Financial integration, financial deepness, and global imbalances’, CEPR Discussion Paper no
Bibliographic record
Abstract
1We thank participants at the NBER Summer Institute “Aggregate Implica-tions of Microeconomic Consumption Behavior”, “Research on Money and Mar-kets ” conference at the University of Toronto, SED meeting in Vancouver. A previous version of this paper has circulated under the titled “Capital markets liberalization, savings and global imbalances” Large global financial imbalances need not be the harbinger of a world fi-nancial crash as many authors believe. Instead, this paper shows that large and persistent global imbalances can be the outcome of financial integration when countries have different financial markets characteristics. In particular, countries with more advanced financial markets accumulate foreign liabilities vis-a-vis countries with less developed financial systems in a gradual, long-lasting process. Moreover, differences in financial development affect the composition of foreign portfolios, so that a country with negative net for-eign asset positions can receive positive factor payments. Three empirical observations support these arguments: (1) financial deepness varies widely even amongst industrial countries, with the United States ranking at the top; (2) the secular decline in the U.S. net foreign assets position started with a gradual process of financial markets liberalization; (3) net exports and cur-rent account balances are negatively correlated with indicators of financial markets development. 1
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".