This setting lets you change the way you view articles. You can choose to have articles open in a dialog window, a new tab, or directly in the same window.
Open in Dialog
Open in New Tab
Open in same window
Open in New Tab
Open in same window

American Economic Review: Vol. 102 No. 3 (May 2012)
AER Volume. 102, Issue 3 |
Previous ArticleNext Article
Sign up for Email Alerts Follow us on Twitter
AER Forthcoming Articles
Full-text Article
Previous ArticleNext Article
Expand
Quick Tools:
Print Article Summary Email Link to this Article Export CitationSign up for Email Alerts Follow us on Twitter
Explore:
AER Forthcoming Articles
Assumptions Matter: Model Uncertainty and the Deterrent Effect of Capital Punishment
Article Citation
Durlauf, Steven N.,
Chao Fu, and
Salvador Navarro. 2012. "Assumptions Matter: Model Uncertainty and the Deterrent Effect of Capital Punishment."
American Economic Review,
102(3): 487-92.
DOI: 10.1257/aer.102.3.487
DOI: 10.1257/aer.102.3.487
Abstract
This paper examines how estimates of the deterrent effect of capital punishment depend on alternate choices of assumptions concerning the homicide process. Specific models of the homicide process represent bundles of these assumptions, which involve the unobserved heterogeneity, the relevant penalty probabilities for homicide choices, possible cross-polity parameter variation, and exchangeability between polity-time pairs that do and do not experience positive numbers of murders. We demonstrate how various assumptions have driven the conflicting findings from studies on capital punishment, and isolate a particular set of assumptions that are required to find a positive deterrent effect.
Article Full-Text Access
Full-text Article
Authors
Durlauf, Steven N. (U WI)
Fu, Chao (U WI)
Navarro, Salvador (Social Science Centre, U Western Ontario)
Fu, Chao (U WI)
Navarro, Salvador (Social Science Centre, U Western Ontario)
JEL Classifications
K42: Illegal Behavior and the Enforcement of Law
C52: Model Evaluation, Validation, and Selection
C52: Model Evaluation, Validation, and Selection

