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Replication Data for: Asymmetric Crime Dynamics In and Out of Lockdowns

Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)

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Title Replication Data for: Asymmetric Crime Dynamics In and Out of Lockdowns
 
Identifier https://doi.org/10.7910/DVN/GNSQ0A
 
Creator Poblete-Cazenave, Ruben
 
Publisher Harvard Dataverse
 
Description This article studies the dynamic impact of a temporary policy restricting social encounters due to coronavirus disease 2019 (COVID-19) on criminal activity in Bihar, India. Using a regression discontinuity design in time and criminal case—level and arrest data, I document an immediate drop in crime of over 35% due to the lockdown. Analysis over a longer timespan shows asymmetric dynamics by crime type. The lockdown was more effective in preventing personal crimes such as murders but was less effective in preventing property crimes, which increased beyond pre-lockdown levels once the lockdown was lifted. The increase in property crimes seems to be driven by temporal crime displacement from “former offenders” and not by “new offenders.” These asymmetric dynamics across crime types provide new insights into criminals’ intertemporal decisions
 
Subject Social Sciences
Crime
COVID-19
Police
Lockdown
 
Date 2024-02-09
 
Contributor Poblete-Cazenave, Ruben