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Unraveling determinants of inferred and stated attribute nonattendance: Effects on farmers’ willingness to accept to join agri‐environmental schemes

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  • Macario Rodríguez‐Entrena
  • Anastasio J. Villanueva
  • José A. Gómez‐Limón

Abstract

Attribute nonattendance (ANA) has received very little attention in the context of willingness to accept (WTA), although an increasing number of studies analyze the preferences of ecosystem service providers toward incentive‐based schemes. We add to the understanding of ANA behavior by analyzing stated and inferred ANA in a choice experiment investigating farmers’ WTA for participating in agri‐environmental schemes (AES) in southern Spain. We use mixed logit models, following Hess and Hensher for the inferred ANA approach. Evidence is found of ANA behavior for both stated and inferred approaches, with models accounting for ANA clearly outperforming those that do not account for it; however, we produce no conclusive results as to which ANA approach is best. WTA estimates are only moderately affected, which to some extent is consistent with the low level of non‐attendance found for the monetary attribute. Stated and inferred approaches show very similar WTA estimates. Additionally, we investigate sources of observed heterogeneity related to ANA behavior by using a sequence of bivariate probit models for each attribute. Overall, our results hint at a positive relationship between ease of scheme adoption and nonattendance to attributes. However, further research is still needed in this field. Malgré un nombre croissant d'études portant sur la préférence des fournisseurs de services d'écosystèmes pour les plans incitatifs, l'ignorance d'attributs (IA) a reçu peu d'attention dans le contexte de la volonté d'acceptation. Nous contribuons à la compréhension des comportements d'IA en analysant l'IA formulée et inférée dans une expérimentation des choix portant sur la volonté d'accepter la participation aux plans agroenvironnementaux au sud de l'Espagne. Nous nous sommes servis des modèles logit mixtes, suivant Hess et Hensher pour l'approche de l'IA inférée. Des preuves de comportement d'IA à la fois dans les approches formulées et inférées sont établies, les modèles d'IA surpassant ceux qui n'en tiennent pas compte. Par contre, nous n'avons pas produit de résultats identifiant laquelle des approches d'IA s'avère la meilleure. Les estimations de la volonté d'acceptation ne sont que modérément affectées, ce qui, en partie, correspond aux bas niveaux de l'ignorance d'attributs identifiés pour l'attribut financier. Les approches formulées et inférées engendrent des estimations très similaires de volonté d'acceptation. De plus, nous enquêtons sur les sources d'hétérogénéité observées liées au comportement d'IA au moyen d'une séquence de modèles probits bivariés pour chaque attribut. Globalement, nos résultats font allusion à une relation positive entre la simplicité d'adoption d'un plan et l'ignorance d'attributs. Toutefois, d'autres études demeurent nécessaires dans ce domaine.

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  • Macario Rodríguez‐Entrena & Anastasio J. Villanueva & José A. Gómez‐Limón, 2019. "Unraveling determinants of inferred and stated attribute nonattendance: Effects on farmers’ willingness to accept to join agri‐environmental schemes," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 67(1), pages 31-52, March.
  • Handle: RePEc:bla:canjag:v:67:y:2019:i:1:p:31-52
    DOI: 10.1111/cjag.12169
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    3. Lu, Jing & Meng, Yucan & Timmermans, Harry & Zhang, Anming, 2021. "Modeling hesitancy in airport choice: A comparison of discrete choice and machine learning methods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 147(C), pages 230-250.

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