Keeping them honest? Exploring the impact of labor unions on CSR
Bibliographic record
Abstract
Purpose The role of labor unions in relation to corporate social responsibility (CSR) remains both ambiguous and crucial for union members and business leaders. Given the complex relationship between labor unions and corporations, this study aims to address whether labor unions keep corporations honest (by monitoring CSR activities) or potentially render CSR initiatives less necessary. Design/methodology/approach Using data from the MSCI Kinder, Lydenberg, Domini Database for firms in the Russell 1000 Index, this study examines the link between labor unions and CSR in U.S. companies over a six year period. Generalized least squares models were used to test the hypotheses for 3,937 firm-year observations. Findings The findings show that unionized companies generally pay less attention to CSR compared to nonunionized ones. The presence of labor unions and positive union-management relations both show a significant negative impact on CSR ratings, where positive union-management relations negatively affect CSR ratings more than just the presence of labor unions. Further, when considering the environmental, social and governance aspects of CSR separately, the results are more complex, suggesting that the relationship between labor unions and CSR varies depending on specific ESG dimensions. Originality/value CSR, a well-researched area, rarely addresses the companies' relationships with labor unions. Studies in South Korea and the UK have touched on the impact of labor unions on CSR, but in the USA it remains unexplored. This study extends this line of work by examining U.S. companies.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".