The Geography of Capital Flows: What We Can Learn from Benchmark Surveys of Foreign Equity Holdings
Bibliographic record
Abstract
To provide insight into the accuracy of U.S. data on international equity transactions, we compare estimates of U.S. holdings of equities in over 40 countries with actual holdings given by comprehensive U.S. benchmark surveys. If the rate of return used to revalue U.S. holdings in a given country is accurate, accurate holdings estimates imply accurate transactions data. For some countries, such as Canada and much of Latin America, the holdings estimates are quite accurate. For the majority of countries, however, there is a great disparity between our estimates and actual amounts, likely because U.S. data on international equity transactions record the country of the transactor, not the country of the issuer. Our estimates are far too high for financial centers--because many U.S. transactions that go through these countries involve securities issued in other countries--and far too low in most other countries, particularly in Europe and Asia. To illustrate the potential pitfalls of using estimated country-specific holdings data, we briefly present two cases in which the use of actual data leads to different conclusions. One case examines the determinants of U.S. equity holdings across countries; the other concerns the turnover rate of foreign equity portfolios.
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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 | 0.005 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".