JOURNAL ARTICLE

Desperation on the Battlefield, the Ethnic Security Dilemma, or Economic Competition? Mass Shootings in Chicago's Gang Wars, 2010–20.

  • Published In: British Journal of Criminology, 2024, v. 64, n. 1. P. 88 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Burke, Patrick J 3 of 3

Abstract

This study analyzes gang-related mass shootings in Chicago from 2010 to 2020, arguing that gangs strategically use mass shootings as a form of coercion to capture increased drug market share during periods of rising demand, proxied by opioid overdose deaths. Testing this "economic competition" theory alongside two established theories of indiscriminate violence—"desperation on the battlefield" and the "ethnic security dilemma"—the findings support the economic competition and ethnic security dilemma hypotheses, while variables representing desperation (search warrants and gang member arrests) are associated with reductions in mass shootings. The research utilizes original datasets obtained mainly through Freedom of Information Act requests, focusing on police districts with gang territories and employing fixed-effects regression models. The study highlights mass shootings as a rational, high-risk investment by gangs to intimidate rivals and secure drug market dominance, and calls for further research on the micro-dynamics of indiscriminate violence in U.S. gang contexts and comparisons with Latin American criminal organizations.

Additional Information

  • Source:British Journal of Criminology. 2024/01, Vol. 64, Issue 1, p88
  • Document Type:Article
  • Subject Area:Ethnic and Cultural Studies
  • Publication Date:2024
  • ISSN:0007-0955
  • DOI:10.1093/bjc/azad010
  • Accession Number:174273374
  • Copyright Statement:Copyright of British Journal of Criminology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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