Steven Durlauf, Chao Fu, Salvador Navarro

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.

capital punishment
model uncertainty