Research Papers

This paper examines how estimates of the deterrent effect of capital punishment depend on alternate choices of assumptions concerning the homicide process. 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.

Results on the deterrent effect of capital punishment are disparate. This article is designed to understand the sources of the disparate literature findings. Specifically, we consider how different substantive assumptions about the homicide process affect deterrence effect estimates.

The effectiveness of capital punishment in deterring homicides has remained unclear despite the fact that the Supreme Court’s moratorium on capital punishment and the subsequent adoption of capital punishment by a subset of states, combined with very different rates of execution across polities, would appear to be an ideal environment for revealing deterrence effects using panel data methods.

We explored the presence of nonlinearities in economic growth and their implications for policy, considering how different growth regimes may require different policy responses.

We aimed to provide insights into the effectiveness of policy interventions by acknowledging and understanding the nonlinearities in economic growth processes.

Our recognition of nonlinearities in economic growth help policymakers tailor interventions to specific growth contexts, enhancing the precision and effectiveness of economic policies.

We explored the concept of poverty traps with a focus on the Appalachian region, examining factors contributing to persistent poverty.

We aimed to provide a nuanced understanding of poverty traps in a specific geographic context, offering insights that could inform targeted anti-poverty policies.

We contribute to the literature on poverty traps, providing region-specific insights that can guide policymakers in designing effective interventions to break the cycle of poverty.

We explored the implications of complexity theory for economics and public policy, considering how complex systems thinking can enhance policy analysis.

Our goal was to integrate insights from complexity theory into economic and policy analysis, providing a more realistic framework for understanding and addressing complex social and economic phenomena.

We contribute to advancing the application of complexity theory in policy analysis, offering a more robust and adaptable approach to addressing real-world challenges.

We investigated the observational implications of taste-based discrimination in racial profiling, considering how biases in law enforcement decisions may manifest in observed data.

We aimed to provide insights into the challenges of identifying and addressing taste-based discrimination in racial profiling through statistical and econometric analysis.

We contribute to the understanding of the complexities involved in studying and combating racial profiling, emphasizing the importance of careful empirical analysis.

We explored the relationship between imprisonment and crime rates, considering the possibility of reducing both imprisonment rates and crime.

We wanted to challenge the prevailing notion that increasing imprisonment rates are the only effective strategy for reducing crime, suggesting alternative approaches.

We contribute to the debate on criminal justice policies, advocating for a balanced approach that considers both crime prevention and the social costs of imprisonment.

We reexamined the relationship between religion and economic growth, considering the impact of specific religious beliefs and practices on growth rates.

We aimed to provide a nuanced analysis of the role of religion in economic growth by investigating the detailed aspects of religious beliefs and practices, which previous research may have overlooked.

We contribute to a deeper understanding of the complex relationship between religion and economic growth, emphasizing the importance of considering specific aspects of religious influence for more accurate policy implications.

This paper provides a general description of the relationship between individual decision problems and aggregate crime regressions to elucidate the behavioral and statistical assumptions that are implicit in the use of aggregate crime regressions.

We wish to understand how aggregate crime regressions may be appropriately used to inform positive and normative questions. We hope our discussion will prove useful in highlighting some of the limitations of the use of these regressions and, in particular, indicate how empirical findings may be overinterpreted when careful attention is not given to the link between the aggregate data and individual behavior.

Despite recent efforts to employ microeconomic data and natural experiments, aggregate crime regressions continue to play a significant role in criminological analyses.

We reviewed linear and nonlinear models and their applications, including results regarding partial identification. We also examined conceptual and methodological links with spatial econometrics and the social networks literature. We considered empirical applications in the context of the methodological overview.

We wanted to provide a template for understanding the broad social interactions literature by outlining a theoretical framework for social interactions and describing the econometric implementation of social interaction ideas.

The study of social interactions enriches the domain of inquiry of economists and the way economists conceptualize individual decision-making.

We critically reviewed the identification strategies of the social determinants of individual behaviors and interactions.

While a rich literature on the identification strategies for uncovering empirical evidence of social interactions exists, a more mature system of identification strategies is needed to identify the conditions under which social interactions occur.

This overview of the existing identification strategies of social interactions clarifies the directions for future research that improves the empirical modeling of social interactions.