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Economists (and Economics) in Tech Companies

Author

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  • Susan Athey

    (Stanford Graduate School of Business)

  • Michael Luca

    (Harvard Business School, Negotiation, Organizations & Markets Unit)

Abstract

As technology platforms have created new markets and new ways of acquiring information, economists have come to play an increasingly central role in tech companies - tackling problems such as platform design, strategy, pricing, and policy. Over the past five years, hundreds of PhD economists have accepted positions in the technology sector. In this paper, we explore the skills that PhD economists apply in tech companies, the companies that hire them, the types of problems that economists are currently working on, and the areas of academic research that have emerged in relation to these problems.

Suggested Citation

  • Susan Athey & Michael Luca, 2018. "Economists (and Economics) in Tech Companies," Harvard Business School Working Papers 19-027, Harvard Business School.
    • Handle: RePEc:hbs:wpaper:19-027
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    References listed on IDEAS

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    Cited by:

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    2. Abraham K. Song, 2019. "The Digital Entrepreneurial Ecosystem—a critique and reconfiguration," Small Business Economics, Springer, vol. 53(3), pages 569-590, October.
    3. Gallin, Joshua & Molloy, Raven & Nielsen, Eric & Smith, Paul & Sommer, Kamila, 2021. "Measuring aggregate housing wealth: New insights from machine learning ☆," Journal of Housing Economics, Elsevier, vol. 51(C).
    4. Deshpande, Advait, 2020. "The potential influence of machine learning and data science on the future of economics: Overview of highly-cited research," SocArXiv 9nh8g, Center for Open Science.
    5. Brett R Gordon & Kinshuk Jerath & Zsolt Katona & Sridhar Narayanan & Jiwoong Shin & Kenneth C Wilbur, 2019. "Inefficiencies in Digital Advertising Markets," Papers 1912.09012, arXiv.org, revised Feb 2020.
    6. Gary Charness & Brian Jabarian & John List, 2023. "Generation Next: Experimentation with AI," Artefactual Field Experiments 00777, The Field Experiments Website.
    7. Gružauskas Valentas & Čalnerytė Dalia & Fyleris Tautvydas & Kriščiūnas Andrius, 2021. "Application of Multivariate Time Series Cluster Analysis to Regional Socioeconomic Indicators of Municipalities," Real Estate Management and Valuation, Sciendo, vol. 29(3), pages 39-51, September.
    8. Stefan Wager & Kuang Xu, 2019. "Experimenting in Equilibrium," Papers 1903.02124, arXiv.org, revised Jun 2020.
    9. Thomas V Maher & Charles Seguin & Yongjun Zhang & Andrew P Davis, 2020. "Social scientists’ testimony before Congress in the United States between 1946-2016, trends from a new dataset," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-13, March.
    10. Kuroki, Masanori, 2021. "Using Python and Google Colab to teach undergraduate microeconomic theory," International Review of Economics Education, Elsevier, vol. 38(C).
    11. Larin, Sergey & Khrustalev, Evgeny & Ermakova, Yasmina, 2023. "Characteristics of markets for the creation of innovative products and their impact on the mechanisms of interaction between the subjects of the innovation infrastructure," MPRA Paper 119340, University Library of Munich, Germany, revised 28 Apr 2023.
    12. Valentiny, Pál, 2024. "Mennyire innovatívak a Big Tech vállalatok? [How innovative are Big Tech companies?]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 22-56.
    13. Bessen, James & Impink, Stephen Michael & Reichensperger, Lydia & Seamans, Robert, 2022. "The role of data for AI startup growth," Research Policy, Elsevier, vol. 51(5).
    14. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    15. Michael G Jacobides & Ioannis Lianos, 2021. "Regulating platforms and ecosystems: an introduction [Ecosystem as structure: an actionable construct for strategy]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(5), pages 1131-1142.
    16. Greiff, Matthias & Paetzel, Fabian, 2020. "Information about average evaluations spurs cooperation: An experiment on noisy reputation systems," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 334-356.
    17. Rickard Nyman & Paul Ormerod, 2020. "Understanding the Great Recession Using Machine Learning Algorithms," Papers 2001.02115, arXiv.org.
    18. Stefan Wager & Kuang Xu, 2021. "Experimenting in Equilibrium," Management Science, INFORMS, vol. 67(11), pages 6694-6715, November.
    19. Nicolas Petit & David J Teece, 2021. "Innovating Big Tech firms and competition policy: favoring dynamic over static competition [Patterns of industrial innovation]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(5), pages 1168-1198.
    20. Krügel, Jan Philipp & Paetzel, Fabian, 2021. "The Impact of Fake Reviews on Reputation Systems and Efficiency," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242415, Verein für Socialpolitik / German Economic Association.

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    More about this item

    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • D4 - Microeconomics - - Market Structure, Pricing, and Design
    • L0 - Industrial Organization - - General
    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics

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