JOURNAL ARTICLE

Youth, Crime, and the Potential Cost Offset to Housing First Programs.

  • Published In: Canadian Journal of Criminology & Criminal Justice, 2023, v. 65, n. 4. P. 82 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Kneebone, Ronald; Dutton, Daniel J.; Jadidzadeh, Ali 3 of 3

Abstract

This article examines the impact of Housing First (HF) programs—an approach that provides housing as a precondition for assisting homeless individuals—on the frequency and severity of police interactions involving homeless youth in Calgary, Alberta, Canada. Using linked administrative data from the Calgary Homeless Foundation and Calgary Police Service, the study employs unconditional quantile regression to analyze changes in police-recorded criminal incidents and their seriousness before and after youth enrollment in HF programs. The findings indicate only weak evidence that HF reduces the number or seriousness of criminal incidents among youth who had prior police interactions, with no statistically significant changes observed overall. The study also notes that a majority of youth enrolled in HF had no police interactions either before or after housing, suggesting limited potential for HF programs to generate substantial cost savings for the justice system, though it emphasizes that the value of HF lies in improving the lives of homeless youth rather than in cost offsets.

Additional Information

  • Source:Canadian Journal of Criminology & Criminal Justice. 2023/10, Vol. 65, Issue 4, p82
  • Document Type:Article
  • Subject Area:Politics and Government
  • Publication Date:2023
  • ISSN:1707-7753
  • DOI:10.3138/cjccj-2023-0051
  • Accession Number:176341830
  • Copyright Statement:Copyright of Canadian Journal of Criminology & Criminal Justice is the property of University of Toronto Press 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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