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Anti-corruption campaign and corporate tax evasion: evidence from China

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Abstract

While the literature has well-documented economic consequences of corruption on economic development, whether and how corruption affects corporate tax evasion is unclear. This study uses anti-corruption investigations in China as an exogenous shock to local corruption to identify the link between corruption and firms’ tax evasion behavior in a difference-in-differences framework. We present strong evidence that the anti-corruption campaign has a negative effect on corporate tax evasion. One plausible explanation is that the anti-corruption campaign conducted by the central inspection team undermined the political umbrella of companies and increased the chances of local government-enterprise collusion being detected, thereby inhibiting tax avoidance. This effect is more significant among firms in provinces with high inspection intensity, firms in economically-developed regions, large firms and firms in politically-sensitive industries. Our findings enrich the existing studies on the economic and fiscal effects of anti-corruption campaigns.

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Notes

  1. Data Source: Central Commission for Discipline Inspection State Supervision Website.

  2. CSMAR database is a comprehensive research-oriented database focusing on China finance and economy. CSMAR was developed by Shenzhen CSMAR Data Technology Co., Ltd based on academic research needs, meeting with the international professional standards (such as CRSP, COMPUSTAT) while adapting to China’s features. This database has been widely used in research on Chinese corporate issues (Fang et al., 2022).

  3. Wind database has built a complete and accurate large-scale financial engineering and financial data center on financial and securities data in China, which covers stocks, bonds, funds, foreign exchange, financial derivatives, commodities, macroeconomics, and financial news. Wind’s data is also frequently cited by authoritative Chinese and English media, research reports, and academic papers (Kong et al., 2021).

  4. See http://www.CCDI.gov.cn.

  5. There are some limitations using ETR as a measure for tax evasion. Most researchers in the field of tax evasion acknowledge this fact but cannot come up with proper measurements. For example, Hanlon and Heitzman (2010) made an outstanding review of tax research, and pointed out that an important issue highlighted by the growing research on tax avoidance is how to measure avoidance, and empirical measures of tax avoidance that rely on financial statements have known limitations in part because they capture variation in tax avoidance as well as the choice between conforming and non-conforming tax avoidance. However, as there is no better measure for tax evasion, the recent literature began to use the effective tax rate measures (ETR) as a tax evasion indicator (such as, Armstrong et al., 2015; Li et al., 2017). This measure is not perfect but widely accepted. Given the facts above, our findings should be interpreted with caution. We thank the editor for pointing out this issue.

  6. We also expand the pre-treatment period in the dynamic difference-in-differences design to get a more comprehensive perspective on trends in treatment and control group prior to treatment in Appendix Table 13. We find that the coefficients of Prek (k = 1, 2, 3, 4, 5) are negative and insignificant, which does not show any pre-trends, suggesting that firm tax evasion has no significant change prior to China’s anti-corruption campaign. The coefficient estimates of Postk (k = 1, 2) are positive and significant at 5%. This finding is consistent with the parallel pre-event trend hypothesis.

  7. Please refer to Rambachan and Roth (2019) and Clarke and Mühlrad (2021) for the details of “Honest DID” method.

  8. However, we find that the results in table 6 are not as significant using these other indicators from the statistical perspective, which are significant at 5% or 10% level. A plausible explanation is that, the observation here is reduced a lot due to the missing data. In addition, we follow the literature (such as, Armstrong et al., 2015; Li et al., 2017), and use the effective tax rate measures (ETR) in our main regression, a measure which has been widely accepted. So, the results in Table 6 are just the supplements to our main findings, which help to strengthen our empirical results.

  9. We repeat the study for SOEs, controlling for the same fixed effects as the base results. The results are presented in Appendix Table 14. We find that SOEs are more significantly influenced by anti-corruption actions.

  10. The positions of civil servants are divided into leadership and non-leadership positions. The pyramid of leadership positions is divided into 10 levels: national-level positions, national-level deputy positions, provincial-ministerial-level positions, provincial-ministerial-level deputy positions, department-bureau-level positions, department-bureau-level deputy positions, county-level positions, county-level deputy positions, township-level positions, and township-level deputy positions. Non-leadership positions are set below the department and bureau level.

  11. The number of officials punished can be used to measure not only the inspection intensity, but also the corruption levels in the province.

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Acknowledgement

We thank the editor, Nadine Riedel, two referees, Chen Lin, Rui Shen, Shasha Liu, Jian Zhang, and seminar participants at Jinan University, Shanghai University of Finance and Economics, and Zhongnan University of Economics and Law for helpful comments. We acknowledge the financial support of the Major Project of the National Social Science Foundation of China (Grant No. 21ZDA010), the National Natural Science Foundation of China (Grant No. 72202078), the Humanities and Social Science Fund of Ministry of Education of China (Grant No. 22YJC790097), and Huazhong University of Science and Technology Double First-Class Funds for Humanities and Social Sciences, and the Fundamental Research Funds for the Central Universities HUST:2021WKFZZX007. All authors contributed equally to this paper.

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Authors and Affiliations

  1. School of Economics, Huazhong University of Science and Technology, Zhongnan Huazhong University of Economics and Law, 1037# Luoyu Road, Wuhan, 430074, China

    Dongmin Kong

  2. School of Economics, Huazhong University of Science and Technology, 1037# Luoyu Road, Wuhan, 430074, China

    Yue Zhang & Ni Qin

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  1. Dongmin Kong
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We thank the editor, Nadine Riedel, two referees, Chen Lin, Rui Shen, Shasha Liu, Jian Zhang, and seminar participants at Jinan University, Shanghai University of Finance and Economics, and Zhongnan University of Economics and Law for helpful comments. We acknowledge the financial support of the Major Project of the National Social Science Foundation of China (Grant No. 21ZDA010), the National Natural Science Foundation of China (Grant No. 72202078), the Humanities and Social Science Fund of Ministry of Education of China (Grant No. 22YJC790097), Huazhong University of Science and Technology Double First-Class Funds for Humanities and Social Sciences, and the Fundamental Research Funds for the Central Universities HUST:2021WKFZZX007. All authors contributed equally to this paper.

Appendixes

Appendixes

See Tables 10, 11,12, 13, 14 and 15.

Table 10 CCDI’s inspection
Full size table
Table 11 Variable Definitions
Full size table
Table 12 Cox Proportional Hazard Model Results
Full size table
Table 13 Parallel Trend Test Using Widen Sample Periods
Full size table
Table 14 Effects of CCDI Inspection on SOEs’ Corporate Tax Evasion
Full size table
Table 15 List of Officials Punished in the Inspection
Full size table

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Kong, D., Zhang, Y. & Qin, N. Anti-corruption campaign and corporate tax evasion: evidence from China. Int Tax Public Finance (2023). https://doi.org/10.1007/s10797-023-09777-x

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