Measuring the distribution of wages in the United States from 1996 through 2010 using the Occupational Employment Survey
The microdata collected by the Occupational Employment Statistics (OES) program provide a unique opportunity to study wage inequality in the United States using employer-provided survey data. These data contain information on establishment characteristics as well as on wages and occupations for the millions of employees who work in the 400,000 establishments surveyed each year. Using these data, we replicate many of the wage variance trends that other authors have found using data from the Current Population Survey (CPS) of households. We show that most of the growth in wage inequality during the 1996–2009 period occurred within the private sector, within particular industry groups such as professional and business services, and within occupational groups such as healthcare occupations. Industry and particularly occupation explain more of overall wage variation in the (employer-reported) OES than in the (employee-reported) CPS. The amount of wage variance explained by occupation is also growing more quickly in the OES than in the CPS. In an examination not possible with the CPS data, we find that within the private sector, wage differences among establishments explain far more of the level and the trend of wage variance than do wage differences among occupations.
By nearly every measure, the distribution of wages in the United States has been growing more unequal since the late 1970s.1 In this article, we show how data from the Occupational Employment Statistics (OES) survey can be used to measure the changing distribution of wages in the United States since the late 1990s, and we describe aspects of this changing distribution of wages that the OES data are uniquely well suited to describe.
There is an enormous literature devoted to understanding the nature and sources of growing wage inequality. Much of this literature relies on data on the wages and characteristics of individual workers from the Current Population Survey (CPS). For example, many studies have examined the changing composition of the workforce and changing returns to education and experience2 and the growing dispersion of wages among the most educated and experienced workers.3 Growing inequality has been attributed to the differential impact of technology on differing portions of the worker skill distribution,4 to the differential ‘offshorability’ of occupational tasks,5 to changing labor market institutions such as declining unionization levels,6 to the declining real value of the minimum wage,7 and to the growing fraction of workers subject to performance-based pay from their employers.8 Although these explanations for growing inequality are concerned with the policies and incentives faced by employers, the CPS data used in this literature contain little if any information on the businesses employing these workers.
Other researchers have studied wage inequality using data collected from employers. These studies have built on evidence that establishments play an important role in determining individual wages.9 Several authors have used employer microdata to study growing earnings variance in the United States from the mid-1970s to the early 2000s, and have found that the increasing variance is due more to variation between establishments than to variation within establishments.10 However, the employer-provided data used in this literature have limited information on the characteristics of workers in each establishment.
This disconnect between wage inequality studies based on individual worker microdata versus studies based on employer-provided microdata can be bridged with data from the Occupational Employment Statistics program. The OES data are unique in containing information on establishment characteristics as well as information on wages and occupations for all employees within surveyed establishments. Our analysis of the occupational data in the OES complements a small yet growing literature that analyzes the relationship between occupations and increasing wage inequality.11
The OES data are collected from a large semiannual survey of establishments. These data allow us to use a single source of wage information as we decompose increasing wage inequality in the United States into its within- and between-occupation components and into its within- and between-establishment components. However, the OES was not designed to study the time series of wage inequality (or any other time series12), and as such, we describe the adjustments we have made to the confidential OES microdata that are necessary for analyzing wage variance trends.
2 John Bound and George Johnson, “Changes in the structure of wages in the 1980's: an evaluation of alternative explanations,” American Economic Review, June 1992, pp. 371–392; Lawrence F. Katz and Kevin M. Murphy, “Changes in relative wages, 1963–1987: supply and demand factors,” Quarterly Journal of Economics, February 1992, pp. 35–78; and Thomas Lemieux, “Increasing residual wage inequality: composition effects, noisy data, or rising demand for skill?” American Economic Review, June 2006, pp. 461–498.
3 Thomas Lemieux, “The changing nature of wage inequality,” Journal of Population Economics, January 2008, pp. 21–48; and David H. Autor, Lawrence F. Katz, and Melissa S. Kearney, “The polarization of the U.S. labor market,” American Economic Association annual meeting papers and proceedings, 2006, pp. 189–194 and “Trends in U.S. wage inequality: re-assessing the revisionists,” Review of Economics and Statistics, May 2008, pp. 300–323.
