Report | Economic Growth

The promise and limits of high-pressure labor markets for narrowing racial gaps

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Press release

One of the most compelling but underrecognized reasons that the Federal Reserve should aim for running the economy hot is the potential to narrow troubling racial gaps in wages and employment. Expansionary macroeconomic policies—policies that prioritize low unemployment over preemptively slowing growth in aggregate demand in the name of controlling potential inflation—create “high-pressure” labor markets characterized by very low unemployment and rapid job growth. While the legacy and present effects of structural racism mean that high-pressure labor markets by themselves are unlikely to fully erase race-based gaps in labor market outcomes, the potential to narrow these gaps is an undeniable benefit of more-expansionary macroeconomic policy.

The growing evidence that high-pressure labor markets can narrow these gaps calls for macroeconomic policies that test the absolute limits of how low unemployment can be pushed. Macroeconomic policymakers frequently weigh the potential benefits of higher-pressure labor markets against the potential risks, namely accelerating price inflation driven by excessively fast wage growth. The potential to close race-based gaps in the labor market should be counted as a substantial benefit in these deliberations and should convince policymakers to take on more inflation risk. Furthermore, running labor markets at the maximum sustainable pressure will provide much-needed information on what else we need to do to foster racial equity in labor markets.

This paper explores the promise and limits of high-pressure labor markets in reducing racial labor market gaps and how too-slack labor markets have helped thwart progress in closing the gaps. It then draws lessons from these investigations for policymakers. Its main findings are:

  • Reductions in the unemployment rate boost hourly wages of typical (median) Black workers more than they boost hourly wages of typical white workers.
    • In 2019, the median Black worker was paid 32.2% less in hourly wages than the median white worker, up from 28.6% in 1973. Had the unemployment rate averaged 1 percentage point less annually from 1973 to 2019, the median Black–white wage gap could have declined to 18.0%. If the unemployment rate had averaged 2 percentage points less (a very ambitious target), the median Black–white wage gap could have fallen to just 5.4% (an 80% reduction in the size of this wage gap).
  • Reductions in the unemployment rate provide an even bigger relative boost for median Black annual earnings, by increasing both hours worked and hourly wages.
    • In 2019, the annual earnings of the typical (median) Black worker amounted to just 80% of the annual earnings of the median white worker. Had the unemployment rate averaged 2 percentage points less annually over the 1970–2019 period, the Black–white median earnings gap (measured as a ratio) could have essentially closed. Had the unemployment rate averaged 1 percentage point less annually over that period, the typical Black worker in 2019 could have been paid 90.1% as much as the typical white worker—reflecting a 50% decrease in the Black–white median annual earnings gap.
  • The Black–white unemployment gap (how much, in percentage points, the Black unemployment rate exceeds the white unemployment rate) closes significantly when the overall economy has fewer idle resources (i.e., when potential output climbs closer to actual output, leading to a rise in the measured “output gap”). For example, the Black unemployment rate falls more than twice as much as white unemployment when the economy’s output gap rises by 1 percentage point.
    • Even this disproportionate reduction in Black unemployment might understate how equalizing overall economic tightening can be. When the output gap measure rises 1 percentage point, the share of Black persons who are employed (the Black employment-to-population ratio, or EPOP) actually rises nearly seven times as much as the share of white persons employed (the white EPOP). But because the share of Black persons who are either working or actively looking for work (the Black labor force participation rate) also rises faster than white labor force participation when the output gap improves, the Black unemployment rate reduction is muted relative to gains in employment.
  • Sustained high-pressure labor markets may have more power than we thought to close the Black–white unemployment ratio. For decades, the Black unemployment rate has been, on average, roughly twice the white unemployment rate. This persistent and distressingly high Black–white unemployment ratio (the Black unemployment rate divided by the white unemployment rate) has traditionally been seen as much more resistant to closing with high-pressure labor markets. However, the Black unemployment rate has only been included in most data sets since the early 1970s and, since then, genuinely high-pressure labor markets have been quite rare. Pre-1970s data that provide a potential proxy for the Black–white unemployment ratio show that sustained high-pressure labor markets may well actually reduce it significantly.

The policy lessons from this data are clear. While overall wage and price inflation remain the proper targets of policy (employment measures are not reliable enough to make good policy guides), policymakers need to change how they balance those targets:

  • As they weigh the potential benefits of higher-pressure labor markets against the risks, policymakers should count, on the benefits side, potential reductions in chronic racial gaps in labor market outcomes.
  • More forbearance should be exercised as wages and prices rise during economic recoveries and expansions, and at a bare minimum wage and price targets should be kept symmetric over business cycles: Every year that sees wage and price inflation come in 1% below target must be matched by a year with wage and price inflation coming in 1% above target.
  • The potential to close race-based gaps in the labor market should convince policymakers to take on more inflation risk than they otherwise would have (that is, they should wait for actual and sustained, rather than forecast, inflation to appear before raising interest rates).

Background on the unemployment and inflation trade-off

All else equal, policymakers should aim for an unemployment rate so low that it reflects only the transitory and voluntary shifts of workers in and out of work or between employers. However, because of the way policymakers have traditionally sought to affect the rate of unemployment, they have instead aimed for a rate that was high enough to avoid any chance, even remote, of sparking inflation.

The primary way policymakers influence the unemployment rate is through measures that change the pace of aggregate demand growth. Aggregate demand is economywide spending of households, businesses, and governments. When this spending is strong, employers need workers to produce the output of goods and services needed to satisfy customer demand, which keeps unemployment low and employment growth strong. When this spending lags, less output and hence fewer workers are needed to satisfy demand, so employment growth lags and unemployment rises.

If policymakers boost economywide spending too much, however, demand might outstrip the productive capacities of firms. As demand runs ahead of supply, this puts upward pressure on wages and prices as firms scrambling to meet demand find they need to hire more workers and can charge customers a bit more for scarce goods. This “inflation barrier” to further efforts to boost demand—the point of tightness in labor markets that sparks an upward drift of inflation—may well be hit before the unemployment rate that reflects only voluntary job transitions is attained.

