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Record W4231599287 · doi:10.17722/ijme.v11i3.1030

Cost Efficiency of Thrift Banks in the Philippines: A Data Envelopment Approach

2018· article· en· W4231599287 on OpenAlexvenueno aff
John Vianne Murcia, Joey Yares, Rey Cabilan, Honey Lorie Arat

Bibliographic record

VenueInternational Journal of Management Excellence · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsLoanData envelopment analysisCost efficiencyPortfolioEconomicsReturns to scaleNet interest incomeVariable (mathematics)EconometricsTotal costVariable costScale (ratio)BusinessActuarial scienceFinanceInterest rateComputer scienceMicroeconomicsProduction (economics)MathematicsStatistics

Abstract

fetched live from OpenAlex

The aim of this study is to determine the cost efficiency of thrift banks in the Philippines. Data were taken from the financial statements of thrift banks in the Central Bank of the Philippines from 2013 to 2016. In deriving cost efficiency, the inputs used were total liability, net of real and other property acquired and net of bank premises, furniture and fixture, and equipment, while outputs were represented in terms of total loan portfolio (TLP) and other financial assets. With the input and output variables employed, evident in the outcomes of data envelopment analysis that variable return-to-scale (VRS) assumption resulted to greater number of cost efficient banks as compared under constant return-to-scale (CRS) assumption. Although findings had revealed that most thrift banks cost efficiency percentages lie below 50 percent, there were still identified thrift banks that has been cost efficient in terms of inputs and outputs used in the study. Hence, majority of the thrift banks in the Philippines were not cost-efficient with respect to the inputs and outputs. Additionally, the number of thrift banks found to be cost-efficient are significantly low in both CRS and VRS assumptions, although VRS reflected greater number than CRS assumption. Implications were discussed in this paper based on the findings gleaned from the econometric analysis.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.801
Threshold uncertainty score0.419

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.097
GPT teacher head0.294
Teacher spread0.197 · 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.

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

Citations0
Published2018
Admission routes1
Has abstractyes

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