Understanding black-white disparities in labor market outcomes requires models that account for persistent discrimination and unequal bargaining power

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Unequal Power

Part of the Unequal Power project, an EPI initiative to reestablish the understanding in law, politics, economics, and philosophy, that equal bargaining power between workers and employers does not exist. Recognizing this inherent workplace inequality will bolster freedom, economic fairness, workplace protections and democracy.

Executive summary 

The assumption of a perfectly competitive labor market is central to some of the most widely accepted theories in the field of labor economics. But the persistent threat of unemployment, in combination with prohibitive conditions imposed by employer practices, public policy, incomplete information about job opportunities, and geographic immobility, means that workers often cannot change jobs or employers easily and without cost.

This imbalance of power disproportionately disadvantages black workers: One of the most durable and defining features of the U.S. labor market is the 2-to-1 disparity in unemployment that exists between black and white workers. Attempts to explain the gap often cite observed average differences in human capital—particularly, education or skills—between black and white workers as a primary cause. But African Americans have made considerable gains in high school and college completion over the last four-and-a-half decades—both in absolute terms as well as relative to whites—and those gains have had virtually no effect on equalizing employment outcomes. Indeed, the significant racial disparities in unemployment that are observed at each level of education, across age cohorts, and among both men and women are the strongest evidence against the notion that education or skills differentials are responsible for the black-white unemployment gap.

Another defining feature of racial inequality in the labor market is the significant pay disparities between black and white workers. In 2019, the typical (median) black worker earned 24.4% less per hour than the typical white worker. This is an even larger wage gap than in 1979, when it was 16.4%. Controlling for racial differences in education, experience, and the fact that black workers are more likely to live in lower-wage Southern states leaves an unexplained gap of 14.9% in 2019 (out of a total average gap of 26.5%). This is up from an unexplained gap of 8.6% in 1979 (out of a total average gap of 17.3%). Any simple or rational explanation for this disparity is further complicated by the fact that racial wage gaps among men are significantly larger than among women.

Racial wage gaps also have widened amid the broader trend of growing wage inequality, as black workers have reaped even fewer gains from increased aggregate productivity than white workers. While net productivity per hour worked increased 69.6% (1.8% per year) between 1979 and 2019, median wages grew by only 14% (0.4% per year). Over this same time, the median wage of black workers grew at a meager 5.2% (0.1% per year) and the median wage of white workers grew 20.0% (0.5% per year).

The economic theories most often invoked to explain observed racial differentials in labor market outcomes are human capital theory, taste-based models of discrimination, and statistical models of discrimination. But each of these models falls short in its attempt to explain long-standing racial disparities in unemployment and pay while blatantly denying the persistence of discrimination. Despite compelling empirical evidence and a solid historical record that points to discrimination as a significant factor in the persistence of racial disparities in the labor market, the interpretation of those disparities is an ongoing debate in the field of economics.

When economists get a statistically significant coefficient on race after estimating a wage equation that controls for standard measures of individual productive capacity (e.g., education, experience) and macroeconomic conditions (e.g., state or regional fixed effects), as well as race and gender, what does that mean? Do we interpret that coefficient as evidence of racial discrimination, or does it reflect some unobserved or omitted variable? Devotees of the conventional economic theories described above tend to dismiss discrimination as a valid or significant explanation of the gaps in favor of the latter interpretation. But if there is some unobserved variable that would explain the statistically significant coefficient on race, it would also have to be strongly correlated with race. In its most basic form, race is nothing more than a socially constructed identifier, defined in the United States primarily by skin color—an arbitrary and superficial physical characteristic that has no relationship to one’s productive capacity. How then should we interpret that correlation?

Stratification economics was developed in response to the inadequacy of conventional economic theory to explain intergroup inequality in general and the persistence of racial disparities in particular. According to stratification economics, while discrimination is unjust, it also serves the functional role of preserving hierarchy. Therefore, persistent racial inequality arises when a dominant group seeks to maintain the hierarchy that affords it some degree of social or economic privilege. Under this framework, identity can be structured so that investing in, or associating with, a group identity can lead to economic returns and benefits. This treatment of identity as endogenous represents a major departure from more conventional economic models but is consistent with a set of alternative theories for explaining stubborn racial gaps in economic outcomes.

When we look at race and labor market discrimination in the context of workers’ bargaining power, it is important we recognize there are at least two complementary goals. With respect to wages, we want to shift the balance of power in a way that puts upward pressure on wages—particularly for wage earners at or below the median—and at the same time close racial wage gaps. We cannot rely on competitive markets alone to do this. Rather, interventions are required to address these inequalities. Appropriate design of those interventions requires that we expand the frameworks we use for understanding power, race, gender, and inequality so that we restructure systems and institutions to prevent discriminatory outcomes rather than enable them. 

Introduction 

The assumption of a perfectly competitive labor market is central to some of the most widely accepted theories in the field of labor economics. On the demand side of this market structure are many firms seeking to fill identical jobs. On the supply side are many workers who possess the same set of requisite skills for a given job opening, all of whom have perfect information about wages and job conditions and are able to move their labor freely, or without cost. The equilibrium price and quantity of labor—the market wage and level of employment, respectively—are those at which the amount of labor supplied by workers is equal to the amount of labor demanded by firms. Workers are paid the marginal product of their labor, and, in the long run, such a perfectly competitive labor market is theoretically at “full employment,” since all who are willing to work at the market wage can find a job that pays that wage.

Under these market conditions, employers are assumed to be wage takers: They are unable to hire or retain workers for less than the going market wage because no workers would willingly accept a job for less when they could easily transfer their labor to a competing firm that pays the market wage. In such markets, any differences in wages must be due to differences in productivity-related human capital.

In reality, however, labor market structures are far from perfectly competitive, and employers are rarely so powerless as to have no discretion in setting wages below marginal productivity or in paying different wages to equally productive and qualified workers. Since there is no absolute empirical standard of full employment—a condition implied by perfect competition—economists often disagree over what the “full employment” unemployment rate is or should be. Nevertheless, the nation’s actual unemployment rate has been above even the Congressional Budget Office’s far-too-conservative estimates of the “natural rate of unemployment”—i.e., the NAIRU, the nonaccelerating inflation rate of unemployment—more often than not during the last 40 years (Bivens and Zipperer 2018), a period characterized by rising inequality. Indeed, since 1979 the monthly unemployment rate has been below 6% only approximately half the time; for blacks the rate has fallen below 6% for only a brief five months preceding the Covid-19 pandemic, and it has remained nearly double the national rate as racial wage gaps have widened.

The threat of unemployment, in combination with prohibitive conditions imposed by employer practices, public policy, incomplete information about job opportunities, and geographic immobility, means that workers often cannot change jobs or employers easily and without cost. This imbalance of power between employers and employees disproportionately disadvantages black workers when racial identity is used to assign privilege or disadvantage in the labor market context.

This paper presents persistent racial inequality in unemployment and wages as outcomes that have been ignored or dismissed and remain unsatisfactorily explained by conventional economic theory. For instance:

  • The significant racial disparities in unemployment that are observed at each level of education, across age cohorts, and among both men and women are the strongest evidence against the notion that education or skills differentials are responsible for the black-white unemployment gap.
  • Less than half of the observed black-white difference in average hourly wages is explained by differences in education, experience, or region—the main factors presumed to determine pay. However, changes in the racial wage gap track closely with changes in policy, such as civil rights enforcement, and with structural trends contributing to greater wage inequality. 
  • Black workers in the public sector face smaller unexplained wage gaps than their counterparts in the private sector. Historically, the appeal of better job opportunities and greater pay equity in the public than in the private sector has contributed to black workers’ disproportionate employment share in the public sector as well as higher average rates of union membership.
  • The economic theories most often invoked to explain the observed racial differentials in labor market outcomes—human capital theory, taste-based models of discrimination, and statistical models of discrimination—are historically and empirically inconsistent with the persistence of black-white wage disparities. 
  • In contrast to conventional economic models, stratification economics treats group identity (race) as a construct and acknowledges the persistence of racial inequality resulting from discrimination’s functional role in preserving a hierarchy that benefits the dominant group.

The paper proceeds as follows. Section I summarizes the data describing racial disparities in unemployment, and Section II looks at wages. In Section III we highlight examples of how disparities in bargaining power play out for black workers. In Section IV we review the prevailing economic theories used to interpret racial inequalities in labor market outcomes and present challenges to and shortcomings of those theories. In Section V we present stratification economics as a more appropriate framework for understanding the imbalance of power inherent in the social structures that perpetuate racial inequality in labor market outcomes. 

I. Racial disparities in unemployment

One of the most durable and defining features of the U.S. labor market is the large and persistent disparity in unemployment that exists between black and white workers. This disparity is well-documented in decades of publicly reported official estimates from the Bureau of Labor Statistics (BLS) dating back to 1954, when the agency first began reporting rates of unemployment by race (i.e., white and nonwhite1). In 1972, BLS began disaggregating the nonwhite unemployment rate and reporting an unemployment rate for blacks alone. According to this measure, black job seekers are about half as likely to secure employment during a consecutive four-week search period as are white job seekers. Figure A illustrates this pattern, showing that the ratio between the black and white unemployment rates has consistently been about 2-to-1 since 1972. The pattern has persisted across multiple periods of economic growth and contraction, including in 2019 when, after 10 years of job growth, the black unemployment rate fell to a historic low of 6.1% but was still twice as high as the white unemployment rate of 3.0%.

Figure A

Unemployment rate by race, 1972–2019

B-W ratio Black White
1972 2 10.4% 5.1%
1973 2.2 9.4 4.3
1974 2.1 10.5 5
1975 1.9 14.8 7.8
1976 2 14 7
1977 2.3 14 6.2
1978 2.5 12.8 5.2
1979 2.4 12.3 5.1
1980 2.3 14.3 6.3
1981 2.3 15.6 6.7
1982 2.2 18.9 8.6
1983 2.3 19.5 8.4
1984 2.4 15.9 6.5
1985 2.4 15.1 6.2
1986 2.4 14.5 6
1987 2.5 13 5.3
1988 2.5 11.7 4.7
1989 2.5 11.4 4.5
1990 2.4 11.4 4.8
1991 2 12.5 6.1
1992 2.2 14.2 6.6
1993 2.1 13 6.1
1994 2.2 11.5 5.3
1995 2.1 10.4 4.9
1996 2.2 10.5 4.7
1997 2.4 10 4.2
1998 2.3 8.9 3.9
1999 2.2 8 3.7
2000 2.2 7.6 3.5
2001 2 8.6 4.2
2002 2 10.2 5.1
2003 2.1 10.8 5.2
2004 2.2 10.4 4.8
2005 2.3 10 4.4
2006 2.2 8.9 4
2007 2 8.3 4.1
2008 1.9 10.1 5.2
2009 1.7 14.8 8.5
2010 1.8 16 8.7
2011 2 15.8 7.9
2012 1.9 13.8 7.2
2013 2 13.1 6.5
2014 2.1 11.3 5.3
2015 2.1 9.6 4.6
2016 2 8.4 4.3
2017 2 7.5 3.8
2018 1.9 6.5 3.5
2019 2 6.1 3
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Note: Black and white are both non-Hispanic.

Source: 1972–2019 series by race & ethnicity, EPI analysis of monthly CPS microdata, downloaded from EPI SWA Data Library.

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Attempts to explain the black-white unemployment rate gap often cite observed average differences in human capital—particularly, education or skills—between black and white workers as a primary reason for the disparity. While conventional human capital theory does not explain the presence of unemployment apart from wage or price rigidities, patterns of unemployment by educational attainment and age are clearly documented in national statistics. According to these data, those with higher levels of education and more potential work experience, as indicated by their age, tend to have lower rates of unemployment than those with lower levels of education and less work experience.