This balancing between demand growth that is strong enough to keep unemployment low, but not strong enough to generate accelerating inflation, is a central problem of macroeconomic policy (often called stabilization policy). Traditionally, the entity doing this balancing in the United States has almost always been the Federal Reserve, which tries to spur demand primarily by lowering interest rates and can brake escalating demand by raising interest rates. However, the Great Recession exposed the extreme limits of the Fed’s ability to generate strong enough demand growth and has elevated the role of fiscal policymakers (Congress and the president) in boosting (or failing to boost) demand by adjusting spending levels and taxation in the economy.1

Far too often in recent decades, policymakers have erred in targeting—or at least unnecessarily tolerating—demand growth that was too weak to generate enough pressure in labor markets to give workers leverage in wage negotiations with employers.2 This toleration of low-pressure labor markets was often done in the name of keeping inflationary pressures in check. But given that genuine inflationary pressures in the U.S. economy have been extraordinarily rare since the 1970s, the targeting of too-weak demand growth has often been about guarding against even the risk of inflation. A growing body of recent research notes that the benefits of low unemployment are large enough to justify taking on substantially more inflation risk than has previously been tolerated.

The most obvious benefits of low unemployment are more job opportunities for more people and more hours of work available to U.S. families. A less obvious benefit, but one that shows up strongly in the data, is faster hourly wage growth for the vast majority of U.S. workers, a particularly important benefit given the anemic pace of wage growth for these workers in recent decades.3 Yet another increasingly discussed benefit of low unemployment is its ability to put sustained pressure on compressing race-based gaps in the labor market. The rest of this paper largely tries to put some empirical bounds on just how large this last benefit might be.

Why aim for ‘high-pressure’ labor markets and not ‘full employment’?

The Fed’s legal mandate is to pursue maximum employment consistent with price stability. Over the years “maximum employment” has often been referred to as “full employment.” However, there is no universally agreed upon definition of full employment. For some, full employment simply means that anybody who wants a job can find a job. For others, particularly macroeconomists, it means attaining the rate of unemployment (often called “the natural rate”) below which further increases in economywide spending will mostly lead to accelerating inflation rather than greater output. “High-pressure” labor markets just mean labor markets characterized by low unemployment, fast rates of job creation, rapid job-finding among the unemployed, and sustained effort by employers to keep their enterprises properly staffed. Labor markets can be “high pressure” yet still tolerate further reductions in unemployment without leading to unsustainable wage or price inflation, and the term “high-pressure” may better connote a continuum of labor market states rather than a single fixed point.

Further, old theories of “disguised unemployment” and new developments in advanced capitalist economies (the rise of “gig work”) argue that “full employment” and “high-pressure labor markets” might not always coincide.

Joan Robinson (1936) defined “disguised unemployment” as follows:

In a society in which there is no regular system of unemployment benefit, and in which poor relief is either nonexistent or “less eligible” than almost any alternative short of suicide, a man who is thrown out of work must scratch up a living somehow or other by means of his own efforts. And under any system in which complete idleness is not a statutory condition for drawing the dole, a man who cannot find a regular job will naturally employ his time as usefully as he may. Thus, except under peculiar conditions, a decline in effective demand which reduces the amount of employment offered in the general run of industries will not lead to “unemployment” in the sense of complete idleness, but will rather drive workers into a number of occupations—selling match-boxes in the Strand, cutting brushwood in the jungles, digging potatoes on allotments—[that] are still open to them.

The modern U.S. economy obviously does not totally lack relief for the unemployed, and the reach and influence of the gig economy is often wildly overstated. But it seems clear that one margin of survival that many U.S. workers draw on when regular work is slack due to weak aggregate demand is to engage in gig or otherwise irregular work. But gig work generally does not provide high-quality jobs or economic security. In some deeply unsatisfactory sense, the rise of gig work could theoretically help fulfill the promise of one definition of full employment—that anybody “who wants a job can find a job.” But, in an economy with measured unemployment kept low only by a large incidence of gig work, if policymakers boosted aggregate demand, it is highly likely that many gig workers would leave the gigs behind and look for and find more regular work. In short, describing labor markets as “high pressure” might better describe the condition that employers are competing actively among themselves to attract workers.

High-pressure labor markets and median racial wage gaps

Since 1979, wage gaps between Black and white workers have widened significantly. Figure A shows the gap in two ways: how much less in percent terms the median Black worker earns in hourly wages than the median white worker, and the percent by which the average hourly wage of Black workers is less than the average hourly wage of white workers, holding other characteristics constant. The latter, a regression-adjusted average gap, controls for educational attainment, gender, ethnicity, and age. Both gaps widened significantly over time, but the median gap started larger and has expanded more rapidly since the late 1970s.

Figure A

Black–white wage gaps have widened since the late 1970s: Median and regression-adjusted average Black–white wage gaps, 1970s through 2020

  Median Average, adjusted
1973 28.6%
1974 24.8%  
1975 23.7%  
1976 23.4%  
1977 23.2%  
1978 25.1%  
1979 19.6% 10.4%
1980 20.9% 10.4%
1981 21.1% 10.0%
1982 24.0% 12.2%
1983 22.5% 11.4%
1984 24.2% 12.6%
1985 23.9% 13.3%
1986 24.9% 13.1%
1987 24.4% 13.2%
1988 22.5% 12.2%
1989 25.6% 13.4%
1990 25.2% 13.9%
1991 28.4% 13.6%
1992 25.8% 13.6%
1993 24.8% 13.5%
1994 25.1% 13.4%
1995 26.3% 13.5%
1996 27.8% 16.8%
1997 27.3% 15.7%
1998 25.6% 13.4%
1999 24.4% 13.7%
2000 26.2% 13.0%
2001 29.3% 15.0%
2002 27.1% 14.7%
2003 25.3% 13.7%
2004 24.8% 13.4%
2005 26.7% 16.1%
2006 25.9% 14.6%
2007 28.7% 16.0%
2008 30.0% 16.6%
2009 27.1% 15.3%
2010 26.9% 15.7%
2011 28.5% 16.3%
2012 32.7% 16.8%
2013 29.1% 17.4%
2014 31.8% 18.5%
2015 34.4% 19.7%
2016 29.8% 18.4%
2017 33.8% 21.4%
2018 36.4% 22.3%
2019 32.2% 20.3%
2020 30.9% 19.0%
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Notes: The median wage gap is measured as the ratio of the white median wage to the Black median wage, minus 1, i.e., the gap measures how much less in percent terms the median-earning Black worker makes than the median-earning white worker. The average, adjusted wage gap shows how much less the average Black worker makes than their white counterpart of similar educational attainment, gender, ethnicity, and age.

Source: Author’s analysis using data from the Economic Policy Institute State of Working America Data Library (EPI 2021b).

