Bibliographic record
Abstract
This thesis includes three independent chapters on the interactions between families and the Canadian tax system. All three chapters make use of administrative longitudinal tax data. The first of these chapters measures the impact of an important reform to federal child benefits in terms of propensity to be partnered among parents. The Canada Child Benefit (CCB), introduced in 2016, is reduced according to the combined income of any partnered adults with whom the child lives. This family means-testing schedule combined with the increased generosity of the CCB could alter incentives for partnering versus singledom. We observe that the share of partnered mothers among recipients of the CCB is 1.2 percentage points lower than it would otherwise have been and that the comparable share among men is 0.3 lower. The second chapter measures the responsiveness of parents to the different taxes they face, with a particular focus on implicit joint taxation from family means-testing. I use variation in such means-testing as well as income tax rates over time and between provinces to estimate a set of elasticity parameters, proportional response rates to changes in effective taxation. I observe that parents are very unresponsive to changes in the income tax rate that they face. They are more sensitive to implicit tax rates imposed by means-testing, but these effects go in the opposite direction theory predicts. My last chapter documents how many families have simple tax returns that the Canada Revenue Agency (CRA) could readily complete with information it already collects from third parties. If so directed, it could complete returns for two-thirds of the families with income from provincial social assistance programs. Because tax-filing is a prerequisite to access to public benefits, I believe that doing so would meaningfully contribute to alleviating poverty. I also describe ways for the CRA to collect third-party information from new sources or institute alternative administrative procedures which could substantially increase the number of simple returns.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 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".