Disability, discrimination, and the effectiveness of wage subsidies: A job-search approach
Bibliographic record
Abstract
In this paper we develop and estimate a job search model with matching and bargaining in the presence of employer taste-based discrimination. The model is estimated using a longitudinal panel data from Canada’s Survey of Labour and Income Dynamics (SLID). Estimates suggest that employer discrimination and individual labour costs explain the majority of labour market disparities between persons living with and without disabilities. We use our model to estimate several counterfactuals. We find that implementing a hiring wage subsidy policy could increase the employment rate of persons with disabilities by 7 percentage points. Eliminating discrimination, on the other hand, would have an even greater impact, raising the employment rate by 14 percentage points for men, and 19 percentage points for women. Combining both measures — removing discrimination and introducing a hiring wage subsidy — would lead to an employment rate increase of 20 percentage points for men, and 24 percentage points for women. This combined approach would significantly reduce the existing employment rate gap between persons with and without disabilities. In particular, the employment rate gap is predicted to fall to 33 percentage points for men (relative to 53 percentage points in the data) and to 13 percentage points for women (relative to 39 percentage points in the data).
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".