Impact of Microcredit on Rural Poverty Alleviation in the Context of Bangladesh
Bibliographic record
Abstract
Microcredit and poverty alleviation have become the two sides of a coin as the role of microcredit on poverty alleviation is well accepted in the arena of economic development. This study is an attempt to analyse the impact of microcredit on poverty alleviation in the rural areas namely Hathazari, Mirsharai and Sitakunda upazilla (sub-units of district ) of Chittagong district, Bangladesh. A cross sectional survey was conducted on the rural part of these three upazillas. Data have been collected through a well-structured questionnaire from 100 microcredit-recipients/borrowers of Bangladesh Rural Advancement Committee (BRAC) and Association for Social Advancement (ASA) - two giant microcredit providers in Bangladesh and from 50 non-borrowers of the study areas. Respondents were selected randomly. Tabular method was used to describe the data. Hypothetically, the outcomes were found significant resulted from chi-square test (X ² -test) and ANOVA (Analysis of Variance) without an exception for clothing expenditure. The study revealed that microcredit disbursed through BRAC and ASA, plays a dynamic role to reduce poverty in the study areas by income generating activities of the poor women borrowers and by improving their living standard. It is found from the study that microcredit has positive impact on income, expenditure, condition of dwelling house, education, health and decision making ability of the poor women borrowers who spent at least five years in BRAC and ASA comparing to the non-borrowers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".