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Does Mandatory Diversion to Drug Treatment Eliminate Racial Disparities in the Incarceration of Drug Offenders? An Examination of California's Proposition 36

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  • Nancy Nicosia
  • John M. MacDonald
  • Rosalie Liccardo Pacula
Abstract
Like other states, minorities are disproportionately represented in the California's state prison system, particularly for drug offenses. Unlike other states, California has had a policy of mandatory diversion to drug treatment for non-violent drug offenders since mid-2001 (Proposition 36). Using a rich dataset including current and prior criminal charges from 1995 through 2005 in California, we examine whether disparities in court dispositions to prison and drug treatment between White and Blacks male drug offenders are explained by observable case and criminal justice characteristics. We estimate the extent to which remaining observable disparities are affected by Proposition 36. We find that Black and White male drug offenders differ considerably on covariates, but by weighting on the inverse of a nonparametric estimate of the propensity score, we can compare Blacks to Whites that are on average equivalent on covariates. Unadjusted disparities in the likelihood of being sentenced to prison are substantially reduced by propensity score weighting. Proposition 36 reduces the likelihood of prison overall, but not differentially for Blacks. By contrast, racial disparity in diversion to drug treatment is not reduced by propensity score weighting. There is some evidence that Proposition 36 increased diversion for Blacks.

Suggested Citation

  • Nancy Nicosia & John M. MacDonald & Rosalie Liccardo Pacula, 2012. "Does Mandatory Diversion to Drug Treatment Eliminate Racial Disparities in the Incarceration of Drug Offenders? An Examination of California's Proposition 36," NBER Working Papers 18518, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:18518
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    References listed on IDEAS

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    1. James J. Heckman & Petra E. Todd, 2009. "A note on adapting propensity score matching and selection models to choice based samples," Econometrics Journal, Royal Economic Society, vol. 12(s1), pages 230-234, January.
    2. Harold Pollack & Peter Reuter & Eric L. Sevigny, 2011. "If Drug Treatment Works So Well, Why Are So Many Drug Users in Prison?," NBER Working Papers 16731, National Bureau of Economic Research, Inc.
    3. Keisuke Hirano & Guido W. Imbens & Geert Ridder, 2003. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometrica, Econometric Society, vol. 71(4), pages 1161-1189, July.
    4. A. Smith, Jeffrey & E. Todd, Petra, 2005. "Does matching overcome LaLonde's critique of nonexperimental estimators?," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 305-353.
    5. Mustard, David B, 2001. "Racial, Ethnic, and Gender Disparities in Sentencing: Evidence from the U.S. Federal Courts," Journal of Law and Economics, University of Chicago Press, vol. 44(1), pages 285-314, April.
    6. Golembeski, C. & Fullilove, R., 2005. "Criminal (in)justice in the city and its associated health consequences," American Journal of Public Health, American Public Health Association, vol. 95(10), pages 1701-1706.
    7. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    8. Hser, Y.-I. & Teruya, C. & Brown, A.H. & Huang, D. & Evans, E. & Anglin, M.D., 2007. "Impact of California's Proposition 36 on the drug treatment system: Treatment capacity and displacement," American Journal of Public Health, American Public Health Association, vol. 97(1), pages 104-109.
    9. Busso, Matias & DiNardo, John & McCrary, Justin, 2009. "New Evidence on the Finite Sample Properties of Propensity Score Matching and Reweighting Estimators," IZA Discussion Papers 3998, Institute of Labor Economics (IZA).
    10. Ridgeway, Greg & MacDonald, John M., 2009. "Doubly Robust Internal Benchmarking and False Discovery Rates for Detecting Racial Bias in Police Stops," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 661-668.
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    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • K42 - Law and Economics - - Legal Procedure, the Legal System, and Illegal Behavior - - - Illegal Behavior and the Enforcement of Law

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