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A straightforward diagnostic tool to identify attribute non-attendance in discrete choice experiments

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  • Espinosa-Goded, María
  • Rodriguez-Entrena, Macario
  • Salazar-Ordóñez, Melania

Abstract

To distinguish between respondents that have attended to/ignored an attribute in discrete choice experiments (DCE), Hess and Hensher (HH) apply the coefficient of variation of the conditional distribution, setting a threshold of 2 as a conservative rule of thumb. This paper develops an analytical framework (piecewise regression analysis — PWRA) to refine the HH approach, offering a flexible method to identify attribute non-attendance (ANA) in highly context-dependent DCE. It is empirically tested on a dataset used to value agricultural public goods. The results suggest that the identification of non-attendance and goodness of fit of different random parameter logit models that accommodate ANA are better when the framework developed in this research is applied. When comparing welfare estimates from the HH and PWRA approach, significant differences are observed. Consequently, the flexibility of the PWRA notably contributes to revealing context-specific ANA patterns that can help to provide more accurate welfare measures and therefore policy recommendations.

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  • Espinosa-Goded, María & Rodriguez-Entrena, Macario & Salazar-Ordóñez, Melania, 2021. "A straightforward diagnostic tool to identify attribute non-attendance in discrete choice experiments," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 211-226.
    • Handle: RePEc:eee:ecanpo:v:71:y:2021:i:c:p:211-226
    DOI: 10.1016/j.eap.2021.04.012
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    More about this item

    Keywords

    Attribute non-attendance (ANA); Inferred ANA; Piecewise regression; Coefficient of variation; Willingness to pay (WTP);
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • Q51 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Valuation of Environmental Effects

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