The pandemic has only underscored how caregiving systems lack resilience, affordability, or even just staff, and how that lack holds people back from achieving their dreams. The result is renewed policymaker interest in the U.S. care economy, as evidenced by President Biden’s April 2023 executive order to improve access to care and support care workers and New Mexico’s landmark decision to offer free childcare to most families in 2022. Designing and implementing cost-effective, evidence-based policies crucially depends upon rigorous empirical analysis coupled with theoretical models of care. Yet care relationships are well-represented by neither canonical economic models nor traditional economic indicators like GDP. A new research program on the “Social Science of Caregiving” at Stanford’s Center for Advanced Study in the Behavioral Sciences (CASBS) aims to help rethink the philosophical, biological, political, and economic foundations of care, and consider how to translate those insights for policymaking. The project’s two-pronged approach involves developing a research program on the cognitive economics of care, and coordinating and disseminating the findings of a broad, interdisciplinary research effort. Alison Gopnik, Professor of Psychology at UC Berkeley, and Margaret Levi, Professor of Political Science at Stanford, are leading the project. So far, they have organized multiple workshops convening economists, psychologists, political scientists, neuroscientists, computer scientists, and policy experts, among others, to outline a preliminary research agenda and produce a general scientific understanding of care. Topics include studying how humans conceptualize care; the ways in which new AI technologies affect our understanding of care; and the economic consequences of cultural assumptions about gender roles in care. Sloan funding will support research led by Gopnik on the cognitive economics of care; virtual and in-person research meetings; the recruitment and convening of an interdisciplinary Advisory Board to ensure that the program’s basis research meets the needs of economists and applied social scientists; and the dissemination of the project’s research outputs to broad audiences through a variety of academic and non-academic channels.

Cognitive Economics studies how people make practical decisions in practice. Like Behavioral Economics, it acknowledges that choices do not always follow the rational patterns posited by traditional theorists. But whereas Behavioral Economists are often content to describe, classify, and try to 'nudge' away these anomalies, Cognitive Economists seek explanations for how, why, and when making mistakes might actually make sense.   PI Andrew Caplin has been pursuing this goal through the 'Sloan-NOMIS Program on the Attentional and Perceptual Foundations of Economic Behavior.' Under its auspices, an active community conducting research on decision-making has developed new concepts and approaches that now need testing in the real world. Caplin therefore plans to work with distinguished researchers in psychology, labor economics, and artificial intelligence on experiments that specifically address important workforce questions, too. Cognitive Economics starts with the notion that, in real life, there are costs that constrain decision-making other than monetary ones. Gathering, processing, storing, and analyzing information about the choices available are tasks that take time, effort, and attention. Though harder to measure, cognitive costs can be estimated and included in economic models to show why, how, and when it may make sense to make mistakes. This requires different kinds of analysis performed on different kinds of data. Specifically needed are datasets about what people would have done under other circumstances since the notion of a decision-making error can hardly even be defined otherwise. Having obtained such information about workforce decisions, and having obtained considerable conceptual progress with previous Sloan support, this project will test how well Cognitive Economics can actually answer practical research questions of significant current concern, including how cognitive costs affect individuals’ managerial skills, career trajectories, and ability to work with artificial intelligence. Besides producing novel research, Caplin’s team will organize workshops and conferences, develop new resources that make Cognitive Economics more accessible to students and researchers, and convene a broader Steering Committee to provide coordination among the many research groups and disciplines now affiliated with the field.

When Sloan helped launch an NBER Working Groups on Digitization in 2010 and another on Artificial Intelligence in 2017, there were only scattered researchers studying the economics of these topics. Since then, three developments are especially notable: Both groups have produced blockbuster research. Topics range from the gig economy to the surveillance economy, and from employee selection to employee displacement. While some has been done by distinguished senior faculty, also contributing fresh perspectives are the hundreds of junior faculty who participated in mentoring and training programs run by these two Working Groups for graduate students from departments that did not yet have inhouse expertise. The pace of advances in digital technology and artificial intelligence has only accelerated. So concepts, findings, and models that seemed to explain a great deal just a few years ago now have much more explaining to do. The need for creative and careful research in these areas has, despite significant progress, become even more urgent. Although they started as distinguishable topics, research on Digitization and on AI are converging. The people, problems, and principles associated with one subfield are increasingly the same as those associated with the other. Given that we cannot continue funding both communities indefinitely anyway, a plan was hatched to merge the two working groups going forward. Catherine Tucker of MIT, a leader of the Digitization group, and Avi Goldfarb from the University of Toronto, a leader of the AI Group, will form a unified program dedicated to 'Digital Economics and the Use of AI.' Over the next three years, the combined group will concentrate on (i) the impact of digital technologies on the nature of work, (ii) political economy and digital technology (including surveillance, media, and political protest), and (iii) the relationship between competition and innovation for digital technology. That work will be facilitated by activities that have proven successful to date, including workshops for PhD students, spring and fall meetings in San Francisco and Toronto, respectively, as well as a very popular session at the NBER Summer Institute. The Sloan Economics Program is always looking for ways to help grantees make the whole more than the sum of the parts. In this case, merging two successful Working Groups should result in even greater research on society’s most pressing questions about the economics of digital technologies.