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CAREER: Economic Prediction of Labor Sequence Data Under Distribution Shift
Keyon Vafa · Emil Palikot · Tianyu Du · Ayush Kanodia · Susan Athey · David Blei
Event URL: https://openreview.net/forum?id=zETSaPIQOM »

Labor economists regularly analyze employment data by fitting predictive models to small, carefully constructed longitudinal survey datasets. Although modern machine learning methods offer promise for such problems, these survey datasets are too small to take advantage of them. In recent years large datasets of online resumes have also become available, providing data about the career trajectories of millions of individuals. However, the distribution of these large resume datasets differ in meaningful ways from the survey datasets used for economic estimation; standard econometric models cannot take advantage of their scale or make predictions under distribution shift. To this end we develop CAREER, a transformer-based model that uses transfer learning to learn representations of job sequences. CAREER is first fit to large, passively-collected resume data and then fine-tuned on samples of the downstream data distribution of interest. We find that CAREER forms accurate predictions of job sequences, achieving state-of-the-art predictive performance on three widely-used economics datasets. We also find that CAREER is adept at making predictions under distribution shifts in time.

Author Information

Keyon Vafa (Columbia University)
Emil Palikot (Stanford University)
Tianyu Du (Stanford University)
Ayush Kanodia (Stanford University)

My name is Ayush Kanodia. I’m a PhD student in Computer Science at Stanford University, currently in my third year. I mix Machine Learning and Economics. I am lucky to be advised by Susan Athey.

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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