However, observed racial differences in education fail to account for the 2-to-1 black-white unemployment rate disparity, and an exposition of trends in educational attainment and unemployment by race helps to clarify why this explanation falls short. African Americans have made considerable gains in high school and college completion over the last four-and-a-half decades—both in absolute terms as well as relative to whites—but those gains have had virtually no effect on equalizing employment outcomes between black and white workers.

Figure B shows that in 1972 fewer than four out of 10 (36.6%) African American adults age 25 or older had a high school diploma. By 2019 that share had grown to almost nine out of 10 (87.9%); among 25- to 29-year-old African Americans the share had grown to more than nine out of 10 (91.0%), indicating a continuation of the longer-term upward trend. This large increase in high school completion rates among black students helped to narrow the black-white difference in high school completion. In 1972, African Americans trailed whites by 23.8 percentage points (60.4% of whites, compared with 36.6% of blacks). In the most recent data, the difference is only 6.7 percentage points (94.6% for whites versus 87.9% for African Americans).2 

Figure B

Share of population with a high school diploma or higher, age 25 or older, 1972–2019

Year White Non-Hispanic white Black
1972 60.4 36.6
1973 61.9 39.2
1974 63.3 40.8
1975 64.5 42.5
1976 66.1 43.8
1977 67.0 45.5
1978 67.9 47.6
1979 69.7 49.4
1980 70.5 51.2
1981 71.6 52.9
1982 72.8 54.9
1983 73.8 56.8
1984 75.0 58.5
1985 75.5 59.8
1986 76.2 62.3
1987 77.0 63.4
1988 77.7 63.5
1989 78.4 64.6
1990 79.1 66.2
1991 79.9 66.7
1992 80.9 67.7
1993 81.5 84.1 70.4
1994 82.0 84.9 72.9
1995 83.0 85.9 73.8
1996 82.8 86.0 74.3
1997 83.0 86.3 74.9
1998 83.7 87.1 76.0
1999 84.3 87.7 77.0
2000 84.9 88.4 78.5
2001 84.8 88.6 78.8
2002 84.8 88.7 78.7
2003 85.1 89.4 80.0
2004 85.8 90.0 80.6
2005 85.8 90.1 81.1
2006 86.1 90.5 80.7
2007 86.2 90.6 82.3
2008 87.1 91.5 83.0
2009 87.1 91.6 84.1
2010 87.6 92.1 84.2
2011 88.1 92.4 84.5
2012 88.1 92.5 85.0
2013 88.6 92.9 85.1
2014 88.8 93.1 85.8
2015 88.8 93.3 87.0
2016 89.5 93.8 87.1
2017 90.1 94.1 87.3
2018 90.2 94.3 87.9
2019 90.5 94.6 87.9
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Note: In this chart, white is inclusive of those who also report Hispanic as their ethnicity. The non-Hispanic white series was only available beginning in 1993.

Source: 1978–2019 series by race & ethnicity, EPI analysis of monthly CPS microdata, downloaded from EPI SWA Data Library.

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College graduation rates have also increased for African Americans. Figure C shows that among those age 25 or older just 5.1% had a four-year college degree in 1972, but by 2019 that share had grown to 26.1%, a fivefold increase. Over the same period, college completion also expanded for whites, but the increase was just over threefold, from 12.6% in 1972 to 40.1% in 2019. As a result, the relative situation of African Americans also improved over this time: In 1972 blacks were just 40.5% as likely as whites to have a four-year college degree (12.6% for whites and 5.1% for blacks), compared to 71.9% today (40.1% for whites and 26.1% for blacks). 

Figure C

Share of the population age 25 or older with a bachelor’s degree or higher, by race and ethnicity, 1972–2019

White Non-Hispanic white Black
1972 12.6 5.1
1973 13.1 6.0
1974 14.0 5.5
1975 14.5 6.4
1976 15.4 6.6
1977 16.1 7.2
1978 16.4 7.2
1979 17.2 7.9
1980 17.8 7.9
1981 17.8 8.2
1982 18.5 8.8
1983 19.5 9.5
1984 19.8 10.4
1985 20.0 11.1
1986 20.1 10.9
1987 20.5 10.7
1988 20.9 11.2
1989 21.8 11.8
1990 22.0 11.3
1991 22.2 11.5
1992 22.1 11.9
1993 22.6 23.8 12.2
1994 22.9 24.3 12.9
1995 24.0 25.4 13.2
1996 24.3 25.9 13.6
1997 24.6 26.2 13.3
1998 25.0 26.6 14.7
1999 25.9 27.7 15.4
2000 26.1 28.1 16.5
2001 26.6 28.7 15.7
2002 27.2 29.4 17.0
2003 27.6 30.0 17.3
2004 28.2 30.6 17.6
2005 28.1 30.6 17.6
2006 28.4 31.0 18.5
2007 29.1 31.8 18.5
2008 29.8 32.6 19.6
2009 29.9 32.9 19.3
2010 30.3 33.2 19.8
2011 31.0 34.0 19.9
2012 31.3 34.5 21.2
2013 32.0 35.2 21.8
2014 32.3 35.6 22.2
2015 32.8 36.2 22.5
2016 33.7 37.3 23.3
2017 34.5 38.1 23.9
2018 35.2 38.8 25.2
2019 36.3 40.1 26.1
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Notes: In this chart, white is inclusive of those who also report Hispanic as their ethnicity. The non-Hispanic white series was only available beginning in 1993.

Source: U.S. Census Bureau, 1970 to 2002 March Current Population Survey, 2003 to 2020 Annual Social and Economic Supplement to the Current Population Survey (noninstitutionalized population, excluding members of the Armed Forces living in barracks).

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However, focusing only on the share of people with a four-year college degree obscures the broader shift to a more highly educated black workforce. Currently, a majority of black high school graduates (55.3%) go on to pursue some level of postsecondary education. More than one-fourth (29.2%) of African Americans age 25 or older have some college education, even if they are not bachelor’s degree holders. This includes 10.6% who earned a two-year associate degree.

Further, there is evidence that, compared to white youth from families with similar income levels, black students are actually more likely to seek higher levels of education, in part because they have fewer less-formal opportunities for economic advancement, such as social networks, family relationships, and institutional mechanisms (Mason 1997; Mangino 2010, 2012). But while these investments in higher education improve employment prospects for black college graduates relative to black noncollege graduates, on average they do not yield outcomes equivalent to those of similarly educated whites.

The significant racial disparities in unemployment that are observed at each level of education (Figure D) are the strongest evidence against the notion that education or skills differentials are responsible for the black-white unemployment gap. In terms of education, the black-white unemployment rate ratio has hovered around 2-to-1 at every level for most of the last 41 years. In that time, only black workers with advanced degrees have approached anything near parity with their white counterparts, as measured by the unemployment rate. In practical terms, this means that black workers are not just twice as likely to be unemployed as similarly educated white workers, but they are often more likely to be unemployed than less-educated whites.

Figure D

Black-white unemployment rate ratios by educational attainment, 1978–2019

Year Less than HS High school Some college Bachelor’s degree Advanced degree
1978 1.9 2.6 2.8 2.3 1.5
1979 1.8 2.5 2.7 2.1 1.2
1980 1.7 2.3 2.7 2.0 1.8
1981 1.7 2.5 2.6 2.2 1.3
1982 1.6 2.2 2.6 2.2 2.6
1983 1.6 2.5 2.5 2.4 1.9
1984 1.8 2.6 2.6 2.2 1.7
1985 1.8 2.6 2.7 2.1 1.4
1986 1.8 2.5 2.7 1.9 1.6
1987 1.9 2.5 2.6 2.1 1.5
1988 2.0 2.7 2.6 1.7 1.5
1989 2.1 2.7 2.4 2.0 1.6
1990 2.0 2.5 2.3 1.6 1.3
1991 1.7 2.1 2.1 1.6 1.5
1992 1.7 2.3 2.2 1.4 1.5
1993 1.8 2.3 2.1 1.4 1.6
1994 1.8 2.4 2.1 1.3 1.7
1995 1.9 2.3 2.2 1.4 1.5
1996 1.9 2.6 2.1 1.5 1.5
1997 2.1 2.5 2.3 2.0 1.7
1998 2.2 2.5 2.1 1.6 2.0
1999 2.2 2.4 1.9 1.6 1.4
2000 2.0 2.5 1.8 1.6 1.7
2001 2.1 2.2 1.8 1.4 1.1
2002 1.9 2.1 1.8 1.6 1.2
2003 1.8 2.2 2.0 1.5 1.6
2004 2.0 2.1 2.1 1.5 2.1
2005 2.0 2.3 2.1 1.6 1.6
2006 1.9 2.3 2.1 1.5 1.3
2007 1.7 2.1 1.8 1.6 1.6
2008 1.7 1.9 2.0 1.6 1.8
2009 1.6 1.7 1.7 1.8 1.6
2010 1.6 1.8 1.7 2.0 1.8
2011 1.8 2.0 2.0 1.8 2.0
2012 1.7 2.0 1.9 1.7 1.9
2013 1.9 2.0 1.9 1.7 1.7
2014 1.9 2.2 2.1 1.9 1.9
2015 2.0 2.3 2.0 1.6 2.2
2016 1.9 2.1 1.8 1.6 1.8
2017 1.8 2.1 1.8 1.7 1.7
2018 1.8 2.0 1.9 1.5 1.4
2019 1.8 2.1 1.7 1.5 1.4
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Source: 1978–2019 series by race & ethnicity, EPI analysis of monthly CPS microdata, downloaded from EPI SWA Data Library.

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The 2-to-1 ratio is also remarkably consistent across age cohorts (Figure E) and among both men and women (Figure F). Older black workers have lower rates of unemployment than younger black workers, but in every age cohort black workers remain roughly twice as likely to be unemployed as white workers.

Figure E

Black-white unemployment rate ratio by age cohort, 1978–2019

Year Age 16–24 Age 25–54 Age 55–64
1978 2.84158 2.27778 1.70370
1979 2.65000 2.34286 1.80000
1980 2.41667 2.20833 1.80000
1981 2.50394 2.32000 1.67647
1982 2.41060 2.16176 1.75000
1983 2.60563 2.25000 1.76000
1984 2.71930 2.43137 1.75610
1985 2.74312 2.41667 1.81579
1986 2.72642 2.36170 1.77778
1987 2.68367 2.40476 1.83333
1988 2.76136 2.55556 1.67857
1989 2.60227 2.72727 1.75000
1990 2.66667 2.43243 1.50000
1991 2.34513 2.10417 1.10256
1992 2.55752 2.21154 1.30435
1993 2.59048 2.12500 1.75000
1994 2.48485 2.30769 1.43243
1995 2.53125 2.19444 1.28125
1996 2.55319 2.38235 1.48276
1997 2.71264 2.53333 1.66667
1998 2.61250 2.48148 1.72727
1999 2.47436 2.32000 1.62500
2000 2.50000 2.33333 1.40909
2001 2.40698 2.16667 1.44444
2002 2.31633 2.02500 1.51429
2003 2.30392 2.15000 1.70270
2004 2.26531 2.29730 1.58824
2005 2.46739 2.29412 1.89655
2006 2.26966 2.29032 1.64286
2007 2.14607 2.09677 1.57143
2008 2.00000 2.10256 1.87879
2009 1.95918 1.85714 1.38333
2010 1.94118 2.00000 1.51563
2011 2.04225 2.26984 1.67241
2012 2.04545 2.10526 1.72549
2013 2.08661 2.17647 1.76596
2014 2.19626 2.28571 2.00000
2015 2.06383 2.34286 1.81250
2016 2.04706 2.11765 1.70968
2017 1.97368 2.20000 1.85185
2018 1.92857 2.03704 1.68000
2019 1.95652 1.92308 1.60870
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Source: 1978–2019 series by race & ethnicity, EPI analysis of monthly CPS microdata, downloaded from EPI SWA Data Library.

