Do Social Trust and Tolerance Affect Board Gender Diversity? An International Evidence
Bibliographic record
Abstract
ABSTRACT Research question/Issue The paper investigates how social trust and tolerance affect board gender diversity levels in 67 countries. Research Findings/Insights We find robust evidence that countries in which people display trust toward strangers, people of other countries, and other religions have more gender‐diverse boards. We also find that tolerance toward homosexuals, immigrants, and people of other religions and different races promotes gender‐diverse boards. Theoretical/Academic Implications This paper relies on social trust, tolerance, and institutional theories. Trust and tolerance theories suggest that trusting others and displaying tolerance toward people who are different from oneself have many important social and economic consequences. We relate social trust and tolerance theories to governance and board gender diversity in a large cross‐section of countries. Practitioner/Policy Implications Board gender diversity has both practical and policy implications. Our findings suggest that legislating comprehensive gender quotas may not be sufficient. More importantly, unlike cultural values, trust and tolerance can be more readily cultivated to promote policies and workplace practices. JEL Classification G38, G41, J18, L25, M14, Z13
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".