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Previous work has indicated that median Black wage growth responds more strongly to changes in unemployment than does median white wage growth.4 Figure B confirms this. The figure shows the relationship between the unemployment rate and wage growth. Specifically, it shows the change in wage growth that occurs if the unemployment rate rises by 1 percentage point. For white median hourly wages, a 1-percentage-point increase in overall unemployment is associated with wage growth that is 0.52% slower. For Black median wages, wage growth declines by 0.76%. As the figure shows, the coefficient for median Black wage growth is nearly 50% larger than for median white wages.5

Figure B

Black workers’ wages are more sensitive to labor market slack: Regression coefficients showing change in Black and white worker hourly and annual wages given a 1-percentage-point increase in the unemployment rate

Outcome Regression coefficient
Black -0.756
White -0.522
Black -0.487
White -0.289
Black -1.75
White -0.45
Black  -1.25
White -0.57
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Notes: For all regressions, a three-year centered moving average of all variables is used, with Newey-West calculations of standard errors. Controls in the wage and annual earnings regressions include inflation, productivity growth, a time trend, and dummies for the following periods: 1979–1988, 1989–2000, 2001–2007, and 2008–2014. The annual earnings measure includes only workers with earnings during the year.

Source: Author’s analysis of Current Population Survey microdata (EPI 2021a; Flood et al. 2021) and CPS, CPI, and productivity data from the Bureau of Labor Statistics (BLS-CPS 2021; BLS-CPI 2021; BLS-LPC 2021).

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Figure C uses the estimated coefficients from Figure B and calculates counterfactual median Black–white wage gaps under three scenarios: unemployment rates that averaged 1, 1.5, and 2 percentage points lower over the 1973–2019 period. These scenarios are plausible alternatives of what might have been under a policy regime that determinedly aimed for high-pressure labor markets. Over this period, the unemployment rate was high, averaging 6.2%. One closely watched measure of the unemployment rate consistent with stable inflation—the nonaccelerating inflation rate of unemployment (or NAIRU) estimated by the Congressional Budget Office (CBO)—averaged 5.3% over this same period, almost a full percentage point lower. Additionally, between 1947 and 1973, the unemployment rate averaged 4.7%, exactly 1.5 percentage points lower than in the post-1973 period, and inflation before the oil price shock of 1973 was generally contained. Finally, when unemployment fell more than 2 percentage points beneath the 1973–2019 average in the late 1990s, and again in 2018–2019, there was no marked uptick in wage or price inflation requiring that macroeconomic policymakers slow demand growth.

Figure C

Sustained lower unemployment would help shrink Black–white wage gaps: Black–white median wage gap, actual and under three counterfactual scenarios, 1973–2019

Actual 1.0 ppt. lower average unemployment 1.5 ppt. lower average unemployment 2.0 ppt. lower average unemployment
1973 28.6% 28.6% 28.6% 28.6%
1974 24.8% 24.5% 24.3% 24.2%
1975 23.7% 23.1% 22.8% 22.5%
1976 23.4% 22.5% 22.0% 21.6%
1977 23.2% 22.0% 21.4% 20.8%
1978 25.1% 23.6% 22.9% 22.1%
1979 19.6% 17.9% 17.1% 16.3%
1980 20.9% 18.9% 17.9% 16.9%
1981 21.1% 18.8% 17.6% 16.5%
1982 24.0% 21.3% 19.9% 18.6%
1983 22.5% 19.5% 18.1% 16.6%
1984 24.2% 20.9% 19.3% 17.7%
1985 23.9% 20.3% 18.5% 16.8%
1986 24.9% 21.0% 19.1% 17.2%
1987 24.4% 20.2% 18.1% 16.1%
1988 22.5% 18.1% 16.0% 13.9%
1989 25.6% 20.7% 18.4% 16.1%
1990 25.2% 20.0% 17.6% 15.2%
1991 28.4% 22.8% 20.1% 17.5%
1992 25.8% 20.0% 17.2% 14.5%
1993 24.8% 18.7% 15.9% 13.1%
1994 25.1% 18.8% 15.8% 12.8%
1995 26.3% 19.6% 16.4% 13.3%
1996 27.8% 20.7% 17.3% 14.1%
1997 27.3% 20.0% 16.5% 13.1%
1998 25.6% 18.0% 14.5% 11.1%
1999 24.4% 16.7% 13.0% 9.5%
2000 26.2% 18.0% 14.2% 10.5%
2001 29.3% 20.6% 16.5% 12.6%
2002 27.1% 18.3% 14.1% 10.2%
2003 25.3% 16.3% 12.1% 8.1%
2004 24.8% 15.6% 11.3% 7.2%
2005 26.7% 17.1% 12.6% 8.3%
2006 25.9% 16.1% 11.5% 7.1%
2007 28.7% 18.3% 13.4% 8.8%
2008 30.0% 19.2% 14.2% 9.4%
2009 27.1% 16.3% 11.3% 6.5%
2010 26.9% 15.8% 10.7% 5.8%
2011 28.5% 16.9% 11.6% 6.6%
2012 32.7% 20.4% 14.8% 9.4%
2013 29.1% 16.9% 11.3% 6.0%
2014 31.8% 19.0% 13.2% 7.6%
2015 34.4% 21.1% 15.0% 9.2%
2016 29.8% 16.6% 10.6% 5.0%
2017 33.8% 20.0% 13.7% 7.7%
2018 36.4% 22.0% 15.4% 9.2%
2019 32.2% 18.0% 11.5% 5.4%
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Notes: The wage gap is how much less in percent terms the median Black worker earns in hourly wages than the median white worker. Regression coefficients from Figure B are used to construct counterfactual wage growth based on lower average unemployment rates over the entire period, and the median wages of Black workers are compared with the median wages of white workers to estimate the gaps. The higher wage growth implied in the counterfactual scenarios is distributed evenly over each year in the figure.

Source: Author’s analysis using data from the State of Working America Data Library (EPI 2021b) and the regression coefficients reported in Figure B. 

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We should be clear that examining a counterfactual that assumes substantially lower unemployment on average does not reflect an assumption that recessions never happen. Instead, it simply assumes that macroeconomic policymakers do their job and ensure that every period of above-average unemployment is matched by an equivalent period of below-average unemployment. Estimates of the natural rate of unemployment are not hard floors below which the economy is never meant to go; instead they are averages that the unemployment rate should fluctuate both above and below. Running the economy far above even too-conservative natural rate estimates over decades is a policy failure that can clearly be addressed.

