Identifying dynamic spillovers of crime with a causal approach to model selection

• 成果类型: Article
• 署名作者: Caetano, Gregorio; Maheshri, Vikram
• 署名单位: University of Rochester; University of Houston System; University of Houston
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE756
• 发表日期: 2018
• 页码: 343-394
• 关键词: Neighborhood crime; broken windows; model selection; test of exogeneity

摘要

Does crime in a neighborhood cause future crime? Without a source of quasi-experimental variation in local crime, we develop an identification strategy that leverages a recently developed test of exogeneity (Caetano (2015)) to select a feasible regression model for causal inference. Using a detailed incident-based data set of all reported crimes in Dallas from 2000 to 2007, we find some evidence of dynamic spillovers within certain types of crimes, but no evidence that lighter crimes cause more severe crimes. This suggests that a range of crime reduction policies that target lighter crimes (prescribed, for instance, by the broken windows theory of crime) should not be credited with reducing the violent crime rate. Our strategy involves a systematic investigation of endogeneity concerns and is particularly useful when rich data allow for the estimation of many regression models, none of which is agreed upon as causal ex ante.