Commuting while Black: compensating differentials and Black–White wage gap in Canada
Bibliographic record
Abstract
Purpose Using the Canadian Census of 2016, the present study examines the Black and White gap in compensating differentials for their commute to work. Design/methodology/approach The data are from the Canadian Census of 2016. The standard Mincerian wage regression, augmented by commute-related variables and their confounders, is estimated by OLS. The estimations use sample weights and heteroscedasticity robust standard errors. Findings In the standard Mincerian wage regressions, Black men are found to earn non-negligibly less than White men. No such gap is found among women. When the Mincerian wage equation is augmented by commute duration and its confounders, commute duration is revealed to positively predict wages of White men and negatively associate with wages of Black men. At the same time, in the specifications including commute duration and its confounders, the coefficient for the dummy variable identifying Black men is positive with a non-negligible size. The latter pattern indicates wage discrepancies among Black men by their commute duration. Again, no difference is found between Black and White women in these estimations. Research limitations/implications The main caveat is that due to data limitations, causal estimates could not be produced. Practical implications For the Canadian working men, the uncovered patterns indicate both between and within race gaps in the impact of commuting on wages. Particularly, Black men seem to commute longer towards relatively lower paying jobs, while the opposite holds for their White counterparts. However, Black men who reside close to their work earn substantially more than both otherwise identical White men and Black men who live far away from their jobs. The implications for research and policy are discussed. Originality/value This is the first paper focused on commute compensating differentials by race using Canadian data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".