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

2021 ◽  
Vol 13 (1) ◽  
pp. 333-372
Author(s):  
Erik Brynjolfsson ◽  
Daniel Rock ◽  
Chad Syverson
Keyword(s):  
Productivity Growth ◽  
General Purpose ◽  
Early Years ◽  
Computer Hardware ◽  
National Accounts ◽  
General Purpose Technologies ◽  
J Curve ◽  
Intangible Investments

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 US data and find that adjusting for intangibles related to computer hardware and software yields a TFP level that is 15.9 percent higher than official measures by the end of 2017. (JEL E22, E23, G31, L63, L86)

How Motion Pictures Industrialized Entertainment

2012 ◽  
Vol 72 (4) ◽  
pp. 1036-1063 ◽  
Author(s):  
Gerben Bakker
Keyword(s):  
Productivity Growth ◽  
Manufacturing Industry ◽  
New Technology ◽  
Motion Pictures ◽  
General Purpose ◽  
Business Leaders ◽  
Human Voice ◽  
General Purpose Technologies ◽  
Present Generation ◽  
Personal Services

Motion pictures constituted a revolutionary new technology that transformed entertainment—a rival, labor-intensive service—into a non-rival commodity. Combining growth accounting with a new output concept shows productivity growth in entertainment surpassed that in any manufacturing industry between 1900 and 1938. Productivity growth in personal services was not stagnant by definition, as current understanding has it, but instead was unparalleled in some cases. Motion pictures’ contribution to aggregate GDP and TFP growth was much smaller than that of general purpose technologies steam, railways, and electricity, but not insignificant. An observer might have noted that “motion pictures are everywhere except in the productivity statistics.”“So long as the number of persons who can be reached by a human voice is strictly limited, it is not very likely that any singer will make an advance on the £10,000 said to have been earned in a season by Mrs. Billington at the beginning of the last century, nearly as great as that which the business leaders of the present generation have made on the last.”1Alfred Marshall

General Purpose Technologies and Economic Growth

2014 ◽  
Vol 28 (2) ◽  
pp. 337-360
Author(s):  
Inyong Shin ◽  
Hyunho Kim
Keyword(s):  
Economic Growth ◽  
General Purpose ◽  
General Purpose Technologies

GPTs and growth: evidence on the technological adoption of electrical and electronic technologies in the 1920s

2020 ◽  
Author(s):  
Sergio Petralia
Keyword(s):  
The United States ◽  
General Purpose ◽  
Geographical Information ◽  
General Purpose Technologies ◽  
Technological Adoption ◽  
Transformative Potential ◽  
The Us ◽  
The Impact ◽  
Electronic Technologies ◽  
Patenting Activity

Abstract The pervasive diffusion of electricity-related technologies at the beginning of the twentieth century has been studied extensively to understand the transformative potential of general purpose technologies (GPTs). Most of what we know, however, has been investigated in relation to the diffusion of their use. This article provides evidence on the county-level economic impact of the technological adoption of electrical and electronic (E&E) technologies in the 1920s in the United States (US). It focuses on measuring the impact of a GPT on technological adopters, i.e., those who are able to develop, transform, and complement it. It is shown that places with patenting activity in E&E technologies grew faster and paid higher wages than others between 1920 and 1930. This analysis required constructing a novel database identifying detailed geographical information for historical patent documents in the US since 1836, as well as developing a text-mining algorithm to identify E&E patents based on patent descriptions.

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