Educational Attainment and Family Gaps in Women's Wages: Evidence from Five Industrialized Countries
Bibliographic record
Abstract
This paper employs Luxembourg Income Study data for women in five industrialized countries to answer the following questions: Do family gaps in women's wage vary across levels of education? Does educational attainment help to 'insure' a woman against child wage penalties? Cross-national analysis of 'family gaps' in women's wages provides clear evidence that wage penalties to motherhood vary significantly in magnitude across countries. Harkness and Waldfogel (1999) estimate these differentials between the wages of mothers and non-mothers for seven industrialized countries. They find that family gaps appear to be largest in Anglo-Saxon countries. The character of our research is primarily exploratory, but some basic conclusions can be drawn from our results. In Canada and the United States, we find that a high educational attainment acts as a 'shock absorber,' almost eliminating the large negative effects of children on a woman's wages; results for Germany are similar. We find these results to be robust to the inclusion of part-time workers in the sample. We conclude that educational attainment does help to offset the family gaps faced by mothers in some countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".