Effectiveness of the Microcredit Program in Enhancing Micro-Enterprise Entrepreneurs’ Income in Selangor
Bibliographic record
Abstract
Poverty has been one of the pressing issues in developing countries like Malaysia. Amanah Ikhtiar Malaysia (AIM) was the first microcredit institution and one of the dominant players contributing to the poverty eradication in Malaysia through the provision of microcredit to the poor. Thus, the study aimed to investigate the effectiveness of the microcredit program on poverty eradication as experienced by AIM microcredit recipients in Selangor. Systematic random sampling was conducted to sample 326 Sahabat (from this point onwards the AIM microcredit recipients will be known as Sahabat) from February to April 2016. Descriptive analysis and multiple regression were used to analyse the data distribution and relationship between the dependent variable as measured by income-investment ratio and independent variables represented by socio-demographic as well as other related variables necessary to achieve the study objective. The findings of the study show that most of Sahabat were married (95.7 percent) and have secondary educated (72.7 percent). In terms of income distribution, most Sahabat earn less than RM1,500.00. Nevertheless, all Sahabat showed positive income changes after receiving different microcredit program schemes from AIM. Multiple regression analysis have identified two variables which are the family workers and hired workers where both significantly influenced the income-investment ratio after joining the microcredit program. This study affirmed the effectiveness of the AIM program in poverty eradication among the poor. AIM also plays an important role in meeting the financial needs of Sahabat which is necessary to enhance their microenterprises.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".