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Racial Wage Disparity in US Cities

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Abstract

This paper estimates the conditional wage gaps between black and white full-time male workers at the metropolitan statistical area (MSA) level using data from the 1990 and 2000 U.S. Censuses. The magnitudes of the wage gaps are found to vary substantially across location. As predicted in Becker's (The economics of discrimination, University of Chicago Press, Chicago, 1957) seminal theory on wage discrimination, we find that the wage gaps are greater in MSAs that have a larger proportion of black workers in the labor force. This is the most consistent result across all specifications and years. We also find the gaps to be greater where there is an overrepresented black population in jail and a more segregated population if the MSA is in the South. The proportion of workers covered by a collective bargaining agreement in the private sector is associated with greater relative black earnings. We find that although the relationship between race and wages has diminished over time as famously suggested in Wilson (The declining significance of race: Blacks and changing American institutions, University of Chicago Press, Chicago, 1978), the significance of race remains.

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Notes

  1. See Altonji and Blank (1999) for a review of the literature covering black and white economic inequality.

  2. Two notable exceptions cited here are Sundstrom (2007), who estimates wage gaps at the SEA level for the South, and Charles and Guryan (2008), who estimate wage gaps at the state level.

  3. The National Jail Census of 1999 surveyed all jails in the USA between 1989 and 1999.

  4. For a summary of the empirical evidence on the effects of incarceration on the subsequent employment an earnings on less-educated young prisoners, see Holzer (2007).

  5. For additional examples of spatial mismatch, see Holzer (1991).

  6. Card and Krueger (1992) show that relative school quality across race accounts for 20 % of the narrowing of the wage gap between 1915 and 1966. Furthermore, the authors found that over 90 % of school-aged children in 1940 were living in their state of birth and 82 % of all blacks born in between 1990 and 1945 grew up in the state of their birth. Thus, these interaction variables are meant to control for school quality that may differ by race and state. However, Card and Krueger (1992) tabulations are from data sets much older than those used in our analysis, so it is possible that these percents are lower in our data if black school-aged children are more mobile in our sample than they were in the 1940s. We do not interact birth-state with race in this specification but do so when estimating a specification that mimics that of Sundstrom (2007) not reported here. Interacting birth-state with race in Eq. 3 leads to little difference in the estimated wage gaps.

  7. See the “Appendix” for list of industries, occupations. An example of a industry-occupation group that would have its own indicator would be all managerial and professional workers in the manufacturing industry.

  8. A worker is designated full-time if he reported working at least 30 h a week and 27 weeks a year.

  9. Available at http://www.usa.ipums.org/usa/slavepums.

  10. Available at https://www.nhgis.org.

  11. We would like to thank the authors for providing this measure.

  12. When looking at the most common industry-occupation pairs rather than considering the two variables separately, white men were most likely to be employed as management in the professional and related services industry. This held true for both years. The list of occupations and industries are displayed in Tables 2 and 3, respectively.

  13. Kernel density estimates for the individual education groups are, for the most part, qualitatively the same as the full sample and are displayed in Figs. 2, 3, and 4.

  14. Maps for the education-specific samples are displayed in Figs. 7, 8, 9, 10, 11, and 12.

  15. Sundstrom (2007) used the proportion black among adult males (age 21 and older) as a proxy for the proportion black in the labor force.

  16. For MSAs that cross state boundaries, prejudice is taken to be the average across the respective states.

  17. See Table 6

  18. We would only expect to find significant results perhaps with the 10th or 50th percentile as the most discriminating firms in the upper tail of the distribution should have little to no effect on the wage gap. Although the coefficients were all insignificant, they did tend to be more negative when using the lower end of the distribution.

  19. When simultaneously modeling worker demand for union jobs and unionized firms demand for workers, Farber (1983) finds that non-whites were more likely to be in unions almost entirely due to their relatively greater desire to be in a union and not from any demand of the firm.

  20. Census block level data are available for the year 1990, but using this as the geographic unit of observation does not alter the results.

