Restatement of <scp>CSR</scp> Reports: Frequency, Magnitude, and Determinants*
Bibliographic record
Abstract
ABSTRACT We provide the first direct analysis of the magnitude of unreliable quantitative information disclosed in corporate social responsibility (CSR) reports. CSR report reliability is of particular interest to fund managers for investment decisions as well as to policymakers for regulating and monitoring purposes. However, surprisingly little is known about CSR reporting reliability despite concerns raised in the prior literature. We examine how often CSR reports for the Global Fortune 250 (G250) are restated, the magnitude of restatements, and factors associated with restatements during the period 2006 to 2013. During this sample period, the occurrence of restatements increased monotonically, with 39% of G250 CSR reports including one or more line‐item restatements. The magnitude of the line‐item restatements is quite high, with a median restatement of about 10%. We also find evidence of bias in the revised items toward overstatement. We find that restatements occur more frequently in firms that have reported a high level of social performance and that have environmental targets. The occurrence of restatement is also positively associated with firms residing in strong law countries and having their CSR reports audited. Our analysis of reporting bias indicates a negative association between use of Global Reporting Initiative (GRI) reporting guidelines and the likelihood of an overstatement. We also find a positive association between having the CSR report audited and the likelihood of revisions associated with overstatements. Together, our exploratory results indicate that CSR information may be unreliable and firms that face pressure to perform well have more restatements. However, our evidence is consistent with the restatements resulting from improvements in information systems over time rather than intentional bias. Our findings will help investors and fund managers better judge the reliability of CSR disclosures, and inform regulators and standard setters on ways to enhance the reliability of CSR reporting. Finally, we contribute to the audit literature examining sustainability assurance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".