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Figure F

Unemployment rate by race and gender, 1978–2019

Year Black men, non-Hispanic White men, non-Hispanic Black women, non-Hispanic White women, non-Hispanic
1978 11.7% 4.4% 14.3% 6.1%
1979 11.2% 4.3% 13.6% 5.8%
1980 14.4% 6.0% 14.8% 6.4%
1981 15.9% 6.3% 15.9% 6.7%
1982 20.0% 8.5% 18.2% 8.0%
1983 20.3% 8.4% 19.0% 7.6%
1984 16.3% 6.2% 15.9% 6.3%
1985 15.3% 5.7% 15.0% 6.0%
1986 14.6% 5.6% 14.4% 5.7%
1987 12.8% 5.1% 13.3% 5.0%
1988 11.7% 4.4% 12.0% 4.4%
1989 11.6% 4.2% 11.3% 4.2%
1990 11.9% 4.6% 11.0% 4.4%
1991 13.1% 6.0% 12.1% 5.3%
1992 15.2% 6.5% 13.1% 5.6%
1993 13.8% 5.8% 12.2% 5.2%
1994 12.0% 4.9% 11.1% 4.7%
1995 10.8% 4.5% 10.2% 4.4%
1996 11.2% 4.3% 10.0% 4.1%
1997 10.3% 3.9% 10.0% 3.8%
1998 8.9% 3.5% 9.0% 3.4%
1999 8.2% 3.3% 7.8% 3.3%
2000 8.1% 3.1% 7.2% 3.2%
2001 9.3% 3.9% 8.0% 3.6%
2002 10.8% 5.0% 9.7% 4.5%
2003 11.6% 5.3% 10.0% 4.4%
2004 11.1% 4.7% 9.8% 4.3%
2005 10.7% 4.2% 9.5% 4.0%
2006 9.6% 3.9% 8.5% 3.8%
2007 9.2% 4.0% 7.5% 3.7%
2008 11.4% 5.0% 9.0% 4.4%
2009 17.5% 8.8% 12.4% 6.7%
2010 18.4% 8.9% 13.7% 7.0%
2011 17.8% 7.7% 14.1% 6.7%
2012 15.4% 6.8% 12.7% 6.3%
2013 14.2% 6.3% 12.2% 5.6%
2014 12.5% 5.1% 10.6% 4.7%
2015 10.4% 4.3% 9.0% 4.0%
2016 9.1% 4.1% 8.0% 3.8%
2017 8.3% 3.6% 6.9% 3.4%
2018 7.1% 3.3% 6.0% 3.0%
2019 6.7% 3.1% 5.6% 2.9%
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Source: 1978–2019 series by race & ethnicity, EPI analysis of monthly CPS microdata, downloaded from EPI SWA Data Library.

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These empirical data are consistent with field experiments revealing that black job applicants with equivalent, and sometimes superior, credentials to white applicants are less likely to receive job callbacks (Turner, Fix, and Struyk 1991; Fix, Galster, and Struyk 1993; Bendick, Jackson, and Reinoso 1994). Among the starkest findings in this regard is the audit study of Pager (2003), demonstrating that employers treated whites with criminal records more favorably than blacks without criminal records. Agan and Starr (2018) find that failure to distinguish between applicants with criminal records and those without, as is done through Ban the Box policies, actually reduces outcomes for black applicants without a criminal record. Though research on the impact of Ban the Box policies yields mixed results, discrimination against black workers remains the central unresolved issue. 

While audit and correspondence studies have been criticized for not adequately capturing unobserved characteristics that might influence hiring decisions, Neumark (2012) has shown how robust these findings can be to such considerations. Quillian et al. (2017) show that subsequent field experiments reveal a pattern of discrimination experienced by blacks in particular that has remained constant over time.

Together, these patterns strongly suggest that racial discrimination—and not inadequate education or lack of skills on the part of black workers—is the most plausible explanation for persistent racial disparities in unemployment. Moreover, currently observed racial differences in employment and education have been shaped by the United States’ long history of racial oppression that outright denied or severely limited black American access to the same formal educational and employment opportunities available to whites.

II. Racial disparities in pay 

Another defining feature of racial inequality in the labor market is the significant pay disparities between black and white workers. Most empirical research on black-white wage inequality has taken one of two approaches to estimating and explaining observed differences in pay. Trend analysis has focused on understanding the causes behind the black-white wage gap’s four distinct periods of change: the gap’s dramatic narrowing during the latter part of the 1960s through the 1970s, the reversal of that pattern during the 1980s, the brief period of improvement during the late 1990s, and the post-2000 expansion of the gap.

The other common approach has been to examine how much of the observed pay gap between black and white workers can be attributed to differences in so-called “cognitive skills.” In this section, we focus primarily on presenting patterns and trends in black-white wage disparities between 1979 and 2019. We provide our review and critique of the cognitive skills and human capital literature in subsequent sections.

In 2019, the typical (median) black worker earned 24.4% less per hour than the typical white worker. This is an even larger wage gap than in 1979, when it was 16.4%. Black workers face these significant and growing pay penalties relative to white workers even after controlling for characteristics assumed to be related to productive capacity, like education and experience.

As shown in Figure G, controlling for racial differences in education, experience, and the fact that black workers are more likely to live in lower-wage Southern states leaves an unexplained gap of 14.9% in 2019 (out of a total average gap of 26.5%). This is up from an unexplained gap of 8.6% in 1979 (out of a total average gap of 17.3%).

Figure G

Black-white wage gap, 1979–2019

Year B-W gap (average) B-W gap (regression-based)
1979 17.3% 8.6%
1980 17.4% 8.6%
1981 17.4% 8.2%
1982 19.1% 9.9%
1983 18.2% 9.3%
1984 19.0% 10.2%
1985 20.0% 10.6%
1986 20.3% 10.4%
1987 20.2% 10.5%
1988 19.7% 9.8%
1989 20.5% 10.7%
1990 21.2% 10.9%
1991 20.6% 10.8%
1992 20.4% 10.8%
1993 20.0% 10.8%
1994 20.0% 10.7%
1995 20.9% 10.7%
1996 23.0% 12.9%
1997 22.8% 12.1%
1998 21.8% 10.5%
1999 22.1% 10.7%
2000 21.8% 10.2%
2001 23.1% 11.5%
2002 23.2% 11.3%
2003 22.0% 10.7%
2004 22.1% 10.5%
2005 23.5% 12.3%
2006 22.1% 11.4%
2007 23.5% 12.2%
2008 24.1% 12.6%
2009 24.0% 11.7%
2010 23.9% 11.9%
2011 23.7% 12.4%
2012 24.6% 12.7%
2013 24.8% 13.1%
2014 24.8% 13.9%
2015 26.1% 14.5%
2016 26.2% 13.6%
2017 27.5% 15.5%
2018 27.5% 16.2%
2019 26.5% 14.9%
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Source: 1978–2019 series by race & ethnicity, EPI analysis of monthly CPS microdata, downloaded from EPI SWA Data Library.

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Any simple or rational explanation for this disparity is further complicated by the fact that racial wage gaps among men are significantly larger than among women. Over this same period, the unexplained black-white wage gap increased 7.3 percentage points among men (from 8.6% in 1979 to 14.9% in 2019) and 6.8 percentage points among women. Notably, the unexplained portion of the racial wage gap among women was minimal (1.4%) in 1979 but had expanded to 8.6% by 2019.

These patterns run counter to the notion that productive capacity, as measured by education specifically, is the prevailing determinant of wages. Less than half of the observed black-white difference in average hourly wages is explained by differences in education, experience, or region—some of the main factors presumed to determine pay. While black-white pay differentials are smaller among women than among men, the intersection of race and gender imposes much larger wage penalties for black women relative to white men. As shown in Figure H, in 2019 black women were paid 33.7% less than their white male counterparts, which was a much larger gap than that faced by either white women (25.7%) or black men (22.2%).

Figure H

Hourly wage gaps of black and white women and black men relative to white men (regression-adjusted), 1979–2019

Black men Black women White women
1979 14.9% 42.5% 39.7%
1980 15.6% 41.5% 38.8%
1981 14.7% 40.3% 37.8%
1982 17.1% 40.5% 36.6%
1983 16.2% 38.9% 35.5%
1984 16.6% 39.7% 35.2%
1985 18.6% 39.4% 35.4%
1986 17.7% 38.9% 35.0%
1987 17.5% 38.2% 34.3%
1988 16.0% 37.9% 33.5%
1989 17.8% 36.1% 31.1%
1990 17.7% 35.3% 29.8%
1991 18.4% 32.7% 28.3%
1992 18.4% 31.2% 26.7%
1993 18.2% 30.6% 25.7%
1994 16.7% 31.6% 25.4%
1995 17.5% 31.7% 26.4%
1996 19.8% 33.3% 25.9%
1997 19.5% 32.9% 26.4%
1998 17.3% 31.3% 25.9%
1999 17.7% 31.8% 26.5%
2000 17.8% 30.8% 26.7%
2001 18.6% 32.1% 25.7%
2002 17.8% 30.8% 24.6%
2003 18.0% 30.0% 24.8%
2004 17.7% 30.0% 25.3%
2005 19.4% 31.3% 24.5%
2006 18.4% 30.5% 24.8%
2007 19.8% 31.3% 25.4%
2008 20.5% 31.7% 25.1%
2009 19.7% 30.6% 25.2%
2010 19.1% 29.7% 23.9%
2011 18.7% 29.6% 22.8%
2012 19.2% 31.7% 24.4%
2013 20.2% 31.3% 23.9%
2014 20.9% 31.7% 23.7%
2015 21.0% 32.7% 24.5%
2016 19.9% 31.8% 24.2%
2017 22.2% 33.4% 23.8%
2018 23.6% 35.0% 25.4%
2019 22.2% 33.7% 25.7%
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Note: Black and white are both non-hispanic.

Source: Economic Policy Institute (EPI), 2020. Current Population Survey Extracts, Version 1.0.4, https://microdata.epi.org.

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The trend analysis research finds that changes in the racial wage gap track closely with changes in policy. The narrowing of the gap from the late 1960s through the 1970s can be attributed to the passage of important civil rights legislation (Bound and Freeman 1989; Card and Krueger 1992; Donohue and Heckman 1991), combined with the 1960s economic boom, active enforcement of anti-discrimination and affirmative action policy (Betsey 1994; Fosu 1992; Heckman and Payner 1989; Leonard 1990), and the narrowing of the educational attainment gap between blacks and whites (Carlson and Swartz 1988; Cunningham and Zalokar 1992; Zalokar 1990). On the other hand, the widening of the gap during the 1980s was the result of retrenchment on anti-discrimination policy (Leonard 1990), growing general wage inequality (Blau and Beller 1992), deterioration in the manufacturing sector, and a decline in union representation (Bound and Freeman 1989; Wilson and Rodgers 2016).

The expansion of the black-white wage gap since the 1980s, and certainly during the post-2000 period, is also consistent with the structural trends contributing to greater wage inequality. These include: (1) limited wage growth among middle- and low-wage workers (Gould 2020); (2) above-average growth among the highest-wage workers, particularly chief executive officers (CEOs) and the top 1% (Mishel and Kandra 2021); and (3) racial inequality in hiring, pay, and opportunities for promotion that results in overrepresentation of black workers among low- to middle-wage occupations and underrepresentation among high-wage occupations (Hamilton, Austin, and Darity 2011; Abayomi and Hawkins 2009).

