Abstract
This study examines the impact of Sarbanes–Oxley (SOX) on the nonprofit sector. Focusing on three key SOX policies applicable to charities—conflict-of-interest policies, records retention policies, and whistleblower policies—this study tests the relationship between the existence and addition of these policies on subsequent ethical and governance lapses as reflected in the issuance of “donor advisories” by the large third-party ratings agency Charity Navigator. The findings suggest that, controlling for other relevant organizational factors, the three SOX-inspired written policies are related to a reduced likelihood of donor advisories in the organizations rated by Charity Navigator.
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Notes
While SOX criminalizes the destruction of documents to impede a federal investigation and protects employees from retaliation against whistleblowing, Sarbanes–Oxley does not require organizations to have written policies covering whistleblowers or document retention.
Nezhina and Brudney (2012) document increased expenditure of both time and money, including longer and more frequent meetings, increased audit fees, board and executive training expenses, and re-allocation of resources to administrative expenses, etc.
Prior studies have either included the written policies as part of a broader governance index or governance construct or, in the case of Harris et al. (2017), did not include all three written policies (Harris et al. 2017 do not separately consider records retention policies). Nezhina and Brudney’s (2012) survey-based and qualitative research, in contrast, represented a valuable early set of univariate analyses of the impacts of SOX on the nonprofit sector.
Note that while SOX protects employees of all organizations from retaliation against whistleblowing, and criminalizes the destruction of documents to impede a federal investigation, Sarbanes–Oxley does not require organizations to have written policies covering whistleblowers or document retention. Best practice, however, encourages nonprofit organizations to have such written policies. Moreover, states can require written policies pertaining to conflicts of interest, document retention, and/or protection of whistleblowers.
While diminishing judgment is certainly not always a positive outcome, in certain instances—such as reducing organizational malfeasance—the benefits of removing judgment from the equation may outweigh the costs.
We began by downloading all available data from the Charity Navigator site, which provided information on donor advisories as well as basic financial and organizational data that were derived from the organizations’ IRS Form 990 returns. Organizations’ age was then determined by examining the IRS Business Master Files records, while data for the governance controls were gathered by accessing the IRS’ e-file 990 database (available at https://aws.amazon.com/public-datasets/irs-990/) using customized Python scripts (a detailed replication tutorial for downloading and re-creating the 990 e-file data is available at http://social-metrics.org/sox). Governance data for non-e-filing organizations were gathered from the IRS Statistics of Income micro-data files (as in Yetman and Yetman 2012), available at https://www.irs.gov/statistics/soi-tax-stats-charities-and-other-tax-exempt-organizations-statistics. We also gathered data on the state regulatory environment from the Multi-State Filer Project website (http://multistatefiling.org). In the following sections we provide further details on the specific data sources and operationalization procedures for our variables.
The mean length of time for remediation in our dataset is 1.6 years. According to Charity Navigator, “…in order for a charity's rating to be restored, it must provide public domain documentation or similarly reliable and accessible information that demonstrates that the issues identified in the Donor Advisory have been resolved. Once the Donor Advisory has been lifted, a record of it will remain in the charity’s ‘Historical’ section of its profile page so that donors have an opportunity to learn about issues the charity previously faced and, if available, how it resolved those issues” (www.charitynavigator.org/index.cfm?bay=search.donoradvisories).
We also tested an alternative version of the variable that counted all non-zero revenue sources, with values ranging from 1 to 8, and there were no changes in sign or significance for any of the model variables in any of our regressions.
We also ran logit versions of these three models including all control variables and the three individual SOX policies as the independent variables. The results for the three SOX variables as well as the control variables were similar; as a result, for ease of presentation, we present the two-stage models shown in Table 4.
This test has fewer cases than Models 6 or 7 because it is missing a third category of cases: those that had zero SOX policies in 2011 but added one or more SOX policy by 2016 (n = 194). While including these cases would facilitate a test of the effects of moving from a position of the absence of SOX policies to having such policies, a statistical test is not necessary: none of the 194 organizations in our sample that had 0 SOX policies in 2011 but subsequently added one or more ended up with a donor advisory in 2016. With a perfect correspondence, the 194 cases are dropped from the regression analysis.
Specifically, Fig. 2 shows the predicted likelihood of an organization receiving a donor advisory (y-axis) across our sample’s empirical range of Program Efficiency Ratios (x-axis) for different values of the independent variable (the different lines for 0, 1, 2, and 3 SOX policies). The NTEE category is set as Human Services, and all remaining control variables from Model 5 in Table 5 are held constant at their mean values.
