Research Papers

We investigated how the timing of exposure to disadvantaged neighborhoods during childhood versus adolescence affects high school graduation and if these effects differ by family income. Tracking 6,137 children from the PSID from childhood through adolescence, we used a structural nested mean model via two-stage regression with residuals to address the joint endogeneity of neighborhood context and family income.

We aimed to understand the nuanced effects of disadvantaged neighborhoods on educational outcomes, considering that these effects might vary depending on the timing of exposure and family economic resources. By identifying critical periods and vulnerable populations, we sought to inform targeted interventions and policies to mitigate the negative impacts of disadvantaged neighborhoods on children’s education.

We find that exposure to disadvantaged neighborhoods, especially during adolescence, negatively impacts high school graduation rates, particularly for children from poor families. This emphasizes the need for policy interventions addressing neighborhood disadvantage and economic support for families, particularly during critical developmental periods, to improve educational outcomes and promote social mobility.

We critically examined Thomas Piketty’s book “Capital in the Twenty-First Century” and its arguments about rising wealth inequality.

We wanted to provide an alternative perspective on wealth inequality and assess the validity of Piketty’s claims and policy prescriptions.

We offer a valuable contribution to the ongoing debate about wealth inequality and challenges some of Piketty’s central arguments.

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.