps the most prominent examples are in the context of economic development. South Korea is an often-lauded success story, due to its subsidies for a targeted set of industries that helped build a series of large, family run business conglomerates called the Chaebol, including well-known brands like Hyundai and Samsung (Albert 2018; Westphal 1990). One study found that targeted industries grew more than 80 percent more than nontargeted ones from 1973 to 2017 (Lane 2017). In contrast, several industrial policy pushes in SubSaharan Africa, North Africa, and the Middle East have been largely unsuccessful, with corruption, existing distortions, and weak government capacity limiting their effectiveness (Devarajan 2016). Even in cases where industrial policy has been successful in the development context, such as Japan, it is difficult to disentangle industry support from other factors that influence economic growth, such as favorable domestic economic conditions or high savings rates (Goodman 2020). The anecdotal evidence of developed countries supporting domestic producers in emerging high growth industries offers notable successes and failures. Denmark has successfully leveraged a national strategy to build world-leading capabilities in offshore wind energy, while the billions of dollars spent by France, Germany, and the European Union in the early 2000s to fund search engines that could compete with Google were unsuccessful (Lewis 2021; Goodman 2020). Efforts by the U.S. government to support domestic industry have similarly produced mixed results. Some of the largest anecdotal successes of government interventions have come in the face of threats, like the Space Race or the War Production Board during World War II (Chatzky, Siripurapu, and Markovich 2021). Facing intense competition from Japan in the 1980s, subsidization of the semiconductor industry created a globally competitive industry by the 1990s (Hof 2011). In contrast, the United States has provided strong support to the domestic shipping industry for a century—yet U.S. ships still cannot compete on cost with foreign vessels, in part due to poor labor standards in the industry abroad (which is also a highly relevant concern for clean energy production abroad) (Frittelli 2003, 2019; Ha et al. 2020; Kaplan, Buckley, and Plumer 2021). and impact evaluations. This will enable policymakers to experiment with policy design, figure out what works, and take sufficient risks to reap the rewards of economy-boosting investments. Accelerating and Smoothing the Clean Energy Transition | 239 The Second Challenge: Supporting Communities That Rely on a Carbon-Intensive Economy The geographic concentration of many of the industries most affected by the energy transition, including fossil fuel extraction and the manufacturing of high-carbon products, implies disproportionate risks for the regions of the country that rely on these industries for jobs and tax revenue, and important opportunities for public policies to mitigate these risks and invest in the residents of these same regions. There is considerable overlap between the dual challenges of smoothing the energy transition for domestic economic sectors and for local communities. After all, clean energy-related investments in fossil fuel-dependent local economies can serve to boost both the industries and places most affected by the energy transition. However, these two challenges also differ in marked ways. As described above, supporting domestic industries most effectively entails a national strategy that will lead to investments across the entire country, including but not limited to local economies that currently depend on fossil fuels. Similarly, effectively supporting fossil fuel-dependent communities will involve a commitment to these local economies with measures that are not limited to clean energy investments. The remainder of this section describes the rationale for government interventions to support fossil fuel-dependent communities and the lessons learned from prior experience with place-based policies. The Geographic Concentration of Fossil-Fuel-Dependent Communities As a case study, consider the automobile industry’s shift away from ICE vehicles. Certain industry jobs, including vehicle assembly and sales, may translate to jobs on the EV line relatively seamlessly. However, many of the jobs specific to ICE components and supply chains will decline. For example, the ICE and EV powertrains—the system by which the engine and motor deliver power to the wheels—require different parts. Of the 140,000 workers in the U.S. powertrain sector, 70 percent are mostly concentrated in small communities in Michigan, Ohio, and Indiana. In Monroe County, Michigan, more than one-quarter of employment relates to ICE vehicle powertrains (Raimi et al. 2021). The risks of the energy transition may be even more acute for communities dependent on the extraction and combustion of fossil fuels. The U.S. fossil fuel industry is highly geographically concentrated, as shown in figure 7-6. The coal extraction industry (panel A) is largely located in Appalachia and portions of the Mountain West—about 90 percent of U.S. coal production takes place in 50 counties (U.S. Energy Information Administration 240 | Chapter 7 Figure 7-6. Fossil Fuel Employment by County A B Fraction of county WOfkforce •0.10-026 •0.05-0.10 o.o, -0.05 0.00-0.01 No data ..., Fraction of counly WOf1d0tce ao.10-0.2• ■0.05-0.10 o.o, -o.05 0.00-0.01 No data C • ...... 1/� Fraction of county wOfkforee ao.,o-o:ia ■0.05-0.10 o.o, -0.05 0.00-0.01 No date Sources: Quarterly Census of Employment and Wages; Bureau of Labor Statistics (BLS); CEA calculations. Note: Figure panels, from left to right: A, coal mining; B, oil and gas extraction; C, support services for the mining and quarrying of minerals and for the extraction of oil and gas. Industries are defined by NAICS codes 211 (oil and gas extraction), 2121 (coal mirung), and 213 (support activities for mining and oiVgas extraction). Each panel displays the fraction of the county's workforce in the NAICS industry. Cells with small employment are suppressed by the BLS. 2021h). In some counties, fossil fuel employment is as high as 30 to 50 percent of all employment (panels A, B, and C); these figures are higher when including jobs directly supported by the region’s dominant industry, such as in the service sector, supply chain, and local government (Tomer, Kane, and George 2021). Employment and economic activity associated with fossil fuel production is already declining in many regions of the country. Coal-mining jobs have decreased by about three-quarters since 1980, and employment in the oil and gas sector has declined by about 30 percent in the last decade (Interagency Working Group 2021; Federal Reserve Bank of Saint Louis 2022). The underlying reasons are myriad: automation; cheap natural gas causing a shift away from coal-fired electricity; lower prices of renewable energy; resource decisions that account for the damage caused by climate change and air pollution; volatility in oil markets; and weak international demand, which may continue to fall as countries seek to meet their Paris Agreement commitments (Look et al. 2021; Bowen et al. 2018). Fossil fuel-dependent communities that are unprepared for the energy transition risk further reductions in employment and economic activity (Larson et al. 2020). These areas are often rural, undiversified, and have preexisting economic challenges—poverty rates are higher in fossil fuel-reliant communities than in neighboring counties and the Nation as a whole, as are mortality rates due to such issues as opioid abuse and black lung disease (Interagency Working Group 2021; Bowen et al. 2018; Metcalf and Wang 2019; National Institute for Occupational Safety and Health 2018). Large Accelerating and Smoothing the Clean Energy Transition | 241 populations in coal communities depend on pensions and other benefit funds with questionable solvency (Randles 2019). More broadly, rural locations often lack both the basic infrastructure (e.g., roads and broadband Internet) and the financial infrastructure (e.g., easily accessible credit) necessary to transition to new industries (Raimi et al. 2021). Many rural locations also suffer from a dearth of opportunities, with undiversified economies and workers that are specialized for the jobs in the region. For instance, workers in Appalachia are 25 percent less likely than the national average to have a college degree (Appalachian Regional Commission 2022). The loss of dominant employers can precipitate fiscal spirals from which jurisdictions struggle to recover, as previously shown in the experiences of steel towns in Pennsylvania, coal-producing regions of the United Kingdom, and the automobile-dominated economy of Detroit, among others. When major industrial firms depart, the supporting service sectors and nearby supply chains shrivel in size. Reduced economic activity leads to reduced government revenues from property and sales taxes, which often results in cuts to government services. Combined with reduced employment opportunities, these factors make it difficult for distressed communities to attract new businesses and for dislocated workers to find new job opportunities (Morris, Kaufman, and Doshi 2021). The Inadequacy of Place-Neutral Policies The geographic concentration of the risks of the energy transition does not, by itself, imply that government support should specifically target these regions. Instead of targeting economically distressed regions, policies could target struggling people, regardless of where they live. Indeed, many government programs already support people in communities that face economic shocks, even though they are often not targeted at specific communities. For example, Federal and State governments have implemented trade adjustment assistance programs to directly compensate workers who lose their jobs because of increased exposure to trade,1 and assistance programs such as the Supplemental Nutrition Assistance Program (formerly known as Food Stamps) and Medicaid help people during times of economic hardship (Higdon and Robertson 2020).2 Multiple reports have found limited effectiveness of trade adjustment assistance (TAA) programs at transitioning workers to new, higher-paying lines of work (Rodrik 2017; U.S. Government Accountability Office 2012a, 2012b). While TAA has a large, positive causal effect on employment and earnings, take-up of TAA is low, so some of the limited effectiveness of TAA may be explained by how few people use it (Hyman 2018; Autor et al. 2014). 2 Social safety net programs may be especially important for aiding fossil-fuel-reliant communities, given preexisting economic challenges and the growing concerns about the solvency of industryfunded pension programs (Higdon and Robertson 2020; Walsh 2019). 1 242 | Chapter 7 However, new evidence suggesting that people largely do not move in response to economic shocks has challenged the argument for targeting people rather than places for transition assistance. For example, researchers who have studied the effect on U.S. communities of increased trade with China have found that trade-induced manufacturing job losses led to nearly one-to-one decreases in the employment-to-population ratio in affected communities, indicating that workers were not migrating to other communities or sectors (Autor, Dorn, and Hanson 2021). Similarly, Hershbein and Stuart (2021) find persistent decreases in employment-to-population ratios after severe recessions. Over half of Americans spend most of their career in their childhood metropolitan area (Bartik 2009). The reasons people do not move in response to shocks likely include their attachment to local communities (including support from family and neighbors), the falling housing prices in declining communities, and lower wages for noncollege workers in high-income cities (Notowidigdo 2020; Autor, Dorn, and Hanson 2021). What often sparks migration is opportunity elsewhere, not the shock in one’s community. Monras (2020) finds that the local differences to migration in response to recessions are driven by differences in in-migration, not in out-migration. In other words, conditional on deciding to move, people respond to local economic conditions when choosing a new location. The workers most likely to stay behind are those with lower earnings capacity (Notowidigdo 2011; Bound and Holzer 2000). For minority households, housing discrimination has also restricted mobility (Neumark and Simpson 2015). This tendency to remain in economically distressed communities and the inadequacy of assistance programs alone in ameliorating long-standing economic hardships (see box 7-4) implies the need for policies that help people where they are. This has led to an increase in scholarship on placebased, economic development policies aimed at improving the well-being of individuals in particular areas. Though the earlier literature highlighted the potential for inefficiencies, more recent findings focus on the conditions that may justify place-based policies. These include the invariance of location choices to local economic conditions; geographically segregated income groups that make investing in regions a reasonable proxy for investing in lower-income individuals (Akerlof 1978; Fajgelbaum and Gaubert 2021); the desire for insurance against location-specific economic shocks (Neumark and Simpson 2015), which may become more important as temperatures continue to rise; differences in the optimal hiring subsidies across regions based on local productivity levels (Kline and Moretti 2013); heterogeneity in local public goods provisions (Bartik 2020); and the desire to take advantage of agglomeration effects (Kline 2010). Accelerating and Smoothing the Clean Energy Transition | 243 Box 7-4. The Broader Issue of Distressed Local Economies A proactive energy transition could prevent exacerbating problems in already distressed areas. The economies of many local communities are struggling, and some local economies have been distressed for a long time. Before the COVID-19 pandemic, in 2019, about 14 percent of U.S. counties had an unemployment rate above 8 percent (see figure 7-ii). Distressed local economies are concentrated in portions of the Black Belt, Appalachia, industrial Midwestern cities, and rural Western areas. The causes of these struggles vary—the “China trade shock” (Autor et al. 2013), migration to urban centers, and technological change (Acemoglu and Restrepo 2020), to name a few—and the struggles are often persistent: about one-third of the counties with unemployment rates above 8 percent in 2019 also had unemployment rates in the worst quartile of U.S. counties in 1980, 1990, 2000, and 2010. Similarly, Kline and Moretti (2013) find that a plot of unemployment rates in 1990 and 2008 across 239 metropolitan areas shows “a remarkable degree of persistence,” with a regression coefficient of 0.509 (.045) and R2 of 0.35; they note that European labor markets show a similar (perhaps larger) degree of persistence. Figure 7-ii. Distressed Counties in the United States A .. B Unemployment rate I ��n��� Oumbte l( •5 .4 •3 2 1 a No data ► •7.5-34.1 •6.0- 7.5 ■ 5.0-6.0 4.1 - 5.0 3.1 -4.1 3.1 a.a - No data Source: American Community Survey, Census Bureau. Note: Figure panels, from left to right: A, number of decennial censuses in \\11ich the comity is the highest quintile of unemployment rate; B, unemployment rate in 2019. Strategies for Place-Based Policies While there is no established playbook for policymakers to follow in designing policies to support local economic development (Rodrik 2014), the following are general principles, drawn from the literature, for the design of place-based policies to support communities affected by the energy transition. First, revitalizing communities requires a sustained commitment from the Federal Government to forming partnerships with local communities to 244 | Chapter 7 fund suitable opportunities for economic development—a type of high-level national mission called out above in the context of industrial policy design. Indeed, perhaps the most important cause of our limited understanding of successful place-based policies is how few resources have been devoted to these efforts at the Federal level. According to Bartik (2020), the U.S. government spends about $10.1 billion a year on Federal programs and tax credits that could fall under the umbrella of place-based policies. Such spending is a drop in the bucket compared with the resources spent on other Federal Government priorities, such as the annual grants of $417 billion to States and localities for Medicaid and the Children’s Health Insurance Program (Shambaugh and Nunn 2018). If the Federal Government committed to providing communities with opportunities to rebuild after economic shocks, the subsequent policy experimentation would likely lead to a far better understanding of the most successful strategies for implementing place-based policies. State and local governments spend above five times more per year than the Federal Government on place-based policies (Bartik 2020), and some State governments are an important source of support for distressed communities within their jurisdictions. However, for struggling regions facing binding budget constraints, economic development programs come in lieu of other public services—or, even worse, create a race to the bottom, in which local governments outbid one another to attract new businesses, depleting government coffers (Mast 2018). The Federal Government is the sole entity that can fund and implement a nationwide strategy to revitalize distressed areas. A second principle for the design of place-based policies is to target the communities that will benefit most from the support. Austin, Glaeser, and Summers (2019) note that spending to boost employment is more effective in areas where unemployment is high. Bartik (2020) estimates that the benefits of added jobs are at least 60 percent greater in distressed regions than in booming local economies. Designing effective place-based policies therefore requires a process of selecting which communities to target. Avoiding political influence in making such decisions will be important for a program’s success and credibility. A third common recommendation for successful place-based policies is to avoid one-size-fits-all solutions. Place-based policies can be designed so that the same measure will be applied to any eligible region; or, at the cost of additional complexity, measures can be differentiated to accommodate local conditions and the relative strengths, needs, and existing assets of individual communities. Forming partnerships with communities and catering to local circumstances may be especially important for fossil fuel communities, given their distinctive characteristics noted above. For example, ReImagine Appalachia is a think tank that has proposed a blueprint for expanding Accelerating and Smoothing the Clean Energy Transition | 245 opportunities for high-quality jobs with public investments that aim to match the skills of fossil fuel workers and contribute to sustainable economic development in the region (ReImagine Appalachia 2021). Other recommendations for successful policy design include encouraging hubs of research and development activity, including in distressed communities, to take advantage of agglomeration effects (Gruber and Johnson 2019); and directing place-based policies toward industries for which investments create larger boosts in economic activity, which are referred to as higher-multiplier industries. For example, Bartik (2020) argues that multipliers in high-technology industries are especially large because the ideas and workers of one high-tech firm boost the productivity of nearby high-tech firms (Rodrik 2014, 2020; Mast 2018). (See box 7-5.) The Clean Energy Transition Provides Unique Opportunities to Implement Successful Place-Based Policies Place-based policies largely have not followed the principles described above (Bartik 2020), so it is perhaps unsurprising that the empirical evidence evaluating previous attempts at place-based policies is mixed. Bartik (2020) finds evidence supportive of the potential for place-based policies to generate large long-run benefits. He points to numerous examples of successful local economic development policies, including experiences involving the Tennessee Valley Authority and the Appalachian Regional Commission. At the same time, Neumark and Simpson (2015) conclude that, though placebased policies may increase economic activity when they are in effect, it is not clear from the evidence that place-based policies typically achieve their goal of jump-starting lasting economic development. While support for struggling communities cannot focus only on clean energy investments, there are various reasons to believe that the energy transition will provide opportunities to improve the track record of place-based policies. The first reason is scale. Climate action requires large investments in a diverse set of emerging clean energy technologies. A recent National Academies panel estimated that roughly $2 trillion in incremental capital investments needs to be mobilized over the next decade to put the United States on track to achieve the goal of net zero emissions by 2050 (National Academies 2021). Princeton University’s Net Zero America report estimates the need for 0.5 to 1 million additional jobs in the U.S. energy sector in the 2020s (Larson et al. 2020). Indeed, many clean energy investments will vastly exceed the scale of the typical place-based policies of the past. For example, the Bipartisan Infrastructure Law includes money for large-scale demonstration projects for low-carbon hydrogen production and carbon capture retrofits for large steel, cement, and chemical production (see box 7-5) (White House 2021g). 