Tax Incentives and Older Workers: Evidence from Quebec's Tax Credit for Career Extension
Bibliographic record
Abstract
We present empirical evidence on the effectiveness of a tax policy designed to boost employment among older workers in Quebec, Canada. To evaluate its impact on employment and earnings, we draw on multiple data sources and use a range of identification strategies. We begin with a difference-in-differences approach comparing Quebec and Ontario but find no robust effect on employment. Moreover, the common trend assumption fails to hold for most age groups, limiting the credibility of this strategy. To address this, we use Longitudinal and International Study of Adults data to implement a staggered adoption design, leveraging variation across birth cohorts within Quebec. This analysis also shows no impact on labour force transitions, although we detect a modest increase in earnings for women. We further use an alternative identification strategy using the Longitudinal Administrative Databank, exploiting variation in treatment intensity over time within Quebec. Consistent with prior findings, we observe no significant effect on labour force transitions but again identify a small positive effect on women's earnings and a reduction in net tax liability among affected workers. Taken together, our results suggest that the tax measure neither effectively increases employment among older workers nor represents a cost-efficient approach to raising public revenues.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".