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Capital Punishment and Deterrence: Understanding Disparate Results

  • Salvador Navarro

    (University of Western Ontario)

  • Chao Fu

    (University of Wisconsin-Madison)

  • Steven Durlauf

    (University of Wisconsin)

The panel data literature on deterrence and capital punishment contains a wide range of empirical claims despite the use of common data sets for analysis. We interpret the diversity of findings in the literature in terms of differences in statistical model assumptions. Rather than attempt to determine a "best" model from which to draw empirical evidence on deterrence and the death penalty, this paper asks what conclusions about deterrence may be drawn given the presence of model uncertainty, i.e. uncertainty about which statistical assumptions are appropriate. We consider four sources of model uncertainty that capture some of the economically substantive differences that appear across studies. We explore which dimensions of these assumptions are important in generating disparate findings on capital punishment and deterrence from a standard county-level crime data set.

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Paper provided by Society for Economic Dynamics in its series 2012 Meeting Papers with number 53.

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Date of creation: 2012
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Handle: RePEc:red:sed012:53
Contact details of provider: Postal: Society for Economic Dynamics Christian Zimmermann Economic Research Federal Reserve Bank of St. Louis PO Box 442 St. Louis MO 63166-0442 USA
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  1. James J. Heckman & Daniel A. Schmierer & Sergio S. Urzua, 2009. "Testing the Correlated Random Coefficient Model," NBER Working Papers 15463, National Bureau of Economic Research, Inc.
  2. 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.
  3. Durlauf, Steven N. & Navarro, Salvador & Rivers, David A., 2010. "Understanding aggregate crime regressions," Journal of Econometrics, Elsevier, vol. 158(2), pages 306-317, October.
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Ethan Cohen-Cole & Steven 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.
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