Understanding the Determinants of Rural Credit Accessibility: The Case of Ehiaminchini, Fanteakwa District, Ghana
Bibliographic record
Abstract
Rural areas in developing countries are known to lack access to credit facilities. Lack of credit limits production activities and stifles agricultural productivity. The objective of this study is to identify the determinants of credit accessibility to more effectively aid alleviate poverty using Ehiaminchini, a village in the Fanteakwa District of Eastern Ghana as a case study. The study utilizes cross-sectional data collected with the use of structured questionnaires from 109 farm households. Interviews and focus group discussions were also conducted to supplement the data. A probit model was used to analyze the factors that determine households’ access to credit. The results show that livelihood diversification, household productivity, savings accounts and household size are factors that have a significant influence on households’ ability to access credit. Furthermore, the marginal effect of household productivity indicates that the predicted probability of accessing credit increases as productivity increases. We argue that improving household productivity and diversifying livelihoods in rural households will, to a large extent, address the problem of credit constraint.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".