Bibliographic record
Abstract
Purpose Research on gender and finance finds that women chief executive officers (CEOs) are relatively risk-averse and more ethical than their male counterparts. These differences are often presented as reasons for lower earnings management by firms led by women. A strand of contrasting literature however finds the notions of women being risk-averse and ethical not necessarily true for women occupying top leadership positions as women successful in shattering the glass ceiling adopt behaviors like men. This study attempts to understand the differences between the ethical tendencies of the two genders by examining if CEO power impacts the relation between CEO gender and earnings management. Design/methodology/approach The authors begin the analysis using standard regressions using the propensity score matched (PSM) samples and examine if CEO power mediates or amplifies relationship between CEO gender and earnings management. The authors use ordinary least squares (OLS) regression approach and instrumental variables (IV) estimation to address the endogeneity concerns. Findings This study’s results suggest that the relationship between CEO gender and earnings management is mediated by CEO power. The authors find that women CEOs with lower power engage in lower earnings management. However, women CEOs with more power tend to engage in greater levels of earnings management than their male counterparts. Originality/value This study contributes the finance literature by showing women leaders successful in occupying top leadership positions are not necessarily more risk averse and more ethical. Less powerful women CEOs are subjected to potentially higher levels of scrutiny and are forced into an environment where they have to be seen as ethical. However, powerful women face the same concerns as their male counterparts and not necessarily more ethical.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".