Does Board Gender Diversity Really Improve Firm Performance? Evidence from Greek Listed Firms
Bibliographic record
Abstract
In recent decades, the contribution of board gender diversity to corporate performance has drawn the interest of researchers, politicians and regulators. This paper examines whether board gender diversity affected the financial performance of 111 Greek listed firms from 2008 to 2020. We use the two-step system GMM estimator to address the endogeneity problem, which is the appropriate method used in governance literature. Our main empirical finding supports the existence of a positive relation between board gender diversity and firm performance. This finding remains robust to three different proxies of gender diversity and under two alternative performance measures, i.e., return on assets and Tobin’s Q. We also find that there is an inverted U-shaped relation between the proportion of female directors and firm performance (measured by Tobin’s Q). Moreover, we find that gender diversity could lead to maximization of corporate performance when female participation in the boardroom reaches 33%. Thus, the imposition of an ad-hoc 25% female representation in corporate boardrooms, dictated by the new Law 4706/2020 on corporate governance, could most probably be an underproductive policy. Our findings have practical implications for Greek regulators and legislators and contribute to the governance literature for the case of companies that operate in a small open economy.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".