About

David Deming is the Isabelle and Scott Black Professor of Public Policy at Harvard Kennedy School, the William Henry Bloomberg Professor of Economics at Harvard Faculty of Arts and Sciences, and holds a teaching appointment at Harvard Graduate School of Education. He serves as the Danoff Dean of Harvard College and has served as the Academic Dean of HKS from 2021 to 2024. His research focuses on higher education, skills, technology, artificial intelligence, and the future of the labor market. He co-directs the Project on Workforce, a cross-Harvard initiative aimed at building better pathways to economic mobility through the school-to-work transition, and the Skills Lab, which creates performance-based measures of soft skills such as teamwork and decision-making. Deming is a Principal Investigator at the CLIMB Initiative, studying and improving the role of higher education in social mobility. He has received awards including the Sherwin Rosen Prize for outstanding contributions to Labor Economics in 2022 and the David N. Kershaw Prize for distinguished contributions to public policy and management under age 40 in 2018. He writes columns for The Atlantic, previously for the New York Times Economic View, and maintains a Substack newsletter called Forked Lightning.

Research topics

• Psychology
• Labour economics
• Economics
• Artificial Intelligence
• Political Science
• Computer Science
• Management
• Social psychology
• Knowledge management
• Marketing
• Accounting
• Demographic economics
• Applied psychology
• Keynesian economics
• Business
• Developmental psychology

Selected publications

• How Do You Identify a Good Manager?

  The Quarterly Journal of Economics · 2026-01-20 · 1 citations

  articleOpen accessSenior author

  Abstract We introduce and validate a novel approach to identifying good managers. In a preregistered lab experiment, we causally identify managerial contributions by randomly assigning managers to teams and controlling for individual skill. We find that manager contributions are crucial for team success, and that people who self-select into management roles perform worse than randomly assigned managers. Managerial performance is strongly predicted by economic decision-making skill but not by demographic characteristics. Two validation studies support our experimental results. Participants who succeed in the lab receive more real-world promotions and, in a separate study of retail store managers, skill measures strongly predict store sales. A one standard deviation increase in manager quality increases annual per store sales by US$4.1 million (25% increase). Selecting managers on skills rather than demographic characteristics or the desire to lead could substantially improve organizational performance.

  PublisherDOI

• Measuring Human Leadership Skills with AI Agents

  National Bureau of Economic Research · 2025-04-01 · 5 citations

  reportOpen accessSenior author

  We show that leadership skill with artificially intelligent (AI) agents predicts leadership skill with human groups.In a large pre-registered lab experiment, human leaders worked with AI agents to solve problems.Their performance on this "AI leadership test" was strongly correlated (=0.81) with their causal impact as leaders of human teams, which we estimate by repeatedly randomly assigning leaders to groups of human followers and measuring team performance.Successful leaders of both humans and AI agents ask more questions and engage in more conversational turn-taking; they score higher on measures of social intelligence, fluid intelligence, and decisionmaking skill, but do not differ in gender, age, ethnicity or education.Our findings indicate that AI agents can be effective proxies for human participants in social experiments, which greatly simplifies the measurement of leadership and teamwork skills.

  PublisherDOI

• Measuring Human Leadership Skills with AI Agents

  SSRN Electronic Journal · 2025-01-01

  articleOpen accessSenior author
  PublisherDOI

• Why do Wages Grow Faster for Educated Workers?

  Journal of Labor Economics · 2025-08-06

  article1st authorCorresponding
  PublisherDOI

• Diversifying Society’s Leaders? The Determinants and Causal Effects of Admission to Highly Selective Private Colleges

  The Quarterly Journal of Economics · 2025-10-30 · 8 citations

  article

  Abstract We use anonymized admissions data from several colleges linked to income tax records and SAT and ACT test scores to study the determinants and causal effects of attending Ivy-Plus colleges (Ivy League, Stanford, MIT, Duke, and Chicago). Children from families in the top 1% are more than twice as likely to attend an Ivy-Plus college as those from middle-class families with comparable SAT/ACT scores. Two-thirds of this gap is due to higher admission rates for students with comparable test scores from high-income families; the remaining third is due to differences in rates of application and matriculation. In contrast, children from high-income families have no admissions advantage at flagship public colleges. The high-income admissions advantage at Ivy-Plus colleges is driven by three factors: (i) preferences for children of alumni, (ii) weight placed on nonacademic credentials, and (iii) athletic recruitment. Using a new research design that isolates idiosyncratic variation in admissions decisions for waitlisted applicants, we show that attending an Ivy-Plus college instead of the average flagship public college increases students’ chances of reaching the top 1% of the earnings distribution by 50%, nearly doubles their chances of attending an elite graduate school, and almost triples their chances of working at a prestigious firm. The three factors that give children from high-income families an admissions advantage are uncorrelated or negatively correlated with postcollege outcomes, whereas academic credentials such as SAT/ACT scores are highly predictive of postcollege success.

  PublisherDOI

• The ABCs of Who Benefits from Working with AI: Ability, Beliefs, and Calibration

  Management Science · 2025-10-24 · 1 citations

  article

  We use a controlled experiment to show that ability and belief calibration jointly determine the benefits of working with artificial intelligence (AI). AI improves performance more for people with low baseline ability. However, holding ability constant, AI assistance is more valuable for people who are calibrated, meaning they have accurate beliefs about their own ability. People who know they have low ability gain the most from working with AI. In a counterfactual analysis, we show that eliminating miscalibration would cause AI to reduce performance inequality nearly twice as much as it already does. This paper was accepted by Marie Claire Villeval, behavioral economics and decision analysis. Funding: This work was supported by the Alfred P. Sloan Foundation (Cognitive Economics at Work). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2024.08994 .

  PublisherDOI

• How do you identify a good manager?

  2025-09-23

  report
  PublisherDOI

• Toward understanding the impact of artificial intelligence on labor

  UNC Libraries · 2025-07-18

  articleOpen access

  Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human-machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.

  PublisherDOI

• How Do You Find a Good Manager?

  National Bureau of Economic Research · 2024-07-01 · 5 citations

  reportOpen access

  This paper develops a novel method to identify the causal contribution of managers to team performance.The method requires repeated random assignment of managers to multiple teams and controls for individuals' skills.A good manager is someone who consistently causes their team to produce more than the sum of their parts.Good managers have roughly twice the impact on team performance as good workers.People who nominate themselves to be in charge perform worse than managers appointed by lottery, in part because self-promoted managers are overconfident, especially about their social skills.Managerial performance is positively predicted by economic decision-making skill and fluid intelligence -but not gender, age, or ethnicity.Selecting managers on skills rather than demographics or preferences for leadership could substantially increase organizational productivity.

  PublisherDOI

• The ABD's of Who Benefits from Working with AI: Ability, Beliefs, and Calibration

  SSRN Electronic Journal · 2024-01-01 · 1 citations

  articleOpen access
  PublisherDOI

Frequent coauthors

• Justine Hastings

  Coventry University

  72 shared

• Douglas O. Staiger

  Dartmouth College

  71 shared

• Thomas J. Kane

  Center For Policy Research

  71 shared

• Claudia Goldin

  Harvard University

  21 shared

• Lawrence F. Katz

  National Bureau of Economic Research

  20 shared

• Marco Trizzino

  Imperial College London

  18 shared

• Michael Lovenheim

  17 shared

• Richard Patterson

  16 shared

Awards & honors

• Sherwin Rosen Prize for outstanding contributions to Labor E…
• David N. Kershaw Prize for distinguished contributions to th…