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Record W3122146912 · doi:10.5539/ass.v7n7p141

Investigating the Effect of the Utilization of Microcredit on Hardcore Poor Clients Household Income and Assets

2011· article· en· W3122146912 on OpenAlexvenueno aff
C. A. Malarvizhi, Sazali Abdul Wahab, Mohammad Nurul Huda Mazumder

Bibliographic record

VenueAsian Social Science · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMicrofinance and Financial Inclusion
Canadian institutionsnot available
Fundersnot available
KeywordsLoanBusinessStratified samplingAsset (computer security)Household incomeService (business)MicrofinanceFinanceEconomicsMarketingEconomic growth

Abstract

fetched live from OpenAlex

The objective of this study is to examine how Amanah Ikhtiar Malaysia (AIM)’s clients have been using the microcredit received and the effect of microcredit utilization on household income and asset. To obtain the above mentioned objectives, this study employed a cross sectional stratified random sampling method. Based on the literature review, it was found that there is a lack of studies to find out how AIM’s hardcore poor clients were utilizing the microcredit they received. In addition, there is a paucity of literature which examines the effect of usage of loan on household income and asset. Findings of this study show that a relatively high percentage of old respondents used credit in trading activities and they engaged in self-employed production, trade and service activities more than new clients. Average monthly household income and market value of total household assets were also found to be higher for the respondents who used credit in income generating activities. Therefore, this study proposes that AIM should focus on the usage of credit in income generating activities by its clients. It is also recommended that AIM should review and re-structure the existing policies to increase the employment rate and income generating opportunities of client’s household members. This can be done by providing appropriate training, diversifying the loan program and offering loan for non-income generating activities.

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 score0.149
Threshold uncertainty score0.373

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.249
Teacher spread0.196 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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