Are Immigrants Positively or Negatively Selected? The Role of Immigrant Selection Criteria and Self-Selection
Bibliographic record
Abstract
This paper specifies and estimates a structural model of international migration using micro data. This provides a direct test of human capital theory that suggests that individuals respond to the earnings differentials across countries while making their migration decisions. The paper specifies migration as a joint outcome of two decision makers, i.e. the individual who decides to apply for migration and the host country that reviews applications, and identifies the factors determining the decision of these two players. The empirical results provide evidence in support of the human capital model. It is also shown that both the host country and the individual have significant impacts on the resulting charatersitics of immigrants. The results suggest negative self-selection at the application stage both in terms of observed and unobserved characteristics and a positive selection at the review step by the host country. Although there is negative self- selection in terms of schooling among applicants, as a result of the positive selection at the review step the resulting migrants are positively selected. However, in terms of unobservable characteristics the review step is unable to reverse the negative self-selection that occurs at the application stage, and the resulting migrants are negatively selected in this dimension.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".