Board Tenure and Specific Skills as Determinants of ESG Reporting: Evidence from ASEAN Listed Companies
Bibliographic record
Abstract
This study investigates the influence of board characteristics—specifically board tenure and board-specific skills—on the quality of ESG reporting among listed firms in five ASEAN countries (Indonesia, Malaysia, Singapore, Thailand, and the Philippines) from 2021 to 2023. Using panel data of 609 firms (1827 firm-year observations) obtained from Refinitiv Eikon, ESG reporting is measured through the reporting score, while board tenure is proxied by the average years of directors’ service and board-specific skills by the proportion of directors with financial or industry expertise. The analysis employs fixed-effects regression with firm-level clustered standard errors to account for unobserved heterogeneity and robust inference. The findings reveal that board tenure has no significant effect on ESG reporting, suggesting that accumulated experience does not necessarily enhance disclosure. In contrast, board-specific skills exhibit a positive and significant impact, highlighting the importance of technical competence in driving transparency. Control variables show that firm age contributes positively to ESG disclosure, while robustness checks confirm the stability of results across alternative specifications and clustering dimensions. Sub-sample country analyses further indicate institutional variations, with board expertise mattering more in Singapore and Indonesia, and firm age in Malaysia, Thailand, and the Philippines. The study offers theoretical and policy implications for strengthening governance reforms and advancing ESG transparency in emerging markets.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".