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Alternative Solutions to the Problem of Selection Bias in an Analysis of Federal Residential Drug Treatment Programs

Author

Listed:
  • William Rhodes

    (Abt Associates)

  • Bernadette Pelissier

    (Federal Bureau of Prisons)

  • Gerald Gaes

    (Federal Bureau of Prisons)

  • William Saylor

    (Federal Bureau of Prisons)

  • Scott Camp

    (Federal Bureau of Prisons)

  • Susan Wallace

    (Federal Bureau of Prisons)

Abstract

In an evaluation of prison-based residential drug treatment programs, the authors use three different regression-based approaches to estimating treatment effects. Two of the approaches, the instrumental variable and the Heckman approach, attempt to minimize selection bias as an explanation for treatment outcomes. Estimates from these approaches are compared with estimates from a regression in which treatment is represented by a dummy variable. The article discusses the advantage of using more than one method to increase confidence in findings when possible selection bias is a concern. Three-year outcome data for 2,315 federal inmates are used in analyses where the authors separately examine criminal recidivism and relapse to drug use for men and women. Statistical tests lead the authors to conclude that treatment reduces criminal recidivism and relapse to drug use. The treatment effect was largest when the inference was based on the Heckman approach, somewhat smaller when based on the instrumental variable approach, and smallest when based on the traditional dummy variable approach. Treatment effects for females were not statistically significant.

Suggested Citation

  • William Rhodes & Bernadette Pelissier & Gerald Gaes & William Saylor & Scott Camp & Susan Wallace, 2001. "Alternative Solutions to the Problem of Selection Bias in an Analysis of Federal Residential Drug Treatment Programs," Evaluation Review, , vol. 25(3), pages 331-369, June.
    • Handle: RePEc:sae:evarev:v:25:y:2001:i:3:p:331-369
    DOI: 10.1177/0193841X0102500303
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    References listed on IDEAS

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    1. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
    2. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    3. James J. Heckman & Jeffrey A. Smith, 1995. "Assessing the Case for Social Experiments," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 85-110, Spring.
    4. James Heckman & Hidehiko Ichimura & Jeffrey Smith & Petra Todd, 1998. "Characterizing Selection Bias Using Experimental Data," Econometrica, Econometric Society, vol. 66(5), pages 1017-1098, September.
    5. Gary Burtless, 1995. "The Case for Randomized Field Trials in Economic and Policy Research," Journal of Economic Perspectives, American Economic Association, vol. 9(2), pages 63-84, Spring.
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    Cited by:

    1. Miller, Holly Ventura & Miller, J. Mitchell & Barnes, J.C., 2016. "Reentry programming for opioid and opiate involved female offenders: Findings from a mixed methods evaluation," Journal of Criminal Justice, Elsevier, vol. 46(C), pages 129-136.

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