246 | Chapter 7 Box 7-5. The Administration’s Actions on PlaceBased Policies for Energy Communities The Biden-Harris Administration has taken actions in its first year that are intended to help energy communities. On January 27, 2021, President Biden signed Executive Order 14008, which established the Interagency Working Group (IWG) on Coal and Power Plant Communities and Economic Revitalization. The IWG’s initial report identifies $37.9 billion in existing Federal funding that could be used to help energy communities; so far, IWG member agencies have delivered more than $2.8 billion in direct Federal funding to 25 priority energy communities across the country. The American Rescue Plan Act of 2021 allocates $3 billion to the Economic Development Administration (EDA) to benefit underserved communities affected by COVID-19. The EDA has allocated $300 million to support communities that are dependent on the coal industry through Build Back Better Regional Challenge grants and Economic Adjustment Assistance grants. The Bipartisan Infrastructure Law (BIL) includes a number of place-based investment provisions for which energy communities are prioritized (see table 7-i). Over the next five years, the BIL will allocate more than $27 billion to these programs—which includes $8 billion for regional clean hydrogen hubs, $3.5 billion for regional direct air capture hubs, and $2.5 billion for carbon capture demonstration projects. The BIL also includes programs that target support to communities in other ways, including $55 billion for clean drinking water and eliminating lead pipes, $65 billion to ensure universal access to high-quality broadband, $110 billion to repair roads and bridges, and $21 billion for cleaning up legacy pollution by reclaiming mines and plugging orphaned oil and gas wells (White House 2021f). Table 7-i. Selected BIL Programs That Target Energy Communities BIL Program Name Regional Clean Hydrogen Hubs Total (thousand dollars) 8,000,000 Regional Direct Air Capture Hubs 3,500,000 Battery Material Processing Grants 3,000,000 Battery Manufacturing and Recycling Grants 3,000,000 Carbon Capture Demonstration Projects Program 2,537,000 Carbon Storage Validation and Testing 2,500,000 Advanced Reactor Demonstration Program 2,477,000 Carbon Dioxide Transportation Infrastructure Finance and Innovation Clean Hydrogen Electrolysis Program 2,100,000 1,000,000 Source: U.S. House of Representatives (2022). Note: BIL = Bipartisan Infrastructure Law. This table only includes programs with at least $1 billion in funding. Accelerating and Smoothing the Clean Energy Transition | 247 Such projects can involve many millions of dollars in investments in local economies (Jones and Lawson 2021). Though place-based policies have not historically been well targeted to individual distressed communities, the diversity of clean energy solutions provides an opportunity to tailor investments to a community’s strengths and needs, including characteristics related to geography, workforce skills, education levels, and preexisting infrastructure (Bartik 2020; Tomer, Kane, and George 2021). Importantly, the employment opportunities created by the energy transition may not, absent policy intervention, arise in fossil fuel-dependent communities that often support more extractive and laborintensive industries. Yet place-based policies can channel investment to these communities. Some are well suited for a carbon capture project, while others are better suited for projects involving wind, solar, geothermal, nuclear, or other climate solutions. In many cases, policies can leverage the existing infrastructure and workforce skills in fossil fuel-dependent communities, including measures to repurpose retired power plants or equip facilities with the ability to sequester carbon underground (Tomer, Kane, and George 2021). The energy transition also presents a unique opportunity to implement measures that raise the quality of jobs for American workers in the energy industry. Though roughly 30 percent of the clean energy workforce will require at least a bachelor’s degree, 70 percent will require fewer than four years of related work experience (Larson et al. 2020). And though some clean energy jobs are already high paying, policy measures that incentivize high-quality clean energy jobs can help to ensure that opportunities in clean industries are suitable replacements for the relatively high-paying blue-collar jobs that constitute much of the employment in fossil fuel-reliant communities (Muro et al. 2019). Once again, the growing EV industry provides an important example. The existing auto industry presents a unique economic opportunity to build a successful domestic EV industry in many of the same locations. For instance, Ford recently announced that it is converting its Van Dyke Transmission Plant in Sterling, Michigan, into the Van Dyke Electric Powertrain Center (Ford Motor Company 2021). Though market forces alone may be sufficient to incentivize such conversions in certain instances, policy support will often be needed to encourage automakers to take advantage of opportunities to shift to EVs in the communities where they currently operate. Finally, it is worth reemphasizing that clean energy investments often carry atypical growth potential. The world needs clean energy solutions to rapidly scale up to successfully address the risks of climate change. Though clean energy investments are not devoid of risk, the likelihood that the demand for clean products will rapidly increase in coming decades is a major advantage compared with a generic, place-based investment. 248 | Chapter 7 Discussion and Conclusions This chapter has emphasized that carefully designed policies are needed to accelerate the United States’ transition to a clean energy economy. The host of market failures inhibiting this transition justifies the implementation of policies that reduce the relative prices of low-carbon products, offer incentives for innovation and energy efficiency, and provide public goods and regulatory measures that effectively support the development of a clean energy economy. These policies should be designed to ensure that they help to mitigate rather than exacerbate preexisting inequities in the economy. Policies are also needed to smooth the transition to clean energy by lessening the risks to U.S. competitiveness in global markets and by supporting vulnerable communities. The literature points to numerous principles for how government can successfully intervene to boost domestic industries by setting transparent and high-level goals, providing regulatory certainty, creating a diversified portfolio of government investments, focusing on nonmature technologies, and pursuing measures that avoid having industry stakeholders exercise undue influence on the policy process. Governments can also make sustained commitments to supporting and diversifying fossil fuel-dependent regional and local economies, by forming partnerships with these communities for measures that fit their particular characteristics, strengths, and challenges. Fortunately, the energy transition provides opportunities for bolstering domestic firms in emerging carbon-free industries and for economic development in the communities that are most vulnerable to the transition’s risks. Taking advantage of these opportunities is at the core of the Biden-Harris Administration’s economic and climate strategies. Given the lack of an established playbook for green industrial policies and place-based policies, policymakers need to be open to experimentation and must expect failures—along with lessons learned from these failures— as necessary aspects of what will become a successful portfolio of policies and investments. The stakes are high. Although this chapter has separated the discussion of policies that accelerate the transition to clean energy from policies that smooth it, the fates of these two policy strategies are very much intertwined. The transition to clean energy has begun, but its pace is difficult to predict. 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Gilford. 2017. “Centuries of Thermal Sea-Level Rise Due to Anthropogenic Emissions of Short-Lived Greenhouse Gases.” Proceedings of the National Academy of Sciences 114, no. 4: 657–62. https:// www.pnas.org/doi/10.1073/pnas.1612066114#:~:text=Our%20study%20 shows%20that%20short,additional%20future%20sea%2Dlevel%20rise. 332 | References Appendix A Report to the President on the Activities of the Council of Economic Advisers during 2021 333 Letter of Transmittal Council of Economic Advisers Washington, December 31, 2021 Mr. President: The Council of Economic Advisers submits this report on its activities during calendar year 2021 in accordance with the requirements of Congress, as set forth by Section 10(d) of the Employment Act of 1946, as amended by the Full Employment and Balanced Growth Act of 1978. Sincerely yours, Cecilia Elena Rouse Chair Jared Bernstein Member Heather Boushey Member Activities of the Council of Economic Advisers during 2021 | 335 Council Members and Their Dates of Service Name Position Oath of office date Separation date Edwin G. Nourse Leon H. Keyserling Chairman Vice Chairman Acting Chairman Chairman Member Vice Chairman Member Member Chairman Member Member Member Chairman Member Member Member Member Chairman Member Member Member Chairman Member Member Member Chairman Member Member Member Chairman Member Member Chairman Member Member Member Member Chairman Member Member Chairman Member Member Member Member Chairman Member Member August 9, 1946 August 9, 1946 November 2, 1949 May 10, 1950 August 9, 1946 May 10, 1950 June 29, 1950 September 8, 1952 March 19, 1953 September 15, 1953 December 2, 1953 April 4, 1955 December 3, 1956 May 2, 1955 December 3, 1956 November 1, 1958 May 7, 1959 January 29, 1961 January 29, 1961 January 29, 1961 August 3, 1962 November 16, 1964 May 17, 1963 September 2, 1964 November 16, 1964 February 15, 1968 February 2, 1966 February 15, 1968 July 1, 1968 February 4, 1969 February 4, 1969 February 4, 1969 January 1, 1972 September 9, 1971 March 13, 1972 July 23, 1973 October 31, 1973 September 4, 1974 June 13, 1975 July 22, 1975 January 22, 1977 March 18, 1977 March 18, 1977 June 6, 1979 August 20, 1980 February 27, 1981 June 12, 1981 July 14, 1981 November 1, 1949 John D. Clark Roy Blough Robert C. Turner Arthur F. Burns Neil H. Jacoby Walter W. Stewart Raymond J. Saulnier Joseph S. Davis Paul W. McCracken Karl Brandt Henry C. Wallich Walter W. Heller James Tobin Kermit Gordon Gardner Ackley John P. Lewis Otto Eckstein Arthur M. Okun James S. Duesenberry Merton J. Peck Warren L. Smith Paul W. McCracken Hendrik S. Houthakker Herbert Stein Ezra Solomon Marina v.N. Whitman Gary L. Seevers William J. Fellner Alan Greenspan Paul W. MacAvoy Burton G. Malkiel Charles L. Schultze William D. Nordhaus Lyle E. Gramley George C. Eads Stephen M. Goldfeld Murray L. Weidenbaum William A. Niskanen Jerry L. Jordan 336 | Appendix A January 20, 1953 February 11, 1953 August 20, 1952 January 20, 1953 December 1, 1956 February 9, 1955 April 29, 1955 January 20, 1961 October 31, 1958 January 31, 1959 January 20, 1961 January 20, 1961 November 15, 1964 July 31, 1962 December 27, 1962 February 15, 1968 August 31, 1964 February 1, 1966 January 20, 1969 June 30, 1968 January 20, 1969 January 20, 1969 December 31, 1971 July 15, 1971 August 31, 1974 March 26, 1973 August 15, 1973 April 15, 1975 February 25, 1975 January 20, 1977 November 15, 1976 January 20, 1977 January 20, 1981 February 4, 1979 May 27, 1980 January 20, 1981 January 20, 1981 August 25, 1982 March 30, 1985 July 31, 1982 Council Members and Their Dates of Service Name Position Oath of office date Separation date Martin Feldstein William Poole Beryl W. Sprinkel Thomas Gale Moore Michael L. Mussa Michael J. Boskin John B. Taylor Richard L. Schmalensee David F. Bradford Paul Wonnacott Laura D’Andrea Tyson Alan S. Blinder Joseph E. Stiglitz Chairman Member Chairman Member Member Chairman Member Member Member Member Chair Member Member Chairman Member Member Chair Member Member Chairman Member Member Chairman Member Member Chairman Member Member Chairman Chairman Member Member Chairman Member Chair Member Chairman Member Member Member Chairman Member Chairman Member Member Member Member October 14, 1982 December 10, 1982 April 18, 1985 July 1, 1985 August 18, 1986 February 2, 1989 June 9, 1989 October 3, 1989 November 13, 1991 November 13, 1991 February 5, 1993 July 27, 1993 July 27, 1993 June 28, 1995 June 30, 1995 January 29, 1996 February 18, 1997 April 23, 1997 October 22, 1998 August 12, 1999 August 12, 1999 May 31, 2000 May 11, 2001 July 25, 2001 November 30, 2001 May 29, 2003 November 21, 2003 November 21, 2003 February 23, 2005 June 21, 2005 November 18, 2005 November 18, 2005 February 27, 2006 July 17, 2008 January 29, 2009 March 11, 2009 September 10, 2010 March 11, 2009 April 19, 2011 April 19, 2011 November 7, 2011 February 7, 2013 August 4, 2013 August 6, 2013 July 21, 2014 August 10, 2015 August 31, 2015 July 10, 1984 January 20, 1985 January 20, 1989 May 1, 1989 September 19, 1988 January 12, 1993 August 2, 1991 June 21, 1991 January 20, 1993 January 20, 1993 April 22, 1995 June 26, 1994 Martin N. Baily Alicia H. Munnell Janet L. Yellen Jeffrey A. Frankel Rebecca M. Blank Martin N. Baily Robert Z. Lawrence Kathryn L. Shaw R. Glenn Hubbard Mark B. McClellan Randall S. Kroszner N. Gregory Mankiw Kristin J. Forbes Harvey S. Rosen Ben S. Bernanke Katherine Baicker Matthew J. Slaughter Edward P. Lazear Donald B. Marron Christina D. Romer Austan D. Goolsbee Cecilia Elena Rouse Katharine G. Abraham Carl Shapiro Alan B. Krueger James H. Stock Jason Furman Betsey Stevenson Maurice Obstfeld Sandra E. Black Jay C. Shambaugh February 10, 1997 August 30, 1996 August 1, 1997 August 3, 1999 March 2, 1999 July 9, 1999 January 19, 2001 January 12, 2001 January 19, 2001 February 28, 2003 November 13, 2002 July 1, 2003 February 18, 2005 June 3, 2005 June 10, 2005 January 31, 2006 July 11, 2007 March 1, 2007 January 20, 2009 January 20, 2009 September 3, 2010 August 5, 2011 February 28, 2011 April 19, 2013 May 4, 2012 August 2, 2013 May 19, 2014 January 20, 2017 August 7, 2015 August 28, 2015 January 20, 2017 January 20, 2017 Activities of the Council of Economic Advisers during 2021 | 337 Council Members and Their Dates of Service Name Position Oath of office date Separation date Kevin A. Hassett Richard V. Burkhauser Tomas J. Philipson Chairman Member Member Acting Chairman Vice Chairman Member Acting Chairman Vice Chairman Chair Member Member September 13, 2017 September 28, 2017 August 31, 2017 July 1, 2019 July 24, 2019 May 22, 2019 June 23, 2020 June 23, 2020 March 2, 2021 January 20, 2021 January 20, 2021 June 30, 2019 May 18, 2019 Tyler B. Goodspeed Cecilia Elena Rouse Jared Bernstein Heather Boushey 338 | Appendix A June 22, 2020 January 6, 2021 Report to the President on the Activities of the Council of Economic Advisers during 2021 Established by the Employment Act of 1946, the Council of Economic Advisers is charged with advising the President on economic policy based on data, research, and evidence. The Council is composed of three members: a Chair, who is appointed by the President with the advice and consent of the Senate; and two members, who are appointed by the President. Along with a team of economists, they analyze and interpret economic developments and formulate and recommend economic policies that advance the interests of the American people. The Chair of the Council Cecilia Elena Rouse was confirmed by the Senate on March 2, 2021, as the 30th Chair of the Council of Economic Advisers. She is the first African American to hold this position. In this role, she serves as President Biden’s Chief Economist and a Member of the Cabinet. She is the Katzman-Ernst Professor in the Economics of Education and Professor of Economics and Public Affairs at Princeton University. From 2012 to 2021, Rouse was Dean of Princeton University’s School of Public and International Affairs. Rouse served as a Member of President Barack Obama’s Council of Economic Advisers from 2009 to 2011. She also worked at the National Economic Council in the Clinton Administration as a Special Assistant to the President from 1998 to 1999. Her academic research has focused on the economics of education, including the economic benefits of community college attendance and impact of student loan debt on postgraduation outcomes, as well as other issues in labor economics, such as discrimination. The Members of the Council Jared Bernstein was appointed to the Council by the President on January 20, 2021. Before this appointment, Bernstein spent 16 years in senior roles at the Economic Policy Institute, and worked at the Department of Labor. He was a Senior Fellow at the Center on Budget and Policy Priorities from Activities of the Council of Economic Advisers during 2021 | 339 2011 to 2020. From 2009 to 2011, he was Chief Economist and Economic Adviser to then–Vice President Biden. Heather Boushey was appointed to the Council by the President on January 20, 2021. Before assuming this position, Boushey co-founded the Washington Center for Equitable Growth, where she was President and CEO from 2013 to 2020. She previously served as Chief Economist for Secretary Hillary Clinton’s 2016 transition team and as an economist at the Center for American Progress, the Joint Economic Committee of the U.S. Congress, the Center for Economic and Policy Research, and the Economic Policy Institute. Areas of Activity A central function of the Council is to advise the President on all economic issues and developments. Over the past year, the priorities of the Council have included analysis on policies to spur economic growth and job creation while recovering from the global COVID-19 pandemic. The Council works closely with officials at various government entities, including the National Economic Council, the Domestic Policy Council, the Office of Management and Budget, and Administrative Agencies to engage in discussions on numerous policy matters. The areas on which the Council focused this year include economic stimulus and pandemic recovery; income inequality and inclusive growth; investment in resilient infrastructure and supply chains; innovation and competition, including in the labor market; inflation and unemployment; climate-related risks; and the cost of care, housing, and other household necessities. The Council prepares almost-daily memos for the President, the Vice President, and White House senior staff on key economic data releases and policy issues. The Council, the Department of Treasury, and the Office of Management and Budget—the Administration’s economic “troika”—are responsible for producing the economic forecasts that underlie the Administration’s budget proposals. The Council initiates the forecasting process twice each year, consulting with an array of outside sources, including leading private sector forecasters and other government agencies. The Council provides analysis and opinions on a range of trade-related issues involving the enforcement of existing trade agreements and the analysis of proposed trade policies. The Council is a leading participant in the Organization for Economic Cooperation and Development (OECD), an important forum for economic cooperation among high-income industrial economies. The Council chairs the Economic Policy Committee, coordinating—including the Departments of Commerce, State, Treasury, and Labor, as well as the Office of 340 | Appendix A Management and Budget—to provide information for the OECD’s review of the U.S. economy. Council Members and staff economists participate in working meetings on macroeconomic policy and contribute to the OECD’s research agenda. The Council produces economic analyses in a series of blogs and issue briefs. This past year, these included: • An issue brief on how the economic stimulus of the American Rescue Plan (ARP) could help launch an equitable pandemic recovery (February 2021). • A blog assessing the pandemic’s effect on wage growth, employment, and prices, outlining the role of composition and base effects in wage volatility (April 2021). • A blog describing the economic downturn caused by the COVID-19 pandemic and the potential for higher inflation driven by base effects, supply chain disruptions, and pent-up service demand (April 2021). • A blog on how government support during the pandemic helped boost personal income and spending, thus contributing to economic growth (April 2021). • An issue brief on the barriers that inhibit private sector investment in clean energy innovation and the importance of public-private partnerships (April 2021). • An issue brief on the