EconPapers    
Economics at your fingertips  
 

Predictive Power in Behavioral Welfare Economics

Elias Bouacida and Daniel Martin

No 296961902, Working Papers from Lancaster University Management School, Economics Department

Abstract: When choices are inconsistent due to behavioral biases, there is a theoretical debate about whether the structure of a model is necessary for providing 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 “model-free†approaches to behavioral welfare analysis. We find they typically have high predictive power, which means there is little ambiguity about what should be selected from each choice set. We also identify properties of revealed preferences that help to explain the predictive power of these approaches.

Keywords: Welfare economics; behavioral economics; predictive power; revealed preferences (search for similar items in EconPapers)
JEL-codes: C91 D12 I30 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-cbe, nep-ore and nep-upt
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.lancaster.ac.uk/media/lancaster-univers ... casterWP2020_008.pdf (application/pdf)

Related works:
Journal Article: Predictive Power in Behavioral Welfare Economics (2021) Downloads
Working Paper: Predictive Power in Behavioral Welfare Economics (2021) Downloads
Working Paper: Predictive Power in Behavioral Welfare Economics (2021) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:lan:wpaper:296961902

Access Statistics for this paper

More papers in Working Papers from Lancaster University Management School, Economics Department Contact information at EDIRC.
Bibliographic data for series maintained by Giorgio Motta ().

 
Page updated 2024-06-21
Handle: RePEc:lan:wpaper:296961902