Bridging governance gaps: politically connected boards, gender diversity and the ESG performance puzzle in Iberian companies
Bibliographic record
Abstract
Purpose The main goal of this paper is to analyse the impact of political connections and gender diversity shaping Environmental, Social and Governance (ESG) components’ effects on the performance of Iberian companies. Design/methodology/approach To achieve this aim, we have used panel data methodology, specifically the generalized method of moments system estimation method by Arellano and Bond (1991), using data from listed Iberian companies for the period between 2015 and 2020. Findings Our findings suggest that, although ESG components positively influence company performance, the presence of political connections weakens ESG commitments, compromising ethical standards and suggesting a lack of transparency or inadequate regulations. Our results also highlight that the presence of women on boards of directors has a nuanced impact on firm performance, as measured by the Market-to-Book ratio. While gender diversity interacts with ESG scores, external investors' perceptions may not always reflect immediate performance improvements. Research limitations/implications This work faces some limitations associated with challenges in securing comprehensive data for all variables, along with the complexity of acquiring information about political connections. Often, we had to rely on multiple sources and cross-reference the data to enhance its reliability. Another limitation for potential consideration or exploration in future research pertains to the omission of distinct industry sectors due to the limited number of companies, particularly notable in the context of Portugal. Originality/value Although there is a large volume of literature on the relationship between ESG and companies’ performance, as far as the authors are aware, this article is original and covers an important gap in the literature when considering political connections and board gender diversity impact on ESG components as determinants of the performance of Iberian 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".