Is Access to Agribank Credit an Effective Tool in Improving Household Income? Evidence from the Northern Mountainous Region of Vietnam
Bibliographic record
Abstract
Access to credit has been thought to be a key factor in rural development and poverty reduction. In Vietnam, the Vietnam Bank for Agriculture and Rural Development (Agribank) emerged from the mono-tier banking system in 1988 and performs as a profit-oriented commercial bank sustaining the development of rural areas. During the last two decades, the bank has clearly expanded its share of credit outstanding in total rural credit market volume and this process is in line with the trending development of the national economy. The aim of this study is to examine whether Agribank credit improves household income in the Northern Mountains of Vietnam, where the poor and ethnic minorities are overrepresented in the population. In order to create robust estimates, a joint consideration of all four matching algorithms (nearest-neighbor matching, radius matching, Kernel matching and stratification matching) is applied to the Propensity Score Matching. The study found that access to extension services, ethnicity, and total savings emerged as reliable predictors of credit access among household endowments. Loan volumes increase with total value of household assets. In addition, the impact of credit lies in the range increase of 14.56% to 43.78% of total income, 12.09% to 51.83% of per capita income and 43.64% to 111.60% of nonfarm income of household with credit access. The agricultural bank credit has contributed in improving household income in the Northern Mountains of Vietnam. Results in this study provide further support for the hypothesis that the remarkable progress in poverty reduction in the last two decades in Vietnam is partly attributed to the development of Agribank credit. Experiences of the Agribank in lending to rural areas could be worthwhile for intermediary financial institutions to support rural development in Vietnam.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".