Currency Manipulation, the US Economy, and the Global Economic Order
Bibliographic record
Abstract
More than 20 countries have increased their aggregate foreign exchange reserves and other official foreign assets by an annual average of nearly $1 trillion in recent years. This buildup—mainly through intervention in the foreign exchange markets—keeps the currencies of the interveners substantially undervalued, thus boosting their international competitiveness and trade surpluses. The corresponding trade deficits are spread around the world, but the largest share of the loss centers on the United States, whose trade deficit has increased by $200 billion to $500 billion per year. The United States has lost 1 million to 5 million jobs as a result of this foreign currency manipulation. The United States must eliminate or at least sharply reduce its large trade deficit to accelerate growth and restore full employment. The way to do so, at no cost to the US budget, is to insist that other countries stop manipulating their currencies and permit the dollar to regain a competitive level. A US strategy to terminate currency manipulation, especially if undertaken together with some of the other countries that are adversely affected by the practice (including Australia, Canada, the euro area, Brazil, India, Mexico, and numerous developing countries), would be fully compatible with its international obligations. The proposed coalition should first seek voluntary agreement from the manipulators to sharply reduce or eliminate their intervention. If they do not do so, however, the United States should adopt four new policy measures against their currency activities: (1) undertake countervailing currency intervention (CCI) against countries with convertible currencies by buying amounts of their currencies equal to the amounts of dollars they are buying themselves, to neutralize the impact on exchange rates, (2) tax the earnings on, or restrict further purchases of, dollar assets acquired by intervening countries with inconvertible currencies (where CCI could therefore not be fully effective) to penalize them for building up these positions, (3) treat manipulated exchange rates as export subsidies for purposes of levying countervailing import duties, and (4) hopefully with other adversely affected countries, bring a case against the manipulators in the World Trade Organization that would authorize more wide-ranging trade retaliation. In the first instance, this approach should be taken against eight of the most significant currency manipulators: China, Denmark, Hong Kong, Korea, Malaysia, Singapore, Switzerland, and Taiwan. Japan may need to be added if it pursues new Prime Minister Abe's stated intention to force a sharply weaker yen through dollar purchases. Bergsten and Gagnon believe that cessation of intervention by these countries will permit most of the other interveners to desist as well, without their being directly approached, because much of their intervention is aimed at avoiding competitive loss to the largest manipulators (especially China).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".