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AMBIGUITY AVERSION AND INDIVIDUAL ADAPTATION TO CLIMATE CHANGE: EVIDENCE FROM A FARMER SURVEY IN NORTHEASTERN THAILAND

Nagisa Shiiba, Hide-Fumi Yokoo, Voravee Saengavut () and Siraprapa Bumrungkit ()
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Nagisa Shiiba: Institute for Global Environmental Strategies, 2108-11, Kamiyamaguchi, Hayama, Kanagawa 240-0115, Japan
Voravee Saengavut: ��Faculty of Economics, Khon Kaen University, 123 Mittraphap Road, Muang District, Khon Kaen, Thailand
Siraprapa Bumrungkit: ��Faculty of Economics, Khon Kaen University, 123 Mittraphap Road, Muang District, Khon Kaen, Thailand

Climate Change Economics (CCE), 2023, vol. 14, issue 01, 1-29

Abstract: Understanding the triggers of individual adaptation behavior is critical for empowering those who are highly vulnerable to climate change. This study explores the effect of ambiguity aversion on adaptation behaviors in the context of climate change. We conduct a field survey among 230 rice farmers in northeastern Thailand to examine the association between elicited ambiguity aversion and the implementation of climate change adaptation. We find that ambiguity aversion does not encourage farmers’ adaptation behaviors and can even discourage the uptake of adaptation strategies. The role of ambiguity aversion varies depending on the characteristics of the adaptation strategy: ambiguity-averse farmers are less likely to adopt adaptation strategies that entail shifts from the status-quo. A deliberate approach is needed to understand farmers’ adaptation behaviors outside the laboratory setting and to reduce ambiguity in the results concerning adaptation to increasing climate risk.

Keywords: Ambiguity aversion; climate change adaptation; Thailand; weather index insurance (search for similar items in EconPapers)
JEL-codes: O13 Q12 Q54 (search for similar items in EconPapers)
Date: 2023
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http://www.worldscientific.com/doi/abs/10.1142/S2010007823500057
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Working Paper: Ambiguity Aversion and Individual Adaptation to Climate Change: Evidence from a Farmer Survey in Northeastern Thailand (2020) Downloads
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DOI: 10.1142/S2010007823500057

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