The Impact of Converting Federal Non-Refundable Tax Credits Into Refundable Credits
Bibliographic record
Abstract
With economic inequality on the rise in Canada, the federal government needs to consider innovative solutions. One possibility for improving the tax-transfer system involves refundable tax credits (RTCs). Making all tax credits refundable wouldn’t require Ottawa to introduce new tax measures; the Canadian tax system already contains a mix of RTCs and NRTCs, so the government could simply continue its practice of designing tax credit programs to be refundable. Using Statistics Canada’s Social Policy Simulation Database and Model, this paper examines the impacts and cost of converting NRTCs to RTCs, with and without an income exemption equal to 25 percent of the before-tax lowincome standard for a census family, the Census Family Low-Income Line. Under the Option Without Exemption (OW/OE), RTC recipients are taxed at a single rate of 15 percent, regardless of family size, right up to the Line. Under the Option With Exemption (OWE), RTC recipients are taxed at zero percent up to 25 percent of the Line and at a single rate of 20 percent, regardless of family size, up to 100 percent of the Line. The incremental cost of switching NRTCs to RTCs under the OW/OE is $6.6 billion, as additional benefits are provided to 6.4 million families — slightly less than 37 percent of all families. The cost of the OWE is $7.2 billion, as benefits flow to slightly more families — 6.45 million. However, the percentage of benefits reaching low-income families is much higher under the OWE (69 percent vs. 49 percent). Additionally, the OWE provides an average of nine percent more RTC benefits to low-income tax filers, making it clearly the superior option for poverty reduction. Moreover, the paper shows that alternative conversion schemes that set benefit reduction rates to differ by family size can further increase the benefits to low-income families at a lower overall cost. Such changes would elicit a labour-supply response in terms of a reduction in hours worked, and while the effect is smaller under the less expensive OW/OE, the difference between the two options is slight. This paper simulates the conversion of NRTCs to RTCs in comprehensive detail, besides providing practical advice on how such a shift would be funded. It offers valuable food for thought on an issue that is increasingly critical to Canadian society.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".