Abstract Financial aid from the Susan Thompson Buffett Foundation (STBF) provides comprehensive support to a student population similar to that served by a host of state aid programs. In conjunction with STBF, we randomly assigned aid awards to thousands of Nebraska high school graduates from low-income, minority, and first-generation college households. Randomly assigned STBF awards boost bachelor’s (BA) degree completion for students targeting four-year schools by about 8 points. Degree gains are concentrated among four-year college applicants who would otherwise have been unlikely to pursue a four-year program. Degree effects are mediated by award-induced increases in credits earned toward a BA in the first year of college. The extent of initial four-year college engagement explains differences in impact by target campus and across covariate subgroups. The projected lifetime earnings effect of awards exceeds marginal educational spending for all of the subgroups examined in the study. Projected earnings gains exceed funder costs for urban students and for students with relatively weak academic preparation.

We study the impact of artificial intelligence (AI) on labor markets using establishment-level data on the near universe of online vacancies in the United States from 2010 onward. There is rapid growth in AI-related vacancies over 2010–18 that is driven by establishments whose workers engage in tasks compatible with AI’s current capabilities. As these AI-exposed establishments adopt AI, they simultaneously reduce hiring in non-AI positions and change the skill requirements of remaining postings. While visible at the establishment level, the aggregate impacts of AI-labor substitution on employment and wage growth in more exposed occupations and industries is currently too small to be detectable.