Research Papers

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 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 introduced the General Gender Gap (GGG), a new framework for measuring gender disparities across multiple outcome dimensions, including wages, health, and leisure. This approach improves upon traditional univariate measures by considering substitution and complementarity between different aspects of well-being, providing a more comprehensive view of gender inequality.

Existing gender gap measures primarily focus on singular variables like income, failing to capture the multidimensional nature of well-being. By incorporating multiple outcomes, we aimed to develop a more accurate and policy-relevant measure of gender disparities that reflects the real trade-offs individuals face in economic and social life.

Our findings show that gender disparities in the U.S. differ significantly when examined through the GGG framework, particularly in the trade-off between income and leisure. This new measure offers critical insights for policymakers, enabling more effective interventions to address gender inequality in well-being beyond traditional income-based assessments.

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 study how socio-economic status (SES) gradients in learning outcomes have evolved over 20+ years in Europe and investigate possible determinants. Using PISA data, which assesses students’ proficiency in math, reading, and science, we measure SES gradients in test scores, allowing cross-country and temporal comparisons.

Family background is a strong determinant of educational achievement and labor market success. Large SES gradients in test scores emerge early, persist, and account for much of the intergenerational transmission of earnings. Understanding the roots of this persistence in socio-economic status can help us design policies to break this cycle.

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 estimated the labor supply response to the 2021 Child Tax Credit using Current Population Survey microdata, comparing labor supply outcomes among households with varying income increases due to their income level and household size.

We conducted this study to understand the impact of the Child Tax Credit on labor supply, assessing if the additional income influenced families’ decisions to work more or less.

We aim to inform policymakers about the labor market effects of the Child Tax Credit, helping to evaluate the effectiveness of such financial support measures on family labor supply decisions.

We developed and tested a model explaining how growing up in a poor neighborhood impedes cognitive development, focusing on early life exposure to neurotoxic air pollution. We analyzed data from a national sample of American infants, examining their exposure to over 50 pollutants known or suspected to harm the central nervous system, using causal inference methods and supervised machine learning.

We aimed to uncover the mechanisms by which neighborhood poverty affects cognitive development. By identifying specific factors, such as neurotoxic air pollution, that mediate these effects, we sought to provide insights that could inform interventions and policies aimed at mitigating the adverse impacts of growing up in impoverished neighborhoods.

Our study shows that living in a high-poverty neighborhood increases exposure to harmful air pollutants during infancy, reducing cognitive abilities at age 4 by about one-tenth of a standard deviation. Understanding these mechanisms is crucial for developing targeted interventions to improve cognitive outcomes for children in poor neighborhoods and addressing environmental health disparities.

We investigated the impact of the early withdrawal penalty on Individual Retirement Accounts (IRAs) by examining withdrawal behavior shortly before and after the penalty is lifted.

We aimed to understand how the expiration of the early withdrawal penalty affects withdrawal behavior, particularly the timing and magnitude of withdrawals, and to explore the influence of liquidity constraints.

Our goal was to inform policymakers about the effects of the early withdrawal penalty on retirement savings behavior, which can guide decisions on adjusting or temporarily removing such penalties.

We measured the impact of the Alaska Permanent Fund Dividend on price levels using a synthetic control method to compare Alaska with similar states before the dividend’s introduction.

We aimed to determine whether unconditional cash transfers, like the Alaska Permanent Fund Dividend, stimulate consumer spending and increase price levels.

Our findings help us understand the inflationary effects of unconditional cash transfers, informing policymakers about potential economic impacts and guiding future decisions on similar programs.