Research Papers

We developed new measures of intergenerational persistence using “memory curves” to track how socioeconomic origins influence mobility across generations. Using U.S. data, we analyzed how initial conditions shape transitions between occupational classes and aggregated insights to describe mobility trends in the population.

Understanding intergenerational mobility requires tools that capture the long-term influence of socioeconomic origins. By modeling these dynamics as “memory,” we aim to better explain how class persistence varies and uncover patterns missed by traditional metrics.

Our findings show that class origins often fade within three generations, but mobility varies significantly by occupational background. These insights help inform policies aimed at addressing inequality and promoting equal opportunities across diverse socioeconomic contexts.

We analyzed a generalized mobility model using administrative data from Denmark to examine the roles of family selection and locational characteristics in generating spatial heterogeneity in intergenerational mobility across nearly 300 larger and 2,000 smaller neighborhoods.

We aimed to understand why intergenerational mobility varies across neighborhoods, an essential issue for shaping economic and social policies.

Understanding the mechanisms behind spatial differences in mobility helps policymakers target interventions to improve equality of opportunity across different neighborhoods.

The article reviews two significant 2022 works on economic growth, How the World Became Rich by Mark Koyama and Jared Rubin, and Slouching Towards Utopia by J. Bradford DeLong, analyzing their approaches to understanding modern economic development.

We aimed to assess how these books contribute to the understanding of economic growth by integrating qualitative and quantitative evidence with historical reasoning, offering a deeper insight into growth mechanisms.

Understanding economic growth through these complementary works helps clarify the historical contingencies that shaped modern growth, offering valuable perspectives beyond formal models.

We study how socio-economic status (SES) gradients in learning outcomes have evolved over 20+ years in Europe and investigate possible determinants. Using PISA data, which assesses students’ proficiency in math, reading, and science, we measure SES gradients in test scores, allowing cross-country and temporal comparisons.

Family background is a strong determinant of educational achievement and labor market success. Large SES gradients in test scores emerge early, persist, and account for much of the intergenerational transmission of earnings. Understanding the roots of this persistence in socio-economic status can help us design policies to break this cycle.

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.