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, we show that full worker history explains about 25% of the gender wage gap than is unexplained by standard summary statistics and covariates. We conclude by using the representations of worker history to identify clusters of history that are most important for explaining wage gaps. This is joint work with Susan Athey and David Blei.
Bio: Keyon Vafa is a postdoctoral fellow at the Harvard Data Science Initiative. His research develops machine learning methodology to uncover insights into human behavior in labor economics and political science, among other fields in the social sciences. He completed his PhD in computer science at Columbia University in 2023, where he was advised by David Blei. During his PhD, he was an NSF GRFP Fellow and Cheung-Kong Innovation Doctoral Fellow.