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Predictive Power in Behavioral Welfare Economics

Elias Bouacida and Daniel Martin ()
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Elias Bouacida: PSE - Paris School of Economics, PJSE - Paris Jourdan Sciences Economiques - UP1 - Université Panthéon-Sorbonne - ENS Paris - École normale supérieure - Paris - INRA - Institut National de la Recherche Agronomique - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique

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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: Scanner data; Experimental economics; Welfare economics; Behavioral economics; Revealed preferences (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-upt
Date: 2017-04-04
Note: View the original document on HAL open archive server: https://halshs.archives-ouvertes.fr/halshs-01489252v4
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