It is largely forgotten that [Enron] had been a favorite of the environmental Left and an advocate/practitioner of the trendy notion of corporate social responsibility.
R. L. Bradley, 2009.
Abstract
This study investigates how managers in firms that have committed fraud strategically use socially responsible activities in coordination with their fraudulent financial reporting practices. Using propensity score matching to select control firms that have a similar probability of fraud in the pre-fraud benchmark period, we find that the corporate social responsibility (CSR) performance of fraudulent firms in the fraud-committing period is significantly higher compared with the CSR performance of non-fraudulent control firms during this period, and compared with that during their own pre-fraud benchmark periods. This higher CSR performance by fraudulent firms is achieved by means of investing in both stakeholder and third-party CSR categories and by improving in CSR strengths. Furthermore, the increase in CSR performance is more pronounced for fraudulent firms with a weak governance environment, and for firms located in high-religiosity states. Overall, our findings suggest that fraudulent firms strategically adjust their CSR performance to coordinate with their fraudulent financial activities.
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Notes
While studies such as that of Kim et al. (2012) show that CSR-engaged firms are less likely to be subject to SEC enforcement actions, a positive relationship between fraud and CSR performance before the public discovery of fraud, if observed, would not necessarily be in conflict with these studies. Because regulatory enforcement actions by the SEC occur after the fraud is detected, whereas our study focuses on CSR performance during the period in which fraud is being committed. Furthermore, the lower likelihood of the SEC’s regulatory enforcement actions for CSR firms may be exactly due to these firms’ improvement in CSR during the fraud-committing period.
Audit Analytics data is only available to us up to 2014; thus, we supplement the years 2015–2016 using the Stanford Securities Class Action Clearinghouse database.
MSCI STATS is the successor to Kinder, Lydenberg & Domini (KLD), Innovest, and the Investor Responsibility Research Center (IRRC).
The coverage of the Audit Analytics Corporate and Legal database starts in 1960. However, there are fewer than 100 cases from 1960 to 1994. Consequently, after the sample selection process, all of our fraud cases are taken from the period from 1995 onwards.
A major advantage of using the Audit Analytics class action litigation database is that each fraud case has a start date (exposure begin date) and an end date (exposure end date), enabling us to accurately construct the fraud-committing period of each firm.
The ex ante probability of financial fraud is computed using the following procedure. First, we compute the predicted value using the estimated coefficients of Dechow et al. (2011): Predicted value = − 7.893 + 0.79 × rsst_acc + 2.518 × d_rec + 1.191 × d_inv + 1.979 × %soft_at + 0.171 × d_cs – 0.932 × d_roa + 1.029 × issue, where rsst_acc is total accruals; d_rec, d_inv, d_cs, and d_roa are changes in receivables, inventory, cash sales, and return on assets (ROA), respectively; %soft_at is the percentage of soft assets; and issue is a dummy variable that equals 1 if a firm issues equity or debt and 0 otherwise. Next, the ex ante predicted probability of financial fraud is computed as p(Fraud) = exp(Predicted_value)/[1 + exp(Predicted_value)].
For example, in 2010 data there are eight community strengths—charitable giving, innovative giving, support for housing, support for education, non-US charitable giving, volunteer program, community engagement, and other community strengths—and four community concerns—investment controversies, negative economic impact, tax disputes, and other community concerns.
For example, the maximum possible number of environmental concerns was six in the period from 1991 to 1998, increasing to seven in 1999.
For example, the community CSR score is defined as the number of community strengths divided by the maximum number of community strengths minus the number of community concerns divided by the maximum number of community concerns, and possible values range between − 1 and 1.
Our results are not affected if we define Leverage as the ratio of long-term debt (item 9) plus debt in current liabilities (item 34) to total assets.
The definition of these variables can be found in “Appendix 1”.
We also include the model-based measures of EM in our main regressions as additional control variables, and our results remain unchanged.
As a robustness check, we also investigate the association between corporate within-GAPP earnings management and CSR performance. The (untabulated) results show that corporate CSR performance is positively associated with its previous earnings management activities (both accrual-based and real earnings management).
The Bebchuk E-index is provided up to 2006; we thus construct the index for more recent years using the method proposed by Bebchuk et al. (2009).
Our results are not affected if we also include the interaction of Entrenchment with the Fraud and During dummies—that is, Fraud × Entrenchment and During × Entrenchment.
