Socio-economic Impact of Rural Credit in Northern Vietnam: Does It Differ between Clients Belonging to the Ethnic Majority and the Minorities?
Bibliographic record
Abstract
The paper evaluates the impact of rural credit program on socio-economic development with emphasis on two classifications of clients at household level: the majority and the ethnic minorities in Vietnam. Our paper utilizes the regression analysis of survey data collected in 10 provinces from the North to the South of Vietnam. The analysis reveals that rural credit poses positive impacts on Vietnamese population living in both rural and remote areas. More specifically, an increased VND 1 million of loan would raise the income by VND 0.249 million. At the same time, it would contribute annually to create 1.548 jobs. Besides, it is worth noting that the effects of income improvement and job creation are strongly determined by household’s characteristics, i.e. number of working adults, experience of heads of households, and lending conditions in terms of loan size. In addition, rural credit facilitates the access to more nutrition of food in the minorities. Empirical results support that micro-credit intervention in the ethnic minority community has tendency to focus on job-creation and food nutrition rather than income improvement. The t-tests used in this paper support for the alternative hypothesizes that significantly different impact of rural credit program between two separated groups is reflected in terms of various variables.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".