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Partially Identified Treatment Effects Under Imperfect Compliance: The Case of Domestic Violence

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  • Zahra Siddique

Abstract

The Minneapolis Domestic Violence Experiment (MDVE) is a randomized social experiment with imperfect compliance that has been extremely influential in how police officers respond to misdemeanor domestic violence. This article reexamines data from the MDVE, using recent literature on partial identification to find recidivism associated with a policy that arrests misdemeanor domestic violence suspects rather than not arresting them. Using partially identified bounds on the average treatment effect, I find that arresting rather than not arresting suspects can potentially reduce recidivism by more than two-and-a-half times the corresponding intent-to-treat estimate and more than two times the corresponding local average treatment effect, even when making minimal assumptions on counterfactuals.

Suggested Citation

  • Zahra Siddique, 2013. "Partially Identified Treatment Effects Under Imperfect Compliance: The Case of Domestic Violence," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 504-513, June.
    • Handle: RePEc:taf:jnlasa:v:108:y:2013:i:502:p:504-513
    DOI: 10.1080/01621459.2013.779836
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    Citations

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    Cited by:

    1. Ho, Kate & Rosen, Adam M., 2015. "Partial Identification in Applied Research: Benefits and Challenges," CEPR Discussion Papers 10883, Centre for Economic Policy Research.
    2. Charles F. Manski & John V. Pepper, 2018. "How Do Right-to-Carry Laws Affect Crime Rates? Coping with Ambiguity Using Bounded-Variation Assumptions," The Review of Economics and Statistics, MIT Press, vol. 100(2), pages 232-244, May.
    3. 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.
    4. Mogstad, Magne & Torgovitsky, Alexander, 2024. "Instrumental variables with unobserved heterogeneity in treatment effects," Handbook of Labor Economics,, Elsevier.
    5. Lukáš Lafférs, 2019. "Bounding average treatment effects using linear programming," Empirical Economics, Springer, vol. 57(3), pages 727-767, September.
    6. Hongming Pu & Bo Zhang, 2021. "Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(2), pages 318-345, April.
    7. Ting Ye & Luke Keele & Raiden Hasegawa & Dylan S. Small, 2020. "A Negative Correlation Strategy for Bracketing in Difference-in-Differences," Papers 2006.02423, arXiv.org, revised Jun 2022.
    8. Zahra Siddique, 2014. "Randomized control trials in an imperfect world," World of Labour, LISER, pages 110-110, December.
    9. Andrei Voronin, 2025. "Linear programming approach to partially identified econometric models," Papers 2503.14940, arXiv.org.

    More about this item

    JEL classification:

    • C9 - Mathematical and Quantitative Methods - - Design of Experiments
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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