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The Role of Industry and Occupation in Recent US Unemployment Differentials by Gender, Race, and Ethnicity

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Abstract

This paper documents historical unemployment trends by gender, race, and ethnicity, and examines the role of the industrial and occupational composition of employment in explaining recent trends. We show that the labor force proportions of women, non-Whites, and Hispanics have increased dramatically over the past 50 years and the unemployment rates for these groups have been converging to those of the rest of the population. We also find that in recent years, underlying differences in the industrial and occupational distributions hide substantial gender, race, and ethnicity differences in the unemployment experience within industry and occupation.

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Notes

  1. Figures 1, 2, 3, 4 and 5 are based on 12-month moving averages of monthly CPS data to remove seasonal effects.

  2. Non-Whites are mostly Black, but include other races as well (Asians, American Indians, and those who identify with more than one race). Hispanics are discussed later; they are not classified as a racial group.

  3. For clarity, we present only four industry categories. Figures 4, 5 and 6, and Figure 10 extend only through 2002 because a change in the classification of major industries and occupations makes comparisons with later years inconsistent. In subsequent sections, our analyses will be based on individual data so we will use more detailed categories, and the analyses will extend to 2007.

  4. White-collar high-skill occupations include: CEOs and managers; professional specialty occupations; engineers and scientists; and health-care occupations. White-collar low-skill occupations include: teachers, community, and social services; sales; and office and administrative support occupations.

  5. Blue-collar high-skill occupations include: protective service; installation, maintenance, and repair; transportation and material moving; construction workers; and production occupations. Blue-collar low-skill occupations include: building and grounds cleaning and maintenance; food preparation and serving; farming, fishing, and forestry; other service-related occupations; and laborers.

  6. Although the unemployment rate in construction is higher during winter months for both genders, the male rate is consistently higher and much more volatile than the female rate. This analysis is available upon request.

  7. Industry categories reported in Table 1 are based on our tabulations of the March CPS microdata, which have been coded to correspond with the North American Industry Classification System, instituted in the early 2000s. These are not fully reconcilable with the industry categories used in Figure 4, which are based on aggregated data for earlier periods and use the Standard Industrial Classification System.

  8. Occupation tabulations in Table 2 use an occupational classification system that was adopted in the early 2000s and applied to existing March CPS microdata. These categories are not fully consistent with the categories reported in Figure 5, which are based on aggregated tabulations of the occupation classifications used for earlier periods.

  9. Our adjustments for occupation and industry are conceptually similar to the industry adjustments of Fairlie and Sundstrom [1999] and the occupational adjustments of Rives and Sosin [2002]. Industry and occupation play a small role in the extensive models of Mohanty [2003] and Garston et al. [2006]; although they assume additivity for industry and occupation, their controls are similar to those used here. In contrast, the shift-share analyses of DeBoer and Seeborg [1984] and Seeborg and DeBoer [1987] identify short-term unemployment effects due to differential growth by industry, which is inherently different from our approach.

  10. For example, let men and women be employed in only two industry/occupation groups, A and B; 50 percent of men in the experienced labor force are in A and 50 percent are in B, while 80 percent of women are in A and 20 percent are in B. Also assume that the unemployment rates for men are 4 percent for A and 8 percent for B, and for women are 5 percent for A and 10 percent for B. The actual overall unemployment rate would then be 6 percent for men (0.5 × 8%+0.5 × 4%) and 6 percent for women (0.8 × 5%+0.2 × 10%). So, even though women face higher unemployment rates than men within each group, men and women have the same overall unemployment rate. Our decomposition approach will adjust the women's unemployment rate based on the men's employment distribution (50 percent in A, 50 percent in B); thus, the adjusted unemployment rate for women would be 7.5 percent (0.5 × 5%+0.5 × 10%). Comparing the actual male rate (6 percent) with the adjusted female rate (7.5 percent) reveals that women would have higher unemployment rates if they had the same employment distribution as men. This also shows that women face higher unemployment rates than men within industry-occupation group, when weighted by the male distribution.

  11. The method requires modification for those industry-occupation cells that only contain men (for UR F ADJ) or only contain women (UR M ADJ). In such cases, we use the unemployment rate for the industry-occupation cell that is available. Given the relatively small weight that applies to such cells, the exact method used in dealing with them does not affect the results.

  12. To simplify interpretation, no interaction is fitted between the industry-occupation cell and the other variables, allowing us to identify a single impact of each measure. Such an assumption is consistent with our findings, reported below, showing that results are not sensitive to whether adjustment is to one distribution or the other.

  13. Non-Whites are more likely to live in the South, but regional differences in unemployment rates are not substantial enough to alter our results.

  14. The UI program is designed and implemented by states but must conform to federal guidelines. Federal legislation specifies that the program serve those who have lost their jobs through no fault of their own and are available for work. See Nicholson and Needels [2006] for a review of the UI program.

  15. Labor force in the previous year is estimated as the number of individuals in the March CPS with earnings or with UI benefits in the previous year. Unemployed individuals who did not receive UI benefits and were never employed during the year are therefore omitted. The receipt rate constructed here differs from a measure of UI participation at a single point in time both because the time period for receipt covers a full year and because occupation and industry apply to March, when the survey was taken, in the subsequent year. Note that our analysis of unemployment above is based on reports of employment status at the time of the March survey, so it is not subject to these limitations. These measures of UI benefit receipt could cause bias in our estimates of group differentials if some groups were appreciably more likely to be omitted from the risk pool than others because they were unemployed but received no UI payments. Although such selection may exist, we would expect such individuals to be disproportionately individuals with no labor market experience, given that this group is not eligible for UI benefits. Our comparisons reported above between the overall and experienced unemployment rate differentials suggest that the bias due to this difference is minor.

  16. Note that the period covered in Figures 13, 14 and 15 is 1991–2006, reflecting the fact that UI benefit receipt refers to the prior year, rather than 1992–2007 for unemployment in prior figures, which applied to the year of the survey.

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Acknowledgements

This paper was based on research conducted for the US Department of Labor, Employment and Training Administration (DOL/ETA) by IMPAQ International, under contract number DOLJ041A000031. The views expressed are those of the authors and should not be attributed to DOL/ETA, nor does mention of trade names, commercial products, or organizations imply endorsement of same by the US Government. The authors would like to thank a number of people who contributed to this paper: Eileen Poe-Yamagata, Jacob Benus, Goska Grodsky, Dharmendra Tirumalasetti, Alan Dodkowitz and seminar participants at the University of Missouri. Special thanks are due to Michael J. White.

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Michaelides, M., Mueser, P. The Role of Industry and Occupation in Recent US Unemployment Differentials by Gender, Race, and Ethnicity. Eastern Econ J 39, 358–386 (2013). https://doi.org/10.1057/eej.2012.16

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