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Estimating the Number of Crimes Averted by Incapacitation: An Information Theoretic Approach

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Abstract

This paper presents an information theoretic approach for estimating the number of crimes averted by incapacitation. It first develops models of the criminal history accumulation process of a sample of prison releasees using their official recorded arrest histories prior to incarceration. The models yield individual offending trajectories that are then used to compute the number of crimes these releasees could reasonably have been expected to commit had they not been incarcerated—the counterfactual of interest. The models also afford the opportunity to conduct a limited set of policy simulations. The data reveal a fair amount of variation among individuals both in terms of the number of crimes averted by their incarceration and the responsiveness of these estimates to longer incarceration terms. Estimates were found not to vary substantially across demographic groups defined by offender race, gender, or ethnicity; variations across states and offense types were more pronounced. Implications of the findings and promising avenues for future research are discussed.

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Notes

  1. For more recent theoretical and empirical developments in this field, see the 2002 special issue of the Journal of Econometrics (Vol 107, Issues 1&2), Chapter 13 of Mittelhammer et al. (2000), Fomby and Hill (1997), and the Golan et al. (1996) monograph. See also Maasoumi (1993), Soofi (1994), and Golan (2002) for historical discussions and general surveys.

  2. It should be noted here that the strategy entails adjusting the event histories by this correction factor (at the micro-level) before estimating λ and not adjusting the crimes averted estimates after their computation. See Bhati (2007) for complete details.

  3. These include Arizona, California, Florida, Illinois, Michigan, Minnesota, New Jersey, New York, North Carolina, Ohio, Oregon, Texas, and Virginia.

  4. While simulating the counterfactual, however, this flag was set to 0 for all individuals, in effect, simulating the micro-trajectory “as if” the individual had not been confined.

  5. Since the samples include multiple arrest events per individual, standard errors need to be corrected for this clustering. The modified sandwich variance estimator (Ezell et al. 2003)—a modified version of sandwich estimators (Huber 1967; White 1980) that account for this clustering—is used here.

  6. These statements are based on a casual review of the estimates in Table 2 and not on rigorous statistical testing.

  7. Examining the causes of the state variation uncovered here is left for future work as it would require careful modeling not only of state policy levers, but also variation in relevant offender attributes across states. The sample of offenders vary considerably across states with respect to attributes like age at release or age at first arrest that are included in the models.

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Authors and Affiliations

  1. Justice Policy Center, Urban Institute, 2100 M Street, NW, Washington, DC, 20037, USA

    Avinash Singh Bhati

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  1. Avinash Singh Bhati
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Correspondence to Avinash Singh Bhati.

Additional information

Peter Reuter, Shawn Bushway, Christy Visher, Dan Mears, and Jennifer Castro provided very thoughtful comments on earlier drafts of this paper, as did three anonymous reviewers. The author is solely responsible for any remaining errors. Funding from the National Institute of Justice, Office of Justice Programs, US Department of Justice through an Institute for Law and justice subcontract agreement is greatfully acknowledged. Points of view expressed here are the author’s and do not represent the official positions or policies of the US Department of Justice, the Institute for Law and Justice, nor of the Urban Institute, its trustees and funders.

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Bhati, A.S. Estimating the Number of Crimes Averted by Incapacitation: An Information Theoretic Approach. J Quant Criminol 23, 355–375 (2007). https://doi.org/10.1007/s10940-007-9034-2

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