Draft:Pascual Restrepo

Pascual Restrepo
Born	Colombia
Citizenship	Colombian, American
Education	Universidad de los Andes (B.A., Economics & Mathematics, 2010) MIT (Ph.D., 2016)
Known for	Task-based framework of automation and labor markets
Scientific career
Fields	Labor economics, automation
Institutions	Yale University Boston University (2017–2023) Cowles Foundation, Yale (2016–17)
Doctoral advisor	Daron Acemoglu

Pascual Restrepo is a Colombian-American economist and Associate Professor of Economics at Yale University. His research focuses on the impact of technology on society, with particular attention on automation and artificial intelligence and how these impact labor markets, wages, inequality, and economic growth. He is known for his collaboration with Daron Acemoglu, winner of the 2024 Nobel Prize in Economics, who was also his PhD supervisor and with whom he developed the task-based framework for analyzing how automation displaces and reinstates labor.

Biography

Restrepo was born and raised in Colombia. He studied economics and mathematics at Universidad de los Andes in Bogotá, graduating in 2010, then crossed to MIT's Department of Economics, where he finished a Ph.D. in 2016 under Daron Acemoglu. His interests drifted over those years—he started out working on conflict economics and drug markets, and only later turned toward how technological change reshapes labor demand and income distribution over the long run.

He worked a year as a postdoctoral fellow at Yale's Cowles Foundation followed graduation. From there he went to Boston University in 2017, moving from Assistant to Associate Professor over the next six years, before returning to Yale in 2023. He now holds a joint appointment at the Economic Growth Center (EGC) alongside an affiliation with the National Bureau of Economic Research (NBER), where he serves as a Faculty Research Fellow in Economic Fluctuations & Growth and Labor Studies.

Research

Task-based framework

Restrepo is best known for the task-based model of the labor market he developed with Acemoglu. The framework breaks production down into discrete tasks, each carried out by either labor or capital. Automation, in this view, is simply the expansion of the set of tasks machines can handle. What makes the model distinctive is its second half: automation does not just displace workers from the tasks it takes over—it also tends to generate new tasks where human labor still has an edge. The authors call this the "race between man and machine," and argue the balance between the two forces determines whether automation ultimately helps or hurts employment and the labor share of income. Ultimately, their simulations point out to inequality introduced as an effect of this race with the machine (or better their use) winning, recommending policy of retraining and safetynets for workers.

Robots and jobs

"Robots and Jobs: Evidence from U.S. Labor Markets" pairs data from the International Federation of Robotics with U.S. commuting-zone data to estimate what industrial robots actually do to local labor markets. The finding that got attention: each additional robot per thousand workers was associated with both lower employment and lower wages in the surrounding area. The New York Times put the study on its front page..

AI, inequality, and the future of work

Restrepo and Acemoglu trace much of the rise in U.S. wage inequality since 1980 to task displacement, a claim they develop with extensive empirical evidence in "Tasks, Automation, and the Rise in U.S. Wage Inequality" (Econometrica, 2022). A companion working paper draws on online job postings to track how AI adoption is shifting employer skill demands .

Their 2024 NBER paper "Automation and Rent Dissipation" looks at a narrower mechanism: automation tends to target tasks where workers had been earning wage premiums above what they could get elsewhere. When machines eliminate those premiums, the resulting wage losses—and the inequality they produce—turn out to be larger than a simple displacement story would predict.

Restrepo took a more speculative turn in 2025 with "We Won't Be Missed: Work and Growth in the AGI World" (NBER Working Paper 34423), which models a labor market where AI systems are capable of performing nearly any task humans currently do .

Other contributions

Earlier in his career, Restrepo worked on the political economy of conflict, the economics of illicit drug markets in Colombia and Mexico, and the relationship between democracy and redistribution (with Acemoglu, Naidu, and Robinson, published in the Handbook of Income Distribution, 2014). This work focused on the identification of strategy for estimating the causal impact of drug markets on violence using coca-cultivation suitability as an instrument.

Restrepo has also contributed to policy-oriented debates related to universal basic income (UBI) as a primary policy response to automation, arguing that more targeted interventions are needed given the uneven geographic and sectoral distribution of automation's effects

Selected academic papers

Journal articles

• Acemoglu, D. & Restrepo, P. (2022). "Tasks, Automation, and the Rise in U.S. Wage Inequality." Econometrica, 90(5), 1973–2016. doi:10.3982/ECTA19815
• Acemoglu, D. & Restrepo, P. (2019). "Automation and New Tasks: How Technology Displaces and Reinstates Labor." Journal of Economic Perspectives, 33(2), 3–30. doi:10.1257/jep.33.2.3
• Acemoglu, D. & Restrepo, P. (2018). "The Race Between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment." American Economic Review, 108(6), 1488–1542. doi:10.1257/aer.20160696
• Acemoglu, D., Autor, D., Hazell, J. & Restrepo, P. (2022). "Artificial Intelligence and Jobs: Evidence from Online Vacancies." Journal of Labor Economics, 40(S1), S293–S340. doi:10.1086/718327
• Restrepo, P. (2024). "Automation: Theory, Evidence, and Outlook." Annual Review of Economics, 16(1), 1–25. doi:10.1146/annurev-economics-090523-113355
• Acemoglu, D. et al. (2024). "Automation and the Workforce: A Firm-Level View from the 2019 Annual Business Survey." In Technology, Productivity, and Economic Growth. NBER, 13–55.
• Acemoglu, D. et al. (2020). "Does the US Tax Code Favor Automation?" Brookings Papers on Economic Activity, Spring 2020.
• Hubmer, J. & Restrepo, P. (2023). "Not a Typical Firm: The Joint Dynamics of Firms, Labor Shares, and Capital–Labor Substitution." PIER Working Paper Archive 23-015.

Coverage in news media

• Lohr, S. (2017). "Evidence That Robots Are Winning the Race for American Jobs." The New York Times.

• "Yes, Robots Are Replacing Workers. But There's More to the Story." Bloomberg (2017).
• "The Robot Takeover Is Greatly Exaggerated." Bloomberg Opinion (2017).

External links

• Yale faculty page
• NBER profile
• IDEAS/RePEC
• Google Scholar

Category:Colombian economists Category:Yale University faculty Category:MIT alumni Category:Labor economists