Assimilation via Prices or Quantities? Sources of Immigrant Earnings Growth in Australia, Canada and the United States. Journal of Human Resources 41: 821-840 Barth E, Bratsberg B and Raaum O (2004) Identifying Earnings Assimilation of Immigrants under Ch
Bibliographic record
Abstract
Using 1980/81 and 1990/91 census data from Australia, Canada, and the United States, we estimate the effects of time in the destination country on male immigrants ’ wages, employment, and earnings. We find that total earnings assimilation is greatest in the United States and least in Australia. Employment assimilation explains all of the earnings progress experienced by Australian immigrants, whereas wage assimilation plays the dominant role in the United States, and Canada falls in-between. We argue that relatively inflexible wages and generous unemployment insurance in countries like Australia may cause assimilation to occur along the “quantity ” rather than the price dimension.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".