Catering to their pleasure: female employees and corporate philanthropy
Bibliographic record
Abstract
Purpose The paper aims to examine how employee gender diversity shapes corporate philanthropy (CP) as a strategic employee governance tool. Drawing on stakeholder salience theory, the authors argue that firms in female-dominated industries are more motivated to use CP to appeal to women employees, who are more responsive to such initiatives. They further explore how this effect is moderated by employee human capital, organizational slack and board gender composition, highlighting conditions under which firms adjust philanthropic strategies to engage employees effectively and manage intra-stakeholder heterogeneity. Design/methodology/approach The paper uses panel data of Chinese A-share listed firms from 2009–2020 and uses Tobit regression to examine the relationship between female employee representation and CP. CP is measured by firms’ charitable contributions reported in financial statements, while female employee representation is proxied by industry classification (female-dominated vs others). To address potential endogeneity and ensure robustness, propensity score matching is applied. Findings The paper provides empirical insights confirms that firms in female-dominated industries exhibit significantly higher levels of CP. This effect strengthens when employees have higher education or firms possess greater slack resources, but weakens with increased board gender diversity. Originality/value The paper addresses how gender-specific stakeholder pressures shape CP. It shows that intra-employee gender heterogeneity drives CP, refining stakeholder salience theory by demonstrating how employee composition influences firm strategies. It also uncovers a countervailing effect of female directors, who may reduce the influence of female employees on CP, challenging assumptions of gender-aligned governance. By integrating stakeholder salience theory with gender dynamics, the study offers theoretical insights and practical guidance for firms seeking to use CP strategically to engage employees and manage intra-stakeholder heterogeneity.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".