Abstract
Using data from the IPUMS-USA, the present research focuses on trends in the gender earnings gap in the United States between 1970 and 2010. The major goal of this article is to understand the sources of the convergence in men’s and women’s earnings in the public and private sectors as well as the stagnation of this trend in the new millennium. For this purpose, we delineate temporal changes in the role played by major sources of the gap. Several components are identified: the portion of the gap attributed to gender differences in human-capital resources; labor supply; sociodemographic attributes; occupational segregation; and the unexplained portion of the gap. The findings reveal a substantial reduction in the gross gender earnings gap in both sectors of the economy. Most of the decline is attributed to the reduction in the unexplained portion of the gap, implying a significant decline in economic discrimination against women. In contrast to discrimination, the role played by human capital and personal attributes in explaining the gender pay gap is relatively small in both sectors. Differences between the two sectors are not only in the size and pace of the reduction but also in the significance of the two major sources of the gap. Working hours have become the most important factor with respect to gender pay inequality in both sectors, although much more dominantly in the private sector. The declining gender segregation may explain the decreased impact of occupations on the gender pay gap in the private sector. In the public sector, by contrast, gender segregation still accounts for a substantial portion of the gap. The findings are discussed in light of the theoretical literature on sources of gender economic inequality and in light of the recent stagnation of the trend.
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Notes
More details are available online (http://usa.ipums.org/usa-action/variables/OCC1990#description_tab).
The Oaxaca-Blinder decomposition also allows decomposing the gap into three components. The third component is the interaction term that accounts for the fact that gender differences in characteristics and coefficients exist simultaneously. To check this, we applied the triple decomposition to our data. The analysis yielded results that were very similar to those of the dual decomposition because the interaction term was found to be negligible. We therefore present the dual decomposition.
The table shows the disaggregated coefficients of both the explained and unexplained portions of the gap. However, the figure displays only the disaggregated components of the explained portion. A visual demonstration of the disaggregated components of the unexplained portion of the gap is problematic because the intercept is dependent on the values of the coefficients and has no substantive meaning.
The coefficients of occupations (about 80 in each decade) are not presented.
The effect of work experience is negligible, perhaps because of the problematic use of age (minus education) as a proxy for work experience. Because women in the sample are slightly older than men in all decades, their work experience is slightly greater than that of men. The returns on work experience, however, are greater for men in all decades (see Table 4 in the appendix).
Indeed, the suppressing effect of education is eliminated (and in some decades even reversed to a modest positive effect) after we control for occupations at the three-digit classification. Apparently, within detailed occupations, men have slightly higher education and greater experience than women. Unfortunately, we were not able to implement the Oaxaca-Blinder decomposition with the three-digit occupational classification because the matrix was too complicated.
To verify the strong impact of working hours, we reanalyzed the data using hourly earnings as the dependent variable. We compared the results with those presented by the present analysis in which weekly wage is used as the independent variable. The results of the two strategies are very similar. Specifically, the contribution of working hours to the gender pay gap is very similar under the two alternatives, as is the contribution of each of the other components.
We used the two-digit occupational classification (instead of three-digit (300 to 400) detailed occupational categories) because the statistical software could not estimate the equation using the large matrix created by the detailed occupations.
An advantage of using the two-digit over the three-digit occupational classifications, however, is that it offers a better adjustment between decades in the case of occupational crosswalks.
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Acknowledgments
We thank Richard Barrett, William Bridges, Yitzhak Haberfeld, Noah Lewin-Epstein, and Anthony Orum for advice and comments; and Yael Navon for valuable assistance in the analysis of the data. The project was supported by the Israel Science Foundations (Grant No. 491/13).
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Mandel, H., Semyonov, M. Gender Pay Gap and Employment Sector: Sources of Earnings Disparities in the United States, 1970–2010. Demography 51, 1597–1618 (2014). https://doi.org/10.1007/s13524-014-0320-y
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DOI: https://doi.org/10.1007/s13524-014-0320-y