Average female wages in traditionally male occupations have steeply risen over the past couple of decades in Germany. This trend led to a new and substantial pay gap between women working in male-typed occupations and other women. I dissect the emergence of these wage disparities between women, using data from the German Socio-Economic Panel (1992–2015). Compositional change with respect to education is the main driver for growing inequality. Other factors are less influential but still relevant: marginal returns for several wage-related personal characteristics have grown faster in male-typed occupations. Net of individual-level heterogeneity, traditionally male occupations have also become more attractive because of rising returns to task-specific skills. Discrimination of women in typically male lines of work seems to have declined, too, which erased part of the wage penalty these women had previously experienced. In sum, I document changes in the occupational sorting behavior of women as well as shifts in occupation-level reward mechanisms that have had a profound impact on the state of inequality between working women.
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Data sets used in this study are the Socio-Economic Panel (DOI: https://doi.org/10.5684/soep.v32.1) and the Employment Surveys of BIBB/IAB (DOIs: https://doi.org/10.4232/1.2565 and https://doi.org/10.4232/1.12247) and BIBB/BAuA (DOIs: https://doi.org/10.4232/1.11072 and https://doi.org/10.7803/501.12.1.1.40). These can be accessed through the Research Data Centers of the SOEP and of the BIBB, respectively. Replication source code is deposited on https://www.github.com/febusch/research.
Substantively, this means that in 2009, 51% of all workers would have had to switch into another occupational group in order for each group’s sex compositions to be identical with the overall sex composition in the labor market.
Other groups that saw a marked inflow of women were higher-skill administration, technical occupations, and semiprofessional occupations (Hausmann and Kleinert 2014:7).
This variable is constructed using the current monthly labor income and weekly work hours multiplied by 4.35 (roughly the average number of weeks in a month). I also restrict the leverage of outliers by top- and bottom-coding hourly wages. This affects about 0.37% of all observations and makes the wage gap estimates slightly more conservative.
The German KldB-1992 (Statistisches Bundesamt 1992) is a typical hierarchical classification of occupations, comparable with the U.S. Standard Occupational Classification.
Results in this article are similar but a bit attenuated if I use slightly different sex thresholds (e.g., 70%) to compute occupational gender groups.
I hold them constant for two reasons. First, changes in gender types can be expected to have very minor effects over shorter time spans (Busch 2018; England et al. 2007). Second, I would have to merge many more occupational cells if I wanted to compute a sex ratio per cell-year, which would make the analysis much more imprecise.
Category 1: basic vocational or elementary education and below; category 2: intermediate education (general or vocational); category 3: tertiary education.
The choice about which group is represented by A and B is consequential for estimation results. Therefore, it is customary to display decomposition results by changing group assignment between A and B in a second run of all analyses. However, this makes sense only if we are strictly interested in comparing two groups. In these analyses, I make comparisons among three groups: outcomes in male-typed versus mixed-typed occupations and in male- versus female-typed occupations. To make these results comparable between each other, I fix A to represent women in male-typed occupations.
See Blau and Kahn (1997:6–8) for excellent insight into what is behind ∆θt.
In some studies, the observed and unobserved interactions are included in one of the other components, but I ignore them for two reasons: (1) these interactions do not have a substantively useful interpretation for this study, and (2) I would find it misleading to add the joint contribution of quantities and prices to either the quantities or the prices component.
These changes are substantial in size. In 2015 euros, changes in wage differences amount to 4.55 EUR (vs. female-typed occupations) and 2.5 EUR (mixed occupations). In comparison, mean wages of the entire female population in 2015 were about 16 EUR.
Following Pannenberg (2005), I also run analyses on a restricted data set until 2014, showing that uncompensated overtime contributed similarly to increasing wage disparities as overwork (results not displayed).
As discussed earlier, several changes occurred in the early to mid-2000s: women started to integrate more quickly into paid work; returns to education were rising; changes in family policy were implemented; and women who had grown up in a more gender-egalitarian society entered work.
Results are based on an interaction term of the gender type variable and the biennial period dummy variables. Bold figures designate significance at p < .05.
Although the supply of women with a tertiary degree has increased steeply since 1992, this was also the case for men (see Figure A3, online appendix). Hence, it is not plausible that there was demand-driven upgrading skewed toward female workers because of a lack of men with sufficient qualifications.
This result is based on an Oaxaca-Blinder decomposition (Kitagawa 1955) of current wage differences. Results are not displayed.
A wage bonus for those in caring activities is found in the early 2000s. Personal care can be considered a strongly female-typed activity, and its positive effect on wages is another indication that female-typed work has not been culturally devalued in Germany in the observed period.
In pooled regressions (results not displayed), I find that across all occupational groups, having children is positively associated with wages, which is another indicator for the positive selection of employed mothers.
A slight inverse trend is observed for only the 1999–2007 subperiod.
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I am grateful for financial support of the Economic and Social Research Council (Grant No. ES/J500112/1). Thank you to Colin Mills, Richard Breen, Paula England, and Helen Buchs for their helpful feedback on earlier drafts of the article. Thank you to Marlis Buchmann, who generously supported me during the writing of the article at the University of Zurich. I also benefitted from discussions at the 2018 European Consortium for Sociological Research conference and at the 2018 Zurich Sociology thesis workshop. I also thank the Editors and the anonymous reviewers for their very constructive comments and help throughout the publication process. All errors are my own.
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Analyses in this article are based solely on secondary survey data. The author followed good practice standards and the data providers’ regulations to protect data privacy and to prevent unauthorized proliferation of microdata.
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Busch, F. Gender Segregation, Occupational Sorting, and Growth of Wage Disparities Between Women. Demography 57, 1063–1088 (2020). https://doi.org/10.1007/s13524-020-00887-3
- Occupational segregation
- Wage inequality