Explaining Gender Wage Differentials: Findings from a Random Effects Model
Bibliographic record
Abstract
In this paper we evaluate the extent to which the gender wage gap in the Finnish manufacturing sector is attributable to within-job wage differentials, sex differences in individual qualifications, and disproportionate concentration of women in lower-paying firms and lower-paying jobs within firms. We use matched employer-employee data to compare wage differentials between similarly qualified female and male workers who are doing the same job for the same employer. Our modelling approach employs a nested random effects specification to account for the hierarchical grouped structure of the underlying data. White-collar women are found to earn 22% less on average than their male counterparts do. Among blue-collar workers, women?s mean wage is 16% lower than men?s mean wage. The major part of the gender wage gap of white-collar workers results from sex segregation among jobs within firms. By contrast, most of the gap of blue-collar workers is attributable to sex segregation among firms. Unexplained within-job wage differentials account for a quarter of the overall gap of white-collar workers and one-fifth of the overall gap of blue-collar workers.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".