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 uncertain...
| Authors: | ; ; |
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| Format: | Electronic Article |
| Language: | English |
| Published: |
2013
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| In: |
Journal of quantitative criminology
Year: 2013, Volume: 29, Issue: 1, Pages: 103-121 |
| Online Access: |
Presumably Free Access Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
| Journals Online & Print: | Drawer...
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| Check availability: | HBZ Gateway |
| Keywords: |
| Summary: | 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 an 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 effects differ mainly because of the choice of linear versus logistic specifications. Conclusions The question about the deterrence effect of capital punishment cannot be answered independently from substantive assumptions on what determines individual behavior. The need for judgment cannot be escaped in empirical work. |
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| ISSN: | 1573-7799 |
| DOI: | 10.1007/s10940-012-9171-0 |