  21. Full results are reported in Tables 9, 10, 11, 12, 13, and 14.

  22. We also used birth-state-race indicators and did not find a significant difference in estimated wage gaps. See footnote 6.

References

  • Altonji, J. G., & Blank, R. M. (1999). Race and gender in the labor market. Handbook of Labor Economics, 3(2), 3143–3259.

    Article  Google Scholar 

  • Arcidiacono, P., Bayer, P., & Hizmo, A. (2010). Beyond signaling and human capital: Education and the revelation of ability. Applied Economics, 2(1), 76–104.

    Google Scholar 

  • Becker, G. (1957). The economics of discrimination. Chicago: University of Chicago Press.

    Google Scholar 

  • Bertrand, M., & Mullainathan, S. (2004). Are emily and greg more employable than lakisha and jamal? A field experiment on labor market discrimination. American Economic Review, 94(4), 991–1013.

    Article  Google Scholar 

  • Bloch, F. E., & Kuskin, M. S. (1978). Wage determination in the union and nonunion sectors. Industrial and Labor Relations Review, 31(2), 183–192.

    Article  Google Scholar 

  • Blumstein, A. (1982). The continuing significance of race: Racial conflict and racial discrimination in construction. The Journal of Criminal Law and Criminology, 73(3), 1259–1281.

    Article  Google Scholar 

  • Bound, J., & Freeman, R. (1992). What went wrong? The erosion of relative earnings and employment among young black men in the 1980s. The Quarterly Journal of Economics, 107(1), 201–232.

    Article  Google Scholar 

  • Boustan, L., & Margo, R. (2011). White suburbanization and African–American home ownership, 1940–1980. Technical report, National Bureau of Economic Research.

  • Braddock, J. H., & McPartland, J. M. (1987). How minorities continue to be excluded from equal employment opportunities: Research on labor market and institutional barriers. Journal of Social Issues, 43(1), 5–39.

    Article  Google Scholar 

  • Brown, C. (1984). Black–white earnings ratios since the civil rights act of 1964: The importance of labor market dropouts.

  • Card, D., & Krueger, A. (1992). School quality and black–white relative earnings: A direct assessment. The Quarterly Journal of Economics, 107(1), 151–200.

    Article  Google Scholar 

  • Center, M. P. (2011). National historical geographic information system: Version 2.0. Minneapolis, MN: University of Minnesota.

    Google Scholar 

  • Chandra, A. (2003). Is the convergence of the racial wage gap illusory?. Technical report, National Bureau of Economic Research.

  • Charles, K. K., & Guryan, J. (2008). Prejudice and wages: An empirical assessment of Becker’s the economics of discrimination. Journal of Political Economy, 116(5), 773–809.

    Article  Google Scholar 

  • Collins, W. J., & Margo, R. A. (2000). Residential segregation and socioeconomic outcomes: When did ghettos go bad? Economics Letters, 69(2), 239–243.

    Article  Google Scholar 

  • Cutler, D., & Glaeser, E. (1997). Are ghettos good or bad? The Quarterly Journal of Economics, 112(3), 827–872.

    Article  Google Scholar 

  • Cutler, D., Glaeser, E., & Vigdor, J. (2008). When are ghettos bad? Lessons from immigrant segregation in the United States. Journal of Urban Economics, 63(3), 759–774.

    Article  Google Scholar 

  • Duncan, G., & Leigh, D. (1980). Wage determination in the union and nonunion sectors: A sample seletivity approach. Industrial and Labor Relations Review, 34, 24.

    Article  Google Scholar 

  • Farber, H. S. (1983). The determination of the union status of workers. The Econometric Society, 51(5), 1417–1437.

    Article  Google Scholar 

  • Freeman, R. B. (1980). Unionism and the dispersion of wages. Industrial and Labor Relations Review, 34(1), 3–23.

    Article  Google Scholar 

  • Glazer, N., & Moynihan, D. P. (1963). Beyond the melting pot: The Negroes, Puerto Ricans, Jews, Italians and Irish of New York City. Cambridge: Massachusetts Institute of Technology Press.

