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Record W1591152189 · doi:10.62986/dp2004.05

In-depth Analysis on the Access to and Suitability of the Loans

2004· preprint· en· W1591152189 on OpenAlexfundno aff
Ma. Chelo V Manlagnit

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsLoanMicrofinanceDescriptive statisticsBusinessSmoothingFinanceEconomicsEconomic growthStatisticsMathematics

Abstract

fetched live from OpenAlex

This paper examines the borrowing behavior of the households and the suitability of the loans obtained from the community oriented financial intermediary (COFI) and reinforces the significance of using a household approach in evaluating the effects of microfinance. Using descriptive and statistical analyses, results show that the effects of credit in household income and expenses are positive and statistically significant with client households experiencing greater positive effects than nonclient households. Moreover, nonclient households, unlike client households, allot a greater percentage of their loans in proportion to their income on food and nonfood consumptions suggesting that they are more engaged in borrowing for smoothing their consumptions. It has also been shown that the access to COFI loans is relatively easy for the client household members since the requirements and processing are fast and reasonable. This indicates that credit cooperatives rarely disapprove loan applications and if there are numbers of pending loan applications, they usually reduce the amount of loan approved instead of disapproving the application. In general, COFI loans reasonably suit the needs of the COFI clients. Given that both household types obtained their credits from various lenders, those who have access to the COFI system have a reliable source of loans indicating that the COFI performs a particularly important role in providing services, especially credit lines.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.054
GPT teacher head0.276
Teacher spread0.222 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

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