role of public sector investment in promoting sustained and equitable economic growth, highlighting the importance of investing in innovation, social programs, and physical and human infrastructure (May 2021). • A blog outlining how supports that meet the needs of workers’ families—such as affordable, high-quality childcare, home health care, and paid family and medical leave—can increase the U.S. labor supply and boost economic growth (May 2021). • A blog describing pandemic-borne supply chain disruptions, ways in which supply chains have adjusted to disruptions in the past, and possible solutions (May 2021). • A blog on the harm of exclusionary zoning laws and the potential of proposed policies to address persistent inequities in the housing market (June 2021). • A blog on data volatility and the need to examine trends and a wide range of indicators rather than data from any single month or source Activities of the Council of Economic Advisers during 2021 | 341 (June 2021). • An issue brief on the effects of earlier Medicaid expansions on qualifying individuals’ insurance coverage, health, food and housing security, financial well-being, and the like (June 2021). • A blog outlining how additional Federal aid in the form of stimulus checks and supplemental Unemployment Insurance benefits was followed by marked improvements in food insecurity among households facing financial hardship (July 2021). • A blog that examines historical parallels during periods of heightened inflation (July 2021). • A blog on the importance of product and labor market competition in the American economy (July 2021). • A blog on the importance of voting rights to secure economic wellbeing (August 2021). • A blog on how the President’s proposed policies can reduce inflationary pressure and increase economic capacity through long-term investments in physical infrastructure, human capital, clean energy, housing, and health care (August 2021). • An issue brief, cowritten with the Office of Management and Budget (OMB), laying out how the rising prices of necessities—such as prescription drugs, childcare, and education—has have made an impact on U.S. families’ budgets (August 2021). • A blog on price increases and supply constraints in the housing market (September 2021). • A blog on the relationship between rent, housing prices, and measured inflation (September 2021). • A blog, cowritten with OMB, on estimating the average Federal individual income tax rate paid by America’s 400 wealthiest families (September 2021). • A blog on how the President’s policy proposals can reduce greenhouse gas emissions while keeping energy costs low for consumers (September 2021). • A blog on the economic benefits of extending permanent legal status to unauthorized immigrants (September 2021). • A blog on the debt ceiling and the services that would be affected should 342 | Appendix A Congress not vote to raise the debt limit (October 2021). • An issue brief on the economic benefits of investing in modern, climateresilient physical infrastructure and the risks that continued disinvestment pose to the nation’s economy (November 2021). • An issue brief on the importance of incorporating climate change into the economic projections that underlie assessments of financial risk and government finances (November 2021). • An issue brief analyzing disaggregated data provided by the U.S. Small Business Administration as a way for the Federal government to review its current procurement practices (December 2021). • A monthly publication of Economic Indicators (January–December 2021). • A monthly blog analyzing the employment situation to correspond to the monthly Jobs Report (January–December 2021). The Council also contributed to the public’s understanding of economic issues and the Administration’s policies through briefings and interviews with the economic and financial press, speeches, discussions with outside economists, Congressional testimony, and regular updates on major data releases. The Chair and Members also regularly met to exchange views on the economy with the Chair and Members of the Board of Governors of the Federal Reserve System. Public Information The Economic Report of the President, together with the Annual Report of the Council of Economic Advisers, is an important vehicle for presenting the Administration’s domestic and international economic policies. It is available for purchase through the Government Publishing Office, and is viewable at no cost at www.gpo.gov/erp. All the Council’s written materials noted above can be found at www.whitehouse.gov/cea. Activities of the Council of Economic Advisers during 2021 | 343 The Staff of the Council of Economic Advisers Front Office Elisabeth Hirschhorn Donahue ���������� Chief of Staff & General Counsel Martha Gimbel ���������������������������������� Senior Adviser Saharra Griffin ���������������������������������� Special Assistant to the Chair Abaigeal O’Shea ������������������������������ Special Assistant to the Members Zehra Khan �������������������������������������� Communications Specialist Senior Economists Lisa Barrow ����������������������������������������Education, Labor Steven Braun ������������������������������������Director of Macroeconomic Forecasting Nathan Converse ��������������������������������Macroeconomics, International Finance Gopi Shah Goda ��������������������������������Health, Long-term Care, Social Insurance Kari Heerman ������������������������������������International Trade Susan Helper ������������������������������������Supply Chains, Manufacturing Damon Jones ������������������������������������Social Insurance, Inequality, Racial Equity Noah Kaufman ����������������������������������Climate Helen Knudsen ����������������������������������Industrial Organization, Small Business, Health Greg Leiserson ����������������������������������Tax, Regulation Kevin Rinz ����������������������������������������Education, Labor Ernie Tedeschi ����������������������������������Macroeconomics Laura Tiehen ������������������������������������Poverty, Rural Issues Jeffery Zhang ������������������������������������Macroeconomics, Finance, Housing National Security Economist Meghan Greene ����������������������������������Senior Adviser for National Security Staff Economists R. Daniel Bressler ����������������������������Climate Elliot Charette ����������������������������������Macroeconomics, Trade, Finance Ryan Cummings ������������������������������Macroeconomics, Finance, Energy Brandon Enriquez ������������������������������Climate, Rural Issues Victoria Lee ��������������������������������������Education, Labor Lindsey Raymond ����������������������������Industrial Organization, Supply Chains, Innovation Evan Soltas ��������������������������������������Education, Labor 344 | Appendix A Research Assistants Bradley Clark ������������������������������������Climate, Finance, Housing Matthew Maury ��������������������������������Climate, Finance, Housing Stephen Nyarko ��������������������������������Health, Supply Chains, Small Business Anna Pasnau ������������������������������������Climate, Social Insurance, Inequality, Infrastructure Sarah Robinson ��������������������������������Macroeconomics Safia Sayed ��������������������������������������Tax, Regulation, Social Insurance Sarah Wheaton ����������������������������������Education, Labor Statistical Office Brian Amorosi ������������������������������������Director of Statistical Office Administrative Office Megan Packer ������������������������������������Director of Finance and Administration Interns Malhaar Agrawal, Umang Bansal, Prosser Cathey, Aditya Dhar, Jay Philbrick, Dylan Saez, and Shoshana Singer ERP Production Alfred Imhoff ������������������������������������Editor Susan Kellam ������������������������������������Editor Activities of the Council of Economic Advisers during 2021 | 345 Appendix B Statistical Tables Relating to Income, Employment, and Production 347 Contents National Income or Expenditure B–1. Percent changes in real gross domestic product, 1971–2021�������������� 354 B–2. Contributions to percent change in real gross domestic product, 1971–2021������������������������������������������������������������������������������������������� 356 B–3. Gross domestic product, 2006–2021��������������������������������������������������� 358 B–4. Percentage shares of gross domestic product, 1971–2021������������������ 360 B–5. Chain-type price indexes for gross domestic product, 1971–2021����� 362 B–6. Gross value added by sector, 1971–2021�������������������������������������������� 364 B–7. Real gross value added by sector, 1971–2021������������������������������������� 365 B–8. Gross domestic product (GDP) by industry, value added, in current dollars and as a percentage of GDP, 1997–2020��������������������������������� 366 B–9. Real gross domestic product by industry, value added, and percent changes, 1997–2020�������������������������������������������������������������� 368 B–10. Personal consumption expenditures, 1971–2021�������������������������������� 370 B–11. Real personal consumption expenditures, 2002–2021������������������������ 371 B–12. Private fixed investment by type, 1971–2021������������������������������������� 372 B–13. Real private fixed investment by type, 2002–2021����������������������������� 373 B–14. Foreign transactions in the national income and product accounts, 1971–2021������������������������������������������������������������������������������������������� 374 B–15. Real exports and imports of goods and services, 2002–2021������������� 375 B–16. Sources of personal income, 1971–2021��������������������������������������������� 376 B–17. Disposition of personal income, 1971–2021��������������������������������������� 378 B–18. Total and per capita disposable personal income and personal consumption expenditures, and per capita gross domestic product, in current and real dollars, 1971–2021���������������������������������� 379 B–19. Gross saving and investment, 1971–2021������������������������������������������� 380 B–20. Median money income (in 2020 dollars) and poverty status of families and people, by race, 2013–2020�������������������������������������������� 382 B–21. Real farm income, 1957–2022������������������������������������������������������������ 383 Contents | 349 Labor Market Indicators B–22. Civilian labor force, 1929–2021���������������������������������������������������������� 384 B–23. Civilian employment by sex, age, and demographic characteristic, 1976–2021������������������������������������������������������������������������������������������� 386 B–24. Unemployment by sex, age, and demographic characteristic, 1976–2021������������������������������������������������������������������������������������������� 387 B–25. Civilian labor force participation rate, 1976–2021����������������������������� 388 B–26. Civilian employment/population ratio, 1976–2021���������������������������� 389 B–27. Civilian unemployment rate, 1976–2021�������������������������������������������� 390 B–28. Unemployment by duration and reason, 1976–2021�������������������������� 391 B–29. Employees on nonagricultural payrolls, by major industry, 1976–2021������������������������������������������������������������������������������������������� 392 B–30. Hours and earnings in private nonagricultural industries, 1976–2021������������������������������������������������������������������������������������������� 394 B–31. Employment cost index, private industry, 2004–2021������������������������ 395 B–32. Productivity and related data, business and nonfarm business sectors, 1971–2021������������������������������������������������������������������������������ 396 B–33. Changes in productivity and related data, business and nonfarm business sectors, 1971–2021��������������������������������������������������������������� 397 Production and Business Activity B–34. Industrial production indexes, major industry divisions, 1976–2021������������������������������������������������������������������������������������������� 398 B–35. Capacity utilization rates, 1976–2021������������������������������������������������� 399 B–36. New private housing units started, authorized, and completed and houses sold, 1976–2021���������������������������������������������������������������� 400 B–37. Manufacturing and trade sales and inventories, 1979–2021��������������� 401 Prices B–38. Changes in consumer price indexes, 1979–2021�������������������������������� 402 B–39. Price indexes for personal consumption expenditures, and percent changes, 1972–2021�������������������������������������������������������������� 403 350 | Appendix B Money Stock, Credit, and Finance B–40. Money stock and debt measures, 1982–2021�������������������������������������� 404 B–41. Consumer credit outstanding, 1970–2021������������������������������������������� 405 B–42. Bond yields and interest rates, 1950–2021������������������������������������������ 406 B–43. Mortgage debt outstanding by type of property and of financing, 1960–2021������������������������������������������������������������������������������������������� 408 B–44. Mortgage debt outstanding by holder, 1960–2021������������������������������ 409 Government Finance B–45. Federal receipts, outlays, surplus or deficit, and debt, fiscal years 1958–2023������������������������������������������������������������������������������������������� 410 B–46. Federal receipts, outlays, surplus or deficit, and debt, as percent of gross domestic product, fiscal years 1953–2023����������������������������� 411 B–47. Federal receipts and outlays, by major category, and surplus or deficit, fiscal years 1958–2023������������������������������������������������������������ 412 B–48. Federal receipts, outlays, surplus or deficit, and debt, fiscal years 2018–2023������������������������������������������������������������������������������������������� 413 B–49. Federal and State and local government current receipts and expenditures, national income and product accounts (NIPA) basis, 1971–2021������������������������������������������������������������������������������������������� 414 B–50. State and local government revenues and expenditures, fiscal years 1956–2019������������������������������������������������������������������������ 415 B–51. U.S. Treasury securities outstanding by kind of obligation, 1980–2021������������������������������������������������������������������������������������������� 416 B–52. Estimated ownership of U.S. Treasury securities, 2007–2021������������ 417 Corporate Profits and Finance B–53. Corporate profits with inventory valuation and capital consumption adjustments, 1971–2021������������������������������������������������ 418 B–54. Corporate profits by industry, 1971–2021������������������������������������������� 419 B–55. Historical stock prices and yields, 1949–2003������������������������������������ 420 B–56. Common stock prices and yields, 2000–2021������������������������������������� 421 Contents | 351 International Statistics B–57. U.S. international transactions, 1971–2021���������������������������������������� 422 B–58. U.S. international trade in goods on balance of payments (BOP) and Census basis, and trade in services on BOP basis, 1992–2021���� 424 B–59. U.S. international trade in goods and services by area and country, 2000–2020������������������������������������������������������������������������������������������� 425 B–60. Foreign exchange rates, 2000–2021���������������������������������������������������� 426 B–61. Growth rates in real gross domestic product by area and country, 2003–2022������������������������������������������������������������������������������������������� 427 352 | Appendix B General Notes Detail in these tables may not add to totals due to rounding. Because of the formula used for calculating real gross domestic product (GDP), the chained (2012) dollar estimates for the detailed components do not add to the chained-dollar value of GDP or to any intermediate aggregate. The Department of Commerce (Bureau of Economic Analysis) no longer publishes chained-dollar estimates prior to 2002, except for selected series. Because of the method used for seasonal adjustment, the sum or average of seasonally adjusted monthly values generally will not equal annual totals based on unadjusted values. Unless otherwise noted, all dollar figures are in current dollars. Symbols used: p Preliminary. ... Not available (also, not applicable). NSA Not seasonally adjusted. Data in these tables reflect revisions made by source agencies through March 8, 2022. Excel versions of these tables are available at www.gpo.gov/erp. General Notes | 353 National Income or Expenditure Table B–1. Percent changes in real gross domestic product, 1971–2021 [Percent change, fourth quarter over fourth quarter; quarterly changes at seasonally adjusted annual rates] Personal consumption expenditures Year or quarter 1971 ���������������������� 1972 ���������������������� 1973 ���������������������� 1974 ���������������������� 1975 ���������������������� 1976 ���������������������� 1977 ���������������������� 1978 ���������������������� 1979 ���������������������� 1980 ���������������������� 1981 ���������������������� 1982 ���������������������� 1983 ���������������������� 1984 ���������������������� 1985 ���������������������� 1986 ���������������������� 1987 ���������������������� 1988 ���������������������� 1989 ���������������������� 1990 ���������������������� 1991 ���������������������� 1992 ���������������������� 1993 ���������������������� 1994 ���������������������� 1995 ���������������������� 1996 ���������������������� 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Gross domestic product 4.4 6.9 4.0 –1.9 2.6 4.3 5.0 6.7 1.3 .0 1.3 –1.4 7.9 5.6 4.2 2.9 4.5 3.8 2.7 .6 1.2 4.4 2.6 4.1 2.2 4.4 4.5 4.9 4.8 2.9 .2 2.0 4.3 3.4 3.0 2.6 2.2 –2.5 .1 2.8 1.5 1.6 2.5 2.6 1.9 2.0 2.7 2.3 2.6 –2.3 5.6 3.1 3.4 1.9 .9 2.4 3.2 2.8 1.9 –5.1 –31.2 33.8 4.5 6.3 6.7 2.3 7.0 Fixed investment Nonresidential Total 5.4 7.3 1.8 –1.6 5.1 5.4 4.2 4.0 1.7 .0 .1 3.5 6.6 4.3 4.8 4.4 2.8 4.6 2.4 .8 .9 4.9 3.3 3.8 2.8 3.4 4.5 5.6 5.2 4.3 2.5 2.0 3.8 3.8 2.8 3.2 2.0 –1.5 –.2 2.8 1.0 1.5 1.9 3.5 2.6 2.3 2.8 2.6 2.3 –2.4 7.0 2.4 3.5 2.7 1.7 .6 3.6 3.2 1.7 –6.9 –33.4 41.4 3.4 11.4 12.0 2.0 3.1 See next page for continuation of table. 