OECD Economic Surveys
UNITED US COVID-19STATES job crisis Executive Will thisSummary time be different? July 2020
By Damien Azzopardi, Mikkel Hermansen, Patrick Lenain, and Douglas Sutherland, OECD Economics Department*
2 - US COVID-19 EMPLOYMENT CRISIS – WILL THIS TIME BE DIFFERENT?
US COVID-19 employment crisis – Will this time be different? A remarkable regularity of the US business cycle is that unemployment always increases very fast during the recession, but declines only slowly during the recovery (Figure 1). In past business cycles, the unemployment rate always returned to a low range of 3-5%, but at a slow pace averaging only 0.55 percentage point per year (Hall and Kudlyak, 2020). After the 2008 financial crisis, it took 10 years for the economy to return to full employment (Table 1). Long periods of high unemployment inflict large income losses on job seekers and is detrimental to their well-being (Davis et al., 2011). The COVID-19 recession has witnessed a sharp rise of unemployment, and the U.S. economy may not return to full employment until the early 2030s if it follows past patterns. This note discusses regulatory reforms that could help to avoid such a long spell of joblessness. Figure 1.Unemployment rises much faster than it falls US Unemployment rate % of labour force 30 25 20 15 10 5 0 1929
Source: Source: Bureau of Labour Statistics; National Bureau of Economic Research.
The long time required by some categories of dismissed employees to find a job explains the slow pace of return to full employment, as shown by research using matched employer-employee datasets. Some groups of job seekers are able to keep a close link with their previous employers and are fast to return to work (“stable workers”). However, others struggle in their job search, may have to move to another industry or another state, may need some retraining, and are likely to go through several temporary jobs before obtaining a long-lasting contract (“fickle workers) (Gregory et al., 2020). Hence, for “fickle workers”, the return to a secure job may take several years. For example, low-skilled and senior workers are more vulnerable and tend to be long-term unemployed, with risks of earning losses and social exclusion. The different prevalence of “fickle” workers across industries makes each industry-specific shock unique. The prevalence of firm bankruptcies also affects the pace of return to full employment: when many firms go bankrupt, even “stable” workers cannot return to their previous job and have therefore to go through a time-consuming search process. When the COVID-19 outbreak hit the United States, unemployment suddenly surged to post-war record high levels. As businesses were shutting down and employers were dismissing large numbers of workers, the unemployment rate went from a low point of 3.5% in early 2020 to a post-war record high of 14.7% in just two months. With the easing of confinement and the reopening of businesses, many workers were recalled by their previous employers, and the unemployment rate fell back to 8.4% in August 2020. The OECD projects further decline, but under-unemployment would still prevail at the end of 2021, depending on how fast the coronavirus epidemics is controlled. If previous regularities apply once again, the decline of unemployment may be very slow after 2021, and there is a risk that the United States will not return to full employment before the early 2030s. However, the current crisis is unique in many ways, so a key question is whether the historical regularity will apply once again, or if this time will be different.
