Discrimination and the fiscal benefits of immigration
Bibliographic record
Abstract
In recent decades, there has been a lengthy debate about the fiscal costs or benefits of immigration, and much of the literature has found fiscal impacts that are close to zero. However, these studies have ignored the possibility that immigrants may be victims of wage discrimination in the labour market, despite evidence of such discrimination in various countries. In the presence of such discrimination, existing estimates of the fiscal impact of immigration will be biased: if immigrants are paid less than their marginal products, then someone else is receiving that income - mostly likely the firm's owners or other workers - and paying taxes on it, and that fiscal benefit is ignored by a model that disregards discrimination. In this paper, I evaluate the quantitative importance of this mechanism, by calibrating a search-and-matching model to Canadian data and simulating the fiscal impact of increases in immigration. When the model and calibration omits wage discrimination against immigrants, the average fiscal impact of immigration is negative, but it becomes positive if discrimination explains the wage gaps between natives and immigrant workers: at an economy-wide level, an annual fiscal cost of about $3 billion in the absence of discrimination becomes a fiscal benefit of about $4 billion in the presence of discrimination. My results indicate that wage discrimination against immigrants could significantly affect our estimates of the fiscal impact of immigration.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".