贵州财经大学学报

贵州财经大学学报 ›› 2023 ›› Issue (04): 63-71.

• 工商管理 • 上一篇    下一篇

大数据税收征管能够缓解企业税负粘性吗——基于“金税三期工程”的政策效应分析

邓菊秋1, 杨加裕1, 杨春宇2   

  1. 1. 四川大学, 四川 成都 610065;
    2. 贵州财经大学, 贵州 贵阳 550025
  • 收稿日期:2022-09-16 出版日期:2023-07-15 发布日期:2023-07-13
  • 作者简介:邓菊秋(1968—),女,四川广安人,四川大学经济学院教授,研究方向为税收理论与政策;杨加裕(1993—),四川眉山人,四川大学经济学院博士研究生,研究方向为税收政策、税收信息化;杨春宇(1977—),男,山东曹县人,贵州财经大学贵州旅游经济与管理研究院教授,博士生导师,研究方向为旅游经济与管理演化理论。
  • 基金资助:
    四川大学中央高校基本科研业务费研究专项项目"企业税负对企业财务可持续增长的影响"。

Big Data in Tax Enforcement and Stickiness of Corporate Tax Burden - Analysis of policy effect based on “the Third Phase of the Golden Tax Project”

DENG Juqiu1, YANG Jiayu1, YANG Chunyu2   

  1. 1. School of Economics, Sichuan University, Chengdu, Sichuan 610065, China;
    2. School of Business Administration, Guizhou University of Finance and Economics, Guiyang, Guizhou 550025, China
  • Received:2022-09-16 Online:2023-07-15 Published:2023-07-13

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摘要： 基于2008~2016年上市公司数据,借助"金税三期工程"在全国逐步试点作为准自然实验,运用双重差分法探究大数据税收征管对企业税负粘性的影响及作用机制。结果表明:大数据税收征管能够有效缓解企业税负粘性,这种缓解效应在民营企业、市场化程度较高地区的企业以及财政创收压力较小地区的企业中较为突出。机制检验发现,大数据税收征管通过减少征纳双方信息不对称、规范税务机关征税行为、切实落实税收优惠政策等路径可有效缓解企业税负粘性。此结论可为持续完善税收信息化建设、进一步优化税收营商环境和实施新的减税降费政策提供理论支撑和经验证据。

关键词: 大数据税收征管, 金税三期, 企业税负, 企业税负粘性

Abstract: Big data in tax enforcement inhibits corporate tax avoidance, which shows the situation of "tax reduction is difficult to reduce burden". However, few literatures reveal the positive significance of big data in tax enforcement in alleviating corporate tax burden. Based on the data of listed companies from 2008 to 2016, and with the help of the "Golden Tax Phase III Project" as a quasi-natural experiment, the double-difference method is used to explore the impact and mechanism of big data tax collection and management on corporate tax burden. The results show that big data in tax enforcement can effectively alleviate the stickiness of corporate tax burden, and this mitigation effect is more prominent in private enterprises, enterprises in areas with a high degree of marketization, and enterprises in areas with less financial income pressure. Mechanism inspection finds that big data in tax enforcement can effectively alleviate the stickiness of corporate tax burden by reducing the information asymmetry between the two parties, regulating the tax collection behavior of tax authorities, and effectively implementing preferential tax policies. The above conclusions provide theoretical support and empirical evidence for the continuous improvement of tax informatization construction, the further optimization of the tax business environment, and the implementation of new tax and fee reduction policies.

Key words: big data in tax enforcement, third phase of the golden tax project, corporate tax burden, stickiness of corporate tax burden

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