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Table 1 . U.S. unemployment rate during 4 recessions Lowest pre-crisis unemployment
Peak of unemployment
7.2% (April 1981)
10.8% (November 1982)
5.0% (March 1989)
3.9% (December 2000)
6.3% (June 2003)
4.4% (October 2006)
4.4% May 2007
10.0% (October 2009)
4.4% (March 2017)
3.5% February 2020
14.7% April 2020
Return to full employment
Number of months
To learn from prior experience, we examine three earlier shocks to the US labour market: 1) the sharp decline of manufacturing employment of 2000-10; 2) the sharp losses of construction jobs in 2007-09; and 3) the job destructions caused by Hurricane Katrina in the state of Louisiana. Keeping in mind that the heterogeneity of workers matter to understand the slow return to full employment, we make use of the novel Census Bureau Job-to-Job dataset, a matched employer-employee statistical record of all job moves, to obtain details on how different groups of workers were affected by these three shocks. Our main findings are: • The US labour market seems to have a built-in capacity to return to full employment following shocks, although this may take many years. • During the return to full employment, workers find new jobs at different speeds. The most “stable” workers are often recalled to their old jobs or are able to find a similar job when the economy starts to recover. In contrast, “fickle” workers face a long period of search, which may involve moving to another industry or state (“re-matching”). • While the “China shock” and, more broadly, the decline of manufacturing jobs, hit the MidWest severely, many individuals were able to return other industries (services and construction) or moved to other parts of the country. Manufacturing workers are often highly skilled and therefore can be rehired more easily than other workers. Women were apt at moving to services, while low skilled and seniors faced greater difficulties. • After the financial crisis, construction workers suffered severely from the bust of the housing market bubble. The unemployment rate of construction workers surged above 20% and remained elevated for many years, coming down below 10% only in 2014. Because many construction workers are low skilled and have patchy work history, their job search is more time-consuming. • After Hurricane Katrina devastated New Orleans, employment collapsed in a handful of sectors. Employment losses were protracted and recovered only slowly in industries heavily dependent on tourism, such as leisure and hospitality, and in government. On the other hand, employment rebounded sharply in other industries not as exposed to the subsequent demand shock. For example, retail trade recovered two-thirds of employment losses within 6 months of starting the recovery. • With the Covid-19 epidemics and the shuttering of many non-essential businesses, the unemployment rate surged temporarily to a post-war record, before retreating a little as some workers were recalled by reopening businesses. Further decline of unemployment is likely to be more gradual as workers in industries facing long-lasting hits, such as leisure and hospitality, are frequently low skilled and therefore will need to go through a long period of job search and adaptation. The Covid-19 crisis has been labelled a “reallocation shock”, which will require many workers to move to new firms and new industries. Lighter regulation in the labour market -- such as deregulation of occupational licensing, non-compete contract, and housing construction -- would fasten the return to full employment, as argued in the 2020 Economic Survey of the United States (OECD, 2020a).
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The great manufacturing job recession of 2000-10 The US manufacturing sector suffered very large employment losses between 2000 and 2010. One third of manufacturing jobs were lost over the decade, the equivalent of 6 million workers. In eleven states traditionally specialized in this industry (most of them in the Midwest), manufacturing employment losses were as large as 40% (Figure 2). The “China shock” explains largely these job losses during the period of 2001 to 2003: the access of China to the World Trade Organisation in December 2001 opened U.S. markets to Chinese exporters (Autor et al., 2013), making uncompetitive many (labour-intensive) manufacturing firms. The financial crisis of 2008-09 was a second shock that severely hit manufacturing employment as the banking system suddenly lost its funding, credit conditions tightened and aggregate demand shrunk. Figure 2 . Employment has lagged behind in the Midwest
A. Manufacturing employment (Jan 2000 = 100) 120
B. Non-farm employment (Jan 2000 = 100) 120
Note: Midwest = AR, IN, MI, MS, NC, NH, OH, PA, RI, SC, TN Source: Bureau of Labour Statistics.
Although manufacturing employment remained depressed after 2010, the Midwest nonetheless returned to full employment – unemployment was back to the low point of 3 ½ percent in these states when the coronavirus struck. In the Midwest like in other states, the recovery from the global financial crisis was driven by the sector of services. Many job seekers previously employed by manufacturing firms returned to work in services firms. However, job gains was weaker in the Midwest than in other states during the decade following the financial crisis because the closure of manufacturing plants has negative spillover effects on its local economy (Hall et al. 2020). Hence, a share of dismissed workers had to leave the Midwest and seek employment elsewhere in the country. By early 2020, the Midwest accounted for only 20% of US jobs, compared to 25% some decades earlier. Using the Job to job dataset, it is possible to analyse some of the characteristics of these moves to new jobs. The data shows that about 7% of workers in manufacturing left their job every quarter during 2001-2003 (Figure 3, Panel A). Only one third of these workers moved to new jobs within a quarter, with the rest either remaining unemployed or leaving the labour force for a longer period. Low-skilled workers and seniors were unlikely to be recalled or to find a new job immediately; being less employable, they faced a period of unemployment. About 58% of workers regained employment by moving to another industry in the same State, with young and female workers most likely to succeed in doing so (Figure 3, Panel B). Such massive reallocation is not restricted to distressed areas and periods of shocks. As we show in separate work (Azzopardi et al., 2020), changing job is key to move up the job ladder and boost earnings. Hence, policies to lower barriers to mobility and support to help job seekers adapt to the changing types of work are important to avoid earning losses and make sure that those facing a job loss have an opportunity to recover.
