Research Papers

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 conducted a multidisciplinary analysis of Guilluy’s concept of “peripheral France” and applied polarization indices to income data at the postal code level across France’s 36,000 municipalities.

We aimed to empirically capture the territorial dimension of inequality and social unrest, particularly as it relates to the emergence of the yellow jackets movement.

Our findings highlight how income polarization aligns with the geography of poverty, offering a deeper understanding of the socio-economic roots of discontent in peripheral France.

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.

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 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.