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Capital Punishment and Deterrence: Understanding Disparate Results

  • Steven N. Durlauf


    (University of Wisconsin at Madison)

  • Chao Fu


    (University of Wisconsin at Madison)

  • Salvador Navarro


    (University of Western Ontario)

Objectives: Investigate how different model assumptions have driven the conflicting findings in the literature on the deterrence effect of capital punishment. Methods: The deterrence effect of capital punishment is estimated across different models that reflect the following sources of model uncertainty: 1) the uncertainty about the probability model generating the aggregate murder rate equation, 2) the uncertainty about the determinants of individual’s choice of committing a murder or not, 3) the uncertainty about state level heterogeneity, and 4) the uncertainty about the exchangeability between observations with zero murder case and those with positive murder cases. Results: First, the estimated deterrence effects exhibit great dispersion across models. Second, a particular subset of models -linear models with constant coefficients - always predict a positive deterrence effect. All other models predict negative deterrence effects. Third, the magnitudes of the point estimates of deterrence effect differ mainly because of the choice of linear versus logistic specifications. Conclusions: The question about the deterrence effect of capital cannot be answered independently from substantive assumptions on what determines individual behavior. The need for judgment cannot be escaped in empirical work.

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Paper provided by Human Capital and Economic Opportunity Working Group in its series Working Papers with number 2012-005.

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Date of creation: Dec 2011
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Handle: RePEc:hka:wpaper:2012-005
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  1. Donohue, John J & Wolfers, Justin, 2006. "Uses and Abuses of Empirical Evidence in the Death Penalty Debate," CEPR Discussion Papers 5493, C.E.P.R. Discussion Papers.
  2. Hashem Dezhbakhsh & Paul H. Rubin & Joanna M. Shepherd, 2003. "Does Capital Punishment Have a Deterrent Effect? New Evidence from Postmoratorium Panel Data," American Law and Economics Review, Oxford University Press, vol. 5(2), pages 344-376, August.
  3. Paul R. Zimmerman, 2004. "State executions, deterrence, and the incidence of murder," Journal of Applied Economics, Universidad del CEMA, vol. 0, pages 163-193, May.
  4. Hashem Dezhbakhsh & Paul Rubin, 2011. "From the 'econometrics of capital punishment' to the 'capital punishment' of econometrics: on the use and abuse of sensitivity analysis," Applied Economics, Taylor & Francis Journals, vol. 43(25), pages 3655-3670.
  5. Heckman, James J. & Schmierer, Daniel & Urzua, Sergio, 2009. "Testing the Correlated Random Coefficient Model," IZA Discussion Papers 4525, Institute for the Study of Labor (IZA).
  6. Mocan, H Naci & Gittings, R Kaj, 2003. "Getting Off Death Row: Commuted Sentences and the Deterrent Effect of Capital Punishment," Journal of Law and Economics, University of Chicago Press, vol. 46(2), pages 453-78, October.
  7. Durlauf, Steven N. & Navarro, Salvador & Rivers, David A., 2010. "Understanding aggregate crime regressions," Journal of Econometrics, Elsevier, vol. 158(2), pages 306-317, October.
  8. Ethan Cohen-Cole & Steven N. Durlauf & Jeffrey Fagan & Daniel Nagin, 2007. "Model uncertainty and the deterrent effect of capital punishment," Risk and Policy Analysis Unit Working Paper QAU07-3, Federal Reserve Bank of Boston.
  9. Abbring, Jaap H. & Heckman, James J., 2007. "Econometric Evaluation of Social Programs, Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 72 Elsevier.
  10. Theo S. Eicher & Alex Lenkoski & Adrian Raftery, 2009. "Bayesian Model Averaging and Endogeneity Under Model Uncertainty: An Application to Development Determinants," Working Papers UWEC-2009-19-FC, University of Washington, Department of Economics.
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