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Figure 3. Many workers found jobs outside manufacturing, except for the low skilled and seniors
A. Job moves in and out of manufacturing
% of job-to-job outflows
% of employment 10 8 6 4 2 0 -2 -4 -6 -8
Outflow to nonemployment Inflow from nonemployment Net employment change
B. Destination of job-to-job moves out of manufacturing
Job-to-job outflows Job-to-job inflows
To another manufacturing job To job in another State
To job in another industry
80 60 40 20 0
Note: Includes the manufacturing industry in IL,KS,MI,MN,NC,PA,SC,WI. Job-to-job flows are moves between jobs with no or only a brief nonemployment period of less than a quarter. Nonemployment reflect a period of a least one quarter with no main job. Includes both voluntary job moves and layoffs. Source: J2J Data.
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The collapse of construction jobs during the 2008 financial crisis The global financial crisis inflicted a severe hit to the US labour market in 2007-09. Over 30 million individuals lost their jobs by some measures (Kalleberg et al., 2017), and the unemployment rate surged from 4.4% to 10%. Employment losses were particularly severe in the construction sector, which had previously experienced a boom fuelled by easy credit conditions. When credit markets suddenly froze, construction came to a sudden halt. The construction sector lost about 2 million jobs (Figure 4, Panel A) and the sectorâ&#x20AC;&#x2122;s unemployment rate surged to a peak of almost 25% in early 2010 (Figure 4, Panel B). Other sectors related to housing also contracted: credit-related activities lost 306 000 jobs, real estate lost 162 000 jobs and architectural and related services lost 144 000 jobs. Fourteen states suffered severely1 : unsurprisingly, this includes states where the construction sector was a large source of employment such as California, Florida, Idaho, Nevada, North Carolina and South Carolina. Figure 4 .Long-lasting unemployment in construction after the financial crisis
A. Employment in construction
B. Unemployment rate 25
Construction and extraction occupations Total
Source: Bureau of Labour Statistics (construction = NAICS 23).
These states were selected because their non-farm employment declined by more than 7% between 2007 and 2009, or their unemployment rate increased by more than 5% during the same period: Alabama, Arizona, California, Florida, Idaho, Indiana, Kentucky, Michigan, Nevada, North Carolina, Oregon, Rhode Island, South Carolina and Tennessee. 1.
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It took 10 years for employment in construction to return to its pre-crisis peak. The unemployment rate of construction sectors fell below 10% only in 2014, with a large number of construction workers having experienced a long period of job search. Although the sector is subject to large cyclical fluctuations and workers understand that they may have to change jobs repeatedly, such long spells of unemployment come with heavy costs in terms of earning losses and well-being. The Job-to-Job dataset indicates that construction employment declined by more than 4% every quarter during the global financial crisis in the hardest hit States. However, this masks even larger gross flows of 16% leaving their construction job and 12% new hires every quarter (Figure 5, Panel A). Again, more than a third of job leavers found new employment within a quarter and the majority moved to other industries or crossed State borders for new employment (18%) (Figure 5, Panel B). While a higher share of workers stayed in construction compared to manufacturing workers, reallocation in construction was notably high among women and youth.
Figure 5. More than 15% of construction workers left their job each quarter during the crisis Average quarterly job flows in the construction industry in 13 hardest hit States, 2007Q4-2009Q2
A. Job moves in and out of construction
% of job-to-job outflows
% of employment 20 15 10 5 0 -5 -10 -15 -20
Outflow to nonemployment Inflow from nonemployment Net employment change
B. Destination of job-to-job moves out of construction
Job-to-job outflows Job-to-job inflows
To another construction job To job in another State
To job in another industry
80 60 40 20 0
Note: Includes the construction industry in AL,AZ,CA,FL,ID,IN,KY,MI,NV,NC,OR,SC,TN. Job-to-job flows are moves between jobs with no or only a brief nonemployment period of less than a quarter. Nonemployment reflect a period of a least one quarter with no main job. Includes both voluntary job moves and layoffs. Source: J2J Data.
