Public Policy in a Time of Crisis: A Framework for Evaluating Canada’s COVID-19 Income Support Programs
Bibliographic record
Abstract
Income support programs introduced for workers during the first wave of coronavirus disease 2019 (COVID-19) lockdowns faced criticism for their negative labour supply effects. We propose that these concerns about work disincentives are embedded in restrictive assumptions about work and led to suboptimal design of crisis support policies. We describe a framework for analyzing alternative crisis income support programs predicated on more realistic assumptions of labour markets and human motivation. Our framework proposes that balancing efficiency, equity, and voice objectives should be the goal of crisis labour market policies. We argue that adoption of a basic income targeted toward low-income workers, in combination with Canada's pre-existing Employment Insurance program, would have balanced efficiency, equity, and voice better than the combination of the Canada Emergency Response Benefit and Canada Emergency Wage Subsidy. A targeted basic income would also have been more effective at achieving stated public health objectives.
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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 | 0.023 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.022 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".