Nothing but the Truth? Private Information and Reporting on Corporate Social Responsibility
Bibliographic record
Abstract
This paper develops and tests a model in which 1) purpose-driven firms emerge as an optimal organizational form even for profit-maximizing entrepreneurs; and 2) CSR arises endogenously as a response to imperfect regulatory oversight.Purpose-driven organizations allow entrepreneurs to create rents for socially responsible (e.g.environmentally concerned) workers by allowing them to reduce the negative externalities (e.g.pollution) that would be generated without them, and to extract these rents through lower wages.Through this rent extraction entrepreneurs internalize the pro-social preferences of their responsible workers, and in turn engage in CSR through self-regulation, provided that regulatory oversight is poor enough -and hence regulation is loose enough -to make self-regulation worthwhile.The key prediction of the model is a negative impact of regulatory oversight on CSR activity.To test this, we exploit the UK's 2012 decision to mandate greenhouse gas emissions disclosure in all public firms.Consistent with our theory, we find that firms in the UK receive lower CSR ratings after increased regulatory oversight compared to firms from the other 15 European countries which did not experience mandatory disclosure requirements.We also perform a number of robustness checks and explore the interaction between oversight, wages and CSR.These empirical findings provide further support for the model.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".