Bibliographic record
Abstract
Many world regions, including Europe, have the perception that their best students and researchers leave to study and work in the United States. This phenomenon has been coined ‘the elite brain drain’. With a sample of European students who obtain a PhD in economics in the US, we study whether the most promising among them are indeed less likely to return. We find that PhD recipients from top institutes, or with a highly cited advisor, or a pre-PhD publication or a higher impact factor on their first publication are more likely to stay in the US or Canada at a top institute. This indicates that the quality of the working environment is of crucial importance to top researchers, and that the attraction of the US consists in a big part in its many top economics departments. The location choice made for the first job strongly predicts the location of the current job. Once a top researcher has made the decision to stay, particularly at a top institute, the probability of his or her return becomes very small. This suggest that from the European perspective, there is indeed an ‘elite brain drain’, as its most talented researchers, once embedded in the North American research system, are not very likely to return. This is work in progress. Please do not cite without the authors ’ permission.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.906 | 0.829 |
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".