Predictive Power in Behavioral Welfare Economics

Elias Bouacida () and Daniel Martin
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Elias Bouacida: LUMS - Lancaster University Management School - Lancaster University

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Abstract: When choices are inconsistent due to behavioral biases, there is a theoretical debate about whether it is necessary to impose the structure of a model in order to provide precise welfare guidance based on those choices. To address this question empirically, we use standard data sets from the lab and field to evaluate the predictive power of two conservative "model-free" approaches to behavioral welfare analysis. We find that for most individuals, these approaches have high predictive power, which means there is little ambiguity about what should be selected from each choice set. We show that the predictive power of these approaches correlates highly with two properties of revealed preferences: the number of direct revealed preference cycles and the fraction of revealed preference cycles that are direct.

Keywords: Welfare economics; Revealed preferences; behavioral economics; predictive power (search for similar items in EconPapers)
Date: 2021-06
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-01489252v5
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Published in Journal of the European Economic Association, 2021, 19 (3), pp.1556-1591. ⟨10.1093/jeea/jvaa037⟩

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Related works:
Journal Article: Predictive Power in Behavioral Welfare Economics (2021)
Working Paper: Predictive Power in Behavioral Welfare Economics (2021)
Working Paper: Predictive Power in Behavioral Welfare Economics (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:pseptp:halshs-01489252

DOI: 10.1093/jeea/jvaa037

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