Does Corporate Social Responsibility Provide Protection Against Systemic Risks? Evidence from Taiwan during the US-China Trade War
Bibliographic record
Abstract
The primary aim of this study is to examine the protection against systemic risks of the financial and stock performance of firms in receipt of ‘corporate social responsibility’ (CSR) awards. Our 2016-2018 study sample, obtained from the Taiwan Economic Journal (TEJ), comprised of CSR-award-recipient firms (CSR firms) voted for by the Common Wealth and Global Views magazines, for a sample period running from the third quarter of 2017 to the third quarter of 2018. Our empirical results reveal that in terms of their financial performance, as compared to non-CSR-award-recipient (non-CSR) firms, CSR firms failed to demonstrate any better protection against systemic risks (such as the US-China trade war). However, the stock performance of CSR firms clearly provided better protection than that of non-CSR firms; the reason for this observation is assumed to be the higher operational costs faced by CSR firms seeking to continue to pursue their CSR goals when encountering systemic risks (like the US-China trade war). Nevertheless, participation in CSR is found to have an insurance-like effect on firm value, which clearly helps to increase the confidence of investors and reduce stock volatility.
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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 | 0.002 | 0.008 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".