This paper is published as part of the Hutchins Center on Fiscal and Monetary Policy’s Productivity Measurement Initiative.
Associate Professor of Operations, Information and Decisions - The Wharton School, The University of Pennsylvania
Professor of Operations, Information and Decisions - The Wharton School, The University of Pennsylvania
Assistant Professor of Operations, Information and Decisions - The Wharton School, The University of Pennsylvania
Director - MIT Initiative on the Digital Economy
Professor - MIT Sloan School of Management
Superstar firms, unique in their capabilities to scale up innovations, have become increasingly important in the US economy. Investments related to digital technologies are likely to play a particularly important role, reflecting, among other things, economies of scale and network effects. Much of the rise in the concentration of power in these firms has been attributed to intangible investments. For digitally-focused firms, investments in the intangible assets needed to realize value from new technologies – like cumulative investment in skills training, new decision-making structures within the firm, management practices, and software customization – often account for significantly greater total costs than the technologies themselves. As the economy becomes increasingly digitized, these assets can be expected to grow even further in importance. For instance, there has been a wave of interest in the potential of data analytics and artificial intelligence (AI) to become the next important general purpose technology that drives economic growth and business value.
This paper uses new firm-level data on IT investment to develop panel measures of digital capital prices and quantities. In particular, building upon earlier work, authors compute firm-level measures of intangible IT capital quantities that allow them to: 1) generate estimates of the annual growth of this asset, 2) to compare how these growth rates differ among firms of different value, and 3) analyze how the accumulation of these assets contributes to productivity differences among firms. Authors find that by 2016, the stock of digital capital accounted for about 25% of total capital stock for firms in our sample. Changes in the value of digital capital in the years before and after the dot-com boom and bust appear to primarily be due to price fluctuations; after the bust, firms continued to accumulate significant amounts of digital capital while prices varied little. The most recent technology-related increases in the market value of firms appear to be due to changes in quantity, not price, as firms accumulate more and more digital capital.
Authors find evidence of striking firm-to-firm heterogeneity in digital capital value, with most of the value concentrated in a small group of superstar firms with market values in the top decile. Inequality in digital capital among firms is growing as the top firms pull further away from the rest. Moreover, per-capita digital capital stocks are substantially greater in firms with more educated workers. These findings are consistent with the emphasis that technology-intensive firms place on making investments in training and skills.
Their findings suggest that the higher values the financial markets have assigned to firms with large digital investments in recent years reflect greater digital capital quantities, rather than simply higher prices for existing assets. In other words, they reflect genuine improvements to firms’ productive capacity. In fact, the authors find that digital capital, if included as a separate factor in firm-level production functions, predicts differences in output and productivity among firms.
The authors’ estimates of the output elasticity of digital capital suggest that it is several times greater than the output elasticity of IT capital. Their estimates are broadly consistent with earlier evidence (Saunders and Brynjolfsson, 2016) that indicates that IT hardware accounts for only about 10% of total digital investment, with investments in complementary intangibles–that is, digital capital– accounting for the rest.
One interpretation of these findings is that translating organizational innovations into productive capital requires significant investment in organizational re-engineering and skill development. Therefore, even if firms have the appropriate absorptive capacity, knowledge of how to construct digital assets will not automatically generate productive digital capital any more than access to the blueprints of a competitor’s plant will directly lead to productive capacity.
The authors did not receive financial support from any firm or person for this article or from any firm or person with a financial or political interest in this article. They are currently not officers, directors, or board members of any organization with an interest in this article. LinkedIn’s Economic Graph Research and Insights team, as the data provider, had the chance to review for possible release of confidential information and trade secrets prior to publication. They did not have editorial control over other aspects of the paper.
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