Did Tax Cuts on Earned Income Reduce Welfare Participation in Canada
Bibliographic record
Abstract
From 1986 to 2005, Canadian provinces used earnings exemptions to modify effective marginal tax rates on welfare participants’ labour market earnings. Two policy variables were used: an exemption threshold and an above-threshold marginal tax rate. We estimate the effects of these two policy variables on provincial rates of welfare participation, while controlling for heterogeneous combinations of other welfare reforms and macroeconomic conditions across provinces and through time. The data reveal large and statistically significant effects of earnings exemptions on welfare participation. Reducing the marginal tax rate levied on welfare participants’ earned income by 50 percentage points was associated with a two percentage point reduction in the average province-year’s participation rate or, equivalently, a 23% relative reduction below the unconditional mean rate of participation. Earnings thresholds and above‑threshold marginal tax rates interact in ways that make it difficult to predict how changing either policy parameter in isolation is likely to affect participation. Cutting above-threshold marginal tax rates would appear to be the strongest variable through which earnings exemptions may effectively reduce welfare participation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".