Corporate Social Responsibility and the Misclassification of Income Statement Items during the Coronavirus Pandemic
Bibliographic record
Abstract
The purpose of this study was to examine the relationship between corporate social responsibility (CSR) and earnings management in Gulf Cooperation Council (GCC)-listed companies. It specifically addresses the question of whether companies that practice greater corporate social responsibility are less likely to engage in earnings management practices. The study sample consisted of 300 firms listed between 2015 and 2021 on GCC bourses (Saudi Arabia, United Arab Emirates, Bahrain, Qatar, Oman, and Kuwait). In this study, we developed multiple linear regression models and collected data from the Bloomberg database, Refinitiv, annual reports, official firms’ websites, and the GCC’s bourse websites for the period from 2015 to 2021. In the pre-pandemic period, firms that engaged in corporate social responsibility activities were more likely to have fewer classification-shifting practices. During the pandemic era, however, this relationship became significantly positive, suggesting that firms’ corporate social responsibility practices may be used to hide their opportunistic classification-shifting practices during difficult times, such as a pandemic. This paper presents a thorough investigation of how businesses may alter their behavior toward increasingly applied but understudied earnings management strategies and CSR practices during a difficult period such as a pandemic.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".