This project seeks to explain how climate adaptation projects mitigate the unequal effects of climate change. They plan to model efficient investment in climate adaptation from a global perspective to inform how the United States should fund global activities to mitigate climate change. They will develop and calibrate a multiregion, multisector spatial equilibrium model that incorporates heterogenous exposure to climate change and adaptive capacity to deal with climate change. They will use the model to explore the equity-efficiency frontier of adaptation funding. Importantly, the model will account for global production networks, which play a key role in the propagation of shocks. They seek to understand how these shocks propagate across countries and affect workers with different characteristics, such as education level or race.

This project will answer how campaigns for unionization run outside of the National Labor Relations Board differ from NLRB campaigns in terms of firm, industry, and bargaining unit characteristics, as well as employer and union tactics. There is existing evidence that NLRB-certified elections are fraught with employer labor law violations and other barriers. Card check campaigns, on the other hand, offer a potentially compelling alternative way of organizing. This study will use an in-depth national survey of lead organizers in private-sector, non-Board organizing campaigns to examine the characteristics and effectiveness of the campaigns. The author will compile a database of non-Board organizing campaigns from 2016–2022 to show the extent of non-Board organizing, whether these campaigns have declined in number and size in accordance with NLRB campaigns, and which unions and industries have the most non-Board organizing activity. The author will then conduct a survey of 500 non-Board campaign organizers on employer and union tactics, unit demographics, and the election and first contract process.

This project will challenge an accepted finding in economics on the lifecycle pattern of inequality—namely, that inequality increases with age within a birth cohort. The research team builds on existing research from the Global Repository of Income Dynamics database to develop a model to explain an observation in the data that inequality is generated more from differences in childhood experiences such as parental income or school quality than from labor market experiences.

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.