A Gap in Brain Gain for Emerging Countries: Evidence of International Immigration on Non-Resident Patents
Why this work is in the frame
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Bibliographic record
Abstract
Immigration is a controversial topic that draws much debate. From a human sustainability perspective, immigration is disadvantageous for home countries causing brain drains. Ample evidence suggests the developed host countries benefit from immigration in terms of diversification, culture, learning, and brain gains, yet less is understood for emerging countries. The purpose of this paper is to examine the presence of brain gains due to immigration for emerging countries, and explore any gaps as compared to developed countries. Using global data from 88 host and 109 home countries over the period from 1995 to 2015, we find significant brain gains due to immigration for emerging countries. However, our results show that there is still a significant brain gain gap between emerging and developed countries. A brain gain to the developed host countries is about 5.5 times greater than that of the emerging countries. The results hold after addressing endogeneity, self-selection, and large sample biases. Furthermore, brain gain is heterogenous by immigrant types. Skilled or creative immigrants tend to benefit the host countries about three times greater than the other immigrants. In addition, the Top 10 destination countries seem to attract the most creative people, thus harvest the most out of the talented immigrants. In contrast, we find countries of origin other than the Top 10 seem to send these creative people to the rest of the world.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it