Effects of New Welfare Reform Strategies on Welfare Participation: Microdata Estimates from Canada
Bibliographic record
Abstract
This paper introduces newly coded information describing province- and year-specific variation in work requirements, diversion, earning exemptions, and time limits. This new information reveals a large decline in the chance of welfare participation of at least 1.1 percentage points (9.2% relative to the unconditional mean rate of participation) associated with stringent combinations of those four new welfare reforms, even after controlling for benefit levels, eligibility requirements, province-specific GDP growth and unemployment. These results replicate previous findings based on aggregate data and extend them with controls for individual-level characteristics. Microdata with individual-level characteristics enable estimates of the effects of new welfare reforms on 46 subpopulations, suggesting that immigrants, native Canadians, single parents and disabled people were far more effected by provinces' aggressive new attempts to limit welfare participation than other Canadians receiving social assistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".