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

William Rhodes, Bernadette Pelissier, Gerald Gaes, William Saylor, Scott Camp and Susan Wallace
Additional contact information
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

Evaluation Review, 2001, vol. 25, issue 3, 331-369

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.

Date: 2001
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Persistent link: https://EconPapers.repec.org/RePEc:sae:evarev:v:25:y:2001:i:3:p:331-369

DOI: 10.1177/0193841X0102500303

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