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Public Governance and Corporate Fraud: Evidence from the Recent Anti-corruption Campaign in China

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Abstract

Taking advantage of the China’s recent anti-corruption campaign, we attempt to examine the effect of public governance on a firm’s incentive to commit fraud. Using enforcement actions data from the Chinese Securities Regulatory Commission (CSRC) from 2004 to 2014, we find that, due to enhanced public governance, firms are less likely to commit fraud in the post-campaign period than in the pre-campaign period. We further show that the effect of public governance is more evident in privately held listed firms, in firms with weak legal environment, and in firms in areas with poor local economies. In addition, we find that older CEOs respond less actively to the public governance caused by anti-corruption regulations. This paper offers clear policy implications for business ethics by indicating that public governance provides external monitoring of corporate decisions.

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Notes

  1. See Demirgüç-Kunt and Maksimovic (1998, 1999); Rajan and Zingales (1995); Levine (1999); La Porta et al. (1997a, b, 1998, 1999b, 2000a, b); Booth et al. (2001); Beck and Levine (2002); Giannetti (2003); Fan et al. (2012); La Porta et al. (1999a); Fan et al. (2008).

  2. For detailed information about the Transparency International Survey, please visit https://www.transparency.org/research/cpi.

  3. See Jensen and Meckling (1976), Jensen (1986, 1993), Shleifer and Vishny (1989, 1997).

  4. The paper is one part of the joint research project by the Supervision Bureau and Research Bureau of The People’s Bank of China, the central bank of China.

  5. Source: China Securities Regulatory Commission Annual Report 2012. The figure is also used in Li et al. (2014).

  6. The financial industry belongs to the regulated industry. Firms in regulated industries are highly regulated by the government, which distinguishes them from firms in typically unregulated industries. Therefore, following the accounting and finance literature, we exclude firms in the financial industry from our sample.

  7. We adopt the 2012 CSRC industry classification standard provided by the CSRC. Since most of the firms belong to the manufacturing industry, the code for which begins with ‘‘C’’, we use first three codes to classify the industries within the manufacturing industry. We use the first one code to classify other industries (Wu et al. 2014).

  8. The industry of Waste resources recycling has a relatively large number of ratio of frauds. The industry code for the Waste resources recycling industry is “C42”. However, there is only one listed firm in the industry of Waste resources recycling. The firm went public in 2010, and thus we only have 5 firm-year observations in our sample for the industry of Waste resources recycling. The limited number of firm-year observations in the industry is the reason for why we end up with a relatively large number of ratio of frauds for the industry.

  9. For example, we have six fraud cases out of ten firm-year observations in a specific industry in the full sample. We also have 993 fraud cases in our fraud sample. Thus, the percentage of frauds will be around 0.6 % (=6/993), and the ratio of frauds will be 60 % (=6/10).

  10. A referee raises the point that the different types of fraud are important as not all frauds involve bribery or are connected to politicians. We agree with the referee. However, after looking at the details of the data, we are unable to make further analysis about the types of fraud for two reasons. First, most fraud cases involve multiple types of fraud. 32.43 % of the fraud observations involve only one type of fraud. 25.88 % of the fraud observations involve two types of fraud. 21.55 % of the fraud observations involve three types of fraud. 14.7 % of the fraud observations involve four types of fraud. The rest of the observations involve more than four types of fraud. As the referee pointed out, different types of fraud might have different effects for the firm; therefore, we cannot disentangle the effects of different types of fraud if a firm commits multiple types of fraud. Second, we also cannot study the effect of fraud type in the fraud observations with only one type of fraud since a large proportion of them are included in the category of Others.

  11. For example, we have 100 observations in the sample, in which 36 observations are taken from the post-campaign period, and 64 observations are taken from the pre-campaign period. We assign the value of one to the variable POST if the observations are from the post-campaign period, otherwise zero. The mean value of POST equals the total value of POST (36 × 1 + 64 × 0 = 36) divided by the number of observations (100). Therefore, the mean value of POST is 0.36, which indicates that we have 36 % (36/100) of observations from the post-campaign period.

  12. Since the legal index is constructed from 1997 to 2009 and is stable across years, we use the average value of the index as proxy for the local legal protection in each province.

  13. There is no firm-fixed effect model for the probit regression. Therefore, we can only adopt a logit model if we want to control for the firm-fixed effect model.

  14. In reality, the situation described here is nearly impossible for several reasons. First, as stated above, according to the statistics of CSRC, 12.7 % of public firms were involved in corporate misconduct in 2012. Although 12.7 % may not be a small proportion, it is still not large enough to ruin financial markets and the Chinese economy. Second, even if the fraud firms have a detrimental effect on financial markets and economy, the first choice for the government to restore the economy is to enact new security laws instead of disciplining government officials. Third, the reason for President Xi Jinping to launch anti-corruption campaign may include various factors such as economy, politics, culture, and so on. Corporate fraud is only one problem in the economy, and is not on its own sufficiently important to influence President Xi to launch the boldest and the most serious anti-corruption campaign.

  15. The CSR data is obtained from the China Stock Market and Accounting Research (CSMAR) database.

  16. The undetected fraud problem here is that we do not observe the corporate misconducts if they are not caught by the CSRC. We may treat those undetected fraud firms as no-fraud firms. If that is the case, those fraud firms without being caught will be included in the non-fraud sample. Thus, our regression results might be biased. Yu and Yu (2012) argues that the fraudulent behaviors of a large firm are less likely to be undetected. In another word, the issue of undetected fraud is likely to be more pronounced among smaller firms. Therefore, if we limit the sample in the large firms, we should have a lower probability to include the undetected fraud into the non-fraud sample, which make our regression more reliable.

  17. Following Wang (2013) and Poirier (1980), we also adopt a bivariate probit model with partial observability to explicitly solve the problem of partial observability. However, due to the data characteristics, the bivariate probit fails to converge and report any results. Therefore, we are not able to report the results in the paper.

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  1. School of Finance, Southwestern University of Finance and Economics, 555 Liutai Avenue, Wenjiang District, Chengdu, 611130, China

    Jian Zhang

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Zhang, J. Public Governance and Corporate Fraud: Evidence from the Recent Anti-corruption Campaign in China. J Bus Ethics 148, 375–396 (2018). https://doi.org/10.1007/s10551-016-3025-x

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