Measuring efficiency in the Chilean wine industry: A robust DEA approach

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dc.contributor.authorVaras, Mauricio
dc.contributor.authorBasso, Franco
dc.contributor.authorMaturana, Sergio
dc.contributor.authorPezoa, Raúl
dc.contributor.authorWeyler, Marcelo
dc.date.accessioned2021-08-31T16:43:10Z
dc.date.available2021-08-31T16:43:10Z
dc.date.issued2020
dc.description.abstractThe Chilean wine industry has been quite innovative in terms of winemaking and trading. Yet, to survive in this competitive industry, wine managers should be aware of the relevance of monitoring their performance. In thispaper, we assess how the five wineries listed on the Santiago Stock Exchange of Chile are efficient while using their critical resources for making profits. Particularly, we apply data envelopment analysis (DEA) to benchmark and rank these wineries’ technical efficiency based on four inputs and one output. We use data gathered from consolidated financial statements that are prepared using estimates, judgments, and assumptions. To account for some level of ex-post adjustments in data, we evaluate these wineries’ relative efficiency using a robust DEA model, which deals with ambiguous, imprecise, and uncertain input-output parameters. We analyze several levels of variability suitable for this data source, and we evaluate how changing the conservatism level affects technical efficiency and the rankings of the wineries. We also conduct a comparison between the five Chilean wineries and nine others from the New World. As the main conclusion, we found that Chilean wineries keep their efficiency level when including international firms in the analysis.es
dc.identifier.citationApplied Economics, 2020es
dc.identifier.urihttps://doi.org/10.1080/00036846.2020.1826400es
dc.identifier.urihttp://hdl.handle.net/11447/4547
dc.language.isoenes
dc.subjectWine Industryes
dc.subjectData Envelopment Analysis.es
dc.subjectRobust Optimizationes
dc.subjectMonte-Carlo Simulationes
dc.subjectCase studyes
dc.titleMeasuring efficiency in the Chilean wine industry: A robust DEA approaches
dc.typeArticlees

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