Abstract
Using the IPUMS-USA data for the years 1960–2015, this study examines trends in the effect of occupational feminization on occupational pay in the U.S. labor market and explores some of the mechanisms underlying these trends. The findings show that the (negative) association between occupational feminization and occupational pay level has declined, becoming insignificent in 2015. This trend, however, is reversed after education is controlled for at the individual as well as the occupational level. The two opposite trends are discussed in light of the twofold effect of education: (1) the entry of women into occupations requiring high education, and (2) the growing returns to education and to occupations with higher educational requirements. These two processes have concealed the deterioration in occupational pay following feminization. The findings underscore the significance of structural forms of gender inequality in general, and occupational devaluation in particular.
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Notes
Levanon et al. (2009) used fixed effects at the occupational level, whereas multilevel models (with data on both individuals and occupations) are used in this study. Also, because the fixed-effects model requires all occupations to appear in all decades, the analysis is based on only 164 selected occupations, relative to approximately 400 used in this study.
Here again a comparison between the results is not straightforward because the study by Mandel (2013) focused on a comparison between different groups of occupations, based on pay and feminization levels, so the effect of feminization on occupational pay is analyzed after disaggregating the samples into different groups.
I use the 5 % sample censuses of 1980 through 2000 and the 1 % censuses of 1960 and 1970.
In the dynamic analysis (where OCC1990 is used) the 2009, 2010, and 2011 ACS data files are combined to enlarge the sample. In Fig. S1 (Online Resource 1), all post-2000 years were analyzed to validate the consistency of the trend.
The census data have drawbacks of inconsistency between the earnings variable (measured for the prior year) and the variables of hours and occupations (measured for the current year). This may affect the results because women, more than men, tend to change occupations. However, this potential bias should randomly affect all census years, so the over-time trend—the main focus of this article—is likely to be preserved.
Given the absence of “usual working time” in the data for 1960 and 1970, I use the total number of hours the respondent worked during the previous week instead. Because the variable is given in intervals, I use the middle of the category.
The measure of potential work experience (age – education – 6) assumes continuous work experience after the completion of education, an assumption that is more problematic for women. I tested the robustness of this measure using an alternative measure by the variable number of years the respondent has worked in his/her current job from the MORG subsample of the biennial January Job Tenure Supplement (years 2000–2012). Indeed, the correlations between the two measures were stronger among men, but differences between the gender groups remained relatively stable across the years, so even if this measure affects results, it is not expected to affect the over-time trend.
Occupations with fewer than 30 workers were selected out.
I also controlled for percentage of unemployed in an occupation as an indicator for demand and supply of workers, which is relevant to both women’s odds of being hired in an occupation and occupational pay levels. The coefficients were not significant across all decades, and their inclusion in the regressions did not change the coefficients of percentage of women. Therefore, I did not include this model.
In the multilevel model, this is accomplished by explaining the intercept (male = 0) after introducing the gender covariate into the equation. See also in Raudenbush and Bryk (2002).
For example, suppose that returns accrue to particular unobserved skill—for example, leadership skills, such as assertiveness—and that men are overrepresented in occupations that demand such skills. In this case, the increase in the effect of female percentage on occupational pay may be affected by the increased returns to assertiveness.
The ACS and the census data are not perfectly comparable. The census measures earnings in the previous calendar year, whereas the ACS measures earnings in the past 12 months.
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Acknowledgments
I thank Amit Lazarus for his valuable assistance in the analysis of the data. I also greatly appreciate the generous support of the Israel Science Foundations (ISF Grant No. 491/13) and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 724351).
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Mandel, H. A Second Look at the Process of Occupational Feminization and Pay Reduction in Occupations. Demography 55, 669–690 (2018). https://doi.org/10.1007/s13524-018-0657-8
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DOI: https://doi.org/10.1007/s13524-018-0657-8