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The Analysis of Prison-Prisoner Data Using Cluster-Sample Econometrics: Prison Conditions and Prisoners' Assessments of the Future

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  • Entorf, Horst

    (Goethe University Frankfurt)

  • Sattarova, Liliya

    (Goethe University Frankfurt)

Abstract

The study investigates whether and how strong prison conditions contribute to the perceived propensity to recidivate after controlling for personal characteristics and criminal background. In order to combine different sources of information on personal characteristics of prison inmates and administrative prison data in an efficient way, we propose the use of matched prison-prisoner data and application of cluster-sample methods such as GEE (generalized estimating equations). Estimated average partial effects based on GEE and random-effects Probit modeling reveal that prison conditions show significant effects on the perceived likelihood of future reincarceration. Particularly, we find that inmates facing prison overcrowding show a reduced likelihood of recidivism.

Suggested Citation

  • Entorf, Horst & Sattarova, Liliya, 2016. "The Analysis of Prison-Prisoner Data Using Cluster-Sample Econometrics: Prison Conditions and Prisoners' Assessments of the Future," IZA Discussion Papers 10209, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp10209
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    More about this item

    Keywords

    matched prison-prisoner data; perceived specific deterrence; recidivism; GEE;
    All these keywords.

    JEL classification:

    • K40 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - General
    • C34 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Truncated and Censored Models; Switching Regression Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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