This study will explore access, use, and outcomes associated with paid leave during the pandemic in New York City utilizing The New York City Longitudinal Study of Health and Wellbeing, also known as the Poverty Tracker. This survey follows representative samples of New York City residents, interviewing them every three months for up to four years and collecting a wealth of data on poverty, hardship (e.g. food insecurity), health and wellbeing, and specialized topics such as assets and debts. The research team will administer a post-COVID-19 survey with members of their second panel, for whom they have four years of pre-COVID-19 data, including information on employment and employer-provided paid sick leave. Interviewing this panel again will allow the researchers to gather important post-COVID-19 data on (1) use of employer-provided paid sick leave, (2) use of New York State paid family and medical leave and temporary disability insurance, and (3) use of the new federal emergency paid sick leave and paid family leave; as well as 4) post-COVID-19 data on poverty, hardship, and health and wellbeing.

This study will examine whether state-mandated paid sick leave and state-mandated paid family and medical leave has helped control the early spread of COVID-19 and ameliorated the economic distress caused by the pandemic. In particular, the research team will explore whether people in states with guaranteed paid sick leave fared better in the pandemic and were better able to adopt social distancing measures compared to those in states without such a guarantee. State administrative data show that, early in the pandemic, there was a surge of initial claims in some states with their own paid leave systems—well before the Families First Coronavirus Response Act was signed into law. The research team will explore whether this surge in leave-taking is reflected in measures of social distancing or staying at home as captured by cell-phone location data, and whether that is reflected in the COVID-19 incidence data. Finally, the authors are also interested in whether there are differences across states with and without paid leave systems in reported measures of illness, leave-taking, and economic and psychological distress associated with the pandemic.

Forty-two percent of Americans report that they do not have savings that could be used to cover unexpected expenses, a staggeringly high number. And there are stark racial differences, with 38 percent of White households and 55 percent of Black households saying they don’t have money to cover an emergency expense—one manifestation of the Black-White wealth divide. Yet there is surprisingly little research on how typical month-to-month fluctuations in income affect consumption and even less evidence on how this consumption smoothing varies with wealth. Given how central consumption dynamics are for macroeconomics, it’s important to understand the sensitivity of consumption to income and how that might vary by race and wealth. This project uses exciting new data to explore how income shocks may be passed through to consumption. By linking deidentified administrative bank data with self-reported race information from voter registration records, the authors will be able to identify the response of consumption by race with a large enough dataset (the analysis sample consists of 1.8 million matched bank-voter records) to identify racial differences credibly. Understanding how well households can smooth consumption, and how and why some groups—such as Black and Hispanic households who have lower-than-average wealth—may face greater challenges in doing so, is central for developing policy to address economic inequality and ensure vulnerable households achieve economic security.

This research project will examine the short- and long-run impacts of being suddenly removed from critical government programs, including the Supplemental Nutrition Assistance Program, Medicaid, and the Temporary Assistance for Needy Families program. The author will utilize the case of Indiana, which, in 2007, attempted to automate its welfare systems, resulting in a number of individuals being removed from essential welfare programs. The author will use linked administrative and survey data to first analyze the effects of the policy change on enrollment and demographics in the programs and then identify the short- and long-term impact of being removed from welfare on earnings, occupation, financial solvency, and health outcomes.

This project will analyze how the labor market absorbs an increase in the minimum wage. Utilizing the case study of Costa Rica’s highly binding and relatively more comprehensive minimum wage policy that includes multiple wage floors based on workers’ skill levels, the author will use employer-employee microdata and administrative data to explain how minimum wages shape the earnings distribution and the labor market equilibrium.

Environmental inequity is intertwined with income inequality in a variety of ways. Demand for housing, for example, is higher in cleaner areas than in polluted ones, and, at the same time, evidence is accumulating that the communities in which children grow up have long-lasting impacts on their economic and other social outcomes. Other research finds that pollution exposure in utero and in early childhood can have lifelong effects on economic outcomes, suggesting pollution may be one important characteristic of the communities in which children grow up. This project engages with these issues by investigating the relationships among race/ethnicity, income, pollution, and human capital in Pittsburgh from 1910 to 2010. The two main areas of research are sorting by race that leads to inequality in pollution exposure, and the effects of childhood exposure to pollution on adult income. Although limited to Pittsburgh, it is a strategic site. Once considered "Hell with a lid off" because of the intense pollution arising from the furnaces of the steel industry, exposure to pollution used to be extremely high in the early 20th century but has since declined dramatically, allowing for the comparison over time. To do this, the authors will take advantage of never-before-used historical data and link it to demographic characteristics of individuals with known residential locations to pollution exposure, jobs, and future outcomes. An anonymized version of these data will be made publicly available, creating a valuable resource for future research.