Sharing the Burden: Monetary and Fiscal Responses to a World Liquidity Trap
Bibliographic record
Abstract
With integrated trade and financial markets, a collapse in aggregate demand in a large country can cause 'natural real interest rates' to fall below zero in all countries, giving rise to a global 'liquidity trap'.This paper explores the optimal policy response to this type of shock, when governments cooperate on both fiscal and monetary policy.Adjusting to a large negative demand shock requires raising world aggregate demand, as well as redirecting demand towards the source (home) country.The key feature of demand shocks in a liquidity trap is that relative prices respond perversely.A negative shock causes an appreciation of the home terms of trade, exacerbating the slump in the home country.At the zero bound, the home country cannot counter this shock.Because of this, it may be optimal for the foreign policy-maker to raise interest rates.Strikingly, the foreign country may choose to have a positive policy interest rate, even though its 'natural real interest rate' is below zero.A combination of relatively tight monetary policy in the foreign country combined with substantial fiscal expansion in the home country achieves the desired mix in terms of the level and composition of world expenditure.Thus, in response to conditions generating a global liquidity trap, there is a critical mutual interaction between monetary and fiscal policy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".