Research Papers

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 proposed a framework for evaluating the effectiveness of policies in the face of model uncertainty, where different economic models may yield conflicting predictions.

Our goal was to address challenges in policy evaluation when relying on models with inherent limitations and improve the reliability of policy recommendations.

We offer a novel approach to policy evaluation that acknowledges model uncertainty and promotes robust, evidence-based policy decisions.

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 introduced and analyzed linear social interactions models, which capture how individual decisions are influenced by the behavior of others in their social networks.

Our goal was to understand the dynamics of social interactions and their impact on economic phenomena like cultural diffusion and collective behavior.

We provide a powerful tool for analyzing social interactions and their consequences for various economic and social issues.

We explored the limitations of dynamic policy analysis, which uses models to evaluate the long-term effects of policy changes.

We aimed to understand the challenges of using models for long-term forecasting and policy planning in a complex and evolving economic environment.

We raise important concerns about the limitations of models and emphasize the need for caution in relying on them for long-term policy decisions.

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.

We discussed the importance of incorporating process and context into economic models of individual choice behavior.

We aimed to improve the accuracy and realism of economic models by taking into account the dynamics of decision-making and the influence of context.

We advocate for more nuanced and context-aware models of economic behavior, acknowledging the influence of cognitive and social factors on individual choices.