Using Machine Learning and Digital Technology to Identify Challenges and Improve Outcomes for Labor Market Transitions
This talk will review several recent papers that focus on labor market transitions. The first project analyzes worker resilience in response to layoffs, using administrative data from Sweden. Recently developed machine learning methods identify and characterize groups of workers who are predictably less resilient to layoffs. There is substantial heterogeneity within firms and within markets in terms of worker resilience. Second, recent work applies foundation models based on custom-created transformer neural networks and/or large language models to model worker careers and decompose gender wage gaps, identifying areas where unexplained wage gaps remain relatively large. Two additional projects create, implement, and evaluate digital interventions that help disadvantaged workers successfully transition into growing occupations related to information technology and data science.
Lecture by:
Susan Athey, The Economics of Technology Professor, Stanford Graduate School of Business; Professor of Economics (by courtesy), School of Humanities and Sciences, Stanford University; Senior Fellow, Stanford Institute for Economic Policy Research
Discussants:
- Bentley MacLeod, Sami Mnaymneh Professor Emeritus of Economics, Columbia University
- Suresh Naidu, Jack Wang and Echo Ren Professor of Economics, Prof. of International and Public Affairs, Columbia University
- Joseph Stiglitz, 2001 Nobel Laureate and University Professor, Columbia University
Introductory remarks by:
Michael Woodford, John Bates Clark Professor of Economics, Columbia University
