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

Name	File Type	Size	Last Modified
Code			04/14/2020 05:36:PM
Derived Data			04/14/2020 05:36:PM
Input Data			04/14/2020 05:36:PM
README_jcurve.md	text/x-web-markdown	6.3 KB	04/14/2020 01:36:PM

Project Description

Summary: 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:
      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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Published Versions

V1 [2020-12-30]

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DDI 2.5

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