Corporate social responsibility transparency and trade credit financing
Bibliographic record
Abstract
Purpose This study aims to examine whether a company’s corporate social responsibility (CSR) transparency (reflected in two separate dimensions of social transparency and environmental transparency) affects a company’s dependence on expensive trade credit (TC) financing. Design/methodology/approach The authors use a panel of S&P 500 index companies between 2012 and 2019 and ordinary least squares estimators. Transparency ratings represented by Bloomberg scores capture both the quantity and quality of verified CSR practice information. Findings CSR transparency (CSRT) is negatively associated with a firm’s dependence on expensive TC financing. This study’s results continue to hold after a battery of robustness tests like substitute proxies for TC, use of two-stage least squares regression, industry-adjusted dependent variable, generalized linear model and bootstrapping approach. This association is stronger among companies with higher information asymmetry (IASY) and lower quality regarding governance and financial reporting. Further investigation indicates that potential channels through which CSRT mitigates a company’s reliance on TC financing are the cost of debt (CoD) and stock liquidity. This study’s findings suggest that transparent companies have a lower CoD and higher stock liquidity. This helps these companies to be more financially flexible and eventually less dependent on expensive TC financing. Originality/value By combining two separate research lines of TC and CSR, this study adds to both works of literature as it is the first (to the best of the authors’ knowledge) to present evidence of the effect of CSRT proxied by Bloomberg scores on a company’s reliance on TC (a real economic decision and financial policy). Additionally, this study documents the moderating effects of financial reporting quality, IASY and corporate governance on the relationship between CSRT and TC financing. In conclusion, this study provides empirical evidence regarding the potential mechanisms of CoD and stock liquidity, through which CSRT influences a company’s reliance on TC financing.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".