Data-Driven Empowerment: How Government Data Governance Facilitates Firm Upgrading

D Wang et al.

Engineering Economics2026https://doi.org/10.5755/j01.ee.37.1.41789article
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Abstract

Promoting deep industrial transformation and upgrading serves as the micro-level foundation for improving the institutional framework of new quality productive forces and advancing a high-standard socialist market economy. Enhancing government data governance mechanisms represents a critical pathway to achieving this objective. Drawing on panel data from 2011 to 2021 concerning the establishment of government big data administration agencies and Chinese A-share listed firms, this paper employs a multi-period difference-in-differences (DID) approach to systematically examine the impact of government data governance on firm upgrading, along with its underlying mechanisms. The empirical results reveal that government data governance significantly facilitates firm upgrading, and this effect remains robust across a series of sensitivity checks. Heterogeneity analyses indicate that the positive effects are more pronounced among firms in heavily regulated industries, high-tech sectors, enterprises with higher levels of data utilization, and regions with higher administrative levels of data governance institutions. Mechanism tests further suggest that government data governance improves the quality of public service delivery and empowers corporate digital governance, both of which play key intermediary roles in promoting firm upgrading. These findings confirm the enabling role of government data governance in advancing firm upgrading and offer policy implications for stimulating innovation among market entities and strengthening the micro-foundations of China’s economic recovery and growth momentum.

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https://doi.org/https://doi.org/10.5755/j01.ee.37.1.41789

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@article{d2026,
  title        = {{Data-Driven Empowerment: How Government Data Governance Facilitates Firm Upgrading}},
  author       = {D Wang et al.},
  journal      = {Engineering Economics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.5755/j01.ee.37.1.41789},
}

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