MétaCan
Menu
Back to cohort
Record W2903214105 · doi:10.1111/ajae.12403

Rainfall shocks and risk aversion: Evidence from Southeast Asia

2023· article· en· W2903214105 on OpenAlex
Sabine Liebenehm, Ingmar Schumacher, Eric Strobl

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Agricultural Economics · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural risk and resilience
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsRisk aversion (psychology)EconomicsPovertyLoss aversionEconometricsMicroeconomicsFinancial economicsExpected utility hypothesisEconomic growth

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.196
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it