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Interval Based Composite Indicators

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  • Carlo Drago

    (Università degli Studi ”Niccolò Cusano”)

Abstract

Composite indicators are increasingly important in country comparisons and in policy making. At the same time, the robustness of the results obtained and in particular of the rankings and the conclusions obtained from the analysis it is usually accepted with doubts. In this sense our proposal is to use interval data in order to measure the uncertainty related to the different composite indicators based on the different assumptions used as input. In this sense where composite indicators can be considered as models, for this reason it could be necessary to assess the uncertainties related to the different choices in the construction. The uncertainty can be represented by the interval data. The intervals keep the information related to the initial value of the composite indicator, but at the same time give information on the range of the results.

Suggested Citation

  • Carlo Drago, 2017. "Interval Based Composite Indicators," Working Papers 2017.42, Fondazione Eni Enrico Mattei.
    • Handle: RePEc:fem:femwpa:2017.42
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    References listed on IDEAS

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    1. Saltelli, Andrea & Ratto, Marco & Tarantola, Stefano & Campolongo, Francesca, 2006. "Sensitivity analysis practices: Strategies for model-based inference," Reliability Engineering and System Safety, Elsevier, vol. 91(10), pages 1109-1125.
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    3. Michael Freudenberg, 2003. "Composite Indicators of Country Performance: A Critical Assessment," OECD Science, Technology and Industry Working Papers 2003/16, OECD Publishing.
    4. L Cherchye & W Moesen & N Rogge & T Van Puyenbroeck & M Saisana & A Saltelli & R Liska & S Tarantola, 2008. "Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 239-251, February.
    5. Michela Nardo & Michaela Saisana & Andrea Saltelli & Stefano Tarantola & Anders Hoffman & Enrico Giovannini, 2005. "Handbook on Constructing Composite Indicators: Methodology and User Guide," OECD Statistics Working Papers 2005/3, OECD Publishing.
    6. Zaman, Kais & Rangavajhala, Sirisha & McDonald, Mark P. & Mahadevan, Sankaran, 2011. "A probabilistic approach for representation of interval uncertainty," Reliability Engineering and System Safety, Elsevier, vol. 96(1), pages 117-130.
    7. M. Saisana & A. Saltelli & S. Tarantola, 2005. "Uncertainty and sensitivity analysis techniques as tools for the quality assessment of composite indicators," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(2), pages 307-323, March.
    8. Gómez-Limón, José A. & Sanchez-Fernandez, Gabriela, 2010. "Empirical evaluation of agricultural sustainability using composite indicators," Ecological Economics, Elsevier, vol. 69(5), pages 1062-1075, March.
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    Citations

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    Cited by:

    1. Carlo Drago & Andrea Gatto, 2022. "An interval‐valued composite indicator for energy efficiency and green entrepreneurship," Business Strategy and the Environment, Wiley Blackwell, vol. 31(5), pages 2107-2126, July.
    2. Drago, Carlo & Gatto, Andrea, 2022. "Policy, regulation effectiveness, and sustainability in the energy sector: A worldwide interval-based composite indicator," Energy Policy, Elsevier, vol. 167(C).
    3. Carlo Drago, 2021. "The Analysis and the Measurement of Poverty: An Interval-Based Composite Indicator Approach," Economies, MDPI, vol. 9(4), pages 1-17, October.
    4. Carlo Drago & Roberto Ricciuti, 2019. "An interval variables approach to address measurement uncertainty in governance indicators," Economics Bulletin, AccessEcon, vol. 39(1), pages 626-635.
    5. Drago, Carlo, 2021. "Interval-Based Composite Indicators with a Triplex Representation: A Measure of the Potential Demand for the "Ristori" Decree in Italy," MPRA Paper 106904, University Library of Munich, Germany.

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    More about this item

    Keywords

    Composite Indicators; Interval Data; Robustness; Sensitivity Analysis; Uncertainty Analysis;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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