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 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 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 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 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.