Valuing the U.S. Data Economy Using Machine Learning and Online Job Postings (NBER|CRIW)


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. In our experimental estimates, we find that annual current-dollar investment in own-account data assets for the U.S. business sector grew from $82.6 billion in 2003 to $159.5 billion in 2020, with an average annual growth rate of 3.9 percent. Cumulative current-dollar investment for the period 2003–2020 was $2.1 trillion. In addition to annual currentdollar investment, we present historical-cost net stocks, real growth rates, and effects on value-added by industrial sector.

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