Bibliographic record
Abstract
Abstract This article explores the “brain drain” explanation for the concentration of incomes in Canada during the past 30 years, namely, that high‐skilled Canadians make use of high salaries on offer in the United States to extract higher salaries at home. If this is the case, then for a given level of US salaries, the threat to accept outside offers should be more credible when the Canadian dollar is depreciating against the US dollar, and weaker when the Canadian dollar is appreciating. The data are broadly consistent with this claim: income concentration worsened during the depreciations of the 1980s and 1990s, and eased when the Canadian dollar began to appreciate in value. The article develops a simple two‐parameter model based on the propositions that high earners in Canada can use US salaries to bargain for higher salaries, and that Canadian high earners can shelter part of their income from personal income taxes. It also offers some preliminary evidence about the parameter values consistent with available data. The results suggest that higher top marginal personal income tax rates may potentially accentuate top‐end after‐tax income inequality. If high earners are able to use their bargaining power to extract pay increases to offset higher tax rates, then the burden of increased personal income taxes will be deflected elsewhere, and may even have the perverse effect of making the after‐tax income distribution more unequal than it was before.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".