Achieving and sustaining high-pressure labor markets since the early 1970s would have dramatically narrowed the median Black–white wage gap. Had unemployment averaged 2 percentage points less over the entire period, 80% of the median Black–white wage gap that appeared in 1973 could have been erased (as the gap shrank from 28.6% to 5.4%). With unemployment averaging just 1 percentage point less (essentially just hitting conventional measures of the natural rate of unemployment), the median wage gap could have fallen slightly (to 18.0%) rather than rising by almost 8 percentage points over this period. In short, high-pressure labor markets hold great potential to reduce this particular measure of racial inequality in the labor market.

The gap-narrowing power of high-pressure labor markets is even more evident when looking at median annual earnings. Annual earnings can be affected by tighter labor markets not only through higher hourly wages but also through increased hours worked during the year. As shown in Figure B, the decline in Black worker annual earnings associated with an uptick in the unemployment rate is an even larger decline than the decline in Black worker hourly earnings. Applying the same counterfactual scenarios of unemployment rates that average 1, 1.5, and 2 percentage points lower over the 1973–2019 period yields dramatic results for the median Black–white annual earnings gaps, shown in Figure D. In this figure, the gaps are presented as ratios—how much Black workers earn as a share of what white workers earn.

Figure D

Lower average unemployment would substantially reduce the Black–white annual earnings gap: Ratio of Black to white annual median earnings in 1970 and 2019 (actual and under three counterfactual scenarios based on lower unemployment)

Black-white ratio
1970 62.4%
2019, actual 80.0%
1.0 ppt. lower 90.1%
1.5 ppt. lower 95.6%
2.0 ppt. lower 101.4%
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Notes: Annual earnings measure excludes workers who earned zero dollars in the year. Regression coefficients from Figure B are used to construct counterfactual wage and earnings growth based on lower unemployment, and the annual median earnings of Black workers are compared with the annual median earnings of white workers to estimate the gaps.  

Source: Author’s analysis based on data from Flood et al. 2021 and the Bureau of Labor Statistics (BLS-CPI 2021). 

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Between 1970 and 2019, the ratio of Black to white annual earnings rose from 62.4% to 80.0%. If the unemployment rate had averaged 1 percentage point lower after 1973, then this ratio could have surpassed 90% by 2019. If the unemployment rate had averaged 2 percentage points lower, the Black–white annual earnings ratio would have essentially been 1, indicating near-complete equality in this measure.

High-pressure labor markets and gaps in employment and unemployment

As we have frequently noted, the Black unemployment rate has been, on average, roughly twice the white unemployment rate since 1972 (the first year that Black unemployment is measured by the Bureau of Labor Statistics). Further, as shown in Figure E, this rough 2-to-1 ratio prevails if one looks at the measure of “nonwhite” unemployment compiled by the BLS before 1972 (Black workers accounted for a very large majority of nonwhite workers over that pre-1972 period).

Figure E

Large racial gaps in unemployment rates are the norm: Unemployment rates for Black, white, and ‘nonwhite’ workers, and for Black workers had the composition of the Black labor force shared the same age, educational credentials, and gender mix as the white labor force, 1954–2020

Nonwhite Black Black adjusted White
1954 10.4% 5.1%
1955 9.7% 3.8%
1956 9.4% 3.6%
1957 7.6% 3.9%
1958 15.1% 6.1%
1959 13.1% 4.8%
1960 11.4% 4.9%
1961 14.0% 5.9%
1962 12.2% 4.9%
1963 11.9% 5.0%
1964 9.9% 4.7%
1965 8.6% 4.1%
1966 9.0% 3.3%
1967 8.0% 3.3%
1968 6.7% 3.2%
1969 7.1% 3.1%
1970 9.2% 4.5%
1971 10.8% 5.5%
1972 10.4% 5.0%
1973 9.4% 4.3%
1974 10.5% 5.0%
1975 14.8% 7.7%
1976 13.9% 12.5% 6.8%
1977 14.1% 12.7% 6.1%
1978 12.8% 11.4% 5.1%
1979 12.3% 10.8% 4.9%
1980 14.2% 12.5% 6.2%
1981 15.7% 13.8% 6.5%
1982 19.0% 16.9% 8.3%
1983 19.6% 17.4% 8.0%
1984 15.9% 13.8% 6.2%
1985 15.1% 13.1% 5.8%
1986 14.5% 12.5% 5.7%
1987 12.9% 11.1% 5.0%
1988 11.8% 9.9% 4.4%
1989 11.4% 9.7% 4.2%
1990 11.4% 9.7% 4.5%
1991 12.5% 10.7% 5.7%
1992 14.1% 12.1% 6.1%
1993 12.9% 11.1% 5.5%
1994 11.5% 9.8% 4.8%
1995 10.4% 8.8% 4.5%
1996 10.5% 8.9% 4.2%
1997 10.0% 8.5% 3.8%
1998 9.0% 7.5% 3.4%
1999 8.0% 6.8% 3.3%
2000 7.5% 6.4% 3.1%
2001 8.6% 7.2% 3.8%
2002 10.3% 8.9% 4.7%
2003 10.8% 9.4% 4.8%
2004 10.4% 9.1% 4.5%
2005 10.1% 8.6% 4.1%
2006 8.9% 7.5% 3.8%
2007 8.3% 7.1% 3.9%
2008 10.1% 8.9% 4.7%
2009 14.8% 13.2% 7.8%
2010 15.9% 14.2% 8.0%
2011 15.8% 13.8% 7.2%
2012 13.9% 12.0% 6.6%
2013 13.0% 11.1% 6.0%
2014 11.3% 9.7% 4.9%
2015 9.5% 8.2% 4.2%
2016 8.4% 7.2% 4.0%
2017 7.5% 6.5% 3.5%
2018 6.4% 5.7% 3.2%
2019 6.0% 5.3% 3.0%
2020 11.4% 10.2% 6.5%
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Notes: The adjusted Black unemployment rate is obtained by creating 80 demographic “cells” in the data using four age categories, five educational credential categories, two races, and two genders. Each demographic cell’s unemployment rate is constructed and its weight in the overall workforce is calculated. Black adjusted unemployment rate is then calculated by applying the white workforce’s weights on age, educational credentials, and gender cells. This essentially assigns the Black workforce an underlying structure of age, educational credentialing, and gender that is identical to the white workforce and recalculates the Black unemployment rate.

Source: Author’s analysis of CPS data from the Bureau of Labor Statistics (BLS-CPS 2021) and EPI (2021a).