    Google Scholar 

  • Handlin, O. (1959). The newcomers: Negroes and Puerto Ricans in a changing metropolis, volume 3. Cambridge, MA: Harvard University Press.

    Book  Google Scholar 

  • Harris, C. T., Steffensmeier, D., Ulmer, J. T., & Painter-Davis, N. (2009). Are blacks and hispanics disproportionately incarcerated relative to their arrests? Racial and ethnic disproportionality between arrest and incarceration. Race and Social Problems, 1(4), 187–199.

    Article  Google Scholar 

  • Hirsch, B., & Macpherson, D. (2003). Union membership and coverage database from the current population survey: Note. Industrial and Labor Relations Review, 56(2), 349–354.

    Article  Google Scholar 

  • Holzer, H. (1991). The spatial mismatch hypothesis: What has the evidence shown? Urban Studies, 28(1), 105–122.

    Article  Google Scholar 

  • Holzer, H. J. (1994). Black employment problems: New evidence, old questions. Journal of Policy Analysis and Management, 13(4), 699–722.

    Article  Google Scholar 

  • Holzer, H. J. (2007). Collateral costs: The effects of incarceration on the employment and earnings of young workers. Discussion Paper: IZA. 3118.

  • Holzer, H. J., Raphael, S., and Stoll, M. (2003). Employer demand for ex-offenders: Recent evidence from los angeles. Paper No. 1268–03 Institute for Research on Poverty, University of Wisconsin.

  • Holzer, H. J., Raphael, S., & Stoll, M. A. (2004). Imprisoning America: The social effects of mass incarceration. New York: Russell Sage Foundation.

    Google Scholar 

  • Kain, J. (1968). Housing segregation, Negro employment, and metropolitan decentralization. The Quarterly Journal of Economics, 82(2), 175–197.

    Article  Google Scholar 

  • Menard, R., Alexander, T., Digman, J., and Hacker, D. J. (2004). Public use microdata samples of the slave population of 1850–1860 [Machine-readable database]. Minneapolis, MN: Minnesota Population Center [producer and distributor], 2009.

  • Pager, D. (2003). The mark of a criminal record. American Journal of Sociology, 108(5), 937–975.

    Article  Google Scholar 

  • Pager, D. (2007). The use of field experiments for studies of employment discrimination: Contributions, critiques, and directions for the future. Annals of the American Academy of Political and Social Science, 609(1), 104–133.

    Article  Google Scholar 

  • Pager, D., Western, B., & Bonikowski, B. (2009a). Discrimination in a low-wage labor market: A field experiment. American Sociological Review, 74(5), 777–799.

    Article  Google Scholar 

  • Pager, D., Western, B., & Sugie, N. (2009b). Sequencing disadvantage: Barriers to employment facing young black and white men with criminal records. Annals of the American Academy of Political and Social Science, 623(1), 195–213.

    Article  Google Scholar 

  • Raphael, S., & Riker, D. A. (1999). Geographic mobility, race, and wage differentials. Journal of Urban Economics, 45(1), 17–46.

    Article  Google Scholar 

  • Ruggles, S., Sobek, M., Alexander, T., Fitch, C., Goeken, R., Hall, P., King, M., and Ronnander, C. (2009). Integrated public use microdata series: Version 4.0 [Machine-readable database]. Minneapolis, MN: Minnesota Population Center [producer and distributor], 2009.

  • Schwartz, R., & Skolnick, J. (1962). Two studies of legal stigma. Social Problems, 133–142.

  • Shook, J. J., & Goodkind, S. A. (2009). Racial disproportionality in juvenile justice: The interaction of race and geography in pretrial detention for violent and serious offenses. Race and Social Problems, 1(4), 257–266.

    Article  Google Scholar 

  • Smith, S. S. (2000). Mobilizing social resources: Race, ethnic, and gender differences in social capital and persisting wage inequalities. The Sociological Quarterly, 41(4), 509–537.

    Article  Google Scholar 

  • Smith, S. S. (2005). Dont put my name on it: Social capital activation and job-finding assistance among the black urban poor. American Journal of Sociology, 111(1), 1–57.

