Impact of Crop Diversification on Rural Poverty in Nepal
Bibliographic record
Abstract
Abstract Crop diversification into high‐value crops (HVCs) can be an important strategy to augment income, generate employment, and reduce poverty in developing countries. We study the impact of crop diversification (share of production value obtained from the HVCs) on household (HH) welfare measures in Nepal. We use three rounds of the nationally representative Nepal Living Standard Surveys: NLSS I (1994/95), NLSS II (2004/05), and NLSS III (2010/11). The dose–response function, propensity score matching, and instrumental variable techniques are used to estimate the impact of crop diversification. Results show the positive impact of HVCs on the monthly per capita consumption expenditure and poverty outcomes. Among HVCs growers, HHs growing vegetables have the better welfare outcomes. While establishing the relationship between degree of agricultural diversity and poverty measures, we find that the marginal farmers need to at least derive 35% of the share of revenue from HVCs to escape from poverty.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".