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 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 conducted an analysis of language acquisition behavior among minority agents in an economy with a dominant majority language and multiple minority groups. Unlike previous studies, we highlight role of conformity concerns as a key factor in shaping the language acquisition decisions of minority agents and assesses individuals’ incentives to learn another language through a cost-benefit analysis.

Immigrants in economies with a dominant native language show significant variation in majority language acquisition. Data from Brock et al. (2024) show many immigrants to English-speaking countries attain only intermediate proficiency. We depart from the binary model and allow choices among full, partial, or no acquisition, each with different communicative benefits and costs.

Our findings offer insights into effective language policies in economics where multiple levels of language acquisition are present. This is significant for policy considerations, as small adjustments in language acquisition costs may unpredictably influence language acquisition patterns across minority groups.

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.