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

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  • Gregorio Caetano
  • Vikram Maheshri

Abstract

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.

Suggested Citation

  • Gregorio Caetano & Vikram Maheshri, 2018. "Identifying dynamic spillovers of crime with a causal approach to model selection," Quantitative Economics, Econometric Society, vol. 9(1), pages 343-394, March.
    • Handle: RePEc:wly:quante:v:9:y:2018:i:1:p:343-394
    DOI: 10.3982/QE756
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    Cited by:

    1. Chalfin, Aaron & Mitre-Becerril, David & Williams, Morgan C., 2024. "Does Proactive Policing Really Increase Major Crime? A Replication Study of Sullivan and O'Keeffe (Nature Human Behaviour, 2017)," Journal of Comments and Replications in Economics (JCRE), ZBW - Leibniz Information Centre for Economics, vol. 3(2024-6), pages 1-34.
    2. Khalil, Umair & Yıldız, Neşe, 2022. "A test of the selection on observables assumption using a discontinuously distributed covariate," Journal of Econometrics, Elsevier, vol. 226(2), pages 423-450.
    3. Bertanha, Marinho & McCallum, Andrew H. & Seegert, Nathan, 2023. "Better bunching, nicer notching," Journal of Econometrics, Elsevier, vol. 237(2).
    4. Mechoulan, Stéphane, 2020. "Civil unrest, emergency powers, and spillover effects: A mixed methods analysis of the 2005 French riots," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 305-326.
    5. Marinho Bertanha & Andrew H. McCallum & Alexis Payne & Nathan Seegert, 2022. "Bunching estimation of elasticities using Stata," Stata Journal, StataCorp LP, vol. 22(3), pages 597-624, September.
    6. Fe, Hao & Sanfelice, Viviane, 2022. "How bad is crime for business? Evidence from consumer behavior," Journal of Urban Economics, Elsevier, vol. 129(C).
    7. Huang, Liquan & Khalil, Umair & Yıldız, Neşe, 2019. "Identification and estimation of a triangular model with multiple endogenous variables and insufficiently many instrumental variables," Journal of Econometrics, Elsevier, vol. 208(2), pages 346-366.
    8. Aaron Chalfin & Michael LaForest & Jacob Kaplan, 2021. "Can Precision Policing Reduce Gun Violence? Evidence from “Gang Takedowns” in New York City," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(4), pages 1047-1082, September.
    9. Carolina Caetano & Gregorio Caetano & Hao Fe & Eric R. Nielsen, 2021. "A Dummy Test of Identification in Models with Bunching," Finance and Economics Discussion Series 2021-068, Board of Governors of the Federal Reserve System (U.S.).

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