Research Papers

We combined a special class of deep neural networks with directed acyclic graphs to improve our ability to draw inferences about entire systems of causal relationships.

Social science theories often postulate causal relationships among a set of variables or events, but when it comes to empirically evaluating them, researchers typically resort to methods that impose unrealistic assumptions on the data, leading to incorrect inferences. Our approach can model these relationships without invoking such restrictive assumptions.

Our approach allows researchers to more accurately learn causal relationships from data. It therefore has a very wide scope for application, ranging from studies of inequality and social mobility to health, education, and psychology.

We offered a comparison between the distributional Recentered Influence Function (RIF) and the conventional Oaxaca-Blinder decomposition methods. Additionally, we compared the multi-year cross-sectional Survey of Consumer Finances with the longitudinal Panel Study of Income Dynamics.

We tested the hypothesis that inheritance receipt explains a larger portion of the black-white gaps in net worth compared to human capital, as measured by educational attainment.

The black-white wealth gap exhibits a surge in inequality across the percentiles of the net worth distribution. It is important to determine whether the decomposition at the mean yields different results compared to the decomposition at the median or over the entire distribution.

In the context of Health and Human Resource outcomes, we developed and exemplified comparison instruments for ordered categorical data that don’t require artificial Cantril or Likert scaling of outcomes to facilitate level and variation comparisons.

Artificial cardinal scaling of ordered categorical data has been heavily criticized recently because of the inherent ambiguity it engenders (different, equally valid scales can result in substantially different conclusions). These new instruments resolve the issue by providing scale independent comparators that have an unambiguous probabilistic basis.

The generation and use of ordered categorical data has proliferated in the modern era in all areas of the empirical sciences and there is a need for analysis tools which are not subject to ambiguity critiques.

We evaluated the hypothesis that neighborhood effects on academic achievement are mediated or interact with school quality using data from the Early Childhood Longitudinal Study.

We aimed to explore the mechanisms through which neighborhood poverty affects academic achievement, focusing on the role of school quality. By examining diverse measures of school effectiveness, resources, and climate, we sought to provide insights into whether and how these factors mediate or interact with neighborhood effects.

Our findings indicate that living in a disadvantaged neighborhood reduces academic achievement, but find little evidence that this effect is mediated by measures of school quality. These results challenge prevailing hypotheses and suggest that neighborhood effects operate independently of school quality factors. Addressing concentrated poverty directly may therefore be crucial in reducing educational inequalities.

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.