Rainfall shocks and risk aversion: Evidence from Southeast Asia
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We analyze how individual risk aversion changes in response to shocks in an agrarian setting, and the role of changes in yields and prices as two potential channels. To do so we specify a theoretical model that describes temporal alterations in risk aversion. Empirically, we test the model's proposition by combining individual‐level panel data with historical rainfall data for rural Thailand and Vietnam. We find that rainfall shocks increase individuals risk aversion, whereby the largest effects are observed among households that are net buyers of food commodities. Regarding potential channels, only prices seem to explain–and even then just to a very small extent–the increase in net buyers' risk aversion. Our findings imply that shocks can increase risk aversion, and, in the absence of functioning credit and insurance markets, may ultimately lead to decisions that perpetuate poverty.
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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 | 0.001 |
| 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