The divergence of productivity growth and median hourly wage growth also points to growing wage inequality in a way that challenges assumptions about competitive labor markets. In competitive labor markets, where it is assumed that employers have no power to set wages below the market wage and workers are paid a wage equal to their marginal productivity, productivity and wages should move together. While this was generally true in the three decades following World War II, beginning around 1973 inflation-adjusted hourly compensation (including employer-provided benefits and wages) grew at a markedly slower rate than economywide productivity. This pattern is documented by Bivens and Mishel (2015), who estimate that over two-thirds of this productivity-pay gap can be explained by rising inequality, which is characterized by greater inequality in compensation and the fact that a smaller share of national income has been going to workers relative to capital owners.

The emergence of the productivity-pay gap calls into question the assumption of wage-taking behavior on the part of employers. An emerging literature on monopsony offers a broader interpretation of employers’ power that goes beyond the simple definition of labor market concentration (i.e., the proverbial one-company town). Rather, monopsony encompasses any power employers have that allows them to cut wages without fear of losing a large share of their workers. While there are several studies estimating employees’ likelihood to exit jobs in response to wage changes (Webber 2015, 2020; Dube, Giuliano, and Leonard 2019; Dube et al. 2020; Bassier, Dube, and Naidu 2020; Azar, Marinescu, and Steinbaum 2019; Langella and Manning 2020; Card et al. 2018; Sokolova and Sorensen 2020), Webber (2020, 18) succinctly concludes: 

The majority of firms compete for workers in labor markets where the typical employee is highly unlikely to move in response to small or even modest changes in their wage. This gives these firms considerable latitude to pay lower wages without worrying about a mass exodus of employees. 

Further, Bassier, Dube, and Naidu (2020) report that employers can “mark down” wages by anywhere from 20% to 50%. While evidence of rising monopsony power in the years since the late 1990s is mixed, studies consistently find that employers can exert more power over low-wage workers, affirming the link between employer power and wage inequalities. Among low-wage hourly workers, indirect wage cuts can also take place in the form of unstable work hours that are too low to qualify the worker for employer-provided benefits, such as health insurance or retirement savings. 

Racial wage gaps also have widened amid the broader trend of growing wage inequality, as black workers have reaped even fewer gains from increased aggregate productivity than white workers. Figure I shows that between 1979 and 2018 median hourly real wage growth fell far short of productivity growth. While net productivity per hour worked increased 69.6% (1.8% per year) during this period, median wages grew by only 14% (0.4% per year). Over this same time, the median wage of black workers grew at a meager 5.2% (0.1% per year) and the median wage of white workers grew 20.0% (0.5% per year).

Figure I

Productivity and median wage growth by race, 1979–2018

Year White median Black median Net productivity per hour worked
1979 0.0% 0.0% 0.0%
1980 -0.3% -1.4% -0.6%
1981 -2.7% -3.9% 1.1%
1982 -1.1% -4.6% 0.1%
1983 -1.9% -4.1% 3.1%
1984 -0.7% -4.3% 5.8%
1985 -1.0% -4.5% 7.5%
1986 1.9% -2.4% 9.7%
1987 1.7% -2.2% 9.9%
1988 2.2% -0.2% 11.5%
1989 1.9% -2.9% 12.5%
1990 2.1% -2.4% 13.9%
1991 3.7% -3.4% 14.6%
1992 2.2% -2.7% 18.9%
1993 0.7% -3.4% 19.4%
1994 -0.5% -4.9% 20.4%
1995 1.1% -4.2% 20.9%
1996 2.1% -4.5% 23.1%
1997 3.9% -2.3% 25.3%
1998 7.6% 2.5% 27.9%
1999 9.2% 5.1% 31.4%
2000 10.5% 4.8% 34.3%
2001 13.8% 5.3% 36.3%
2002 14.8% 8.2% 40.1%
2003 15.6% 10.4% 44.7%
2004 16.1% 11.2% 48.6%
2005 13.3% 7.1% 51.5%
2006 13.5% 7.9% 52.6%
2007 14.1% 6.0% 53.6%
2008 14.8% 5.7% 54.4%
2009 17.0% 10.1% 58.0%
2010 16.1% 9.5% 62.5%
2011 13.8% 6.0% 62.5%
2012 14.5% 3.3% 63.2%
2013 13.8% 5.6% 63.8%
2014 13.8% 3.4% 64.8%
2015 17.0% 4.2% 66.1%
2016 18.0% 8.8% 66.4%
2017 19.5% 6.9% 68.1%
2018 20.0% 5.3% 69.6%
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Note: Black and white are both non-Hispanic.

Source: 1978–2019 series by race & ethnicity, EPI analysis of monthly CPS microdata, downloaded from EPI SWA Data Library.

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It is also clear from Figure I that the strongest period of wage growth during this time occurred between the mid-1990s and early 2000s. The period of low unemployment and strong wage growth between 1995 and 2000 has been cited as a key contributor to some brief narrowing of the black-white wage gap during this time (Wilson 2015), while others assert that the rise in mass incarceration during the 1990s is responsible for artificially increasing the average wage of black men by removing a disproportionate share of those who were “less skilled” or lower-wage earners from the labor force (Neal and Rick 2014).

Since 2000, the black-white wage gap has continued to widen (Wilson and Rodgers 2016; Gould 2020). One of the most troubling trends of the post-2000 period has been the fact that the black-white wage gap has grown most among workers with a bachelor’s degree, and discriminatory differentials are also higher among the more highly educated (Tomaskovic-Devy, Thomas, and Johnson 2005; Wilson and Rodgers 2016). In fact, while the tight labor market of the late 1990s delivered faster wage growth to black college graduates than white college graduates in the five-year period from 1996 to 2000, the wages of black college graduates fell between 2015 and 2019—corresponding to the last five years of the recovery from the Great Recession, when unemployment rates were closest to those during 1996–2000 (Figure J). By contrast, the wages of white college graduates increased between 2015 and 2019 (Gould and Wilson 2019).

Figure J

Real average wage growth, workers with a bachelor’s degree, 1996–2000 and 2015–2019

1996–2000 2015–2019
Men 12.5% 5.3%
Women 11.5% 3.9%
White 12.4% 4.8%
Black 12.8% -0.7%
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III. Examples of bargaining power in action 

The imbalance of power between employers and employees is both a cause and consequence of the racial disparities in labor market outcomes that we have detailed above. One of the things that gives an employee or potential employee greater leverage at the bargaining table is the existence of equally or more attractive employment alternatives—a condition that is facilitated by tighter labor markets. However, over the last four decades there has been insufficient vigilance in fighting unemployment.

As shown in Figure K, between 1979 and 2019 the actual unemployment rate exceeded estimates of the NAIRU by an average of roughly 0.8 percentage points each year. Failure to meet even this arguably too-conservative employment target has weakened the bargaining power of the vast majority of workers, as evidenced by growing wage inequality over this period. Black workers have suffered some of the greatest harm from policy decisions that allowed excessive unemployment in pursuit of misguided inflation targets intended to limit wage growth.

