- Personal website and CV:
https://pinchofdata.github.io/germaingauthier/
- Email address:
germain.gauthier@polytechnique.edu
- 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:
Title: Measuring Crime Reporting and Incidence: Method and Application to #MeToo
Link: https://www.dropbox.com/s/jepq64dfauyo1t6/metoo_crime_v6.pdf?dl=0
- 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.