Interprovincial Migration and Retirement Income Transfers among Canada's Older Population: 1996–2001
Bibliographic record
Abstract
Given the propensity of Canadians to migrate, it is likely that migration has a large impact upon the distribution and redistribution of income across regions. Such impacts may be magnified within the older population, as their relocation involves the transfer of nonearned income such as pensions, retirement investments, or other income supplements from province to province and so return migration to a province of birth following retirement subsidizes local economies. By using methods proposed by Plane in 1999, income-based versions of demographic effectiveness are applied to evaluate the movement of nonearned income in the Canadian context among Canada's older population. The analysis uses data drawn from the 2001 Canadian Census, and focuses upon the older population (aged 60+ in 2001) who reported nonearned incomes in 2000. The paper distinguishes between four types of nonearned income, including (i) Old Age Security and Guaranteed Income Supplements; (ii) Canada/Québec pension plan benefits; (iii) Retirement Investment income; and (iv) Investment Income. The objectives of the paper are twofold. First, it documents the movement of nonearned income between Canada's provinces, focusing upon the demographic effectiveness of the observed flows. Second, the paper explores the potential for primary, return, and onward migration to redistribute nonearnings income across Canada, and the significance of regional income redistributions by each type of migration. Results illustrate the importance of migration in transferring nonearned incomes over space, and the particular importance of return migration as a vehicle to redistribute nonearned income to economically depressed provinces.
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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 | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".