Estimating the Impact of the Québec’s Work Incentive Program on Labour Supply: An Ex Post Microsimulation Analysis
Bibliographic record
Abstract
In 2005, a wage subsidy program was established in Québec to encourage low-income individuals, particularly recipients of social assistance, to work, by offering them fiscal relief. We analyse the effect of this program (the Prime au travail) with a microsimulation model which determines the impact on the labour supply. We estimate the variation in the labour supply at the extensive and intensive margins which allows us to grasp both the income effect and the substitution effect of the Prime au travail on individuals’ willingness to work. On the other hand, our labour supply model has the necessary characteristics to link it to a general equilibrium model and offer an integrated macro-microsimulation analysis. Nonetheless, unlike the usual microsimulation models employed in integrated macro-microsimulation analysis, we provide a number of innovations, notably the analysis at the intensive margin so that it captures both the substitution effect and the income effect. Our results show that a number of individuals entered the labour market in response to the Prime au travail, while others decided to work fewer hours, due to increased income linked to the program. Ultimately, the variation in labour supply was less in the intensive margin than in the extensive margin and it is positive for all types of households, with the exception of female single parents.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".