Gender Discrimination? Evidence from the Belgian Public Accounting Profession*
Bibliographic record
Abstract
ABSTRACT Prior research finds that women receive lower salaries than men. Similarly, we show that female audit partners in Belgium receive significantly lower compensation than male partners. However, there are alternative explanations for the pay gap other than gender discrimination. For example, the gap in compensation could reflect that men are paid more because they have higher levels of productivity. We provide new predictions and tests of gender discrimination by comparing the fees generated by audit partners (a measure of partner productivity) and the types of clients assigned to partners. Consistent with our prediction of female partners having to meet higher performance thresholds than male partners, we show that female partners generate larger fee premiums, but they are less likely to be assigned to prestigious clients. To test whether these patterns are attributable to gender discrimination, we examine whether the results are stronger in male‐dominated offices, because this is where we would expect to find the most discrimination against women. We find the fee premiums generated by female partners are larger in male‐dominated offices, while the negative association between prestigious clients and female partners is stronger in male‐dominated offices. Collectively, our combined predictions and tests are consistent with female partners facing gender discrimination in audit offices that are dominated by male partners.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".