Geographical indications and welfare: Evidence from US wine demand
dc.contributor.author | Chandra, Raj | |
dc.contributor.author | Moschini, Giancarlo | |
dc.contributor.author | Lade, Gabriel E. | |
dc.contributor.department | Economics | |
dc.contributor.department | Center for Agricultural and Rural Development | |
dc.date.accessioned | 2024-10-15T13:19:10Z | |
dc.date.available | 2024-10-15T13:19:10Z | |
dc.date.issued | 2024-10-03 | |
dc.description.abstract | A systematic component of wine quality is believed to depend on the geoclimatic factors of its production conditions. This belief has long motivated the development of geographical indications for wines. American viticulture areas (AVAs) represent the most common geographic identifier firms use to differentiate their products in the United States. In this paper we contribute new empirical evidence on the effectiveness and impact of GIs by studying consumers' valuation of US wine appellations within a structural model of wine demand. The model is rooted in the discrete-choice framework, under the basic premise that observable information concerning wine attributes is credible and key to consumers' choices. Specifically, we develop a two-level, nested-logit model featuring many wine products and characteristics—including wine type, brands, and varietals, in addition to geographic origin. The model is estimated using NielsenIQ Consumer Panel data over the 2007–2019 period. We find that US consumers place a relatively high value on wines' geographic origins, distinct from the value of brand and varietal information, as documented by their marginal willingness to pay estimates. Furthermore, a counterfactual experiment shows significant welfare impacts from information about the geographic origin of wines. Over the period of interest, the welfare gain attributable to US geographic origin designation is estimated at about $5.37 billion, with wine producers and retailers capturing ~78% of this surplus. Virtually all consumer welfare gains are due to product differentiation and increased product variety enabled by information about the wine's origin. | |
dc.description.comments | <p>JEL Classification: D82, O34, Q11, Q18<p/><p>This article is published as Chandra, Raj, GianCarlo Moschini, and Gabriel E. Lade. 2024. “Geographical Indications and Welfare: Evidence from US Wine Demand.” American Journal of Agricultural Economics 1–26. https://doi.org/10.1111/ajae.12499.<p/> | |
dc.identifier.uri | https://dr.lib.iastate.edu/handle/20.500.12876/OrD8MRYr | |
dc.language.iso | en | |
dc.publisher | Wiley Periodicals LLC on behalf of Agricultural & Applied Economics Association | |
dc.relation.hasversion | Geographical Indications and Welfare: Evidence from the US Wine Market | |
dc.rights | © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | |
dc.source.uri | https://doi.org/10.1111/ajae.12499 | * |
dc.subject.disciplines | DegreeDisciplines::Social and Behavioral Sciences::Agricultural and Resource Economics | |
dc.subject.disciplines | DegreeDisciplines::Life Sciences::Agriculture::Viticulture and Oenology | |
dc.subject.disciplines | DegreeDisciplines::Social and Behavioral Sciences::Economics::Behavioral Economics | |
dc.subject.keywords | American viticulture areas | |
dc.subject.keywords | geographic indications | |
dc.subject.keywords | nested logit | |
dc.subject.keywords | product differentiation | |
dc.subject.keywords | welfare | |
dc.subject.keywords | willingness to pay | |
dc.subject.keywords | wine demand | |
dc.title | Geographical indications and welfare: Evidence from US wine demand | |
dc.type | Article | |
dspace.entity.type | Publication | |
relation.hasVersion | 5b56c2e0-e5aa-47f1-b916-49eb27056dc1 | |
relation.isAuthorOfPublication | 875efd32-d51f-480d-8d4b-3d87da3f2678 | |
relation.isOrgUnitOfPublication | 4c5aa914-a84a-4951-ab5f-3f60f4b65b3d | |
relation.isOrgUnitOfPublication | 1a6be5f1-4f64-4e48-bb66-03bbcc25c76d |
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