Research Papers

I estimate income inequality in China from 2012–2018 using new methods that adjust for sparse top-income data in the China Household Finance Survey. I find Gini index estimates of 0.616 (2012), 0.604 (2014), 0.581 (2016), and 0.590 (2018). I also document rural-urban, regional, and provincial inequality patterns in Shanghai, Guangdong, Liaoning, Henan, and Gansu.

Academic and policy discussions are complicated by a lack of consensus on income inequality in China. The existing discrepancies result from the data and methodological issues. In response, I provide estimates of income inequality levels in China using a dataset designed to oversample rich households, and by developing new robust measurement procedures applicable to the datasets with extreme high-income values.

Most researchers share the view that existing high levels of income inequality pose significant warning signs for future economic progress and remain an important challenge for Chinese society and its policymakers. New empirical evidence on the levels and trends of income inequality in China can help inform the design of effective policy interventions.

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 developed a Structural Mixed Autoregression (SMAR) model to analyze how macroeconomic aggregates and income distribution interact dynamically. By incorporating functional variables, we measured the effects of monetary policy and income distribution shocks on macroeconomic outcomes. Our model identifies how shifts in income distribution impact aggregate output and how macroeconomic shocks influence inequality.

Existing studies often treat income distribution and macroeconomic aggregates separately. We sought to understand their joint dynamics to uncover whether redistributive policies could enhance growth while reducing inequality. Our approach addresses gaps in traditional models by capturing the full distributional effects rather than focusing solely on mean or median outcomes.

Redistributive policies can boost growth without harming income equality, challenging the growth-equity trade-off. Monetary policy shocks have mixed effects: contractionary policies may reduce inequality but often worsen it by lowering aggregate income. These findings guide policies balancing macroeconomic stability and income equality.

We analyzed how race was socially constructed during the U.S. Reconstruction Era (1865–1877) by linking detailed skin tone data from the Freedman’s Bank with racial classifications in the 1870 Census. We found that individuals with the same skin tone were racialized differently depending on their socioeconomic status (wealth, literacy, occupation), highlighting how race was shaped by social and economic factors.

We aimed to investigate how racial categories, often treated as fixed, were fluid and influenced by socioeconomic conditions during a pivotal time in U.S. history. By understanding how race was constructed in the aftermath of slavery, we sought to reveal the processes that laid the groundwork for segregation and enduring racial disparities.

Our findings show that wealthier and more literate individuals with the same skin tone were more likely to be classified as White or Mulatto. This underscores how race, as a social construct, has historically been intertwined with class. Recognizing this helps explain persistent racial inequalities and challenges assumptions in research that treats race as a static variable.

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 analyzed how political competition influences state capture when business leaders fund campaigns for economic rents. Based on three African countries, we modeled (1) a unified business sector lobbying for a fixed rent and (2) multiple firms competing for rents. Capture rises with more candidates before declining, but when firms share rents, outcomes depend on politicians’ credibility and business rivalry.

We aimed to understand the unintended consequences of political liberalization, particularly how increased political competition may facilitate or mitigate business-driven state capture. Given that standard democratic theories often assume liberalization reduces corruption, our study investigates conditions under which political reforms might actually increase elite influence over policymaking.

Political liberalization does not automatically reduce state capture; its effects depend on economic structures and political incentives. Delaying liberalization until economic transformation is underway may better limit capture. This challenges conventional governance assumptions and provides insights for policymakers managing transitions in developing economies.

We introduced the General Gender Gap (GGG), a new framework for measuring gender disparities across multiple outcome dimensions, including wages, health, and leisure. This approach improves upon traditional univariate measures by considering substitution and complementarity between different aspects of well-being, providing a more comprehensive view of gender inequality.

Existing gender gap measures primarily focus on singular variables like income, failing to capture the multidimensional nature of well-being. By incorporating multiple outcomes, we aimed to develop a more accurate and policy-relevant measure of gender disparities that reflects the real trade-offs individuals face in economic and social life.

Our findings show that gender disparities in the U.S. differ significantly when examined through the GGG framework, particularly in the trade-off between income and leisure. This new measure offers critical insights for policymakers, enabling more effective interventions to address gender inequality in well-being beyond traditional income-based assessments.

We developed a nonparametric model to study intergenerational income mobility, allowing for nonlinearities and interactions among factors like race, education, and parental age. Using PSID data, we analyzed how these dynamics affect children’s economic outcomes.

Standard models often overlook complex interactions in mobility. Our approach captures how race and parental education shape opportunities, offering a more nuanced understanding of barriers to economic mobility.

Our findings reveal how interactions between parental income, race, and education create mobility bottlenecks, providing insights for policies aimed at reducing economic inequality and promoting upward mobility.

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.