Bibliographic record
Abstract
Purpose This paper aims to explore an extensive set of determinants of earnings and to offer recent empirical evidence of their effects on gender and racial earnings gaps. Design/methodology/approach Most previous studies looked at gender and racial comparisons independently of each other. This study extends previous studies by considering the interaction between gender and race. Using administrative data from a large Canadian firm, this paper explores the determinants of earnings based on a standard human capital model, comparing the earnings of white females, minority males and minority females with their white male counterparts. Both the dummy variable approach and a decomposition analysis are employed. Findings The results show that ranking in the organizational hierarchy accounts for most of the differences in gender and racial earnings, and ranking, together with human capital and job characteristics variables, explains over 90 percent of the earnings gap. Research limitations/implications The analyses in the paper are based on data from a Canadian organization with nation‐wide operations. The findings may not apply to small or medium sized enterprises in Canada and in other non‐Western economies. Practical implications To eliminate the earnings gap, equal pay programs need to be supplemented by effective employers' programs and policies targeted at equal advancement opportunity. Originality/value The paper uses firm‐level data, which provides natural controls for variations across firms and allows for more in‐depth analysis of the impact of various factors on earnings differentials.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".