The exports of higher education services from OECD countries to Asian countries: A gravity approach
Bibliographic record
Abstract
Abstract We analyse bilateral exports of higher education services between OECD countries and Asia, using a gravity equation approach, panel data from 1998 to 2016 and PPML regression. The approach treats higher education consumption by Asian countries as a consumable durable good reflecting investment in human capital. Asian students come to OECD countries to obtain degrees from their universities. Structurally, the flow of students from Asian country j to OECD country i depends on the higher education capacity of i, the perceived quality of universities in i, expected earnings in i, a series of bilateral transaction costs between i and j, the income per capita in j, school‐age demographics in j and the usual multilateral trade resistance terms. We find that bilateral flows of students are strongly influenced by wage levels in the host country, bilateral distance, importers’ income, demographics, common language, the visa regime prevailing in bilateral country pairs and the network of migrants from j in i. These results hold through a variation of specifications, proxies and estimation methods. We find mixed evidence on the role of tertiary education capacity in OECD countries and no evidence of a country's university reputations explaining the flow of students. The evolution over time of education capacity, earnings, visa regimes, migrant networks, strong income growth and changes in demographics in nearby export markets explain the emergence of Australia, Canada, Korea and New Zealand and the loss of market share by the US, which still strongly dominates international trade in higher education services. The decline in Chinese students coming to the US is also predicted for the most recent years driven by the reduction in its college‐age population.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".