Figure K

Estimate of the natural rate of unemployment and actual unemployment, 1949–2018

Month-year Unemployment rate NAIRU
Jan-1949 4.3% 5.26%
Feb-1949 4.7
Mar-1949 5.0
Apr-1949 5.3 5.26
May-1949 6.1
Jun-1949 6.2
Jul-1949 6.7 5.25
Aug-1949 6.8
Sep-1949 6.6
Oct-1949 7.9 5.25
Nov-1949 6.4
Dec-1949 6.6
Jan-1950 6.5 5.26
Feb-1950 6.4
Mar-1950 6.3
Apr-1950 5.8 5.26
May-1950 5.5
Jun-1950 5.4
Jul-1950 5.0 5.27
Aug-1950 4.5
Sep-1950 4.4
Oct-1950 4.2 5.28
Nov-1950 4.2
Dec-1950 4.3
Jan-1951 3.7 5.29
Feb-1951 3.4
Mar-1951 3.4
Apr-1951 3.1 5.31
May-1951 3.0
Jun-1951 3.2
Jul-1951 3.1 5.33
Aug-1951 3.1
Sep-1951 3.3
Oct-1951 3.5 5.34
Nov-1951 3.5
Dec-1951 3.1
Jan-1952 3.2 5.36
Feb-1952 3.1
Mar-1952 2.9
Apr-1952 2.9 5.37
May-1952 3.0
Jun-1952 3.0
Jul-1952 3.2 5.38
Aug-1952 3.4
Sep-1952 3.1
Oct-1952 3.0 5.38
Nov-1952 2.8
Dec-1952 2.7
Jan-1953 2.9 5.37
Feb-1953 2.6
Mar-1953 2.6
Apr-1953 2.7 5.37
May-1953 2.5
Jun-1953 2.5
Jul-1953 2.6 5.37
Aug-1953 2.7
Sep-1953 2.9
Oct-1953 3.1 5.37
Nov-1953 3.5
Dec-1953 4.5
Jan-1954 4.9 5.37
Feb-1954 5.2
Mar-1954 5.7
Apr-1954 5.9 5.37
May-1954 5.9
Jun-1954 5.6
Jul-1954 5.8 5.37
Aug-1954 6.0
Sep-1954 6.1
Oct-1954 5.7 5.37
Nov-1954 5.3
Dec-1954 5.0
Jan-1955 4.9 5.37
Feb-1955 4.7
Mar-1955 4.6
Apr-1955 4.7 5.38
May-1955 4.3
Jun-1955 4.2
Jul-1955 4.0 5.38
Aug-1955 4.2
Sep-1955 4.1
Oct-1955 4.3 5.39
Nov-1955 4.2
Dec-1955 4.2
Jan-1956 4.0 5.40
Feb-1956 3.9
Mar-1956 4.2
Apr-1956 4.0 5.41
May-1956 4.3
Jun-1956 4.3
Jul-1956 4.4 5.41
Aug-1956 4.1
Sep-1956 3.9
Oct-1956 3.9 5.41
Nov-1956 4.3
Dec-1956 4.2
Jan-1957 4.2 5.40
Feb-1957 3.9
Mar-1957 3.7
Apr-1957 3.9 5.40
May-1957 4.1
Jun-1957 4.3
Jul-1957 4.2 5.40
Aug-1957 4.1
Sep-1957 4.4
Oct-1957 4.5 5.40
Nov-1957 5.1
Dec-1957 5.2
Jan-1958 5.8 5.40
Feb-1958 6.4
Mar-1958 6.7
Apr-1958 7.4 5.40
May-1958 7.4
Jun-1958 7.3
Jul-1958 7.5 5.40
Aug-1958 7.4
Sep-1958 7.1
Oct-1958 6.7 5.40
Nov-1958 6.2
Dec-1958 6.2
Jan-1959 6.0 5.41
Feb-1959 5.9
Mar-1959 5.6
Apr-1959 5.2 5.42
May-1959 5.1
Jun-1959 5.0
Jul-1959 5.1 5.43
Aug-1959 5.2
Sep-1959 5.5
Oct-1959 5.7 5.45
Nov-1959 5.8
Dec-1959 5.3
Jan-1960 5.2 5.48
Feb-1960 4.8
Mar-1960 5.4
Apr-1960 5.2 5.49
May-1960 5.1
Jun-1960 5.4
Jul-1960 5.5 5.51
Aug-1960 5.6
Sep-1960 5.5
Oct-1960 6.1 5.51
Nov-1960 6.1
Dec-1960 6.6
Jan-1961 6.6 5.51
Feb-1961 6.9
Mar-1961 6.9
Apr-1961 7.0 5.51
May-1961 7.1
Jun-1961 6.9
Jul-1961 7.0 5.51
Aug-1961 6.6
Sep-1961 6.7
Oct-1961 6.5 5.51
Nov-1961 6.1
Dec-1961 6.0
Jan-1962 5.8 5.50
Feb-1962 5.5
Mar-1962 5.6
Apr-1962 5.6 5.50
May-1962 5.5
Jun-1962 5.5
Jul-1962 5.4 5.51
Aug-1962 5.7
Sep-1962 5.6
Oct-1962 5.4 5.51
Nov-1962 5.7
Dec-1962 5.5
Jan-1963 5.7 5.53
Feb-1963 5.9
Mar-1963 5.7
Apr-1963 5.7 5.54
May-1963 5.9
Jun-1963 5.6
Jul-1963 5.6 5.55
Aug-1963 5.4
Sep-1963 5.5
Oct-1963 5.5 5.56
Nov-1963 5.7
Dec-1963 5.5
Jan-1964 5.6 5.57
Feb-1964 5.4
Mar-1964 5.4
Apr-1964 5.3 5.59
May-1964 5.1
Jun-1964 5.2
Jul-1964 4.9 5.60
Aug-1964 5.0
Sep-1964 5.1
Oct-1964 5.1 5.62
Nov-1964 4.8
Dec-1964 5.0
Jan-1965 4.9 5.64
Feb-1965 5.1
Mar-1965 4.7
Apr-1965 4.8 5.66
May-1965 4.6
Jun-1965 4.6
Jul-1965 4.4 5.69
Aug-1965 4.4
Sep-1965 4.3
Oct-1965 4.2 5.71
Nov-1965 4.1
Dec-1965 4.0
Jan-1966 4.0 5.74
Feb-1966 3.8
Mar-1966 3.8
Apr-1966 3.8 5.76
May-1966 3.9
Jun-1966 3.8
Jul-1966 3.8 5.78
Aug-1966 3.8
Sep-1966 3.7
Oct-1966 3.7 5.78
Nov-1966 3.6
Dec-1966 3.8
Jan-1967 3.9 5.78
Feb-1967 3.8
Mar-1967 3.8
Apr-1967 3.8 5.78
May-1967 3.8
Jun-1967 3.9
Jul-1967 3.8 5.78
Aug-1967 3.8
Sep-1967 3.8
Oct-1967 4.0 5.78
Nov-1967 3.9
Dec-1967 3.8
Jan-1968 3.7 5.78
Feb-1968 3.8
Mar-1968 3.7
Apr-1968 3.5 5.79
May-1968 3.5
Jun-1968 3.7
Jul-1968 3.7 5.80
Aug-1968 3.5
Sep-1968 3.4
Oct-1968 3.4 5.81
Nov-1968 3.4
Dec-1968 3.4
Jan-1969 3.4 5.82
Feb-1969 3.4
Mar-1969 3.4
Apr-1969 3.4 5.84
May-1969 3.4
Jun-1969 3.5
Jul-1969 3.5 5.85
Aug-1969 3.5
Sep-1969 3.7
Oct-1969 3.7 5.86
Nov-1969 3.5
Dec-1969 3.5
Jan-1970 3.9 5.88
Feb-1970 4.2
Mar-1970 4.4
Apr-1970 4.6 5.89
May-1970 4.8
Jun-1970 4.9
Jul-1970 5.0 5.90
Aug-1970 5.1
Sep-1970 5.4
Oct-1970 5.5 5.91
Nov-1970 5.9
Dec-1970 6.1
Jan-1971 5.9 5.92
Feb-1971 5.9
Mar-1971 6.0
Apr-1971 5.9 5.93
May-1971 5.9
Jun-1971 5.9
Jul-1971 6.0 5.95
Aug-1971 6.1
Sep-1971 6.0
Oct-1971 5.8 5.97
Nov-1971 6.0
Dec-1971 6.0
Jan-1972 5.8 6.00
Feb-1972 5.7
Mar-1972 5.8
Apr-1972 5.7 6.02
May-1972 5.7
Jun-1972 5.7
Jul-1972 5.6 6.05
Aug-1972 5.6
Sep-1972 5.5
Oct-1972 5.6 6.07
Nov-1972 5.3
Dec-1972 5.2
Jan-1973 4.9 6.10
Feb-1973 5.0
Mar-1973 4.9
Apr-1973 5.0 6.12
May-1973 4.9
Jun-1973 4.9
Jul-1973 4.8 6.14
Aug-1973 4.8
Sep-1973 4.8
Oct-1973 4.6 6.15
Nov-1973 4.8
Dec-1973 4.9
Jan-1974 5.1 6.16
Feb-1974 5.2
Mar-1974 5.1
Apr-1974 5.1 6.17
May-1974 5.1
Jun-1974 5.4
Jul-1974 5.5 6.17
Aug-1974 5.5
Sep-1974 5.9
Oct-1974 6.0 6.17
Nov-1974 6.6
Dec-1974 7.2
Jan-1975 8.1 6.17
Feb-1975 8.1
Mar-1975 8.6
Apr-1975 8.8 6.17
May-1975 9.0
Jun-1975 8.8
Jul-1975 8.6 6.17
Aug-1975 8.4
Sep-1975 8.4
Oct-1975 8.4 6.18
Nov-1975 8.3
Dec-1975 8.2
Jan-1976 7.9 6.19
Feb-1976 7.7
Mar-1976 7.6
Apr-1976 7.7 6.20
May-1976 7.4
Jun-1976 7.6
Jul-1976 7.8 6.21
Aug-1976 7.8
Sep-1976 7.6
Oct-1976 7.7 6.21
Nov-1976 7.8
Dec-1976 7.8
Jan-1977 7.5 6.22
Feb-1977 7.6
Mar-1977 7.4
Apr-1977 7.2 6.23
May-1977 7.0
Jun-1977 7.2
Jul-1977 6.9 6.24
Aug-1977 7.0
Sep-1977 6.8
Oct-1977 6.8 6.25
Nov-1977 6.8
Dec-1977 6.4
Jan-1978 6.4 6.26
Feb-1978 6.3
Mar-1978 6.3
Apr-1978 6.1 6.27
May-1978 6.0
Jun-1978 5.9
Jul-1978 6.2 6.27
Aug-1978 5.9
Sep-1978 6.0
Oct-1978 5.8 6.27
Nov-1978 5.9
Dec-1978 6.0
Jan-1979 5.9 6.26
Feb-1979 5.9
Mar-1979 5.8
Apr-1979 5.8 6.26
May-1979 5.6
Jun-1979 5.7
Jul-1979 5.7 6.25
Aug-1979 6.0
Sep-1979 5.9
Oct-1979 6.0 6.24
Nov-1979 5.9
Dec-1979 6.0
Jan-1980 6.3 6.23
Feb-1980 6.3
Mar-1980 6.3
Apr-1980 6.9 6.22
May-1980 7.5
Jun-1980 7.6
Jul-1980 7.8 6.21
Aug-1980 7.7
Sep-1980 7.5
Oct-1980 7.5 6.20
Nov-1980 7.5
Dec-1980 7.2
Jan-1981 7.5 6.19
Feb-1981 7.4
Mar-1981 7.4
Apr-1981 7.2 6.17
May-1981 7.5
Jun-1981 7.5
Jul-1981 7.2 6.16
Aug-1981 7.4
Sep-1981 7.6
Oct-1981 7.9 6.15
Nov-1981 8.3
Dec-1981 8.5
Jan-1982 8.6 6.13
Feb-1982 8.9
Mar-1982 9.0
Apr-1982 9.3 6.12
May-1982 9.4
Jun-1982 9.6
Jul-1982 9.8 6.11
Aug-1982 9.8
Sep-1982 10.1
Oct-1982 10.4 6.10
Nov-1982 10.8
Dec-1982 10.8
Jan-1983 10.4 6.09
Feb-1983 10.4
Mar-1983 10.3
Apr-1983 10.2 6.08
May-1983 10.1
Jun-1983 10.1
Jul-1983 9.4 6.07
Aug-1983 9.5
Sep-1983 9.2
Oct-1983 8.8 6.06
Nov-1983 8.5
Dec-1983 8.3
Jan-1984 8.0 6.05
Feb-1984 7.8
Mar-1984 7.8
Apr-1984 7.7 6.05
May-1984 7.4
Jun-1984 7.2
Jul-1984 7.5 6.04
Aug-1984 7.5
Sep-1984 7.3
Oct-1984 7.4 6.03
Nov-1984 7.2
Dec-1984 7.3
Jan-1985 7.3 6.03
Feb-1985 7.2
Mar-1985 7.2
Apr-1985 7.3 6.02
May-1985 7.2
Jun-1985 7.4
Jul-1985 7.4 6.02
Aug-1985 7.1
Sep-1985 7.1
Oct-1985 7.1 6.01
Nov-1985 7.0
Dec-1985 7.0
Jan-1986 6.7 6.00
Feb-1986 7.2
Mar-1986 7.2
Apr-1986 7.1 6.00
May-1986 7.2
Jun-1986 7.2
Jul-1986 7.0 5.99
Aug-1986 6.9
Sep-1986 7.0
Oct-1986 7.0 5.99
Nov-1986 6.9
Dec-1986 6.6
Jan-1987 6.6 5.98
Feb-1987 6.6
Mar-1987 6.6
Apr-1987 6.3 5.97
May-1987 6.3
Jun-1987 6.2
Jul-1987 6.1 5.97
Aug-1987 6.0
Sep-1987 5.9
Oct-1987 6.0 5.96
Nov-1987 5.8
Dec-1987 5.7
Jan-1988 5.7 5.95
Feb-1988 5.7
Mar-1988 5.7
Apr-1988 5.4 5.94
May-1988 5.6
Jun-1988 5.4
Jul-1988 5.4 5.93
Aug-1988 5.6
Sep-1988 5.4
Oct-1988 5.4 5.92
Nov-1988 5.3
Dec-1988 5.3
Jan-1989 5.4 5.91
Feb-1989 5.2
Mar-1989 5.0
Apr-1989 5.2 5.91
May-1989 5.2
Jun-1989 5.3
Jul-1989 5.2 5.90
Aug-1989 5.2
Sep-1989 5.3
Oct-1989 5.3 5.89
Nov-1989 5.4
Dec-1989 5.4
Jan-1990 5.4 5.89
Feb-1990 5.3
Mar-1990 5.2
Apr-1990 5.4 5.87
May-1990 5.4
Jun-1990 5.2
Jul-1990 5.5 5.86
Aug-1990 5.7
Sep-1990 5.9
Oct-1990 5.9 5.84
Nov-1990 6.2
Dec-1990 6.3
Jan-1991 6.4 5.82
Feb-1991 6.6
Mar-1991 6.8
Apr-1991 6.7 5.79
May-1991 6.9
Jun-1991 6.9
Jul-1991 6.8 5.77
Aug-1991 6.9
Sep-1991 6.9
Oct-1991 7.0 5.74
Nov-1991 7.0
Dec-1991 7.3
Jan-1992 7.3 5.71
Feb-1992 7.4
Mar-1992 7.4
Apr-1992 7.4 5.68
May-1992 7.6
Jun-1992 7.8
Jul-1992 7.7 5.65
Aug-1992 7.6
Sep-1992 7.6
Oct-1992 7.3 5.61
Nov-1992 7.4
Dec-1992 7.4
Jan-1993 7.3 5.58
Feb-1993 7.1
Mar-1993 7.0
Apr-1993 7.1 5.54
May-1993 7.1
Jun-1993 7.0
Jul-1993 6.9 5.51
Aug-1993 6.8
Sep-1993 6.7
Oct-1993 6.8 5.48
Nov-1993 6.6
Dec-1993 6.5
Jan-1994 6.6 5.44
Feb-1994 6.6
Mar-1994 6.5
Apr-1994 6.4 5.41
May-1994 6.1
Jun-1994 6.1
Jul-1994 6.1 5.38
Aug-1994 6.0
Sep-1994 5.9
Oct-1994 5.8 5.35
Nov-1994 5.6
Dec-1994 5.5
Jan-1995 5.6 5.33
Feb-1995 5.4
Mar-1995 5.4
Apr-1995 5.8 5.30
May-1995 5.6
Jun-1995 5.6
Jul-1995 5.7 5.28
Aug-1995 5.7
Sep-1995 5.6
Oct-1995 5.5 5.25
Nov-1995 5.6
Dec-1995 5.6
Jan-1996 5.6 5.23
Feb-1996 5.5
Mar-1996 5.5
Apr-1996 5.6 5.21
May-1996 5.6
Jun-1996 5.3
Jul-1996 5.5 5.19
Aug-1996 5.1
Sep-1996 5.2
Oct-1996 5.2 5.17
Nov-1996 5.4
Dec-1996 5.4
Jan-1997 5.3 5.15
Feb-1997 5.2
Mar-1997 5.2
Apr-1997 5.1 5.13
May-1997 4.9
Jun-1997 5.0
Jul-1997 4.9 5.12
Aug-1997 4.8
Sep-1997 4.9
Oct-1997 4.7 5.10
Nov-1997 4.6
Dec-1997 4.7
Jan-1998 4.6 5.09
Feb-1998 4.6
Mar-1998 4.7
Apr-1998 4.3 5.07
May-1998 4.4
Jun-1998 4.5
Jul-1998 4.5 5.06
Aug-1998 4.5
Sep-1998 4.6
Oct-1998 4.5 5.05
Nov-1998 4.4
Dec-1998 4.4
Jan-1999 4.3 5.04
Feb-1999 4.4
Mar-1999 4.2
Apr-1999 4.3 5.03
May-1999 4.2
Jun-1999 4.3
Jul-1999 4.3 5.03
Aug-1999 4.2
Sep-1999 4.2
Oct-1999 4.1 5.02
Nov-1999 4.1
Dec-1999 4.0
Jan-2000 4.0 5.01
Feb-2000 4.1
Mar-2000 4.0
Apr-2000 3.8 5.01
May-2000 4.0
Jun-2000 4.0
Jul-2000 4.0 5.01
Aug-2000 4.1
Sep-2000 3.9
Oct-2000 3.9 5.00
Nov-2000 3.9
Dec-2000 3.9
Jan-2001 4.2 5.00
Feb-2001 4.2
Mar-2001 4.3
Apr-2001 4.4 5.00
May-2001 4.3
Jun-2001 4.5
Jul-2001 4.6 5.00
Aug-2001 4.9
Sep-2001 5.0
Oct-2001 5.3 5.00
Nov-2001 5.5
Dec-2001 5.7
Jan-2002 5.7 5.00
Feb-2002 5.7
Mar-2002 5.7
Apr-2002 5.9 5.00
May-2002 5.8
Jun-2002 5.8
Jul-2002 5.8 5.00
Aug-2002 5.7
Sep-2002 5.7
Oct-2002 5.7 5.00
Nov-2002 5.9
Dec-2002 6.0
Jan-2003 5.8 5.00
Feb-2003 5.9
Mar-2003 5.9
Apr-2003 6.0 5.00
May-2003 6.1
Jun-2003 6.3
Jul-2003 6.2 5.00
Aug-2003 6.1
Sep-2003 6.1
Oct-2003 6.0 5.00
Nov-2003 5.8
Dec-2003 5.7
Jan-2004 5.7 5.00
Feb-2004 5.6
Mar-2004 5.8
Apr-2004 5.6 5.00
May-2004 5.6
Jun-2004 5.6
Jul-2004 5.5 5.00
Aug-2004 5.4
Sep-2004 5.4
Oct-2004 5.5 5.00
Nov-2004 5.4
Dec-2004 5.4
Jan-2005 5.3 5.00
Feb-2005 5.4
Mar-2005 5.2
Apr-2005 5.2 5.00
May-2005 5.1
Jun-2005 5.0
Jul-2005 5.0 5.00
Aug-2005 4.9
Sep-2005 5.0
Oct-2005 5.0 4.98
Nov-2005 5.0
Dec-2005 4.9
Jan-2006 4.7 4.97
Feb-2006 4.8
Mar-2006 4.7
Apr-2006 4.7 4.97
May-2006 4.6
Jun-2006 4.6
Jul-2006 4.7 4.96
Aug-2006 4.7
Sep-2006 4.5
Oct-2006 4.4 4.95
Nov-2006 4.5
Dec-2006 4.4
Jan-2007 4.6 4.95
Feb-2007 4.5
Mar-2007 4.4
Apr-2007 4.5 4.94
May-2007 4.4
Jun-2007 4.6
Jul-2007 4.7 4.94
Aug-2007 4.6
Sep-2007 4.7
Oct-2007 4.7 4.93
Nov-2007 4.7
Dec-2007 5.0
Jan-2008 5.0 4.93
Feb-2008 4.9
Mar-2008 5.1
Apr-2008 5.0 4.93
May-2008 5.4
Jun-2008 5.6
Jul-2008 5.8 4.92
Aug-2008 6.1
Sep-2008 6.1
Oct-2008 6.5 4.92
Nov-2008 6.8
Dec-2008 7.3
Jan-2009 7.8 4.92
Feb-2009 8.3
Mar-2009 8.7
Apr-2009 9.0 4.97
May-2009 9.4
Jun-2009 9.5
Jul-2009 9.5 5.00
Aug-2009 9.6
Sep-2009 9.8
Oct-2009 10.0 5.02
Nov-2009 9.9
Dec-2009 9.9
Jan-2010 9.8 5.06
Feb-2010 9.8
Mar-2010 9.9
Apr-2010 9.9 5.08
May-2010 9.6
Jun-2010 9.4
Jul-2010 9.4 5.10
Aug-2010 9.5
Sep-2010 9.5
Oct-2010 9.4 5.11
Nov-2010 9.8
Dec-2010 9.3
Jan-2011 9.1 5.13
Feb-2011 9.0
Mar-2011 9.0
Apr-2011 9.1 5.14
May-2011 9.0
Jun-2011 9.1
Jul-2011 9.0 5.15
Aug-2011 9.0
Sep-2011 9.0
Oct-2011 8.8 5.15
Nov-2011 8.6
Dec-2011 8.5
Jan-2012 8.3 5.13
Feb-2012 8.3
Mar-2012 8.2
Apr-2012 8.2 5.12
May-2012 8.2
Jun-2012 8.2
Jul-2012 8.2 5.10
Aug-2012 8.1
Sep-2012 7.8
Oct-2012 7.8 5.07
Nov-2012 7.7
Dec-2012 7.9
Jan-2013 8.0 5.05
Feb-2013 7.7
Mar-2013 7.5
Apr-2013 7.6 5.03
May-2013 7.5
Jun-2013 7.5
Jul-2013 7.3 5.00
Aug-2013 7.2
Sep-2013 7.2
Oct-2013 7.2 4.98
Nov-2013 6.9
Dec-2013 6.7
Jan-2014 6.6 4.95
Feb-2014 6.7
Mar-2014 6.7
Apr-2014 6.3 4.93
May-2014 6.3
Jun-2014 6.1
Jul-2014 6.2 4.92
Aug-2014 6.2
Sep-2014 5.9
Oct-2014 5.7 4.88
Nov-2014 5.8
Dec-2014 5.6
Jan-2015 5.7 4.83
Feb-2015 5.5
Mar-2015 5.5
Apr-2015 5.4 4.79
May-2015 5.5
Jun-2015 5.3
Jul-2015 5.2 4.77
Aug-2015 5.1
Sep-2015 5.0
Oct-2015 5.0 4.76
Nov-2015 5.0
Dec-2015 5.0
Jan-2016 4.9 4.75
Feb-2016 4.9
Mar-2016 5.0
Apr-2016 5.0 4.75
May-2016 4.7
Jun-2016 4.9
Jul-2016 4.9 4.74
Aug-2016 4.9
Sep-2016 5.0
Oct-2016 4.9 4.74
Nov-2016 4.6
Dec-2016 4.7
Jan-2017 4.8 4.74
Feb-2017 4.7
Mar-2017 4.5
Apr-2017 4.4 4.74
May-2017 4.3
Jun-2017 4.3
Jul-2017 4.3 4.74
Aug-2017 4.4
Sep-2017 4.2
Oct-2017 4.1 4.74
Nov-2017 4.1
Dec-2017 4.1
Jan-2018 4.1 4.73
Feb-2018 4.1
Mar-2018 4.1
ChartData Download data

