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Record W4312569788 · doi:10.1590/198055272623

PUBLIC BANKS AND BANKING COMPETITION

2022· article· en· W4312569788 on OpenAlexfundno aff
Kamaiaji de Souza Castor

Bibliographic record

VenueRevista de Economia Contemporânea · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
FundersOffice of Energy Research and Development
KeywordsPanel dataCompetition (biology)SubsidyBusinessPrivate sectorFinancial systemOligopolyMargin (machine learning)Monetary economicsFinanceEconomicsMarket economyEconomic growth

Abstract

fetched live from OpenAlex

ABSTRACT How margins of private banks are affected by public banks’ conduct is a relevant question for both competition policy and credit market development in emerging economies. In this article, this question is addressed using an exogenous variation on the conduct of public banks between 2008 and 2015 when a pro-state government implemented a broad counter-cyclical policy in Brazil on major credit lines financed by the National Development Bank (BNDES). Given this event, we estimate the best reply function of private banks in a mixed oligopolistic market structure where private and public firms differ in their objective function. Using a detailed data set from a large BNDES credit line, in a dynamic panel data, results point to a significant but low reaction of private financial institutions. In the long run, a private bank’s margin is reduced by 0.03 p.p for 1 p.p lower final interest rate set by state-owned institutions. In this sense, the reduction in margins observed between 2008-2014 is more associated with a lower subsidized funding cost.

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)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.039
GPT teacher head0.220
Teacher spread0.181 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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