Estimating deterrent effects requires modeling many correlated causal factors simultaneously.

Evidence

Verifying causal relations in human behavior is complicated by the multiplicity of possible causal factors. To complicate matters further, many factors correlate with each other to some degree, requiring statistical methods to estimate how much each of the various factors contributes to behavior on its own. A reliable casual theory is required, to provide guidance on which of many factors should be selected for the causal analysis. The strength of this analytic model lies in its ability to gauge the deterrent influence of the death penalty after the influence of other possible causes of murder rates are taken into account. However, controversy has arisen over its application. Some critiques are directed at the explanatory value of the causal model. This is a key issue because it determines which factors among the many possible causal ones are selected. Other concerns center on the quality and completeness of the data and whether they are measured at the individual or aggregated group level. Often, data for key factors are unavailable so researchers have to rely on secondary or proxy measures for them. For example, the unemployment rate might be used as a proxy for incentives to engage in criminal activities. Still other issues involve disagreements about the statistical methods used. Are the findings based on cross-sectional or time-series data? Are data on social conditions that are conducive to criminal behavior aggregated at the national level or for local jurisdictions? How do law enforcement and judicial practices affect the risks and consequences of crime? — Albert Bandura, Moral Disengagement, 1949

Categorization

Category: Definitional Content

• DEFINE: Lays out the econometric causal-model approach to deterrence and its key methodological troubles (proxies, aggregation level, missing data).

Related

• Multi-factor causality and proxy data complicate deterrence research (similarity 0.94)
• Econometric modeling shifted death-penalty research and produced contested deterrence claims (similarity 0.84)
• Confounding social factors undermine cross-state and time-series deterrence studies (similarity 0.84)
• Claims that executions deter specific numbers of murders rest on weak data (similarity 0.83)
• Death penalty deterrence claims cannot be supported by existing research (similarity 0.82)
• Moral disengagement mediates the link between demographics and death penalty support (similarity 0.81)
• Isaac Ehrlich’s econometric model claimed each execution spares several lives. (similarity 0.81)
• Death-penalty deterrence cannot be tested through randomized experiments (similarity 0.80)
• The death-penalty deterrence question resists controlled experiments because society cannot randomize executions. (similarity 0.80)
• Cross-state and time-series comparisons of death penalty effects suffer from uncontrolled concurrent social factors. (similarity 0.79)
• No credible evidence shows the death penalty deters homicide (similarity 0.79)
• Legal deterrence based on threatened punishment works poorly in practice. (similarity 0.79)