The Impact of Income Support Programs on Labour Market Behaviour in Canada
Bibliographic record
Abstract
Employment insurance (EI) and social assistance (SA) represent two key income support programs in Canada. The impact of these programs on labour market behaviour has been well documented in the literature. There is little analysis, however, of the nature of the interface between the programs and their overall impact on labour market outcomes. In this paper we use the 1997 Canadian Out of Employment Panel dataset to examine labour market behaviour for a set of individuals following the loss of employment. A generalized transition probability model is estimated that identifies the use of both income support programs and employment patterns following the loss of a job. The approach allows labour market behaviour to be simulated under a variety of policy scenarios. Key results from the analysis indicate that reductions in the generosity of SA results in lower use of both income support programs. Conversely, if the generosity of the EI program is curtailed this results in greater use of the SA program. Further, changes that make establishing EI eligibility more onerous have a more pronounced impact on the use of the SA program than changes that reduce weeks of EI entitlement given that EI eligibility has already been established. These results have important policy implications in an environment where responsibility for the programs is shared by different levels of government.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| 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".