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The Black–white unemployment ratio shrinks only slightly if one adjusts for age, educational credentials, and gender composition of the workforces. In the figure, this ratio is calculated by comparing the “Black adjusted” line (see figure note) with the line for the white unemployment rate. For example, between 1976 and 2019, the overall Black–white unemployment ratio averaged 2.3 while the adjusted ratio averaged 2.0. This is an improvement for sure, but a depressingly small one, at roughly 15%.

One conclusion that can be drawn from Figure E is that the ratio of Black to white unemployment is pretty stubborn: It does not seem to fall quickly during periods of labor market tightness (when all rates fall together). However, even if the Black-to-white unemployment ratio never moved, the raw gap in unemployment rates between Black and white workers (simply the Black unemployment rate minus the white unemployment rate) would shrink rapidly during periods of overall labor market tightness, and would expand rapidly during periods of overall labor market distress. At a minimum, this means that Black workers see disproportionate gains and losses from effective and ineffective macroeconomic stabilization policy, respectively. Thus, getting macroeconomic stabilization policy right is a key issue for racial equity.

Figure F confirms this intuition, using the output gap as a proxy for overall economic, and thus labor market, health.6 The output gap is a measure of how fully the economy’s resources are being utilized at any given point in time (resources including potential workers). Specifically, it is calculated as the quotient of actual gross domestic product (GDP) divided by a measure of potential GDP (what GDP could have been had the economy’s resources been fully utilized), minus 1. When actual GDP is lower than potential GDP, the output gap is negative. As actual GDP falls further and further behind potential GDP, the gap measure becomes more negative; as it comes closer to potential GDP, it rises.

Figure F

Black workers’ unemployment is more sensitive to overall conditions: Regression coefficient showing change in Black and white labor market indicators given a 1-percentage-point increase in the output gap

Outcome Regression coefficient
White -0.643
Black -1.571
Nonwhite -1.539
Black–white  -0.703
Nonwhite–white -0.896
Nonwhite–white (post-1971) -0.826
White 0.18
Black 1.2
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Note: Regression coefficients stemming from a regression (with Newey-West standard errors) of three-year centered average of labor market indicators on the three-year centered average of the output gap and a time trend.

Source: Author’s analysis of Current Population Survey data from Bureau of Labor Statistics (BLS-CPS 2021) and output data from the Congressional Budget Office (CBO 2021).

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Given this, as the gap rises, resource utilization increases and the overall unemployment rate generally falls. The figure shows the relationship between the overall output gap and Black and white labor market indicators. As can be seen, the Black unemployment rate is twice as responsive as the white unemployment rate to a change in the output gap: specifically, a 1-percentage-point increase in the output gap (moving actual GDP 1% closer to potential GDP) is associated with a 1.57-percentage-point decline in the Black unemployment rate, compared with a 0.64-percentage-point reduction in the white unemployment rate. Because the BLS measures the unemployment rate of Black workers only after 1971, we also include a measure of the responsiveness of what the BLS labels “nonwhite” unemployment—a series that goes back to 1954. In the years before 1972, Black workers made up more than 90% of those labeled nonwhite.7 The advantage of using a series with a longer historical perspective is that one can examine a period of very tight labor markets that were achieved in the mid-to-late 1960s. The overall unemployment rate, for example, fell to under 4% for three straight years in the late 1960s. In this longer time series, the overall responsiveness of the nonwhite unemployment rate to an increase in the output gap measure (-1.54) is quite close to the responsiveness of Black unemployment in the more recent series.

These differential rates of responsiveness translate into substantial closing of the gap between Black and white unemployment rates when the overall economy tightens up, with this gap defined simply as the Black unemployment rate minus the white unemployment rate (i.e., the gap is by how many percentage points the Black unemployment rate exceeds the white unemployment rate). The figure shows that each percentage-point increase in the output gap (i.e., a tightening of the economy and labor market generally) is associated with a 0.70-percentage-point reduction in the Black–white unemployment gap. Each percentage-point increase in the output gap is also associated with a 0.90-percentage-point reduction in the nonwhite–white unemployment gap. It is possible that relatively greater responsiveness of the nonwhite–white unemployment gap is due to the inclusion of workers who are not Black in the nonwhite unemployment calculation. It is also possible that the difference is due to the longer time series available with the nonwhite–white unemployment rate gap. By restricting this series to just post-1971 data points (to make it consistent with the Black unemployment rate coverage), the responsiveness of the nonwhite–white unemployment gap to a 1-percentage-point reduction in the output gap shrinks to 0.83 percentage points.

Race-based differentials in the responsiveness of labor market indicators to a change in the output gap are even larger when examining the responsiveness of the Black and white employment-to-population ratios. The Black EPOP rises by 1.2 percentage points as the output gap increases, while the white EPOP rises by 0.18 percentage points, just over a seventh as much.

If the ratio of Black to white unemployment rates was constant, then the change in the gap between these rates would also just equal this ratio multiplied by the change in the white unemployment rate. Given the relative stubbornness of the Black–white unemployment ratio (for example, as seen in Figure E), it might seem that it is essentially constant regardless of the state of labor market pressure. But it may not be.

Can high-pressure labor markets reduce Black–white unemployment ratios, not just gaps?

Looking at the Black and white unemployment rates over time—like those displayed in Figure E—can easily convince observers that the ratio of Black to white unemployment is nearly constant. In good times and in bad, the Black unemployment rate looks to be roughly twice the white unemployment rate. But there are actually some reasons for optimism—tempered, to be sure—that this ratio is not as unyielding to change as it seems. For one, there seems to be a shallow but steady downward trend in this ratio over time. For another, more detailed evidence indicates that the Black–white unemployment ratio may indeed respond measurably to high-pressure labor markets. That evidence is highlighted in the discussion of the next two figures, which show the overall unemployment rate and the Black–white unemployment ratio (Figure G) and the nonwhite–white unemployment ratio (Figure H) prevailing at business cycle peaks. Both show a clear positive relationship between the overall unemployment rate and the respective ratios (i.e., an increase in one measure coincides with an increase in the other). All else equal, this would indicate that a higher-pressure labor market overall does indeed put downward pressure on the Black–white unemployment ratio.

Figure G

Lower unemployment is associated with a lower Black–white unemployment rate ratio: Overall unemployment and the Black–white unemployment ratio at business cycle peaks

Unemployment rate Black–white unemployment ratio
(1979) 5.9 2.4
(1989) 5.2 2.5
(2000) 4.0 2.1
(2007) 4.7 2.2
(2019) 3.6 1.7
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Source: Author’s analysis of Current Population Survey data from the Bureau of Labor Statistics (BLS-CPS 2021).

