David Autor of Massachusetts Institute of Technology and NBER reviews “The Means of Prediction: How AI Really Works (and Who Benefits)” by Maximilian Kasy. The Econlit abstract of this book begins: “Presents a framework for understanding how artificial intelligence (AI) will proceed in a society that is shaped by power and inequality, focusing on the limits of AI and how it can be made to work for all people.”

Over the past 3 decades, the U.S. Temporary Help Services (THS) industry grew five times more rapidly than overall employment. Contemporaneously, courts in 46 states adopted exceptions to the common law doctrine of employment at will that limited employers' discretion to terminate workers and opened them to litigation. This article assesses the contribution of "unjust dismissal" doctrine to THS employment specifically, and outsourcing more generally, finding that it is substantialexplaining 20% of the growth of THS between 1973 and 1995 and contributing 500,000 additional outsourced workers in 2000. States with smaller declines in unionization also saw substantially more THS growth.

Goldin and Katz's The Race between Education and Technology is a monumental achievement that supplies a unified framework for interpreting how the demand and supply of human capital have shaped the distribution of earnings in the U.S. labor market over the twentieth century. This essay reviews the theoretical and conceptual underpinnings of this work and documents the success of Goldin and Katz's framework in accounting for numerous broad labor market trends. The essay also considers areas where the framework falls short in explaining several key labor market puzzles of recent decades and argues that these shortcomings can potentially be overcome by relaxing the implicit equivalence drawn between workers' skills and their job tasks in the conceptual framework on which Goldin and Katz build. The essay argues that allowing for a richer set of interactions between skills and technologies in accomplishing job tasks both augments and refines the predictions of Goldin and Katz's approach and suggests an even more important role for human capital in economic growth than indicated by their analysis. (JEL I20, J24, J31, O30)

Because minorities typically fare poorly on standardized tests, job testing is thought to pose an equality-efficiency trade-off: testing improves selection but reduces minority hiring. We develop a conceptual framework to assess when this trade-off is likely to apply and evaluate the evidence for such a trade-off using hiring and productivity data from a national retail firm whose 1,363 stores switched from informal to test-based worker screening over the course of one year. We document that testing yielded more productive hires at this firm—raising mean and median tenure by 10% or more. Consistent with prior research, minorities performed worse on the test. Yet, testing had no measurable impact on minority hiring, and productivity gains were uniformly large among minority and nonminority hires. These results suggest that job testing raised the precision of screening without introducing additional negative information about minority applicants, most plausibly because both the job test and the informal screen that preceded it were unbiased.

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.