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The Productivity J-Curve: How Intangibles Complement General Purpose Technologies

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  • Erik Brynjolfsson
  • Daniel Rock
  • Chad Syverson

Abstract

General purpose technologies (GPTs) such as AI enable and require significant complementary investments, including co-invention of new processes, products, business models and human capital. These complementary investments are often intangible and poorly measured in the national accounts, even when they create valuable assets for the firm. We develop a model that shows how this leads to an underestimation of productivity growth in the early years of a new GPT, and how later, when the benefits of intangible investments are harvested, productivity growth will be overestimated. Our model generates a Productivity J-Curve that can explain the productivity slowdowns often accompanying the advent of GPTs, as well as the increase in productivity later. We use our model to analyze empirically the historical roles of intangibles tied to R&D, software, and computer hardware. We find substantial and ongoing Productivity J-Curve effects for software in particular and computer hardware to a lesser extent. Our adjusted measure TFP is 11.3% higher than official measures at the end of 2004, and 15.9% higher than official measures at the end of 2017. We then assess how AI-related intangible capital may be currently affecting measured productivity and find the effects are small but growing.

Suggested Citation

  • Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies," NBER Working Papers 25148, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:25148
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    References listed on IDEAS

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    Cited by:

    1. de Ridder, Maarten, 2019. "Market power and innovation in the intangible economy," LSE Research Online Documents on Economics 100946, London School of Economics and Political Science, LSE Library.
    2. Cecilia Jona-Lasinio & Stefano Schiavo & Klaus Weyerstrass, 2019. "How to revive productivity growth?," EconPol Policy Reports 13, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    3. Wendy C.Y. LI & NIREI Makoto & YAMANA Kazufumi, 2019. "Value of Data: There's No Such Thing as a Free Lunch in the Digital Economy," Discussion papers 19022, Research Institute of Economy, Trade and Industry (RIETI).
    4. Jerome H. Powell, 2019. "Data-Dependent Monetary Policy in an Evolving Economy : A speech at \\"Trucks and Terabytes: Integrating the 'Old' and 'New' Economies\\" 61st Annual Meeting of the National Association for ," Speech 1093, Board of Governors of the Federal Reserve System (U.S.).
    5. Jeffrey Ding & Allan Dafoe, 2020. "The Logic of Strategic Assets: From Oil to Artificial Intelligence," Papers 2001.03246, arXiv.org.

    More about this item

    JEL classification:

    • D2 - Microeconomics - - Production and Organizations
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • 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

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