The data below can be saved or copied directly into Excel.

Notes: NAIRU refers to the nonaccelerating inflation rate of unemployment (another term for the natural rate of unemployment). Shaded areas represent recessions.

Source: Data on the natural rate of unemployment from the Congressional Budget Office, “Online Data on Potential Output and Its Underlying Inputs,” 2018; data on actual unemployment rate come from the Bureau of Labor Statistics, “Series ID: LNS14000000. (Seas) Unemployment Rate,” accessed August 2018. Shaded areas represent recessions.

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The widening of wage gaps between black and white workers over these same years further suggests that the perpetual 2-to-1 unemployment disparity further eroded black workers’ bargaining power relative to white workers. This diminished leverage can affect a worker’s willingness to challenge unfair and unsafe working conditions given concerns about how long it may take to find another job if the worker were to leave or be terminated from the current job as an act of employer retaliation. Long-standing racial disparities in income and wealth also raise the stakes associated with leaving or losing a job more for black workers than for white workers.

Race, unequal power, and the Covid-19 crisis 

The Covid-19 pandemic and recession offer the most recent example of the resilience of racial inequality and stratification in the labor market and how they generate disparate outcomes and unequal bargaining power. When businesses, schools, and other public places responded to the public health crisis in mid-March 2020 by simultaneously closing their doors, three distinct groups of workers quickly emerged. The first group included tens of millions of workers who lost jobs. Workers in the second and third groups both retained their jobs but under very different conditions.

“Essential” frontline workers were required to continue physically reporting to work, while those not in that category were able to work remotely from the safety of their homes. Black workers were least likely to be among those able to retain employment under remote working arrangements: Less than one-fifth had the option to work from home, compared to almost one-third of white workers. Therefore, black (along with Hispanic and Native American) workers were more likely than whites to suffer job loss or be compelled to put their health at risk in exchange for a measure of job security (Gould and Wilson 2020).

One of the structures contributing to such racially disparate impacts is occupational segregation, characterized by overrepresentation of black workers, and especially black women workers, in low-wage occupations and underrepresentation in higher-wage occupations. The Covid-19 crisis popularized the term essential worker, drawing attention to the fact that black workers occupy a disproportionate share of lower-wage jobs in major frontline industries, often with inconsistent work hours (thus, inconsistent pay) and without paid leave or employer-provided health coverage. Rho, Brown, and Fremstad (2020) report that black workers make up a disproportionate share of frontline workers across six sectors of the economy that are considered essential.

While black workers represent 11.9% of all workers, they make up about one in six (17%) of frontline-industry workers. This category includes employment in public transit (26.0%); child care and social services (19.3%); trucking, warehouse, and postal service (18.2%); health care (17.5%); and grocery, convenience, and drug stores (14.2%).

Except for those in health care, none of these workers had any prior professional obligation that would require them to put their health at risk. Absent policy intervention, union representation, or a sympathetic employer, few had any assurances that they would be compensated for the increased risk.

