The impact of artificial intelligence industry agglomeration on economic complexity

Yang Shoufu, Ma Dan, Shen Zuiyi, Wen Lin and Dong Li

Economic Research-Ekonomska Istraživanja, 2023, vol. 36, issue 1, 1420-1448

Abstract: Artificial intelligence (AI) is a fundamental driver of technological and economic growth. However, few studies have focused on the impact of AI industry agglomeration on economic complexity. This study uses a unique dataset of 2,503,795 AI enterprises in China collected through web crawlers to measure AI industrial agglomeration and examine the relationship between AI industry agglomeration and economic complexity in 194 Chinese cities based on Marshall industry agglomeration theory. The study’s results show that AI industry clustering increases economic complexity. The mechanism analysis indicates that people and knowledge are the channels through which it boosts economic complexity. Unexpectedly, AI industry agglomeration does not improve the economic complexity index (ECI) through the goods path. This study proposes three possible explanations for this result. First, AI industrial clustering may lead to excessive rivalry in China’s intermediate product market. Hence, sharing intermediate inputs has no increasing returns effect. Second, the city's high-end talent is not fairly distributed due to China's uneven development. Finally, policies drive the formation of China’s AI industrial agglomeration, which does not develop naturally. Consequently, China should implement a talent- and knowledge-driven AI agglomeration. To avoid overcrowding, policies must match regional development.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/1331677X.2022.2089194 (text/html)
Access to full text is restricted to subscribers.

Related works:
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:taf:reroxx:v:36:y:2023:i:1:p:1420-1448

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rero20

DOI: 10.1080/1331677X.2022.2089194

Access Statistics for this article

Economic Research-Ekonomska Istraživanja is currently edited by Marinko Skare

More articles in Economic Research-Ekonomska Istraživanja from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().