Bibliographic record
Abstract
This paper examines the rise of high-skill migration from Asia to the United States over the past three decades and its consequences for both sending and receiving economies.Between 1990 and 2019, migrants from five Asian countries-India, China, South Korea, Japan, and the Philippinesaccounted for over one-third of the growth in US software developers and a quarter of the increase in scientists, engineers, and physicians.Drawing on census microdata, visa records, and administrative sources, I show how US demand for talent in information technology, higher education, and healthcare interacted with Asia's demographic and educational transformations to generate this migration boom.Policy reforms (notably the H-1B, F-1, and J-1 visa programs) and sectoral shifts-such as the internet revolution, declining public support for universities, and aging-related healthcare demand-created persistent needs for foreign students and workers.Asian economies were uniquely positioned to meet this demand through rapid tertiary expansion, strong STEM institutions, English proficiency, and diaspora networks.These inflows boosted US innovation, entrepreneurship, and service-sector productivity while fostering "brain gain" and "brain circulation" in Asia.Together, these trends reveal how talent flows from Asia have become central to the structure and growth of the modern US economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.021 |
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".