Bibliographic record
Abstract
Prior studies have shown that women are more willing to trade off wages for short commutes than men.Given the gender difference in commuting preferences, we show that the wage return to commuting (i.e., the wage penalty for reducing commute time) that stems from the spatial distribution of jobs contributes to the gender wage gap.We propose a simple job choice model, which predicts that differential commuting preferences would lead to a larger gender wage gap for workers who face greater wage returns to commuting based on their locations of residence and occupations.We then show empirical evidence that validates the model's prediction.Moreover, we estimate the model components: (i) the indifference curves between wages and commutes by gender, and (ii) the wage return to commuting faced by each worker.Our model shows that differential commuting choices account for about 16-21% of the gender wage gap on average, but the contribution varies widely across residential locations.The model also shows that policies that increase commute speed or density in the central city neighborhoods could moderately lower the gender wage 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.009 | 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".