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The loss of services jobs following Hurricane Katrina The nature of the shock causing a recession matters for the speed of the recovery. A recession generated by the unravelling of accumulated imbalances, such as the burst of the housing bubble in 2008, tends to be followed by slow recoveries (U-shaped) and possibly long lasting negative effects (L-shaped). Indeed, a weak recovery was predicted to follow the financial crisis based on the observation that it would take time to work out the complex financial imbalances and the stock of subprime credits at the origin of the crisis (Reinhart and Rogoff, 2008). By contrast, a recession caused by an external shock such as a natural disaster can be temporary and followed by a V-shaped recovery. To learn how the economy recovers from natural disasters, we look at activity and employment in the state of Louisiana following Hurricane Katrina, which devastated New Orleans and destroyed 200,000 homes in August 2005. About 80,000 jobs (4% of nonfarm employment) were lost in the months following the disaster. The severity and instantaneous nature of the job losses following hurricane Katrina are comparable with the coronavirus shock. The latter shock is larger, particularly in leisure and hospitality. Other sectors experiencing marked downturns in both shocks include government, retail trade as well as education and health. The remaining industries largely escaped serious employment losses during both hurricane Katrina and the current coronavirus shock. Following hurricane Katrina the job losses were quickly reversed and by end-2007, two years after the disaster, employment had returned to its earlier level (Figure 6, Panel A). Econometric approaches to make local projections of employment growth after a crisis suggest that statistically significant above-trend growth was sustained for up to 6 months following the nadir in employment levels. However, there is considerable heterogeneity across industries. Whereas retail trade employment bounced back very rapidly - with two-thirds of job losses reversed in 6 months (Figure 6, Panel B) - the rebound in the leisure and hospitality was more modest recovering only one third of the jobs lost during the same period (Figure 6, Panel D). Indeed, employment in leisure and hospitality had not fully recovered by the time of the global financial crisis hit around 3 years later. Two types of job losses occurred:
• Temporary dismissals in sectors such as retail trade and professional services – customers returned quickly to these essential services after the hurricane, and employers rapidly recalled their former employees. Most job losses only lasted a few months. • Longer lasting dismissals in sectors linked to tourism (leisure and hospitality) and in government and education & social services as it took time to repair the infrastructure, rehire staff, and resume normal operations. Nonetheless, after a few years, the leisure and hospitality sector was back on its feet and exceeded earlier levels of employment. The tourism industry adopted a proactive approach with a new branding and marketing campaigns to attract tourists. By the time to COVID-19 epidemics struck, the city had become once again a key magnet of tourists. Government services were also hit severely, but they recovered after a few years and resumed normal operations thanks to the large financial support provided by Congress to help the region recover from the natural disaster.
In comparison with hurricane Katrina, the 2008 financial crisis inflicted more severe damages to public employment, due to the decline of tax revenue collected by the state and local governments, in Louisiana as in many other States. The contrast between these two shocks show the importance of federal support to state and local governments, which due to balanced budget requirements are forced to cut their spending sharply unless federal government support comes to the rescue. Thanks to federal help, employment in the state of Louisiana returned to its pre-disaster level by end-2007, two years after the disaster. A study of tax returns suggest that there were no persistent impact on the victims’ incomes, which fully recovered after a few years, and even performed better than in comparable cities: “At least in this particular disaster, aid to cover destroyed assets and short-run income declines was sufficient to make victims financially whole” (Deryugina et al., 2014).
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It is still unclear what type of shock will result from the coronavirus outbreak and whether it will have a lasting negative impact on demand for services in industries, particularly services, where distancing is an unavoidable concern. This will depend on a possible second wave of contagion, the availability of effective treatments and vaccines, and the possible mutation of the coronavirus. If the epidemics is rapidly brought under control, the experience of Louisiana gives hope that employment could come back rapidly and that unemployment might decline rapidly. We discuss these prospects in the next section. Figure 6. Post-Katrina employment patterns in the state of Louisiana
B. Retail trade
7.55 5.40 7.50 5.35
D. Leisure and hospitality
Source: Bureau of Labour Statistics.
