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Publishing Language: Chinese

Big Data Audit and Government Procurement——Empirical Evidence from the Golden Audit Ⅲ Project

School of Government Audit, Nanjing Audit University
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Abstract

Standardizing government procurement behavior is an important means to improve the efficiency of fiscal expenditure and cope with the fiscal pressure. Based on the micro data of government procurement, this paper exploits the implementation of the Golden Audit Ⅲ Project and uses the difference-in-differences method to explore the impact of big data audit on government procurement. The benchmark results show that big data audit can significantly promote the development of government procurement, which is reflected in the improvement of government procurement compliance, and the significant positive effect of government procurement to promote the construction of a unified national market. The dynamic effect analysis shows that the role of big data audit in promoting the development of government procurement continues to increase over time. Using methods such as placebo test, instrumental variable method and robust estimators of staggered DID for robustness tests, the benchmark results are still valid. The mechanism analysis results show that big data audit works through two channels: improving the effectiveness of audit supervision and reducing information asymmetry in government procurement audit. Further analysis reveals that the positive effect of big data audits on the development of government procurement is more pronounced in regions with greater fiscal pressure and lower marketability indices. This paper confirms the positive effect of big data audit on improving government governance, and provides insights for policy-making to promote the development of government procurement.

CLC number: H57, H83, M42

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China Journal of Economics
Pages 275-304

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Cite this article:
Zhang J. Big Data Audit and Government Procurement——Empirical Evidence from the Golden Audit Ⅲ Project. China Journal of Economics, 2024, 11(3): 275-304. https://doi.org/10.26599/CJE.2024.9300309

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Published: 17 December 2025
© 2024 Tsinghua University Press