    Article  Google Scholar 

  • Smith, S. S. (2010). A test of sincerity: How black and latino service workers make decisions about making referrals. The Annals of the American Academy of Political and Social Science, 629(1), 30–52.

    Article  Google Scholar 

  • Sundstrom, W. (2007). The geography of wage discrimination in the pre-civil rights south. The Journal of Economic History, 67(02), 410–444.

    Article  Google Scholar 

  • Turner, M. A., Freiberg, F., Godfrey, E., Herbig, C., Levy, D. K., & Smith, R. R. (2002). All other things being equal: A paired testing study of mortgage lending institutions. Washington, DC: US Department of Housing and Urban Development.

    Google Scholar 

  • Waldfogel, J. (1994). The effect of criminal conviction on income and the trust reposed in the workmen. Journal of Human Resources, 62–81.

  • Waldinger, R., & Bailey, T. (1991). The continuing significance of race: Racial conflict and racial discrimination in construction. Politics and Society, 19(3), 291–323.

    Article  Google Scholar 

  • Western, B. (2002). The impact of incarceration on wage mobility and inequality (pp. 526–546). American Sociological Review.

  • Western, B., & Pettit, B. (2005). Black–white wage inequality, employment rates, and incarceration1. American Journal of Sociology, 111(2), 553–578.

    Article  Google Scholar 

  • Wilson, W. J. (1978). The declining significance of race: Blacks and changing American institutions. Chicago: University of Chicago Press.

    Google Scholar 

  • Wilson, W. J. (1987). The truly disadvantaged: The inner city, the underclass, and public policy. Chicago: University of Chicago Press.

    Google Scholar 

  • Yinger, J. (1986). Measuring racial discrimination with fair housing audits: Caught in the act (pp. 881–893). The American Economic Review.

  • Zax, J., & Kain, J. (1996). Moving to the suburbs: Do relocating companies leave their black employees behind? Journal of Labor Economics, 472–504.

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Authors and Affiliations

  1. Department of Economics, California State Polytechnic University - Pomona, 3801 West Temple Ave., Pomona, CA, 91768, USA

    Craig Kerr

  2. Department of Economics, University of Pittsburgh, 4901 Wesley W. Posvar Hall, 230 S. Bouquet St., Pittsburgh, PA, 15260, USA

    Randall Walsh

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Correspondence to Craig Kerr.

Appendix

Appendix

Metropolitan Areas

One problem with using Census data is that the public use micro-areas in which our individuals resided (PUMAs) do not always identify the MSA in which they resided. That is, some PUMAs cross MSA boundaries and so if an individual resides in such a PUMA, we cannot be certain whether or not he resides within the MSA. We are, however, able to identify the percentage of each race (black or white) within each PUMA that lived in each MSA using aggregated Census data. We then assigned any questionable individual to the location that most of his race belonged to. We also assigned the MSA in which each individual worked to the MSA for which the majority of his race resided in the same manner.

In earlier versions of this paper, we randomly assigned each individual to any of his possible locations with probability p = percent of own race in that location. Although this preserves the total population of each race in each MSA, assigning the individuals to the MSA for which the majority of their race resides in can be shown in expectation to be a better predictor for where any individual resided.

Lastly, in 2000, the place of work variable in the publicly available Census data, which gives the public use micro-area (PUMA) of work, does not identify the MSA where individuals work for 8 PUMAs. So an additional 10 MSAs were taken out of the sample in 2000. However, only seven of these had estimated wage gaps in 1990. These were Davenport-Rock Island-Moline, IA-IN, Anderson, IN, Indianapolis, IN, Minneapolis-St. Paul, MN, Omaha, NE, Syracuse, NY, and San Antonio, TX.

See Tables 2, 3, 8, 9, 10, 11, 12, 13, and 14.

See Figures 2, 3, 4, 7, 8, 9, 10, 11, and 12.

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Kerr, C., Walsh, R. Racial Wage Disparity in US Cities. Race Soc Probl 6, 305–327 (2014). https://doi.org/10.1007/s12552-014-9127-0

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