Nothing to Lose or Much to Lose? The Gendered Employment Consequences of Leaving Engineering Majors
Bibliographic record
Abstract
Are there gender differences in the employment consequences of leaving the engineering major? Given the intense interest shown by scholars and policy makers alike in promoting women’s persistence in engineering careers, it is surprising that we could find no prior studies that answer this question. Rather, the employment consequences of leaving engineering have largely been assumed. Using wage decomposition, this paper tests for gender differences in the post-graduation wages of engineering major stayers versus leavers. We do so for three distinct longitudinal datasets. Our results are significant, consistent, and challenge prior assumptions. The unexplained portion of the stayer-leaver wage gap for male engineers across all three datasets is large, significant, and substantive – from 22% to 35% of male engineers’ mean initial annual salary. Men experience a large wage penalty for beginning but not completing an engineering degree. The unexplained portion of the stayer-lever wage gap for female engineers is small, mostly insignificant, and far less substantial – from 0% to 5% of female engineers’ mean initial annual salary. Women experience little to no wage penalty for beginning but not completing an engineering degree. These results have direct implications for the policy goals of using women’s participation and persistence in engineering to address gender wage inequality and provides novel insights regarding the mechanisms and nature of gender inequalities in engineering. Surprisingly, it is men who have much to lose by leaving engineering. The women who start engineering majors have little to lose by leaving.
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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 | 0.003 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".