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| AI Investment of Chinese Firms: Findings Based on Large-Scale Job Posting Data |
| FANG Ying, WANG Xiangyu, YE Mengqian, ZHAO Xiliang
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| Wang Yanan Institute for Studies in Economics/Interdisciplinary Laboratory of Big Data Finance/Paula and Gregory Chow Institute for Studies in Economics, Xiamen University; Fujian Institute of Scientific and Technical Information |
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Abstract Measuring firm-level artificial intelligence (AI) investment is central to understanding technology adoption, productivity upgrading, and market valuation, yet direct accounting measures are limited because most AI inputs are intangible and rarely separated in financial statements. Existing proxies based on annual-report wording, IT hardware spending, or patent output capture only part of the process and can blur real investment behavior. Building on the view that AI adoption is human-capital intensive, this study develops a recruitment-based measure that tracks whether firms are actually allocating resources to AI capability development. The core idea is that hiring demand for AI-related skills reflects concrete investment decisions at the implementation stage, thereby providing a scalable, behavior-based indicator that complements disclosure-based metrics.
To operationalize this idea, we construct a Chinese labor-skill dictionary with 99,463 skills by integrating Lightcast, O*NET, and ESCO and then localizing the lexicon with a BERT-based named-entity-recognition pipeline trained on Chinese job texts. Using skill co-occurrence with four anchor concepts (AI, machine learning, image recognition, and natural language processing), we compute an AI relevance score for each skill, aggregate scores to the posting level, and classify a posting as AI-relatedwhen its average relevance exceeds 0.05. We then define AIRatio at the firm-year level as the share of AI-related postings in total postings. This framework follows a transparent skill-to-position-to-firm aggregation logic and can be extended to other technologies by replacing anchor skills.
The empirical sample combines 7.13 million postings from theZhaopin platform (organized by CnOpenData and matched to listed firms) with annual-report disclosures from CNINFO, AI patent information identified under China National Intellectual Property Administration (CNIPA) rules, and accounting and market variables from CSMAR, covering the period from 2015-2022. We validate AIRatio through three complementary tests. First, benchmark validation shows strong discrimination: firms on the CEIDI Top 100 AI list have a much higher mean AIRatio than other firms (0.0617 versus 0.0097). Second, patent validation shows a significantly positive association between AIRatio and AI patent applications. Third, a capital-market validation based on the release of ChatGPT on November 30, 2022, shows that high-AIRatio portfolios outperform low-AIRatio portfolios in China's event window (form December 30, 2022 to April 1, 2023), and firm-level cumulative abnormal returns are positively related to AIRatio.
UsingAIRatio, we document a substantial increase in AI investment among Chinese listed firms from 0.0023 in 2015 to 0.0157 in 2020, followed by a mild normalization to 0.0130 in 2022. Industry heterogeneity is substantial, with information services and finance leading, while agriculture, mining, and hospitality lagging behind. We then compare investment-based and disclosure-based AI measures and find only a modest correlation with frequent firm-level mismatch, including many cases of high disclosure but low contemporaneous hiring. Mechanism analysis indicates that mismatch is not purely cosmetic: disclosure contains forward-looking signals that predict next-period hiring, and it also captures technology positioning and business-AI relatedness beyond current input. In asset pricing, a disclosure premium is broad, while an investment premium is strongest when disclosure is already high, indicating a disclosure-first information hierarchy.
The study contributes in three ways.First, it introduces a scalable and behavior-based AI investment metric that is less dependent on voluntary narrative disclosure. Second, it clarifies the different information content of disclosure and investment indicators. Third, it provides evidence on how AI adoption in the real economy is transmitted into equity valuation under a major technology shock. Policy implications are to support AI diffusion in traditional sectors through application-oriented incentives, strengthen AI talent training and mobility, monitor widening inter-firm AI gaps, and improve verifiable AI disclosure standards by linking narrative claims to recruitment, R&D, and patent evidence. Future research can extend the framework to non-listed firms, add new input channels such as cloud and compute spending, and develop stronger causal designs for productivity, labor, and competition effects of AI diffusion.
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Received: 24 October 2025
Published: 02 June 2026
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