Exploring Machine Learning's Contributions to Economic Productivity and Innovation

"Exploring Machine Learning’s Contributions to Economic Productivity and Innovation." The International Journal of Technology, Knowledge, and Society 14 (3): 1-25. doi:10.18848/1832-3669/CGP/v14i03/1-25.

Posted: 5 Aug 2017

See all articles by Christopher Hooton

Christopher Hooton

George Washington University Institute of Public Policy; Internet Association

Davin Kaing

Independent

Date Written: 2018

Abstract

What role does computational power play in economic productivity and innovation? How will machine learning and AI change this? Building off previous work quantifying historical computational power levels, we explore the relationship of metrics for computing power with US GDP and US internet sector GDP from 1960 to today. We develop forecast scenarios incorporating machine learning development using internet data production volumes and forecasted growth of computational power. The goal of the research is not to build a full model of productivity that incorporates computational power, but to begin to get a sense of potential role and impacts of artificial intelligence on the economy. The research has three main findings. First, we find a modest, but statistically significant relationship between computational power and economic productivity, linked to approximately 0.3-0.7% of GDP per capita and to approximately 2-3% of internet sector GDP per capita. Second, and as expected, the relationship is stronger for internet sector GDP per capita, which is linked more closely to AI. Third, and as expected, when we narrow our window of analysis to more recent windows of analysis in our regressions and in robustness tests, we see a strengthening of the relationship between computational power and GDP per capita.

Keywords: productivity, machine learning, artificial intelligence, computational power

JEL Classification: O3, O4

Suggested Citation

Hooton, Christopher and Kaing, Davin, Exploring Machine Learning's Contributions to Economic Productivity and Innovation (2018). "Exploring Machine Learning’s Contributions to Economic Productivity and Innovation." The International Journal of Technology, Knowledge, and Society 14 (3): 1-25. doi:10.18848/1832-3669/CGP/v14i03/1-25.. Available at SSRN: https://ssrn.com/abstract=

Christopher Hooton (Contact Author)

George Washington University Institute of Public Policy ( email )

Institute of Public Policy
2121 I Street NW
Washington, DC DC 20052
United States

HOME PAGE: http://https://gwipp.gwu.edu/

Internet Association ( email )

Washington, DC
United States

HOME PAGE: http://https://internetassociation.org/

Davin Kaing

Independent ( email )

No Address Available

Register to save articles to
your library

Register

Paper statistics

Abstract Views
106
PlumX Metrics