Stéphane Bonhomme is a French economist currently at the University of Chicago, where he is the Ann L. and Lawrence B. Buttenwieser Professor of Economics. Bonhomme specializes in microeconometrics. His research involves latent variable modeling, modeling of unobserved heterogeneity in panel data, and its applications in labor economics, in particular the analysis of earnings inequality and dynamics.
Career
During his elementary education, Bonhomme attended the Conservatoire de Lyon where while acquiring basic education knowledge he simultaneously acquired music education with a focus on viola. Afterwards, he attended Lycee du Parc to prepare for entrance to the Grandes Écoles. He received his bachelor's degree and master’s degree in pure mathematics from École normale supérieure de Lyon.
His interest in politics led him to obtain the degree of political sciences from Science Po, Paris. Later, he studied at CREST and at Université Paris I. And, under the supervision of Jean-Marc Robin, Stéphane Bonhomme received his Ph.D. in economics from the Université Paris I Panthéon-Sorbonne.
He initiated his tenure track at the Center for Monetary and Financial Studies (CEMFI) in 2005. From 2009 to 2010, he was assistant professor at New York University. In 2010, he became professor at CEMFI, where he remained until 2013, when he joined The University of Chicago as professor in the Kenneth C. Griffin Department of Economics.
In parallel, he has held different editorial positions, including Managing Editor at the Review of Economic Studies (2011-2015), Co-editor of the Econometric Society Monograph Series (2018-2021), Associate Editor of the Journal of Econometrics (2018-2021), and member of the Editorial Board of the Journal of Economic Methods. Currently, he is the Editor of Quantitative Economics.
Since 2018, he is co-director of the Big Data Initiative at the Becker Friedman Institute for Research in Economics.
Awards
From 2011 to 2015, he was awarded with a Starting Grant from the European Research Council.
In 2017, together with Thibaut Lamadon, he received a National Science Foundation (NSF) Grant.
In 2017, he was elected fellow of the Econometric Society, and in 2019, fellow of the International Association for Applied Econometrics.
Work
Bonhomme’s contributions are both on the theory and empirical sides of econometrics.
Nonlinear panel models and unobserved heterogeneity
Bonhomme has developed several new approaches to estimating panel models with unobserved heterogeneity. He has characterized the class of prior distributions of individual effects that produce integrated-likelihood estimators of common parameters and average marginal effects which are first-order unbiased. He has developed a systematic “functional-differencing” method to construct moment restrictions on common parameters of likelihood models that are free from individual effects. These moment restrictions lead to method-of-moment estimators that are free from incidental-parameter bias in short panels. Bonhomme has also introduced a class of quantile regression (QR) estimators for short panels with random effects, which relies on a stochastic EM algorithm that alternates between draws of unobservables and QRs based on those draws. He has successfully applied this method to estimating nonlinear models of income and consumption.
Bonhomme has advocated for the use of clustering methods in panel data models to capture unobserved heterogeneity in parsimonious ways. In particular, his work combines k-means with a regression approach to estimate models with time-varying group patterns of heterogeneity. In recent work, he has put forward two-step grouped fixed-effects (GFE) estimators, where individuals are first classified into groups using k-means clustering, and the model is then estimated allowing for group-specific heterogeneity. In this work discrete heterogeneity is used as a dimension reduction tool rather than as an assumption about the real form of heterogeneity.