354 | Appendix B Gross private domestic investment Goods 6.6 8.5 .4 –5.6 6.1 6.4 4.9 3.5 .3 –2.5 –.2 3.6 8.3 5.3 4.6 6.5 .4 4.5 1.8 –1.6 –.8 5.3 4.4 5.5 2.3 4.8 5.3 8.1 6.6 4.0 4.9 1.7 6.6 4.3 3.0 4.6 1.8 –6.8 .6 4.3 .9 2.4 3.5 5.0 3.8 3.4 5.1 2.7 3.7 7.7 7.4 1.4 4.2 2.9 2.1 1.3 7.0 4.9 1.8 .3 –10.0 49.5 –.3 27.4 13.0 –8.8 1.5 Services 4.3 6.2 3.2 2.4 4.1 4.5 3.7 4.4 2.9 2.2 .3 3.4 5.3 3.6 5.0 3.0 4.6 4.7 2.7 2.3 2.0 4.7 2.7 2.8 3.0 2.7 4.0 4.3 4.5 4.5 1.3 2.1 2.3 3.6 2.7 2.5 2.0 1.2 –.6 2.1 1.0 1.1 1.1 2.7 2.1 1.8 1.8 2.5 1.6 –6.9 6.9 2.9 3.1 2.6 1.5 .3 2.0 2.4 1.7 –10.0 –42.4 37.5 5.3 3.9 11.5 8.2 3.9 Total 13.1 15.0 10.2 –10.4 –9.8 15.2 14.9 14.3 –3.4 –7.2 6.7 –17.3 31.3 14.2 1.9 –4.1 9.8 –.5 .7 –6.5 2.1 7.7 7.6 11.5 .8 11.2 11.4 9.7 8.5 4.3 –11.1 4.4 8.7 8.0 6.1 –1.5 –1.8 –15.3 –9.2 12.1 10.4 4.0 9.3 5.3 2.3 1.8 4.2 5.2 .8 2.4 8.9 8.5 .7 9.7 2.2 6.4 2.6 1.1 –6.5 –5.3 –48.8 82.1 24.7 –2.3 –3.9 12.4 33.5 Total 10.5 12.0 3.5 –9.9 –2.6 12.1 12.1 13.1 1.1 –4.8 1.5 –8.0 18.3 11.3 3.7 .6 1.5 3.7 1.5 –4.2 –1.9 8.7 8.4 6.6 5.5 9.9 8.3 11.5 7.2 5.9 –4.7 –1.5 8.6 6.5 5.8 .0 –1.1 –11.1 –10.5 6.1 9.2 7.2 5.7 7.0 1.7 2.8 4.7 3.8 2.9 .5 4.4 6.7 6.0 .8 1.8 3.7 6.1 3.1 –1.1 –2.3 –30.4 27.5 17.7 13.0 3.3 –.9 2.6 Total 4.7 11.5 10.6 –3.9 –5.9 7.8 11.9 16.0 5.5 –.9 9.0 –9.5 10.4 13.9 3.2 –3.2 2.2 5.1 4.5 –.9 –3.4 7.1 7.6 8.5 7.4 11.3 9.7 11.6 8.4 8.5 –6.8 –5.1 6.8 6.5 6.1 8.1 7.3 –7.0 –10.3 8.9 10.0 5.6 5.4 6.9 –.1 2.5 4.7 6.1 3.1 –3.8 6.6 10.2 6.8 2.8 4.8 4.7 6.7 2.9 –1.7 –8.1 –30.3 18.7 12.5 12.9 9.2 1.7 3.1 Structures –1.1 5.1 7.9 –6.4 –8.1 3.8 5.7 21.7 8.8 2.7 14.1 –13.5 –3.9 15.7 3.3 –14.3 4.9 –3.3 3.3 –3.2 –12.8 1.0 .2 1.6 4.7 10.9 4.4 4.3 –.1 10.8 –10.6 –15.7 1.9 .3 1.5 9.0 17.7 –.8 –27.1 –3.6 8.6 4.0 6.7 9.3 –7.3 3.6 .0 1.8 5.8 –20.0 –2.9 20.2 7.1 –4.2 –12.8 4.4 14.3 14.0 –8.0 –.9 –46.8 –15.3 –8.2 5.4 –3.0 –4.1 –9.4 Equipment 8.5 17.0 13.5 –3.7 –6.7 9.0 17.2 14.5 2.7 –4.4 4.6 –10.0 19.9 13.4 1.7 .8 .1 8.2 2.5 –2.7 –3.2 11.3 13.1 12.5 8.1 11.1 10.7 14.8 9.5 8.5 –7.7 –3.7 9.6 9.8 8.7 7.1 3.9 –15.9 –8.4 22.6 12.7 7.8 5.4 5.6 1.5 –2.2 6.4 6.0 –.9 –.3 6.3 5.6 3.0 5.4 10.3 4.4 2.5 –5.1 –4.9 –21.3 –36.2 55.9 26.4 14.1 12.1 –2.3 2.4 Intellectual property products 4.8 6.2 5.1 1.6 2.8 11.8 4.8 10.3 9.4 4.7 12.1 3.4 13.0 12.6 7.7 5.4 4.2 9.8 11.3 6.2 7.2 4.8 2.9 5.8 8.3 12.1 12.4 11.5 13.3 6.6 –2.1 .9 5.8 5.7 5.1 9.3 4.0 .9 3.8 1.6 7.2 3.7 4.5 6.9 3.3 8.4 5.8 9.2 6.3 2.5 11.9 9.6 11.6 4.6 11.0 5.4 7.2 6.0 6.7 3.8 –10.6 8.1 10.2 15.6 12.5 9.1 10.6 Residential 25.2 12.9 –10.5 –24.6 7.8 23.8 12.6 6.8 –9.1 –15.3 –22.0 –1.7 49.7 3.7 5.2 11.8 –.5 .1 –6.5 –13.6 2.9 13.6 10.6 1.6 .1 5.6 4.0 11.3 3.5 –1.5 2.0 8.1 12.7 6.6 5.2 –15.2 –21.2 –24.7 –11.5 –5.7 5.3 15.4 7.1 7.7 9.2 4.0 4.5 –3.9 2.2 15.7 –1.7 –4.2 3.3 –5.8 –8.3 .1 4.1 3.6 1.1 20.4 –30.7 59.9 34.4 13.3 –11.7 –7.7 1.0 Change in private inventories ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� ���������������� Table B–1. Percent changes in real gross domestic product, 1971–2021—Continued [Percent change, fourth quarter over fourth quarter; quarterly changes at seasonally adjusted annual rates] Net exports of goods and services Year or quarter 1971 ���������������������� 1972 ���������������������� 1973 ���������������������� 1974 ���������������������� 1975 ���������������������� 1976 ���������������������� 1977 ���������������������� 1978 ���������������������� 1979 ���������������������� 1980 ���������������������� 1981 ���������������������� 1982 ���������������������� 1983 ���������������������� 1984 ���������������������� 1985 ���������������������� 1986 ���������������������� 1987 ���������������������� 1988 ���������������������� 1989 ���������������������� 1990 ���������������������� 1991 ���������������������� 1992 ���������������������� 1993 ���������������������� 1994 ���������������������� 1995 ���������������������� 1996 ���������������������� 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Government consumption expenditures and gross investment Federal Net exports Exports Imports ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� ������������� –4.5 19.5 18.4 3.1 1.6 4.3 –1.4 18.8 10.5 3.9 .7 –12.2 5.5 9.1 1.5 10.6 12.8 14.0 10.2 7.4 9.2 4.5 4.4 10.8 9.4 10.1 8.3 2.6 6.2 6.0 –12.2 4.0 7.2 7.2 7.4 9.9 9.2 –2.0 1.4 10.6 4.7 3.0 5.2 2.4 –1.5 1.3 5.9 .2 .3 –10.7 5.2 1.8 5.0 –6.1 .5 3.1 –2.2 –.8 1.2 –16.3 –59.9 54.5 22.5 –2.9 7.6 –5.3 23.6 1.3 17.9 –.5 –1.0 –5.6 19.2 5.7 9.9 .9 –9.3 6.2 –3.9 24.6 18.9 5.6 7.9 6.3 3.8 2.6 –.2 5.7 6.5 9.9 12.2 4.8 11.1 14.2 11.0 12.4 11.1 –7.6 9.6 5.9 10.9 6.1 4.0 1.6 –5.4 –5.1 11.5 3.3 .5 2.9 6.5 3.3 2.2 5.1 3.4 –2.0 .3 9.6 2.6 1.4 5.9 3.9 .0 1.7 –1.1 –8.5 –13.1 –53.1 89.2 31.3 9.3 7.1 4.7 17.6 Total –2.4 –.1 –.3 3.0 3.0 –1.3 1.9 4.4 .9 .3 2.5 2.6 1.9 6.3 6.1 4.7 3.0 1.4 2.5 2.6 .0 1.3 –.7 .0 –.6 2.6 1.7 2.8 3.9 .5 4.9 3.8 1.8 .9 .9 1.9 2.3 2.6 3.1 –1.5 –3.4 –2.1 –2.4 .3 2.2 1.6 .7 1.0 3.2 1.2 .1 .9 2.8 1.0 –.8 2.7 5.0 2.1 3.0 3.7 3.9 –2.1 –.5 4.2 –2.0 .9 –2.6 Total –7.3 –2.6 –3.6 3.7 .8 –1.0 2.3 3.5 1.2 4.0 6.0 4.5 2.7 7.1 6.7 5.3 3.6 –1.4 .5 1.5 –2.3 1.6 –4.5 –4.2 –4.8 1.1 .2 –.3 3.3 –1.9 5.5 8.1 6.5 2.6 1.8 2.4 3.6 6.4 6.2 1.8 –3.6 –2.6 –6.1 –1.0 1.2 .1 1.3 3.0 4.3 3.1 –1.1 1.8 5.1 3.4 1.9 1.4 8.9 3.6 3.5 2.4 20.6 –5.4 –3.1 11.3 –5.3 –5.1 –4.5 National Nondefense defense –11.5 –5.8 –5.0 1.2 .5 –2.1 .1 2.9 2.4 3.7 7.9 7.3 6.5 5.6 8.2 4.7 5.3 –.8 –1.3 .0 –4.9 –.4 –5.4 –6.7 –5.0 .3 –.8 –2.4 3.9 –3.3 4.7 8.1 8.9 2.8 1.8 3.1 3.9 7.4 4.9 1.3 –3.6 –4.7 –6.5 –3.4 –.4 –.6 2.2 4.2 5.0 2.3 –3.7 –1.2 7.9 3.5 6.8 5.2 4.2 4.5 6.0 –.7 3.2 1.7 5.3 –5.8 –1.1 –1.7 –6.1 5.6 6.1 –.3 9.5 1.4 1.3 6.8 4.8 –1.1 4.6 2.0 –1.6 –6.6 11.5 2.8 6.8 –1.0 –3.0 5.8 5.4 4.3 6.2 –2.5 1.1 –4.3 2.6 1.9 3.3 2.4 .4 6.8 8.2 2.6 2.3 1.9 1.3 3.1 4.5 8.9 2.7 –3.5 1.2 –5.4 2.8 3.7 1.1 .0 1.4 3.4 4.4 2.7 6.3 1.1 3.4 –5.0 –3.9 16.2 2.2 .0 7.4 50.1 –14.3 –14.1 40.8 –10.7 –9.5 –2.2 State and local 2.8 2.3 2.9 2.4 4.9 –1.6 1.7 5.2 .7 –2.9 –.7 .8 1.1 5.4 5.5 4.1 2.4 4.1 4.3 3.6 1.9 1.1 2.2 3.1 2.2 3.6 2.7 4.6 4.2 1.8 4.6 1.5 –.8 –.2 .3 1.6 1.5 .3 1.1 –3.7 –3.2 –1.7 .2 1.2 2.8 2.5 .4 –.3 2.5 .0 .9 .3 1.5 –.5 –2.4 3.5 2.7 1.1 2.7 4.4 –5.5 .1 1.2 –.1 .2 4.9 –1.4 Final Final Gross sales to Gross sales of domestic private domestic Average of GDP domestic pur- domestic income and GDI pur(GDI) 3 product chases 1 chasers 2 4.0 6.4 2.8 –1.7 3.9 3.8 4.5 6.4 2.2 .4 .3 .4 6.0 5.0 4.6 3.9 3.0 4.6 2.9 1.0 .5 4.5 2.7 3.3 3.0 4.2 3.9 5.2 4.6 3.2 1.5 .9 4.3 3.1 2.9 2.9 2.3 –1.8 –.2 2.0 1.3 2.0 1.9 2.8 1.8 2.2 2.8 2.1 2.9 –2.6 4.7 2.8 4.3 .4 .8 1.9 3.8 3.1 2.9 –4.6 –27.6 25.9 3.4 9.1 8.1 .1 2.0 4.7 6.8 2.8 –2.3 2.0 5.4 5.6 6.0 .5 –1.4 1.8 –.7 9.5 6.5 4.5 2.9 4.1 3.0 2.1 –.1 .9 4.6 3.2 4.3 1.8 4.6 5.2 5.9 5.6 3.7 .4 2.7 4.2 4.0 3.0 2.1 1.3 –3.1 –.9 3.2 1.4 1.2 2.2 3.2 2.5 2.1 2.7 2.7 2.2 –1.0 6.1 3.2 2.9 3.5 1.4 2.0 3.6 2.6 .5 –4.9 –30.8 37.8 6.1 7.7 6.7 3.5 6.8 6.5 4.8 4.6 8.3 7.1 7.0 2.2 3.8 3.9 –3.5 –2.9 –2.4 3.4 2.7 2.6 6.7 3.8 4.1 5.9 6.0 5.5 6.1 5.4 6.0 1.5 .8 1.0 –1.2 1.3 .6 .4 1.2 1.2 .8 –1.3 –1.3 9.1 6.6 7.3 5.9 6.7 6.1 4.6 3.4 3.8 3.5 2.7 2.8 2.5 5.5 5.0 4.4 4.7 4.2 2.2 1.0 1.9 –.3 1.0 .8 .3 .7 .9 5.6 3.9 4.1 4.3 3.0 2.8 4.4 4.3 4.2 3.3 2.9 2.6 4.8 4.8 4.6 5.3 5.5 5.0 6.9 4.9 4.9 5.7 4.4 4.6 4.7 3.6 3.2 .9 –.4 –.1 1.3 3.2 2.6 4.8 2.7 3.5 4.4 3.8 3.6 3.4 4.2 3.6 2.5 2.5 2.6 1.3 –.3 .9 –3.5 –2.6 –2.6 –2.1 .6 .3 3.4 3.3 3.1 2.4 2.0 1.8 2.5 3.1 2.3 2.6 1.3 1.9 4.2 4.0 3.3 2.5 1.2 1.5 2.4 1.2 1.6 3.2 2.9 2.8 2.8 2.9 2.6 2.4 1.8 2.2 –1.8 –.2 –1.2 6.5 ������������� ��������������� 3.3 4.0 3.6 4.0 .8 2.1 2.3 5.1 3.5 1.7 1.5 1.2 1.2 2.3 2.3 4.1 .8 2.0 3.2 .9 1.9 1.1 3.0 2.4 –6.0 –.8 –3.0 –32.8 –32.7 –32.0 38.4 24.4 29.0 6.2 19.6 11.9 11.8 6.3 6.3 10.1 4.3 5.5 1.4 6.4 4.3 3.0 ������������� ��������������� 1 Gross domestic product (GDP) less exports of goods and services plus imports of goods and services. 2 Personal consumption expenditures plus gross private fixed investment. 3 Gross domestic income is deflated by the implicit price deflator for GDP. Note: Percent changes based on unrounded GDP quantity indexes. Source: Department of Commerce (Bureau of Economic Analysis). National Income or Expenditure | 355 Table B–2. Contributions to percent change in real gross domestic product, 1971–2021 [Percentage points, except as noted; annual average to annual average, quarterly data at seasonally adjusted annual rates] Personal consumption expenditures Year or quarter 1971 ���������������������� 1972 ���������������������� 1973 ���������������������� 1974 ���������������������� 1975 ���������������������� 1976 ���������������������� 1977 ���������������������� 1978 ���������������������� 1979 ���������������������� 1980 ���������������������� 1981 ���������������������� 1982 ���������������������� 1983 ���������������������� 1984 ���������������������� 1985 ���������������������� 1986 ���������������������� 1987 ���������������������� 1988 ���������������������� 1989 ���������������������� 1990 ���������������������� 1991 ���������������������� 1992 ���������������������� 1993 ���������������������� 1994 ���������������������� 1995 ���������������������� 1996 ���������������������� 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Gross domestic product (percent change) 3.3 5.3 5.6 –.5 –.2 5.4 4.6 5.5 3.2 –.3 2.5 –1.8 4.6 7.2 4.2 3.5 3.5 4.2 3.7 1.9 –.1 3.5 2.8 4.0 2.7 3.8 4.4 4.5 4.8 4.1 1.0 1.7 2.8 3.9 3.5 2.8 2.0 .1 –2.6 2.7 1.5 2.3 1.8 2.3 2.7 1.7 2.3 2.9 2.3 –3.4 5.7 3.1 3.4 1.9 .9 2.4 3.2 2.8 1.9 –5.1 –31.2 33.8 4.5 6.3 6.7 2.3 7.0 Fixed investment Nonresidential Total 2.29 3.66 2.97 –.50 1.36 3.41 2.59 2.68 1.44 –.19 .85 .88 3.51 3.30 3.20 2.58 2.15 2.65 1.86 1.28 .12 2.36 2.24 2.51 1.91 2.26 2.45 3.42 3.49 3.29 1.63 1.70 2.13 2.54 2.38 1.95 1.63 .10 –.88 1.31 1.16 .94 1.01 1.82 2.20 1.67 1.65 1.96 1.48 –2.55 5.30 1.64 2.34 1.79 1.16 .43 2.37 2.12 1.13 –4.79 –24.10 25.51 2.26 7.44 7.92 1.35 2.13 See next page for continuation of table. 356 | Appendix B Gross private domestic investment Goods 1.23 1.90 1.52 –1.08 .20 2.03 1.26 1.19 .45 –.72 .33 .19 1.69 1.91 1.38 1.45 .47 .96 .64 .16 –.49 .76 .99 1.26 .71 1.06 1.12 1.54 1.83 1.23 .72 .92 1.15 1.21 .98 .87 .65 –.71 –.70 .62 .49 .48 .70 .89 1.03 .73 .82 .84 .71 .96 2.70 .30 .89 .61 .44 .29 1.42 .99 .35 .04 –1.89 9.92 –.07 5.69 2.99 –2.21 .36 Services 1.06 1.76 1.45 .58 1.16 1.38 1.33 1.49 .99 .53 .52 .69 1.82 1.39 1.83 1.13 1.67 1.69 1.21 1.12 .61 1.60 1.26 1.26 1.20 1.20 1.33 1.88 1.66 2.06 .92 .78 .98 1.34 1.40 1.08 .98 .81 –.18 .68 .68 .46 .31 .93 1.18 .94 .83 1.13 .78 –3.52 2.60 1.34 1.45 1.18 .72 .14 .95 1.13 .77 –4.83 –22.21 15.59 2.34 1.75 4.93 3.57 1.76 Total 1.63 1.90 1.95 –1.24 –2.91 2.91 2.47 2.22 .72 –2.07 1.64 –2.46 1.60 4.73 –.01 .03 .53 .45 .72 –.45 –1.09 1.11 1.24 1.90 .55 1.49 2.01 1.76 1.62 1.31 –1.11 –.16 .76 1.64 1.26 .60 –.48 –1.52 –3.51 1.85 .94 1.64 1.10 .95 .95 –.18 .68 .98 .60 –.99 1.69 1.45 .14 1.64 .39 1.13 .48 .22 –1.18 –.92 –9.64 11.71 4.01 –.37 –.65 2.05 5.38 Total 1.08 1.85 1.47 –.98 –1.68 1.54 2.23 2.10 1.11 –1.18 .50 –1.16 1.32 2.83 1.02 .34 .11 .59 .55 –.25 –.84 .83 1.17 1.29 .99 1.48 1.49 1.82 1.65 1.34 –.27 –.64 .77 1.23 1.33 .50 –.24 –1.05 –2.69 .43 .99 1.47 .87 1.06 .64 .35 .69 .82 .55 –.47 1.37 1.14 1.03 .15 .31 .64 1.06 .54 –.19 –.41 –5.63 4.88 2.92 2.25 .61 –.16 .48 Total –0.01 .97 1.51 .10 –1.13 .66 1.26 1.72 1.34 .00 .87 –.43 –.06 2.18 .91 –.24 .01 .63 .71 .14 –.48 .33 .84 .91 1.15 1.13 1.38 1.44 1.36 1.31 –.31 –.94 .30 .67 .92 1.00 .89 .08 –1.95 .52 .99 1.16 .53 .95 .32 .12 .53 .85 .59 –.73 .98 1.31 .90 .38 .65 .63 .90 .40 –.23 –1.14 –4.28 2.72 1.57 1.65 1.21 .22 .43 Structures –0.06 .12 .30 –.08 –.42 .09 .15 .52 .51 .26 .39 –.09 –.56 .58 .31 –.49 –.11 .02 .07 .05 –.38 –.18 –.01 .05 .16 .15 .21 .16 .01 .24 –.04 –.56 –.09 .00 .06 .22 .42 .23 –.71 –.50 .07 .34 .04 .33 –.03 –.14 .13 .12 .06 –.39 –.23 .57 .22 –.13 –.42 .13 .42 .42 –.26 –.02 –1.77 –.46 –.22 .14 –.08 –.11 –.25 Equipment 0.05 .75 1.12 .14 –.73 .39 1.01 1.08 .62 –.35 .28 –.47 .32 1.29 .39 .08 .03 .43 .35 –.14 –.28 .34 .73 .75 .78 .65 .76 .91 .89 .71 –.31 –.35 .26 .49 .60 .57 .25 –.29 –1.21 .91 .69 .62 .28 .42 .19 –.11 .16 .36 .19 –.48 .69 .32 .18 .31 .57 .25 .15 –.31 –.29 –1.30 –1.99 2.73 1.29 .75 .66 –.13 .14 Intellectual property products 0.01 .11 .08 .05 .01 .18 .11 .12 .20 .09 .21 .12 .17 .30 .21 .17 .10 .18 .29 .22 .18 .17 .12 .11 .20 .33 .41 .37 .45 .36 .04 –.03 .14 .18 .26 .21 .23 .14 –.02 .11 .24 .20 .22 .20 .16 .37 .25 .36 .33 .14 .52 .42 .51 .21 .49 .25 .34 .29 .32 .18 –.51 .45 .50 .76 .62 .46 .53 Residential 1.08 .87 –.04 –1.08 –.54 .88 .97 .38 –.22 –1.19 –.37 –.72 1.38 .65 .11 .58 .10 –.05 –.16 –.38 –.35 .49 .32 .38 –.15 .35 .11 .38 .29 .03 .04 .29 .47 .57 .41 –.50 –1.13 –1.14 –.74 –.08 .00 .31 .33 .12 .33 .23 .15 –.02 –.04 .26 .39 –.17 .13 –.24 –.34 .00 .15 .14 .04 .73 –1.36 2.16 1.34 .60 –.60 –.38 .05 Change in private inventories 0.56 .06 .48 –.26 –1.24 1.37 .24 .12 –.40 –.89 1.13 –1.31 .28 1.90 –1.03 –.31 .41 –.13 .17 –.21 –.26 .28 .07 .61 –.44 .02 .52 –.07 –.03 –.03 –.84 .48 –.02 .41 –.07 .10 –.25 –.46 –.82 1.42 –.05 .17 .23 –.12 .31 –.53 –.01 .16 .05 –.52 .32 .31 –.89 1.50 .08 .49 –.57 –.32 –.99 –.51 –4.01 6.84 1.10 –2.62 –1.26 2.20 4.90 Table B–2. Contributions to percent change in real gross domestic product, 1971–2021—Continued [Percentage points, except as noted; annual average to annual average, quarterly data at seasonally adjusted annual rates] Government consumption expenditures and gross investment Net exports of goods and services Year or quarter 1971 ���������������������� 1972 ���������������������� 1973 ���������������������� 1974 ���������������������� 1975 ���������������������� 1976 ���������������������� 1977 ���������������������� 1978 ���������������������� 1979 ���������������������� 1980 ���������������������� 1981 ���������������������� 1982 ���������������������� 1983 ���������������������� 1984 ���������������������� 1985 ���������������������� 1986 ���������������������� 1987 ���������������������� 1988 ���������������������� 1989 ���������������������� 1990 ���������������������� 1991 ���������������������� 1992 ���������������������� 1993 ���������������������� 1994 ���������������������� 1995 ���������������������� 1996 ���������������������� 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Net exports –0.18 –.19 .80 .73 .86 –1.05 –.70 .05 .64 1.64 –.15 –.59 –1.32 –1.54 –.39 –.29 .17 .81 .51 .40 .62 –.04 –.56 –.41 .12 –.15 –.31 –1.14 –.90 –.85 –.24 –.67 –.49 –.63 –.31 –.06 .52 1.04 1.07 –.43 .12 .12 .20 –.31 –.78 –.17 –.16 –.27 –.18 –.29 –1.39 –.16 .40 –1.66 –.51 .39 –.50 .07 1.43 –.05 1.53 –3.25 –1.65 –1.56 –.18 –1.26 –.07 Exports Total 0.10 .42 1.08 .56 –.05 .36 .19 .80 .80 .95 .12 –.71 –.22 .61 .24 .53 .77 1.23 .97 .78 .61 .66 .31 .84 1.02 .86 1.26 .26 .52 .86 –.59 –.19 .19 .88 .67 .95 .94 .67 –1.00 1.43 .90 .54 .40 .52 .04 .05 .49 .35 –.01 –1.57 .48 .24 .62 –.78 .05 .36 –.26 –.08 .17 –1.95 –8.34 4.64 2.07 –.30 .80 –.59 2.35 Goods 0.00 .43 1.05 .49 –.14 .34 .12 .64 .69 .88 –.05 –.63 –.21 .41 .20 .27 .62 .99 .72 .56 .45 .52 .22 .65 .83 .68 1.10 .17 .32 .72 –.49 –.24 .19 .58 .52 .71 .53 .48 –1.00 1.13 .65 .37 .27 .41 –.03 .05 .32 .34 .00 –.76 .52 .14 .89 –.75 .13 .31 –.41 .10 –.04 –.32 –6.24 4.75 1.59 –.10 .48 –.39 1.63 Imports Services 0.10 –.01 .02 .08 .09 .02 .07 .17 .11 .07 .17 –.08 .00 .20 .05 .25 .15 .24 .26 .22 .16 .14 .09 .19 .19 .18 .16 .08 .20 .13 –.10 .05 .01 .30 .15 .24 .41 .19 .00 .30 .26 .17 .13 .11 .07 .00 .17 .01 .00 –.81 –.04 .10 –.27 –.03 –.08 .05 .15 –.18 .21 –1.63 –2.09 –.11 .49 –.20 .32 –.19 .72 Total –0.28 –.61 –.28 .17 .91 –1.41 –.89 –.76 –.16 .69 –.26 .12 –1.10 –2.16 –.63 –.82 –.60 –.41 –.46 –.37 .01 –.70 –.87 –1.25 –.90 –1.01 –1.57 –1.39 –1.42 –1.71 .35 –.48 –.68 –1.51 –.98 –1.01 –.42 .37 2.07 –1.86 –.79 –.42 –.20 –.84 –.81 –.22 –.65 –.62 –.17 1.28 –1.87 –.40 –.22 –.88 –.57 .02 –.24 .15 1.26 1.90 9.87 –7.89 –3.73 –1.26 –.99 –.68 –2.42 Goods –0.32 –.55 –.33 .17 .85 –1.31 –.82 –.66 –.13 .66 –.18 .20 –.98 –1.78 –.50 –.80 –.39 –.35 –.37 –.25 –.04 –.76 –.82 –1.15 –.84 –.91 –1.40 –1.18 –1.31 –1.45 .39 –.41 –.67 –1.28 –.88 –.81 –.27 .47 2.10 –1.73 –.74 –.38 –.28 –.75 –.75 –.14 –.53 –.62 –.07 .65 –1.61 –.54 –.04 –.87 –.29 .01 .01 .19 1.16 .85 7.27 –7.37 –3.04 –1.21 –.51 .04 –2.11 Federal Services 0.04 –.06 .05 .00 .06 –.10 –.07 –.10 –.02 .03 –.09 –.08 –.12 –.38 –.13 –.02 –.21 –.07 –.09 –.13 .05 .05 –.05 –.10 –.06 –.10 –.17 –.21 –.11 –.26 –.04 –.07 –.01 –.22 –.09 –.20 –.15 –.10 –.03 –.13 –.05 –.04 .07 –.09 –.07 –.08 –.12 .00 –.11 .63 –.26 .14 –.18 –.01 –.27 .01 –.25 –.03 .10 1.05 2.59 –.52 –.69 –.05 –.48 –.72 –.31 Total –0.45 –.12 –.07 .47 .49 .12 .26 .60 .36 .36 .20 .37 .79 .74 1.37 1.14 .62 .26 .58 .65 .25 .10 –.17 .02 .10 .18 .30 .44 .59 .33 .67 .82 .39 .30 .15 .30 .34 .49 .72 –.02 –.67 –.42 –.47 –.17 .33 .35 .09 .24 .38 .43 .09 .15 .49 .17 –.14 .47 .86 .36 .52 .63 .97 –.19 –.09 .77 –.36 .17 –.45 Total –0.80 –.37 –.39 .06 .05 .01 .21 .23 .20 .38 .43 .35 .65 .33 .78 .61 .38 –.15 .15 .20 .01 –.15 –.32 –.31 –.21 –.09 –.06 –.06 .12 .02 .24 .47 .45 .31 .15 .17 .14 .46 .48 .34 –.23 –.16 –.44 –.19 .00 .03 .02 .20 .25 .33 .04 .12 .32 .22 .12 .09 .57 .23 .23 .16 1.42 –.32 –.22 .78 –.38 –.35 –.30 National Nondefense defense –0.97 –.60 –.40 –.07 –.07 –.04 .06 .04 .15 .22 .40 .47 .51 .38 .62 .52 .38 –.04 –.02 .02 –.06 –.31 –.32 –.28 –.21 –.08 –.13 –.09 .06 –.04 .13 .30 .35 .26 .11 .07 .13 .33 .29 .16 –.12 –.18 –.33 –.19 –.09 –.02 .04 .13 .20 .11 –.04 –.05 .29 .13 .26 .20 .16 .18 .23 –.03 .16 .11 .22 –.25 –.04 –.07 –.24 0.17 .22 .01 .14 .13 .06 .15 .19 .05 .16 .03 –.11 .14 –.04 .16 .09 .01 –.12 .18 .18 .07 .16 .00 –.02 .00 –.01 .07 .03 .06 .06 .12 .18 .10 .05 .04 .10 .01 .14 .20 .18 –.12 .02 –.10 .00 .09 .06 –.01 .07 .04 .21 .08 .16 .03 .09 –.14 –.11 .40 .06 .00 .20 1.26 –.43 –.44 1.02 –.34 –.29 –.06 State and local 0.35 .25 .32 .41 .43 .10 .05 .37 .16 –.02 –.23 .01 .14 .41 .59 .53 .24 .42 .43 .45 .24 .25 .15 .32 .31 .27 .36 .50 .47 .31 .43 .35 –.06 –.01 .00 .13 .20 .03 .24 –.36 –.44 –.26 –.03 .02 .33 .31 .07 .04 .14 .10 .04 .03 .17 –.05 –.26 .38 .29 .12 .28 .47 –.45 .13 .14 –.01 .02 .52 –.15 Final sales of domestic product 2.74 5.20 5.16 –.28 1.03 4.01 4.38 5.42 3.56 .63 1.41 –.50 4.31 5.34 5.20 3.77 3.05 4.31 3.51 2.09 .15 3.24 2.68 3.41 3.13 3.76 3.92 4.55 4.82 4.11 1.80 1.21 2.81 3.45 3.56 2.68 2.26 .58 –1.77 1.29 1.60 2.11 1.61 2.41 2.40 2.20 2.26 2.76 2.24 –2.89 5.36 2.78 4.26 .44 .82 1.92 3.78 3.09 2.88 –4.60 –27.23 26.95 3.44 8.90 7.99 .10 2.09 Source: Department of Commerce (Bureau of Economic Analysis). National Income or Expenditure | 357 Table B–3. Gross domestic product, 2006–2021 [Quarterly data at seasonally adjusted annual rates] Personal consumption expenditures Year or quarter Gross private domestic investment Fixed investment Gross domestic product Nonresidential Total Goods Services Total Total Total Structures Equipment Intellectual property products Residential Change in private inventories Billions of dollars 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� 13,815.6 14,474.2 14,769.9 14,478.1 15,049.0 15,599.7 16,254.0 16,843.2 17,550.7 18,206.0 18,695.1 19,479.6 20,527.2 21,372.6 20,893.7 22,997.5 20,143.7 20,492.5 20,659.1 20,813.3 21,001.6 21,289.3 21,505.0 21,694.5 21,481.4 19,477.4 21,138.6 21,477.6 22,038.2 22,741.0 23,202.3 24,008.5 9,277.2 9,746.6 10,050.1 9,891.2 10,260.3 10,698.9 11,047.4 11,363.5 11,847.7 12,263.5 12,693.3 13,239.1 13,913.5 14,428.7 14,047.6 15,746.9 13,667.4 13,864.8 14,002.6 14,119.3 14,155.6 14,375.7 14,529.5 14,653.9 14,439.1 12,989.7 14,293.8 14,467.6 15,005.4 15,681.7 15,964.9 16,335.5 3,239.7 3,367.0 3,363.2 3,180.0 3,317.8 3,518.1 3,637.7 3,730.0 3,863.0 3,923.0 3,991.8 4,158.6 4,353.7 4,478.9 4,653.8 5,482.8 4,298.4 4,354.4 4,373.2 4,388.8 4,382.8 4,479.4 4,512.7 4,540.8 4,530.9 4,349.9 4,867.2 4,867.3 5,245.0 5,529.8 5,500.1 5,656.2 6,037.6 6,379.6 6,686.9 6,711.2 6,942.4 7,180.7 7,409.6 7,633.6 7,984.8 8,340.5 8,701.4 9,080.6 9,559.8 9,949.8 9,393.7 10,264.1 9,369.0 9,510.3 9,629.4 9,730.5 9,772.7 9,896.3 10,016.8 10,113.2 9,908.2 8,639.8 9,426.6 9,600.4 9,760.4 10,151.9 10,464.8 10,679.2 2,701.0 2,673.0 2,477.6 1,929.7 2,165.5 2,332.6 2,621.8 2,826.0 3,044.2 3,237.2 3,205.0 3,381.4 3,637.8 3,826.3 3,637.8 4,113.4 3,550.8 3,603.2 3,679.6 3,717.5 3,801.9 3,843.0 3,858.2 3,801.9 3,752.4 3,167.0 3,708.8 3,923.2 3,928.0 3,925.1 4,099.6 4,501.1 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� 15,315.9 15,623.9 15,643.0 15,236.3 15,649.0 15,891.5 16,254.0 16,553.3 16,932.1 17,390.3 17,680.3 18,079.1 18,606.8 19,032.7 18,384.7 19,428.4 18,436.3 18,590.0 18,679.6 18,721.3 18,833.2 18,982.5 19,112.7 19,202.3 18,952.0 17,258.2 18,560.8 18,767.8 19,055.7 19,368.3 19,478.9 19,810.6 10,386.2 10,638.7 10,654.7 10,515.6 10,716.0 10,898.3 11,047.4 11,211.7 11,515.3 11,892.9 12,187.7 12,483.7 12,845.0 13,126.3 12,629.9 13,629.4 12,707.6 12,816.4 12,900.6 12,955.5 12,975.1 13,088.8 13,192.3 13,249.0 13,014.5 11,756.4 12,820.8 12,927.9 13,282.7 13,665.6 13,732.4 13,836.7 3,509.7 3,607.6 3,498.9 3,389.8 3,485.7 3,561.8 3,637.7 3,752.2 3,905.1 4,090.9 4,231.7 4,395.2 4,569.3 4,723.0 4,942.5 5,545.1 4,511.9 4,558.8 4,591.4 4,615.2 4,630.6 4,709.1 4,765.5 4,786.9 4,790.2 4,665.8 5,158.9 5,155.0 5,476.6 5,646.7 5,518.3 5,538.8 6,873.1 7,027.0 7,154.9 7,125.8 7,230.4 7,336.7 7,409.6 7,460.3 7,613.2 7,809.8 7,968.5 8,110.1 8,305.7 8,443.7 7,808.5 8,261.4 8,223.8 8,287.3 8,339.7 8,371.8 8,377.8 8,420.2 8,471.0 8,505.9 8,284.4 7,217.3 7,815.2 7,917.0 7,993.4 8,214.3 8,378.5 8,459.4 2,752.4 2,684.1 2,462.9 1,942.0 2,216.5 2,362.1 2,621.8 2,801.5 2,959.2 3,121.8 3,089.9 3,212.5 3,394.8 3,510.6 3,316.2 3,634.3 3,346.3 3,352.5 3,430.9 3,449.6 3,503.4 3,526.0 3,535.9 3,477.1 3,430.1 2,901.9 3,371.0 3,561.9 3,541.3 3,506.0 3,609.7 3,880.2 2,632.0 2,639.1 2,506.9 2,080.4 2,111.6 2,286.3 2,550.5 2,721.5 2,960.2 3,100.4 3,168.8 3,351.9 3,579.1 3,752.6 3,697.4 4,139.4 3,505.0 3,579.0 3,602.5 3,629.9 3,683.4 3,754.5 3,791.2 3,781.4 3,773.0 3,456.9 3,693.8 3,865.9 4,022.2 4,099.4 4,159.8 4,276.1 1,793.8 1,948.6 1,990.9 1,690.4 1,735.0 1,907.5 2,118.5 2,211.5 2,400.1 2,466.6 2,469.3 2,591.6 2,780.6 2,938.7 2,799.6 3,053.9 2,716.0 2,770.1 2,798.1 2,838.1 2,886.9 2,946.1 2,969.3 2,952.6 2,900.1 2,659.1 2,776.6 2,862.7 2,956.7 3,029.2 3,073.9 3,155.7 425.2 510.3 571.1 455.8 379.8 404.5 479.4 492.5 577.6 584.4 560.4 599.3 633.3 672.6 597.2 579.7 627.2 641.6 638.2 626.1 639.9 669.4 696.0 685.3 687.1 585.9 563.5 552.3 565.0 572.8 581.9 599.1 862.3 893.4 845.4 670.3 777.0 881.3 983.4 1,027.0 1,091.9 1,119.5 1,087.8 1,117.4 1,190.5 1,231.3 1,123.9 1,274.5 1,166.4 1,175.7 1,195.7 1,224.2 1,240.2 1,247.4 1,227.4 1,210.3 1,141.9 1,020.6 1,135.5 1,197.5 1,244.5 1,270.4 1,277.2 1,306.1 506.3 544.8 574.4 564.4 578.2 621.7 655.7 691.9 730.5 762.7 821.2 875.0 956.7 1,034.8 1,078.5 1,199.7 922.3 952.7 964.2 987.7 1,006.7 1,029.3 1,046.0 1,057.0 1,071.1 1,052.6 1,077.6 1,112.9 1,147.2 1,186.0 1,214.9 1,250.5 838.2 690.5 516.0 390.0 376.6 378.8 432.0 510.0 560.2 633.8 699.4 760.3 798.5 813.9 897.8 1,085.5 789.0 808.9 804.3 791.8 796.5 808.5 821.9 828.8 872.9 797.8 917.2 1,003.2 1,065.5 1,070.2 1,085.9 1,120.4 69.0 34.0 –29.2 –150.8 53.9 46.3 71.2 104.5 84.0 136.8 36.3 29.5 58.7 73.6 –59.6 –25.9 45.9 24.2 77.1 87.7 118.5 88.4 67.0 20.6 –20.6 –289.9 15.0 57.3 –94.2 –174.3 –60.2 225.1 501.7 568.6 605.4 492.2 412.8 424.1 479.4 485.5 538.8 534.1 511.0 532.5 553.6 565.0 494.2 454.3 554.2 563.7 557.7 538.9 544.7 563.2 582.0 570.0 568.8 485.8 466.0 456.1 462.1 458.6 453.8 442.7 832.6 865.8 824.4 649.7 781.2 886.2 983.4 1,029.2 1,101.1 1,134.6 1,114.6 1,145.5 1,218.8 1,258.8 1,154.0 1,304.4 1,196.6 1,205.4 1,221.3 1,251.7 1,265.2 1,273.1 1,256.4 1,240.6 1,168.3 1,044.0 1,166.6 1,237.1 1,278.5 1,315.7 1,307.9 1,315.6 521.5 554.3 575.3 572.4 588.1 624.8 655.7 691.4 724.8 752.4 818.8 865.2 935.5 1,002.9 1,031.3 1,136.1 905.2 930.3 940.8 965.8 978.5 995.7 1,010.5 1,027.1 1,036.6 1,008.0 1,027.7 1,053.0 1,091.9 1,124.6 1,149.3 1,178.5 818.9 665.8 504.6 395.3 383.0 382.5 432.0 485.5 504.1 555.4 592.1 615.9 612.3 606.7 648.0 707.3 616.5 621.5 612.2 599.0 599.1 605.2 610.6 612.2 641.2 584.9 657.8 708.2 730.6 708.2 694.2 696.0 87.1 40.6 –32.7 –177.3 57.3 46.7 71.2 108.7 86.3 137.6 35.7 33.6 65.7 75.1 –42.3 –38.1 63.5 11.1 101.0 87.3 131.7 84.3 68.3 16.3 –30.4 –252.8 25.3 88.8 –88.3 –168.5 –66.8 171.2 Billions of chained (2012) dollars See next page for continuation of table. 