Public-sector vs. private-sector racial wage gaps and the role of unions 

Given the amount of power an employer holds over any individual worker, it becomes necessary to establish a countervailing force that builds sufficient power among workers through a stronger collective voice with which to advocate for higher pay, better benefits, training and promotional opportunities, and protections against discrimination and harassment. In a unionized workforce, for example, collective bargaining results in labor contracts that help to create greater transparency and consistency through clearly defined policies and pay structures. These contracts play a critical role in reducing the potential for pay discrimination by limiting an employer’s discretion in paying different wages to comparably qualified individuals doing the same job and providing workers with critical protections and recourse against other forms of exploitation or mistreatment.

These conditions are more likely to exist in the public sector than in the private sector because in the former a larger share of the workforce is covered by a union contract: 39% of public-sector employees are in a union or covered by a union contract, compared to only 7% of private-sector workers. Historically, the appeal of better job opportunities and greater pay equity in the public than in the private sector has contributed to black workers’ disproportionate employment share in the public sector as well as higher average rates of union membership. Based on empirical analysis of wages among public- and private-sector employees, black workers in the public sector face smaller unexplained wage gaps than their counterparts in the private sector—6.9% versus 16.9%, respectively.3 Within the public sector, postal service jobs have been particularly valuable to black workers because of the uniform wage and benefit structure (all postal employees who have the same job title and job tenure are paid the same nationwide) and higher pay relative to comparable private-sector employment. However, since the 1980s the postal service has been under sustained assault by those who believe the compensation provided by these jobs is too generous.

Newly developed historical data from the early postwar period affirm that collective bargaining has been an effective tool for reducing wage inequality. Based on data compiled for men in several U.S. cities in 1951, Callaway and Collins (2017) found “the [union] wage premium was larger at the bottom of the income distribution than at the middle or higher, larger for African Americans than for whites, and larger for those with low levels of education,” findings that are “consistent with the view that unions substantially narrowed urban wage inequality at mid-century.”

Using data on union households from Gallup surveys dating back to 1936, Farber et al. (2021) similarly found that unions raised wages “between ten and twenty log points, with the less-educated receiving an especially large premium.” While this union effect has been relatively consistent over the last 80 years, patterns of union membership have not been. Unions’ disproportionate representation of “disadvantaged” workers (i.e., not white and not college-educated) began in the mid-1940s and peaked during the 1960s. While black workers continued to have higher rates of union membership than whites in the decades since, as overall union density has declined rates of union membership for black and white workers have converged.

Despite unions being a powerful force for increasing wages among the working class, racism within the labor movement has at times served to perpetuate rather than reduce racial inequality. While racially integrated unions have been instrumental in building support for policies that benefit black workers (Day 2020), tragically there are also examples of white workers using their unions to defend rather than defeat white supremacy.

IV. Challenging the prevailing economic theories used to explain racial disparities in labor market outcomes 

The economic theories most often invoked to explain the observed racial differentials in labor market outcomes described above are human capital theory, taste-based models of discrimination, and statistical models of discrimination. In this section, we briefly review these models and their core assumptions. We then make a case for why each of these models is historically and empirically inconsistent with the facts. 

Challenges to the conventional wisdom of human capital theory 

Adam Smith first introduced the concept of human capital as having an economic value analogous to physical capital in Wealth of Nations. Building upon this concept, Mincer (1958), Schultz (1961), and Becker (1964) popularized what we now know as human capital theory, formalizing a relationship between education, productivity, and earnings. Productivity is the assumed link between education and earnings in the Mincerian earnings function that operationalizes modern human capital theory.

Human capital theory posits that a worker’s earnings are related, directly and solely, to the worker’s productive capacity, represented by an individual’s particular set of skills, knowledge, and abilities, or human capital. Workers can increase their earnings by investing in human-capital-enhancing activities that, presumably, make them more productive. While human capital investments can take multiple forms, including formal education and on-the-job training, we will focus on formal education, the most frequently studied and most often deployed explanation for observed labor market differentials between black and white workers.

For a majority of economists, education is treated as a means of skill accumulation, which translates into greater productivity and higher compensation. However, Darity and Underwood (2021) argue there are three major issues with the human capital theory of wages: (1) it is difficult to precisely calculate productivity, and evidence on the link between more education and increased productivity is far from definitive; (2) the huge wage differential between CEOs, or even the top 1% of all wage earners (Bivens and Mishel 2013), and the typical “line” worker creates an important conundrum; and (3) of primary importance to the discussion outlined in this paper, the presence of labor market discrimination directly contradicts human capital theory.

Regarding the first point, the fundamental question raised by Darity and Underwood (2021) is, what is the function of higher education? Does it generally impart skill- or productivity-enhancing knowledge, or do credentials function as artificial entry barriers to certain occupations? Of primary concern here is the difficulty in precisely calculating one’s productivity apart from the circular move of using wages as a proxy.

We might conceive of one set of occupations where the skills-productivity nexus is consistent with the association between higher education and higher earnings, another set of occupations where the exclusion associated with gaining the credential produces scarcity that raises earnings, and a third set of professions where both factors are at play in determining earnings. The problem is there is no existing research of which we are aware that approaches the connection between higher education and higher earnings, empirically. Thus, we have no way of quantifying which or for how many jobs higher education—or education at any level—leads to a greater capacity to perform job functions. In jobs where employers rate their employees, there is considerable evidence that job performance ratings do not rise uniformly with higher levels of educational attainment for employees. Rather, in some instances there is an inverse relationship between employer ratings and employee educational attainment (Berg 1970).

A second challenge to human capital theory lies with the mere existence of corporate “super salaries.” In 2019, the average CEO at one of the top 350 largest U.S. firms (by sales) earned almost 320 times what the typical employee in those firms’ same industry earned ($21.3 million versus $66,800), constituting an increase of $2.6 million for CEOs and $1,100 for employees from 2018 (Mishel and Kandra 2021). If one were to accept human capital theory at face value, this differential would imply that the average CEO is 320 times as productive as the typical worker, a claim that has the ring of absurdity about it, even if there was a reasonable way to measure the difference.

But one need not consider the extreme cases of corporate executives’ huge compensation packages to recognize the difficulty of our lack of criteria for measuring productivity apart from comparative earnings. The Georgetown University Center on Education and the Workforce’s standard for a good job is one that pays at least $35,000 for workers 25–44 and at least $45,000 for workers 45–64. Inclusive of workers with a bachelor’s degree or higher, this corresponds to median earnings of $65,000 (Carnevale et. al 2018). That is consistent with the average annual compensation for tractor/trailer drivers. Is an investment banker who earns $250,000 in the same year necessarily close to four times as productive? Furthermore, if we did have a reliable independent standard for measuring productivity and found that the investment banker truly is about four times as productive, would that higher level of productivity be attributable to the banker having had a university education? 

The third challenge to human capital theory is the phenomenon of labor market discrimination as evidenced by the persistence of racial disparities in wages and employment that cannot be accounted for by differences in skills. According to human capital theory, black-white differences in unemployment and earnings can largely be explained by black-white differences in skills or education. While, empirically, higher levels of educational attainment are associated with lower average rates of unemployment and higher average wages, there is also strong evidence that the returns to educational attainment are unequal for black and white workers.

By definition, racial discrimination results in an unfair devaluation of black labor, since equivalently productive workers—or in the context of higher education, workers with similar advanced degrees—receive different pay. Thus, discrimination drives a wedge between any ostensible consistent relationship between wages, skills, and productivity. 

Nevertheless, deniers of labor market discrimination as an explanation for the persistence of the black-white wage gap raise questions about whether there are other unobserved characteristics that explain these differences. The literature seeking to explain the racial wage gap as a racial gap in cognitive skills has been controversial, to say the least. This body of research stemmed from cross-sectional analysis of the National Longitudinal Survey of Youth (NLSY), which included scores from the Armed Forces Qualification Test (AFQT). These AFQT scores were used as a proxy for cognitive skills in the estimation of relative wage differentials among black and white men in their 20s and 30s. The finding that inclusion of AFQT scores in a standard human capital model substantially reduced the black-white wage gap (O’Neil 1990; Maxwell 1994; Neal and Johnson 1996) led some to conclude that racial discrimination in the labor market was not a significant factor in the persistence of the wage gap because racial disparities in wages could be explained by differences in cognitive skills.

The validity of that conclusion was challenged on multiple grounds. First, because there was little overlap in AFQT scores of blacks and whites, high correlation between race and test scores presented a problem in estimating the effect on the wage gap (Ferguson 1995; Rodgers and Spriggs 1996). Using AFQT scores to explain black-white pay gaps was also challenged on the basis of the lack of robustness to different model specifications (Mason 1998; Goldsmith, Veum, and Darity 1997). For example, Mason (1998) found that a different measure of intelligence reported in the Panel Study of Income Dynamics (PSID) failed to significantly explain the racial wage gap. Goldsmith, Veum, and Darity (1997) used data from the NLSY to show that the AFQT no longer explains the racial wage gap once psychological measures of “self-esteem” and “locus of control” are included in the wage equation.

Finally, the argument that there are unobserved variables that account for relatively lower skills among black workers is inconsistent with the fact that the racial wage gap is different for men and women (Darity, Guilkey, and Winfrey 1996; Wilson and Rodgers 2016). In fact, given that black women had a slight wage advantage over white women as recently as the early 1980s—something that has never been observed among men—it seems unlikely that some unobserved or unexplained pre-market force at once disadvantages the skill attainment of black men relative to white men while providing an advantage for black women over white women.

Challenges to the conventional wisdom of taste-based model of discrimination 

Becker’s (1957) taste-based model of discrimination is perhaps the best-known neoclassical competitive model used to explain labor market discrimination. Becker’s original model has three central assumptions: (1) labor markets are competitive and employers are motivated by profit maximization; (2) black and white workers are equally productive; and (3) whites have an “externally” acquired “taste for discrimination,” functioning as a preference for white workers. This model posits that discrimination is in fact intentional, if not rational, and allows tastes for discrimination to function through three kinds of agents: employers, employees, and customers.

The biggest challenge to Becker’s taste-based model of discrimination lies in the conclusion that discriminatory wage outcomes are only temporary. In the context of black-white wage differentials, the preference for white workers, or, equivalently, the distaste for black workers, requires black workers to compensate discriminating employers by accepting lower wages. While the discriminatory tastes of employers, employees, or customers may create incentives for workplace segregation, in the long run racial wage differentials are eliminated through competition.

This conclusion is directly refuted by the historical record outlined in Section II, which demonstrates that anti-discrimination policy intervention, not market competition, was responsible for the most significant narrowing of racial wage gaps in the decade following the passage of the Civil Rights Act of 1964, and that lax enforcement of those laws in the decades since, along with other policies that weakened the bargaining power of workers, have contributed to further widening of those gaps.

A second weakness of this model is that it assumes full employment, since all job seekers are presumed hired. The set of possible outcomes then are differentiated by the racial composition of a firm’s workforce, but unemployment—the possibility that someone actively seeking employment will not be hired at all—is not a consideration. 

Challenges to the conventional wisdom of statistical models of discrimination 

Statistical models of discrimination, pioneered by Phelps (1972) and Arrow (1973), allow for less overt and unintentional means by which disparate or discriminatory labor market outcomes manifest. These models are based on the idea that employers have incomplete information about the actual productivity of individual job applicants. However, they may have beliefs about the average productivity of a given group of workers (e.g., black workers, women, or formerly incarcerated individuals) that they assign to individuals belonging to that group. Based on those beliefs, workers belonging to the group assumed to be less productive will have a lower probability of being hired (i.e., a higher unemployment rate) or, when hired, will be offered a lower wage, resulting in a wage gap.

According to statistical models of discrimination, profit-maximizing employers in an environment of imperfect information believe they can distinguish visually between candidates from groups A and B that are drawn from different frequency distributions for ability to perform. Based on the assumption that the mean of group B’s ability distribution is markedly higher than the mean of A’s ability distribution, and there is little difference, if any, in the variance, these employers display a preference for members of group B. 

