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Executive Compensation and Corporate Fraud in China

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Abstract

This study investigates the relation between CEO compensation and corporate fraud in China. We document a significantly negative correlation between CEO compensation and corporate fraud using data on publicly traded firms between 2005 and 2010. Our findings are consistent with the hypothesis that firms penalize CEOs for fraud by lowering their pay. We also find that CEO compensation is lower in firms that commit more severe frauds. Panel data fixed effects and propensity score methods are used to demonstrate these effects. Our results also indicate that corporate governance mechanisms influence the magnitude of punishment. We find that CEOs of privately controlled firms, firms that split the posts of CEO and chairman, and CEOs of firms located in developed regions suffer larger compensation penalties for committing financial fraud. Finally, we show that CEOs at firms that commit fraud are more likely to be replaced compared to those at non-fraud firms.

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Notes

  1. The information is translated by the author from original Chinese documents of these firms. Information on Beifang Chuangye Corp is obtained from

    http://www.bfcy.cc/%28hyhvc1jpwgx1no45g0w2v145%29/UploadFile/2010511165428515.pdf. Information on Industrial Bank Corp. is downloaded from http://download.cib.com.cn/netbank/download/cn/Investor_Relations/20130917.pdf. Information on Sichuan Mingxing Electric Power Co. could be obtained from http://www.mxdl.com.cn/uploadfile/2013/2/18111137328.pdf.

    Information on Great Wall Technology could be assessed from http://www.cec.com.cn. Information on Capital Environmental Protection could be assessed from ftp://www.tjcep.com/29aa37a4-ba25-45f2-9767-4946630877f8.pdf. Information on Copote is obtained from http://www.copote.com/pdf/zlgz/600476_2012_6.pdf.

  2. Prior to this, only average data for the top management team are accessible. We discuss this in the sensitivity analysis section later in this paper.

  3. The title “Chief Executive Officer” or “CEO” is not commonly used in the GTA dataset. In this study, we identify the CEO position by the title “General Manager” or “President.” This captures most CEOs. We also manually checked other titles such as “Administrative President,” “Executive President” in cases where current CEO compensation data were missing.

  4. The firm might have been committing fraud for several years prior to this announcement.

  5. The GTA provides information on “Date of violation” (Vltyear) to identify financial years affected by a specific fraud. For example, an entry may be “1998, 1999, 2000, 2002,” which suggests the fraud affects and involves all these financial years. We count the number of years listed in the entry to capture Serious Fraud. It is coded as 4 in the above-mentioned example. When there are multiple violations in a given year, the count is aggregated within the year.

  6. The NERI index covers marketization level of four direct-controlled municipalities and twenty-seven provinces. The four direct-controlled municipalities are Beijing, Shanghai, Tianjin, and Chongqing. The 27 mainland provinces (excluding Taiwan, Hong Kong, and Macau) include Anhui, Fujian, Gansu, Guangdong, Guangxi, Guizhou, Hainan, Hebei, Heilongjiang, Henan, Hubei, Hunan, Jiangsu, Jiangxi, Jilin, Liaoning, Neimeng (Inner Mongolia), Ningxia, Qinghai, Sangxi, Shandong, Shanxi, Sichuan, Xizang (Tibet), Xinjiang, Yunnan, and Zhejiang.

  7. The CSRC classifies Chinese industries into 22 categories: Agriculture and fishery; Mining; Manufacturing-food/beverage; Manufacturing-Textiles; Manufacturing-Furniture; Manufacturing-Paper/Printing; Manufacturing-Petroleum; Manufacturing-Electronic; Manufacturing-Metal/Non-metal; Manufacturing-Machines; Manufacturing-Pharmaceutical; Manufacturing-others; Electricity, water, and other energy manufacturing and supply; Construction; Transportation and logistics; Information technology; Wholesales and retails; Finance and insurance; Real estate; Service; Communication; and Others.

  8. For example, the statistician does not typically observe CEO and management quality but these are nevertheless important determinants of executive pay. Their exclusion leads to an omitted variable bias problem that can be partially ameliorated by including firm fixed effects.

  9. The restriction reduced the number of firms in the sample to 307 and the number of firm years to 1028.

  10. Endogenous selection is potentially a serious problem especially as the number and frequency of firms committing fraud in the population of public enterprises are, in fact, low. Another solution to the problem is instrumental variables. However, the difficulty here is that it is problematic to find a legitimate theoretical instrument that is correlated with the propensity to commit fraud (the relevance criteria) and is also uncorrelated to executive compensation (the exclusion criteria). In consequence, any chosen instrument set might turn out to be theoretically somewhat arbitrary.

  11. The results are not affected by this choice. For example, re-estimation using the logit method yields qualitatively similar results.

  12. It is necessary for this exercise to construct a binary variable from the count variable.

  13. We would like to thank an anonymous reviewer for this suggestion.

  14. We also find this set of qualitative results held for the OLS estimates and propensity score estimates as well.

  15. We would like to thank a referee for suggesting this point.

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Acknowledgments

We would like to thank Peter Cappelli, Douglas Cumming, Wenxuan Hou, Edward Lee, Joseph Fan, Justin Tan, two anonymous reviewers for comments during the preparation of this paper. We would also like to thank Xin Zhou for providing data on CEO political connections. We thank the organizers and participants of the Sustainable and Ethical Entrepreneurship, Corporate Finance and Governance, and Institutional Reform in China conference in Beijing, China 2013. Financial support from the Center for Human Resources at the Wharton School is gratefully acknowledged as well as funding and support from Lancaster University (Conyon) and SUNY-Brockport (He).

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Authors and Affiliations

  1. Lancaster University, Lancaster, UK

    Martin J. Conyon

  2. The Wharton School, University of Pennsylvania, Philadelphia, USA

    Martin J. Conyon

  3. College at Brockport, State University of New York, Brockport, USA

    Lerong He

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  1. Martin J. Conyon
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Correspondence to Lerong He.

Appendix: Variable Definitions

Appendix: Variable Definitions

Log (CEO pay)

=

Logarithm of CEO compensation, which is calculated as the total of salary, bonus, and other cash compensation as reported by the firm

Fraud

=

1 if fraud is revealed in a given year, and zero otherwise

Serious fraud

=

The number of years the fraud was perpetrated

Serious fraud: CSRC is prosecutor

=

1 if the fraud is prosecuted by the CSRC or Finance Department, and zero otherwise

Serious fraud: multiple violations

=

1 if the fraud involves more than one violations, and zero otherwise

Serious fraud: severe violation

=

1 if the fraud is related to false financial information, misleading statements, price manipulation, illegal provisions, and insider trading, and zero otherwise

Comp. Comm.

=

1 if there is a compensation committee, and zero otherwise.

Audit Comm.

=

1 if there is an audit committee, and zero otherwise

Combine

=

1 if the CEO also holds the chairperson position, and zero otherwise

Board size

=

The number of directors on a board

Outside directors

=

1 if the proportion of outside directors on the board is more than one-third, and zero otherwise

SOE

=

1 if the ultimate owner of the firm is state, and zero otherwise.

Largest shareholder

=

Percentage ownership of the single largest shareholder

Foreign

=

1 if the ultimate owner is a foreign entity, and zero otherwise

Auditor

=

1 if the auditor is one of the top 8 largest auditors ranked by total assets, and zero otherwise.

Log sales

=

Logarithm of total firm sales

ROA

=

Return on assets ratio measured as earnings before interest and tax divided by total assets.

Stock returns

=

Annualized stock returns calculated from monthly returns data

Market to book

=

Market value of the firm divided by total assets

Volatility

=

Past 3 years stock returns volatility calculated as rolling average

Regional development

=

NERI marketization index of the province where a firm’s headquarter locates

Industry

=

Based on 22 CSRC classification of industries: Agriculture and fishery; Mining; Manufacturing-food/beverage; Manufacturing-Textiles; Manufacturing-Furniture; Manufacturing-Paper/Printing; Manufacturing-Petroleum; Manufacturing-Electronic; Manufacturing- Metal/Non-metal; Manufacturing-Machines; Manufacturing-Pharmaceutical; Manufacturing-others; Electricity, water and other energy manufacturing and supply; Construction; Transportation and logistics; Information technology; Wholesales and retails; Finance and insurance; Real estate; Service; Communication; Others

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Conyon, M.J., He, L. Executive Compensation and Corporate Fraud in China. J Bus Ethics 134, 669–691 (2016). https://doi.org/10.1007/s10551-014-2390-6

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