358 | Appendix B 2,686.8 2,653.5 2,499.4 2,099.8 2,164.2 2,317.8 2,550.5 2,692.1 2,869.2 2,979.0 3,041.0 3,164.3 3,316.2 3,421.3 3,329.4 3,587.5 3,273.2 3,321.2 3,327.9 3,342.6 3,372.8 3,423.2 3,449.3 3,439.9 3,419.6 3,123.0 3,318.5 3,456.6 3,564.1 3,593.0 3,585.0 3,607.8 1,854.2 1,982.1 1,994.2 1,704.3 1,781.0 1,935.4 2,118.5 2,206.0 2,365.3 2,420.3 2,442.0 2,541.4 2,704.4 2,822.0 2,671.1 2,868.8 2,654.0 2,698.0 2,716.7 2,749.0 2,780.7 2,826.0 2,846.5 2,834.7 2,775.5 2,535.7 2,646.9 2,726.2 2,810.4 2,873.1 2,884.8 2,907.0 Table B–3. Gross domestic product, 2006–2021—Continued [Quarterly data at seasonally adjusted annual rates] Net exports of goods and services Year or quarter Government consumption expenditures and gross investment Federal Net exports Exports Imports Total –786.5 –735.9 –740.9 –419.2 –532.3 –579.6 –551.6 –479.4 –510.0 –526.2 –506.3 –539.9 –596.2 –596.3 –651.2 –915.9 –580.1 –539.8 –624.0 –640.9 –606.4 –632.3 –614.0 –532.4 –541.7 –538.9 –725.7 –798.4 –872.5 –881.7 –947.0 –962.2 1,470.2 1,659.3 1,835.3 1,582.8 1,857.2 2,115.9 2,217.7 2,287.0 2,377.4 2,268.7 2,232.1 2,383.8 2,533.5 2,519.7 2,123.4 2,479.9 2,504.4 2,568.3 2,534.2 2,527.1 2,524.6 2,533.4 2,512.1 2,508.7 2,385.5 1,807.9 2,079.6 2,220.7 2,311.9 2,461.5 2,485.2 2,661.1 2,256.6 2,395.2 2,576.2 2,001.9 2,389.6 2,695.5 2,769.3 2,766.4 2,887.4 2,794.9 2,738.4 2,923.7 3,129.7 3,116.0 2,774.6 3,395.8 3,084.5 3,108.1 3,158.2 3,168.1 3,131.0 3,165.7 3,126.1 3,041.1 2,927.3 2,346.7 2,805.3 3,019.1 3,184.5 3,343.2 3,432.3 3,623.2 2,623.8 2,790.6 2,983.0 3,076.3 3,155.6 3,147.9 3,136.5 3,133.0 3,168.8 3,231.6 3,303.1 3,399.1 3,572.0 3,713.9 3,859.5 4,053.0 3,505.5 3,564.3 3,600.9 3,617.4 3,650.5 3,702.9 3,731.3 3,771.0 3,831.6 3,859.6 3,861.7 3,885.3 3,977.3 4,015.9 4,084.9 4,134.0 Total National Nondefense defense State and local Final sales to Gross Final Gross sales of domestic private domestic Average of GDP domestic income and domestic purGDI pur(GDI) 3 product chases 1 chasers 2 Billions of dollars 2006 ������������������ 2007 ������������������ 2008 ������������������ 2009 ������������������ 2010 ������������������ 2011 ������������������ 2012 ������������������ 2013 ������������������ 2014 ������������������ 2015 ������������������ 2016 ������������������ 2017 ������������������ 2018 ������������������ 2019 ������������������ 2020 ������������������ 2021 p ���������������� 2018: I �������������� II ������������� III ������������ IV ������������ 2019: I �������������� II ������������� III ������������ IV ������������ 2020: I �������������� II ������������� III ������������ IV ������������ 2021: I �������������� II ������������� III ������������ IV p ��������� 1,001.2 1,051.0 1,152.0 1,220.8 1,300.2 1,299.8 1,287.0 1,227.2 1,216.0 1,221.8 1,234.5 1,262.8 1,339.0 1,414.9 1,501.8 1,565.0 1,305.8 1,331.7 1,350.8 1,367.7 1,387.0 1,406.9 1,424.1 1,441.7 1,454.7 1,525.0 1,515.1 1,512.3 1,568.6 1,563.3 1,562.0 1,566.1 640.8 679.3 750.3 787.6 828.0 834.0 814.2 764.2 743.4 729.7 727.9 746.5 792.8 847.5 881.3 905.3 767.4 788.1 799.4 816.2 829.3 840.4 852.5 868.0 868.3 872.4 883.9 900.8 897.1 904.1 910.9 909.0 360.4 371.8 401.6 433.2 472.2 465.8 472.8 462.9 472.6 492.0 506.6 516.3 546.2 567.4 620.5 659.7 538.4 543.7 551.4 551.4 557.6 566.6 571.7 573.7 586.4 652.6 631.3 611.5 671.6 659.2 651.1 657.1 1,622.7 1,739.5 1,831.1 1,855.6 1,855.4 1,848.2 1,849.5 1,905.9 1,952.8 2,009.8 2,068.5 2,136.3 2,233.0 2,299.0 2,357.8 2,488.0 2,199.7 2,232.6 2,250.1 2,249.7 2,263.5 2,296.0 2,307.2 2,329.2 2,376.9 2,334.6 2,346.5 2,373.0 2,408.7 2,452.6 2,522.9 2,567.9 13,746.6 14,440.3 14,799.1 14,628.8 14,995.1 15,553.5 16,182.8 16,738.7 17,466.7 18,069.2 18,658.8 19,450.1 20,468.4 21,299.0 20,953.3 23,023.4 20,097.9 20,468.3 20,582.0 20,725.7 20,883.1 21,200.8 21,438.0 21,673.9 21,502.0 19,767.4 21,123.6 21,420.3 22,132.5 22,915.3 23,262.5 23,783.4 14,602.0 15,210.2 15,510.7 14,897.2 15,581.3 16,179.3 16,805.6 17,322.6 18,060.7 18,732.2 19,201.4 20,019.6 21,123.3 21,968.8 21,544.9 23,913.4 20,723.8 21,032.3 21,283.1 21,454.2 21,608.0 21,921.6 22,119.0 22,226.8 22,023.1 20,016.3 21,864.3 22,276.0 22,910.8 23,622.6 24,149.4 24,970.6 11,909.2 12,385.7 12,556.9 11,971.7 12,371.8 12,985.2 13,597.9 14,085.0 14,807.9 15,363.9 15,862.0 16,591.0 17,492.6 18,181.3 17,745.0 19,886.2 17,172.4 17,443.7 17,605.0 17,749.2 17,839.0 18,130.3 18,320.7 18,435.3 18,212.0 16,446.7 17,987.6 18,333.5 19,027.7 19,781.1 20,124.7 20,611.5 14,019.9 13,917.8 14,454.4 14,464.3 14,572.9 14,671.4 14,276.0 14,377.0 14,966.4 15,007.7 15,612.0 15,605.9 16,442.8 16,348.4 16,958.0 16,900.6 17,807.9 17,679.3 18,440.5 18,323.3 18,788.5 18,741.8 19,598.5 19,539.1 20,652.6 20,589.9 21,442.2 21,407.4 21,064.3 20,979.0 ��������������� ��������������� 20,276.4 20,210.0 20,497.4 20,494.9 20,823.8 20,741.5 21,012.9 20,913.1 21,195.6 21,098.6 21,361.6 21,325.4 21,481.9 21,493.4 21,729.8 21,712.2 21,755.9 21,618.6 19,620.2 19,548.8 20,908.5 21,023.6 21,972.6 21,725.1 22,547.9 22,293.1 23,132.7 22,936.8 23,833.2 23,517.8 ��������������� ��������������� 16,246.6 16,476.2 16,332.6 15,757.9 16,238.4 16,462.7 16,805.6 17,073.1 17,505.4 18,100.1 18,423.5 18,857.5 19,443.0 19,910.1 19,306.6 20,632.5 19,238.6 19,376.1 19,545.4 19,611.8 19,706.9 19,883.1 20,013.2 20,036.9 19,787.6 18,046.1 19,551.0 19,841.7 20,211.1 20,540.9 20,716.4 21,061.4 13,104.7 13,317.3 13,169.7 12,613.3 12,878.7 13,215.8 13,597.9 13,903.7 14,384.4 14,871.9 15,228.6 15,647.9 16,161.0 16,547.3 15,959.0 17,216.6 15,980.7 16,137.4 16,228.3 16,297.9 16,347.6 16,511.6 16,641.2 16,688.7 16,433.7 14,879.0 16,139.0 16,384.1 16,846.3 17,258.3 17,317.3 17,444.4 15,542.5 15,429.2 15,602.5 15,613.2 15,434.4 15,538.7 15,023.6 15,130.0 15,563.2 15,606.1 15,904.1 15,897.8 16,442.8 16,348.4 16,666.2 16,609.8 17,180.2 17,056.1 17,614.3 17,502.3 17,768.6 17,724.5 18,189.4 18,134.3 18,720.5 18,663.6 19,094.7 19,063.7 18,534.8 18,459.7 ��������������� ��������������� 18,557.7 18,497.0 18,594.4 18,592.2 18,828.5 18,754.1 18,900.8 18,811.0 19,007.2 18,920.2 19,047.0 19,014.8 19,092.1 19,102.4 19,233.6 19,218.0 19,194.2 19,073.1 17,384.7 17,321.5 18,358.8 18,459.8 19,200.3 18,984.0 19,496.4 19,276.0 19,701.9 19,535.1 20,008.5 19,743.7 ��������������� ��������������� Billions of chained (2012) dollars 2006 ������������������ 2007 ������������������ 2008 ������������������ 2009 ������������������ 2010 ������������������ 2011 ������������������ 2012 ������������������ 2013 ������������������ 2014 ������������������ 2015 ������������������ 2016 ������������������ 2017 ������������������ 2018 ������������������ 2019 ������������������ 2020 ������������������ 2021 p ���������������� 2018: I �������������� II ������������� III ������������ IV ������������ 2019: I �������������� II ������������� III ������������ IV ������������ 2020: I �������������� II ������������� III ������������ IV ������������ 2021: I �������������� II ������������� III ������������ IV p ��������� –927.6 –847.9 –685.7 –516.3 –589.4 –571.0 –551.6 –519.3 –575.3 –721.7 –757.1 –799.5 –864.2 –905.3 –942.7 –1,282.2 –826.4 –807.2 –896.9 –926.5 –906.7 –935.3 –931.5 –847.6 –841.9 –774.8 –1,021.3 –1,132.8 –1,226.1 –1,244.5 –1,316.6 –1,341.7 1,670.5 1,816.9 1,921.9 1,762.5 1,989.5 2,132.1 2,217.7 2,283.6 2,372.3 2,378.7 2,388.4 2,485.8 2,555.6 2,554.0 2,207.6 2,309.0 2,551.6 2,582.9 2,542.5 2,545.6 2,565.3 2,551.3 2,545.9 2,553.3 2,442.1 1,943.0 2,166.3 2,279.0 2,262.3 2,304.2 2,273.0 2,396.6 2,598.2 2,664.8 2,607.6 2,278.8 2,578.9 2,703.1 2,769.3 2,802.9 2,947.6 3,100.4 3,145.4 3,285.2 3,419.9 3,459.2 3,150.3 3,591.3 3,378.0 3,390.1 3,439.4 3,472.1 3,472.0 3,486.6 3,477.4 3,400.9 3,283.9 2,717.7 3,187.5 3,411.8 3,488.4 3,548.7 3,589.6 3,738.3 3,061.8 3,116.9 3,195.8 3,310.7 3,308.0 3,202.7 3,136.5 3,060.7 3,033.2 3,088.4 3,148.8 3,165.2 3,208.8 3,279.5 3,360.2 3,376.5 3,189.7 3,212.2 3,220.0 3,213.4 3,235.2 3,274.9 3,291.7 3,316.3 3,346.3 3,378.1 3,360.2 3,356.0 3,390.9 3,373.8 3,381.6 3,359.7 1,125.3 1,147.3 1,220.0 1,296.0 1,348.4 1,312.0 1,287.0 1,215.8 1,184.7 1,184.5 1,190.5 1,194.7 1,231.0 1,277.2 1,340.7 1,348.7 1,213.0 1,228.1 1,238.5 1,244.2 1,248.7 1,275.5 1,286.8 1,298.0 1,305.8 1,368.4 1,349.6 1,338.8 1,375.2 1,356.7 1,339.1 1,323.9 719.8 740.3 791.5 836.7 861.3 842.9 814.2 759.6 728.4 713.1 709.1 715.7 739.9 778.5 800.9 793.5 723.2 737.1 743.4 755.8 765.4 773.4 781.9 793.4 791.9 798.2 801.6 812.0 799.9 797.8 794.3 781.9 405.6 407.0 428.6 459.4 487.0 469.1 472.8 456.2 456.1 471.0 480.8 478.5 490.7 498.7 539.0 554.0 489.2 490.5 494.6 488.3 483.5 501.9 504.7 504.7 513.7 568.6 547.0 526.7 573.7 557.7 543.9 540.9 1,939.6 1,972.7 1,977.6 2,015.9 1,959.8 1,890.8 1,849.5 1,844.4 1,847.6 1,902.2 1,956.3 1,968.5 1,976.4 2,001.5 2,019.9 2,028.0 1,974.9 1,982.5 1,980.2 1,968.1 1,985.4 1,998.7 2,004.3 2,017.6 2,039.7 2,011.0 2,011.4 2,017.6 2,017.1 2,017.9 2,042.1 2,035.1 15,240.9 15,586.7 15,678.0 15,400.3 15,596.8 15,847.4 16,182.8 16,444.1 16,842.3 17,248.3 17,630.6 18,030.4 18,528.8 18,944.4 18,395.9 19,382.0 18,363.5 18,558.0 18,577.9 18,615.8 18,704.8 18,881.4 19,027.1 19,164.4 18,940.1 17,471.0 18,508.0 18,664.8 19,076.1 19,449.3 19,453.4 19,549.0 1 Gross domestic product (GDP) less exports of goods and services plus imports of goods and services. 2 Personal consumption expenditures plus gross private fixed investment. 3 For chained dollar measures, gross domestic income is deflated by the implicit price deflator for GDP. Source: Department of Commerce (Bureau of Economic Analysis). National Income or Expenditure | 359 Table B–4. Percentage shares of gross domestic product, 1971–2021 [Percent of nominal GDP] Personal consumption expenditures Year or quarter 1971 ���������������������� 1972 ���������������������� 1973 ���������������������� 1974 ���������������������� 1975 ���������������������� 1976 ���������������������� 1977 ���������������������� 1978 ���������������������� 1979 ���������������������� 1980 ���������������������� 1981 ���������������������� 1982 ���������������������� 1983 ���������������������� 1984 ���������������������� 1985 ���������������������� 1986 ���������������������� 1987 ���������������������� 1988 ���������������������� 1989 ���������������������� 1990 ���������������������� 1991 ���������������������� 1992 ���������������������� 1993 ���������������������� 1994 ���������������������� 1995 ���������������������� 1996 ���������������������� 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Gross domestic product (percent) 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Fixed investment Nonresidential Total 60.1 60.1 59.6 60.2 61.2 61.3 61.2 60.5 60.3 61.3 60.3 61.9 62.8 61.7 62.5 63.0 63.4 63.6 63.4 63.9 64.0 64.4 64.9 64.8 65.0 65.0 64.5 64.9 65.2 66.0 66.8 67.2 67.6 67.4 67.3 67.2 67.3 68.0 68.3 68.2 68.6 68.0 67.5 67.5 67.4 67.9 68.0 67.8 67.5 67.2 68.5 67.8 67.7 67.8 67.8 67.4 67.5 67.6 67.5 67.2 66.7 67.6 67.4 68.1 69.0 68.8 68.0 See next page for continuation of table. 360 | Appendix B Gross private domestic investment Goods 29.4 29.2 29.2 29.2 29.2 29.2 28.8 28.2 28.1 28.0 27.1 26.9 26.8 26.3 26.2 26.1 25.9 25.5 25.2 25.0 24.3 24.0 23.9 24.0 23.8 23.8 23.4 23.3 23.7 23.9 23.9 23.8 23.8 23.8 23.6 23.4 23.3 22.8 22.0 22.0 22.6 22.4 22.1 22.0 21.5 21.4 21.3 21.2 21.0 22.3 23.8 21.3 21.2 21.2 21.1 20.9 21.0 21.0 20.9 21.1 22.3 23.0 22.7 23.8 24.3 23.7 23.6 Services 30.7 30.8 30.4 31.0 32.0 32.1 32.4 32.3 32.3 33.3 33.2 35.0 36.0 35.4 36.3 36.9 37.5 38.1 38.2 38.9 39.7 40.4 41.0 40.8 41.2 41.2 41.2 41.6 41.5 42.1 43.0 43.5 43.8 43.6 43.6 43.7 44.1 45.3 46.4 46.1 46.0 45.6 45.3 45.5 45.8 46.5 46.6 46.6 46.6 45.0 44.6 46.5 46.4 46.6 46.8 46.5 46.5 46.6 46.6 46.1 44.4 44.6 44.7 44.3 44.6 45.1 44.5 Total 16.9 17.8 18.7 17.8 15.3 17.3 19.1 20.3 20.5 18.6 19.7 17.4 17.5 20.3 19.1 18.5 18.4 17.9 17.7 16.7 15.3 15.5 16.1 17.2 17.2 17.7 18.6 19.2 19.6 19.9 18.3 17.7 17.7 18.7 19.4 19.6 18.5 16.8 13.3 14.4 15.0 16.1 16.8 17.3 17.8 17.1 17.4 17.7 17.9 17.4 17.9 17.6 17.6 17.8 17.9 18.1 18.1 17.9 17.5 17.5 16.3 17.5 18.3 17.8 17.3 17.7 18.7 Total 16.2 17.1 17.6 16.9 15.6 16.3 18.0 19.2 19.9 18.8 18.8 17.8 17.7 18.7 18.6 18.4 17.8 17.5 17.2 16.4 15.3 15.3 15.8 16.4 16.8 17.4 17.8 18.5 19.0 19.4 18.6 17.5 17.6 18.1 19.0 19.1 18.2 17.0 14.4 14.0 14.7 15.7 16.2 16.9 17.0 16.9 17.2 17.4 17.6 17.7 18.0 17.4 17.5 17.4 17.4 17.5 17.6 17.6 17.4 17.6 17.7 17.5 18.0 18.3 18.0 17.9 17.8 Total 11.2 11.5 12.1 12.4 11.7 11.7 12.4 13.4 14.2 14.2 14.7 14.5 13.3 14.0 14.0 13.3 12.7 12.6 12.7 12.4 11.8 11.4 11.7 11.9 12.6 12.9 13.4 13.8 14.2 14.6 13.8 12.4 12.0 12.0 12.4 13.0 13.5 13.5 11.7 11.5 12.2 13.0 13.1 13.7 13.5 13.2 13.3 13.5 13.7 13.4 13.3 13.5 13.5 13.5 13.6 13.7 13.8 13.8 13.6 13.5 13.7 13.1 13.3 13.4 13.3 13.2 13.1 Structures 3.7 3.7 3.9 4.0 3.6 3.5 3.6 4.0 4.5 4.8 5.2 5.3 4.2 4.4 4.5 3.9 3.6 3.5 3.4 3.4 3.0 2.6 2.6 2.6 2.7 2.8 2.9 3.0 3.0 3.1 3.2 2.6 2.5 2.5 2.7 3.1 3.5 3.9 3.1 2.5 2.6 2.9 2.9 3.3 3.2 3.0 3.1 3.1 3.1 2.9 2.5 3.1 3.1 3.1 3.0 3.0 3.1 3.2 3.2 3.2 3.0 2.7 2.6 2.6 2.5 2.5 2.5 Equipment 5.9 6.2 6.7 6.8 6.4 6.5 7.1 7.7 7.9 7.6 7.5 7.0 6.8 7.2 7.1 6.9 6.6 6.6 6.6 6.2 5.9 5.9 6.2 6.5 6.9 7.0 7.1 7.3 7.4 7.5 6.7 6.0 5.9 5.9 6.1 6.2 6.2 5.7 4.6 5.2 5.6 6.1 6.1 6.2 6.1 5.8 5.7 5.8 5.8 5.4 5.5 5.8 5.7 5.8 5.9 5.9 5.9 5.7 5.6 5.3 5.2 5.4 5.6 5.6 5.6 5.5 5.4 Intellectual property products 1.6 1.6 1.6 1.7 1.7 1.7 1.7 1.7 1.8 1.9 2.0 2.2 2.2 2.4 2.4 2.5 2.5 2.5 2.7 2.8 2.9 2.9 2.9 2.8 3.0 3.1 3.4 3.5 3.8 4.0 3.9 3.7 3.7 3.6 3.6 3.7 3.8 3.9 3.9 3.8 4.0 4.0 4.1 4.2 4.2 4.4 4.5 4.7 4.8 5.2 5.2 4.6 4.6 4.7 4.7 4.8 4.8 4.9 4.9 5.0 5.4 5.1 5.2 5.2 5.2 5.2 5.2 Residential 5.0 5.7 5.5 4.5 4.0 4.6 5.5 5.9 5.6 4.5 4.0 3.3 4.4 4.7 4.6 5.1 5.1 4.9 4.5 4.0 3.6 3.9 4.2 4.4 4.2 4.4 4.4 4.6 4.8 4.7 4.8 5.1 5.6 6.1 6.6 6.1 4.8 3.5 2.7 2.5 2.4 2.7 3.0 3.2 3.5 3.7 3.9 3.9 3.8 4.3 4.7 3.9 3.9 3.9 3.8 3.8 3.8 3.8 3.8 4.1 4.1 4.3 4.7 4.8 4.7 4.7 4.7 Change in private inventories 0.7 .7 1.1 .9 –.4 .9 1.1 1.1 .7 –.2 .9 –.4 –.2 1.6 .5 .1 .6 .4 .5 .2 .0 .3 .3 .9 .4 .4 .8 .7 .6 .5 –.4 .2 .1 .5 .4 .5 .2 –.2 –1.0 .4 .3 .4 .6 .5 .8 .2 .2 .3 .3 –.3 –.1 .2 .1 .4 .4 .6 .4 .3 .1 –.1 –1.5 .1 .3 –.4 –.8 –.3 .9 Table B–4. Percentage shares of gross domestic product, 1971–2021—Continued [Percent of nominal GDP] Government consumption expenditures and gross investment Net exports of goods and services Year or quarter 1971 ����������������������� 1972 ����������������������� 1973 ����������������������� 1974 ����������������������� 1975 ����������������������� 1976 ����������������������� 1977 ����������������������� 1978 ����������������������� 1979 ����������������������� 1980 ����������������������� 1981 ����������������������� 1982 ����������������������� 1983 ����������������������� 1984 ����������������������� 1985 ����������������������� 1986 ����������������������� 1987 ����������������������� 1988 ����������������������� 1989 ����������������������� 1990 ����������������������� 1991 ����������������������� 1992 ����������������������� 1993 ����������������������� 1994 ����������������������� 1995 ����������������������� 1996 ����������������������� 1997 ����������������������� 1998 ����������������������� 1999 ����������������������� 2000 ����������������������� 2001 ����������������������� 2002 ����������������������� 2003 ����������������������� 2004 ����������������������� 2005 ����������������������� 2006 ����������������������� 2007 ����������������������� 2008 ����������������������� 2009 ����������������������� 2010 ����������������������� 2011 ����������������������� 2012 ����������������������� 2013 ����������������������� 2014 ����������������������� 2015 ����������������������� 2016 ����������������������� 2017 ����������������������� 2018 ����������������������� 2019 ����������������������� 2020 ����������������������� 2021 p ��������������������� 2018: I ������������������� II ������������������ III ����������������� IV ����������������� 2019: I ������������������� II ������������������ III ����������������� IV ����������������� 2020: I ������������������� II ������������������ III ����������������� IV ����������������� 2021: I ������������������� II ������������������ III ����������������� IV p �������������� Net exports 0.1 –.3 .3 –.1 .9 –.1 –1.1 –1.1 –.9 –.5 –.4 –.6 –1.4 –2.5 –2.6 –2.9 –3.0 –2.1 –1.5 –1.3 –.5 –.5 –1.0 –1.3 –1.2 –1.2 –1.2 –1.8 –2.7 –3.7 –3.6 –4.0 –4.6 –5.2 –5.7 –5.7 –5.1 –5.0 –2.9 –3.5 –3.7 –3.4 –2.8 –2.9 –2.9 –2.7 –2.8 –2.9 –2.8 –3.1 –4.0 –2.9 –2.6 –3.0 –3.1 –2.9 –3.0 –2.9 –2.5 –2.5 –2.8 –3.4 –3.7 –4.0 –3.9 –4.1 –4.0 Exports Total 5.4 5.5 6.7 8.2 8.2 8.0 7.7 7.9 8.8 9.8 9.5 8.5 7.6 7.5 7.0 7.0 7.5 8.5 8.9 9.3 9.7 9.7 9.5 9.9 10.6 10.7 11.1 10.5 10.3 10.7 9.7 9.1 9.0 9.6 10.0 10.6 11.5 12.4 10.9 12.3 13.6 13.6 13.6 13.5 12.5 11.9 12.2 12.3 11.8 10.2 10.8 12.4 12.5 12.3 12.1 12.0 11.9 11.7 11.6 11.1 9.3 9.8 10.3 10.5 10.8 10.7 11.1 Goods 4.0 4.1 5.3 6.7 6.7 6.5 6.2 6.4 7.1 8.1 7.6 6.7 5.9 5.7 5.2 5.1 5.5 6.3 6.6 6.8 7.0 7.0 6.8 7.1 7.8 7.8 8.2 7.6 7.4 7.8 7.0 6.5 6.4 6.8 7.1 7.6 8.0 8.7 7.3 8.5 9.4 9.4 9.3 9.2 8.2 7.7 7.9 8.1 7.7 6.8 7.6 8.1 8.3 8.1 8.0 7.9 7.7 7.6 7.5 7.4 5.9 6.7 7.1 7.3 7.6 7.5 7.8 Imports Services 1.4 1.4 1.4 1.5 1.6 1.5 1.5 1.6 1.6 1.8 1.9 1.8 1.7 1.8 1.7 2.0 2.0 2.1 2.3 2.5 2.7 2.7 2.7 2.8 2.9 3.0 3.0 2.9 2.9 2.9 2.7 2.7 2.6 2.9 2.9 3.1 3.5 3.7 3.6 3.9 4.2 4.2 4.3 4.3 4.2 4.2 4.3 4.2 4.1 3.4 3.2 4.3 4.2 4.2 4.1 4.1 4.2 4.1 4.1 3.7 3.4 3.2 3.3 3.2 3.2 3.2 3.3 Total 5.4 5.8 6.4 8.2 7.3 8.1 8.8 9.0 9.6 10.3 9.9 9.1 9.0 10.0 9.6 9.9 10.5 10.6 10.5 10.6 10.1 10.2 10.5 11.2 11.8 11.9 12.3 12.3 13.0 14.4 13.3 13.2 13.6 14.8 15.7 16.3 16.5 17.4 13.8 15.9 17.3 17.0 16.4 16.5 15.4 14.6 15.0 15.2 14.6 13.3 14.8 15.3 15.2 15.3 15.2 14.9 14.9 14.5 14.0 13.6 12.0 13.3 14.1 14.4 14.7 14.8 15.1 Goods 4.0 4.5 5.0 6.8 5.9 6.7 7.3 7.5 8.1 8.7 8.4 7.5 7.5 8.3 7.9 8.1 8.5 8.6 8.6 8.5 8.1 8.4 8.6 9.3 9.9 10.0 10.3 10.3 10.9 12.2 11.1 11.0 11.3 12.4 13.2 13.8 13.8 14.5 11.0 12.9 14.3 14.1 13.6 13.6 12.6 11.9 12.2 12.5 11.8 11.1 12.4 12.6 12.4 12.5 12.4 12.1 12.0 11.7 11.3 11.1 10.0 11.2 11.8 12.2 12.4 12.3 12.6 Federal Services 1.4 1.4 1.4 1.5 1.4 1.4 1.4 1.5 1.5 1.6 1.6 1.6 1.5 1.7 1.7 1.8 1.9 1.9 1.9 2.0 2.0 1.9 1.9 1.9 1.9 1.9 2.0 2.0 2.1 2.2 2.1 2.2 2.3 2.4 2.4 2.6 2.7 2.9 2.9 2.9 3.0 2.9 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.2 2.4 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.5 2.1 2.1 2.2 2.2 2.3 2.5 2.5 Total 23.0 22.4 21.4 22.1 22.6 21.6 20.9 20.3 20.0 20.6 20.4 21.3 21.1 20.5 21.0 21.3 21.2 20.6 20.4 20.8 21.1 20.6 19.9 19.2 19.0 18.5 18.0 17.8 17.9 17.8 18.4 19.1 19.3 19.1 19.0 19.0 19.3 20.2 21.2 21.0 20.2 19.3 18.6 18.1 17.8 17.7 17.4 17.4 17.4 18.5 17.6 17.4 17.4 17.4 17.4 17.4 17.4 17.4 17.4 17.8 19.8 18.3 18.1 18.0 17.7 17.6 17.2 Total 11.5 11.1 10.3 10.3 10.3 9.9 9.6 9.3 9.2 9.6 9.8 10.4 10.5 10.2 10.4 10.5 10.4 9.8 9.5 9.4 9.5 9.0 8.5 7.9 7.5 7.2 6.8 6.5 6.3 6.2 6.3 6.8 7.2 7.3 7.3 7.2 7.3 7.8 8.4 8.6 8.3 7.9 7.3 6.9 6.7 6.6 6.5 6.5 6.6 7.2 6.8 6.5 6.5 6.5 6.6 6.6 6.6 6.6 6.6 6.8 7.8 7.2 7.0 7.1 6.9 6.7 6.5 National defense Nondefense 8.4 7.9 7.2 7.1 7.0 6.7 6.5 6.2 6.1 6.4 6.7 7.3 7.5 7.4 7.6 7.7 7.7 7.3 6.9 6.8 6.7 6.2 5.7 5.2 4.9 4.7 4.3 4.1 4.0 3.8 3.9 4.2 4.5 4.7 4.7 4.6 4.7 5.1 5.4 5.5 5.3 5.0 4.5 4.2 4.0 3.9 3.8 3.9 4.0 4.2 3.9 3.8 3.8 3.9 3.9 3.9 3.9 4.0 4.0 4.0 4.5 4.2 4.2 4.1 4.0 3.9 3.8 3.1 3.2 3.1 3.2 3.3 3.2 3.2 3.1 3.0 3.2 3.1 3.1 3.0 2.8 2.8 2.8 2.7 2.5 2.5 2.6 2.7 2.8 2.7 2.6 2.6 2.5 2.5 2.4 2.4 2.3 2.4 2.6 2.7 2.6 2.6 2.6 2.6 2.7 3.0 3.1 3.0 2.9 2.7 2.7 2.7 2.7 2.7 2.7 2.7 3.0 2.9 2.7 2.7 2.7 2.6 2.7 2.7 2.7 2.6 2.7 3.4 3.0 2.8 3.0 2.9 2.8 2.7 State and local 11.4 11.3 11.1 11.8 12.3 11.7 11.2 10.9 10.8 11.0 10.6 10.9 10.6 10.3 10.5 10.8 10.9 10.8 11.0 11.3 11.6 11.6 11.4 11.4 11.4 11.3 11.2 11.3 11.5 11.6 12.1 12.3 12.1 11.8 11.7 11.7 12.0 12.4 12.8 12.3 11.8 11.4 11.3 11.1 11.0 11.1 11.0 10.9 10.8 11.3 10.8 10.9 10.9 10.9 10.8 10.8 10.8 10.7 10.7 11.1 12.0 11.1 11.0 10.9 10.8 10.9 10.7 Source: Department of Commerce (Bureau of Economic Analysis). National Income or Expenditure | 361 Table B–5. Chain-type price indexes for gross domestic product, 1971–2021 [Index numbers, 2012=100, except as noted; quarterly data seasonally adjusted] Personal consumption expenditures Gross private domestic investment Fixed investment Year or quarter 1971 ���������������������� 1972 ���������������������� 1973 ���������������������� 1974 ���������������������� 1975 ���������������������� 1976 ���������������������� 1977 ���������������������� 1978 ���������������������� 1979 ���������������������� 1980 ���������������������� 1981 ���������������������� 1982 ���������������������� 1983 ���������������������� 1984 ���������������������� 1985 ���������������������� 1986 ���������������������� 1987 ���������������������� 1988 ���������������������� 1989 ���������������������� 1990 ���������������������� 1991 ���������������������� 1992 ���������������������� 1993 ���������������������� 1994 ���������������������� 1995 ���������������������� 1996 ���������������������� 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Gross domestic product 22.761 23.745 25.045 27.292 29.827 31.469 33.424 35.775 38.741 42.251 46.240 49.099 51.018 52.860 54.533 55.638 57.004 59.018 61.331 63.636 65.777 67.278 68.874 70.342 71.819 73.132 74.399 75.219 76.272 78.016 79.814 81.013 82.635 84.842 87.490 90.212 92.653 94.397 95.019 96.164 98.157 100.000 101.769 103.662 104.662 105.703 107.742 110.326 112.279 113.740 118.490 109.312 110.156 110.647 111.191 111.502 112.142 112.524 112.947 113.397 112.969 113.984 114.611 115.826 117.546 119.259 121.329 Nonresidential Total 21.798 22.542 23.756 26.229 28.415 29.974 31.923 34.145 37.178 41.182 44.871 47.363 49.378 51.243 53.031 54.184 55.855 58.038 60.572 63.231 65.345 67.087 68.758 70.193 71.671 73.204 74.478 75.070 76.164 78.090 79.656 80.702 82.398 84.443 86.876 89.322 91.614 94.325 94.062 95.747 98.170 100.000 101.354 102.887 103.116 104.148 106.051 108.318 109.922 111.225 115.529 107.557 108.184 108.546 108.986 109.100 109.835 110.141 110.612 110.958 110.505 111.507 111.928 112.989 114.772 116.277 118.078 See next page for continuation of table. 