The gender gap in pre-career salary expectations: a test of five explanations
Bibliographic record
Abstract
Purpose – The purpose of this paper is to investigate the gender gap in pre-career salary expectations. Five major explanations are tested to explain the gap, as well as understand the relative contribution of each explanation. Design/methodology/approach – Data were collected from 452 post-secondary students from Canada. Findings – Young women had lower initial and peak salary expectations than their male counterparts. The gap in peak salary could be explained by initial salary expectations, beta values, the interaction between beta values and gender, and estimations of the value of the labor market. Men and women in this study expected to earn a considerably larger peak salary than they expected for others. Research limitations/implications – Cross-sectional data cannot infer causality, and the Canadian sample may not be generalizable to other countries given that an economic downturn occurred at time of data collection. Research should continue to investigate how individuals establish initial salary expectations, while also testing more dynamic models given the interaction effect found in terms of gender and work values in explaining salary expectations. Practical implications – The majority of the gender gap in peak salary expectations can be explained by what men and women expect to earn immediately after graduation. Further, women and men have different perceptions of the value they attribute to the labor market and what might be a fair wage, especially when considering beta work values. Social implications – The data suggests that the gender-wage gap is likely to continue and that both young men and women would benefit from greater education and information with respect to the labor market and what they can reasonably expect to earn, not just initially, but from a long-term perspective. Originality/value – This study is the first to simultaneously investigate five theoretical explanations for the gender gap in pre-career expectations.
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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 | 0.012 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".