Migrants as Engines of Financial Globalization: The Case of Global Banking
Bibliographic record
Abstract
Abstract Does international migration contribute to the spread of global commerce? Recent work demonstrates the relevance of international migration for patterns of investment, focusing on migrants’ role in facilitating investment from their host country to their country of origin. By contrast, we investigate the potential for migrants to attract inward investment into their host country. As customers, migrants shape the composition of the market in the country where they live, such that global networks of migrants can shape investment decisions of co-national firms. Focusing on a single sector, banking, we identify the mechanisms by which international migration attracts foreign investment. First, migrants may prefer home country banks, especially if they are excluded from financial services in their host country. Second, migrants require financial institutions to send remittances to their country of origin. We test these arguments using a global sample of foreign bank ownership data and find that bilateral migrant networks are a predictor of cross-border banking investment. These findings speak to how the composition of markets affects international investment, as well as the reinforcing relationship between two features of globalization: international migration and financial integration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".