Research Papers

We study residential segregation and public service access across 1.5 million neighborhoods in India. Linking census data to schools, clinics, water, and electricity, we measure how separated Muslims and Scheduled Castes are from other Indians and whether their neighborhoods have equal access. We systematically decompose disparities across various levels of government to identify where inequalities emerge.

Prior research examines inequality in large administrative units, but lack data on neighborhood-level segregation and service access. Aggregate analyses can’t reveal if people actually access services where they live. Qualitative studies suggested barriers, but systematic evidence remained unavailable. We build the first dataset linking neighborhood demographics to public facilities at scale in a developing country.

Affirmative action appears effective at district level—areas with more Scheduled Castes receive more facilities. But local allocation systematically diverts resources from marginalized neighborhoods, erasing higher-level progressivity. This creates a blind spot: policymakers using aggregate data see success while marginalized groups lack services where they live. As cities grow rapidly, these patterns may persist.

We study intergenerational class mobility using synthetic dynasties modeled using Markov chains. This yields measures of movement (class change across generations) and memory (how long class origins persist). Applied to U.S. cohorts born 1945-1990, we find overall stability masking offsetting trends: upper/lower classes show declining movement and rising memory; middle classes show the reverse.

Existing research relies on origin–destination associations from log-linear models, capturing only one mobility dimension with restrictive assumptions. Prior evidence is mixed, limited by small samples or volatile income measures. We develop a dynamic, nonparametric framework using Markov chains that separately identifies movement and memory across generations.

When inequality grows but mobility stays constant, stable mobility may reduce public concern about inequality and weaken redistribution support. Yet it amplifies mobility’s significance: moving up feels more rewarding, moving down carries steeper penalties. These divergent high-stakes experiences can deepen social and political divisions.

We challenge Magness and Makovi’s (2023) claim that Marx’s prominence depends on the Bolshevik Revolution rather than his ideas. Their synthetic control approach lacks a credible counterfactual and cannot separate competing explanations for citation growth, leaving the role of merit versus history unresolved.

Strong claims about intellectual prominence need rigorous empirical grounding. Magness and Makovi’s analysis faces standard identification issues—weak counterfactuals, limited evidence, and unclear mapping from data to theory—producing unsubstantiated conclusions about whether Marx’s influence stems from substance or circumstance.

Understanding how ideas gain prominence requires methods that address identification problems. A sound framework for citation analysis can separate historical accident from intellectual merit, underscoring the need for theoretical models that yield credible insights into which thinkers truly shaped scholarship.

Our goal is to call attention to how complexity science can add to our understanding of inequality and aid in the development of policies and interventions that might reduce it. To explore what complexity has added or can add to our understanding of inequality, we argue that the economics profession has become more expansive in the types of assumptions it makes about how people act and how the world works.

Compounded disadvantages create system of system effects where each disadvantage reinforces others, making inequality a pressing issue that nations have struggled to reduce. Given that some economic phenomena require moving in the direction of complexity economics, the failure to think systemically may produce ineffective policies and lead to misleading inferences.

Viewing inequality as resulting from interacting complex systems offers insights into its causes and solutions. A complexity lens can identify system features for lasting interventions. Adopting a complex adaptive systems perspective highlights the need to coordinate interventions across interdependent systems, deepening understanding and creating more effective policies

I outline ways to think about meritocracy, which I interpret as a set of claims about who deserves positions in schools, firms, and government. My goal is to show challenges in evaluating meritocracy. I explore how different concepts of merit can lead to different policies and consider equality/efficiency tradeoffs that arise under meritocratic rules.

Beyond its current salience, meritocracy has long been controversial among scholars. The notion of merit is contestable in ways that lead to different meritocratic rules, so an abstract belief in meritocracy may offer little concrete guidance. Meritocracy can also lead to enhanced inequality, highlighting how its pursuit can result in dysfunction or conflict with other ideals.

Claims about meritocracy are integral to policy debates and often involve claims of efficiency. Historically, meritocratic employment rules have helped individuals maximize potential, making societies more productive. However, recent critiques argue meritocracy causes social harm. Well-intended policies may falter if their implementation relies on flawed empirical or normative stances.

We built a trajectories-based model of intergenerational mobility using PSID data, analyzing how parental income and family structure across childhood shape adult income. Using an ordered multinomial probability model with functional covariates, we captured the timing of effects while flexibly accounting for high-dimensional factors.

Traditional measures of intergenerational mobility often rely on static or average indicators, obscuring when during childhood parental income and family structure matter most. Our approach allows us to test the timing of these effects and explore whether family resources and stability operate as substitutes or complements in shaping long-term outcomes.

Our findings show that both parental income and two-parent family structure have the strongest influence during middle childhood and adolescence, and that they reinforce each other as complements. These results highlight the need for coordinated policy interventions that address both financial resources and family stability at critical developmental stages, with the goal of maximizing upward mobility.

We study how delaying pregnancy affects women’s labor market outcomes by leveraging quasi-random variation in unplanned pregnancies among women using long-acting reversible contraceptives to delay childbirth. Using linked Swedish health and labor market data, we compare career trajectories of women who experience an unplanned pregnancy to those who delay as intended.

The effects of children on women’s careers are central to understanding gender inequality, yet observed earnings losses may reflect only part of the total cost because women avoid especially costly births. Unplanned pregnancies offer a unique lens into career impacts typically hidden by planning, revealing how timing and pregnancy intentions shape economic consequences.

Our findings show that unplanned births have substantial and persistent effects on earnings, skills, and career progression, with larger impacts for younger women and those enrolled in education. These results highlight the critical role of pregnancy timing in shaping long-term economic outcomes and underscore the importance of policies that affect pregnancy intentions and access to effective contraception.

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.