Research Papers

We introduced a new method, regression with residuals, to estimate moderated causal effects in longitudinal studies where both treatment and effect moderators vary over time.

We aimed to address the limitations of traditional regression models in longitudinal settings, where time-varying moderators of future treatment can be influenced by prior treatment.

Our method provides a more accurate approach to understanding how individual characteristics modify the effects of treatments over time. This is crucial for social scientists seeking to identify the nuanced ways in which interventions impact different subgroups.

We developed a theory of social class based on workplace ownership and authority relations and examined its link to growth in personal income inequality since the 1980s.

We aimed to establish a theoretical framework linking social class to income inequality trends and empirically investigate these dynamics over time. By focusing on between-class income differences, class size variations, and income dispersion within classes, we sought to identify key factors driving the growth in personal income inequality.

Our findings reveal a significant increase of about 60% in between-class income differences since the 1980s, with the relative size of different social classes remaining stable. Decomposition analysis indicates that these income differences substantially contributed to rising personal income inequality, underscoring the importance of class-based perspectives in understanding economic disparities.

We examined the exclusion restriction assumption in interpreting IV estimates as a local average treatment effect (LATE) and highlighted cases where this assumption may be violated among inframarginal agents.

We aimed to identify and address potential biases in IV estimates due to violations of the exclusion restriction, which can affect the validity of LATE interpretations, especially among always- and never-takers.

Our findings underscore the need for careful consideration and potential adjustments in IV estimates to ensure accurate interpretations, thereby improving the reliability of econometric analyses involving heterogeneous treatment effects.

We conducted a field experiment to test theories of time-inconsistency, specifically the “Beta-Delta” model of present bias, in the context of saving decisions made by low-income tax filers.

We aimed to understand how present-biased preferences influence the decision to deposit tax refunds into illiquid accounts, providing insights into the tradeoff between immediate and delayed payments.

Our findings provide evidence of significant present bias in financial decisions, with an estimated annual discount rate of 164%, offering valuable implications for designing policies and interventions to encourage saving among low-income individuals.

We provide a systematic analysis of identification in linear social interactions models. We develop an incomplete information game that describes individual choices in the presence of social interactions.

The theoretical models used to study social interactions are distinct from the econometric environments in which identification is studied, while empirical work generally does not systematically exploit the implications of theory and econometrics for formulating data analyses. This paper aims to facilitate the integration of the theoretical, econometric, and empirical sides of the social interactions literature.

While the proposition that individuals are subject to social influence states the obvious, the study of social influences on individual behavior in economics is a relatively recent phenomenon. In the last two decades, however, a rich theoretical, econometric, and empirical literature in social economics, has emerged.

We investigated the mixed empirical evidence on the deterrent effect of capital punishment and potential explanations for these inconsistencies.

Our findings can contribute to the ongoing debate about capital punishment and inform policy decisions regarding its use as a criminal deterrent.

We shed light on the complexities of measuring deterrence and highlight the need for careful consideration of various factors before drawing conclusions about capital punishment’s effectiveness.

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.