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Relating risk preferences and risk perceptions over different agricultural risk domains : Insights from Ethiopia

Authors :
Feyisa, Ashenafi Duguma
Maertens, Miet
de Mey, Yann
Feyisa, Ashenafi Duguma
Maertens, Miet
de Mey, Yann
Source :
ISSN: 0305-750X
Publication Year :
2023

Abstract

Households in developing countries are exposed to various shocks and risks, which leaves them vulnerable as they typically have limited resources to cope with them. Even though a large body of development literature has focused on the role of risk in rural livelihoods, the focus is often on single sources of risk and taking a unidimensional view on risk preference. This paper explores the diversity in risk perception and risk preferences of Ethiopian households by combining incentivized field experiments with detailed primary household survey data. We disentangle the relationship between risk perception and risk preferences using an innovative combination of time framing and instrumental variable estimation approaches. We find that our respondents are exposed to multiple past shocks and perceive multiple sources of future threats across different agricultural risk domains. Our respondents can be characterized as relatively risk-averse and loss-averse, and they also overweight unlikely extreme outcomes. We find a statistically significant association between the prospect theory risk preferences parameters—risk aversion, loss aversion, and probability weighting—and overall risk perception, domain-specific risk perceptions (except for the personal domain) and the impact dimension of future risk. Our findings make an important contribution to our understanding of farm households’ risk behavior, and can guide prioritizing development efforts to stimulate better informed and well-targeted risk management policy interventions.

Details

Database :
OAIster
Journal :
ISSN: 0305-750X
Notes :
application/pdf, World Development 162 (2023), ISSN: 0305-750X, ISSN: 0305-750X, English
Publication Type :
Electronic Resource
Accession number :
edsoai.on1356878879
Document Type :
Electronic Resource