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 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 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 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 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.