Measuring Effective Tax Rates on Human Capital: The Canadian Case
Bibliographic record
Abstract
This paper analyzes the impacts of a wide range of tax provisions on the incentive to invest in human capital, and shows how these effects can be quantified using effective tax rates, or ETRs. The approach is illustrated using data for Canada. For individuals with median earnings, ETRs on the human capital formed in first-degree university study are sizeable, although not as large as for physical capital in Canada. When the expenditure side and its direct subsidies are also taken into account, the net effective tax rate on human capital becomes negative. The taxation of human capital is far from uniform. ETRs vary by income level, gender, part-time vs. full-time study, whether students have loans, number of dependants, and use of sheltered savings plans. Workers at higher percentile levels of the earnings distribution throughout life may face ETRs substantially higher than those for low-income workers, as a result of progressive income
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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 | 0.000 | 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) | 0.009 | 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".