Hours, Occupations and the Gender Wage Gap

Join Professor Gueorgui Kambourov at the Mary Williamson Lecture where he will discuss Hours, Occupations and the Gender Wage Gap. Here is a brief overview of the content he will cover in the lecture.

In some occupations mean hours worked are high while the dispersion is low: that is, all the workers in those occupations work many hours. In other occupations mean hours worked are low, but the dispersion in hours is high: in other words, while hours worked are low on overage, some individuals in those occupations work many hours while others work few hours. Understanding these patterns in hours worked in the data is important for understanding the behaviour of aggregate labor supply in many contexts: over time, both secularly and over the business cycle, across countries, and across demographic groups within an economy at a point in time. A simple model of time allocation and occupational choice is developed in order to highlight the important mechanisms behind the observed patterns in occupational hours in the data.

Then the model is used to isolate and measure some key forces associated with gender differences in labor market outcomes, specifically occupational choice and wages: a simple look at the data suggests that a framework like this is appropriate, if not necessary. First, the occupational distribution of women is very different from that of men: most men work in occupations with high mean and low variance of hours, while most women work in occupations with low mean and high dispersion in hours. Second, even within most three-digit occupations, women work less hours over a year. As a result, employed women have annual hours that are as much as 30% lower than those of employed men. Third, hourly wages are higher on average in the high mean-low dispersion occupations. Since a relatively larger fraction of men than women work in those occupations, this alone would generate a gender wage gap in hourly wages. This indicates that in order to understand the overall gender wage gap one needs to understand the underlying forces determining the sorting of men and women across occupations based on their underlying individual characteristics. The model is quantitatively consistent with most of the moments in the data on occupational sorting, hours worked, and hourly wages for men and women. The model generates a gender wage gap of 10% despite there being no fundamental difference in productivity by gender. Consistent with the data, most of the gender wage gap is accounted for by gender difference in hourly wages within rather than across occupations.

We hope to see you there!