Fiscal transfers to immigrants in Canada: responding to critics and a revised estimate
Bibliographic record
Abstract
In 2011, we estimated that in 2005 Canada’s immigrant selection policies resulted in an average fiscal burden on taxpayers of $6,000 for each immigrant. Later that year, Mohsen Javdani and Krishna Pendakur from the economics department at Simon Fraser University (J&P hereafter)\npresented an alternative estimate of this fiscal burden of $450.\n\nThis study concludes that J&P’s lower estimate is due mainly to their choice of a different immigrant cohort and assumptions about the immigrants’ absorption of government spending on pure public goods, education, and public housing.\n\nAfter taking into account some new data and some issues raised by J&P, this study presents new estimates that show that the fiscal burden imposed by the average recent immigrants is $6,000, which for all immigrants is a total of between $16 billion and $23 billion per year,\nfigures virtually identical to those found in our earlier study.\n\nThis study also rejects arguments made by J&P that immigrants are needed to meet labour shortages, that they bring productivity-increasing economies of scale, and that their children will repay the fiscal burden.\n\nNew evidence does not provide any grounds for optimism that the offspring of recent immigrants are going to be able to earn enough to compensate current and future generations of Canadians for the fiscal transfers made to their parents by existing Canadians.\n\nThis study also presents new evidence showing that immigrants who were admitted mainly on the basis of pre-arranged jobs have superior economic performance, which supports the policy recommendation made in our 2011 study.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| 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".