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Sweeping up Gangs: The Effects of Tough-on-Crime Policies from a Network Approach

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  • Magdalena Domínguez

Abstract

Globally, gang proliferation is fought primarily through sweeps. This paper uses a difference-in-differences strategy to examine their causal impact on crime for arrested individuals and their peers. Analysis focuses on the Metropolitan Area of Barcelona, which underwent a notable policy shift. Findings reveal significant crime reductions among those arrested and their peers of 85 percent and 31 percent, respectively. Evidence suggests the first stems from incapacitation and the second from less criminal environment. A counterfactual exercise suggests that broader targeting of key players could have reduced crime by 43 percentage points more. This implies network analysis could enhance policy formulation.

Suggested Citation

  • Magdalena Domínguez, 2026. "Sweeping up Gangs: The Effects of Tough-on-Crime Policies from a Network Approach," American Economic Journal: Economic Policy, American Economic Association, vol. 18(1), pages 127-158, February.
    • Handle: RePEc:aea:aejpol:v:18:y:2026:i:1:p:127-58
    DOI: 10.1257/pol.20230467
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    References listed on IDEAS

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    1. Yann Bramoullé & Habiba Djebbari & Bernard Fortin, 2020. "Peer Effects in Networks: A Survey," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 603-629, August.
    2. Zenou, Yves & Lindquist, Matthew, 2014. "Key Players in Co-Offending Networks," CEPR Discussion Papers 9889, Centre for Economic Policy Research.
    3. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    4. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    5. Stephen P. Borgatti, 2006. "Identifying sets of key players in a social network," Computational and Mathematical Organization Theory, Springer, vol. 12(1), pages 21-34, April.
    6. Arun Advani & Bansi Malde, 2018. "Credibly Identifying Social Effects: Accounting For Network Formation And Measurement Error," Journal of Economic Surveys, Wiley Blackwell, vol. 32(4), pages 1016-1044, September.
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    More about this item

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • H56 - Public Economics - - National Government Expenditures and Related Policies - - - National Security and War
    • 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
    • Z18 - Other Special Topics - - Cultural Economics - - - Public Policy

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