Black/White Male Earnings gap BEN SCHROCK MIKE SCHUENKEEMILY GROSSKOPFJAMES DUGGAN

Key Problem

This study investigates the determinants of earnings difference between black and white males?

Do black males still face labor market discrimination that limits their opportunities?

Does the earnings gap between black and white males reflect differences in human capital?

The importance of this is to determine if we have fairness and maximum efficiency in the economy.

Smith & Welch (1989) Shows how trends have effected

the economic situation of the black community in America (1940-1980 Cohorts)

Their study found that: Improved quantity and quality of education Migration from South to North Increase in labor force participation and

affirmative action

O’Neill (1990)

O’Neill’s studies paralleled Richard Freeman’s (1981) findings in that: In 1987, differences in background factors (Years

of school completed, AFQT scores) explained ¾ of the earnings difference between black men and white men under age 30

Differences in work experience accounted for most of the remaining gap

Found that human capital is approximately the sole source of earnings amongst the individual, NOT discrimination

Corcoran & Duncan (1979) This article compares the differences in

earnings between Black and White sexes Education and some human capital factors has a

strong effect on black male earnings but cannot fully explain the earnings gap

Found that the wage advantages enjoyed by white men cannot be explained solely by superior qualifications or more attachment to the labor force

Their results prove that there is discrimination in the market

Bostic (1997)

Minorities differ in levels of education, work experience, etc…

Growth for blacks are 67%/88% compared to whites

Earnings growth in the short run for blacks is less than whites

These results are consistent with discrimination in the market Evidence suggest that among potential

homebuyers, blacks are inherently riskier than comparable whites due to differences in earnings variability

Semyonov & Lewin-Epstein (2009)

Trends in racial earnings inequality observed in the market as a whole mask considerable differences between the private and public sectors of the economy

Found that: Little to no racial disparity in the public sector Substantial amounts of discrimination remained in the

private sector even into 2000 In contrast, the private sector is

characterized by racial disparities in earnings even after taking into considerations racial variations in socio-demographic attributes and in occupational distributions

Model to be Tested

Ln Wi = HCiß + Oi∂ + Biα + εi

Where: Wi = Wage/ annual salary income HC = Vector of human capital O = Vector of occupation B = Vector of personal background

Variables Dependent:

Wage Independent:

Human Capital Years of education Experience (Age – Years of Edu – 6) Usual hours worked

Occupation Background

Race Marital status Age Region

Hypothesis: Human Capital

Education Ho: There is no correlation between earnings

and education Ha: There is a positive correlation between

earnings and education

Hypothesis: Human Capital

Experience Ho: There is no correlation between earnings

and experience Ha: There is a positive correlation between

earnings and experience

Hypothesis: Human Capital

Hours Worked Ho: There is no correlation between earnings

and hours worked Ha: There is a positive correlation between

earnings and hours worked

Hypothesis: Race

Race Ho: There is no correlation between earnings

and Race. Ha: There is positive correlation between

earnings and race.

Data sources

Integrated Public Use Microdata Series (IPUMS) American Community Survey Years Analyzed: 2000, 2010 Age analyzed: 18-65

Variable definitions and anticipated signs

Mean values

OLS regressions

Oaxaca Decomposition

w̄W – w̄B= (αW – αB) + (βW – βB)x̄Bi + βWi(x̄wi – x̄Bi)

w̄W – w̄B= (αW – αB) + (βW – βB)x̄Wi + βBi(x̄wi – x̄Bi)

Discrimination

Human Capital and other characteristics

Human Capital and other characteristics

Discrimination

Decomposing the raw wage differential (2000)

Ln w̄W = 11.10 Ln w̄B = 9.396 Ln w̄W – Ln w̄B = 1.704 1.704= (αW – αB) + (βW – βB)x̄βi +.48

1.704 = X + .48 X = 1.224

Decomposing the raw wage differential (2010)

Ln w̄W = 10.461 Ln w̄B = 9.138 Ln w̄W – Ln w̄B = 1.323 1.323= (αW – αB) + (βW – βB)x̄βi +.65

1.323 = X + .65 X = 0.673

Conclusions

Works Cited Altonji, J., & Blank, R. (2010). Race and gender in the labor market. In O.

Ashenfelter & D. Card (Eds.), Handbook of labor economics (Vol. 3, p. 3143–3259). Amsterdam: North Holland.

Burkhauser, R., & Larrimore, J. (2009). Using internal CPS data to reevaluate trends in labor-earnings gaps. Monthly Labor review.

Concoran, M., & Duncan, G. (1979). Work History, Labor Force Attachment, and Earnings Differences between the Races and Sexes. The Journal of Human Resources, 14(1), 3-20.

Federal Reserve, Racial Differences in Short-Run Earnings Stability and Implications for Credit Markets, Doc., at 6-10 (1997).

Lewin-Epstein, N., & Semyonov, M. (2009). The declining racial earnings’ gap in United States: Multi-level analysis of males’ earnings, 1960–2000. Social Science Research, 38(2), 296-311. Retrieved from http://www.sciencedirect.com/science/article/pii/S0049089X08001105

O'Neill, J. (1990). The Role of Human Capital in Earnings Differences Between Black and White Men. The Journal of Economic Perspectives, 4(4), 43-44.

Smith, J. P., & Welch, F. R. (1989). Black Economic Progress After Myrdal. Journal of Economic Literature, 27(2), 519-564.

Steven Ruggles, J. Trent Alexander, Katie Genadek, Ronald Goeken, Matthew B. Schroeder, and Matthew Sobek. Integrated Public Use Microdata Series: Version 5.0 [Machine-readable database]. Minneapolis: University of Minnesota, 2010.