Assumptions Matter: Model Uncertainty and the Deterrent Effect of Capital Punishment

The effectiveness of capital punishment in deterring homicides has remained unclear despite the fact that the Supreme Court’s moratorium on capital punishment and the subsequent adoption of capital punishment by a subset of states, combined with very different rates of execution across polities, would appear to be an ideal environment for revealing deterrence effects using panel data methods. One can find papers that argue that postmoratorium data reveal large deterrent effects (Dezhbakhsh, Rubin, and Shepherd 2003 (DRS hereafter); Zimmerman 2004), fail to provide evidence of a deterrent effect (Donohue and Wolfers 2005; Durlauf, Navarro, and Rivers 2010 (DNR hereafter)), or provide a mixture of positive deterrence and negative deterrence (brutalization) effects depending on the frequency of execution (Shepherd 2005). The presence of disparate results on the deterrent effect of capital punishment is not, by itself, surprising. Social scientists have long understood that the data “do not speak for themselves,” and so empirical analyses that involve substantive social science questions, such as the measurement of deterrence, can do so only conditional on the choice of a statistical model. The disparate findings in the capital punishment literature reflect this model dependence. This is true even when one conditions on the modern panel literature in which the various models typically represent statistical instantiations of Assumptions Matter: Model Uncertainty and the Deterrent Effect of Capital Punishment †