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Ranking sectors using fuzzy output multipliers

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  • Malcolm Beynon
  • Max Munday
  • Annette Roberts

Abstract

Using input-output analysis to model the effects of changes in industry final demands is fraught with problems, many of which relate to the fundamental limitations of the concomitant linear framework. A further issue concerns the accuracy of the results, a consequence of the uncertainty surrounding the values of multipliers. Such uncertainty can create problems where the values of output multipliers are used to inform resource directions. This paper utilizes (and develops) a fuzzy input-output model and investigates the ranking of industries based on fuzzy output multipliers. The non-triviality of the fuzzy model is exposited in a general problem, where imprecision is defined by a proportional level of imprecision (fuzziness) in the technical coefficients. Through a nascent method for ranking fuzzy numbers, comparisons are made between the fuzzy and more traditional (non-fuzzy) analysis.

Suggested Citation

  • Malcolm Beynon & Max Munday & Annette Roberts, 2005. "Ranking sectors using fuzzy output multipliers," Economic Systems Research, Taylor & Francis Journals, vol. 17(3), pages 237-253.
    • Handle: RePEc:taf:ecsysr:v:17:y:2005:i:3:p:237-253
    DOI: 10.1080/09535310500221716
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    References listed on IDEAS

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    Cited by:

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    3. Umed Temurshoev, 2015. "Uncertainty treatment in input-output analysis," Working Papers 2015-004, Universidad Loyola Andalucía, Department of Economics.
    4. Douglas Thomas & Anand Kandaswamy, 2019. "Identifying high resource consumption areas of assembly-centric manufacturing in the United States," The Journal of Technology Transfer, Springer, vol. 44(1), pages 264-311, February.
    5. Barbara Diaz & Laura Moniche & Antonio Morillas, 2006. "A Fuzzy clustering approach to the key sectors of the Spanish economy," Economic Systems Research, Taylor & Francis Journals, vol. 18(3), pages 299-318.
    6. Beynon, Malcolm J. & Munday, Max, 2008. "Considering the effects of imprecision and uncertainty in ecological footprint estimation: An approach in a fuzzy environment," Ecological Economics, Elsevier, vol. 67(3), pages 373-383, October.
    7. Hristov, Jordan & Surry, Yves, 2013. "Considering the Effect of Uncertainty in an Input-Output Analysis of Water Consumption: A Fuzzy Approach Applied to Macedonia," 2013 Conference: Tools for decision support in agriculture and rural development, April 18-19, 2013, Krško, Slovenia 183913, Slovenian Association of Agricultural Economists (DAES).
    8. Caggiani, Leonardo & Ottomanelli, Michele & Dell’Orco, Mauro, 2014. "Handling uncertainty in Multi Regional Input-Output models by entropy maximization and fuzzy programming," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 159-172.

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