Prospects for Global Current Account Rebalancing
Bibliographic record
Abstract
The authors use the Bank of Canada's version of the Global Economy Model, a multi-country, multi-sector dynamic stochastic general-equilibrium model with an active banking system (the BoC-GEM-FIN), to study the evolution of global current account balances following the recent global financial crisis. More specifically, they use several shocks from the model to generate a simulated baseline scenario that mimics: (i) the initial, pre-crisis state of disequilibrium in global current account balances, and (ii) the effects of the crisis, including those of the policy responses undertaken worldwide. The authors find that a sufficient set of conditions and policies for a sustainable resolution of the global current account imbalances relies on three key elements: (i) a continuous upward adjustment of U.S. private savings, (ii) fiscal consolidation in advanced countries, and (iii) an orderly adjustment of exchange rates. These three criteria facilitate a gradual decline in the U.S. current account deficit going forward. A fourth key element, the implementation of policies aimed at stimulating domestic demand in emerging Asia, is needed to ensure that the counterpart of the decrease in the U.S. current account deficit is mainly a reduction in the surpluses of emerging Asia. Sensitivity analysis based on deviations from these conditions illustrates the factors behind the main results and the costs associated with the alternative scenarios considered.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".