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Figure H

Half a century of data shows the connection between lower unemployment and a lower nonwhite–white unemployment rate ratio is not just a recent trend: Overall unemployment and the nonwhite–white unemployment rate ratio at business cycle peaks

Unemployment rate Nonwhite/white unemployment ratio
(1954) 5.5 2.0
(1959) 5.5 2.7
(1969) 3.5 2.3
(1979) 5.9 2.5
(1989) 5.2 2.5
(2000) 4.0 1.9
(2006) 4.7 2.0
(2019) 3.6 1.6

 

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Note: Because the BLS only measures the unemployment rate of Black workers after 1971, data years prior to 1972 reflect what the BLS labeled “nonwhite” unemployment. In the years before 1972, Black workers made up more than 90% of those labeled nonwhite.

Source: Author’s analysis of Current Population Survey data from the Bureau of Labor Statistics (BLS-CPS 2021).

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However, the positive relationship in Figure G is likely driven in some part by the trend in the Black–white unemployment ratio coinciding with the fact that, since 1976, later business cycles have consistently achieved lower unemployment rates. Between the business cycle peaks of 1979 and 2019, the Black–white unemployment rate ratio actually declined by a bit over 20% (as did the adjusted ratio, a ratio that estimates what the Black unemployment rate would have been had the composition of the Black labor force shared the same age, educational credentials, and gender mix as the white labor force). This trend likely would have led to successively lower Black–white unemployment ratios in 1989, 2000, and 2019 anyhow. But on top of this trend, unemployment rates in these business cycle years were successively lower over time. Given this, it is not clear if it is a given year’s unemployment rate or a long-running trend that drives the pattern in Figure G.

To test the connection, Figure H includes data on nonwhite unemployment back to 1959 and thus includes business cycles not characterized by uniformly lower overall unemployment rates over time. The strong positive relationship between rising overall unemployment and an increasing Black–white unemployment rate ratio still holds.

Figure I looks at the responsiveness of various labor market ratios (not gaps, as was analyzed above in Figure F) to changes in the output gap while controlling for a time trend. The first three data points come from a regression that used a lagged measure of the output gap. They show a significant decline in the Black–white and the nonwhite–white unemployment rate ratios associated with each percentage-point increase in the output gap (remember, as the economy improves and actual GDP gets closer and closer to potential GDP, the output gap rises). As before, this analysis includes a look at the coefficient on the nonwhite–white unemployment ratio from this regression just in the years after 1971 to see if some of the difference between its responsiveness and the responsiveness of the Black–white unemployment ratio is simply due to different timespans. The responsiveness of the nonwhite–white unemployment ratio is roughly same (but actually increases slightly) when just looking at the post-1971 period.

Figure I

Key race-based ratios reflecting labor market inequity respond to overall conditions: Regression coefficients showing change in race-based unemployment, employment-to-population, and labor force participation ratios given a 1-percentage-point increase in the output gap

Outcome Regression coefficient
Black–white ratio -0.061
Nonwhite–white ratio -0.067
Nonwhite–white ratio (post-1971) -0.072
Black–white ratio 0.012
Nonwhite–white ratio 0.013
Nonwhite–white ratio (post-1971) 0.009
Black–white ratio 0.0045
Nonwhite–white ratio 0.0075
Nonwhite–white ratio (post-1971) 0.0036
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Note: All regressions use three-year centered averages and include the output gap as estimated by the Congressional Budget Office as an explanatory variable, as well as a time trend.

Source: Author’s analysis of Current Population Survey data from the Bureau of Labor Statistics (BLS 2021) and output data from the Congressional Budget Office (CBO 2021).

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The results from estimating the responsiveness of ratios of EPOPs and labor force participation rates (LFPRs) demonstrates a similar pattern as that shown by Black–white employment gaps. When the output gap rises, the Black–white ratio of EPOPs rises, meaning that the share of Black persons employed approaches closer to the share of white persons employed. A similar increase holds for the ratio of nonwhite-to-white EPOPs and, again, the responsiveness of this ratio falls a bit when only the post-1971 period is examined. The ratio of Black to white LFPR rises when output gaps rise as well, meaning Black labor force participation approaches closer to white labor force participation. Again, the responsiveness of the ratio of nonwhite–white LFPRs is greater than for the Black–white ratios, but much of this seems due to the different time periods; when only post-1971 years are included, the responsiveness is very similar.

The upshot of this examination is that there is some suggestive evidence that even key labor market ratios (not just absolute gaps) that compare labor market performance for Black and white workers might indeed narrow during periods of high-pressure labor markets. This more hopeful interpretation may have been missed by those looking only at raw measures of the Black and white unemployment rates over time (like those shown in Figure E).

First, the post-1971 period might not contain enough episodes of truly tight labor markets to allow the relationships between high-pressure labor markets and the Black–white unemployment ratio to be well estimated. Figure I has some slight suggestive evidence of this: The responsiveness of the nonwhite–white EPOP and LFPR is greater when the pre-1971 period is included. This pre-1971 period includes a long stretch in the 1960s when unemployment was beneath 4% for four straight years (1966–1969). Further, Aizer et al. (2020) found that many racial gaps in labor markets (like the Black–white earnings gap and the measures of occupation segregation) fell significantly in the 1940s. These declines were concentrated in areas with more defense spending. This spending not only contributed to tighter labor markets but also often came attached with anti-discrimination conditions. The authors could not disentangle the precise effect of each of these influences, but the large spillovers of reduced race-based gaps in industries not directly affected by the defense spending suggests a large role for high-pressure labor markets generally. And the U.S. labor market of the 1940s was high pressure in a way not seen since: The unemployment rate was below 2% for three straight years between 1943 and 1945. In short, since we have begun measuring the Black unemployment rate specifically, we just may not have seen enough periods of genuinely high-pressure labor markets to get a solid statistical read on what happens to the Black–white unemployment ratio when labor markets get truly tight and are kept that way for a sustained period of time.

Second, while the employment rate of Black workers rises significantly faster than for white workers as the output gap rises, the labor force participation rate of Black workers also rises faster, muting any disproportionate decline in unemployment for Black workers. If the measure is simple joblessness and not unemployment, then it seems clear that the Black–white jobless ratio clearly declines when labor markets tighten up.

