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Record W3021017634 · doi:10.3390/jrfm13050090

Negative Interest Rates

2020· article· en· W3021017634 on OpenAlexvenueno aff
Sarkis J. Khoury, Poorna C. Pal

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsInterest rateMonetary economicsEconomicsLiquidity trapDebtMarket liquidityNet interest marginMonetary policyMacroeconomicsFinanceLiquidity risk

Abstract

fetched live from OpenAlex

Negative interest rates are an invention of monetary authorities to show that monetary activism does not have boundaries, i.e., as if there is no such thing as a liquidity trap. Their presence in the financial landscape has redefined the benefits to savers and to investors. Governments can now borrow at will without visibly adding to budget deficits. This makes negative interest borrowing an alternative to raising taxes. Banks can now achieve regulatory compliance partially at the expense of depositors. Commercial banks pay to keep money at the central bank instead of earning interest on it. This paper shows the true nature of negative interest rates and their consequences on various economic agents and performance measures, specifically on economic growth and exchange rates. In addition, this paper demonstrates that the arguments in favor of negative interest rates have been largely exaggerated based on the weight of the evidence that shows the United States, which never issued negative interest rates debt, is a leader among developed countries in terms of economic growth in a non-inflationary environment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.356

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.029
GPT teacher head0.224
Teacher spread0.195 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2020
Admission routes1
Has abstractyes

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