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Can recidivism be prevented from behind bars? Evidence from a behavioral program

Author

Listed:
  • William Arbour

    (University of Toronto)

Abstract
Incarcerated offenders are offered a wide range of programs to encourage their chances of successful reintegration into society. Little is known, however, about the degree to which such programs improve prisoners’ reentry. In this paper, I study the effects of a cognitive-behavioral program implemented in Quebec, Canada, with a rich micro-level dataset. To manage the econometric issue of inmates’ self-selection into the program, I exploit inmates’ random assignment to probation officers who exhibit varying propensities to recommend the rehabilitation measure. I find large, negative, and significant effects of the program on recidivism, as measured by an inmate’s probability of serving a subsequent sentence: within one year following release, the program reduces recidivism by up to 18 percentage points. Moreover, the program is shown to decrease the number of future offenses. Further analyses indicate that the most plausible mechanism can be attributed to the program’s success in altering offenders’ preferences towards crime.

Suggested Citation

  • William Arbour, 2021. "Can recidivism be prevented from behind bars? Evidence from a behavioral program," Working Papers 2021.07, International Network for Economic Research - INFER.
    • Handle: RePEc:inf:wpaper:2021.07
    as

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    File URL: https://infer-research.eu/wp-content/uploads/2021/09/WP2021.07.pdf
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    References listed on IDEAS

    as
    1. Manudeep Bhuller & Gordon B. Dahl & Katrine V. Løken & Magne Mogstad, 2020. "Incarceration, Recidivism, and Employment," Journal of Political Economy, University of Chicago Press, vol. 128(4), pages 1269-1324.
    2. Kleibergen, Frank & Paap, Richard, 2006. "Generalized reduced rank tests using the singular value decomposition," Journal of Econometrics, Elsevier, vol. 133(1), pages 97-126, July.
    3. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    4. Zanella, Giulio, 2020. "Prison Work and Convict Rehabilitation," IZA Discussion Papers 13446, Institute of Labor Economics (IZA).
    5. Yang, Crystal S., 2017. "Local labor markets and criminal recidivism," Journal of Public Economics, Elsevier, vol. 147(C), pages 16-29.
    6. Kleibergen, Frank, 2007. "Generalizing weak instrument robust IV statistics towards multiple parameters, unrestricted covariance matrices and identification statistics," Journal of Econometrics, Elsevier, vol. 139(1), pages 181-216, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Rachel Nesbit, 2022. "The Role of Mandated Mental Health Treatment in the Criminal Justice System," Papers 2212.06736, arXiv.org, revised Nov 2023.
    2. Johannes W. Ligtenberg & Tiemen Woutersen, 2024. "Multidimensional clustering in judge designs," Papers 2406.09473, arXiv.org.
    3. William Arbour & Steeve Marchand, 2022. "Parole, Recidivism, and the Role of Supervised Transition," Working Papers tecipa-725, University of Toronto, Department of Economics.

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    More about this item

    Keywords

    program; incarceration; recidivism; cognitive-behavioral; judges fixed effects;
    All these keywords.

    JEL classification:

    • A1 - General Economics and Teaching - - General Economics

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