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Valuing the U.S. Data Economy Using Machine Learning and Online Job Postings

Author

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  • J Bayoán Santiago Calderón
  • Dylan Rassier

    (Bureau of Economic Analysis)

Abstract

With the recent proliferation of data collection and uses in the digital economy, the understanding and statistical treatment of data stocks and flows is of interest among compilers and users of national economic accounts. In this paper, we measure the value of own-account data stocks and flows for the U.S. business sector by summing the production costs of data-related activities implicit in occupations. Our method augments the traditional sum-of-costs methodology for measuring other own-account intellectual property products in national economic accounts by proxying occupation-level time-use factors using a machine learning model and the text of online job advertisements (Blackburn 2021). In our experimental estimates, we find that annual current-dollar investment in own-account data assets for the U.S. business sector grew from $84 billion in 2002 to $186 billion in 2021, with an average annual growth rate of 4.2 percent. Cumulative current-dollar investment for the period 2002–2021 was $2.6 trillion. In addition to the annual current-dollar investment, we present historical-cost net stocks, real growth rates, and effects on value-added by the industrial sector.

Suggested Citation

  • J Bayoán Santiago Calderón & Dylan Rassier, 2022. "Valuing the U.S. Data Economy Using Machine Learning and Online Job Postings," BEA Working Papers 0204, Bureau of Economic Analysis.
    • Handle: RePEc:bea:wpaper:0204
    as

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    References listed on IDEAS

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    1. Alessandro Acquisti & Curtis Taylor & Liad Wagman, 2016. "The Economics of Privacy," Journal of Economic Literature, American Economic Association, vol. 54(2), pages 442-492, June.
    2. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    3. Wendy C.Y. Li & Makoto Nirei & Kazufumi Yamana, 2018. "Value of Data: There’s No Such Thing as a Free Lunch in the Digital Economy," BEA Working Papers 0164, Bureau of Economic Analysis.
    4. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 70(1), pages 65-94.
    5. Ellen Hughes-Cromwick & Julia Coronado, 2019. "The Value of US Government Data to US Business Decisions," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 131-146, Winter.
    6. Max Boisot & Agustí Canals, 2004. "Data, information and knowledge: have we got it right?," Journal of Evolutionary Economics, Springer, vol. 14(1), pages 43-67, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    JEL classification:

    • E22 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Investment; Capital; Intangible Capital; Capacity
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • O51 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - U.S.; Canada

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