Germain Gauthier

Leave a comment
  • References:
    Professor Alessandro Riboni, Ecole polytechnique
    Professor Xavier D’Haultfoeuille, CREST – ENSAE
    Professor Elliott Ash, ETH Zürich
    Professor Roberto Galbiati, Sciences Po
  • Research fields:
    Primary Field: Applied Microeconomics
    Secondary Field: Political Economy, Econometrics, Machine Learning, Text as Data
  • JMP Abstract:
    This paper studies the Me Too movement’s effects on sex criminality. As many victims do not report to the police, a long-standing empirical challenge with reported crime statistics is that they reflect variations in victim reporting and crime incidence. To separate both margins, I develop a duration model that studies the delay between the incident’s occurrence and its report to the police. The model accounts for unobserved heterogeneity, never-reporters, and double-truncation in the data. I apply it to the police records of large US cities. Contrary to the widespread view that #MeToo was a watershed moment, I find that sex crime reporting had already been increasing for years before its sudden mediatization in October 2017. Nonetheless, the movement had a positive, persistent impact on victim reporting, particularly for juveniles, racial minorities, and victims of misdemeanors and old crime incidents. The increase in reporting translates into drastically higher probabilities of arrest for sex offenders. Using reported non-sexual crimes as a control group, difference-in-differences estimates suggest the movement also had a sizable deterrent effect.

Martin Mugnier

Leave a comment
  • References:
    Professor Xavier D’Haultfoeuille, CREST – ENSAE
    Professor Stéphane Bonhomme, The University of Chicago
    Professor Laurent Davezies, CREST – ENSAE
  • Research field:
    Econometrics (theory and applications)
  • JMP Abstract:
    In studies based on longitudinal data, researchers often assume time-invariant unobserved heterogeneity or linear-in-parameters conditional expectations. Violation of these assumptions may lead to poor counterfactuals. I study the identification and estimation of a large class of nonlinear grouped fixed effects (NGFE) models where the relationship between observed covariates and cross-sectional unobserved heterogeneity is left unrestricted but the latter only takes a restricted number of paths over time. I show that the corresponding “clusters” and the nonparametrically specified link function can be point-identified when both dimensions of the panel are large. I propose a semiparametric NGFE estimator whose implementation is feasible, and establish its large sample properties in popular binary and count outcome models. Distinctive features of the NGFE estimator are that it is asymptotically normal unbiased at parametric rates, and it allows for the number of periods to grow slowly with the number of cross-sectional units. Monte Carlo simulations suggest good finite sample performance. I apply this new method to revisit the so-called inverted-U relationship between product market competition and innovation. Allowing for clustered patterns of time-varying unobserved heterogeneity leads to a much flatter estimated curve.

Pierre Picard and Alexis Louaas receive the “SCOR-Geneva Risk and Insurance Review Best Paper Award”
Leave a comment

Congratulations to Pierre Picard and Alexis Louaas who received the “SCOR-Geneva Risk and Insurance Review Best Paper Award” for their article “Optimal insurance coverage of low-probability catastrophic risk” published in 2021 in the Geneva Risk and Insurance Review. The award was presented to them on the occasion of the 49th seminar of the European Group of Risk and Insurance Economists (EGRIE) which took place in Vienna (Austria) from September 18 to 21, 2021.

Workshop Artificial Intelligence and Globalization
Leave a comment

Le 27 octobre 2022 de 9:00 à 17:00 au Ministère de l’économie
L’évènement est organisé par DiPLab (Digital Platform Labor), une équipe de recherche animée par Antonio Casilli, Paola Tubaro et Ulrich Laitenberger à l’Institut Polytechnique de Paris.