The impact of the COVID-19 pandemic on the labour market outcomes of Indigenous persons living off-reserve in Canada
Bibliographic record
Abstract
The recent COVID-19 pandemic had an impact on the labour force activity of many, but the effect of the crisis on the labour market outcomes of Indigenous peoples in Canada is relatively understudied. Using data from the master files of the Canadian Labour Force Survey (LFS), this analysis first considers the relative probability of four mutually exclusive labour force states: employed; unemployed; not in the labour force, but not discouraged; and not in the labour force due to being a discouraged worker. Differences in hours worked as well as earnings comparing Indigenous and non-Indigenous workers are then examined. The findings reveal that, regardless of the COVID-19 pandemic, Indigenous Canadians were relatively more likely to be either unemployed or not in the labour force (including being a discouraged worker) than to be employed as compared to their non-Indigenous counterparts. Indigenous workers earned roughly 3 percent less than their non-Indigenous counterparts. The earnings penalty experienced by Indigenous workers, was slightly smaller at the bottom of the wage distribution at 2 percent, increasing to roughly 3.3 percent at the top 90th percentile of the earnings function. During the COVID-19 period, while the risk of unemployment was lower for Indigenous persons, the risk of being a discouraged worker was even higher for Indigenous Canadians during the pandemic. Using an ad-hoc supplement to the LFS conducted by Statistics Canada in response to the pandemic, this study finds that Indigenous respondents were less likely than non-Indigenous respondents to either work from home or receive any form of Canada relief benefit from April 2020 to October 2021. The implications of these findings suggest a role for policy makers to improve access to funding supports for Indigenous workers and students seeking to pursue higher education to improve labour market outcomes. Additionally, employers play an important role in ensuring that Indigenous workers have fair access to job opportunities.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".