Research Papers

We evaluated inequality of opportunity across 31 EU countries by applying Bayesian model averaging and tree-based machine learning, focusing on how uncertainty in the choice of circumstance variables and functional forms affects estimates.

Standard “kitchen sink” models often overstate the role of circumstances in shaping income outcomes. By systematically addressing model uncertainty, we sought to produce more accurate and robust measures that reflect how social class, parental education, and household conditions shape opportunity.

Our findings show that ignoring model uncertainty can mislead policy by exaggerating the extent of inequality of opportunity. Recognizing the roles of class, education, and migrant background informs better strategies for reducing unjust disparities across societies.

We examine intergenerational educational and occupational mobility in China and Russia during their transitions from centrally planned to market economies. We focus on the extent new generations can escape institutional and structural constraints shaping their parents’ outcomes. We estimate overall and steady-state mobility and decompose it to reveal mechanisms driving intergenerational change.

Analysis of intergenerational mobility in China and Russia must account for the fact that observed mobility arises within an evolving dynamic system. We offer the first attempt to measure intergenerational mobility in these transition economies, isolating the transmission of educational and occupational status between parents and children from the direct effects of exogenous structural change.

We offer a novel perspective on intergenerational mobility in transition economies. Our measures address limits of traditional approaches that overstate mobility in contexts like China and Russia. By isolating transmission of social status from structural changes, this method allows more accurate assessments and enhances comparability of mobility across countries.

We examined differences in the elementary schools attended by students from high- versus- low-poverty neighborhoods and analyzed whether disparities in academic achievement are linked to these differences.

Persistent disparities in academic achievement between students from high- and low-poverty neighborhoods are widely attributed to differences in school quality. We wanted to empirically evaluate this common belief.

By 3rd grade, students from high-poverty neighborhoods show academic disadvantages compared to peers from low-poverty areas. Students from neighborhoods with poverty rates over 20% score about half a standard deviation lower on standardized reading and math tests. This gap is equal to ~5 months of lost learning. Understanding the etiology of these disparities is central to reducing them.

We analyzed textbooks from public, religious private, and home schools using computational and AI tools to measure how people, topics, and values are presented across school types.

With growing public funding for religious schools, we sought to understand what these schools teach and how curricula differ across educational settings.

Our findings reveal important differences—such as gender and racial representation—alongside shared omissions, offering insight into how education shapes values and collective memory.

We conducted the first systematic empirical analysis of income inequality across Chinese provinces using CHFS data and a semiparametric model to estimate Gini indices from 2012 to 2018, identifying human capital differences as key drivers.

We aimed to understand intraprovincial inequality, which accounts for 90% of national income variation and contributes to economic disparity, slower growth, and potential instability.

Our findings suggest that government policies promoting educational development in disadvantaged provinces and reducing barriers to labor mobility can facilitate labor market integration, narrow income gaps, and support sustainable and balanced regional economic development in China.

We applied recent advances in discrete dependence modeling to reassess trends in educational assortative matching in the U.S., using a discrete copula approach that isolates intrinsic dependence from marginal effects.

We sought to clarify long-standing ambiguities in measuring assortative matching by using dependence-preserving tools that overcome distortions from shifting marginals.

Our findings overturn key conclusions in prior research, showing a consistent rise in assortative matching through 2013 and underscoring the need for metrics that capture true dependence between partners’ educational levels.

We analyzed how within-district teacher assignments affect student learning and teacher welfare using Internal Transfer System (ITS) data from a large urban school district. By modeling student achievement, teacher preferences, and principal hiring, we found that strategic reassignments could raise test scores by up to 7% of a standard deviation while maintaining teacher welfare.

Current teacher assignment mechanisms may not maximize student learning, as teachers and principals make decisions based on factors beyond test score improvement. We aimed to assess whether assigning teachers based on comparative advantage—their ability to improve learning in specific classrooms—could lead to better student outcomes without harming teacher welfare.

Our results indicate that maximizing test scores requires placing higher-effectiveness teachers in larger classrooms, rather than focusing on teacher-student match quality. However, prioritizing achievement gains could widen race and income-based disparities, posing an equity-efficiency tradeoff for policymakers. Understanding these dynamics is crucial for designing fair and effective teacher assignment policies.

We evaluated the impact of implementing Restorative Practices (RP) in Chicago Public Schools. Using a difference-in-differences design, we analyzed how RP affected student behavior, arrests, suspensions, attendance, school climate, and academic performance. The study focused on 73 high schools and assessed how these interventions influenced both in-school and out-of-school student outcomes.

Traditional punitive discipline methods, like suspensions, can harm long-term student outcomes. We aimed to assess whether restorative approaches could improve student behavior, reduce disciplinary incidents, and enhance school climate without sacrificing academic achievement. Understanding these effects is crucial for shaping equitable school discipline policies.

RP led to an 18% reduction in out-of-school suspensions and a 19% decline in juvenile arrests, particularly benefiting Black students. Student perceptions of school climate improved, with no significant harm to academic performance. These findings suggest RP can promote safer, more supportive learning environments while reducing reliance on exclusionary discipline.

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.