The Gender Pay Gap across Countries: A Human Capital Approach
Bibliographic record
Abstract
The gender wage gap varies across countries. For example, among OECD nations women in Australia, Belgium, Italy and Sweden earn 80% as much as males, whereas in Austria, Canada and Japan women earn about 60%. Current studies examining cross-country differences focus on the impact of labor market institutions such as minimum wage laws and nationwide collective bargaining. However, these studies neglect labor market institutions that affect women'slifetime work behavior - a factor crucially important in gender wage gap studies that employ individual data. This paper explicitly concentrates on labor market institutions that are related to female lifetime work that affect the gender wage gap across countries. Using ISSP (International Social Survey Programme), LIS (Luxembourg Income Study) and OECD wage data for 35 countries covering 1970-2002, we show that the gender pay gap is positively associated with the fertilityrate (treated exogenously and endogenously with religion as the instrument), positively associated with the husbandwifeage gap at first marriage, and positively related to the top marginal tax rate, all factors which negatively affect women's lifetime labor force participation. In addition, we show that collective bargaining, as found in previous studies, is negatively associated with the gender pay gap.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".