Board gender diversity, government subsidies, and green vehicles sales: Evidence from China
Bibliographic record
Abstract
Abstract This article investigates whether increased female representation on a board improves firm performance in terms of electric vehicle (EV) sales in China when government subsidies are available. The increase in EV sales in China is a direct result of the sustainability efforts spearheaded by the various levels of local and state governments. This area is of importance due to the rising Chinese footprint in global EV sales, the increasing role of subsidies, and a transformation from State‐Owned Enterprises (SOEs) to market‐driven firms that are more likely to pay attention to corporate governance issues such as board diversity. Using the instrumental variable (IV) approach, we estimate a two‐stage least squares (2SLS) regression to control for more women representation on EV boards using firm‐level data. The results indicate that board gender diversity (BGD) positively impacts firm performance in terms of EV sales. Furthermore, the effect amplifies in the presence of government subsidies. The insights provide valuable contributions to bridge the critical literature gap on how board diversity impacts firm performance in the presence of subsidies and offer valuable insights into corporate governance and policymaking.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".