Managerial Efficiency and Human Capital Information: Linkages with the Voluntary Disclosure of Labour Costs
Bibliographic record
Abstract
This research paper examines the information content and managerial incentives for labour cost voluntary disclosures for a sample of United States publicly traded companies. We focus on labour productivity and managerial efficiency in labour usage and argue that these human capital indicators could provide valuable information to capital market participants seeking human resource‐type of performance measures and signals. Labour productivity and efficiency indicators are estimated following a production function approach and are included in logistic regressions to help explain and predict labour cost voluntary disclosure decisions. We find that labour productivity and managerial efficiency in labour use indicators are generally different between disclosing and non‐disclosing firms, and that proprietary information costs and political cost proxies are significantly related to labour costs voluntary disclosure, consistent with previous literature. These empirical results corroborate the ‘proprietary information’ hypothesis of voluntary disclosure where the strategic costs of disclosure outweigh the signaling benefit from disclosing human capital information.
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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 | 0.006 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".