Research Papers

We studied the emergence, persistence, and consequences of urban slums along the structural transformation and urbanization of developing countries. We construct a general equilibrium model in which the size and composition of urban slums emerge endogenously along with disparities in housing costs, labor markets, and schooling opportunities across a country’s rural and urban regions.

The macro development literature has largely ignored exploring the conditions for labor, housing, and schooling markets that give rise to large and persistent shares of a country’s population living in urban slums and has sidestepped the long-term consequences of these extreme forms of metropolitan segregation for human capital of children growing up in slums.

Many rural-to-urban migrants become slum dwellers, segregated from the city. Besides lacking basic services and enduring the dire living conditions, these migrants may be excluded from the learning opportunities offered by the formal cities. As much as 20% of the population in developing countries lives in urban slums, with multiple countries having more of their population in urban slums than in rural areas.

We find COVID rises in states with higher re-employment rates due to UI cuts: case, hospitalization, and death rates more than double. Additional wages of $1.1B offset only one-eighth of UI losses, while UI savings are offset by $14.8B in hospitalization costs. Illness-related work time losses are valued at $1.5B. Beyond monetary and morbidity costs, UI cuts are estimated to cause an additional 27,000 deaths.

No previous research has addressed the possibility that, during a pandemic, unemployment insurance (UI) may have externalities for health.

The results suggest an important role for UI during infectious stages of pandemics that should be considered for future policy design.

Some jobs and employers pay more than others. We showed that people from advantaged family backgrounds tend to work for higher-paying employers.

This pattern accounts for a quarter of the overall intergenerational persistence in pay inequality.

We show how inequality in pay across firms translates into barriers to class mobility across generations.

We examined the impact of the federal Teacher Loan Forgiveness (TLF) program on teacher employment decisions and borrower behavior.

Our study aimed to understand if access to student debt relief influences teacher retention in high-need schools, and if providing targeted information affects application and receipt rates among eligible teachers.

This research is crucial as it reveals that while teachers generally value potential debt relief, program complexity and administrative hurdles significantly limit its uptake. Understanding these dynamics is essential for improving the efficacy and accessibility of student debt relief programs in education.

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 estimated the labor supply response to the 2021 Child Tax Credit using Current Population Survey microdata, comparing labor supply outcomes among households with varying income increases due to their income level and household size.

We conducted this study to understand the impact of the Child Tax Credit on labor supply, assessing if the additional income influenced families’ decisions to work more or less.

We aim to inform policymakers about the labor market effects of the Child Tax Credit, helping to evaluate the effectiveness of such financial support measures on family labor supply decisions.

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 developed and tested a model explaining how growing up in a poor neighborhood impedes cognitive development, focusing on early life exposure to neurotoxic air pollution. We analyzed data from a national sample of American infants, examining their exposure to over 50 pollutants known or suspected to harm the central nervous system, using causal inference methods and supervised machine learning.

We aimed to uncover the mechanisms by which neighborhood poverty affects cognitive development. By identifying specific factors, such as neurotoxic air pollution, that mediate these effects, we sought to provide insights that could inform interventions and policies aimed at mitigating the adverse impacts of growing up in impoverished neighborhoods.

Our study shows that living in a high-poverty neighborhood increases exposure to harmful air pollutants during infancy, reducing cognitive abilities at age 4 by about one-tenth of a standard deviation. Understanding these mechanisms is crucial for developing targeted interventions to improve cognitive outcomes for children in poor neighborhoods and addressing environmental health disparities.