LAWRENCE D. W. SCHMIDT
Victor J. Menezes (1972) Career Development Assistant Professor of Finance
Leveraging big data to uncover the critical role of risky human capital for asset markets and the real economy
Primary Research Areas:
Finance, Macro-Labor
Secondary Research Areas:
Applied Econometrics
Lawrence D. W. Schmidt is the Victor J. Menezes (1972) Career Development Assistant Professor of Finance. He is an an applied economist working at the intersection of finance and macro-labor.
Schmidt’s research has developed new insights about the risk exposures and decision-making processes of households, institutional investors, and financial intermediaries, and in doing so has deepened our understanding of asset prices, financial policy, and the workings of the real economy. His research to date involves two interrelated strands. The first and most active strand studies fundamental risk factors impacting the value of human capital and the causes and consequences of imperfect risk-sharing in labor and financial markets. The second strand aims to understand the underlying drivers of financial markets by focusing on the interplay between individual decision-making, strategic complementarities, and information processing frictions.
Schmidt’s research tackles these questions by a combination of quantitative models and empirical work. This work leverages, and often creates, novel microeconomic datasets, advanced econometric methods, and cutting-edge tools for textual analysis. He specializes in working large, administrative datasets capturing detailed, longitudinal information on millions of firms and workers. His work often features quantitative models that help make sense of the data and better understand financial market dynamics, evaluate welfare, and inform better economic policy.
His research has appeared in the American Economic Review, the Journal of Finance, the Review of Financial Studies, among other outlets, and his research has won multiple awards, including the 2015 AQR Top Finance Graduate Award. Schmidt holds a BA from the University of California, Santa Barbara, and PhD and MA degrees in Economics from the University of California, San Diego. Prior to joining the faculty at MIT Sloan, Schmidt was an Assistant Professor in the Kenneth C. Griffin Department of Economics at the University of Chicago and a senior consultant at Navigant Consulting, Inc.
Leveraging big data to uncover the critical role of risky human capital for asset markets and the real economy
Lawrence D. W. Schmidt is the Victor J. Menezes (1972) Career Development Professor of Finance. He is an applied economist working at the intersection of finance and macro-labor.
Schmidt’s research has developed new insights about the risk exposures and decision-making processes of households, institutional investors, and financial intermediaries, and in doing so has deepened our understanding of asset prices, financial policy, and the workings of the real economy. His research to date involves two interrelated strands. The first and most active strand studies fundamental risk factors impacting the value of human capital and the causes and consequences of imperfect risk-sharing in labor and financial markets. The second strand aims to understand the underlying drivers of financial markets by focusing on the interplay between individual decision-making, strategic complementarities, and information processing frictions.
Schmidt’s research tackles these questions by a combination of quantitative models and empirical work. This work leverages, and often creates, novel microeconomic datasets, advanced econometric methods, and cutting-edge tools for textual analysis. He specializes in working large, administrative datasets capturing detailed, longitudinal information on millions of firms and workers. His work often features quantitative models that help make sense of the data and better understand financial market dynamics, evaluate welfare, and inform better economic policy.
His research has appeared in the American Economic Review, the Journal of Finance, and the Review of Financial Studies, among other outlets, and his research has won multiple awards, including the 2015 AQR Top Finance Graduate Award. Schmidt holds a BA from the University of California, Santa Barbara, and PhD and MA degrees in Economics from the University of California, San Diego. Prior to joining the faculty at MIT Sloan, Schmidt was an Assistant Professor in the Kenneth C. Griffin Department of Economics at the University of Chicago and a senior consultant at Navigant Consulting, Inc.