If, in fact, these employers are wrong and the two distributions are identical, then that fact should be learned over time. As a result, any profit-maximizing employer should become indifferent between members of groups A and B. On the other hand, if those employers are correct, then subsequent inequalities in intergroup outcomes are due to average differences in ability and there is no need for a theory of discrimination at all.

Confounding matters more, the informational assumption that leads employers to rely on knowledge, whether accurate or not, about group affiliation in making individual hiring decisions seems tenuous. It suggests that employers are incapable of making a sound approximation about a candidate’s future potential to perform without relying on the additional signal of group affiliation. This is implausible given the vast resources corporations devote to hiring decisions and the design of screening mechanisms. Over time, an appropriate set of questions or tests should emerge that will facilitate selection, regardless of group affiliation. 

In his open letter to economists, AFL-CIO Chief Economist William Spriggs calls out the inherent racism in the statistical discrimination framework by asking:

How does a model assume that an entire set of actors, observing the infinite diversity of human beings, all settle on race as a meaningful marker independent of history, laws, and social norms? And, miraculously, those same ‘rational’ actors use ‘statistical’ methods to find only negative attributes highly correlated with race. (Spriggs n.d.)

In other words, the only logical reason for taking account of group affiliation or race is to discriminate on the basis of race, and not to improve the accuracy of predictions of an individual’s performance. 

V. Stratification economics as a better framework for understanding persistent racial disparities in the labor market 

Each of the aforementioned economic theories falls short in its attempt to explain long-standing racial disparities in unemployment and pay while blatantly denying the persistence of discrimination. Despite compelling empirical evidence and a solid historical record that points to discrimination as a significant factor in the persistence of racial disparities in the labor market, the interpretation of those disparities is an ongoing debate in the field of economics.

When economists get a statistically significant coefficient on race after estimating a wage equation that controls for standard measures of individual productive capacity (e.g., education, experience) and macroeconomic conditions (e.g., state or regional fixed effects), as well as race and gender, what does that mean? Do we interpret that coefficient as evidence of racial discrimination, or does it reflect some unobserved or omitted variable? Devotees of the conventional economic theories described above tend to dismiss discrimination as a valid or significant explanation of the gaps in favor of the latter interpretation. But if there is some unobserved variable that would explain the statistically significant coefficient on race, it would also have to be strongly correlated with race. In its most basic form, race is nothing more than a socially constructed identifier, defined in the United States primarily by skin color—an arbitrary and superficial physical characteristic that has no relationship to one’s productive capacity. How then should we interpret that correlation?

Getting an answer to that last question requires that we go beyond our standard individual-centered models and consider structural and institutional factors, as well as what we can learn from social, psychological, and historical analyses.

In the absence of that, we continue to lean on human capital models and models of discrimination to our peril. Those models have been inadequate to explain well-documented and persistent patterns of racial inequality, leading to an overemphasis on the shortcomings of individuals and little or no emphasis on fixing biased or discriminatory systems that uphold an economic hierarchy predicated on race. This is the hole that stratification economics was developed to fill.

Stratification economics was developed in response to the inadequacy of conventional economic theory to explain intergroup inequality in general and the persistence of racial disparities in particular. In contrast to Becker’s taste-based discrimination model, or models of statistical discrimination, stratification economics includes a set of theories or models that identify the structural processes that enable the persistence of discrimination and inequality over the long term. This framework emphasizes the effect of group formation, group identity, and group action on an individual’s life outcomes, as opposed to a more conventional framework in which individuals act solely as autonomous optimizers. Stratification economics employs an interdisciplinary approach that incorporates economics, sociology, and social psychology while proposing that one’s relative position matters. Individuals discern relative position by making both intragroup and intergroup comparisons, but a key feature of intergroup comparison is the identification of an outsider group, defined by race, ethnicity, gender, class, religion, or some other demographic characteristic (Darity et al. 2017).

According to stratification economics, while discrimination is unjust, it also serves the functional role of preserving hierarchy. Therefore, persistent racial inequality arises when a dominant group seeks to maintain the hierarchy that affords it some degree of social or economic privilege. Under this framework, identity can be structured so that investing in, or associating with, a group identity can lead to economic returns and benefits. This treatment of identity as endogenous represents a major departure from more conventional economic models but is consistent with a set of alternative theories for explaining stubborn racial gaps in economic outcomes, and these theories help to operationalize stratification economics.4 Those most relevant to the labor market context are Lewis’s (1979) noncompeting groups hypothesis and Swinton’s (1978) labor force competition model of racial discrimination. In each of these, racial identity is associated with aspects of power and social control that are directly incorporated into the analysis.

Lewis (1979) and Swinton (1978) present models of a hierarchical wage or occupational structure and the existence of white worker “coalitions” that allow those who share this group identity to maintain a higher position in that hierarchy by limiting other (i.e., black) workers’ access to higher-status and higher-paying occupations and funneling them into lower-status and lower-wage jobs. The coalitions’ ability to exercise such an influence is based on their position as the majority group, which is a numerical and historical advantage. It is important to note that while applying racial preferences to an existing hierarchical occupational structure (i.e., occupational segregation) is discriminatory, it can be achieved without explicitly invoking or referencing race. Rather, a white worker “coalition” can essentially render excluded workers “noncompeting” by using its majority position in the firm or industry to influence the required credentials for a position, manipulate opportunities to obtain the credentials, or otherwise act as a gatekeeper over entry and promotion to preferred positions, as is often observed in professional environments, including corporate leadership, academia, law, or medicine.

Thus, Lewis concludes that more direct forms of in-market discrimination only become necessary as pre-market efforts to preserve the established racial hierarchy in the occupational structure become less effective. An interesting implication of this conclusion is that investments in human capital that make members of the excluded group more qualified for preferred positions can increase the likelihood that they will experience labor market discrimination. Darity, Dietrich, and Guilkey (1997) find that while black males were making dramatic strides in acquiring literacy between 1880 and 1910 in the United States, simultaneously they were suffering increasing proportionate losses in occupational status due to disadvantageous treatment of their measured characteristics. 

Krueger’s (1963) extension of the trade-based version of the Becker model also has relevance to the discussion of racially disparate labor market outcomes. In that model, white capitalists must value racial group solidarity sufficiently to accept a lower return on their capital as the price they pay for a generally higher level of income for all whites (and higher wages for white workers). In principle, if white capitalists lose from their inability to hire less-expensive black workers, a sufficiently high relative gain in income for white workers can compensate white capitalists for their losses. This prospect advanced by Anne Krueger nearly 60 years ago fits like a glove into stratification economics’ frame of understanding discrimination as an act that yields group benefits and losses.

Further, there is an additional perverse possibility derived from stratification economics. A racial hierarchy of workers can be exploited by owners of capital to subvert worker solidarity and capture a larger share of a worker’s productivity as economic profit. In short, employers can potentially get away with paying black and white workers a wage below their marginal productivity if, on average, the weight white workers place on being relatively better off than black workers is sufficiently high and white workers are paid a wage that is above that of black workers. Thus, both white labor and white capital jointly can benefit from discrimination against black workers. 

Conclusion 

When we look at race and labor market discrimination in the context of workers’ bargaining power, it is important we recognize there are at least two complementary goals. With respect to wages, we want to shift the balance of power in a way that puts upward pressure on wages—particularly for wage earners at or below the median—and at the same time close racial wage gaps. We cannot rely on competitive markets alone to do this. Rather, interventions are required to address these inequalities. Appropriate design of those interventions requires that we expand the frameworks we use for understanding power, race, gender, and inequality so that we restructure systems and institutions to prevent discriminatory outcomes rather than enable them. 

Policies geared toward maximizing employment and limiting the depth and duration of recessions are essential to establishing a new balance of power that makes workers less vulnerable to limited job prospects and low wages. For many of the reasons we have already discussed, these policies are particularly important to improve outcomes of black workers. During the last four decades, the Federal Reserve’s monetary policy decisions have been too contractionary, and those decisions have limited wage growth for the bottom 80% of workers and had an adverse effect on closing the black-white wage gap. While recent revisions to the Federal Reserve’s long-run goals and monetary policy strategy reflect some acknowledgement of the role the central bank plays in reducing or exacerbating racial economic inequality, unless there is an ongoing commitment to avoid prematurely enacting policies that needlessly limit job growth and disproportionately harm black workers, the balance of power is unchanged. Similarly, Congress must avoid excessive and unnecessary fiscal austerity and utilize its power to target funding for job creation in ways that promote racial equity, including a federal job guarantee. A federal job guarantee would eliminate the need for economists to squabble over the full employment unemployment rate, and essentially end the tradeoff between unemployment and inflation by making the Phillips curve vertical at a zero unemployment rate.

Labor unions play an important role in giving workers a stronger collective voice to advocate for higher pay, better benefits, training and promotional opportunities, and protections against discrimination and harassment. The Protecting the Right to Organize (PRO) Act is an important step toward streamlining the process when workers form a union, ensuring that they are successful in negotiating a first agreement, and holding employers accountable for violations of labor law (McNicholas, Poydock, and Rhinehart 2021). Historically, when given an opportunity to join a union, black workers have had the highest rates of union membership and have benefited from better pay and working conditions relative to workers who are not covered by a union contract. Still, the labor movement, like any other U.S. institution, is not immune to racism, and unions must continue to grow as more diverse, inclusive, and dynamic organizations as they serve the vital role of leveling the playing field for all workers. 

Finally, the fact that labor market discrimination has persisted well beyond the passage of Title VII of the Civil Rights Act of 1964 and the establishment of the Equal Employment Opportunity Commission, the federal agency tasked with enforcement of federal anti-discrimination laws, should not be overlooked, or taken lightly. As outlined in Yang and Liu (2021), meaningful accountability for discrimination requires solutions that confront the power and information asymmetries that weaken our enforcement system. Specifically, the authors recommend changes in at least four areas: (1) policies that encourage employer transparency, such as requiring employers to report employment and pay data by race, ethnicity, gender, and occupation, are necessary to fight discrimination and encourage accountability; regular reporting draws attention to discriminatory patterns, but also empowers workers with the information they need to pursue recourse against workplace discrimination; (2) increased funding of federal, state, and local enforcement agencies is necessary to provide the staffing and resources required to investigate the tens of thousands of discrimination charges filed each year and level the playing field for workers seeking justice; (3) revising legal doctrines to better align with the language and purpose of Title VII and other anti-discrimination laws will help to relieve the exceptionally onerous burden workers face in proving cases of discrimination; and (4) legal protections against anti-discrimination should be expanded to cover all workers and protect against practices that coerce employees to waive any rights to legally challenge unfair or unequal treatment.

The pursuit of economic and racial justice requires a serious interrogation of long-accepted assumptions about how the labor market functions and how much power any individual worker has to choose a better alternative. For black workers, there is also a long history of racially motivated exclusion, exploitation, and oppression that contributes to the assumed inferiority of black labor and the normalization of racial inequality. Therefore, meaningful policy changes that will serve to empower all workers and eliminate persistent racial disparities in the labor market also require a serious reckoning with the pervasiveness of racism in the collective thought, actions, institutions, and polices of the United States.

Notes

1. The nonwhite racial category included black workers along with others who did not identify as white, but about 95% of those in the category were black (or “negro”).

2. The 2019 high school and college completion data are based on non-Hispanic white population, while 1972 estimates for whites are not distinguishable by ethnicity.

3. Based on estimates of a wage regression with controls for education, age, state of residence, and union contract coverage status in addition to race and gender. The data used for this analysis included a combined 10 years of data from the CPS ORG (2009–2018).

4. Darity and Mason (1998) provide a more extensive review of economic models with relevance to the theory of stratification economics.

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See more work by Valerie Wilson and William Darity Jr.