Brain Drain or Brain Bank? The Impact of Skilled Emigration on Poor-Country Innovation
Bibliographic record
Abstract
The development prospects of a poor country depend in part on its capacity for innovation.The productivity of its innovators depends in turn on their access to technological knowledge.The emigration of highly skilled individuals weakens local knowledge networks (brain drain), but may also help remaining innovators access valuable knowledge accumulated abroad (brain bank).We develop a model in which the size of the optimal innovator diaspora depends on the competing strengths of co-location and diaspora effects for accessing knowledge.Then, using patent citation data associated with inventions from India, we estimate the key co-location and diaspora parameters; the net effect of innovator emigration is to harm domestic knowledge access, on average.However, knowledge access conferred by the diaspora is particularly valuable in the production of India's most important inventions as measured by citations received.Thus, our findings imply that the optimal emigration level may depend, at least partly, on the relative value resulting from the most cited compared to average inventions.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".