Factors Explaining the Hypothetical Bias: How to Improve Models for Meta-analyses

Baoubadi Atozou, Lota Tamini, Stéphane Bergeron and Maurice Doyon

CIRANO Working Papers from CIRANO

Abstract: Meta-analyses are getting more common in economics. However, little information is available regarding the choice of econometric models and its impact on results. Moreover, outlier data are common in meta-analyses and some authors have simply chosen to remove arbitrarily such data. We use the rich literature of meta-analysis on hypothetical bias (HB) related to contingent valuation methods to illustrate our point. More specifically, we review and update the meta-analyses of HB using a Meta-Regression Hierarchical Mixed Effect (MRHME) model and we apply a Bayesian Gibbs Sampling as classical results robustness check. A set of 462 observations from 87 economic valuation studies is used to this effect. The findings indicate that MRHME model is more efficient to explain HB. While respondents overstate their stated willingness-to-pay for a good by a factor of two, cheap talk, certainty correction, Ex Ante and Ex Post mitigation techniques significantly reduce the HB. Notwithstanding, mitigation techniques are more effective in private goods economic valuation.

Keywords: Contingent Valuation; Hypothetical Bias; Meta-analysis; Willingness-To-Pay; Private Goods; Public Goods; Economic Valuation (search for similar items in EconPapers)
JEL-codes: C10 H40 Q00 Q20 (search for similar items in EconPapers)
Date: 2019-12-11
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https://cirano.qc.ca/files/publications/2019s-30.pdf

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Journal Article: Factors Explaining the Hypothetical Bias: How to Improve Models for Meta-Analyses (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:cir:cirwor:2019s-30

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