This project partners with two guaranteed income experiments in Georgia to understand how guaranteed income affects asset development and credit-building. One experiment, in and around Atlanta, enrolled 654 low-income Black women in the summer of 2022. This experiment uses two different models of benefit delivery—equal-sized regular payments versus a one-time lump-sum payment followed by smaller regular payments. There is also a control group formed by those not randomly selected for the program. The other project takes 200 homeless clients from Project Community Connections Inc. and randomly assigns half of them to receive case management and housing assistance, while the other half receives those services plus $400 per month. For both experiments, the research team will link to credit report data and create a control group from the credit data to provide insight on racially patterned economic disparities and potential policy interventions. This paper will bridge guaranteed income—an income-maintenance intervention—with the study of wealth development and access to credit.

This project will digitize previously thought to be destroyed Federal Housing Administration maps created in the 1930s and 1940s that outline redlining in FHA loans. Furthermore, the authors plan to digitize 10,000 Paid Loan Case files, which describe individual FHA loans. They will then look at how the information in these maps helped decide which neighborhoods were redlined and how important race was in those decisions. Lastly, they will look at who received FHA loans and how these loans affected homeownership.

This project asks, “Can access grant inclusion?” The author will explore this question in the context of open-access coding platforms that allow people to be self-taught coders. The technology sector is notoriously exclusionary, and computer science degrees are difficult to obtain because of the high cost of college, among other factors. Could open-access training platforms create a path for individuals from underrepresented communities of color to take advantage of the explosive job growth in the technology sector? This project will address these questions by combining survey data with in-depth interviews and observations of those who have participated in a specific learn-to-code platform. Preliminary findings indicate that the open-access platform does not aid those who have been excluded from high-quality jobs in the tech industry. The project seeks to understand why open-access training platforms are not leading to success. What barriers do female applicants or applicants of color face in job-seeking? This research will be especially informative for efforts to address job quality and wage stagnation by advocating for worker training. If completing worker training does not lead to the desired outcomes, then policymakers need to understand those programs as only part of the solution and identify necessary complements.

This project ties together neighborhood segregation, migration patterns, and local spending and policies to explore how “structural racism is built into physical infrastructure in cities.” The author will use granular satellite images to measure temperature in Black and White urban neighborhoods. The project will look at the degree to which neighborhoods are segregated and the level of surface imperviousness in each of those neighborhoods. The author also will map Great Migration patterns, White flight, and local government spending patterns. The research uses data from the University of Virginia’s Environmental Inequality Lab and builds on research on Northern cities’ responses to the Great Migration. Preliminary results show that historical Black migrant inflows increased the surface temperature of neighborhoods where Black households live, relative to the neighborhoods where White households live, as well as the Black-White gap in neighborhood imperviousness.

Individuals and their families use a variety of caregiving arrangements, but there is little research on who is likely to use which kind of arrangement. Furthermore, existing scholarship mainly focuses on the characteristics of the care recipient, without considering how family characteristics influence choices for caregiving arrangements. This mixed-methods study will explore how family characteristics and social infrastructure programs shape caregiving arrangements for older adults in the United States. The first two papers quantify and study the relationship between family characteristics and the size and scope of several social infrastructure and caregiving arrangements. The third paper will use semi-structured interviews with families who have eldercare responsibilities, focusing on Black females in the Chicago area, to shed light on their decision-making processes. Through these interviews, the author will explore how access to public programs affects their decisions, how they were selected as caregivers, what their preferences and future expectations are, and how employment plays into their decisions. The sample will include recipients (either care recipients or caretakers) of Social Security Insurance, Social Security Disability Insurance, Old Age and Survivors Insurance, Paid Family Leave, and Home & Community Based Services waivers from Medicaid.

This proposal will utilize a large and comprehensive dataset to evaluate whether historically Black colleges and universities, or HBCUs, can narrow or close racial gaps on numerous measures of economic well-being, not just typical measures such as income. The dataset links College Board SAT data with credit bureau data and National Student Clearinghouse data. It includes students who took the SATs between 2004 and 2010, tracking them from high school through college, and will allow the authors to look at financial outcomes at age 30. Using these data, the authors will compare the longer-term outcomes for Black students who attend these schools versus similar students who applied to but did not attend one. The authors will explore several outcomes, including those where racial disparities exist, such as college-related debt, other forms of debt, and whether the individual has a mortgage (a proxy for homeownership). The credit data also give a more complete picture of income than earnings since it covers all types of income. Existing research shows how important social supports and social capital are to economic mobility. This project will shed light on the distinctive social and psychological value-added features of historically Black colleges and universities.