The finance wage premium: Finnish evidence from a gender perspective
Bibliographic record
Abstract
Abstract The growth in finance wages has contributed to the increase in top incomes over the last decades. The finance wage premium has been studied from various viewpoints in recent years, however, not from the gender perspective. Studies have shown that the gender wage gap tends to increase at top incomes. As finance wages are increasing and if the benefits of working in finance are mostly claimed by men, the overall gender wage gap will persist. Using Finnish registry data from 1990 to 2014, this paper shows that the finance wage premium differs considerably between men and women. Overall, the finance premium has increased over time. The premium of men is larger than that of women at all hierarchy levels. Women at manager and expert positions in finance get a premium, but not at clerical level. Men on the other hand receive a premium at all hierarchy levels. The negative female effect is larger at higher points of the wage distribution, indicative of a glass ceiling effect. For men, the premium has increased especially at the top of the wage distribution.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".