Research Papers

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 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 estimated the impact of entrenched political dynasties on local development in Punjab, Pakistan, using a new 100-year database of political genealogies and a regression discontinuity design.

We aimed to resolve the uncertain relationship between dynastic politics and development in poor countries by providing systematic empirical evidence.

Our findings show that long-standing political families harm development outcomes and persist due to segmented political markets, raising concerns for democratic accountability under clientelism.

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.