Part of the reason why the stronger responsiveness of the Black–white EPOP to output gap increases translates weakly into a reduction in the Black–white unemployment ratio is likely due to different dynamics of labor force participation over the business cycle. A recent paper by Cajner, Coglianese, and Montes (2020) makes a significant contribution in our understanding of the cyclical behavior of labor force participation. Their main finding is that the LFPR is indeed affected by the state of labor market tightness, but that it responds substantially more slowly to positive or negative shocks than employment or unemployment. They further find that the Black LFPR responds substantially more strongly to a negative shock to the overall labor market. So when economic growth slows and the labor market develops slack, the rise in the Black unemployment rate relative to the white unemployment rate can be somewhat muted because of a larger labor supply response from Black workers. This means that the Black–white unemployment ratio may actually fall during recessions. Just to buttress this point, it is striking that the two lowest annual Black–white unemployment ratios on record occurred in 2009 and 2020—two of the worst years for economywide labor market health in the past 70 years or more.

As recoveries begin, Black EPOPs respond more strongly to improving economic conditions. All else equal, this should lead to a reduction in the Black–white unemployment ratio. But because the Black LFPR recovers more quickly than the white LFPR , the progress in reducing the racial unemployment gap is blocked by faster labor force growth among Black workers. By the time late in recoveries when labor markets are getting tight again, the white LFPR likely begins recovering more strongly, which allows for a reduction in the Black–white unemployment ratio. This modestly complicated series of dynamics likely explains part of why the salutary effect of lower unemployment rates on the Black–white unemployment ratio might be harder to detect in a simple eyeballing of trends. In 2019, the last business cycle peak, the Black–white unemployment ratio hit its lowest point at any business cycle peak on record. This likely reflects both a shallow but nontrivial downward trend over time and pressure that tight labor markets put on compressing the ratio. Additionally, the prolonged (if too slow) recovery following the Great Recession allowed ample time for the white LFPR to recover from the negative shock of the Great Recession and to stop putting downward pressure on the white unemployment rate.

Policy implications

The upshot of this examination is that sustained periods of high pressure in U.S. labor markets might significantly narrow racial gaps in unemployment and other key labor market measures. Given the long history of structural racism in the United States and the intentional policy efforts that created these gaps, it seems incumbent upon policymakers to use every tool available to try to close them. High-pressure labor markets look as promising as (or more promising than) any other tool. The large benefits—moral, political, and economic—of closing these labor market gaps call upon macroeconomic policymakers to consider them when assessing the benefits and costs of a “go for growth” strategy targeting high-pressure labor markets. To be explicit: The potential of more aggressive expansionary macroeconomic policy to help close race-based gaps in the labor market is worth taking on more risk of sparking inflation.

This policy recommendation for macroeconomic policymakers (including the Federal Reserve) to take on extra inflation risk in the name of narrowing racial gaps in the labor market is likely frustratingly imprecise to some. Some policymakers would prefer the clarity of, say, a numerical target for the Black unemployment rate. However, excess confidence in the ability of macroeconomic policymakers to use hard-and-fast ex ante labor market targets that precisely define “high pressure” has backfired in the past. Specifically, that unfounded confidence is a prime reason why labor markets were kept too slack for so long in recent decades, as hard targets such as estimates of the NAIRU turned out to be wrong, leading to unemployment rates in excess of what was needed for reasonable inflation control. Further, if using overall unemployment rates as precise labor market targets has proven to lead to unsatisfactory outcomes (and it has), using the Black unemployment rate as a specific target might be even worse, as one would be implicitly targeting not only the overall rate, but also how robustly the ratio between the Black and the overall rate changed as overall unemployment rose and fell depending on labor market conditions.

One of the most direct and thoughtful calls for having the Federal Reserve aim for narrower racial gaps in the labor market was by Bernstein and Jones (2020b). Their paper is often described as calling on the Fed to “target the Black unemployment rate,” but it does so only in the sense described above: It calls upon the Fed to consider the benefits of narrower gaps, and explicitly make them part of their criteria for decision-making. As the authors explain, “It is not just asking the chair to tell us about the gaps; it requires him or her to make closing them a part of their mandate” (Bernstein and Jones 2020a).

These sensible calls to narrow labor market gaps do raise an important question: Why is there reticence to demand that macroeconomic policymakers achieve a full elimination of labor market gaps? The answer is because it is unlikely that macroeconomic policy by itself can neutralize the centuries-long legacy of structural racism. This legacy has led to disadvantages Black workers face along numerous margins in the labor market, and while high-pressure labor markets can help to ameliorate these disadvantages, high-pressure labor markets likely cannot completely undo them before inflationary pressures require some moderating of expansionary policy.

For example, some of the gap in unemployment rates between Black and white workers represents differing levels of educational credentials. As we showed earlier (Figure E), adjusting the Black unemployment rate under a scenario that gives the Black and white workforces the same age and educational profiles does reduce the Black–white unemployment ratio by a small amount, around 15%. This gap in educational credentials obtained by Black and white workers is itself largely a function of historic discrimination, but it is unlikely to be solved simply by boosting aggregate demand. Further, even at the same level of educational credentials, it is certainly possible for the quality of educational investments to differ systematically between Black and white workers. Research has shown that educational investments are not only larger in white neighborhoods, but they have also been systematically reduced in schools with larger shares of Black students.8 Thus it seems likely that equalizing labor market outcomes will require interventions over and above expansionary macroeconomic policy to address the differences in education investment.

Despite these caveats, the results in this paper and previous research clearly show that expansionary macroeconomic policy can have profoundly equalizing effects. It also seems clear that policymakers have not fully accounted for these benefits when weighing benefits against the potential cost of sparking inflationary pressure. Further, ignoring these potential benefits may even result in worse analytical forecasting. Concepts like the natural rate of unemployment and the level of potential output for the U.S. economy often are estimated by assuming a given Black–white unemployment gap that does not close as the economy heats up.

An oft-cited example is the Congressional Budget Office (CBO) estimate of the natural rate of unemployment. The CBO assumes the overall unemployment rate reached in 2005 is consistent with the economy’s natural rate. It then takes group-specific unemployment rates that prevailed in 2005 and allows the overall natural rate to change only as the group-specific shares of the labor force change over time due to demography or immigration flows (Shackleton 2018). In some sense, this method implicitly assumes that group differences in unemployment that prevailed in 2005 are set in stone. Some have gone so far as to call this assumption racist. This seems wrong. The existence of the gaps is evidence of racism. But it would be odd indeed to ignore them entirely when doing forecasting given how persistent they have been. Instead, it seems that this assumption of ever-persisting gaps is evidence more of pessimism (much of it arguably well-earned) than of racism. 

