Does ESG Index Recognition Improve Firm Performance? Evidence from Thailand’s ESG100 Using Staggered Difference-in-Differences
Bibliographic record
Abstract
In the context of rising investor interest in Environmental, Social, and Governance (ESG) benchmarks, this study examines whether first-time inclusion in Thailand’s ESG100 index improves firm performance. Performance is measured along three dimensions: accounting (return on assets, return on equity), market valuation (Tobin’s Q, market-to-book ratio), and payout policy (dividend ratio, dividend yield). Using a rigorous staggered Difference-in-Differences (DiD) framework—incorporating both traditional DiD and modern estimators by Callaway and Sant’Anna and Sun and Abraham—alongside propensity score matching to address treatment timing and selection bias, this methodology ensures robust identification. Results indicate that ESG100 inclusion does not improve short-term accounting or market performance, with robustness tests indicating slight declines. However, firms newly included in ESG100 significantly increase dividend payouts. We also find that firm size moderates these effects: large firms experience improvements in ROA and ROE, while smaller firms show limited or negative responses. In contrast, market valuation and payout responses do not vary by firm size. These findings refine stakeholder and agency theories in an emerging-market context by showing that ESG recognition influences cash distribution policies more than accounting metrics or market prices. By differentiating these effects, this paper contributes to theory and practice around ESG adoption in emerging economies and discusses implications for corporate ESG strategy and policy in the Asia-Pacific region.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".