The Distribution of Income and Taxes/Transfers In Canada: A Cohort Analysis
Bibliographic record
Abstract
Who pays and how much? These are crucial questions for any tax system and, given the complexity of the economy, they are also among the most difficult to answer. This paper undertakes an analysis of the distribution of taxes and transfers in Canada using a static approach based on annual income combined with the novel approach of breaking down taxpayers by age cohort. The paper examines how tax rates net of transfers differ by age and income group, and how those rates change over taxpayers’ lifetimes. It clearly reveals the progressive nature of Canada’s tax system. In our base case scenario, when all age cohorts are considered together and transfers are treated as negative taxes, the first two quintiles of the income distribution are net recipients of government transfers with negative net tax rates equal to about -48 percent for the first quintile and -33 percent for the second quintile. For middle to high-income individuals net tax rates are positive and increase with income, from 10 percent for the median group, to 24 percent for the fourth quintile and 34 percent for the fifth quintile. Looking at net tax rates by age cohort, we find that overall the bottom 20 percent of the income distribution is a net recipient of fiscal transfers at all ages. However, on average for individuals 65 and over all but the top 20 percent of the income distribution are net recipients of fiscal transfers, with negative net tax rates. The age related redistributive nature of Canada’s tax system is further emphasized by an examination of the Gini coefficients for each age cohort, calculated here for the first time. Starting at age 30, before taxes and transfers income inequality is found to rise monotonically with age, leveling off at 65. Taxes and transfers reduce the degree of income inequality significantly for all ages, but substantially more so for the elderly due to age related features of the tax and transfer system. If redistribution can be thought of as a one of the fundamental features of the tax and transfer system in Canada, the extent to which it is targeted at the elderly is an important secondary feature.
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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".