A Pay Raise, with a Bonus Cut: The Unintended Effects of Interventions to Close the Gender Pay Gap
Bibliographic record
Abstract
This paper presents one of the few accounts of the unintended consequences of diversity initiatives that decrease their effectiveness or may even exacerbate inequality. We rely on the personnel records of a multi-unit European bank between 2012 and 2017 to explore the bank’s attempt to reduce the gender pay gap. We find that the gender gap in annual base salaries declines slightly over the period we examine, because women receive larger base salary increases than men, even after controlling for demographic and human capital attributes, types of jobs held or job performance. Nevertheless, we document that managers adjust downward another part of the pay package (bonuses), to counterbalance the higher increases in base salary given to women. We also investigate the factors that may mitigate this unintended consequence and find that women are less likely to be penalized during bonus allocation if they have a female, rather than a male supervisor, and after the employer implements other diversity initiatives to signal its commitment to diversity and inclusion.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".