Capital Punishment and Deterrence: Understanding Disparate Results

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.