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Record W4406135957 · doi:10.55016/ojs/sppp.v9i1.42593

The Disability Tax Credit: Why it Fails and How to Fix It Authors

2016· article· en· W4406135957 on OpenAlexaffabout
Wayne Simpson, Harvey Stevens

Bibliographic record

VenueThe School of Public Policy Publications · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEconomicsTax creditLaw and economicsBusinessPublic economics

Abstract

fetched live from OpenAlex

When the government establishes a social program whose primary purpose is to help provide support to low-income people with disabilities, its success should be measured on how well it achieves that purpose. Unfortunately, there are reasons to seriously question the usefulness of Canada’s disability tax credit since it is helping so very few of the people it is intended to support. In fact, the credit is helping only a small number of Canadians with disability who qualify for it, and least of all those in the poorest families who receive an average of only $29 annually. The reason is not hard to see: Designing the support as a tax credit means that only those Canadians with disability who earn enough income to have them owing taxes can take advantage of it. Yet it is an unfortunate reality that people with disability are often at low incomes precisely because their disability leaves them unable to work in full-time, wellpaid jobs. Thus, the very people who need this support most are the ones least able to take advantage of it. In other words, the neediest disabled Canadians are receiving the least benefit. Far from being a successful policy, the results of the disability tax credit can only be described as disappointing. There is an uncomplicated way to begin rectifying this: By making the disability tax credit refundable. Along the same lines as a guaranteed minimum income, or negative income tax, those low-income Canadians with disabilities who qualify for the credit but lack sufficient income to benefit from the credit could simply be made eligible for a refund of the amount they cannot claim. Simply doing that, turning this non-refundable credit into a refundable credit, would increase the average benefit for Canada’s poorest families with a disabled person from $29 to $511, increasing their total income by a meaningful 4.1 per cent. Just as importantly, where a meagre 0.2 per cent of these families now get any benefit at all from the credit, a refundable credit would now see a majority, 56.4 per cent, receiving benefits. We estimate that this would mean added costs to the federal program of a modest $72 million, or a 17 per cent increase. A similar reform at the provincial level would cost an additional $31 million. An even more effective option for ensuring better outcomes for this policy, however, would be to both make the tax credit refundable and enhance it to triple its value. This would ensure that virtually every family with a disabled person below the low income cut-off, would benefit from the credit and this enhancement would raise their incomes a far more consequential 27 per cent. The costs of this enhanced refundable disability tax credit would be, of course, notably higher, estimated at $516 million federally and $240 million provincially, but it would actually achieve the outcomes that this policy ostensibly intends. The current program may be cheaper, but the value it delivers is trifling and the money, therefore, is arguably heavily wasted. For years there have been calls to make this tax credit work better through refundability. We now have evidence that an enhanced refundable disability tax credit would make the significant difference in the lives of low income Canadians with disabilities that the policy was designed to do, but has so far largely failed to do.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.019
metaresearch head score (Gemma)0.100
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.451

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0190.100
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0110.011
Scholarly communication0.0140.014
Open science0.0040.004
Research integrity0.0180.034
Insufficient payload (model declined to judge)0.0190.007

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.

Opus teacher head0.049
GPT teacher head0.337
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations0
Published2016
Admission routes2
Has abstractyes

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