The Impact of Imperfect Information on the Wages of Native-Born and Immigrant Workers: Evidence from the 2006 Canadian Census
Bibliographic record
Abstract
This paper empirically examines how imperfect information about wage offers and reservation wages among employees and employers respectively impacts on the wages of Canadian born and immigrant workers. We estimate these effects from 2006 census data using a two-tier stochastic wage frontier. Our main contributions are: first, the use 2006 census data allows us to examine how the international transferability of immigrant human capital (or the lack of it) impacts on worker and employer information - this could not be done with earlier censuses, but is a critical factor that separates the labour market experience of immigrants (especially newcomers) from that of native-born Canadians; second, we adopt a more general approach to information gaps by re-parameterizing the frontier model to incorporate the impact of individual differences on labour market information; and third, we allow worker and employer information gaps to vary due to industry fixed effects. Our findings show that Canadian-born and immigrants with similar characteristics tend to experience quite similar wage gaps in the aggregate. While those gaps show significant variation across some industries for both immigrants and Canadian-born workers, and wage gaps due to worker imperfect information are also similar both groups, wage gaps driven by employer imperfect information are much larger among immigrants. As well, the results show that the variability in the amount of information that workers and employers possess is clearly more substantial among immigrants, thereby pointing to greater uncertainty about their wage outcomes. Our analysis of immigrants shows that while the effects of acquiring their degree prior to migration increases the size of wage gaps due to employer and worker imperfect information, these impacts are relatively modest when compared to those arising from a lack of language skills.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".