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Using internal CPS data to reevaluate trends in labor-earnings gaps
Richard V. Burkhauser and Jeff Larrimore
Richard V. Burkhauser is a professor in the Department of Policy Analysis and Management at Cornell University. Jeff Larrimore is a Ph.D. candidate in the Department of Economics at Cornell University. E-mail: rvb1@cornell.edu, jhl42@cornell.edu
The Current Population Survey provides data that are used to compare gaps in the labor earnings of women and men, people of different races, and people of different levels of education; this article presents a data series that uses cell means and more accurately measures gaps and trends in earnings than do other publicly available series.
The Current Population Survey (CPS) is a large, nationally representative sample of households collected each month since 1942 by the U.S. Census Bureau.1 This article focuses on data from the surveys conducted in March because the March survey includes an extensive income questionnaire. The data that are publicly available from the CPS are the primary tool used to investigate yearly trends in United States average labor earnings and their distribution. However, to protect the confidentiality of its respondents, the Census Bureau topcodes the highest values from each source of income that it collects when it reports the income in the public-use CPS data. Topcoding is the replacement of a datum representing part or all of a person’s true income with a lower value. One of the challenges that topcoding presents for those using the public-use data to examine labor-earnings levels and trends is that the topcodes vary over time, which leads to artificial increases or decreases in earnings (when the term “earnings” appears alone in this article, it still refers to “labor earnings”) at the top of the earnings distribution as different fractions of the population are subject to topcoding each year.2 Although the public-use data are used extensively to measure the earnings gaps between men and women and Blacks and Whites,3 until now little was known about how topcoding affects comparisons of labor earnings across these subsets of the population.4
This article finds that gaps between the earnings of men and women, Blacks and Whites, and people of various education levels are all sensitive to topcoding. Ratios of these earnings as well as trends in the gaps and ratios also are sensitive to topcoding. The article arrives at these findings by analyzing 1975–2007 CPS data and comparing the values of gaps and ratios obtained using the public-use CPS data with values found using the internal CPS data.
This excerpt is from an article published in the August 2009 issue of the Monthly Labor Review. The full text of the article is available in Adobe Acrobat's Portable Document Format (PDF). See How to view a PDF file for more information.
Footnotes
1 Each year the U.S. Census Bureau uses March CPS data to calculate yearly average income and poverty rates, and it releases these rates to the public; see www.census.gov/prod/2008pubs/p60-235.pdf (visited July 27, 2009) for more details. The March CPS data that the Census Bureau uses in its calculations are not available, except under certain conditions, to researchers outside of the Census Bureau.
2 For an early review of this problem in the earnings-inequality literature, see Frank Levy and Richard J. Murnane, “U.S. Earnings Levels and Earnings Inequality: A Review of Recent Trends and Proposed Explanations,” Journal of Economic Literature, September 1992, pp. 1333–81. For a more recent discussion see Shuaizhang Feng, Richard V. Burkhauser, and J.S. Butler, “Levels and Long-Term Trends in Earnings Inequality: Overcoming Current Population Survey Censoring Problems Using the GB2 Distribution,” Journal of Business and Economic Statistics, January 2006, pp. 57–62.
3 See, among other sources, Chinhui Juhn, Kevin M. Murphy, and Brooks Pierce, “Accounting for the Slowdown in Black-White Wage Convergence,” in Marvin Kosters, ed., Workers and their Wages (Washington, DC, AEI Press, 1991); David Card and John E. DiNardo, “Skill-Biased Technological Change and Rising Wage Inequality: Some Problems and Puzzles,” Journal of Labor Economics, October 2002, pp. 733–83; Kenneth Couch and Mary C. Daly, “Black-White Wage Inequality in the 1990s: a Decade of Progress,” Economic Inquiry, January 2002, pp. 31–42; and Chinhui Juhn, “Labor Market Dropouts and Trends in the Wages of Black and White Men,” Industrial and Labor Relations Review, July 2003, pp. 643–62.
4 For a discussion of the impact of topcoding on the income gap between men with and without disabilities, see Richard V. Burkhauser and Jeff Larrimore, “Trends in the Relative Household Income of Working-Age Men with Work Limitations: Correcting the Record using Internal Current Population Survey Data,” Journal of Disability Policy Studies, forthcoming article, see http://dps.sagepub.com (visited July 27, 2009).
The results and conclusions presented in this article are those of the authors and do not necessarily reflect the views of the U. S. Census Bureau. This article has been screened to ensure that no confidential data are disclosed.
Earnings by gender: evidence from Census 2000.—Jul./Aug. 2007.
Employment change by occupation, industry, and earnings quartile, 2000-05.—Dec. 2006.
How does gender play a role in the earnings gap? an update—Mar. 2003.
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