Research Papers

We measured the impact of the Alaska Permanent Fund Dividend on price levels using a synthetic control method to compare Alaska with similar states before the dividend’s introduction.

We aimed to determine whether unconditional cash transfers, like the Alaska Permanent Fund Dividend, stimulate consumer spending and increase price levels.

Our findings help us understand the inflationary effects of unconditional cash transfers, informing policymakers about potential economic impacts and guiding future decisions on similar programs.

We developed a new method for estimating randomized intervention analogues to the natural direct effect (rNDE) and the natural indirect effect (rNIE) in the presence of treatment-induced confounders.

We aimed to address the challenge of estimating mediation effects when treatment-induced confounders complicate the mediator-outcome relationship. Existing methods require cumbersome weighting procedures, making them difficult to use. Our method provides a more practical and accessible approach for researchers by utilizing simpler regression-based techniques.

Our approach allows researchers to estimate mediation effects more efficiently and accurately, even when treatment-induced confounders are present. By simplifying the estimation process, this method makes causal mediation analysis more accessible and robust.

We introduced “residual balancing” as an alternative method for constructing weights in marginal structural models (MSMs) to estimate time-varying treatment effects, aiming to address issues inherent in inverse probability weighting (IPW).

We sought to overcome limitations of IPW, such as inefficiency, sensitivity to model misspecification, and difficulty with continuous treatments.

Residual balancing improves the efficiency and robustness of estimating marginal treatment effects in the presence of time-varying confounders. By offering a more reliable and user-friendly alternative to IPW, it enhances causal inference accuracy in observational studies.

We introduced a method to estimate the cost of adjusting earnings and the earnings elasticity with respect to the net-of-tax share, using bunching in the earnings distribution at convex budget set kinks before and after policy changes.

We sought to explore the relationship between tax policy changes and earnings adjustments, specifically focusing on the Social Security Earnings Test, to provide a clearer understanding of the behavioral responses to tax rate changes.

Our results highlight that adjustment costs can significantly dampen the short-run effects of changes in effective marginal tax rates, offering crucial insights for policymakers on the efficiency and impact of tax policy adjustments.

We employed a novel rank-based index of stratification to measure how occupational classes occupy distinct, non-overlapping, hierarchical layers in the US income distribution from 1980 to 2016.

We aimed to address the gap in empirical studies that focus on inequality rather than stratification. By developing a rank-based stratification index, we sought to accurately capture class stratification and provide insights into the structural dynamics of the US income distribution over time, enhancing the understanding of class structure and social stratification.

Our findings show a substantial increase in class stratification in the US from 1980 to 2016, driven by factors like income returns to education, deunionization, and deindustrialization. These results highlight growing economic divides based on occupational class, informing theories of class structure and guiding policies to reduce economic stratification and promote social mobility.

We developed a method to estimate the effect of kinked or notched budget sets on workers’ employment decisions and applied it to the Social Security Old-Age and Survivors Insurance (OASI) Annual Earnings Test (AET).

We aimed to understand how the AET, which reduces OASI benefits based on earnings, influences employment rates, using a Regression Kink Design and Social Security Administration data.

Our findings show that the AET significantly reduces employment, providing valuable insights for policymakers on the labor market effects of benefit reduction policies.

We explored an alternative regression-based method, regression-with-residuals (RWR), for estimating marginal effects of time-varying treatments on end-of-study continuous outcomes. This method was compared with inverse-probability-of-treatment-weighted (IPTW) estimation of a marginal structural model.

We aimed to address challenges in estimating marginal treatment effects with observational data, particularly when dealing with time-varying confounders affected by prior treatments.

Our findings suggest that RWR estimation of a constrained structural nested mean model may outperform IPTW estimation in certain scenarios for estimating marginal treatment effects of time-varying treatments on continuous outcomes.

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.