He is also interested in measuring firm-level idiosyncratic uncertainty and its impact on investment and labor reallocation.
Before joining Stanford, Frank received his PhD in business economics from Ross School of Business at the University of Michigan. He also holds a BA in math and economics from the University of Michigan and an MA in economics from Duke University.
His current focus is developing new well-being metrics by using massive online choice experiments. He also studies the application of machine learning techniques to measure individual-level latent parameters. Lee
Before coming to Stanford, JJ worked at the MIT Initiative on the Digital Economy as a postdoctoral associate. He received his PhD in economics at Claremont Graduate University in 2019.
Lee has work experience as a business consultant, a financial analyst and a policy analyst in South Korea before his doctoral study. He also holds a bachelor degree in economics from Seoul National University and a master’s degree in public policy from KDI School of Public Policy and Management.
Sarah’s research uses novel methods to measure skills, tasks, and technologies, with an emphasis on uncovering fine distinctions using big datasets.
Her most recent work uses state-of-the-art natural language processing techniques to better characterize how jobs have changed over time.
Sarah has been published by the Journal of Policy Analysis and Management and the Sloan Management Review, and has spoken at several events, including the California State Assembly’s Rising Tide Summit on Economic Security. In 2019, she was awarded an honorable mention by the Upjohn Institute for her dissertation, “Three Essays on Vulnerable Workers.”
Sarah received her Ph.D. in economics from the University of California, Santa Barbara, and was a postdoctoral associate at the MIT Initiative on the Digital Economy.