But if these gaps do indeed close further as labor markets enter high-pressure periods, then any estimate of the natural rate of unemployment can be lower and estimates of potential output can be higher than one would otherwise forecast. In essence, we will never know the full extent of other policy interventions that need to be done to foster racial equity until we have fully maximized the reach of high-pressure labor markets. To put this another way, we won’t even know the size of the Black–white unemployment gap until we are sure we have reached the lowest rate of overall unemployment consistent with sustainable inflation. And yet for the vast majority of years over recent decades, we have not been close to this minimum unemployment rate.

In recent months, handwringing about the possibility of eventually “overheating” the U.S. economy due to excessively generous fiscal stimulus has begun. It is true that we are not completely certain about how low unemployment can go or how much the economy’s supply side will respond to growth in aggregate demand—so signs of overheating should indeed be monitored. But we should be very cautious about premature declarations of overheating. We have not seen sustained and broad-based wage and price inflation in the U.S. economy for decades. And we now know much more than in previous years about just how equalizing a high-pressure labor market can be, both for compressing wage growth among low-, middle-, and high-wage workers and for closing race-based gaps in labor market measures. These benefits are utterly enormous, and maximizing them is worth the risk of being very patient before aiming to deflate high-pressure labor markets through policy.

Endnotes

1. See Bivens 2016 for the central role of fiscal policy in conditioning economic growth after the Great Recession of 2008–2009.

2. See Bivens and Zipperer 2018 for some evidence of this.

3. See Mishel and Bivens 2021 for the central role of low-pressure labor markets in suppressing wage growth for most of the post-1979 period. See Gould 2020 for a broad overview of wage trends over the same period.

4. See Wilson 2015 for evidence on this.

5. While the differences between the coefficients are not statistically significant at most conventional levels, the difference in magnitude is economically large and is consistent and robust across the various regression specifications and time periods.

6. We switch to using an output gap measure for the state of the overall economy in this section because there is an arithmetic relationship between the overall unemployment rate and disaggregated measures of unemployment and employment by group. When, for example, the Black unemployment rate falls, this will by definition also reduce the measured overall unemployment rate. Our output gap measure does not have any arithmetic relationship to disaggregated labor force measures, so we use it for the rest of this paper.

7. See Hobbs and Stoop 2002 for evidence of this.

8. See Derenoncourt 2021 for evidence that as neighborhoods’ share of Black residents increased over time, public investments shifted more heavily toward policing and incarceration and white students saw higher enrollments in private schools. See Johnson 2011 for evidence that racial segregation led to lower resources for students in schools with higher shares of Black students.

References

Aizer, Anna, Ryan Boone, Adriane Lleras-Muney, and Jonathan Vogel. 2020. “Discrimination and Racial Disparities in Labor Market Outcomes: Evidence From WWII.” National Bureau of Economic Research (NBER) Working Paper #27689, August 2020, https://doi.org/10.3386/w27689.

Bernstein, Jared, and Janelle Jones. 2020a. “The Federal Reserve Could Help Make the Job Market Fairer for Black Workers.” Washington Post, June 15, 2020.

Bernstein, Jared, and Janelle Jones. 2020b. The Impact of the Covid-19 Recession on the Jobs and Incomes of Persons of Color. Groundwork Collaborative and Center on Budget and Policy Priorities. Policy Futures Report. June 2020.

Bivens, Josh. 2016. Why is Recovery Taking So Long—and Who’s to Blame? Economic Policy Institute. August 2016.

Bivens, Josh, and Ben Zipperer. 2018. The Importance of Locking in Full Employment for the Long Haul. Economic Policy Institute. August 2018.

Bureau of Labor Statistics, Consumer Price Index (BLS-CPI). 2021. Public data series for various years accessed through the CPI National Databases and through series reports. Accessed January 2021.

Bureau of Labor Statistics, Current Population Survey (BLS-CPS). 2021. Public data series for various years accessed through the CPS National Databases and through series reports. Accessed January 2021.

Bureau of Labor Statistics, Productivity and Costs by Major Sector. (BLS-LPC). 2021. Public data series for various years accessed through the LPC National Databases and through series reports. Accessed January 2021.

Cajner, Tomaz, John Coglianese, and Joshua Montes. 2020. “The Long-Lived Cyclicality of the Labor Force Participation Rate.” Working Paper. October 2020.

Congressional Budget Office (CBO). 2021. Historical Data and Economic Projections (online database). Accessed February 2021.

Derenoncourt, Ellora. 2021. “Can You Move to Opportunity? Evidence from the Great Migration.” Working Paper. February 2021.

Economic Policy Institute (EPI). 2021a. Current Population Survey Extracts, Version 1.0.18, https://microdata.epi.org.

Economic Policy Institute (EPI). 2021b. State of Working America Data Library.

Flood, Sarah, Miriam King, Renae Rodgers, Steven Ruggles, and J. Robert Warren. Integrated Public Use Microdata Series, Current Population Survey: Version 7.0 [data set]. Minneapolis, MN: IPUMS, 2021. https://doi.org/10.18128/D030.V7.0

Gould, Elise. 2020. State of Working America 2019: A Story of Slow, Uneven, and Unequal Wage Growth Over the Past 40 Years. Economic Policy Institute. February 2020.

Hobbs, Frank, and Nicole Stoop. 2002. Demographic Trends in the 20th Century. U.S. Census Bureau. November 2002.

Johnson, Rucker. 2011. Long-Run Impacts of School Desegregation and School Quality on Adult Attainments. National Bureau of Economic Research Working Paper #166664.

Mishel, Lawrence, and Josh Bivens. 2021. Identifying the Policy Levers Generating Wage Suppression and Wage Inequality. Economic Policy Institute. May 2021.

Robinson, Joan. 1936. “Disguised Unemployment.” The Economic Journal 36, no. 182): 225–237.

Shackleton, Robert. 2018. “Estimating and Projecting Potential Output Using CBO’s Forecasting Growth Model.” Congressional Budget Office Working Paper Series. February 2018.

Wilson, Valerie. 2015. The Impact of Full Employment on African American Employment and Wages. Report for the Full Employment Project at the Center on Budget and Policy Priorities. March 2015.


See related work on Recession/stimulus | Job creation | Regulation | Macroeconomics | Monetary policy and the Federal Reserve | Unemployment | Labor force participation | Black Americans | Wages

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