Corporate social responsibility and corporate payout policy: the impact of product market competition
Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically investigate if and how firm performance in corporate social responsibility (CSR) is related to corporate payouts and how competition in product markets influences this relation. Design/methodology/approach Logit and Tobit regressions are used to estimate the relation between firm performance in CSR and corporate payouts. Findings The empirical results show that firm performance in CSR is positively related to the propensity and level of dividends, repurchases and total payouts (dividends plus repurchases). However, the positive relation between CSR performance and corporate payouts is significant only for firms that operate in low competition markets. In high competition markets, CSR performance does not seem to have any significant relation with corporate payouts. Research limitations/implications This study uses MSCI social ratings data to measure net scores on CSR. There is no systematic conceptual reason for measuring social performance using MSCI social ratings. Future research should use other measures of social performance (e.g. Dow Jones Sustainability Index, Accountability Ratings and Global Reporting Initiative to estimate the relation between CSR and corporate payouts). Practical implications CSR firms are more likely to choose higher payouts when they operate in low competition markets. Originality/value This study contributes to the stream of research that evaluates the payout choices of CSR firms and competition in product markets. To the author's knowledge, this is the first study that documents the impact of market competition on the relation between firm performance in CSR and corporate payouts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".