Tester l’effet du crédit d’impôt pour le prolongement de carrière sur le taux d’activité des travailleurs de 55 ans et plus
Bibliographic record
Abstract
While we are witnessing an acceleration of the phenomenon of demographic aging in Canada, a spectacular increase in the participation rate of the elderly has been felt since the end of the 1990s. Several factors may be at the origin of this increase, but few studies really focus on empirically verifying these. It is in this register that this research is focused. In fact, in 2012, the government of Quebec implemented a tax initiative dedicated to the maintenance and return to the labor market of the elderly, namely the “Tax credit for experienced workers”, today. renamed as the “Tax Credit for career extension”. However, to date, no study has been carried out on the effectiveness of this Quebec tax measure in which the government has spent nearly $ 700 million and is preparing to spend more in the coming years. Testing the effect of this incentive policy for experienced workers will therefore be the main objective of this research. In order to meet this objective, we performed multivariate regression analyzes using a statistical model developed from our review of the literature on labor force participation rates of people aged 55 and over. Our sample consists of 360 data or 36 observations (one for each of the years covering the period 1983 to 2018) for each of the 10 provinces of Canada. For the purposes of our analyzes, we selected the participation rate of those aged 55 and over as the dependent variable as well as a realistic selection of 10 independent variables divisible into three categories, i.e., trend and non-trend structural variables (term linear trend, squared trend term and set of 9 provincial dichotomies), economic variables (unemployment rate, interest rate and average weekly earnings) and public policy variables (income tax benefit work, incentive nature within pension schemes and tax credit for experienced workers). Our results show that the Quebec incentive, the tax credit for career extension, did not have the expected effects on workers aged 55 and over, while particularly noticeable changes are observable for this age group. It seems that the good performance of the economic situation is sufficient to explain a good part of the behavior of the participation rates of people aged 55 and over in Quebec. These results are sufficient to encourage further evaluation of the tax credit program, as well as all current and future incentive policies put in place by governments.
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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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".