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Record W3124487959 · doi:10.24149/gwp44

Fiscal Deficits, Debt, and Monetary Policy in a Liquidity Trap

2010· article· en· W3124487959 on OpenAlexafffund
Michael B. Devereux

Bibliographic record

VenueFederal Reserve Bank of Dallas, Globalization and Monetary Policy Institute Working Papers · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaRoyal Bank of Canada
KeywordsLiquidity trapEconomicsMonetary economicsStimulus (psychology)Interest rateFiscal policyMonetary policyMarket liquidityRecessionDebtGovernment debtZero lower boundGovernment spendingMacroeconomicsLiquidity crisisMarket economy

Abstract

fetched live from OpenAlex

The macroeconomic response to the economic crisis has revived old debates about the usefulness of monetary and fiscal policy in fighting recessions. Without the ability to further lower interest rates, policy authorities in many countries have turned to expansionary fiscal policies. Recent literature argues that government spending may be very effective in such environments. But a critical element of the stimulus packages in all countries was the use of deficit financing and tax reductions. This paper explores the role of government debt and deficits in an economy constrained by the zero bound on nominal interest rates. Given that the liquidity trap is generated by a large increase in the desire to save on the part of the private sector, the wealth effects of government deficits can provide a critical macroeconomic response to this. Government spending .financed by deficits may be far more expansionary than that financed by tax increases in such an environment. In a liquidity trap, tax cuts may be much more effective than during normal times. Finally, monetary policies aimed at directly increasing monetary aggregates may be effective, even if interest rates are unchanged.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.291
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.043
GPT teacher head0.255
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations27
Published2010
Admission routes2
Has abstractyes

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