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26 août 2024
- handle: 10670/1.9le329
- hal: hal-04677299
- doi: 10.1093/qopen/qoae021/7731124
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info:eu-repo/semantics/altIdentifier/doi/10.1093/qopen/qoae021/7731124
http://creativecommons.org/licenses/by-nc-nd/ , info:eu-repo/semantics/OpenAccess
Mots-clés
Risk attitudes Expert predictions Expert forecasts Multiple prices lists Meta-science C - Mathematical and Quantitative Methods/C.C9 - Design of Experiments/C.C9.C91 - Laboratory, Individual Behavior Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q1 - Agriculture/Q.Q1.Q12 - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets Q - Agricultural and Natural Resource Economics • Environmental and Ecological Economics/Q.Q1 - Agriculture/Q.Q1.Q16 - R&D • Agricultural Technology • Biofuels • Agricultural Extension ServicesSujets proches
Predictions Farm operators Operators, Farm Planters (Persons)Citer ce document
Henning Schaak et al., « Who Can Predict Farmers' Choices in Risky Gambles? », HALSHS : archive ouverte en Sciences de l’Homme et de la Société, ID : 10.1093/qopen/qoae021/7731124
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Résumé
Risk is a pervasive factor in agriculture and a subject of great interest to agricultural economists. However, there is a lack of comprehensive understanding of the knowledge held by farm advisors, students, and economists with regards to farmers' risk preferences. Misconceptions about farmers' willingness to take risks could lead to misguided advice. This study builds upon a recent multinational endeavor that employed a multiple price list to assess risk preferences among European farmers. We expand this research by gathering predictions for farmers' risk preferences from 561 farm advisors, students, and economists. Our objectives are threefold: firstly, we explore variations as to how accurately participants can predict risk preferences in different specializations; secondly, we compare the predictive accuracy of different groups of forecasters; and thirdly, we assess whether modifying incentive mechanisms can improve the accuracy of predictions. Whereas our findings reveal substantial variation in individual predictions, the averages closely align with the observed responses of farmers. Notably, the most accurate predictions were provided by a sample of experimental economics researchers. Furthermore, predictions for different production systems exhibit minimal disparities. Introducing incentive schemes, such as a tournament structure, where the best prediction receives a reward, or a high-accuracy system, where randomly selected participants are compensated for the accuracy of their predictions, does not significantly impact accuracy. Further research and exploration are needed to identify the most reliable sources of advice for farmers.