The Impact of Board Gender Diversity on European Firms’ Performance: The Moderating Role of Liquidity
Bibliographic record
Abstract
This study examines how board gender diversity affects listed non-financial European companies’ financial performance. Data from the Refinitiv Eikon Platform—LSEG and World Bank databases was used to complete the analysis. The total sample included 4257 companies for the period 2011–2023. This study examined board gender diversity and its interaction with liquidity while controlling for board characteristics such as board size, independence, and board meetings. Controlling for firm characteristics (firm size and leverage) and macroeconomic variables like inflation and GDP. This study estimated the connection using panel regression. Due to Hausman test significance, fixed effect estimation was used. The findings demonstrated a notable and favorable influence of board features, such as gender diversity, board independence, and board size, on European nonfinancial companies. Additionally, liquidity positively affects firm performance. Furthermore, the findings indicated that leverage had a significant negative impact on profitability. Finally, both the size and GDP have a significant beneficial impact on profitability. Our findings indicate that an increased representation of women on the board of directors is associated with greater independence among board members and a higher number of board members being hired. This, in turn, has a positive impact on profitability due to the extensive experience shared among board members. Additionally, this leads to improved governance, enabling better control over decisions and a greater focus on the long-term investment strategy of the company. Our results are robust, as are similar results reported by the GMM regression.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".