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Firm-Level Data and Monetary Policy: The Case of a Middle Income Country

2019· article· en· W2986599368 on OpenAlex
Lahcen Bounader, Mohamed Doukali

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

VenueIMF Working Paper · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsMonetary policyContext (archaeology)Balance sheetEconomicsChannel (broadcasting)Panel dataMonetary economicsIdentification (biology)Quality (philosophy)ScarcityEstimationInstrumental variableBusinessFinanceEconometricsMicroeconomics

Abstract

fetched live from OpenAlex

We test the existence of the balance sheet channel of monetary policy in a middle-income country. Firm-level data scarcity and quality, in such a context, make the identification of this channel a steep challenge. To circumvent this challenge, we use panel instrumental variables estimation with measurement error to analyze the financial statements of 58 500 Moroccan firms over the period 2010-2016. Our analysis confirms the existence of this channel. It shows that monetary policy has a significant impact on small and medium enterprises’ access to banks’ financing, and that firm-specific variables are key determinants of firms’ financing decisions.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.485

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 science0.0000.001
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 head0.035
GPT teacher head0.241
Teacher spread0.206 · 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