Bibliographic record
Abstract
Many countries use tax-preferred saving accounts to incentivize individuals to save for retirement. The two main forms of tax-preferred saving accounts – TEE and EET – tax savings at the contribution and withdrawal years respectively. Thus the relative returns of the two savings vehicles depend on the effective marginal tax rates in these two years, which in turn depend on earning dynamics. This paper estimates a model of earning dynamics on a Canadian longitudinal administrative database containing millions of individuals, allowing for substantial heterogeneity in the evolution of income across income groups. The model is then used, together with a tax and credit calculator, to predict how the returns of EET and TEE vary across these groups. The results suggest that TEE accounts yield in general higher returns, especially for low-income groups. Comparing optimal saving choices predicted by the model with observed saving choices in the data suggests that EET are over-chosen, especially in the province of Quebec. These results have important implications for “nudging†policies that are currently being implemented in Quebec, forcing employers to automatically enrol their employees in savings accounts similar to EET. These could yield very low returns for low-income individuals, which are known to be the most sensitive to nudging.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".