Employer learning and statistical discrimination in the Canadian labour market
Bibliographic record
Abstract
Statistical discrimination is frequently applied to illustrate different economic opportunities among equally able individuals. We use statistics from 1994, the second wave of the Survey of Labour and Income Dynamics, to analyze the income received from paid work jobs as the measure of an individual’s economic opportunity. At the same time, Heckman’s two-stage procedure is performed to account for possible bias that arises from estimating with only a pool of paid workers. We are interested in testing the following hypotheses: whether employers statistically discriminate among potential workers on the basis of education and immigration status if they have limited information about those workers and whether they learn to revise their judgments as new information is obtained. The results confirm the employer learning and statistical discrimination based on years of schooling hypotheses for the Canadian labour market. The labour market returns to initially unobservable characteristic increases with time spend in the labour market. In addition, wage becomes less related to education that employers initially use to infer an individual’s productivity. On the other hand, immigration status is not very informative about the productivity of a worker and the results do not support the hypothesis of statistical discrimination on the basis of immigration status. This paper points out the challenges faced by traditional labour market policies in a world of statistical discrimination and employer learning.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".