Dear Applied Statistics Workshop Community,

Welcome back! Our first meeting of the semester will be on September 6 (12:00 EST). Keyon Vafa presents "Decomposing Changes in the Gender Wage Gap over Worker Careers."

<When>

September 6, 12:00 to 1:30 PM, EST

Lunch will be available for pick-up at 11:30 (CGIS K354).

<Where>

In-person: CGIS K354

Zoom:

https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09

Abstract: A large literature in labor economics seeks to decompose gender wage gaps into different sources, including portions explained by cross-gender differences in education and occupation. While career histories contain valuable information about sources of gender wage disparities, they are too high-dimensional to include in standard econometric techniques. This talk presents new machine learning methods for decomposing gender wage gaps over worker careers. We develop a "foundation model" of career trajectories to summarize worker histories with low-dimensional representations. We show how to fine-tune the foundation model on small survey datasets while ensuring that the representations do not omit features of history whose exclusion would bias decompositions. On data from the Panel Study of Income Dynamics, our method explains more of the gender wage gap than standard techniques. Finally, we propose a new decomposition of the change in gender wage gaps over workers careers into two sources: gender differences in initial characteristics and gender differences in worker transitions. Using representations from the foundation model, we show that early in careers, the gender wage gap widens, driven by males transitioning to higher-paying characteristics than females; meanwhile, later in careers, the gender wage gap narrows, driven by female initial characteristics setting up workers for more wage growth than those of males.

<2023 Schedule>

GOV 3009 Website:

https://projects.iq.harvard.edu/applied.stats.workshop-gov3009

Calendar:

https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbmJobmVkOEBncm91cC5jYWxlbmRhci5nb29nbGUuY29t

Best,

Jialu

Jialu Li

Department of Government

Harvard University

jialu_li@g.harvard.edu