Corporate social responsibility and <scp>COVID</scp> ‐19: Prior reporting experience and assurance
Bibliographic record
Abstract
Abstract The novel COVID‐19 has created an exogenous shock to capital markets and, hence, an ideal opportunity for researchers to assess whether CSR‐related activities provide an insurance‐like mechanism to protect firms against the shock. Using a large sample of 4361 firms domiciled in 40 countries, we investigate the roles of CSR reporting and assurance in the negative consequences of COVID‐19 on firm value. The results confirm that prior CSR reporting experience buffers firms against the adverse effects of the health crisis. The results also support that not only does the assurance on CSR reports create a buffering effect against the health crisis, but it also intensifies the buffering effects of prior CSR reporting experience against the pandemic. Moreover, using difference‐in‐difference method for testing the link between CSR reporting and firm value, we show that the positive association of reporting and assurance with firm value is more pronounced during the pandemic as compared with the years preceding it. The results of this study are robust to various analyses. Replicating the analyses to the context of the global financial crisis, we find that prior CSR reporting experience and assurance provide similar buffering effects when a market is exposed to various exogenous shocks. The results also hold for the mandatory disclosure regimes. By distinguishing first and subsequent reports and assurance, we show that, unlike subsequent CSR reports and assurance, the initial ones cannot mitigate the negative effects of the crisis on firm value, indicating that stakeholders take into account longer‐term CSR reporting experiences. Aside from reporting and assurance aspects of CSR, we analyze the role of CSR report's quality and accuracy and show that the adoption of Global Reporting Initiatives (GRI) frameworks can enhance socially responsible firms' resilience against systematic shocks.
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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 | 0.042 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".