The Commute to Work and the Gender Wage Differential: International Analysis
Bibliographic record
Abstract
This paper examines the effects of commute time and commute distance on wages in the United States and Canada, and the impact these have on the gender pay gap. Separate analyses are undertaken for native-born workers and for foreign-born workers. Analyses are presented for all workers, for young workers, and for workers categorized according to the distance or time that they travel to work. This latter set of analyses permits insights into the wage effects for workers who are trapped in local labor markets, as compared to the wage effects for workers who work in broader labor markets. Consistent with the earlier research, the paper shows that the commute to work is associated with modest wage increases in each country. Within each country there are minor variations in these wage increases by gender and nativity. Consideration of the wage effects of commute time and commute distance has little effect on the gender pay gap, which remains a sizeable feature of the pay outcomes in Canada and the United states. The changes in the labor market over the past 20 years, which include higher rates of female labor force participation, a decline in the extent of occupational segregation, a narrowing of the gender wage differential, and a major change (suburbanization) in the spatial organization of work have, therefore, had little effect on the role of the commute to work in the determination of earnings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".