Occupational Sex Segregation and its Consequences for the (Re-)Production of Gender Inequalities in the German Labor Market
First project phase: May 2012 – May 2015
Second project phase: June 2015 – June 2018
Funded by: German Science Foundation, Priority Programme 1646 “Education as Lifelong Process”
- Dörthe Gatermann, Leibniz University Hannover
- Anna Erika Hägglund, Leibniz Universität Hannover
- Ann-Christin Bächmann, Leibniz Institute for Educational Trajectories
In Germany, occupations are the structuring principle connecting the education system with the labor market and are thus considered important institutions for shaping employment histories. Yet empirical research has shown that male and female-dominated occupations come along with unequal employment opportunities. In this project, we therefore study the relevance of occupational sex segregation for the (re)production of gender inequalities in the German labor market. We approach the consequences of occupational sex segregation at an occupational and an individual level. In the first step, we analyze whether there is a relationship between occupational sex segregation and other occupational aspects, such as wage levels, proportion of part-time work, or qualification requirements. In the second step, we ask how these occupational features shape male and female employment histories and thereby contribute to the (re-)production of gender inequalities. Empirically, we first generate an occupational panel based on SIAB and Microcensus data to investigate long-term trends of occupational sex segregation and its causal relation with other occupational characteristics. These findings are then used to identify occupational characteristics that are relevant for generating gender inequality in employment histories. The respective occupational indicators are merged with NEPS Starting Cohort 6 data to examine their effects on different stages of female and male employment biographies, such as labor market entries, subsequent employment mobility, employment interruptions, and returns to work.
In the first project phase, we described long-term trends of occupational sex segregation in Germany and analysed how the share of women in an occupation is causally related with other occupational characteristics, such as wage levels, shares of part-time work, and qualification structure. The findings of these analyses were then used to investigate how various occupational characteristics generate individual gender inequalities in career progressions. Thus, the first project phase focused on the importance of occupational sex segregation for non-monetary aspects of labour market inequalities between women and men.
However, previous research has shown that the uneven distribution of women and men across occupations is particularly important for our understanding of the gender wage gap. Many studies indicate that a higher share of women in an occupation leads to lower monetary returns for both women and men working in this occupation. Yet it is far from clear why occupations dominated by women pay less. Is the mere proportion of women responsible for the gender wage gap, or are other occupational characteristics linked to female-typical occupations the decisive mechanisms? If this is true, how has the influence of different occupational characteristics on the gender wage gap changed throughout the last 30 years in Germany?
To answer these questions, we explore in the second phase of our project how the gendered structure of occupations affects the wages of women and men and how this relationship changed since the mid-1970s in Germany. Theoretically, we distinguish three possible mechanisms: (1) the devaluation of job tasks typically considered female, (2) less demand for specialized skills in female-dominated occupations, and (3) higher potentials of occupational closure strategies in male-dominated occupations. The analyses will be based on unique new wage data on the individual level: The first three waves of NEPS Starting Cohort 6 data were linked with register data of the Institute for Employment Research (IAB). Thus they additionally contain rich longitudinal wage and firm information for the respondents. For modelling and decomposing the gender wage gap from 1976 until 2010, we will merge these data with the occupational panel data generated in the first phase of the project. In addition, we will enrich these data with newly generated indicators on occupational task profiles and social closure.