The Importance of Cost of Living and Education in Estimates of the
Conditional Wage Gap Between Black and White Women
Peter McHenry Melissa McInerney
McHenry and McInerney
A B S T R A C T
While evidence about discrimination in U.S. labor markets typically implies preferential treatment for whites, recent studies document a substantial wage
premium for black women for example, Fryer 2011. Although differential selection of black and white women into the labor market has been a
suggested explanation, we demonstrate that accounting for selection does not eliminate the estimated premium. We then incorporate two additional omitted
variables recently documented in the literature: 1 local cost of living and 2 years of education attained, conditional on AFQT score. After controlling
for these variables, we fi nd no evidence of a wage premium for black women.
I. Introduction
Concerns about discrimination in labor markets have long motivated economists to compare labor market outcomes—wages in particular—between
members of different groups. The history of race relations in the United States puts a focus on black- white differences, and many studies have found that black workers
tend to earn lower wages than white workers with similar characteristics like age and education.
1
More recently, however, several studies document higher wages among black—relative to white—women with similar characteristics for example, Fryer
2011; Fisher and Houseworth 2012; Black et al. 2008. Such black wage premiums
1. See Lang and Lehmann 2012 for an excellent review. Peter McHenry is an assistant professor of economics at the College of William and Mary. Melissa
McInerney is an assistant professor of economics at the College of William and Mary. This research was conducted with restricted access to Bureau of Labor Statistics BLS data. For advice on acquiring the
data, consult the authors beginning January 2015 through December 2017. The views expressed here do not necessarily refl ect the views of the BLS. They thank Sara Gault, Jason Saunders, and Xingchen Wang
for excellent research assistance and three anonymous referees for excellent comments. [Submitted February 2012; accepted June 2013]
ISSN 0022- 166X E- ISSN 1548- 8004 © 2014 by the Board of Regents of the University of Wisconsin System
T H E J O U R N A L O F H U M A N R E S O U R C E S • 49 • 3
are puzzling in light of other fi ndings of discrimination against black workers for example, Bertrand and Mullainathan 2004; Fryer, Pager, and Spenkuch 2011.
2
A common thread in the studies fi nding a black wage premium among women is to control for test scores usually the AFQT as a proxy for general academic ability
andor labor market productivity. For example, Fryer in the 2011 Handbook of Labor Economics fi nds that black women earn 12.7 percent more than otherwise similar
white women controlling for age, AFQT score, and its square. The magnitude of this estimated premium is striking, and Fryer 2011 cites selection into or out of the
labor force as a potential explanation. Whereas the performance of blacks relative to whites is likely overstated in estimates that do not control for selection, prior estimates
of wage disparities that account for selection out of the labor force only reduce black wages relative to white wages by three to fi ve percentage points for example, Neal
2004. We begin by demonstrating that accounting for selection out of the labor market is not suffi cient to eliminate the black wage premium in our sample of women in the
2006 National Longitudinal Survey of Youth 1979 NLSY79.
We then turn to two important omitted variables: cost of living and years of educa- tion see Black et al. 2012; Lang and Manove 2011. Omitting either variable from
estimates of racial wage differences is likely to overstate black wages relative to white wages. We present a model of employer discrimination to show explicitly how racial
wage gap estimates might attribute racial differences in local costs of living to unequal pay due to discrimination. See the Appendix. To the extent that black and white
women face different local costs of living, estimates of raw wage differentials do not provide an adequate measure of labor market disadvantages faced by particular groups
see also Black et al. 2012; DuMond, Hirsch, and Macpherson 1999. Many wage gap studies do not control for differential costs of living for example, Murnane, Willett,
and Levy 1995; Neal and Johnson 1996; Black et al. 2008; Fryer 2011. We show that black women tend to face higher local costs of living for example, in larger cities, so
estimates of wage gaps that fail to account for this will overstate how blacks are per- forming, relative to whites. We show that including controls for local costs reduces the
wage differential. We control for local costs of living using restricted- access geocoded data that are not generally available to researchers, and we demonstrate the sensitivity
of our racial wage gap evidence to alternative proxies for local costs for example, an urban indicator that are available in most public- use microdata sets.
In addition, for a given score on the AFQT, blacks acquire more years of education Lang and Manove 2011. Because additional years of education are rewarded in the
labor market, omitting years of education in estimates of wage gaps that also control for AFQT score results in upward biased estimates of the wage gap. Similar to the
Lang and Manove 2011 results for black men, after we account for AFQT score and education in wage regressions, the estimated black wage premium among women falls
substantially. The combination of cost of living and education controls eliminates the black wage premium.
2. For researchers and policymakers interested in how blacks are doing relative to whites in the labor market, the female black-white wage gap is equally, if not more, informative than the wage gap among males because
there are more black women working than black men. See Table A-2 in “The Employment Situation – April 2013” published by the U.S. Bureau of Labor Statistics. In April 2013, the number of employed black
or African American men was about 8.3 million. The corresponding employment level for black or African American women was about 9.5 million.
After accounting for selection and including these two important omitted variables, we fi nd no evidence of black wage premiums for women. We then test the data further
by examining three settings in which we would be most likely to observe a black- wage premium, if it exists. First, in the previous literature, the most consistent evidence of
a black wage premium is among highly educated women for example, Fisher and Houseworth 2012; Black et al. 2008. We estimate wage gaps separately by education
level and fi nd no evidence of black wage premiums, even among highly educated women. Second, we add controls for occupation to wage regressions. To the extent
that occupational sorting differs by race due to discrimination against black women, wage gaps conditional on occupation understate discrimination against black women
Blau and Ferber 1987. Equivalently, black women’s conditional wages should be high relative to white women’s wages, but our estimates that control for occupation
again show little signifi cant difference between black and white women’s conditional wages. Third, researchers typically control for age or potential experience, but workers
with the same education and age may have very different work experience histories, which imply very different labor market productivities. Because, on average, white
women have more years of work experience than black women, we expect blacks will appear to perform even better, relative to whites, when we control for actual labor
market experience. However, when we control for actual labor market experience, we fi nd no statistically signifi cant wage premium. These fi ndings provide further evidence
of no wage premium for black women.
II. Related Literature
