Impact of Microcredit on Poverty Reduction in Bangladesh: a cross-sectional study
Bibliographic record
Abstract
Purpose: Usually, microcredit is known as the providing of “small loans” to the poorer group of the population although it differs from country to country. Many poor and hardcore poor people have been taken the microcredit to reduce their poverty. This study aimed to detect the impact of microcredit on poverty reduction among different levels of poor people. Methodology: Primary data were collected using cluster sampling from the 14 different slum areas from Sylhet city in Bangladesh (n= 408) based on a semi-structured questionnaire during October 2023 to December 2023. Descriptive statistical analysis and Generalized Estimating Equations (GEE) were used to analyze the data. Findings: It was observed that among the participants 76% are male and 24% are female. Around 84.1% individuals were taking microcredit from the different NGOs and among them 91.6% got benefit from their loan and their income was increasing 14.6% as well. It was also observed that 54.4% people are anxious about their food and 15.9% people are sleeping with hunger. Results also revealed that after taking microcredit the average percentage of hardcore poor people are significantly decreased compared with non-poor. It may indicate that after taking microcredit hardcore poor people invest their loan more appropriately compare to poor and non-poor groups of people. Conclusions: Taken together, we conclude that microcredit might play better role for hardcore poor people. To get more success of microcredit and hence for sustainable development- government and non-government organizations should pay more attention to reduce the interest rate of loans.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".