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The COVID-19 recession – Will it be different? Like most countries, the United States was deeply affected by the outbreak of COVID-19 cases in early 2020. As many businesses were ordered to close during the confinement period, the unemployment rate went from the lowest level in 50 years of 3.5% to the post-war highest level of 14.7% in just two months (Figure 1 and Table 1). Aggregate employment fell by 21% through late-April. This was followed by a partial employment rebound through June as job losses were reversed when lockdown measures were lifted and workers were recalled, but the return to full employment will take time (Bartik et al, 2020). The coronavirus crisis has been characterized as a “reallocation shock” (Barrero et al, 2020) because sectors of activity requiring personal contacts – mainly tourism, airlines, hospitality, food services, health care and entertainment – have been subject to a specific shock and may remain depressed for a long time. In these sectors, about 15 million jobs were lost in April 2020, and only partially reversed in May and June (Figure 7, Panel A). Among the three sectors subject to shutdown orders (Figure 8), the sector of leisure and hospitality was particularly hard hit: workers in this industry faced an unemployment rate of 47% in April, which declined thereafter but was still around 30% in June (Figure 7, Panel B). Figure 7. Employment fell in sectors providing in-person services* Million 65
B. Unemployment rates
A. Employment in shutdown sectors 40
Wholesale and retail trade Leisure and hospitality
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020
* Air transportation, retail trade, leisure and hospitality, health care & social assistance, and other services. Source: Bureau of Labour Statistics.
Figure 8. Three sectors provide the bulk of employment in shutdown businesses Share of jobs in shutdown industries, June 2020 Air transportation Other services
Healthcare and social assistance
Source: Bureau of Labour Statistics.
Leisure and hospitality
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OECD Economic Outlook projections foresee a rapid decline of US unemployment in the short term, as businesses gradually reopen and the economy recovers. By the end of 2021, unemployment is projected to fall to a range of 8-10% depending on how fast the pandemics is brought under control (Figure 9). However, further decline may be slower. Some workers will be quickly recalled to their old jobs in sectors that are not permanently affected. Judging from the experience of New Orleans after Hurricane Katrina, retail trade is likely to recall its workers rapidly; this is comforting because the sector employs many workers who are more prone to long spells of job search and adaptation to new jobs. However, the future of the leisure, entertainment, accommodation and food service industries is more uncertain: it is unclear as of yet how soon tourists will be allowed to travel freely, and whether distancing measures and customer anxiety will prevent the sector from returning to its prior level of activity.
Figure 9. Unemployment is projected to decline to 8-10% depending on the COVID-19 contagion US unemployment rate % of total employment 20 18 16 14 12 10 8 6 4 2 0
Source: BLS, and OECD Economic Outlook projections, June 2020.
This is worrying because these sectors employ a large share of workers who will have difficulties moving to new industries, and are often entry points for workers joining or re-joining the labour force (Table 2). As a consequence, these workers will likely face periods of unemployment, job search, and temporary contracts, with adverse effects on earning and wellbeing. As noted above, the experiences of manufacturing workers after the China shock and construction workers after the 2008 financial crisis serve as a cautionary tale. If previous patterns of slow declines of unemployment occur once again, the United States may not return to full employment until the early 2030s. Lessons from the past also suggest that a set of regulatory easing measures and support to dismissed workers could hasten the return to full employment. Regulations introduced in past decades at the state level â&#x20AC;&#x201C; especially mandatory occupational licenses, non-compete contracts, and housing regulations hinder the fluidity of the worker-job matching process and slow down the reallocation of workers across industries (Hermansen, 2019; Von Rueden et al, 2020). Easing these restrictions, for instance by eliminating