362 | Appendix B Goods 33.079 33.926 35.949 40.436 43.703 45.413 47.837 50.773 55.574 61.797 66.389 68.198 69.429 70.742 71.877 71.541 73.842 75.788 78.704 81.927 83.930 84.943 85.681 86.552 87.361 88.321 88.219 86.893 87.349 89.082 89.015 88.166 88.054 89.292 91.084 92.306 93.331 96.122 93.812 95.183 98.773 100.000 99.407 98.920 95.896 94.332 94.615 95.281 94.832 94.160 98.892 95.270 95.516 95.247 95.092 94.647 95.120 94.697 94.863 94.597 93.243 94.361 94.437 95.790 97.948 99.690 102.141 Services 16.733 17.441 18.284 19.833 21.533 23.027 24.770 26.674 28.911 31.918 35.187 37.949 40.280 42.376 44.450 46.276 47.660 49.939 52.293 54.690 56.829 58.850 60.885 62.540 64.288 66.051 67.914 69.351 70.731 72.740 75.063 77.004 79.574 82.018 84.774 87.844 90.786 93.458 94.182 96.017 97.875 100.000 102.322 104.880 106.796 109.197 111.965 115.100 117.836 120.302 124.213 113.930 114.763 115.471 116.236 116.656 117.536 118.253 118.900 119.604 119.713 120.624 121.267 122.109 123.593 124.904 126.244 Total 31.092 32.388 34.153 37.559 42.059 44.384 47.655 51.517 56.141 61.395 67.123 70.679 70.896 71.661 72.548 74.178 75.723 77.627 79.606 81.270 82.648 82.647 83.627 84.875 86.240 86.191 86.241 85.608 85.690 86.815 87.555 87.841 88.561 91.148 94.839 98.176 99.656 100.474 99.331 97.687 98.704 100.000 100.979 102.922 103.535 103.516 105.230 107.186 108.906 110.212 113.822 106.370 107.029 107.506 107.840 108.443 108.918 109.128 109.134 109.632 109.726 110.490 111.000 111.777 112.574 114.256 116.680 Total 30.134 31.420 33.169 36.449 40.874 43.232 46.550 50.444 54.977 60.105 65.624 69.311 69.575 70.253 71.277 73.021 74.506 76.586 78.561 80.278 81.683 81.728 82.711 83.983 85.378 85.450 85.599 85.133 85.277 86.486 87.241 87.500 88.265 90.843 94.597 97.958 99.456 100.296 99.076 97.568 98.641 100.000 101.091 103.172 104.075 104.202 105.928 107.926 109.684 111.052 115.386 107.086 107.765 108.254 108.597 109.212 109.680 109.915 109.930 110.340 110.701 111.316 111.850 112.864 114.105 116.042 118.532 Total 37.997 39.297 40.882 44.857 50.766 53.562 57.111 60.930 65.830 71.641 78.453 82.911 82.774 83.036 83.893 85.365 86.339 88.514 90.572 92.516 94.267 93.960 94.161 94.904 95.849 95.267 94.735 93.248 92.314 92.718 92.346 91.863 91.156 92.055 94.443 96.745 98.310 99.832 99.184 97.416 98.559 100.000 100.251 101.469 101.909 101.119 101.977 102.815 104.137 104.813 106.433 102.341 102.676 103.001 103.243 103.823 104.251 104.313 104.160 104.487 104.864 104.895 105.005 105.203 105.429 106.549 108.550 Structures Equipment 12.757 13.674 14.734 16.770 18.773 19.692 21.401 23.468 26.194 28.629 32.566 35.136 34.241 34.540 35.361 36.039 36.618 38.171 39.666 40.948 41.689 41.699 42.922 44.437 46.362 47.540 49.355 51.612 53.198 55.283 58.178 60.603 62.769 67.416 75.733 84.749 89.748 94.335 92.613 92.006 95.362 100.000 101.455 107.198 109.403 109.670 112.545 114.391 119.058 120.852 127.655 113.167 113.793 114.422 116.182 117.497 118.881 119.610 120.243 120.805 120.615 120.919 121.071 122.237 124.882 128.200 135.302 63.848 64.686 65.780 70.713 81.484 86.486 91.800 96.900 103.167 112.249 120.463 125.415 125.776 124.748 124.748 127.254 128.083 129.854 132.337 135.042 137.330 137.121 135.518 135.277 133.796 130.762 127.156 121.451 116.763 114.224 110.858 108.531 105.725 104.841 104.598 103.560 103.191 102.542 103.169 99.471 99.447 100.000 99.787 99.169 98.671 97.592 97.542 97.684 97.816 97.388 97.683 97.480 97.539 97.905 97.811 98.035 97.987 97.689 97.552 97.727 97.737 97.309 96.780 97.318 96.536 97.626 99.253 Intellectual property products 39.318 40.490 42.494 46.461 50.190 52.408 54.709 57.557 61.382 66.123 71.058 75.093 77.898 80.081 81.413 82.047 83.518 86.129 87.240 88.147 90.271 89.373 89.998 90.468 93.134 93.544 94.052 93.595 95.105 97.814 97.684 96.376 95.647 95.335 95.952 97.088 98.284 99.834 98.589 98.306 99.517 100.000 100.081 100.791 101.374 100.302 101.125 102.266 103.172 104.574 105.591 101.894 102.410 102.485 102.273 102.881 103.373 103.515 102.920 103.331 104.425 104.853 105.688 105.069 105.464 105.712 106.119 Residential 16.943 17.975 19.571 21.593 23.590 25.117 27.683 31.082 34.593 38.325 41.425 43.646 44.680 46.003 47.267 49.351 51.486 53.278 55.020 56.288 57.021 57.723 60.074 62.247 64.473 65.856 67.444 69.223 71.816 75.004 78.564 80.510 84.325 90.243 96.706 102.355 103.708 102.249 98.671 98.317 99.049 100.000 105.054 111.118 114.114 118.127 123.454 130.417 134.145 138.541 153.768 127.975 130.152 131.373 132.167 132.937 133.586 134.626 135.430 136.224 136.528 139.594 141.817 146.010 151.291 156.609 161.163 Table B–5. Chain-type price indexes for gross domestic product, 1971–2021—Continued [Index numbers, 2012=100, except as noted; quarterly data seasonally adjusted] Exports and imports of goods and services Government consumption expenditures and gross investment Federal Year or quarter Exports 1971 ����������������� 1972 ����������������� 1973 ����������������� 1974 ����������������� 1975 ����������������� 1976 ����������������� 1977 ����������������� 1978 ����������������� 1979 ����������������� 1980 ����������������� 1981 ����������������� 1982 ����������������� 1983 ����������������� 1984 ����������������� 1985 ����������������� 1986 ����������������� 1987 ����������������� 1988 ����������������� 1989 ����������������� 1990 ����������������� 1991 ����������������� 1992 ����������������� 1993 ����������������� 1994 ����������������� 1995 ����������������� 1996 ����������������� 1997 ����������������� 1998 ����������������� 1999 ����������������� 2000 ����������������� 2001 ����������������� 2002 ����������������� 2003 ����������������� 2004 ����������������� 2005 ����������������� 2006 ����������������� 2007 ����������������� 2008 ����������������� 2009 ����������������� 2010 ����������������� 2011 ����������������� 2012 ����������������� 2013 ����������������� 2014 ����������������� 2015 ����������������� 2016 ����������������� 2017 ����������������� 2018 ����������������� 2019 ����������������� 2020 ����������������� 2021 p ��������������� 2018: I ������������� II ������������ III ����������� IV ����������� 2019: I ������������� II ������������ III ����������� IV ����������� 2020: I ������������� II ������������ III ����������� IV ����������� 2021: I ������������� II ������������ III ����������� IV p �������� Imports Total Total National Nondefense defense State and local 30.837 21.114 17.353 20.673 19.817 22.531 15.199 32.187 22.593 18.664 22.488 21.883 23.589 16.163 36.430 26.520 19.938 24.054 23.484 25.028 17.246 44.865 37.942 21.854 25.975 25.404 26.916 19.158 49.453 41.100 23.872 28.258 27.545 29.497 21.000 51.076 42.338 25.183 30.016 29.345 31.137 22.025 53.158 46.068 26.742 31.863 31.268 32.796 23.395 56.391 49.315 28.510 34.012 33.561 34.627 24.915 63.184 57.753 30.856 36.571 36.216 36.968 27.115 69.594 71.945 34.048 40.104 39.919 40.124 30.082 74.748 75.834 37.428 43.849 43.747 43.662 33.228 75.104 73.281 39.973 46.950 47.039 46.309 35.403 75.410 70.535 41.520 48.506 48.778 47.418 36.966 76.116 69.925 43.322 50.644 51.013 49.300 38.546 73.850 67.628 44.663 51.719 51.872 50.929 40.115 72.618 67.627 45.413 51.964 51.894 51.770 41.271 74.222 71.715 46.640 52.325 52.267 52.099 43.198 78.022 75.146 48.181 54.033 53.904 53.997 44.642 79.315 76.789 50.021 55.542 55.365 55.629 46.754 79.762 78.991 52.118 57.258 57.162 57.118 49.156 80.651 78.332 54.010 59.317 58.964 59.813 50.955 80.259 78.396 55.647 60.832 60.678 60.851 52.692 80.391 77.795 56.958 62.159 61.615 63.021 54.004 81.325 78.526 58.468 63.870 63.229 64.926 55.397 83.143 80.677 60.128 65.847 65.027 67.252 56.874 82.039 79.271 61.361 66.946 66.114 68.373 58.180 80.593 76.516 62.566 67.981 67.035 69.621 59.474 78.685 72.396 63.630 68.850 67.871 70.548 60.633 78.091 72.827 65.753 70.532 69.559 72.218 62.963 79.592 76.013 68.577 72.898 71.908 74.616 65.989 78.968 74.046 70.558 74.249 73.270 75.947 68.258 78.287 73.164 72.386 76.648 75.714 78.272 69.792 79.531 75.377 75.044 80.025 79.505 80.946 72.063 82.435 78.971 78.169 82.777 82.263 83.689 75.382 85.289 83.618 82.132 86.222 86.011 86.586 79.631 88.006 86.854 85.695 88.969 89.022 88.858 83.659 91.328 89.887 89.530 91.609 91.750 91.340 88.181 95.493 98.795 93.334 94.397 94.801 93.647 92.590 89.803 87.854 92.921 94.193 94.126 94.308 92.045 93.350 92.655 95.391 96.425 96.128 96.951 94.674 99.237 99.716 98.289 99.069 98.946 99.284 97.747 100.000 100.000 100.000 100.000 100.000 100.000 100.000 100.148 98.697 102.363 100.933 100.609 101.482 103.332 100.216 97.961 104.470 102.643 102.056 103.621 105.698 95.373 90.144 104.638 103.143 102.334 104.466 105.656 93.458 87.058 104.899 103.695 102.650 105.370 105.739 95.897 88.996 107.389 105.702 104.306 107.902 108.524 99.135 91.515 111.319 108.776 107.150 111.325 112.984 98.660 90.078 113.246 110.781 108.865 113.775 114.863 96.188 88.075 114.861 112.018 110.039 115.108 116.725 107.549 94.611 120.041 116.058 114.106 119.123 122.671 98.161 91.314 109.902 107.655 106.122 110.063 111.383 99.440 91.684 110.965 108.447 106.927 110.835 112.615 99.674 91.823 111.832 109.073 107.538 111.484 113.632 99.264 91.239 112.576 109.928 108.011 112.920 114.307 98.390 90.169 112.842 111.078 108.361 115.320 114.014 99.277 90.789 113.070 110.303 108.669 112.858 114.878 98.676 89.904 113.358 110.673 109.024 113.253 115.114 98.295 89.448 113.713 111.068 109.408 113.669 115.447 97.783 89.194 114.503 111.400 109.650 114.143 116.536 93.181 86.424 114.252 111.444 109.303 114.766 116.093 96.164 88.098 114.921 112.269 110.264 115.403 116.659 97.622 88.585 115.768 112.959 110.939 116.118 117.611 102.383 91.385 117.292 114.065 112.152 117.070 119.416 107.030 94.312 119.031 115.228 113.335 118.207 121.544 109.539 95.720 120.796 116.643 114.686 119.720 123.541 111.244 97.027 123.045 118.294 116.254 121.494 126.182 Personal consumption Final expenGross sales of ditures domestic Gross domestic excludpurproduct ing chases 1 domestic product food and energy 22.627 23.609 24.907 27.136 29.661 31.305 33.262 35.614 38.566 42.056 46.016 48.889 50.803 52.637 54.335 55.456 56.814 58.851 61.165 63.477 65.621 67.125 68.722 70.194 71.676 73.009 74.297 75.152 76.221 77.983 79.785 80.978 82.609 84.814 87.470 90.195 92.645 94.392 94.990 96.142 98.146 100.000 101.791 103.707 104.760 105.832 107.874 110.468 112.429 113.902 118.783 109.448 110.297 110.791 111.337 111.649 112.289 112.676 113.101 113.535 113.154 114.143 114.775 116.034 117.833 119.593 121.673 23.112 23.856 24.764 26.726 28.958 30.718 32.694 34.861 37.403 40.840 44.419 47.306 49.727 51.789 53.893 55.752 57.548 59.994 62.484 65.016 67.338 69.384 71.269 72.864 74.451 75.863 77.201 78.183 79.210 80.625 82.153 83.526 84.874 86.544 88.440 90.558 92.578 94.393 95.270 96.651 98.184 100.000 101.535 103.187 104.487 106.138 107.935 110.096 111.959 113.553 117.320 109.292 109.943 110.320 110.829 111.136 111.783 112.269 112.647 113.135 112.919 113.904 114.255 115.010 116.731 118.045 119.493 22.158 23.147 24.469 26.954 29.417 31.033 33.079 35.431 38.539 42.551 46.476 49.154 50.864 52.585 54.149 55.278 56.839 58.850 61.166 63.586 65.583 67.108 68.623 70.062 71.575 72.820 73.893 74.386 75.518 77.480 78.987 80.070 81.807 84.151 87.083 89.885 92.327 94.947 94.534 95.951 98.272 100.000 101.478 103.181 103.464 104.187 106.157 108.648 110.326 111.682 116.014 107.769 108.473 108.939 109.411 109.635 110.242 110.527 110.898 111.346 111.024 111.924 112.434 113.523 115.130 116.708 118.694 5.1 4.3 5.5 9.0 9.3 5.5 6.2 7.0 8.3 9.1 9.4 6.2 3.9 3.6 3.2 2.0 2.5 3.5 3.9 3.8 3.4 2.3 2.4 2.1 2.1 1.8 1.7 1.1 1.4 2.3 2.3 1.5 2.0 2.7 3.1 3.1 2.7 1.9 .7 1.2 2.1 1.9 1.8 1.9 1.0 1.0 1.9 2.4 1.8 1.3 4.2 2.4 3.1 1.8 2.0 1.1 2.3 1.4 1.5 1.6 –1.5 3.6 2.2 4.3 6.1 6.0 7.1 Percent change 2 Personal consumption expenditures Total 4.2 3.4 5.4 10.4 8.3 5.5 6.5 7.0 8.9 10.8 9.0 5.6 4.3 3.8 3.5 2.2 3.1 3.9 4.4 4.4 3.3 2.7 2.5 2.1 2.1 2.1 1.7 .8 1.5 2.5 2.0 1.3 2.1 2.5 2.9 2.8 2.6 3.0 –.3 1.8 2.5 1.9 1.4 1.5 .2 1.0 1.8 2.1 1.5 1.2 3.9 2.7 2.4 1.3 1.6 .4 2.7 1.1 1.7 1.3 –1.6 3.7 1.5 3.8 6.5 5.3 6.3 Gross domestic Excludpuring chases 1 food and energy 4.7 3.2 3.8 7.9 8.4 6.1 6.4 6.6 7.3 9.2 8.8 6.5 5.1 4.1 4.1 3.4 3.2 4.3 4.2 4.1 3.6 3.0 2.7 2.2 2.2 1.9 1.8 1.3 1.3 1.8 1.9 1.7 1.6 2.0 2.2 2.4 2.2 2.0 .9 1.4 1.6 1.8 1.5 1.6 1.3 1.6 1.7 2.0 1.7 1.4 3.3 2.4 2.4 1.4 1.9 1.1 2.3 1.8 1.4 1.7 –.8 3.5 1.2 2.7 6.1 4.6 5.0 5.2 4.5 5.7 10.2 9.1 5.5 6.6 7.1 8.8 10.4 9.2 5.8 3.5 3.4 3.0 2.1 2.8 3.5 3.9 4.0 3.1 2.3 2.3 2.1 2.2 1.7 1.5 .7 1.5 2.6 1.9 1.4 2.2 2.9 3.5 3.2 2.7 2.8 –.4 1.5 2.4 1.8 1.5 1.7 .3 .7 1.9 2.3 1.5 1.2 3.9 2.9 2.6 1.7 1.7 .8 2.2 1.0 1.3 1.6 –1.2 3.3 1.8 3.9 5.8 5.6 7.0 1 Gross domestic product (GDP) less exports of goods and services plus imports of goods and services. 2 Quarterly percent changes are at annual rates. Source: Department of Commerce (Bureau of Economic Analysis). National Income or Expenditure | 363 Table B–6. Gross value added by sector, 1971–2021 [Billions of dollars; quarterly data at seasonally adjusted annual rates] Business 1 Year or quarter 1971 ���������������������� 1972 ���������������������� 1973 ���������������������� 1974 ���������������������� 1975 ���������������������� 1976 ���������������������� 1977 ���������������������� 1978 ���������������������� 1979 ���������������������� 1980 ���������������������� 1981 ���������������������� 1982 ���������������������� 1983 ���������������������� 1984 ���������������������� 1985 ���������������������� 1986 ���������������������� 1987 ���������������������� 1988 ���������������������� 1989 ���������������������� 1990 ���������������������� 1991 ���������������������� 1992 ���������������������� 1993 ���������������������� 1994 ���������������������� 1995 ���������������������� 1996 ���������������������� 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Gross domestic product 1,164.9 1,279.1 1,425.4 1,545.2 1,684.9 1,873.4 2,081.8 2,351.6 2,627.3 2,857.3 3,207.0 3,343.8 3,634.0 4,037.6 4,339.0 4,579.6 4,855.2 5,236.4 5,641.6 5,963.1 6,158.1 6,520.3 6,858.6 7,287.2 7,639.7 8,073.1 8,577.6 9,062.8 9,631.2 10,251.0 10,581.9 10,929.1 11,456.5 12,217.2 13,039.2 13,815.6 14,474.2 14,769.9 14,478.1 15,049.0 15,599.7 16,254.0 16,843.2 17,550.7 18,206.0 18,695.1 19,479.6 20,527.2 21,372.6 20,893.7 22,997.5 20,143.7 20,492.5 20,659.1 20,813.3 21,001.6 21,289.3 21,505.0 21,694.5 21,481.4 19,477.4 21,138.6 21,477.6 22,038.2 22,741.0 23,202.3 24,008.5 Total 882.5 972.5 1,094.0 1,182.8 1,284.8 1,443.3 1,616.2 1,838.2 2,062.8 2,225.8 2,502.0 2,568.6 2,801.9 3,136.7 3,369.6 3,539.3 3,735.2 4,019.3 4,326.7 4,542.0 4,645.0 4,920.2 5,177.4 5,523.7 5,795.1 6,159.5 6,578.8 6,959.2 7,401.8 7,875.9 8,057.7 8,256.0 8,642.9 9,249.3 9,911.0 10,524.7 10,997.8 11,061.8 10,659.6 11,137.8 11,614.9 12,206.4 12,689.6 13,279.8 13,804.8 14,168.5 14,803.1 15,643.7 16,298.1 15,666.4 17,552.0 15,341.2 15,633.8 15,747.4 15,852.3 16,001.0 16,247.2 16,406.9 16,537.3 16,249.0 14,318.1 15,899.9 16,198.6 16,726.8 17,361.1 17,702.2 18,417.9 Nonfarm 1 857.2 942.9 1,047.2 1,138.5 1,239.2 1,400.2 1,572.7 1,787.5 2,002.7 2,174.4 2,437.0 2,508.2 2,757.0 3,072.6 3,305.9 3,479.4 3,673.2 3,957.9 4,252.8 4,464.2 4,574.7 4,840.4 5,106.2 5,440.1 5,726.7 6,066.9 6,490.6 6,880.2 7,330.9 7,799.9 7,979.6 8,181.7 8,551.1 9,129.1 9,805.4 10,427.2 10,880.7 10,943.6 10,557.4 11,021.6 11,464.5 12,058.5 12,506.4 13,113.8 13,659.6 14,038.6 14,663.8 15,506.6 16,175.1 15,531.7 17,348.9 15,201.2 15,489.6 15,615.8 15,719.7 15,880.1 16,127.8 16,283.2 16,409.3 16,106.4 14,214.2 15,766.9 16,039.4 16,554.2 17,148.0 17,484.5 18,208.9 Households and institutions Farm 25.4 29.7 46.8 44.2 45.6 43.0 43.5 50.7 60.1 51.4 65.0 60.4 44.9 64.2 63.7 59.9 62.0 61.4 73.9 77.8 70.4 79.9 71.3 83.6 68.4 92.6 88.1 79.0 70.9 76.0 78.1 74.3 91.8 120.2 105.6 97.5 117.1 118.2 102.2 116.2 150.4 148.0 183.3 166.0 145.2 129.9 139.4 137.1 123.0 134.7 203.1 140.0 144.1 131.6 132.6 120.9 119.4 123.7 128.0 142.6 103.9 133.0 159.2 172.6 213.1 217.6 209.0 Total 104.5 114.0 124.6 137.2 151.6 164.9 179.9 202.1 226.3 258.2 291.6 323.8 352.5 383.8 411.8 447.0 489.5 539.8 586.0 636.3 677.3 720.3 772.8 824.7 877.8 923.2 975.9 1,040.6 1,111.2 1,190.7 1,271.7 1,344.7 1,408.8 1,489.2 1,572.8 1,658.9 1,749.5 1,886.9 1,934.9 1,965.0 2,012.0 2,058.4 2,114.2 2,182.9 2,260.2 2,344.1 2,436.0 2,551.4 2,669.0 2,755.5 2,884.9 2,505.8 2,536.9 2,564.4 2,598.5 2,627.4 2,653.2 2,680.8 2,714.5 2,753.7 2,716.4 2,756.9 2,794.8 2,812.1 2,845.7 2,909.6 2,972.1 Households 67.2 72.7 78.5 85.5 93.7 101.7 110.7 124.8 139.5 158.8 179.2 198.2 213.6 230.9 248.2 268.4 289.8 316.4 341.4 367.6 386.6 407.1 437.6 472.7 506.9 534.6 565.7 601.6 644.0 692.3 748.9 781.6 814.1 862.6 922.3 976.2 1,035.9 1,125.2 1,136.8 1,150.7 1,164.0 1,168.8 1,196.5 1,228.3 1,258.8 1,299.3 1,352.6 1,416.5 1,479.6 1,528.0 1,579.3 1,389.8 1,407.7 1,425.3 1,443.4 1,459.1 1,472.8 1,485.8 1,500.6 1,517.8 1,527.1 1,533.3 1,533.9 1,545.7 1,566.2 1,589.0 1,616.4 Nonprofit institutions serving households 2 37.4 41.4 46.1 51.7 58.0 63.2 69.2 77.3 86.9 99.3 112.4 125.6 138.9 152.8 163.6 178.6 199.7 223.4 244.6 268.8 290.7 313.2 335.1 352.0 370.9 388.7 410.2 439.0 467.2 498.4 522.8 563.0 594.6 626.6 650.5 682.8 713.6 761.7 798.2 814.3 848.0 889.6 917.7 954.6 1,001.4 1,044.8 1,083.4 1,134.9 1,189.4 1,227.4 1,305.6 1,116.1 1,129.2 1,139.1 1,155.1 1,168.3 1,180.4 1,195.0 1,213.9 1,235.8 1,189.4 1,223.6 1,260.9 1,266.3 1,279.6 1,320.6 1,355.8 General government 3 Total 177.8 192.6 206.8 225.3 248.4 265.3 285.7 311.3 338.2 373.4 413.5 451.4 479.7 517.1 557.5 593.3 630.4 677.4 728.8 784.9 835.8 879.8 908.3 938.8 966.9 990.3 1,022.9 1,063.0 1,118.1 1,184.3 1,252.6 1,328.4 1,404.8 1,478.7 1,555.4 1,631.9 1,726.9 1,821.2 1,883.5 1,946.1 1,972.9 1,989.1 2,039.3 2,088.0 2,141.0 2,182.5 2,240.4 2,332.1 2,405.5 2,471.9 2,560.6 2,296.7 2,321.8 2,347.3 2,362.5 2,373.2 2,388.9 2,417.3 2,442.7 2,478.7 2,442.9 2,481.8 2,484.2 2,499.4 2,534.1 2,590.6 2,618.5 Federal 87.5 92.4 96.4 102.5 110.5 117.3 125.2 135.8 145.4 159.8 178.3 195.7 207.1 225.3 240.0 250.6 261.0 278.5 292.8 306.7 323.5 329.6 331.5 332.6 333.0 331.8 333.5 336.8 345.0 360.3 370.3 397.8 434.7 459.4 488.4 509.9 535.7 569.1 603.0 640.0 659.8 663.7 658.4 666.8 673.7 683.9 699.3 726.0 749.4 782.5 817.9 715.1 722.9 730.2 735.8 741.7 745.7 752.5 757.8 765.6 776.7 792.2 795.5 803.0 812.9 822.4 833.5 State and local 90.3 100.2 110.4 122.8 138.0 148.0 160.6 175.5 192.8 213.5 235.2 255.6 272.6 291.9 317.6 342.7 369.4 398.8 436.1 478.2 512.2 550.2 576.9 606.2 633.9 658.6 689.3 726.2 773.1 824.0 882.3 930.6 970.1 1,019.3 1,067.0 1,122.1 1,191.2 1,252.1 1,280.5 1,306.1 1,313.1 1,325.5 1,380.9 1,421.1 1,467.3 1,498.6 1,541.2 1,606.1 1,656.1 1,689.4 1,742.7 1,581.6 1,598.9 1,617.1 1,626.7 1,631.4 1,643.2 1,664.8 1,684.8 1,713.1 1,666.2 1,689.6 1,688.7 1,696.4 1,721.2 1,768.2 1,785.0 Addendum: Gross housing value added 86.4 93.9 101.4 110.4 121.3 130.9 144.2 160.2 177.7 204.0 231.6 258.6 280.6 303.1 333.8 364.5 392.1 424.2 452.7 487.0 515.3 545.2 578.4 619.6 662.6 695.0 731.9 774.8 825.1 880.6 947.7 983.5 1,014.8 1,074.1 1,149.7 1,209.4 1,279.3 1,388.7 1,415.5 1,443.9 1,471.0 1,493.6 1,536.3 1,582.8 1,633.1 1,691.5 1,753.8 1,835.7 1,921.1 1,988.9 2,053.8 1,801.0 1,824.0 1,847.1 1,870.6 1,891.9 1,911.8 1,930.4 1,950.3 1,973.1 1,986.8 1,996.2 1,999.5 2,013.8 2,037.7 2,064.8 2,099.0 1 Gross domestic business value added equals gross domestic product excluding gross value added of households and institutions and of general government. Nonfarm value added equals gross domestic business value added excluding gross farm value added. 2 Equals compensation of employees of nonprofit institutions, the rental value of nonresidential fixed assets owned and used by nonprofit institutions serving households, and rental income of persons for tenant-occupied housing owned by nonprofit institutions. 3 Equals compensation of general government employees plus general government consumption of fixed capital. Source: Department of Commerce (Bureau of Economic Analysis). 