Research Papers

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 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.

Some jobs and employers pay more than others. We showed that people from advantaged family backgrounds tend to work for higher-paying employers.

This pattern accounts for a quarter of the overall intergenerational persistence in pay inequality.

We show how inequality in pay across firms translates into barriers to class mobility across generations.

We combined a special class of deep neural networks with directed acyclic graphs to improve our ability to draw inferences about entire systems of causal relationships.

Social science theories often postulate causal relationships among a set of variables or events, but when it comes to empirically evaluating them, researchers typically resort to methods that impose unrealistic assumptions on the data, leading to incorrect inferences. Our approach can model these relationships without invoking such restrictive assumptions.

Our approach allows researchers to more accurately learn causal relationships from data. It therefore has a very wide scope for application, ranging from studies of inequality and social mobility to health, education, and psychology.

We investigated the effects of merit aid scholarship programs on the migratory patterns and educational outcomes of skilled labor, using Census data on 24 to 32-year-olds in the U.S. from 1990 to 2010.

We aimed to understand whether subsidizing higher education through merit aid can retain high-achieving students in-state and influence their long-term residency and educational attainment.

Our findings suggest that while merit aid slightly increases the likelihood of state natives remaining in-state and extends in-state enrollment, it does not significantly change the share of a cohort living in-state with a BA, questioning the overall effectiveness of such programs in altering skilled labor migration.

We examined the impact of pension plan type on employee mobility by leveraging a natural experiment where an employer transitioned from a defined benefit (DB) plan to a defined contribution (DC) plan, influenced by default rules.

We aimed to determine if the observed relationship between plan type and employee mobility is due to selection, where employees with higher mobility tendencies may self-select into DC plans.

Our findings reveal that employees with higher mobility tendencies indeed self-select into DC plans, providing important insights for employers and policymakers about the role of pension plan types in workforce dynamics and mobility.

We critically examined Thomas Piketty’s book “Capital in the Twenty-First Century” and its arguments about rising wealth inequality.

We wanted to provide an alternative perspective on wealth inequality and assess the validity of Piketty’s claims and policy prescriptions.

We offer a valuable contribution to the ongoing debate about wealth inequality and challenges some of Piketty’s central arguments.

We explored the presence of nonlinearities in economic growth and their implications for policy, considering how different growth regimes may require different policy responses.

We aimed to provide insights into the effectiveness of policy interventions by acknowledging and understanding the nonlinearities in economic growth processes.

Our recognition of nonlinearities in economic growth help policymakers tailor interventions to specific growth contexts, enhancing the precision and effectiveness of economic policies.

We explored the concept of poverty traps with a focus on the Appalachian region, examining factors contributing to persistent poverty.

We aimed to provide a nuanced understanding of poverty traps in a specific geographic context, offering insights that could inform targeted anti-poverty policies.

We contribute to the literature on poverty traps, providing region-specific insights that can guide policymakers in designing effective interventions to break the cycle of poverty.

We reexamined the relationship between religion and economic growth, considering the impact of specific religious beliefs and practices on growth rates.

We aimed to provide a nuanced analysis of the role of religion in economic growth by investigating the detailed aspects of religious beliefs and practices, which previous research may have overlooked.

We contribute to a deeper understanding of the complex relationship between religion and economic growth, emphasizing the importance of considering specific aspects of religious influence for more accurate policy implications.