4 Chinhui Juhn, Kevin M. Murphy, and Brooks Pierce, “Wage inequality and the rise in returns to skill wage inequality and the rise in returns to skill,” Journal of Political Economy, June 1993, pp. 410-442; Katz and Murphy, “Changes in relative wages;” Daron Acemoglu, “Technical change, inequality, and the labor market,” Journal of Economic Literature, March 2002, pp. 7–72; and Autor, Katz, and Kearney, “The polarization of the U.S. labor market” and “Trends in U.S. wage inequality.”
5 Sergio Firpo, Nicole M. Fortin, and Thomas Lemieux, “Occupational tasks and changes in the wage structure,” working paper, February 2011, http://faculty.arts.ubc.ca/nfortin/FFL_Occupations_feb2011.pdf.
6 Lemieux, “The changing nature of wage inequality.”
7 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. 733–783; and David Lee, “Wage inequality in the United States during the 1980s: rising dispersion or falling minimum wage?” Quarterly Journal of Economics, August 1999, pp. 977–1023.
8 Thomas Lemieux, W. Bentley MacLeod, and Daniel Parent, “Performance pay and wage inequality,” Quarterly Journal of Economics, February 2009, pp. 1–49.
9 Erica L. Groshen, “Sources of intra-industry wage dispersion: how much do employers matter?” Quarterly Journal of Economics, August 1991, pp. 869–884, and “Five reasons why wages vary among employers,” Industrial Relations, Fall 1991, pp. 350–381; Stephen G. Bronars and Melissa Famulari, “Wage, tenure, and wage growth variation within and across establishments,” Journal of Labor Economics, 1997, pp. 285–317; David I. Levine, Dale Belman, and Gary Charness, How new is the new employment contract? Evidence from North American pay practice (Kalamazoo, MI: W.E. Upjohn Institute for Employment Research, 2002); John M. Abowd, Francis Kramarz, and David Margolis, “High wage workers and high wage firms,” Econometrica, February 1999, pp. 251–333; and Julia I. Lane, Laurie A. Salmon, and James R. Spletzer, “Establishment wage differentials,” Monthly Labor Review, April 2007, pp. 3–17, http://www.stats.bls.gov/opub/mlr/2007/04/art1full.pdf.
10 Steve J. Davis and John Haltiwanger, “Wage dispersion between and within U.S. manufacturing plants,” Brookings papers on economic activity, 1991, pp. 115–200; Timothy Dunne, Lucia Foster, John Haltiwanger, and Kenneth R. Troske, “Wage and productivity dispersion in United States manufacturing: the role of computer investment,” Journal of Labor Economics, April 2004, pp. 397–429; Fredrik Andersson, Elizabeth E. Davis, Matthew L. Freedman, Julia I. Lane, Brian P. McCall, and Kristin Sandusky, “Decomposing the sources of earnings inequality: assessing the role of reallocation,” Industrial Relations: A Journal of Economy and Society, October 2012, pp. 779–810; and Erling Barth, Alex Bryson, James C. Davis, and Richard Freeman, “The contribution of dispersion across plants to the increase in US earnings dispersion,” unpublished paper, 2011, http://cep.lse.ac.uk/seminarpapers/08-02-11-AB.pdf.
11 Autor, Katz, and Kearney, “Trends in U.S. Wage Inequality;” Firpo, Fortin, and Lemieux, “Occupational tasks and changes in the wage structure;” and Daron Acemoglu and David Autor, “Skills, tasks and technologies: implications for employment and earnings,” in David Card and Orley Ashenfelter, eds., Handbook of labor economics, vol. 4B (North Holland: 2011, pp. 1,043–1,171).
12 As stated on the OES website, “Challenges in using OES data as a time series include changes in the occupational, industrial, and geographical classification systems, changes in the way data are collected, changes in the survey reference period, and changes in mean wage estimation methodology, as well as permanent features of the methodology,” http://www.bls.gov/oes/oes_ques.htm Many further details on the limitations of using OES data to construct time series are given in Katharine G. Abraham and James R. Spletzer, “Addressing the demand for time series and longitudinal data on occupational employment” in Susan N. Houseman and Kenneth F. Ryder, eds., Measurement issues arising from the growth of globalization: conference papers (National Academy of Public Administration and W.E. Upjohn Institute for Employment Research, 2010).