Capital Punishment and Deterrence: Understanding Disparate Results
نویسندگان
چکیده
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. Corresponding Author: Durlauf, Department of Economics, University of Wisconsin, Madison WI 53706, [email protected]. We thank Timothy Conley and David Rivers for many helpful insights. Durlauf thanks the University of Wisconsin Graduate School and Vilas Trust for financial support. Hon Ho Kwok and Xiangrong Yu have provided outstanding research assistance.
منابع مشابه
ORI GIN AL PA PER Capital Punishment and Deterrence: Understanding Disparate Results
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...
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تاریخ انتشار 2011