Farmers’ risk preferences and pesticide use decisions: evidence from field experiments in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract China faces health and environmental problems associated with the use of agricultural chemicals, including pesticides. While previous studies have found that risk aversion affects pesticide use in China, they have focused primarily on commercial cotton farmers. In this study, we consider the case of smaller, semisubsistence and subsistence farmers in a poor and landlocked province of China (Yunnan). We use a field experiment to measure risk aversion and collect detailed data on farm production and input use to specifically ask whether risk aversion affects pesticide use, and whether this effect differs for subsistence farmers producing exclusively for home consumption versus semisubsistence farmers who produce both for home and the market. We find that risk aversion significantly increases pesticide use, particularly for subsistence farmers and for market plots by semisubsistence farmers. Further, this effect of risk aversion significantly decreases with farm size for subsistence farmers, but not for semisubsistence farmers, implying that pesticide use may be used to ensure sufficient food supply for home consumption. Finally, we find barriers to the use of pesticides for subsistence farmers, both in terms of financial constraints and economies of scale. This finding implies that risk‐mitigation strategies, such as crop insurance, may not target food security concerns of subsistence farmers. Given these different motivations for pesticide use, policymakers may wish to consider effective tools to support rural food security for farmers in the poorer regions of China in order to decrease pesticide use.
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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 | 0.001 |
| 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 | 0.001 |
| 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