Artificial intelligence and productivity: global evidence from AI patent and bibliometric data

Aleksandra Parteka and Aleksandra Kordalska

Technovation, 2023, vol. 125, issue C

Abstract: In this paper we analyse the relationship between technological innovation in the artificial intelligence (AI) domain and macroeconomic productivity. We embed recently released data on patents and publications related to AI in an augmented model of productivity growth, which we estimate for the OECD countries and compare to an extended sample including non-OECD countries. Our estimates provide evidence in favour of the modern productivity paradox. We show that the development of AI technologies remains a niche innovation phenomenon with a negligible role in the officially recorded productivity growth process. This general result, i.e. a lack of a strong relationship between AI and registered macroeconomic productivity growth, is robust to changes in the country sample, in the way we quantify labour productivity and technology (including AI stock), in the specification of the empirical model (control variables) and in estimation methods.

Keywords: Technological innovation; Productivity paradox; Productivity growth; Artificial intelligence; Patents (search for similar items in EconPapers)
JEL-codes: O33 O47 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0166497223000755
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Artificial intelligence and productivity: global evidence from AI patent and bibliometric data (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:techno:v:125:y:2023:i:c:s0166497223000755

DOI: 10.1016/j.technovation.2023.102764

Access Statistics for this article

Technovation is currently edited by Jonathan Linton

More articles in Technovation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().