Data and Code for Productivity J-Curve
Principal Investigator(s): View help for Principal Investigator(s) Erik Brynjolfsson, Massachusetts Institute of Technology; Daniel Rock, Massachusetts Institute of Technology; Chad Syverson, University of Chicago
Version: View help for Version V1
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Project Citation:
Brynjolfsson, Erik, Rock, Daniel, and Syverson, Chad. Data and Code for Productivity J-Curve. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-12-30. https://doi.org/10.3886/E117947V1
Project Description
Summary:
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General
purpose technologies (GPTs) like AI enable and require significant
complementary investments. These investments are often intangible and poorly
measured in national accounts. We develop a model that shows how this
can lead to underestimation of productivity growth in a new GPTs early years
and, later, when the benefits of intangible investments are harvested,
productivity growth overestimation. We call this phenomenon the Productivity
J-Curve. We apply our method to U.S. data and find that adjusting for
intangibles related to computer hardware and software yields a TFP level that
is 15.9% higher than official measures by the end of 2017.
Scope of Project
JEL Classification:
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D24 Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
E01 Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
E22 Investment; Capital; Intangible Capital; Capacity
O33 Technological Change: Choices and Consequences; Diffusion Processes
D24 Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
E01 Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
E22 Investment; Capital; Intangible Capital; Capacity
O33 Technological Change: Choices and Consequences; Diffusion Processes
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