About

Dimitris Papanikolaou is the John L. and Helen Kellogg Professor of Finance at the Kellogg School of Management and a Research Associate of the National Bureau of Economic Research. His research has primarily focused on the interaction between technological innovation and financial markets. His recent work has concentrated on the measurement of intangibles, the impact of innovation on firms and workers, the displacement of human capital, the market for corporate executives, and the causal impact of financial frictions on innovation and employment. He has published in leading journals such as the Quarterly Journal of Economics, the Journal of Political Economy, the Journal of Finance, the Review of Financial Studies, and the Journal of Financial Economics. Papanikolaou has received several research awards, including the Anundi Smith Breeden prize twice for the best paper in the Journal of Finance. He currently serves as a Co-Editor of the Journal of Financial Economics and has previously served as an Associate Editor for several prominent finance journals. He earned his Ph.D. in Financial Economics from the Massachusetts Institute of Technology and has held academic positions at Northwestern University since 2007, progressing from Assistant Professor to his current role as Professor of Finance.

Research topics

• Economics
• Business
• Computer Science
• Industrial organization
• Psychology
• Finance
• Demographic economics
• Labour economics
• Geography
• Macroeconomics
• Monetary economics
• Economic growth
• Commerce
• Engineering
• Microeconomics

Selected publications

• Artificial Intelligence and the Labor Market

  SSRN Electronic Journal · 2025-01-01 · 9 citations

  preprintOpen access
  PublisherDOI

• Technology and Labor Markets: Past, Present, and Future; Evidence from Two Centuries of Innovation

  National Bureau of Economic Research · 2025-10-01 · 1 citations

  reportOpen access

  We use recent advances in natural language processing and large language models to construct novel measures of technology exposure for workers that span almost two centuries.Combining our measures with Census data on occupation employment, we show that technological progress over the 20th century has led to economically meaningful shifts in labor demand across occupations: it has consistently increased demand for occupations with higher education requirements, occupations that pay higher wages, and occupations with a greater fraction of female workers.Using these insights and a calibrated model, we then explore different scenarios for how advances in artificial intelligence (AI) are likely to impact employment trends in the medium run.The model predicts a reversal of past trends, with AI favoring occupations that are lower-educated, lowerpaid, and more male-dominated.

  PublisherDOI

• Neutron capture measurements for s-process nucleosynthesis

  The European Physical Journal A · 2025-05-19 · 4 citations

  reviewOpen access

  Abstract This article presents a review about the main CERN n_TOF contributions to the field of neutron-capture experiments of interest for s -process nucleosynthesis studies over the last 25 years, with a special focus on the measurement of radioactive isotopes. A few recent capture experiments on stable isotopes of astrophysical interest are also discussed. Results on s -process branching nuclei are appropriate to illustrate how advances in detection systems and upgrades in the facility have enabled increasingly challenging experiments and, as a consequence, have led to a better understanding and modeling of the s -process mechanism of nucleosynthesis. New endeavors combining radioactive-ion beams from ISOLDE for the production of radioisotopically pure samples for activation experiments at the new NEAR facility at n_TOF are briefly discussed. On the basis of these new exciting results, also current limitations of state-of-the-art TOF and activation techniques will be depicted, thereby showing the pressing need for further upgrades and enhancements on both facilities and detection systems. A brief account of the potential technique based on inverse kinematics for direct neutron-capture measurements is also presented.

  PublisherOA PDFDOI

• Neutron capture measurements for s-process nucleosynthesis; A review about CERN n_TOF developments and contributions

  ArXiv.org · 2025-02-14 · 1 citations

  reviewOpen access

  This article presents a review about the main CERN n\_TOF contributions to the field of neutron-capture experiments of interest for $s$-process nucleosynthesis studies over the last 25 years, with special focus on the measurement of radioactive isotopes. A few recent capture experiments on stable isotopes of astrophysical interest are also discussed. Results on $s$-process branching nuclei are appropriate to illustrate how advances in detection systems and upgrades in the facility have enabled increasingly challenging experiments and, as a consequence, have led to a better understanding and modeling of the $s$-process mechanism of nucleosynthesis. New endeavors combining radioactive-ion beams from ISOLDE for the production of radioisotopically pure samples for activation experiments at the new NEAR facility at n\_TOF are briefly discussed. On the basis of these new exciting results, also current limitations of state-of-the-art TOF and activation techniques will be depicted, thereby showing the pressing need for further upgrades and enhancements on both facilities and detection systems. A brief account of the potential technique based on inverse kinematics for direct neutron-capture measurements is also presented.

  PublisherOA PDFDOI

• Technology and Labor Markets: Past, Present, and Future; Evidence from Two Centuries of Innovation

  SSRN Electronic Journal · 2025-01-01

  articleOpen access
  PublisherDOI

• Artificial Intelligence and the Labor Market

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• Artificial Intelligence and the Labor Market

  National Bureau of Economic Research · 2025-02-01 · 38 citations

  reportOpen access

  We use advances in natural language processing to construct new measures of workers' task-level exposure to artificial intelligence (AI) and machine learning from 2010 to 2023, capturing variation across firms, occupations, and time.Tasks with higher AI exposure subsequently experience reduced labor demand.To interpret these patterns, we develop a model that separates direct substitution from indirect reallocative effects of labor-saving technologies.Two variables summarize the impact of AI on within-firm labor demand: the mean exposure of an occupation's tasks, which depresses demand, and the concentration of exposure in a few tasks, which offsets losses by enabling workers to reallocate effort.Using an instrument based on historical university hiring networks, we find causal evidence consistent with these predictions.Despite strong substitution at the task level, overall employment effects are modest, as reduced demand in exposed occupations is offset by productivity-driven increases in labor demand at AI-adopting firms.

  PublisherDOI

• Winners and Losers: Competition, Creative Destruction, and Labor Income Risk

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Time-Varying Risk Premia, Labor Market Dynamics, and Income Risk

  SSRN Electronic Journal · 2023-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• Technology and Labor Displacement: Evidence from Linking Patents with Worker-Level Data

  SSRN Electronic Journal · 2023 · 21 citations

  • Labour economics
  • Business
  • Demographic economics
  PublisherDOI

Frequent coauthors

• Leonid Kogan

  Massachusetts Institute of Technology

  81 shared

• Amit Seru

  58 shared

• Matt Taddy

  Amazon (United States)

  53 shared

• Bryan Kelly

  Yale University

  51 shared

• Lawrence Schmidt

  20 shared

• Andrea L. Eisfeldt

  19 shared

• Rui Albuquerque

  Boston College

  19 shared

• Efraim Benmelech

  Kellogg's (Canada)

  18 shared

Awards & honors

• Kellogg Research Mentorship Award
• Kellogg Best Paper Award
• LBS Finance Symposium Best Paper Award
• Red Rock Finance Conference Crowell Memorial Prize
• Panagora Asset Management Crowell Memorial Prize