Academic Positions
2019-present
Victor J. Menezes (1972) Career Development Assistant Professor of Finance
MIT Sloan School of Management
2018-present
Assistant Professor of Finance
MIT Sloan School of Management
2015-2018
Assistant Professor of Economics and the College, Kenneth C. Griffin
Department of Economics, University of Chicago
Academic Positions
| 2019-present | Victor J. Menezes (1972) Career Development Assistant Professor of Finance, MIT Sloan School of Management |
| 2018-present | Assistant Professor of Finance, MIT Sloan School of Management |
| 2015-2018 | Assistant Professor of Economics and the College, Kenneth C. Griffin Department of Economics, University of Chicago |
Featured Working Papers
with Maarten Meeuwis, Dimitris Papanikolaou, and Jonathan Rothbaum. Revise and Resubmit at the American Economic Review. Updated December 2023
We show that time variation in risk premia leads to time-varying idiosyncratic income risk for workers. Using US administrative data on worker earnings, we show that increases in risk premia lead to lower earnings for low-wage workers; these declines are primarily driven by job separations. By contrast, productivity shocks affect the earnings mainly of highly paid workers. We build an equilibrium model of labor market search that quantitatively replicates these facts. The model generates endogenous time-varying income risk in response to changes in risk premia and matches several stylized features of the data regarding unemployment and income risk over the business cycle.
with Carter Braxton, Kyle Herkenhoff, and Jonathan Rothbaum. Revised and resubmitted (2nd round) to the American Economic Review. Last updated in October 2024
For whom has earnings risk changed, and why? We answer these questions by combining the Kalman filter and EM-algorithm to estimate persistent and temporary earnings for every individual at every point in time. We apply our method to administrative earnings linked with survey data. We show that since the 1980s, persistent earnings risk rose by 12.5% for both employed and unemployed workers and the scarring effects of unemployment doubled. At the same time, temporary earnings risk declined. Using education and occupation codes, we show that rising persistent earnings risk is concentrated among high-skill workers and related to technology adoption.
with Leonid Kogan, Dimitris Papanikolaou, and Bryan Seegmiller. Updated July 2024
We develop measures of labor-saving and labor-augmenting technology exposure using textual analysis of patents and job tasks. Using US administrative data, we show that exposure to labor-saving technologies negatively affects the earnings of exposed workers. This negative effect is pervasive across both blue- and white-collar workers and across workers of different ages or earnings relative to their peers. In contrast, labor-augmenting technologies have a heterogeneous impact on exposed workers. While the wage bill paid to affected groups rises, this increase is driven primarily by an increase in employment, while earnings rise for new entrants but decline for incumbent workers. This decline is primarily present among white-collar, older, and higher-paid workers, highlighting the importance of vintage-specific human capital. Last, we find positive spillovers of both types of innovation at the industry level, benefiting other workers in the same industry who are not directly exposed to these innovations.
Featured Publications
with Klakow Akepanidtaworn, Rick Di Mascio, and Alex Imas. Journal of Finance, August 2023
Are market experts prone to heuristics, and if so, do they transfer across closely related domains---buying and selling? We investigate this question using a unique dataset of institutional investors with portfolios averaging $573 million. A striking finding emerges: while there is clear evidence of skill in buying, selling decisions underperform substantially---even relative to random selling strategies. This holds despite the similarity between the two decisions in frequency, substance and consequences for performance. Evidence suggests that an asymmetric allocation of cognitive resources such as attention can explain the discrepancy: we document a systematic, costly heuristic process when selling but not when buying.
with Leland Farmer and Allan Timmermann, Journal of Finance, April 2023
For many benchmark predictor variables, short-horizon return predictability in the U.S. stock market is local in time as short periods with significant predictability (‘pockets’) are interspersed with long periods with little or no evidence of return predictability. We document this result empirically using a flexible time-varying parameter model which estimates predictive coefficients as a nonparametric function of time and explore possible explanations of this finding, including time-varying risk-premia for which we only find limited support. Conversely, pockets of return predictability are consistent with a sticky expectations model in which investors only slowly update their beliefs about a persistent component in the cash flow process.
Note: A minor coding error impacted some of the results using the original method in the paper. In this note, we show that a simple adjustment to the estimation procedure restores the key results of the published paper.
with Allan Timmermann and Russ Wermers, American Economic Review, September 2016
We study daily money market mutual fund flows at the individual share class level during September 2008. This fine granularity of data facilitates new insights into investor and portfolio holding characteristics conducive to run risk in cash-like asset pools. Empirically, we find that cross-sectional flow data observed during the week of the Lehman failure are consistent with key implications of a simple model of coordination with incomplete information and strategic complementarities. Similar conclusions follow from daily models fitted to capture dynamic interactions between investors with differing levels of sophistication within the same money fund, holding constant the underlying portfolio.
