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Adjusting the Gender Wage Gap with a Low-Dimensional Representation of Job History
Keyon Vafa · Susan Athey · David Blei

To estimate adjusted gender wage gaps, economists build models that predict wage from observable data. Although an individual's complete job history may be predictive of wage, economists typically summarize experience with hand-constructed summary statistics about the past. In this work, we estimate the adjusted gender wage gap for an individual's entire job history by learning a low-dimensional representation of their career. We develop a transformer-based representation model that is pretrained on massive, passively-collected resume data that is then fine-tuned to predict wages on the small, nationally representative survey data that economists use for wage gap estimation. This dimension-reduction approach produces unbiased estimates of the adjusted wage gap as long as each representation corresponds to the same full job history for males and females. We discuss how this condition relates to the sufficiency fairness criterion; although the adjusted wage gap is not a causal quantity, we take inspiration from the high-dimensional confounding literature to assess and mitigate sufficiency. We validate our approach with experiments on semi-synthetic and real-world data. Our method makes more accurate wage predictions than economic baselines. When applied to wage survey data in the United States, our method finds that a substantial portion of the gender wage gap can be attributed to differences in job history, although this proportion varies by year and across sub-populations.

Author Information

Keyon Vafa (Columbia University)
Susan Athey (Stanford University)

Susan Athey is The Economics of Technology Professor at Stanford Graduate School of Business. She received her bachelor's degree from Duke University and her Ph.D. from Stanford, and she holds an honorary doctorate from Duke University. She previously taught at the economics departments at MIT, Stanford and Harvard. In 2007, Professor Athey received the John Bates Clark Medal, awarded by the American Economic Association to “that American economist under the age of forty who is adjudged to have made the most significant contribution to economic thought and knowledge.” She was elected to the National Academy of Science in 2012 and to the American Academy of Arts and Sciences in 2008. Professor Athey’s research focuses on the economics of the internet, online advertising, the news media, marketplace design, and the intersection of machine learning and econometrics. She advises governments and businesses on marketplace design and platform economics.

David Blei (Columbia University)

David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.

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