The Effect of Asset Thresholds on Income Assistance Flows in British Columbia
Bibliographic record
Abstract
Across Canada, provincial social assistance programs (also sometimes referred to as “welfare”, or “funders of last resort”) impose asset tests for both applicants and recipients. The “good policy” argument argues that asset tests contribute to better program targeting, ensuring that those with high assets cannot access nor continue on social assistance. The “bad policy” argument argues that asset tests force applicants to spend down assets and keep asset levels low, reducing their ability to permanently exit from social assistance. Exploiting a policy change that increased asset thresholds for social assistance recipients in British Columbia, Canada, we test these hypotheses using recipient-level social assistance data. We find that increasing the asset threshold did not motivate people to enter social assistance nor did it help those leaving social assistance to leave permanently. There is some evidence that increasing the asset threshold did reduce the probability of exit from social assistance. From a policy perspective, these findings suggest that further increasing the asset threshold is unlikely to result in non-vulnerable persons accessing social assistance, but it could reduce the burden on those who are vulnerable and do require access to social assistance.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".