Research Papers

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 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 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.

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 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 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 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.