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Quantitative Storytelling in the Making of a Composite Indicator

Author

Listed:
  • Marta Kuc-Czarnecka

    (Gdansk University of Technology)

  • Samuele Lo Piano

    (University of Reading
    Universitat Oberta de Catalunya)

  • Andrea Saltelli

    (Universitat Oberta de Catalunya
    University of Bergen)

Abstract

The reasons for and against composite indicators are briefly reviewed, as well as the available theories for their construction. After noting the strong normative dimension of these measures—which ultimately aim to ‘tell a story’, e.g. to promote the social discovery of a particular phenomenon, we inquire whether a less partisan use of a composite indicator can be proposed by allowing more latitude in the framing of its construction. We thus explore whether a composite indicator can be built to tell ‘more than one story’ and test this in practical contexts. These include measures used in convergence analysis in the field of cohesion policies and a recent case involving the World Bank’s Doing Business Index. Our experiments are built to imagine different constituencies and stakeholders who agree on the use of evidence and of statistical information while differing on the interpretation of what is relevant and vital.

Suggested Citation

  • Marta Kuc-Czarnecka & Samuele Lo Piano & Andrea Saltelli, 2020. "Quantitative Storytelling in the Making of a Composite Indicator," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 149(3), pages 775-802, June.
    • Handle: RePEc:spr:soinre:v:149:y:2020:i:3:d:10.1007_s11205-020-02276-0
    DOI: 10.1007/s11205-020-02276-0
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    More about this item

    Keywords

    Composite indicator; International comparison; Quantitative storytelling; Social convergence;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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