A Question of Fairness: Time to Reconsider Income-Averaging Provisions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A system of progressive marginal income tax rates, as in Canada, tends to impose a greater tax burden on individuals whose incomes are irregular or fluctuate year-by-year, compared to individuals with steadier incomes of the same average value over several years. Take, for example, a person without dependents living in Ontario. Suppose she earns $50, 000 in 2016 and $100, 000 the following year. Thus, her average income is $75, 000 per year. However, her total income tax for the two years is about $1, 900 more than if she had earned, instead, $75, 000 in both years. On an annual basis, her extra tax liability is almost $1, 000, or 1.3 percent of her average annual income. A similar tax penalty on fluctuating income would occur in a case where her income had fallen from $100, 000 in 2016 to $50, 000 in 2017. Call it the “fluctuation penalty, ” for short. The fluctuation penalty is a policy concern for reasons of fairness and the adverse incentives it may create for risk-taking activities, such as entrepreneurship. In terms of fairness, the fluctuation penalty violates the principle of horizontal equity, which is that equals should be taxed equally. Vertical equity is also weakened, if the fluctuation penalty is most acute for lower-income persons. But how severe is the fluctuation penalty in Canada? The answer will depend, not only on the marginal tax rates and tax credits, but also on the actual patterns and sources of incomes received by individuals over several consecutive years. This study uses longitudinal data spanning the six-year period, 2005-2010. After restricting the data to focus on individuals who can be expected to pay taxes, the sample contains about 7, 000 persons. We compare the tax burdens that these individuals paid on their observed incomes with a counterfactual situation, in which the same individuals earned a constant income with the same six-year average value as their observed incomes, adjusted for inflation. The difference in tax burdens is expressed as a percentage of an individual’s income and, hence, represents the increase in the average tax rate paid by an individual taxpayer. The main findings are that the fluctuation penalty is relatively largest for lower-income taxpayers, the unincorporated self-employed, and recipients of capital gains. The fluctuation penalty in Canada appears especially harmful for the poor and for potential entrepreneurs. Prior to 1989, provisions in the tax code allowed taxpayers to smooth their taxable incomes by using an average of more than one year’s income as the basis for calculating the tax liability. Reintroducing one or more of these provisions would help address the fluctuation penalty today.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it