Analysis of the Path to Improving the Efficiency of Regional Economic Factor Allocation through the Development of Digital Finance
DOI:
https://doi.org/10.54097/fq3f6g83Keywords:
Digital finance, regional economy, efficiency of factor allocation, improvement path, empirical analysis.Abstract
Digital finance, supported by information technology, has become the core driving force for optimizing regional economic factor allocation by reconstructing financial service models and expanding service boundaries. This article is based on the theories of financial intermediation and information asymmetry, combined with the development practice of digital finance in China, to systematically analyze the improvement path of its efficiency in the allocation of core production factors such as capital, labor, and technology. Research has found that digital finance improves regional factor allocation efficiency in three dimensions: capital mismatch correction, labor flow optimization, and technological achievement transformation, by lowering financing barriers, breaking geographical barriers, and accelerating information flow. On an empirical level, the positive correlation between the high penetration rate of digital finance in the eastern region and the efficiency of factor allocation, as well as the narrowing effect of digital finance development on regional disparities in the central and western regions, have all verified the effectiveness of this improvement mechanism. Digital finance plays a key role in promoting cross regional flow of factors and alleviating regional development imbalances. The conclusion of this article provides theoretical reference and practical inspiration for improving digital finance infrastructure, strengthening regional digital finance collaborative development, and enhancing the level of market-oriented factor allocation.
Downloads
References
[1] Aghion, P., Howitt, P., & Mayer-Foulkes, D. (2005). The effect of financial development on convergence: Theory and evidence. The quarterly journal of economics, 120(1), 173-222.
[2] Hsieh C T, Klenow P J. Misallocation and manufacturing TFP in China and India [J]. The Quarterly journal of economics, 2009, 124(4): 1403-1448.
[3] Bessen, J. (2019). Automation and jobs: When technology boosts employment. Economic Policy, 34(100), 589-626.
[4] Huang Y, Zhang L, Li Z, et al. Fintech credit risk assessment for SMEs: Evidence from China[J]. 2020.
[5] Jiang, X., Wang, X., Ren, J., & Xie, Z. (2021). The nexus between digital finance and economic development: Evidence from China. Sustainability, 13(13), 7289.
[6] Tan Ying, Wang Pan, Zhang Xun The labor migration effect of digital finance development: micro evidence from Chinese household tracking survey [J]. Financial Research, 2024, (10): 39-57.
[7] Xun, Z., Guanghua, W., Jiajia, Z., & Zongyue, H. (2020). Digital economy, financial inclusion and inclusive growth. China Economist, 15(3), 92-105.
[8] Xu Rui, Wang Yanyan, Yu Lisheng The Innovation Incentive Effect of Patent Pledge Loans [J]. Financial Research, 2024, (10): 58-75.
[9] Zhan, M., Li, S., & Wu, Z. (2023). Can digital finance development improve balanced regional investment allocations in developing countries?—The evidence from China. Emerging Markets Review, 56, 101035.
[10] LeSage J, Pace R K. Introduction to spatial econometrics[M]. Chapman and Hall/CRC, 2009.
[11] Niu, G., Jin, X., Wang, Q., & Zhou, Y. (2022). Broadband infrastructure and digital financial inclusion in rural China. China Economic Review, 76, 101853.
Downloads
Published
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

