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The potential influence of machine learning and data science on the future of economics: Overview of highly-cited research

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  • Deshpande, Advait

Abstract

This working paper provides an overview of the potential influence of machine learning and data science on economics as a field. The findings presented are drawn from highly cited research which was identified based on Google Scholar searches. For each of the articles reviewed, this working paper covers what is likely to change and what is likely to remain unchanged in economics due to the emergence and increasing influence of machine learning and data science methods.

Suggested Citation

  • 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.
    • Handle: RePEc:osf:socarx:9nh8g
    DOI: 10.31219/osf.io/9nh8g
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    References listed on IDEAS

    as
    1. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    2. Susan Athey & Michael Luca, 2019. "Economists (and Economics) in Tech Companies," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 209-230, Winter.
    3. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    4. Ernst Ekkehardt & Merola Rossana & Samaan Daniel, 2019. "Economics of Artificial Intelligence: Implications for the Future of Work," IZA Journal of Labor Policy, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 9(1), pages 7-72, June.
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