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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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.039
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 teacher head, not a consensus.

Study designTheoretical or conceptual
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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