Talent attraction policies and firm-level AI intensity: evidence from listed firms in China

This paper examines whether talent attraction policies promote firm-level artificial intelligence (AI) intensity. Using panel data on Chinese listed firms from 2005 to 2023, we exploit the staggered adoption of city-level talent attraction policies and estimate a staggered difference-in-differences model. We find that these policies significantly increase AI intensity among firms located in treated cities. Mechanism analysis shows that these policies increase the accumulation of highly educated employees in local firms. Additional evidence on R&D and digital asset investment is consistent with the capital–skill complementarity channel. We also find that urban digital infrastructure strengthens the policy effect. Moreover, analysis by keyword category shows increases in references to AI applications and complementary digital activities in firms’ annual reports. Further analysis shows that the policy effects are stronger in higher-tier and wealthier cities. These findings provide firm-level causal evidence on the role of human capital in AI development and offer policy implications for designing talent policies that support the diffusion of AI technologies.

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Publication Details

Journal
Applied Economics
Published
2026-11-02
DOI
https://doi.org/10.1080/00036846.2026.2753037
Primary Topic
Innovation Policy and R&D
Type
article
Field-Weighted Citation Impact
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Talent attraction policies and firm-level AI intensity: evidence from listed firms in China

Tianyu Huang, Ai Liu, Qin Yao
4 links
Applied Economics
Innovation Policy and R&D
article

Talent attraction policies and firm-level AI intensity: evidence from listed firms in China

Tianyu Huang, Ai Liu, Qin Yao
article en

Abstract

This paper examines whether talent attraction policies promote firm-level artificial intelligence (AI) intensity. Using panel data on Chinese listed firms from 2005 to 2023, we exploit the staggered adoption of city-level talent attraction policies and estimate a staggered difference-in-differences model. We find that these policies significantly increase AI intensity among firms located in treated cities. Mechanism analysis shows that these policies increase the accumulation of highly educated employees in local firms. Additional evidence on R&D and digital asset investment is consistent with the capital–skill complementarity channel. We also find that urban digital infrastructure strengthens the policy effect. Moreover, analysis by keyword category shows increases in references to AI applications and complementary digital activities in firms’ annual reports. Further analysis shows that the policy effects are stronger in higher-tier and wealthier cities. These findings provide firm-level causal evidence on the role of human capital in AI development and offer policy implications for designing talent policies that support the diffusion of AI technologies.

Applied Economics
Guangxi University (CN), Shanghai Normal University (CN)
Decent work and economic growth, Industry, innovation and infrastructure
Openalex Percentile: Top 11%
Innovation Policy and R&D
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