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Record W2113598396 · doi:10.1162/qjec.2010.125.1.363

Monetary Policy by Committee: Consensus, Chairman Dominance, or Simple Majority?<sup>*</sup>

2010· article· en· W2113598396 on OpenAlexaffabout
Alessandro Riboni, Francisco J. Ruge‐Murcia

Bibliographic record

VenueThe Quarterly Journal of Economics · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsDominance (genetics)VotingDictatorMonetary policyEconomicsPublic administrationPolitical scienceAccountingMacroeconomicsPoliticsLaw

Abstract

fetched live from OpenAlex

This paper studies the theoretical and empirical implications of monetary policy making by committee under four different voting protocols. The protocols are a consensus model, where a supermajority is required for a policy change; an agenda-setting model, where the chairman controls the agenda; a dictator model, where the chairman has absolute power over the committee; and a simple majority model, where policy is determined by the median member. These protocols give preeminence to different aspects of the actual decision-making process and capture the observed heterogeneity in formal procedures across central banks. The models are estimated by maximum likelihood using interest rate decisions by the committees of five central banks, namely the Bank of Canada, the Bank of England, the European Central Bank, the Swedish Riksbank, and the U.S. Federal Reserve. For all central banks, results indicate that the consensus model fits actual policy decisions better than the alternative models. This suggests that despite institutional differences, committees share unwritten rules and informal procedures that deliver observationally equivalent policy 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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.070
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.070
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.003
Scholarly communication0.0040.008
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.030
GPT teacher head0.233
Teacher spread0.203 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations119
Published2010
Admission routes2
Has abstractyes

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