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Strangulation Laws Save Lives

Author

Listed:
  • Dércio de Assis

    (School of Economics, University of Nottingham)

  • Arpita Ghosh

    (Department of Economics, University of Exeter)

  • Sonia Oreffice

    (Department of Economics, University of Exeter)

  • Climent Quintana-Domeque

    (Department of Economics, University of Exeter)

Abstract

Non-fatal strangulation (NFS) is a common and dangerous form of intimate partner violence (IPV) and a predictor of homicide, yet it was historically neglected by the criminal justice system. Since the year 2000, most U.S. states have enacted laws enlisting NFS as a standalone criminal offense. We compile a novel dataset on state NFS statutes and link it to the FBI Supplementary Homicide Reports from 1990 to 2019 to estimate the causal effects of these laws on intimate partner homicide rates. Using a difference-in-differences strategy, and an estimator that accounts for staggered adoption and treatment heterogeneity, we find that NFS laws led to significant reductions in intimate partner homicides (IPH). We estimate that these laws reduce female-victim IPH by 14% and male-victim IPH by 36%, among victims 18-34. No significant effects are observed for victims 50 and above or for homicides committed by strangers. Event-study estimates support the parallel trends assumption. Our findings suggest that NFS laws can effectively disrupt the escalation of IPV and reduce lethal outcomes.

Suggested Citation

  • Dércio de Assis & Arpita Ghosh & Sonia Oreffice & Climent Quintana-Domeque, 2025. "Strangulation Laws Save Lives," Discussion Papers 2501, University of Exeter, Department of Economics.
    • Handle: RePEc:exe:wpaper:2501
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    File URL: https://exetereconomics.github.io/RePEc/dpapers/DP2501.pdf
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    More about this item

    Keywords

    intimate partner violence; non-fatal strangulation; homicide; difference-in-differences; criminal justice policy;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J1 - Labor and Demographic Economics - - Demographic Economics
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • K14 - Law and Economics - - Basic Areas of Law - - - Criminal Law
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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