MétaCan
Menu
Back to cohort
Record W2137973429 · doi:10.1016/j.rdf.2014.02.001

Why are banks in Africa hoarding reserves? An empirical investigation of the precautionary motive

2014· article· en· W2137973429 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueReview of Development Finance · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMarket liquidityHoarding (animal behavior)Guard (computer science)EconomicsMonetary economicsFinancial intermediaryCredit rationingLiquidity crisisHoardIntermediationBusinessFinancial systemFinanceInterest rateGeography

Abstract

fetched live from OpenAlex

For two decades now, many banks in Africa have been holding large amounts of liquid assets. Prevailing explanations of this phenomenon rely on credit rationing models. Yet, while modern models of financial intermediation show that high exposure to liquidity risk may prompt banks to hoard large amounts of (precautionary) liquid reserves, this hypothesis has often been overlooked. We try to fill the gap in this paper. More specifically, we hypothesize and confirm that bank liquidity hoarding in Africa reflects, at least partially, a precautionary strategy to guard against the risks associated with liquidity services to depositors.

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.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.463

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.050
GPT teacher head0.259
Teacher spread0.209 · 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