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Model uncertainty and the deterrent effect of capital punishment

Author

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  • Ethan Cohen-Cole
  • Steven N. Durlauf
  • Jeffrey Fagan
  • Daniel Nagin
Abstract
The reintroduction of capital punishment after the end of the Supreme Court moratorium has permitted researchers to employ state level heterogeneity in the use of capital punishment to study deterrent effects. However, no scholarly consensus exists as to their magnitude. A key reason this has occurred is that the use of alternative models across studies produces differing estimates of the deterrent effect. Because differences across models are not well motivated by theory, the deterrence literature is plagued by model uncertainty. We argue that the analysis of deterrent effects should explicitly recognize the presence of model uncertainty in drawing inferences. We describe methods for addressing model uncertainty and apply them to understand the disparate findings between two major studies in the deterrence literature, finding that evidence of deterrent effects appears, while not nonexistent, is weak.

Suggested Citation

  • Ethan Cohen-Cole & Steven N. Durlauf & Jeffrey Fagan & Daniel Nagin, 2007. "Model uncertainty and the deterrent effect of capital punishment," Supervisory Research and Analysis Working Papers QAU07-3, Federal Reserve Bank of Boston.
    • Handle: RePEc:fip:fedbqu:qau07-3
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    Cited by:

    1. Koop, Gary & Leon-Gonzalez, Roberto & Strachan, Rodney, 2012. "Bayesian model averaging in the instrumental variable regression model," Journal of Econometrics, Elsevier, vol. 171(2), pages 237-250.
    2. Steven N. Durlauf & Chao Fu & Salvador Navarro, 2011. "Capital Punishment and Deterrence: Understanding Disparate Results," Working Papers 2012-005, Human Capital and Economic Opportunity Working Group.
    3. Gerritzen, Berit & Kirchgässner, Gebhard, 2013. "Facts or Ideology: What Determines the Results of Econometric Estimates of the Deterrence Effect of Death Penalty? A Meta-Analysis," Economics Working Paper Series 1303, University of St. Gallen, School of Economics and Political Science.
    4. León-González, Roberto & Montolio, Daniel, 2015. "Endogeneity and panel data in growth regressions: A Bayesian model averaging approach," Journal of Macroeconomics, Elsevier, vol. 46(C), pages 23-39.
    5. Ampaabeng, Samuel K. & Tan, Chih Ming, 2013. "The long-term cognitive consequences of early childhood malnutrition: The case of famine in Ghana," Journal of Health Economics, Elsevier, vol. 32(6), pages 1013-1027.
    6. Gebhard Kirchgässner, 2011. "Econometric Estimates of Deterrence of the Death Penalty: Facts or Ideology?," Kyklos, Wiley Blackwell, vol. 64(3), pages 448-478, August.
    7. Samuel K. Ampaabeng & Chih Ming Tang, 2012. "The Long-Term Cognitive Consequences of Early Childhood Malnutrition: The Case of Famine in Ghana," Working Paper series 64_12, Rimini Centre for Economic Analysis.
    8. Enrique Moral-Benito, 2010. "Model Averaging in Economics," Working Papers wp2010_1008, CEMFI.
    9. 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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