Investigating the Effect of the Utilization of Microcredit on Hardcore Poor Clients Household Income and Assets
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".