The Benefits of Workforce Well-Being on Profitability in Listed Companies: A Comparative Analysis between Europe and Mexico from an ESG Investor Perspective
Bibliographic record
Abstract
This paper evaluates the relationship between investing in workforce well-being and profitability of listed companies in Mexico compared to European companies from an Environmental, Social, and Governance (ESG) investor perspective. In this case, the Refinitiv workforce score or High-Performance Work Policies (HPWP) is used as an indicator of the quality of workforce well-being by including the industry effects (economic and business sectors) and the behavioral (sentiment) factors as control variables. Specifically, this article examines the relationships between HPWP, stock price changes (measured as a percentage), profitability (ROE), and market risk (betas). We used a sample of companies from the Refinitiv Mexico and European stock indices for this purpose. In the Mexican case, the results show that a higher level of well-being promotion relates to better company profits. The opposite happens in European companies. Regarding market prices, European companies show higher prices when they have higher HPWP and Mexican companies confirm the opposite. Regarding market risk, only European basic materials with high HPWP show less risk. Finally, in almost all Mexican business sectors, the relationship between market risk and workforce well-being is negative.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".