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Record W3134865473 · doi:10.5539/ijef.v13n4p25

Institutions and Cyclicality of the Fiscal and Monetary Policies in Brazil

2021· article· en· W3134865473 on OpenAlexvenueno aff
Aline Gadelha, José Ângelo Divino

Bibliographic record

VenueInternational Journal of Economics and Finance · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsEconomicsBusiness cycleMonetary policyFiscal policyMonetary economicsProxy (statistics)MacroeconomicsQuality (philosophy)Fiscal unionSample (material)Dynamic stochastic general equilibriumGovernment (linguistics)

Abstract

fetched live from OpenAlex

This paper investigates the relationship between the quality of institutions and the cyclical properties of macroeconomic policies in the Brazilian economy in the recent period. We extend the monetary and fiscal policy rules proposed by Taylor (2000) to incorporate a proxy for institutional quality. In the empirical analysis, we estimate reaction functions for monetary and fiscal policies by the Markov-Switching method. This methodology allows us to analyze how changes in the quality of institutions might influence the guidance of the fiscal and monetary policies over the sample period. The major results maintain that both monetary and fiscal policies are significantly countercyclical in periods that exhibit higher levels of institutional quality and are pro-cyclical or acyclical in periods which exhibit lower levels of institutional quality. Thus, the quality of institutions plays a key role in the government's ability to implement countercyclical monetary and fiscal policies to stabilize the Brazilian economy over the business cycle.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.131

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.253
Teacher spread0.225 · 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 designNot applicable
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

Citations1
Published2021
Admission routes1
Has abstractyes

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