The figure shows a clear ordering in the predicted probabilities of a donor advisory with each successive SOX policy—as the number of SOX policies goes up, the probability of a donor advisory goes down. Similarly, there are stark differences in the predicted likelihoods based on the level of organizational efficiency, with greater efficiency being associated with lower probabilities of receiving a donor advisory. When efficiency and SOX adoption are viewed together, we see the differences in predicted likelihoods for the different numbers of SOX policies are more pronounced for organizations with lower efficiency ratios. For instance, moving from 0 to 1 SOX policy at the 0% program spending level results in a 1% drop (34% as percentage change) in the predicted likelihood of a donor advisory (from 2.8 to 1.8%), a much greater change than the 0.5% drop (from 1.45 to 0.94%) seen from moving from 0 to 1 SOX policy at the mean level of efficiency (80.1%) or the 0.26% drop (from 0.74 to 0.48%) for such a move at the 100% efficiency level. On average, if a wholly inefficient organization moves from 0 to 3 SOX policies, it will experience a 2% absolute decrease (from 2.8 to 0.79%) in the predicted likelihood of a donor advisory. In contrast, making the same policy change in an organization at the maximum efficiency level is associated with a decrease in predicted probability of 0.54% (with the predicted likelihood dropping from 0.74 to 0.2%).
Specifically, to analyze the potential influence of unobservable confounding variables, we used the Konfound package in Stata to calculate the ITCV impact statistic for our independent variable SOX Policies in a modified version of Model 5. The test showed that an omitted variable would have to be correlated at − 0.247 or greater with SOX Policies and at 0.247 or greater with our dependent variable Donor Advisory in order to render SOX Policies statistically insignificant. This corresponds to an ITCV impact for an omitted variable of − 0.061 (− 0.247 × 0.247). Given the difficulty in determining absolute standards for impact threshold, we employ the technique recommended by Larcker and Rusticus (2010, p. 203) and use “…the impact for the … control variables to provide a reasonable benchmark to the ITCV.” We thus calculated the impact (in line with Larcker and Rusticus, calculated as the product of the partial correlations of each control with Donor Advisory and SOX Policies) of all control variables shown in Model 5 (Table 5). None of the controls had an impact close to the threshold of -0.061; the controls with the largest impact from Model 5 are Program Efficiency, Total Revenues, Audit Committee, Complexity, and Audited Financial Statements, yet their ITCV impacts range only from .01 to − 0.04.
For the models shown in Table 6, we found only one of the donor advisories was for an organization that had reported a significant diversion, so re-running those results would show no important changes in results. In the sample used in Tables 4 and 5, in turn, we found 12 reported significant diversions; 10 of these had a donor advisory while 2 did not. We thus replicated the test shown in Model 4 omitting these 10 and 12 cases in two separate tests. In both cases, there were no changes in sign or significance in any of the model variables nor in the overall pseudo-R2. The correlation between material diversions and donor advisories in this sample is 0.27 (p < 0.001).
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Appendix: Variable definitions table
Appendix: Variable definitions table
Variable | Definition |
|---|---|
Dependent variables | |
Donor advisory (2016) | Organization has a donor advisory in effect in 2016 |
Donor advisory (2011–2016) | Organization has received a donor advisory between 2011 and 2016 |
Independent variables | |
Conflict-of-interest policy | Organization has written conflict-of-interest policy (0, 1) |
Records retention policy | Organization has written document retention policy (0, 1) |
Whistleblower policy | Organization has written whistleblower policy (0, 1) |
# of SOX policies | Count of number of SOX policies (0–3) |
Financial and environmental controls | |
Program efficiency ratio | Program service expenses/total expenses |
Complexity | Sum of number of revenue sources with > 0 values: contributions/gifts/grants, federated campaigns, membership dues, fundraising events, related organizations, government grants, program services, other revenue (0–8) |
Age | As of 2016, number of years since IRS ruling date |
Total revenues (1000s) | Total revenue (logged) |
Industry | 11 NTEE categories (dummy variables) |
State | State (dummy variables) |
Governance controls | |
Government grant | Nonzero government grants (contributions) (0, 1) |
Restricted donations | Permanently restricted net assets or Temporarily restricted net assets (0, 1) |
Family/business relations | Family/business relationships with key personnel? (0, 1) |
990 Review | Form 990 provided to governing body? (0, 1) |
Audit committee | Does the organization have an audit committee? (0, 1) |
Audited financials | Financial statements audited? (0, 1) |
% Independent directors | # independent voting members/# members governing body |
Outsourced management | Delegation of management duties? (0, 1) |
Municipal bond | Tax-exempt bonds? (0, 1) |
# Voting directors | Number of voting members governing body |
Donor restrictions | Percentage of fund balances that are restricted (%) |
Federal grant audit | Federal grant audit performed? (0, 1) |
Taxable revenue | Net unrelated business taxable income (0, 1) |
Fees for accounting services | Non-employee fees for accounting services (0, 1) |
State audit | State audit required (0, 1) |
State information return | State information return required (0, 1) |
State registration for solicitation | State registration required for solicitation of donations (0, 1) |
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Saxton, G.D., Neely, D.G. The Relationship Between Sarbanes–Oxley Policies and Donor Advisories in Nonprofit Organizations. J Bus Ethics 158, 333–351 (2019). https://doi.org/10.1007/s10551-018-3843-0
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DOI: https://doi.org/10.1007/s10551-018-3843-0