Impacts of Credit Access on Agricultural Production and Rural Household’s Welfares in Northern Mountains of Vietnam
Bibliographic record
Abstract
There is a great consensus on the positive impact of credit access on farmers' incomes and consumption, however, its effect on income inequality among different population segments is still a controversial issue. The paper aims to examine these concerns through using the mixed data collected from the sample of 193 households surveyed (demand-side) and in-depth interviewees with the key credit providers (supply-side) in Lao Cai, the sixth poorest province in Vietnam. At the grass root level, it is evident that better credit access not only significantly positive influences on the effectiveness of agricultural production, but also is the driving force for better structural transition within cultivation versus livestock. Besides this, it enhances both on-farm and off-farm income as well as the well-being of rural households. At the community-impact level, surprisingly, the financial development without agriculture-related supports causes to the negative effect on the distribution of agricultural outcomes and prolongs the inequality in the locality. In addition, an alarm regarding latent social issues has been generating from the preferential credit screen under the community-based lending method. Finally, policy implications are discussed to enhance the effectiveness and outreach of credit in the locality.
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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) | 0.002 | 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".