Our results are not affected by the inclusion of the interaction of Religiosity with the Fraud and During dummies—that is, Fraud × Religiosity and During × Religiosity.
Note that we define Fraud_Length as the logged number of months from the fraud start date to the fraud end date.
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Acknowledgements
We thank Steven Dellaportas (editor) and two anonymous referees for their valuable feedback. We also thank the seminar participants at City University of Hong Kong, Xi’an Jiaotong University, the 2016 Management Theory and Practice Conference for their suggestions.
Funding
Xing Li acknowledges financial supports from the Chinese National Natural Science Foundation (71372163 and 71672141).
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Appendices
Appendix 1: Sample selection
Firm cases | |
Total securities class action litigation lawsuits | 7607 |
Less | |
Cases without the fraud start or end date | (1093) |
Cases are not finished | (1063) |
Cases with non-Compustat firms as lead defendants | (1837) |
Cases within 4 years from the end of previous fraud | (1585) |
Cases with fraud length shorter than 1 year | (1092) |
Remaining fraud cases | 938 |
Fraud firms covered by MSCI STATS | 498 |
Less | |
Fraud firms with missing MSCI STATS data before or after the fraud start date | (356) |
Fraud firms with no matched control firms covered in MSCI STATS | (11) |
Sample fraud firms | 131 |
Matched control firms | 456 |
Final sample (1995–2016) | 587 |
Appendix 2: Variable definitions
CSR variables | |
CSR1 | The summation of raw CSR scores over community, diversity, employee, the environment, and human rights, where the raw CSR score in each category is the number of strengths minus the number of concerns |
CSR2 | The summation of scaled CSR scores over community, diversity, employee, the environment, and human rights, where the scaled CSR score in each category is the scaled value of strengths (scaled by total strength dimensions in that category) minus the scaled value of concerns (scaled by total concern dimensions in that category) |
CSR_Stakeholder | The summation of CSR scores over diversity and employees |
CSR_ThirdParty | The summation of CSR scores over community, the environment, and human rights |
CSR_Strength | The summation of CSR strength scores over community, diversity, employee, the environment, and human rights |
CSR_Concern | The summation of CSR concern scores over community, diversity, employee, the environment, and human rights |
CSR_Disclosure | A dummy variable that takes the value 1 if a firm issues a standalone CSR report during the year and 0 otherwise |
Fraud variables | |
Fraud | A dummy variable that takes the value 1 for fraud firms and 0 for non-fraud firms |
During | A dummy variable that takes the value 1 if a fraud firm is committing financial fraud and 0 otherwise |
After | A dummy variable that takes the value 1 if after the fraud-committing period and 0 otherwise |
Fraud_Length | The logged number of months from the fraud start date to the fraud end date |
Control variables | |
Size | The logged value of total assets (item 6) |
MB | The ratio of market equity to book equity (item 60), where market equity is defined as the closing stock price at the fiscal year end (item 199) multiplied by the number of shares outstanding (item 25) |
Leverage | The ratio of long-term debt (item 9) to total assets |
ROA | The ratio of income before extraordinary items (item 18) to lagged total assets |
Dividends | The ratio of common dividends (item 21) and preferred dividends (item 19) to total assets |
R&D | The ratio of R&D expense (item 46) to total assets |
Advertising | The ratio of advertising expense (item 45) to total assets |
Employee | The logged value of total number of employees |
Insti_holding | The percentage of shares held by institutional shareholders |
Analysts | The logged number of analysts who follow the firm |
Fraud_Length | The logged number of months between the fraud start date and end date |
Religiosity | The dummy variable that takes the value 1 if a firm’s headquarters are in a state with a religiosity level in the highest tercile of the sample and zero if in a state with a religiosity level in the lowest tercile. Religiosity level is measured using the ratio of the number of religious adherents in a state to the total population in that state |
Entrenchment | The value of E-index before 2006 is obtained from Professor Bebchuk’s website (http://www.law.harvard.edu/faculty/bebchuk/data.shtml), while the value after 2006 is computed following the method described in Bebchuk et al. (2009) using data from ISS database |
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Li, X., Kim, JB., Wu, H. et al. Corporate Social Responsibility and Financial Fraud: The Moderating Effects of Governance and Religiosity. J Bus Ethics 170, 557–576 (2021). https://doi.org/10.1007/s10551-019-04378-3
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DOI: https://doi.org/10.1007/s10551-019-04378-3