The Earnings Gap between Black and White Workers in Canada: Evidence from the 2006 Census
Bibliographic record
Abstract
This paper investigates the earnings gap between Black and White workers in the Canadian economy using 2006 Canadian Census data. Several studies have examined visible minority earnings in Canada (e.g., Hou and Coulombe, 2010; Pendakur and Pendakur, 2011; Yap and Konrad, 2009). Recent research consistently finds that Black workers face one of the largest earnings gaps amongst ethnic groups in Canada (Pendakur and Pendakur, 2002, 2007; Hou and Coulombe, 2010). Nonetheless, the literature lacks an investigation of the combined impact of wage discrimination and occupational segregation on the earnings gap faced by Black workers in the Canadian labour market. Howland and Sakellariou (1993) as well as Hou and Coulombe (2010) highlighted the importance of occupational attainment differences in labour market outcomes. Consequently, this research suggests the need for occupational attainment to be incorporated into models investigating earnings gaps. We address the gap in the literature by utilizing the decomposition method developed by Brown, Moon and Zoloth (1980). This BMZ method extends the traditional earnings decomposition methods advanced by Blinder (1973) and Oaxaca (1973) by also identifying the role played by occupational differences. Specifically, the BMZ method estimates the portion of the earnings gap attributable to differences in productive endowments and to unexplained factors (i.e., the traditional decomposition approach) as well as extending the traditional approach by providing a calculation of the portion of the earnings gap explained by occupational attainment differences. The study finds that approximately one-fifth of the Black-White earnings gap (equaling $2,600) can be attributed to productivity-related endowment differences. Furthermore, the remaining four-fifths of the earnings gap (equaling $9,800) is attributable at the upper-bound level to occupational segregation and wage discrimination. In aggregate, the estimates of occupational segregation and wage discrimination translate into annual earnings losses of approximately $1.5 billion for full-time full-year Black workers in the Canadian workforce.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".