About

Daniel Rock is an Assistant Professor of Operations, Information, and Decisions at the Wharton School of the University of Pennsylvania. His research focuses on the economic effects of digital technologies, with a particular emphasis on the economics of artificial intelligence, digitization, information systems, the future of work and automation, productivity, and intangible assets. He has conducted studies on the types of occupations most exposed to machine learning, measuring the value of AI skillsets to employer firms, and adjusting productivity measurement to include investments in intangible assets. His work involves applying advanced data science techniques to analyze datasets from financial markets, online resume sites, and job postings. His research has been published in academic journals and featured in prominent outlets such as The New York Times, Wall Street Journal, Bloomberg, Harvard Business Review, and Sloan Management Review. Daniel Rock holds a B.S. from the Wharton School of the University of Pennsylvania, and an M.S. and Ph.D. from the Massachusetts Institute of Technology. Prior to his academic career, he worked as an Algorithmic Trader at DRW Trading.

Research topics

• Economics
• Sociology
• Computer Science
• Neoclassical economics
• Business
• Engineering
• Medicine
• Mathematics
• Economic growth
• Demography
• Statistics
• Demographic economics
• Econometrics
• Geography
• Macroeconomics

Selected publications

• GPTs are GPTs: Labor market impact potential of LLMs

  Science · 2024 · 535 citations

  • Economics

  Research is needed to estimate how jobs may be affected.

  PublisherDOI

• GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models

  arXiv (Cornell University) · 2023 · 542 citations

  • Sociology
  • Labour economics
  • Economics

  We investigate the potential implications of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), on the U.S. labor market, focusing on the increased capabilities arising from LLM-powered software compared to LLMs on their own. Using a new rubric, we assess occupations based on their alignment with LLM capabilities, integrating both human expertise and GPT-4 classifications. Our findings reveal that around 80% of the U.S. workforce could have at least 10% of their work tasks affected by the introduction of LLMs, while approximately 19% of workers may see at least 50% of their tasks impacted. We do not make predictions about the development or adoption timeline of such LLMs. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software. Significantly, these impacts are not restricted to industries with higher recent productivity growth. Our analysis suggests that, with access to an LLM, about 15% of all worker tasks in the US could be completed significantly faster at the same level of quality. When incorporating software and tooling built on top of LLMs, this share increases to between 47 and 56% of all tasks. This finding implies that LLM-powered software will have a substantial effect on scaling the economic impacts of the underlying models. We conclude that LLMs such as GPTs exhibit traits of general-purpose technologies, indicating that they could have considerable economic, social, and policy implications.

  PublisherOA PDFDOI

• Thoughts on African Swine Fever Vaccines

  Viruses · 2021 · 55 citations

  • Virology
  • Medicine
  • Biology

  African swine fever (ASF) is an acute viral hemorrhagic disease of domestic swine with mortality rates approaching 100%. Devastating ASF outbreaks and continuing epidemics starting in the Caucasus region and now in the Russian Federation, Europe, China, and other parts of Southeast Asia (2007 to date) highlight its significance. ASF strain Georgia-07 and its derivatives are now endemic in extensive regions of Europe and Asia and are "out of Africa" forever, a situation that poses a grave if not an existential threat to the swine industry worldwide. While our current concern is Georgia-07, other emerging ASFV strains will threaten for the indefinite future. Economic analysis indicates that an ASF outbreak in the U.S. would result in approximately $15 billion USD in losses, assuming the disease is rapidly controlled and the U.S. is able to reenter export markets within two years. ASF's potential to spread and become endemic in new regions, its rapid and efficient transmission among pigs, and the relative stability of the causative agent ASF virus (ASFV) in the environment all provide significant challenges for disease control. Effective and robust methods, including vaccines for ASF response and recovery, are needed immediately.

  PublisherOA PDFDOI

• COVID-19 and Remote Work: An Early Look at US Data

  National Bureau of Economic Research · 2020 · 31 citations

  • Sociology
  • Demographic economics
  • Business

  We report the results of a nationally-representative sample of the US population during the COVID-19 pandemic. The survey ran in two waves from April 1-5, 2020 and May 2-8, 2020. Of those employed pre-COVID-19, we find that about half are now working from home, including 35.2% who report they were commuting and recently switched to working from home. In addition, 10.1% report being laid-off or furloughed since the start of COVID-19. There is a strong negative relationship between the fraction in a state still commuting to work and the fraction working from home. We find that the share of people switching to remote work can be predicted by the incidence of COVID-19 and that younger people were more likely to switch to remote work. Furthermore, states with a higher share of employment in information work including management, professional and related occupations were more likely to shift toward working from home and had fewer people laid off or furloughed. We find no substantial change in results between the two waves, suggesting that most changes to remote work manifested by early April.

• Understanding and Addressing the Modern Productivity Paradox

  2020 · 22 citations

  Senior authorCorresponding
  • Computer Science
  • Economics
  • Computer Science

• The Productivity J-Curve: How Intangibles Complement General Purpose Technologies

  American Economic Journal Macroeconomics · 2020 · 535 citations

  • Economics
  • Econometrics
  • Macroeconomics

  General purpose technologies (GPTs) like AI enable and require significant complementary investments. These investments are often intangible and poorly measured in national accounts. We develop a model that shows how this can lead to underestimation of productivity growth in a new GPTs early years and, later, when the benefits of intangible investments are harvested, productivity growth overestimation. We call this phenomenon the Productivity J-curve. We apply our method to US data and find that adjusting for intangibles related to computer hardware and software yields a TFP level that is 15.9 percent higher than official measures by the end of 2017. (JEL E22, E23, G31, L63, L86)

  DOI

Frequent coauthors

• Erik Brynjolfsson

  National Bureau of Economic Research

  27 shared

• Chad Syverson

  University of Chicago

  17 shared

• Prasanna Tambe

  University of Pennsylvania

  5 shared

• Guillermo Lagarda

  Inter-American Development Bank

  3 shared

• Seth Benzell

  3 shared

• Lorin M. Hitt

  University of Pennsylvania

  3 shared

• Erik Brynjolfsson

  2 shared

• John J. Horton

  National Bureau of Economic Research

  2 shared

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