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Public Sector Employment Inequality in the United States and the Great Recession

Abstract

Historically in the United States, the public sector has served as an equalizing institution through the expansion of job opportunities for minority workers. This study examines whether the public sector continues to serve as an equalizing institution in the aftermath of the Great Recession. Using data from the Current Population Survey, I investigate changes in public sector employment between 2003 and 2013. My results point to a post-recession double disadvantage for black public sector workers: they are concentrated in a shrinking sector of the economy, and they are more likely than white and Hispanic public sector workers to experience job loss. These two trends are a historical break for the public sector labor market. I find that race and ethnicity gaps in public sector employment cannot be explained by differences in education, occupation, or any of the other measurable factors that are typically associated with employment. Among unemployed workers who most recently worked for the public sector, black women are the least likely to transition into private sector employment.

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Notes

  1. 1.

    The majority of public sector workers are employed by local government; less than one-fifth are employed by the federal government.

  2. 2.

    In this article, I treat blacks, whites, and Hispanics as separate categories: “black” refers to non-Hispanic black, and “white” refers to non-Hispanic white. Within each gender group, Hispanics have lower public sector representation rates than both whites and blacks (see Table 1).

  3. 3.

    Not all public sector jobs are good jobs. In both the private and the public sectors, black workers have a lower median wage than whites, and women have a lower median wage than men (results of weekly earnings models available upon request). However, black-white and male-female income inequality is significantly lower in the public sector, even after public-private differences in occupation and education are controlled for (Gornick and Jacobs 1998; Grodsky and Pager 2001). Using data from the Panel Study of Income Dynamics (1976–1998), Heywood and Parent (2012) reported that although the raw black-white wage gap in the public sector is positive and significant, the gap disappears after observable characteristics (e.g., education, experience, region, occupation) are controlled for. Heywood and Parent attributed the absence of a positive wage gap between similarly situated white and black workers in the public sector to explicit pay scales and rules determining compensation.

  4. 4.

    Members of the military who reside in military barracks are excluded from the CPS. Because the CPS is designed to measure unemployment in the civilian labor force, members of the armed forces are not part of the universe for many employment-related questions. For these reasons, members of the armed forces are not included in this analysis.

  5. 5.

    Those who are not working, available for work, have looked for a job during the past year but not during the past four weeks are considered by the BLS to be discouraged workers. Approximately 77 % of the 3,885 discouraged workers in the CPS sample have missing occupation information and are therefore dropped from this analysis. Given that discouraged workers are disproportionately male and black, the CPS results most likely underestimate employment disadvantages among men and among blacks.

  6. 6.

    I use the CPS “two-digit” detail occupation recode. The categories are management, business, and financial operations; computer and mathematical science; architecture and engineering; life, physical, and social science occupations; legal occupations; education, training, and library occupations; arts, design, entertainment, sports, and media occupations; healthcare practitioner and technical occupations; healthcare support occupations; protective service occupations; food prep and serving occupations; building and grounds cleaning and maintenance; personal care and service; sales; office and administrative support; farming, fishing, and forestry; construction and extraction; installation, maintenance, and repair; production; and transportation and material moving. I use this occupation scheme because it identifies occupation groups that were disproportionately affected by the recent recession (e.g., administrative support and construction). With several hundred categories, the more detailed occupation scheme would yield cell counts that are too small to quantify race differences within sectors.

  7. 7.

    For public sector workers, veteran status should theoretically reduce the odds of unemployment because preference for veterans is commonly used in in the civil service hiring process (Ban 1995; Lewis 2013). I did not find that veteran status reduced the odds of unemployment in any of my models (even those restricted to the public sector).

  8. 8.

    The independence of irrelevant alternatives assumption of multinomial logit requires that an individual’s probability of being in one outcome category relative to another outcome category should not change if a third (irrelevant) category is added to or dropped from the analysis (e.g., there is a chance that an individual’s probability of voting for a Democrat versus a Republican will change if a third-party candidate is added to the ballot). Under the independence of irrelevant alternatives (IIA) assumption, no systematic change should be evident in the coefficients if one of the outcomes is excluded from the model. I performed a Hausman test for a violation of IIA, comparing the results from the full model and a model that excludes those who are not in the labor force. According to the results of the Hausman test (available upon request), I find no evidence that the IIA assumption is violated in this analysis.

  9. 9.

    I use the tile package in R to produce the figures in this analysis (Adolph 2016).

  10. 10.

    In models that combine public and private sector workers, I find statistically significant interactions between sector (public vs. private) and other predictors of employment, including education, age, and occupation (results available upon request). For this reason, I model employment separately for public and private sector workers. Online Resource 1 includes models predicting the odds of not being in the labor force, as well as results for the private sector. The coefficients in the tables in Online Resource 1 show that the race gaps in unemployment cannot be attributed to differential likelihoods of being out of the labor force.

  11. 11.

    Who is the typical federal, state, or local government employee? Approximately one-quarter of state employees and one-third of local government employees are teachers. Nearly one-third of federal employees work in office or administrative support occupations; one-half of federal administrative support workers are postal workers. Among all public sector employees, 50 % work for local governments, 30 % work for state governments, and nearly 20 % work for the federal government.

  12. 12.

    The CPS does not have enough minority public sector workers in every state to allow a state-level analysis. The large samples in the American Community Survey (ACS) are better suited for an analysis of state-level variation in public sector employment, but the ACS does not measure the timing of employment as precisely as the CPS. (ACS responses can relate to any weekly period throughout the year, whereas CPS responses refer to a particular week.) Figure S1 in Online Resource 1 shows state and local government employment rates in the ACS for those states that had at least 100 black public sector workers (men and women) in 2008.

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Acknowledgments

This research was supported by NICHD Training Grant 5T32HD007543 to the Center for Studies in Demography and Ecology at the University of Washington. Thanks to Jake Rosenfeld, Stew Tolnay, Kyle Crowder, and anonymous reviewers for insights on prior versions of this article.

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Correspondence to Jennifer Laird.

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Laird, J. Public Sector Employment Inequality in the United States and the Great Recession. Demography 54, 391–411 (2017). https://doi.org/10.1007/s13524-016-0532-4

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Keywords

  • Employment
  • Race
  • Gender
  • Recession