The Receipt of Guaranteed Income Supplement (GIS) Status Among Canadian Seniors – Incidence and Dynamics
Bibliographic record
Abstract
Our topic is the receipt patterns of low-income support benefits in the form of the guaranteed income supplement (GIS) benefit amongst Canadians who are 65 and older. The GIS regime is the only means-tested public retirement benefit that is targeted to the group of retired individuals and couples. The primary outcome variables are the incidence of receipt of payment amongst this population and the dynamics of entries and exits from this state. Our study is based on administrative data drawn from tax returns. The analysis is in the spirit of the poverty/low-income literature that is fairly developed in regards to the working-age population. In a point of departure from that literature, however, we take a retrospective approach by including in our analysis several phases of the life cycle. We estimate multivariate econometric models of the incidence of receipt among the eligible population, as well as hazard models of both entry and exit from that state. In our estimating equations we include indicators for age and entry cohort. We subsequently include regressors to reflect demographic variables such as gender, marital status, immigration status, minority language status, and regional effects. The fullest specification includes indicators for permanent income and prior savings activity, all calculated based on retrospective information observed when the individual was 50-52 years old. Among our numerous empirical results are an incidence rate that rises sharply with age and is much lower for married than single individuals. In regards to the dynamics, a majority (but not all) of GIS receipt is characterised as persistent.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".