Research Papers

We identified potential methodological pitfalls in using time series analysis to study the relationship between capital punishment and crime rates.

We wanted to improve the quality of research on capital punishment and prevent misleading conclusions based on flawed methodology.

We encourage rigorous methods in research on capital punishment and highlight the importance of considering potential biases and confounding factors.

We critically reviewed the literature on model uncertainty and its implications for empirical policy analysis in economics.

Our goal was to raise awareness of the challenges posed by model uncertainty and promote robust methods for policy evaluation under such conditions.

We highlight the importance of acknowledging and addressing model uncertainty to ensure reliable and informative policy recommendations.

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 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 examined the inertia in taxpayers’ adjustments to income tax prepayments following changes in tax liability, analyzing how taxpayers’ prepayments change over time.

We aimed to investigate why many US taxpayers receive income tax refunds and to explore the role of inertia in their tax prepayment behavior, beyond the previously suggested precautionary and forced savings motives.

Our findings indicate that default withholding rules significantly impact consumption smoothing, especially for low-income taxpayers, suggesting that policies around withholding can have broader economic implications than previously understood.

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 investigated the effects of long-term exposure to disadvantaged neighborhoods on high school graduation by following 4,154 children in the Panel Study of Income Dynamics, measuring neighborhood context annually from age 1 to 17.

We aimed to address the gap in previous research that typically measures neighborhood context only once, failing to account for the duration of residence and understating the detrimental effects of long-term neighborhood disadvantage.

Our findings reveal that sustained exposure to disadvantaged neighborhoods significantly reduces the probability of high school graduation, with impacts larger than previously reported. For black children, graduation rates drop from 96% to 76%, and for nonblack children, from 95% to 87%. These results highlight the importance of addressing long-term neighborhood disadvantage to improve educational outcomes.

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.