Factors Affecting Risk Attitude of Rice Farmers: Evidence from Vietnam’s Mekong Delta
Bibliographic record
Abstract
Agricultural production accounts for 64.2% of the Vietnam’s Mekong Delta. However, this sector has to face damage risks, especially from the natural disasters, such as flood, drought, severe soil salinity, pests, and erosion, which might factor into the farmers’ risk attitude and their decision-making relative to investment in production activities. This study analyzes the factors influencing the risk attitudes of the rice farmers, based on evidence from the Vietnamese Mekong Delta. The data were collected through face-to-face interviews and experimental games with 145 rice farmers. An ordered probit regression model was applied to estimate how the factors affected the rice farmers’ risk attitudes. The risk-neutral farmers comprised 53.72% of farmers in the survey, while 31.72% and 15.15% were risk-preferred and risk-averse farmers. The study results indicated that age, number of rice crops per year, household assets, income from rice production, and credit accessibility were the main factors affecting the farmers’ risk attitudes. The results suggest that the financial incentives’ policies to compensate for losses in uncertain conditions and increase the household income, diversification of income sources, and improving the accessibility of formal credit might be useful to increase farmers’ willingness to accept the risks of investing in better profitability projects and gaining a higher income.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".