On Current Account Surpluses and the Correction of Global Imbalances
Bibliographic record
Abstract
In this paper I analyze the nature of external adjustments in current account surplus countries.I ask whether a realignment of world growth rates --with Japan and Europe growing faster, and the U.S. growing more slowly --is likely to solve the current situation of global imbalances.The main findings may be summarized as follows: (a) There is an important asymmetry between current account deficits and surpluses.(b) Large surpluses exhibit little persistence through time.(c) Large and abrupt reductions in surpluses are a rare phenomenon.(d) A decline in GDP growth, relative to long term trend, of 1 percentage point results in an improvement in the current account balance --higher surplus or lower deficit --of one quarter of a percentage point of GDP.Taken together, these results indicate that a realignment of global growth --with Japan and the Euro Zone growing faster, and the U.S. moderating its growth --would only make a modest contribution towards the resolution of global imbalances.This means that, even if there is a realignment of global growth, the world is likely to need significant exchange rate movements.This analysis also suggests that a reduction in China's (very) large surplus will be needed if global imbalances are to be resolved.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".