No More Second-Class Taxpayers: How Income Splitting Can Bring Fairness to Canada’s Single Income Families
Bibliographic record
Abstract
The Canadian personal income tax system does not pay much attention to whether the amount of money an individual brings home is supplemented by the income of a spouse or not. That means that families where one spouse earns more than the other get taxed at a higher rate than families where two working partners earn the same total income split evenly between two paycheques. In fact, a family with just a single earner making $70,000 a year pays 30 per cent more in taxes every year than a family with two partners making $35,000 a year. A single-earner family taking in $120,000 a year pays the same income tax as a dualearning couple making $141,000 between them. The federal Conservative government has at least suggested it wants to finally level that playing field — nearly six decades after a royal commission recommended that the income tax system be changed to recognize total family household income, rather than focusing on each individual’s income. Given that Canada’s income tax system aims to treat people in similar circumstances as equally as possible, it is certainly time to let couples split their income so they do not face a penalty in higher tax rates than those faced by couples bringing home the same amount of total pay. While couples with just a single earner enjoy some advantages, a dual-earning couple does not — namely the extra time the stay-at-home spouse is able to use to raise children and produce other unpaid, home-based benefits — that can be accounted for using other means. Specifically, cutting out the transferability of the unused portion of the basic personal tax exemption for couples splitting income — requiring couples splitting their income to each earn money in order to use this credit — is one way to account for the difference in unpaid benefits that single-income families do typically enjoy more than dual-income couples. That is one mechanism; there may still be others the government might consider. But the bottom line is that there are ways to account for certain non-labour-market-related differences between single-earner families and dual-earner families. No doubt many of these proposals would be an improvement over the current, where two different families subsisting on the same pay are taxed very differently, with one type of family penalized simply because of how their income is earned.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.037 | 0.011 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".