An Assessment of the Impact of Conservative Immigration Reform on the Labour Market Performance of Immigrants
Bibliographic record
Abstract
This paper examines the performance of recent immigrants to Canada in the labour market as revealed in the Longitudinal Immigration Database (IMDB). This is an administrative database constructed by Statistics Canada by combining an administrative landing file from Citizenship and Immigration with the T1 Family File (T1FF) of income tax returns from the Canada Revenue Agency. As this database now extends to 2012, it provides the most current evidence on the impact on the labour market performance of recent immigrants of the relatively ambitious immigration reforms introduced by the Conservative Government. These reforms involved tighter criteria for skilled workers, an expansion of the Provincial Nominee Program, and a tightening up on refugee claims. The conclusion of the paper is that the overall performance of recent immigrants has improved enough to modestly reduce the wide earnings gap that has opened up between average recent immigrant and overall earnings. However, the reduction in the earnings gap has not been very large given the ambitiousness of the immigration policy reforms. There are many reasons for this, but the most important is that the Conservative Government has continued to pursue a policy of high mass immigration admitting around 250, 000 new immigrants per year right through the 2008-09 recession. Ironically, while the Government has cut back on the number of relatively high performing skilled workers admitted, it has actually increased the number of live-in caregivers, and their families who predominantly are low earning. On the other hand, it is clear that if the Conservative Government had not tightened up immigration policy as aggressively as it did, particularly by eliminating the backlog of workers admitted under the old less stringent criteria, the labour market performance of immigrants would have probably deteriorated, instead of improving modestly as it did.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".