National Big Data Comprehensive Pilot Zone Policy and Corporate AI Innovation Output —Evidence from Chinese A-Share Listed Companies
国家ビッグデータ総合試験区政策と企業のAIイノベーション成果——中国A株上場企業からの証拠 (AI 翻訳)
Lingbing Feng, Deling Chen
🤖 gxceed AI 要約
日本語
中国の国家ビッグデータ総合試験区の設立を自然実験として、2012〜2024年のA株上場企業データを用い、DID分析により政策が企業のAI特許出願数を有意に増加させることを示す。資金調達制約の緩和とAIへの注目度上昇が経路として示唆され、企業規模や所有制による異質性も確認された。
English
Using the establishment of National Big Data Comprehensive Pilot Zones as a quasi-natural experiment, this study analyzes Chinese A-share listed firms from 2012 to 2024 with a DID design, finding that the policy significantly boosts corporate AI patent applications. Mechanism tests suggest alleviation of financing constraints and increased AI textual attention, with heterogeneous effects across firm characteristics.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本論文は中国のデジタル政策に焦点を当てており、日本企業への直接的な示唆は限定的。ただし、データ要素市場の整備が企業の技術革新に与える影響は、日本のDX推進やGXにおけるデータ活用の参考になり得る。
In the global GX context
This paper provides empirical evidence on how data-factor marketization policies spur corporate AI innovation, relevant to global discussions on digital transformation and innovation policy. While not directly GX-focused, it offers insights into policy-driven technology adoption that could inform climate-tech innovation strategies.
👥 読者別の含意
🔬研究者:AI特許データを用いた政策評価の方法論や、データ要素市場と技術革新の関係に関する知見が得られる。
🏢実務担当者:データ関連政策が企業の技術開発に与える影響を理解し、自社のAI投資戦略の参考にできる。
🏛政策担当者:データ政策が企業イノベーションを促進するメカニズムを理解し、類似政策の設計に活用できる。
📄 Abstract(原文)
Using the establishment of National Big Data Comprehensive Pilot Zones as a quasi-natural experiment, this paper examines the impact of the pilot-zone policy on cor porate AI innovation output based on Chinese A-share listed companies from 2012 to 2024 and a difference-in-differences design. The dependent variable is constructed from the CN RDS listed-firm AI patent application database and measured by the natural logarithm of one plus the number of AI invention patent applications independently filed by a firm in a given year. The results show that the policy significantly increases corporate AI innovation output. The conclusion remains valid after the parallel-trend test, placebo test, PSM-DID test, and a series of robustness checks. Mechanism-clue tests provide evidence consistent with two possi ble channels: alleviation of financing constraints and higher AI textual attention. Heterogeneity analysis further shows that the policy effect is more pronounced among firms located in regions with larger R&D personnel pools, firms with higher financing constraints, and state-owned en terprises, and the corresponding between-group differences are statistically significant. From the perspectives of transition economics, factor market reform, and transaction costs, this paper explains the micro-level innovation effect of digital policy and provides empirical evidence for understanding how data-factor marketization affects corporate AI innovation
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1142/s3082844926500089first seen 2026-08-12 05:25:04 · last seen 2026-08-13 05:34:24
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