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Evaluating and Quantifying the Specific Deterrent Effects of DNA Databases

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  • Avinash Bhati
  • Caterina G. Roman

Abstract

Background: Today, the ability of deoxyribonucleic acid (DNA) evidence to place persons at crime scenes with near certainty is broadly accepted by criminal investigators, courts, policy makers, and the public. However, the public safety benefits of investments in DNA databases are largely unknown and research attempting to quantify these benefits is only gradually emerging. Given the inherent difficulty in randomly assigning offenders to treatment and comparison groups for the purpose of inferring specific deterrence and probative effects (PREs) of DNA databases, this study developed an alternate strategy for extracting these effects from transactional data. Research Design: Reoffending patterns of a large cohort of offenders released from the Florida Department of Corrections custody between 1996 and 2004 were analyzed across a range of criminal offense categories. First, an identification strategy using multiple clock models was developed that linked the two simultaneous effects of DNA databases to different clocks measuring the same events. Then, a semiparametric approach was developed for estimating the models. Results: The estimation models yielded mixed results. Small deterrent effects—2–3% reductions in recidivism risk attributable to deterrence—were found only for robbery and burglary. However, strong PREs—20–30% increase in recidivism risk attributable to PREs—were uncovered for most offense categories. Conclusion: The probative and deterrent effects of DNA databases can be elucidated through innovative semiparametric models.

Suggested Citation

  • Avinash Bhati & Caterina G. Roman, 2014. "Evaluating and Quantifying the Specific Deterrent Effects of DNA Databases," Evaluation Review, , vol. 38(1), pages 68-93, February.
    • Handle: RePEc:sae:evarev:v:38:y:2014:i:1:p:68-93
    DOI: 10.1177/0193841X14531415
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    References listed on IDEAS

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    3. Wooldridge, Jeffrey M., 2007. "Inverse probability weighted estimation for general missing data problems," Journal of Econometrics, Elsevier, vol. 141(2), pages 1281-1301, December.
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