364 | Appendix B Table B–7. Real gross value added by sector, 1971–2021 [Billions of chained (2012) dollars; quarterly data at seasonally adjusted annual rates] Business 1 Year or quarter 1971 ���������������������� 1972 ���������������������� 1973 ���������������������� 1974 ���������������������� 1975 ���������������������� 1976 ���������������������� 1977 ���������������������� 1978 ���������������������� 1979 ���������������������� 1980 ���������������������� 1981 ���������������������� 1982 ���������������������� 1983 ���������������������� 1984 ���������������������� 1985 ���������������������� 1986 ���������������������� 1987 ���������������������� 1988 ���������������������� 1989 ���������������������� 1990 ���������������������� 1991 ���������������������� 1992 ���������������������� 1993 ���������������������� 1994 ���������������������� 1995 ���������������������� 1996 ���������������������� 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Gross domestic product 5,117.6 5,386.7 5,690.9 5,660.1 5,648.5 5,952.8 6,228.1 6,572.8 6,780.9 6,763.5 6,935.2 6,810.1 7,122.3 7,637.7 7,956.2 8,231.7 8,516.4 8,872.2 9,198.0 9,371.5 9,361.3 9,691.1 9,957.7 10,358.9 10,637.0 11,038.3 11,529.2 12,045.8 12,623.4 13,138.0 13,263.4 13,488.4 13,865.5 14,399.7 14,901.3 15,315.9 15,623.9 15,643.0 15,236.3 15,649.0 15,891.5 16,254.0 16,553.3 16,932.1 17,390.3 17,680.3 18,079.1 18,606.8 19,032.7 18,384.7 19,428.4 18,436.3 18,590.0 18,679.6 18,721.3 18,833.2 18,982.5 19,112.7 19,202.3 18,952.0 17,258.2 18,560.8 18,767.8 19,055.7 19,368.3 19,478.9 19,810.6 Total 3,397.4 3,619.3 3,870.6 3,811.6 3,775.4 4,030.5 4,261.3 4,533.1 4,694.1 4,651.7 4,787.4 4,650.0 4,896.4 5,330.7 5,579.3 5,782.0 5,989.5 6,246.0 6,485.1 6,589.0 6,548.8 6,826.1 7,020.7 7,359.3 7,585.5 7,937.6 8,354.3 8,813.8 9,323.0 9,741.3 9,799.7 9,966.6 10,281.2 10,733.3 11,154.8 11,520.2 11,766.1 11,663.6 11,234.6 11,597.6 11,825.7 12,206.4 12,506.7 12,874.0 13,313.2 13,564.1 13,926.2 14,410.8 14,791.5 14,164.4 15,186.8 14,258.6 14,398.2 14,474.9 14,511.3 14,621.2 14,749.3 14,863.7 14,931.8 14,670.6 13,080.9 14,346.5 14,559.8 14,848.7 15,143.8 15,211.1 15,543.6 Nonfarm 1 3,351.5 3,577.2 3,837.0 3,779.4 3,717.7 3,984.3 4,213.0 4,494.3 4,646.4 4,606.8 4,711.8 4,567.7 4,850.7 5,261.1 5,492.8 5,700.6 5,907.8 6,176.9 6,403.9 6,499.6 6,458.7 6,721.2 6,928.5 7,247.4 7,496.4 7,833.7 8,237.5 8,700.2 9,206.0 9,607.5 9,672.8 9,835.1 10,139.9 10,578.9 10,992.0 11,357.8 11,617.4 11,514.9 11,071.4 11,436.4 11,670.5 12,058.5 12,328.6 12,695.2 13,123.8 13,364.9 13,728.1 14,206.7 14,595.9 13,952.9 14,981.5 14,057.5 14,191.7 14,270.2 14,307.7 14,427.6 14,554.7 14,668.2 14,733.1 14,454.4 12,885.1 14,129.8 14,342.4 14,635.8 14,936.7 15,010.8 15,342.8 Households and institutions Farm 48.2 48.2 47.7 46.6 55.5 52.8 55.6 53.5 58.6 56.9 75.2 78.8 54.5 72.7 86.1 82.4 83.2 73.9 84.0 90.7 91.2 105.3 93.4 113.0 90.0 104.1 116.7 112.6 115.5 136.6 126.6 132.3 144.4 159.2 168.6 165.5 145.4 145.7 168.1 162.7 155.3 148.0 177.9 178.3 190.0 203.0 198.2 203.6 186.2 221.0 207.6 200.3 207.7 204.2 202.2 184.2 184.8 185.4 190.4 223.0 204.9 228.8 227.4 218.4 209.4 201.5 201.3 Total 691.1 718.4 742.5 772.8 799.6 810.0 816.4 846.9 870.4 896.6 913.8 941.5 980.4 1,002.9 1,020.3 1,052.2 1,091.6 1,147.8 1,194.3 1,232.6 1,257.9 1,289.8 1,356.2 1,401.9 1,443.7 1,472.5 1,517.7 1,537.4 1,573.1 1,633.7 1,674.3 1,698.4 1,734.8 1,798.6 1,858.1 1,888.4 1,922.7 2,007.0 1,994.8 2,035.1 2,058.7 2,058.4 2,071.4 2,088.0 2,104.4 2,126.4 2,152.0 2,185.2 2,212.5 2,182.8 2,207.1 2,172.0 2,181.2 2,189.9 2,197.7 2,205.9 2,210.4 2,213.8 2,219.9 2,223.4 2,144.6 2,176.1 2,187.0 2,188.0 2,199.1 2,216.9 2,224.5 Households 408.9 425.8 439.5 459.1 472.2 478.4 478.3 501.3 511.5 526.1 531.8 539.1 560.2 570.7 583.6 595.3 610.4 635.7 655.5 668.2 678.5 693.9 727.5 764.4 790.9 807.1 829.9 851.5 878.8 917.7 951.4 956.4 984.2 1,020.5 1,068.7 1,097.0 1,123.2 1,185.0 1,161.9 1,186.5 1,186.4 1,168.8 1,174.6 1,182.5 1,180.9 1,186.6 1,201.1 1,220.8 1,235.0 1,235.1 1,245.3 1,212.0 1,218.1 1,223.9 1,229.4 1,234.4 1,234.8 1,234.7 1,236.1 1,237.0 1,236.9 1,234.5 1,232.2 1,236.1 1,242.4 1,249.5 1,253.3 Nonprofit institutions serving households 2 279.5 289.6 300.0 310.4 324.2 328.4 335.3 342.2 355.7 367.4 379.3 401.1 419.0 431.4 435.4 456.5 481.9 513.6 541.4 568.4 583.9 600.8 634.0 641.5 656.4 669.0 691.8 688.8 696.4 717.7 723.7 743.6 751.6 779.3 789.8 791.4 799.3 821.5 832.8 848.4 872.2 889.6 896.7 905.5 923.2 939.5 950.6 964.1 977.2 947.8 961.6 959.7 962.8 965.8 968.0 971.3 975.3 978.8 983.4 985.9 909.0 941.9 954.6 951.9 956.6 967.2 970.9 General government 3 Total 1,228.7 1,226.9 1,232.9 1,257.1 1,276.0 1,286.8 1,300.3 1,325.1 1,339.9 1,359.9 1,369.5 1,385.7 1,397.7 1,418.3 1,461.1 1,500.5 1,537.5 1,580.7 1,619.4 1,659.8 1,676.7 1,683.9 1,687.9 1,689.5 1,691.9 1,695.2 1,708.1 1,726.8 1,742.1 1,770.3 1,801.4 1,835.6 1,858.5 1,871.5 1,888.4 1,903.9 1,930.9 1,970.9 2,006.7 2,016.3 2,007.2 1,989.1 1,975.7 1,971.9 1,977.2 1,995.5 2,009.6 2,024.3 2,046.0 2,043.5 2,064.2 2,017.8 2,024.1 2,028.8 2,026.5 2,022.0 2,039.9 2,053.4 2,068.8 2,070.3 2,015.2 2,050.2 2,038.3 2,043.5 2,055.6 2,079.6 2,078.1 Federal 506.6 487.2 473.6 473.8 472.1 473.3 475.2 481.5 482.5 490.3 498.5 507.7 520.6 534.1 551.1 564.4 582.2 593.4 602.4 612.9 616.4 606.3 596.3 579.7 561.2 547.8 538.8 533.1 528.9 531.7 533.2 542.6 557.0 565.1 572.3 576.7 584.6 606.3 636.6 658.0 664.3 663.7 652.0 646.9 642.5 645.4 646.2 649.5 656.4 674.4 679.7 647.3 649.7 651.6 649.4 644.2 657.7 661.0 662.8 666.4 672.6 680.8 678.0 678.5 680.5 680.1 679.7 State and local 700.2 724.6 750.1 777.4 801.0 811.7 824.3 843.7 859.1 871.1 871.0 876.9 873.5 879.0 904.3 930.7 949.1 981.6 1,011.9 1,042.2 1,055.9 1,073.9 1,088.7 1,107.7 1,129.6 1,147.1 1,169.7 1,194.6 1,214.4 1,240.0 1,269.6 1,294.4 1,302.8 1,307.5 1,317.0 1,328.3 1,347.3 1,365.3 1,370.5 1,358.5 1,343.0 1,325.5 1,323.7 1,324.7 1,334.2 1,349.5 1,362.6 1,373.9 1,388.7 1,369.1 1,384.5 1,369.6 1,373.5 1,376.3 1,376.2 1,376.8 1,381.4 1,391.6 1,405.0 1,402.9 1,343.1 1,369.7 1,360.7 1,365.3 1,375.3 1,399.4 1,398.2 Addendum: Gross housing value added 522.1 546.8 564.2 591.9 610.8 616.9 625.7 648.2 660.8 684.1 697.5 713.7 741.3 755.5 786.8 808.2 827.0 854.3 872.1 889.6 907.8 929.9 963.2 1,004.4 1,040.2 1,058.1 1,083.6 1,109.0 1,140.3 1,179.4 1,216.8 1,215.6 1,236.4 1,280.1 1,341.3 1,367.7 1,394.1 1,467.0 1,452.4 1,492.0 1,500.9 1,493.6 1,505.6 1,516.6 1,520.7 1,528.9 1,537.4 1,559.8 1,578.6 1,582.1 1,596.5 1,548.9 1,556.5 1,563.5 1,570.2 1,576.5 1,577.9 1,578.8 1,581.2 1,582.8 1,583.5 1,581.8 1,580.5 1,585.9 1,593.5 1,601.1 1,605.6 1 Gross domestic business value added equals gross domestic product excluding gross value added of households and institutions and of general government. Nonfarm value added equals gross domestic business value added excluding gross farm value added. 2 Equals compensation of employees of nonprofit institutions, the rental value of nonresidential fixed assets owned and used by nonprofit institutions serving households, and rental income of persons for tenant-occupied housing owned by nonprofit institutions. 3 Equals compensation of general government employees plus general government consumption of fixed capital. Source: Department of Commerce (Bureau of Economic Analysis). National Income or Expenditure | 365 Table B–8. Gross domestic product (GDP) by industry, value added, in current dollars and as a percentage of GDP, 1997–2020 [Billions of dollars; except as noted] Private industries Gross domestic product Year Total private industries Agriculture, forestry, fishing, and hunting Manufacturing Construction Mining Total manufacturing Durable goods Nondurable goods Utilities Wholesale trade Retail trade Value added 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 8,577.6 9,062.8 9,631.2 10,251.0 10,581.9 10,929.1 11,456.5 12,217.2 13,039.2 13,815.6 14,474.2 14,769.9 14,478.1 15,049.0 15,599.7 16,254.0 16,843.2 17,550.7 18,206.0 18,695.1 19,479.6 20,527.2 21,372.6 7,432.0 7,871.5 8,378.8 8,927.9 9,189.0 9,454.7 9,904.1 10,585.9 11,328.9 12,023.6 12,587.2 12,788.3 12,433.0 12,941.0 13,462.7 14,094.5 14,630.7 15,279.3 15,866.6 16,310.9 17,032.6 17,980.9 18,750.8 108.6 99.8 92.6 98.3 99.8 95.9 114.6 143.8 129.5 126.4 145.5 146.0 129.1 144.9 179.2 178.7 214.3 198.9 180.1 165.8 175.9 175.6 162.6 95.1 81.7 84.6 110.5 123.9 112.4 138.9 166.4 225.4 273.1 314.1 392.5 275.4 306.4 357.8 360.5 387.8 417.0 261.7 218.1 276.9 320.1 295.7 339.6 379.8 417.7 461.2 486.4 493.5 525.2 584.6 651.6 697.1 715.7 649.3 565.4 525.7 525.6 554.9 588.7 637.7 695.3 747.7 800.6 847.1 903.6 1,382.9 1,430.6 1,489.6 1,549.8 1,473.5 1,468.3 1,524.0 1,607.8 1,692.5 1,793.5 1,845.8 1,802.1 1,700.8 1,799.8 1,873.6 1,934.7 1,997.3 2,053.5 2,131.0 2,102.9 2,199.3 2,334.1 2,370.9 823.8 850.7 875.2 924.6 833.3 832.8 863.1 905.0 956.3 1,004.2 1,031.0 1,000.2 880.4 965.5 1,017.9 1,065.2 1,104.5 1,135.6 1,184.4 1,187.8 1,235.4 1,298.9 1,327.9 559.1 579.9 614.4 625.2 640.3 635.6 660.9 702.8 736.2 789.3 814.7 801.8 820.5 834.3 855.7 869.5 892.8 917.9 946.6 915.1 963.8 1,035.3 1,043.0 171.5 163.7 180.0 180.1 181.3 177.6 183.9 199.1 197.9 226.7 231.9 241.7 257.8 279.1 288.3 280.7 286.9 298.3 299.2 302.0 311.6 319.0 333.3 527.5 563.7 584.2 622.5 613.7 613.1 641.4 697.1 754.7 811.4 858.2 884.8 833.8 890.0 937.1 1,000.3 1,042.2 1,089.6 1,143.6 1,135.8 1,165.7 1,217.4 1,275.0 579.9 626.9 652.8 685.4 709.4 732.6 769.5 795.5 840.6 869.8 869.4 848.8 827.3 852.1 873.1 910.0 950.6 975.1 1,020.3 1,053.0 1,081.6 1,119.7 1,166.7 20,893.7 18,223.1 174.5 182.1 895.9 2,272.0 1,268.8 1,003.1 341.7 1,243.3 1,202.2 2.0 1.8 1.9 1.8 1.7 1.6 1.6 1.6 1.5 1.6 1.6 1.6 1.8 1.9 1.8 1.7 1.7 1.7 1.6 1.6 1.6 1.6 1.6 1.6 6.2 6.2 6.1 6.1 5.8 5.6 5.6 5.7 5.8 5.9 5.9 6.0 5.8 5.9 6.0 6.2 6.2 6.2 6.3 6.1 6.0 5.9 6.0 6.0 6.8 6.9 6.8 6.7 6.7 6.7 6.7 6.5 6.4 6.3 6.0 5.7 5.7 5.7 5.6 5.6 5.6 5.6 5.6 5.6 5.6 5.5 5.5 5.8 Percent 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Industry value added as a percentage of GDP (percent) 86.6 86.9 87.0 87.1 86.8 86.5 86.4 86.6 86.9 87.0 87.0 86.6 85.9 86.0 86.3 86.7 86.9 87.1 87.2 87.2 87.4 87.6 87.7 87.2 1.3 1.1 1.0 1.0 .9 .9 1.0 1.2 1.0 .9 1.0 1.0 .9 1.0 1.1 1.1 1.3 1.1 1.0 .9 .9 .9 .8 .8 1.1 .9 .9 1.1 1.2 1.0 1.2 1.4 1.7 2.0 2.2 2.7 1.9 2.0 2.3 2.2 2.3 2.4 1.4 1.2 1.4 1.6 1.4 .9 4.0 4.2 4.3 4.5 4.6 4.5 4.6 4.8 5.0 5.0 4.9 4.4 3.9 3.5 3.4 3.4 3.5 3.6 3.8 4.0 4.1 4.1 4.2 4.3 16.1 15.8 15.5 15.1 13.9 13.4 13.3 13.2 13.0 13.0 12.8 12.2 11.7 12.0 12.0 11.9 11.9 11.7 11.7 11.2 11.3 11.4 11.1 10.9 9.6 9.4 9.1 9.0 7.9 7.6 7.5 7.4 7.3 7.3 7.1 6.8 6.1 6.4 6.5 6.6 6.6 6.5 6.5 6.4 6.3 6.3 6.2 6.1 6.5 6.4 6.4 6.1 6.1 5.8 5.8 5.8 5.6 5.7 5.6 5.4 5.7 5.5 5.5 5.3 5.3 5.2 5.2 4.9 4.9 5.0 4.9 4.8 1 Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing. 2 Consists of utilities; wholesale trade; retail trade; transportation and warehousing; information; finance, insurance, real estate, rental, and leasing; professional and business services; educational services, health care, and social assistance; arts, entertainment, recreation, accommodation, and food services; and other services, except government. Note: Data shown in Tables B–8 and B–9 are consistent with the 2021 annual revision of the industry accounts released in September 2021. For details see Survey of Current Business, October 2021. See next page for continuation of table. 366 | Appendix B Table B–8. Gross domestic product (GDP) by industry, value added, in current dollars and as a percentage of GDP, 1997–2020—Continued [Billions of dollars; except as noted] Private industries—Continued Year Transportation and warehousing Finance, insurance, real estate, Information rental, and leasing Professional and business services Arts, Educational entertainservices, ment, health recreation, care, accommoand dation, social food assistance and services Other services, except government Government Private Private servicesgoodsproducing producing industries 1 industries 2 Value added 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 257.3 280.0 290.1 307.8 308.0 305.6 321.4 352.0 375.6 410.3 414.0 427.0 404.4 433.5 452.5 473.3 492.1 522.5 566.1 582.4 609.4 648.1 685.7 572.0 394.1 434.6 485.3 471.2 502.3 550.5 564.8 620.3 642.0 651.9 707.5 743.8 721.4 754.9 763.0 762.7 831.4 844.4 907.8 970.3 1,004.7 1,064.6 1,134.5 1,167.9 1,612.4 1,710.1 1,835.4 1,974.7 2,129.4 2,210.0 2,294.2 2,392.8 2,611.4 2,745.2 2,865.6 2,816.1 2,903.1 2,990.4 3,080.8 3,289.2 3,362.0 3,560.7 3,713.8 3,883.2 4,020.2 4,257.8 4,451.5 4,592.1 840.6 914.0 997.4 1,104.9 1,155.3 1,189.8 1,247.4 1,340.9 1,446.0 1,546.5 1,667.3 1,777.9 1,688.1 1,768.5 1,860.0 1,968.9 2,020.1 2,120.2 2,237.7 2,306.2 2,434.3 2,586.9 2,731.3 2,689.8 590.6 615.8 654.1 695.4 749.8 807.0 862.7 927.2 970.2 1,035.3 1,088.0 1,185.0 1,267.0 1,311.3 1,356.2 1,409.3 1,448.4 1,492.6 1,571.2 1,652.6 1,710.7 1,784.2 1,871.4 1,798.6 301.8 322.1 354.2 386.5 390.7 413.5 432.1 461.1 481.1 511.4 533.6 542.9 533.0 556.2 581.9 622.7 652.3 691.9 747.0 790.5 828.2 869.6 914.2 672.1 230.3 248.7 260.9 279.7 265.5 284.9 283.8 297.2 310.6 325.0 330.5 330.3 326.4 328.2 333.5 348.6 356.7 376.8 391.6 400.5 413.5 436.5 454.4 419.0 1,145.6 1,191.3 1,252.3 1,323.0 1,392.9 1,474.4 1,552.3 1,631.3 1,710.3 1,792.0 1,887.1 1,981.6 2,045.1 2,108.0 2,137.1 2,159.5 2,212.5 2,271.4 2,339.4 2,384.2 2,447.1 2,546.3 2,621.8 2,670.6 1,926.1 1,991.8 2,084.5 2,219.9 2,183.7 2,170.2 2,302.8 2,502.7 2,698.9 2,890.1 3,021.1 2,989.8 2,670.7 2,776.8 2,936.1 3,028.8 3,188.0 3,307.1 3,268.2 3,234.5 3,452.7 3,677.0 3,732.8 3,524.4 5,505.9 5,879.7 6,294.3 6,708.0 7,005.3 7,284.6 7,601.3 8,083.2 8,630.0 9,133.5 9,566.0 9,798.4 9,762.3 10,164.2 10,526.5 11,065.7 11,442.7 11,972.2 12,598.4 13,076.4 13,579.9 14,303.9 15,018.0 14,698.7 13.4 13.1 13.0 12.9 13.2 13.5 13.5 13.4 13.1 13.0 13.0 13.4 14.1 14.0 13.7 13.3 13.1 12.9 12.8 12.8 12.6 12.4 12.3 12.8 22.5 22.0 21.6 21.7 20.6 19.9 20.1 20.5 20.7 20.9 20.9 20.2 18.4 18.5 18.8 18.6 18.9 18.8 18.0 17.3 17.7 17.9 17.5 16.9 64.2 64.9 65.4 65.4 66.2 66.7 66.3 66.2 66.2 66.1 66.1 66.3 67.4 67.5 67.5 68.1 67.9 68.2 69.2 69.9 69.7 69.7 70.3 70.3 Industry value added as a percentage of GDP (percent) 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 3.0 3.1 3.0 3.0 2.9 2.8 2.8 2.9 2.9 3.0 2.9 2.9 2.8 2.9 2.9 2.9 2.9 3.0 3.1 3.1 3.1 3.2 3.2 2.7 4.6 4.8 5.0 4.6 4.7 5.0 4.9 5.1 4.9 4.7 4.9 5.0 5.0 5.0 4.9 4.7 4.9 4.8 5.0 5.2 5.2 5.2 5.3 5.6 18.8 18.9 19.1 19.3 20.1 20.2 20.0 19.6 20.0 19.9 19.8 19.1 20.1 19.9 19.7 20.2 20.0 20.3 20.4 20.8 20.6 20.7 20.8 22.0 9.8 10.1 10.4 10.8 10.9 10.9 10.9 11.0 11.1 11.2 11.5 12.0 11.7 11.8 11.9 12.1 12.0 12.1 12.3 12.3 12.5 12.6 12.8 12.9 6.9 6.8 6.8 6.8 7.1 7.4 7.5 7.6 7.4 7.5 7.5 8.0 8.8 8.7 8.7 8.7 8.6 8.5 8.6 8.8 8.8 8.7 8.8 8.6 3.5 3.6 3.7 3.8 3.7 3.8 3.8 3.8 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.8 3.9 3.9 4.1 4.2 4.3 4.2 4.3 3.2 2.7 2.7 2.7 2.7 2.5 2.6 2.5 2.4 2.4 2.4 2.3 2.2 2.3 2.2 2.1 2.1 2.1 2.1 2.2 2.1 2.1 2.1 2.1 2.0 Note (cont’d): Value added is the contribution of each private industry and of government to GDP. Value added is equal to an industry’s gross output minus its intermediate inputs. Current-dollar value added is calculated as the sum of distributions by an industry to its labor and capital, which are derived from the components of gross domestic income. Value added industry data shown in Tables B–8 and B–9 are based on the 2012 North American Industry Classification System (NAICS). Source: Department of Commerce (Bureau of Economic Analysis). National Income or Expenditure | 367 Table B–9. Real gross domestic product by industry, value added, and percent changes, 1997–2020 Private industries Gross domestic product Year Total private industries Agriculture, forestry, fishing, and hunting Manufacturing Mining Construction Total manufacturing Durable goods Nondurable goods Utilities Wholesale trade Retail trade Chain-type quantity indexes for value added (2012=100) 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 70.931 74.110 77.663 80.830 81.601 82.985 85.305 88.592 91.678 94.229 96.123 96.241 93.739 96.278 97.770 100.000 101.842 104.172 106.991 108.775 111.229 114.475 117.096 113.109 70.046 73.402 77.229 80.653 81.255 82.670 84.993 88.565 91.949 94.880 96.595 96.324 93.332 95.883 97.540 100.000 101.880 104.617 107.828 109.748 112.474 116.061 119.008 114.529 77.781 75.893 78.203 89.714 86.605 89.789 97.128 104.991 109.754 111.588 99.272 99.372 110.461 107.225 103.127 100.000 116.130 116.724 124.160 131.263 129.287 133.135 124.990 142.510 72.719 75.656 73.421 65.086 75.515 77.598 68.794 69.198 70.313 81.229 87.622 84.835 97.011 85.963 89.386 100.000 103.744 114.972 125.161 118.502 120.941 121.136 135.434 121.647 124.354 130.050 135.421 140.845 138.221 133.748 136.061 140.907 141.526 138.689 134.513 121.342 103.961 98.810 97.298 100.000 102.401 104.349 109.037 113.193 117.231 119.956 121.588 117.208 73.447 76.469 80.746 86.510 83.058 83.793 88.483 94.727 97.591 103.211 106.720 104.522 94.688 100.081 100.599 100.000 102.945 104.601 105.768 105.458 108.976 113.457 115.469 112.050 54.511 58.994 63.131 70.473 66.103 67.503 72.557 77.761 83.117 89.557 93.800 94.301 80.569 90.953 97.223 100.000 102.358 103.902 105.519 105.707 110.096 115.413 116.330 110.615 107.960 106.119 109.889 110.880 109.949 109.179 112.634 120.408 118.265 122.110 124.218 117.744 114.123 112.119 104.835 100.000 103.671 105.467 106.067 105.121 107.530 110.982 114.334 113.857 81.637 77.993 90.800 91.942 76.548 79.187 77.791 82.510 78.281 83.357 85.179 89.539 84.369 94.906 98.679 100.000 98.755 94.883 94.918 100.030 101.224 100.875 102.103 106.381 67.499 74.131 76.606 80.289 81.714 82.739 87.144 91.106 95.261 98.148 101.407 101.651 89.214 94.469 96.638 100.000 102.072 106.032 110.553 109.159 109.730 110.950 110.431 108.354 76.759 84.135 87.240 90.127 93.502 97.602 102.667 104.409 107.790 108.680 105.184 101.255 96.810 99.023 99.257 100.000 103.046 104.956 108.872 112.919 116.680 120.389 123.395 119.834 3.1 –1.7 3.6 .9 –.8 –.7 3.2 6.9 –1.8 3.3 1.7 –5.2 –3.1 –1.8 –6.5 –4.6 3.7 1.7 .6 –.9 2.3 3.2 3.0 –.4 –5.2 –4.5 16.4 1.3 –16.7 3.4 –1.8 6.1 –5.1 6.5 2.2 5.1 –5.8 12.5 4.0 1.3 –1.2 –3.9 .0 5.4 1.2 –.3 1.2 4.2 10.9 9.8 3.3 4.8 1.8 1.3 5.3 4.5 4.6 3.0 3.3 .2 –12.2 5.9 2.3 3.5 2.1 3.9 4.3 –1.3 .5 1.1 –.5 –1.9 7.5 9.6 3.7 3.3 3.7 4.4 5.2 1.7 3.2 .8 –3.2 –3.7 –4.4 2.3 .2 .7 3.0 1.9 3.7 3.7 3.3 3.2 2.5 –2.9 Percent change from year earlier 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 4.4 4.5 4.8 4.1 1.0 1.7 2.8 3.9 3.5 2.8 2.0 .1 –2.6 2.7 1.5 2.3 1.8 2.3 2.7 1.7 2.3 2.9 2.3 –3.4 4.9 4.8 5.2 4.4 .7 1.7 2.8 4.2 3.8 3.2 1.8 –.3 –3.1 2.7 1.7 2.5 1.9 2.7 3.1 1.8 2.5 3.2 2.5 –3.8 8.5 –2.4 3.0 14.7 –3.5 3.7 8.2 8.1 4.5 1.7 –11.0 .1 11.2 –2.9 –3.8 –3.0 16.1 .5 6.4 5.7 –1.5 3.0 –6.1 14.0 3.7 4.0 –3.0 –11.4 16.0 2.8 –11.3 .6 1.6 15.5 7.9 –3.2 14.4 –11.4 4.0 11.9 3.7 10.8 8.9 –5.3 2.1 .2 11.8 –10.2 0.5 4.6 4.1 4.0 –1.9 –3.2 1.7 3.6 .4 –2.0 –3.0 –9.8 –14.3 –5.0 –1.5 2.8 2.4 1.9 4.5 3.8 3.6 2.3 1.4 –3.6 6.6 4.1 5.6 7.1 –4.0 .9 5.6 7.1 3.0 5.8 3.4 –2.1 –9.4 5.7 .5 –.6 2.9 1.6 1.1 –.3 3.3 4.1 1.8 –3.0 9.1 8.2 7.0 11.6 –6.2 2.1 7.5 7.2 6.9 7.7 4.7 .5 –14.6 12.9 6.9 2.9 2.4 1.5 1.6 .2 4.2 4.8 .8 –4.9 1 Consists of agriculture, forestry, fishing, and hunting; mining; construction; and manufacturing. 2 Consists of utilities; wholesale trade; retail trade; transportation and warehousing; information; finance, insurance, real estate, rental, and leasing; professional and business services; educational services, health care, and social assistance; arts, entertainment, recreation, accommodation, and food services; and other services, except government. See next page for continuation of table. 