Aboriginal Earnings in Canada: The Importance of Gender, Education, and Industry
Bibliographic record
Abstract
The contrasting social and economic inequalities that exist between Indigenous and non-Indigenous peoples has been the focus of many Canadian studies. Several interlinking factors have been found to impact the socioeconomic status of Canada's Indigenous peoples, including social distance, education, gender, and occupational characteristics. Few of these studies, however, look at the gender wage gap amongst Indigenous populations, and in this paper, we identify some of the factors that drive both income and differences in income between men and women, amongst Indigenous peoples. Using data from the 2017 Aboriginal Peoples Survey (APS), we examine key socioeconomic factors and identify how these are linked to income. We find that education and employment industry are major indicators of socioeconomic status, as are the interactions between gender and income. We also find a persistent gender wage gap amongst Canada's Indigenous peoples, but we find that this gap can be explained by differences in the returns to education, skill levels, and industry of employment. Résumé: Plusieurs études canadiennes se sont penchées sur les inégalités sociales et économiques contrastées entre les peuples autochtones et non-autochtones. Quelques facteurs interdépendants ont été trouvés pour influencer le statut socio-économique des peuples autochtones du Canada, ainsi que la distance sociale, l'éducation, le sexe et les caractéristiques professionnels. Certains de ces études n'ont cependant pas mis l'accent sur l'écart salarial entre les hommes et les femmes de la population autochtone et, dans cet article, nous avons identifié non seulement les facteurs qui déterminent le revenu, mais aussi les facteurs de différence entre les revenus des hommes et ceux des femmes dans la communauté autochtone. En nous servant des données de Aboriginal Peoples Survey 2017 (APS), nous examinons les principaux facteurs socio-économiques et identifions comment ceux-ci sont liés au revenu. Nous postulons que l'éducation et l'industrie de l'emploi sont des indicateurs majeurs du statut socio-économique, tout comme les interactions entre les hommes, les femmes et le revenu. Nous notons aussi un écart salarial persistant entre hommes et femmes de la population autochtone du Canada, mais trouvons que cet écart peut s'expliquer par des différences dans les rendements de l'éducation, des niveaux de compétence et de l'industrie de l'emploi.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".