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

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  • Mauricio Varas
  • Franco Basso
  • Sergio Maturana
  • Raúl Pezoa
  • Marcelo Weyler

Abstract

The 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 this paper, 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, judgements, 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 analyse 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.

Suggested Citation

  • Mauricio Varas & Franco Basso & Sergio Maturana & Raúl Pezoa & Marcelo Weyler, 2021. "Measuring efficiency in the Chilean wine industry: a robust DEA approach," Applied Economics, Taylor & Francis Journals, vol. 53(9), pages 1092-1111, February.
  • Handle: RePEc:taf:applec:v:53:y:2021:i:9:p:1092-1111
    DOI: 10.1080/00036846.2020.1826400
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    Cited by:

    1. Pejman Peykani & Jafar Gheidar-Kheljani & Reza Farzipoor Saen & Emran Mohammadi, 2022. "Generalized robust window data envelopment analysis approach for dynamic performance measurement under uncertain panel data," Operational Research, Springer, vol. 22(5), pages 5529-5567, November.

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