Earnings Management Behavior and the Stickiness of Income Tax Burden: Big Data Analysis Based on China A-share Listed Companies
- DOI
- 10.2991/978-94-6463-064-0_61How to use a DOI?
- Keywords
- Tax burden stickiness; earnings management; manipulable accruals; tax avoidance
- Abstract
This paper uses the panel data of China A-share listed companies from 2009 to 2019 to calculate the manipulable accruals and explore the impact of corporate earnings management on the stickiness of income tax burden. The empirical results show that for every 1% increase in corporate profit before tax, the current income tax will increase by 0.76%; for every 1% decrease in corporate profit before tax, the current income tax will decrease by 0.41%. There is a significant positive correlation between earnings management behavior and the stickiness of corporate income tax burden. The higher the degree of earnings management, the greater the stickiness of income tax burden. A sample regression of enterprises with different property rights shows that the positive correlation between earnings management and the stickiness of income tax burden in state-owned enterprises is more significant.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Mengyuan He PY - 2022 DA - 2022/12/27 TI - Earnings Management Behavior and the Stickiness of Income Tax Burden: Big Data Analysis Based on China A-share Listed Companies BT - Proceedings of the 2022 3rd International Conference on Big Data and Social Sciences (ICBDSS 2022) PB - Atlantis Press SP - 589 EP - 601 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-064-0_61 DO - 10.2991/978-94-6463-064-0_61 ID - He2022 ER -