these requirements when not clearly justified or through interstate reciprocity agreements, would quicken the return to full employment. Similarly, a reform of unemployment benefits from passive support to active policies favouring labour-market reintegration, reskilling and geographic mobility -- would help the return to work of job seekers and avoid long spells of unemployment and their scarring effects. These policy options are discussed in the 2020 OECD Economic Survey of the United States (OECD, 2020a) and policy options to flatten the unemployment curve are presented in OECD (2020b). Table 2. The age and skills distribution of workers vary across exposed industries Age distribution
Prime age (25-54)
Health Care and Social Assistance
Arts, Entertainment, and Recreation
Accommodation and Food Services
Source: Job-to-Job dataset (2018)
References • Autor, D., D. Dorn and G. Hanson (2016), “The China Shock: Learning from Labor-Market Adjustment to Large Changes in Trade”, Annual Review of Economics. • Azzopardi, D. et al. (2020), ”Why has labour mobility declined in the United States? Insights from a new dataset”, OECD Technical Background Paper, forthcoming. • Barrero, J. M., N. Bloom and S.J. Davis (2020), “COVID-19 Is Also a Reallocation Shock”, Brookings Papers on Economic Activity, Summer 2020. • Bartik, Alexander W., Marianne Bertrand, Feng Lin, Jesse Rothstein and Matthew Unrath (2020), “Measuring the labor market at the onset of the COVID-19 crisis”, Brookings Papers on Economic Activity, Summer 2020. • Cajner, Tomaz, Leland D. Crane, Ryan A. Decker, John Grigsby, Adrian Hamins-Puertolas, Erik Hurst, Christopher Kurz and Ahu Yildirmaz, “The U.S. Labor Market during the Beginning of the Pandemic Recession”, Brookings Papers on Economic Activity, Summer 2020. • Chetty, Raj, John N. Friedman, Nathaniel Hendren, Michael Stepner, and The Opportunity Insights Team (2020), “How Did COVID-19 and Stabilization Policies Affect Spending and Employment? A New Real-Time Economic Tracker Based on Private Sector Data”, NBER Working Paper No. 27431,June. • Davis, Steven J. and Till Von Wachter (2011), “Recessions and the Costs of Job Loss”, Brookings Papers on Economic Activity, Fall. • Deryugina, T. , L. Kawano, S. Levitt (2018), “The economic impact of Hurricane Katrina on its victims: Evidence from individual tax returns”, American Economic Journal: Applied Economics, 10 (2). • Dolfman, Michael L., Solidelle Fortier Wasser, and Bruce Bergman (2007), “The effects of Hurricane Katrina on the New Orleans economy”, Monthly Labor Review, BLS, June. • Goodman, Christopher J. and Steven M. Mance (2011), Employment loss and the 2007–09 recession: an overview, Monthly Labor Review, April, Washington DC. • Gregory, Victoria, Guido Menzio and David G. Wiczer (2020), “Pandemic Recession: L or V-Shaped?”, NBER Working Paper No. 27105 • Hall, Robert E. and Marianna Kudlyak (2020), Why Has the US Economy Recovered So Consistently from Every Recession in the Past 70 Years?, NBER Working Paper No. 27234, May. • Hermansen, Mikkel, (2019). “Occupational Licensing and Job Mobility in the United States,” OECD Economics Department Working Paper No. 1585, OECD Publishing, Paris, https://doi.org/10.1787/4cc19056-en. • Kalleberg, Arne L. & von Wachter, Till M. (2017). The U.S. Labor Market during and after the Great Recession: Continuities and Transformations. The Russell Sage Foundation Journal of the Social Sciences, 3(3), 1-19. • OECD (2020a), OECD Economic Survey of the United States, OECD Publishing, Paris, https://doi.org/10.1787/12323be9-en. • OECD (2020b), “Flattening the unemployment curve? Policies to support workers’ income and promote a speedy labour market recovery” , Issue Note 5 OECD (2020), OECD Economic Outlook, Volume 2020 Issue 1: Preliminary version, OECD Publishing, Paris, https://doi.org/10.1787/0d1d1e2e-en. • Reinhart, Carmen M., and Kenneth S. Rogoff (2008). “Is the 2007 US Sub-prime Financial Crisis So Different? An International Historical Comparison.” American Economic Review, 98 (2): 339-44. • Von Rueden, C. and I. Bambalaite (2020), “Measuring occupational entry regulations: A new OECD approach”, OECD Economics Department Working Papers, No. 1606, OECD Publishing, Paris, https://doi.org/10.1787/296dae6b-en. *This note is based on research prepared in the background of the 2020 OECD Economic Survey of the United States. The views expressed are those of the authors and do not necessarily reflect those of the OECD or of its member countries.
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