368 | Appendix B Table B–9. Real gross domestic product by industry, value added, and percent changes, 1997–2020—Continued Private industries—Continued Year Transportation and warehousing Finance, insurance, estate, Information realrental, and leasing Arts, Educational entertainservices, ment, health recreation, care, accommoand dation, social food assistance and services Professional and business services Other services, except government Government Private Private servicesgoodsproducing producing industries 1 industries 2 Chain-type quantity indexes for value added (2012=100) 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 84.687 88.991 89.731 89.480 83.650 80.670 83.579 90.509 94.860 100.505 99.790 98.863 92.702 97.391 99.295 100.000 101.389 104.448 107.144 108.740 113.484 117.549 119.611 103.584 45.514 50.255 56.341 55.253 58.676 64.338 66.391 74.041 78.974 81.801 89.943 95.681 93.038 98.573 100.202 100.000 108.867 111.584 123.147 134.001 142.545 153.585 164.264 169.990 64.047 66.832 71.072 74.812 78.601 79.078 79.608 81.082 86.517 88.398 90.254 88.297 92.938 94.383 95.951 100.000 99.486 101.652 102.770 103.834 104.288 106.100 108.176 108.494 63.505 66.440 69.573 73.644 75.849 76.729 79.196 81.122 84.732 87.160 90.088 94.308 88.066 91.902 95.650 100.000 101.208 105.828 109.409 111.630 116.872 123.568 129.199 126.159 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 4.4 5.1 .8 –.3 –6.5 –3.6 3.6 8.3 4.8 6.0 –.7 –.9 –6.2 5.1 2.0 .7 1.4 3.0 2.6 1.5 4.4 3.6 1.8 –13.4 –1.3 10.4 12.1 –1.9 6.2 9.6 3.2 11.5 6.7 3.6 10.0 6.4 –2.8 5.9 1.7 –.2 8.9 2.5 10.4 8.8 6.4 7.7 7.0 3.5 4.2 4.3 6.3 5.3 5.1 .6 .7 1.9 6.7 2.2 2.1 –2.2 5.3 1.6 1.7 4.2 –.5 2.2 1.1 1.0 .4 1.7 2.0 .3 7.3 4.6 4.7 5.9 3.0 1.2 3.2 2.4 4.5 2.9 3.4 4.7 –6.6 4.4 4.1 4.5 1.2 4.6 3.4 2.0 4.7 5.7 4.6 –2.4 65.087 65.370 67.564 70.038 71.815 74.683 77.657 81.355 82.879 86.244 86.927 92.430 95.531 96.650 98.364 100.000 101.220 103.045 106.918 109.940 111.886 114.997 118.248 110.728 78.592 80.744 85.167 90.255 87.220 89.599 91.907 96.013 96.269 98.946 98.432 96.222 90.582 94.163 97.550 100.000 102.051 105.746 109.018 110.917 113.644 115.686 118.019 82.895 115.380 120.186 120.996 123.725 111.631 114.679 111.481 112.927 113.691 114.286 111.693 107.558 101.117 99.308 98.489 100.000 99.174 102.044 102.620 101.787 102.423 105.804 105.811 92.653 87.664 88.684 89.743 91.570 92.529 94.176 95.338 96.193 97.080 97.638 98.590 100.494 100.556 101.076 100.755 100.000 99.296 99.081 99.199 100.179 101.164 102.015 102.629 101.667 81.001 84.104 88.164 93.389 91.021 91.172 94.634 100.195 102.577 107.155 108.829 104.591 97.383 98.450 98.726 100.000 103.727 106.526 109.624 110.207 113.558 117.308 119.773 116.329 67.082 70.519 74.287 77.217 78.624 80.375 82.397 85.441 89.093 91.577 93.305 94.115 92.247 95.186 97.215 100.000 101.376 104.095 107.334 109.587 112.156 115.701 118.767 114.042 4.7 4.2 .7 2.3 –9.8 2.7 –2.8 1.3 .7 .5 –2.3 –3.7 –6.0 –1.8 –.8 1.5 –.8 2.9 .6 –.8 .6 3.3 .0 –12.4 1.3 1.2 1.2 2.0 1.0 1.8 1.2 .9 .9 .6 1.0 1.9 .1 .5 –.3 –.7 –.7 –.2 .1 1.0 1.0 .8 .6 –.9 5.4 3.8 4.8 5.9 –2.5 .2 3.8 5.9 2.4 4.5 1.6 –3.9 –6.9 1.1 .3 1.3 3.7 2.7 2.9 .5 3.0 3.3 2.1 –2.9 4.7 5.1 5.3 3.9 1.8 2.2 2.5 3.7 4.3 2.8 1.9 .9 –2.0 3.2 2.1 2.9 1.4 2.7 3.1 2.1 2.3 3.2 2.6 –4.0 Percent change from year earlier 1.8 0.4 3.4 3.7 2.5 4.0 4.0 4.8 1.9 4.1 .8 6.3 3.4 1.2 1.8 1.7 1.2 1.8 3.8 2.8 1.8 2.8 2.8 –6.4 5.7 2.7 5.5 6.0 –3.4 2.7 2.6 4.5 .3 2.8 –.5 –2.2 –5.9 4.0 3.6 2.5 2.1 3.6 3.1 1.7 2.5 1.8 2.0 –29.8 Note: Data are based on the 2012 North American Industry Classification System (NAICS). See Note, Table B–8. Source: Department of Commerce (Bureau of Economic Analysis). National Income or Expenditure | 369 Table B–10. Personal consumption expenditures, 1971–2021 [Billions of dollars; quarterly data at seasonally adjusted annual rates] Goods Year or quarter 1971 ���������������������� 1972 ���������������������� 1973 ���������������������� 1974 ���������������������� 1975 ���������������������� 1976 ���������������������� 1977 ���������������������� 1978 ���������������������� 1979 ���������������������� 1980 ���������������������� 1981 ���������������������� 1982 ���������������������� 1983 ���������������������� 1984 ���������������������� 1985 ���������������������� 1986 ���������������������� 1987 ���������������������� 1988 ���������������������� 1989 ���������������������� 1990 ���������������������� 1991 ���������������������� 1992 ���������������������� 1993 ���������������������� 1994 ���������������������� 1995 ���������������������� 1996 ���������������������� 1997 ���������������������� 1998 ���������������������� 1999 ���������������������� 2000 ���������������������� 2001 ���������������������� 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Personal consumption expenditures 699.9 768.2 849.6 930.2 1,030.5 1,147.7 1,274.0 1,422.3 1,585.4 1,750.7 1,934.0 2,071.3 2,281.6 2,492.3 2,712.8 2,886.3 3,076.3 3,330.0 3,576.8 3,809.0 3,943.4 4,197.6 4,452.0 4,721.0 4,962.6 5,244.6 5,536.8 5,877.2 6,283.8 6,767.2 7,073.8 7,348.9 7,740.7 8,232.0 8,769.1 9,277.2 9,746.6 10,050.1 9,891.2 10,260.3 10,698.9 11,047.4 11,363.5 11,847.7 12,263.5 12,693.3 13,239.1 13,913.5 14,428.7 14,047.6 15,746.9 13,667.4 13,864.8 14,002.6 14,119.3 14,155.6 14,375.7 14,529.5 14,653.9 14,439.1 12,989.7 14,293.8 14,467.6 15,005.4 15,681.7 15,964.9 16,335.5 Durable Total 342.1 373.8 416.6 451.5 491.3 546.3 600.4 663.6 737.9 799.8 869.4 899.3 973.8 1,063.7 1,137.6 1,195.6 1,256.3 1,337.3 1,423.8 1,491.3 1,497.4 1,563.3 1,642.3 1,746.6 1,815.5 1,917.7 2,006.5 2,108.4 2,287.1 2,453.2 2,525.6 2,598.8 2,722.6 2,902.0 3,082.9 3,239.7 3,367.0 3,363.2 3,180.0 3,317.8 3,518.1 3,637.7 3,730.0 3,863.0 3,923.0 3,991.8 4,158.6 4,353.7 4,478.9 4,653.8 5,482.8 4,298.4 4,354.4 4,373.2 4,388.8 4,382.8 4,479.4 4,512.7 4,540.8 4,530.9 4,349.9 4,867.2 4,867.3 5,245.0 5,529.8 5,500.1 5,656.2 Services Nondurable Total 1 Motor vehicles and parts 102.4 116.4 130.5 130.2 142.2 168.6 192.0 213.3 226.3 226.4 243.9 253.0 295.0 342.2 380.4 421.4 442.0 475.1 494.3 497.1 477.2 508.1 551.5 607.2 635.7 676.3 715.5 779.3 855.6 912.6 941.5 985.4 1,017.8 1,080.6 1,128.6 1,158.3 1,188.0 1,098.8 1,012.1 1,049.0 1,093.5 1,144.2 1,189.4 1,242.1 1,307.6 1,345.2 1,396.6 1,469.2 1,513.3 1,616.4 2,026.6 1,449.4 1,471.3 1,478.2 1,477.8 1,473.3 1,509.2 1,531.4 1,539.2 1,484.9 1,468.3 1,753.3 1,759.2 1,957.8 2,092.2 1,995.2 2,061.3 43.2 49.4 54.4 48.2 52.6 68.2 79.8 89.2 90.2 84.4 93.0 100.0 122.9 147.2 170.1 187.5 188.2 202.2 207.8 205.1 185.7 204.8 224.7 249.8 255.7 273.5 293.1 320.2 350.7 363.2 383.3 401.3 401.5 409.3 410.0 394.9 400.6 343.3 318.6 344.5 365.2 396.6 417.5 442.0 475.3 484.3 501.3 519.5 514.5 541.3 700.3 514.7 519.4 522.8 521.0 500.1 512.6 519.0 526.6 482.0 485.2 595.8 602.1 674.9 758.1 667.9 700.5 Total 1 239.7 257.4 286.1 321.4 349.2 377.7 408.4 450.2 511.6 573.4 625.4 646.3 678.8 721.5 757.2 774.2 814.3 862.3 929.5 994.2 1,020.3 1,055.2 1,090.8 1,139.4 1,179.8 1,241.4 1,291.0 1,329.1 1,431.5 1,540.6 1,584.1 1,613.4 1,704.8 1,821.4 1,954.3 2,081.3 2,179.0 2,264.5 2,167.9 2,268.9 2,424.6 2,493.5 2,540.6 2,620.9 2,615.4 2,646.7 2,761.9 2,884.5 2,965.6 3,037.4 3,456.2 2,849.1 2,883.2 2,895.0 2,911.0 2,909.5 2,970.1 2,981.3 3,001.6 3,046.0 2,881.7 3,113.9 3,108.1 3,287.2 3,437.6 3,505.0 3,595.0 Food and beverages Gasoline purchased and for offother premises energy congoods sumption 107.1 114.5 126.7 143.0 156.6 167.3 179.8 196.1 218.4 239.2 255.3 267.1 277.0 291.1 303.0 316.4 324.3 342.8 365.4 391.2 403.0 404.5 413.5 432.1 443.7 461.9 474.8 487.4 515.5 540.6 564.0 575.1 599.6 632.6 668.2 700.3 737.3 769.1 772.9 786.9 819.5 846.2 864.0 896.9 921.0 940.6 973.1 1,000.3 1,030.9 1,146.7 1,235.5 992.8 997.7 1,002.3 1,008.6 1,013.4 1,027.4 1,041.3 1,041.5 1,125.1 1,152.1 1,159.5 1,150.0 1,201.5 1,223.4 1,245.3 1,271.7 Addendum: Personal consumption expendiFinancial tures excluding Health services and food care insurand ance energy 2 Household consumption expenditures 27.6 29.4 34.3 43.8 48.0 53.0 57.8 61.5 80.4 101.9 113.4 108.4 106.5 108.2 110.5 91.2 96.4 99.9 110.4 124.2 121.1 125.0 126.9 129.2 133.4 144.7 147.7 132.4 146.5 184.5 178.0 167.9 196.4 232.7 283.8 319.7 345.5 391.1 287.0 336.7 413.8 421.9 418.2 403.3 309.4 275.7 309.9 350.4 337.6 246.8 359.3 343.2 352.9 354.2 351.3 323.9 350.1 332.8 343.7 305.2 188.5 245.4 247.9 300.3 345.5 376.2 415.2 Total Total 1 357.8 346.1 394.3 381.5 432.9 419.2 478.6 463.1 539.2 522.2 601.4 582.4 673.6 653.0 758.7 735.7 847.5 821.4 950.9 920.8 1,064.6 1,030.4 1,172.0 1,134.0 1,307.8 1,267.1 1,428.6 1,383.3 1,575.2 1,527.3 1,690.7 1,638.0 1,820.0 1,764.3 1,992.7 1,929.4 2,153.0 2,084.9 2,317.7 2,241.8 2,446.0 2,365.9 2,634.3 2,546.4 2,809.6 2,719.6 2,974.4 2,876.6 3,147.1 3,044.7 3,326.9 3,216.9 3,530.3 3,424.7 3,768.8 3,645.0 3,996.7 3,858.5 4,314.0 4,156.0 4,548.2 4,369.1 4,750.1 4,551.8 5,018.2 4,812.6 5,329.9 5,123.6 5,686.1 5,475.9 6,037.6 5,798.4 6,379.6 6,130.8 6,686.9 6,399.6 6,711.2 6,422.0 6,942.4 6,648.0 7,180.7 6,868.9 7,409.6 7,068.1 7,633.6 7,281.0 7,984.8 7,619.2 8,340.5 7,968.9 8,701.4 8,300.0 9,080.6 8,662.6 9,559.8 9,115.1 9,949.8 9,509.9 9,393.7 8,872.9 10,264.1 9,781.0 9,369.0 8,940.9 9,510.3 9,071.4 9,629.4 9,184.8 9,730.5 9,263.5 9,772.7 9,336.7 9,896.3 9,459.1 10,016.8 9,571.5 10,113.2 9,672.3 9,908.2 9,387.7 8,639.8 8,062.8 9,426.6 8,932.1 9,600.4 9,109.0 9,760.4 9,281.7 10,151.9 9,684.8 10,464.8 9,984.4 10,679.2 10,173.0 Housing and utilities 120.0 131.2 143.5 158.6 176.5 194.7 217.8 244.3 273.4 312.5 352.1 387.5 421.2 457.5 500.6 537.0 571.6 614.4 655.2 696.5 735.2 771.1 814.9 863.3 913.7 962.4 1,009.8 1,065.5 1,123.1 1,198.6 1,287.5 1,329.5 1,391.1 1,466.6 1,580.1 1,665.7 1,759.6 1,872.7 1,900.0 1,947.9 1,983.3 2,014.7 2,083.5 2,151.4 2,206.6 2,280.8 2,363.2 2,472.1 2,571.5 2,668.1 2,777.4 2,431.8 2,460.2 2,479.4 2,517.0 2,534.7 2,554.1 2,585.3 2,611.8 2,622.6 2,667.7 2,682.6 2,699.7 2,727.2 2,753.4 2,792.6 2,836.5 53.7 59.8 67.2 76.1 89.0 101.8 115.7 131.2 148.8 171.7 201.9 225.2 253.1 276.5 302.2 330.2 366.0 410.1 451.2 506.2 555.8 612.8 648.8 680.5 719.9 752.1 790.9 832.0 863.6 918.4 996.6 1,082.9 1,154.0 1,238.9 1,320.5 1,391.9 1,478.2 1,555.3 1,632.7 1,699.6 1,757.1 1,821.3 1,858.2 1,940.5 2,057.3 2,159.4 2,238.8 2,339.6 2,458.2 2,308.4 2,548.0 2,300.2 2,326.3 2,364.4 2,367.4 2,408.7 2,449.7 2,469.9 2,504.3 2,406.9 2,000.3 2,369.2 2,457.2 2,464.2 2,534.4 2,574.5 2,618.8 33.1 37.1 39.9 44.1 51.8 56.8 65.1 76.7 83.6 91.7 98.5 113.7 141.0 150.8 178.2 187.7 189.5 202.9 222.3 230.8 250.1 277.0 314.0 327.9 347.0 372.1 408.9 446.1 484.6 541.9 529.3 539.0 574.2 619.3 676.8 719.5 762.7 777.5 720.5 768.0 811.1 830.9 869.3 922.9 974.4 996.1 1,069.0 1,151.9 1,171.6 1,196.3 1,270.3 1,128.5 1,147.2 1,161.3 1,170.5 1,156.3 1,166.6 1,177.0 1,186.5 1,193.9 1,168.7 1,200.5 1,222.0 1,244.9 1,256.4 1,276.3 1,303.8 548.5 605.8 668.5 719.7 797.3 894.7 998.6 1,122.4 1,239.7 1,353.1 1,501.5 1,622.9 1,817.2 2,008.1 2,210.3 2,391.3 2,566.6 2,793.1 3,002.1 3,194.9 3,314.4 3,561.7 3,796.6 4,042.5 4,267.2 4,513.0 4,787.8 5,132.4 5,495.9 5,904.5 6,182.2 6,460.4 6,784.4 7,198.5 7,627.2 8,056.6 8,453.5 8,666.3 8,616.1 8,915.3 9,246.6 9,571.6 9,861.4 10,315.3 10,807.4 11,256.1 11,731.4 12,318.7 12,820.0 12,414.0 13,893.5 12,091.6 12,269.1 12,406.6 12,507.6 12,573.7 12,763.4 12,915.6 13,027.5 12,784.0 11,401.2 12,645.1 12,825.7 13,251.4 13,859.9 14,081.5 14,381.3 1 Includes other items not shown separately. 2 Food consists of food and beverages purchased for off-premises consumption; food services, which include purchased meals and beverages, are not classified as food. Source: Department of Commerce (Bureau of Economic Analysis). 370 | Appendix B Table B–11. Real personal consumption expenditures, 2002–2021 [Billions of chained (2012) dollars; quarterly data at seasonally adjusted annual rates] Goods Year or quarter 2002 ���������������������� 2003 ���������������������� 2004 ���������������������� 2005 ���������������������� 2006 ���������������������� 2007 ���������������������� 2008 ���������������������� 2009 ���������������������� 2010 ���������������������� 2011 ���������������������� 2012 ���������������������� 2013 ���������������������� 2014 ���������������������� 2015 ���������������������� 2016 ���������������������� 2017 ���������������������� 2018 ���������������������� 2019 ���������������������� 2020 ���������������������� 2021 p �������������������� 2018: I ������������������ II ����������������� III ���������������� IV ���������������� 2019: I ������������������ II ����������������� III ���������������� IV ���������������� 2020: I ������������������ II ����������������� III ���������������� IV ���������������� 2021: I ������������������ II ����������������� III ���������������� IV p ������������� Personal consumption expenditures 9,106.2 9,394.4 9,748.6 10,093.8 10,386.2 10,638.7 10,654.7 10,515.6 10,716.0 10,898.3 11,047.4 11,211.7 11,515.3 11,892.9 12,187.7 12,483.7 12,845.0 13,126.3 12,629.9 13,629.4 12,707.6 12,816.4 12,900.6 12,955.5 12,975.1 13,088.8 13,192.3 13,249.0 13,014.5 11,756.4 12,820.8 12,927.9 13,282.7 13,665.6 13,732.4 13,836.7 Durable Total Total 1 2,947.6 3,092.0 3,250.0 3,384.7 3,509.7 3,607.6 3,498.9 3,389.8 3,485.7 3,561.8 3,637.7 3,752.2 3,905.1 4,090.9 4,231.7 4,395.2 4,569.3 4,723.0 4,942.5 5,545.1 4,511.9 4,558.8 4,591.4 4,615.2 4,630.6 4,709.1 4,765.5 4,786.9 4,790.2 4,665.8 5,158.9 5,155.0 5,476.6 5,646.7 5,518.3 5,538.8 820.2 879.3 952.1 1,004.9 1,049.3 1,099.7 1,036.4 973.0 1,027.3 1,079.7 1,144.2 1,214.1 1,301.6 1,400.6 1,476.0 1,568.4 1,678.2 1,749.7 1,884.3 2,225.3 1,647.8 1,676.3 1,692.0 1,696.7 1,693.6 1,737.5 1,773.1 1,794.7 1,738.3 1,731.8 2,030.6 2,036.4 2,253.5 2,316.2 2,158.5 2,173.0 Services Nondurable Motor vehicles and parts 416.9 429.2 441.1 435.1 419.0 427.3 373.1 346.7 360.0 370.1 396.6 415.3 439.4 472.8 487.2 510.4 531.2 524.9 542.0 623.4 527.8 532.5 533.2 531.5 511.7 521.8 528.5 537.5 493.0 498.4 586.8 589.7 661.2 686.1 576.0 570.2 Total 1 2,157.5 2,233.6 2,306.5 2,383.4 2,461.6 2,503.4 2,463.9 2,423.1 2,461.3 2,482.9 2,493.5 2,538.5 2,605.3 2,693.7 2,760.5 2,834.2 2,903.6 2,988.1 3,080.5 3,360.4 2,875.3 2,895.3 2,912.6 2,931.3 2,948.7 2,985.4 3,008.2 3,010.1 3,061.8 2,949.1 3,159.9 3,151.1 3,269.3 3,377.2 3,394.0 3,400.8 Food and beverages Gasoline purchased and for offother premises energy congoods sumption 744.5 761.8 779.5 809.2 834.0 845.2 831.0 825.3 837.7 839.0 846.2 855.5 871.4 884.8 913.2 945.9 967.3 987.1 1,062.0 1,109.4 962.1 966.2 968.4 972.6 970.6 985.1 997.9 994.7 1,066.8 1,056.5 1,066.8 1,057.9 1,103.3 1,112.1 1,111.2 1,111.2 Addendum: Personal consumption expendiFinancial tures excluding Health services and food care insurand ance energy 2 Household consumption expenditures 455.2 455.6 459.4 457.4 456.3 455.4 437.5 440.1 437.9 427.8 421.9 429.7 430.0 450.0 453.0 450.8 448.2 447.6 386.3 423.4 446.6 449.9 447.6 449.0 448.9 450.8 448.0 443.0 414.1 341.7 401.2 388.3 393.7 425.5 437.1 437.5 Total 6,168.7 6,306.3 6,498.5 6,707.4 6,873.1 7,027.0 7,154.9 7,125.8 7,230.4 7,336.7 7,409.6 7,460.3 7,613.2 7,809.8 7,968.5 8,110.1 8,305.7 8,443.7 7,808.5 8,261.4 8,223.8 8,287.3 8,339.7 8,371.8 8,377.8 8,420.2 8,471.0 8,505.9 8,284.4 7,217.3 7,815.2 7,917.0 7,993.4 8,214.3 8,378.5 8,459.4 Total 1 Housing and utilities 5,983.7 6,104.2 6,294.1 6,505.2 6,641.7 6,788.8 6,877.4 6,837.0 6,932.0 7,023.9 7,068.1 7,114.7 7,267.9 7,471.7 7,614.8 7,755.3 7,936.0 8,090.8 7,393.5 7,906.4 7,864.6 7,921.1 7,970.8 7,987.4 8,020.3 8,067.7 8,118.2 8,157.0 7,870.2 6,748.9 7,422.8 7,531.9 7,622.4 7,863.1 8,031.4 8,108.9 1,705.6 1,730.0 1,774.1 1,846.2 1,867.5 1,906.3 1,959.9 1,966.3 2,011.3 2,019.1 2,014.7 2,033.6 2,039.3 2,039.6 2,049.4 2,052.8 2,082.5 2,102.2 2,124.3 2,148.5 2,071.7 2,080.2 2,082.1 2,096.3 2,095.9 2,095.3 2,105.9 2,111.8 2,104.9 2,128.9 2,130.7 2,132.5 2,142.4 2,143.9 2,152.5 2,155.1 1,440.7 1,479.3 1,531.2 1,581.9 1,618.2 1,657.2 1,697.9 1,735.1 1,761.7 1,788.7 1,821.3 1,832.6 1,892.8 1,994.6 2,070.0 2,115.0 2,169.7 2,240.3 2,051.8 2,201.0 2,148.6 2,160.3 2,189.3 2,180.6 2,211.5 2,239.0 2,247.5 2,263.3 2,165.7 1,782.6 2,094.5 2,164.4 2,140.7 2,193.6 2,219.8 2,249.7 710.3 711.3 736.0 775.2 790.5 809.7 829.4 821.2 820.0 841.3 830.9 826.0 828.7 848.8 830.7 846.5 859.1 849.3 851.6 877.4 857.2 860.2 860.0 858.9 852.6 846.7 846.8 850.9 847.3 842.0 852.4 864.7 874.7 867.9 876.6 890.5 7,734.5 7,993.5 8,317.8 8,624.1 8,896.6 9,131.3 9,181.1 9,043.8 9,224.2 9,417.7 9,571.6 9,712.4 9,996.8 10,343.3 10,605.2 10,869.0 11,189.1 11,450.7 10,932.3 11,841.7 11,064.0 11,160.0 11,246.4 11,286.0 11,314.1 11,418.4 11,504.7 11,565.6 11,300.9 10,098.1 11,103.1 11,227.2 11,523.8 11,875.1 11,930.8 12,037.1 1 Includes other items not shown separately. 2 Food consists of food and beverages purchased for off-premises consumption; food services, which include purchased meals and beverages, are not classified as food. Source: Department of Commerce (Bureau of Economic Analysis). National Income or Expenditure | 371 Table B–12. Private fixed investment by type, 1971–2021 [Billions of dollars; quarterly data at seasonally adjusted annual rates] Nonresidential Residential Intellectual property products Equipment Private fixed Year or quarter investment 1971 ����������������� 1972 ����������������� 1973 ����������������� 1974 ����������������� 1975 ����������������� 1976 ����������������� 1977 ����������������� 1978 ����������������� 1979 ����������������� 1980 ����������������� 1981 ����������������� 1982 ����������������� 1983 ����������������� 1984 ����������������� 1985 ����������������� 1986 ����������������� 1987 ����������������� 1988 ����������������� 1989 ����������������� 1990 ����������������� 1991 ����������������� 1992 ����������������� 1993 ����������������� 1994 ����������������� 1995 ����������������� 1996 ����������������� 1997 ����������������� 1998 ����������������� 1999 ����������������� 2000 ����������������� 2001 ����������������� 2002 ����������������� 2003 ����������������� 2004 ����������������� 2005 ����������������� 2006 ����������������� 2007 ����������������� 2008 ����������������� 2009 ����������������� 2010 ����������������� 2011 ����������������� 2012 ����������������� 2013 ����������������� 2014 ����������������� 2015 ����������������� 2016 ����������������� 2017 ����������������� 2018 ����������������� 2019 ����������������� 2020 ����������������� 2021 p ��������������� 2018: I ������������� II ������������ III ����������� IV ����������� 2019: I ������������� II ������������ III ����������� IV ����������� 2020: I ������������� II ������������ III ����������� IV ����������� 2021: I ������������� II ������������ III ����������� IV p �������� 188.6 219.0 251.0 260.5 263.5 306.1 374.3 452.6 521.7 536.4 601.4 595.9 643.3 754.7 807.8 842.6 865.0 918.5 972.0 978.9 944.7 996.7 1,086.0 1,192.7 1,286.3 1,401.3 1,524.7 1,673.0 1,826.2 1,983.9 1,973.1 1,910.4 2,013.0 2,217.2 2,477.2 2,632.0 2,639.1 2,506.9 2,080.4 2,111.6 2,286.3 2,550.5 2,721.5 2,960.2 3,100.4 3,168.8 3,351.9 3,579.1 3,752.6 3,697.4 4,139.4 3,505.0 3,579.0 3,602.5 3,629.9 3,683.4 3,754.5 3,791.2 3,781.4 3,773.0 3,456.9 3,693.8 3,865.9 4,022.2 4,099.4 4,159.8 4,276.1 Total nonresidential Structures 130.4 146.6 172.7 191.1 196.8 219.3 259.1 314.6 373.8 406.9 472.9 485.1 482.2 564.3 607.8 607.8 615.2 662.3 716.0 739.2 723.6 741.9 799.2 868.9 962.2 1,043.2 1,149.1 1,254.1 1,364.5 1,498.4 1,460.1 1,352.8 1,375.9 1,467.4 1,621.0 1,793.8 1,948.6 1,990.9 1,690.4 1,735.0 1,907.5 2,118.5 2,211.5 2,400.1 2,466.6 2,469.3 2,591.6 2,780.6 2,938.7 2,799.6 3,053.9 2,716.0 2,770.1 2,798.1 2,838.1 2,886.9 2,946.1 2,969.3 2,952.6 2,900.1 2,659.1 2,776.6 2,862.7 2,956.7 3,029.2 3,073.9 3,155.7 42.7 47.2 55.0 61.2 61.4 65.9 74.6 93.6 117.7 136.2 167.3 177.6 154.3 177.4 194.5 176.5 174.2 182.8 193.7 202.9 183.6 172.6 177.2 186.8 207.3 224.6 250.3 276.0 285.7 321.0 333.5 287.0 286.6 307.7 353.0 425.2 510.3 571.1 455.8 379.8 404.5 479.4 492.5 577.6 584.4 560.4 599.3 633.3 672.6 597.2 579.7 627.2 641.6 638.2 626.1 639.9 669.4 696.0 685.3 687.1 585.9 563.5 552.3 565.0 572.8 581.9 599.1 Information processing equipment Total 1 Computers and Other Total peripheral equipment 69.1 78.9 95.1 104.3 107.6 121.2 148.7 180.6 208.1 216.4 240.9 234.9 246.5 291.9 307.9 317.7 320.9 346.8 372.2 371.9 360.8 381.7 425.1 476.4 528.1 565.3 610.9 660.0 713.6 766.1 711.5 659.6 670.6 721.9 794.9 862.3 893.4 845.4 670.3 777.0 881.3 983.4 1,027.0 1,091.9 1,119.5 1,087.8 1,117.4 1,190.5 1,231.3 1,123.9 1,274.5 1,166.4 1,175.7 1,195.7 1,224.2 1,240.2 1,247.4 1,227.4 1,210.3 1,141.9 1,020.6 1,135.5 1,197.5 1,244.5 1,270.4 1,277.2 1,306.1 14.9 16.7 19.9 23.1 23.8 27.5 33.7 42.3 50.3 58.9 69.6 74.2 83.7 101.2 106.6 111.1 112.2 120.8 130.7 129.6 129.2 142.1 153.3 167.0 188.4 204.7 222.8 240.1 259.8 293.8 265.9 236.7 242.7 255.8 267.0 288.5 310.9 306.3 275.6 307.5 313.3 331.2 341.7 346.0 352.8 353.0 369.2 390.2 393.9 413.9 472.0 389.5 388.1 392.6 390.6 396.6 397.6 391.0 390.2 379.1 397.0 432.2 447.3 472.1 461.9 461.4 492.7 2.8 3.5 3.5 3.9 3.6 4.4 5.7 7.6 10.2 12.5 17.1 18.9 23.9 31.6 33.7 33.4 35.8 38.0 43.1 38.6 37.7 44.0 47.9 52.4 66.1 72.8 81.4 87.9 97.2 103.2 87.6 79.7 79.9 84.2 84.2 92.6 95.4 93.9 88.9 99.6 95.6 103.5 102.1 101.9 101.3 99.4 104.7 119.3 119.1 128.1 146.5 117.0 120.1 121.0 119.2 120.0 121.8 115.6 118.9 113.5 126.0 133.9 138.9 152.8 137.4 143.2 152.4 12.2 13.2 16.3 19.2 20.2 23.1 28.0 34.8 40.2 46.4 52.5 55.3 59.8 69.6 72.9 77.7 76.4 82.8 87.6 90.9 91.5 98.1 105.4 114.6 122.3 131.9 141.4 152.2 162.5 190.6 178.4 157.0 162.8 171.6 182.8 195.9 215.5 212.4 186.7 207.9 217.7 227.7 239.6 244.1 251.5 253.6 264.5 270.9 274.8 285.8 325.5 272.5 268.0 271.6 271.4 276.6 275.8 275.4 271.3 265.6 270.9 298.3 308.5 319.2 324.5 318.2 340.3 1 Includes other items not shown separately. 2 Research and development investment includes expenditures for software. Source: Department of Commerce (Bureau of Economic Analysis). 372 | Appendix B Indus- Transtrial portation 1 equip- equip- Total ment ment 19.5 21.4 26.0 30.7 31.3 34.1 39.4 47.7 56.2 60.7 65.5 62.7 58.9 68.1 72.5 75.4 76.7 84.2 93.3 92.1 89.3 93.0 102.2 113.6 129.0 136.5 140.4 147.4 149.1 162.9 151.9 141.7 143.4 144.2 162.4 181.6 194.1 194.3 153.7 155.2 191.5 211.2 209.3 218.8 218.2 213.9 225.0 242.7 251.9 241.7 287.4 237.0 240.1 243.7 250.1 249.5 254.2 256.0 247.8 242.8 229.1 241.5 253.4 260.8 284.7 294.9 309.4 18.4 21.8 26.6 26.3 25.2 30.0 39.3 47.3 53.6 48.4 50.6 46.8 53.5 64.4 69.0 70.5 68.1 72.9 67.9 70.0 71.5 74.7 89.4 107.7 116.1 123.2 135.5 147.1 174.4 170.8 154.2 141.6 134.1 159.2 179.6 194.3 188.8 148.7 74.9 135.8 177.8 215.3 242.5 272.8 306.3 292.3 294.0 309.5 320.3 206.0 220.9 303.0 300.6 309.2 325.2 333.0 328.3 312.5 307.4 260.2 155.5 191.2 217.1 225.4 231.2 222.3 204.5 18.7 20.6 22.7 25.5 27.8 32.2 35.8 40.4 48.1 54.4 64.8 72.7 81.3 95.0 105.3 113.5 120.1 132.7 150.1 164.4 179.1 187.7 196.9 205.7 226.8 253.3 288.0 318.1 365.1 411.3 415.0 406.2 418.7 437.8 473.1 506.3 544.8 574.4 564.4 578.2 621.7 655.7 691.9 730.5 762.7 821.2 875.0 956.7 1,034.8 1,078.5 1,199.7 922.3 952.7 964.2 987.7 1,006.7 1,029.3 1,046.0 1,057.0 1,071.1 1,052.6 1,077.6 1,112.9 1,147.2 1,186.0 1,214.9 1,250.5 Structures Total resiResearch denSoftand tial 1 ware development 2 2.4 2.8 3.2 3.9 4.8 5.2 5.5 6.3 8.1 9.8 11.8 14.0 16.4 20.4 23.8 25.6 29.0 33.3 40.6 45.4 48.7 51.1 57.2 60.4 65.5 74.5 93.8 109.2 136.6 156.8 157.7 152.5 155.0 166.3 178.6 189.5 206.4 223.8 226.0 226.4 249.8 272.1 283.7 297.5 307.1 334.8 365.7 401.3 427.7 453.4 504.3 386.9 400.2 405.5 412.8 415.5 423.5 432.7 439.1 449.2 444.9 453.6 466.0 484.2 501.3 511.7 520.0 11.9 12.9 14.6 16.4 17.5 19.6 21.8 24.9 29.1 34.2 39.7 44.8 49.6 56.9 63.0 66.5 69.2 76.4 84.1 91.5 101.0 105.4 106.3 109.2 121.2 134.5 148.1 160.6 177.5 199.0 202.7 196.1 201.0 207.4 224.7 245.6 268.0 284.2 274.6 282.4 303.4 313.4 337.9 359.5 378.3 404.4 423.5 465.6 514.4 537.7 601.5 447.2 463.1 468.3 483.7 499.7 513.4 520.2 524.3 529.8 519.9 539.9 561.3 576.3 594.5 607.2 627.8 58.2 72.4 78.3 69.5 66.7 86.8 115.2 138.0 147.8 129.5 128.5 110.8 161.1 190.4 200.1 234.8 249.8 256.2 256.0 239.7 221.2 254.7 286.8 323.8 324.1 358.1 375.6 418.8 461.8 485.4 513.1 557.6 637.1 749.8 856.2 838.2 690.5 516.0 390.0 376.6 378.8 432.0 510.0 560.2 633.8 699.4 760.3 798.5 813.9 897.8 1,085.5 789.0
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