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Methodological challenges in building composite indexes: Linking theory to practice

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  • Santeramo, Fabio Gaetano

Abstract

Composite indicators are emerging in several fields and disciplines as appealing method to synthesize a multitude of information, in a compact, single, and unique way. The process of aggregating heterogeneous information is itself very challenging and exposed to numerous threats. The chapter deepens on the methodological challenges that scientists, analysts, and final users must be aware of for a correct interpretation of the composite indexes. By mean of a worked example on the construction of composite indicators for food security, the chapter concludes that while different normalization and weighting approaches do not alter composite indicators, data imputation and aggregation methods are the most crucial steps: different methods convey very different results. For instance, the adoption of different aggregation procedures may largely alter the rankings based on composite indicators. In sum, the analysis shows that the index construction decisions matter and comment on policy and practical implications for the construction of composite indicators.

Suggested Citation

  • Santeramo, Fabio Gaetano, 2016. "Methodological challenges in building composite indexes: Linking theory to practice," MPRA Paper 73276, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:73276
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    References listed on IDEAS

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    2. Santeramo, Fabio Gaetano & Shabnam, Nadia, 2015. "The income-elasticity of calories, macro and micro nutrients: What is the literature telling us?," MPRA Paper 63754, University Library of Munich, Germany.
    3. 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.
    4. Giuseppe Munda, 2012. "Choosing Aggregation Rules for Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 109(3), pages 337-354, December.
    5. Eric Tate, 2012. "Social vulnerability indices: a comparative assessment using uncertainty and sensitivity analysis," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 325-347, September.
    6. Per Pinstrup-Andersen, 2009. "Food security: definition and measurement," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 1(1), pages 5-7, February.
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    8. Francesco Caracciolo & Fabio Gaetano Santeramo, 2013. "Price Trends and Income Inequalities: Will Sub-Saharan Africa Reduce the Gap?," African Development Review, African Development Bank, vol. 25(1), pages 42-54, March.
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    11. Carman, Katherine Grace & Zamarro, Gema, 2016. "Does Financial Literacy Contribute To Food Security?," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 4(1), pages 1-19, January.
    12. Santeramo, Fabio Gaetano & Di Pasquale, Jorgelina & Contò, Francesco & Tudisca, Salvatore & Sgroi, Filippo, 2012. "Analyzing risk management in Mediterranean Countries: The Syrian perspective," MPRA Paper 49851, University Library of Munich, Germany.
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    Cited by:

    1. Santeramo, Fabio Gaetano & Carlucci, Domenico & De Devitiis, Biagia & Seccia, Antonio & Stasi, Antonio & Viscecchia, Rosaria & Nardone, Gianluca, 2017. "Emerging trends in European food, diets and food industry," MPRA Paper 82105, University Library of Munich, