数字经济、财政纵向失衡与区域经济包容性增长

——基于双重机器学习的因果推断

陈旭东　沈利芸　秦文晋

（天津财经大学）

内容提要：数字经济是引领经济增长和社会发展的重要引擎，也是推动区域经济包容性增长的现实选择。本文以国家大数据综合试验区政策构造准自然实验，运用双重机器学习模型考察以数据要素为核心的数字经济对区域经济包容性增长的影响。研究结果表明，数字经济能够提升区域经济包容性增长，这一效应在经济发展基础较好、处于区域外围、转移支付规模庞大的城市更加明显。其中，地方财政纵向失衡的降低是重要路径。然而，从地方治理层级的内部来看，尽管数字经济降低了省级财政纵向失衡，但省以下政府仍然存在财政收支分权不相匹配的问题，一定程度上削弱了数字经济对区域经济包容性增长的促进作用。本文将机器学习算法与传统因果推断结合，基于政府间财政关系视角探究了数字经济促进包容性增长的制度成因，为深化财税体制改革、全面促进区域经济包容性增长提供了经验证据和决策参考。

关键词：数字经济；区域经济；财政纵向失衡；包容性增长；双重机器学习

作者简介：陈旭东，天津财经大学财税与公共管理学院教授、博士生导师，天津，300222；沈利芸，天津财经大学财税与公共管理学院博士研究生，通信作者；秦文晋，天津财经大学财税与公共管理学院讲师。

基金项目：国家社会科学基金后期项目“数字经济背景下的财政治理与税收分配研究”（23FJYB007）；2022年度天津市研究生科研创新项目“数字经济背景下税收横向分配对企业数字化转型的影响研究”（2022BKY222）；天津市哲学社会科学规划委托项目“数字政府建设赋能共同富裕的模式创新与政策优化研究”（TJJJJ2302-05）

引用格式：陈旭东,沈利芸,秦文晋.数字经济、财政纵向失衡与区域经济包容性增长——基于双重机器学习的因果推断[J].经济与管理研究,2024,45(9):61-77.DOI:10.13502/j.cnki.issn1000-7636.2024.09.004.

Digital Economy, Vertical Fiscal Imbalances, and Inclusive Growth of Regional Economy

—Causal Inference Based on Double Machine Learning

CHEN Xudong, SHEN Liyun, QIN Wenjin

(Tianjin University of Finance and Economics, Tianjin 300222)

Abstract: With the continuous penetration of data elements into the economy and society, the role of the digital economy in promoting inclusive growth of the regional economy is becoming increasingly obvious. From the perspective of the reform of inter-governmental fiscal relations, this paper constructs a quasi-natural experiment using the policy of national big data comprehensive pilot zones. It employs a double machine learning model to investigate the impact and internal mechanism of the digital economy reform centered on data elements on inclusive growth of the regional economy.

The results show that the digital economy promotes inclusive growth in Chinese cities. This conclusion holds after a series of robustness tests. This promotion effect is more evident in cities with a better economic foundation and in the periphery of the region and areas with high transfer payments. Mechanism tests indicate that this promotion effect is achieved by effectively reducing local vertical fiscal imbalances. Further, from the perspective of the local governance level, although the digital economy reduces the vertical fiscal imbalances of provincial governments, local governments below the provincial level still encounter the mismatch of fiscal revenue and expenditure decentralization, which weakens the promotion role to a certain extent, and hinders the realization of common prosperity. By combining machine learning algorithms with traditional causal inference, this paper explores the institutional causes of the digital economy promoting inclusive growth of the regional economy from the perspective of inter-governmental fiscal relations, providing empirical evidence and decision-making reference for deepening the reform of fiscal and taxation systems and comprehensively promoting inclusive growth of the regional economy.

The policy implications are as follows. On the one hand, it is essential to strengthen the role of the digital economy in promoting economic development and improving regional resource allocation to drive inclusive growth of the regional economy by building up the strength, quality, and size of China’s digital economy. Specifically, it should accelerate the deep integration of data elements with the real economy, share digital dividends, and give full play to the radiating and demonstration roles of the big data pilot zone in the digital economy, digital governments and digital enterprises. On the other hand, it is necessary to accelerate the empowerment of data elements for government governance and fully recognize the role of reducing local vertical fiscal imbalances in promoting inclusive growth of the regional economy. At the same time, it should improve the reform of the fiscal system below the provincial level, reduce the vertical fiscal imbalances of municipal governments, and provide a favorable institutional environment for the digital economy to realize the inclusive growth effect.

Keywords: digital economy; regional economy; vertical fiscal imbalance; inclusive growth; double machine learning

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