Factors Determining Farmers’ Access to and Sources of Credit: Evidence from the Rain-Fed Zone of Pakistan
Why this work is in the frame
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Bibliographic record
Abstract
This study investigates the factors that affect farmers’ access to agricultural credit and its role in adopting improved agricultural technologies in the rain-fed zone of Khyber Pakhtunkhwa (KP), Pakistan. Using logistic models, we assess and compare the relative role of farmers’ socioeconomic attributes in their access to credit and adoption strategies. The results indicate a moderate positive association between farmers’ access to agricultural credit and their adoption of improved agricultural technologies. The binary logit model’s results indicate that farmers with a large-sized farm, high farm income, better access to information, and large physical asset ownership showed a positive influence on credit access. However, farming experience showed a negative effect on farmers’ access to agricultural credit. Regarding farmers’ credit sources, this study found that asset-rich farmers with more farming experience and better access to information relied more on banks than on input providers and informal credit sources. Similarly, older farmers with more education, larger farm sizes and high farm income were more likely to have borrowed from input providers than banks. We conclude that the role of the effective provision of information on credit and agricultural technology is imperative and requires separate policies that are specifically aimed at different groups of farmers with different socioeconomic and farm-related characteristics.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it