Low income among Canadian seniors: interactions between immigration status, racial identity and gender
Bibliographic record
Abstract
Purpose Population aging is prompting concern over the viability of public pension systems in many Organisation for Economic Co-operation and Development (OECD) nations. In Canada, over the past 2 decades, one response has been to significantly increase immigration, increasing the immigrant share of the population. However, policy and academic analyses have largely overlooked the aging of the immigrant population. This study aims to document disparities in low-income rates and labour market outcomes between immigrants and non-immigrants at older ages, focusing on the intersectionality of immigration status, racial identity and gender. Design/methodology/approach We employ the 2021 Canadian Census to undertake a descriptive analysis of low-income patterns and the underlying income sources. Findings Compared to non-racialized non-immigrant men, seniors’ low-income rates increase with each of immigrant, racialized and female status – and the effects are cumulative. Low-income differences are linked to variations in prime-age employment and earnings, and access to pension benefits for immigrants. To a large extent, the gaps are driven by variations in Canada Pension Plan (CPP) benefits, mostly due to limited years of Canadian residency. Originality/value We characterize differences in low income across eight population subgroups and examine the complete set of tax-relevant income sources to understand how each underlying income stream is associated with the observed differences in low income. We emphasize the intersectionality between immigration status and racial identity, and how both vary by gender.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".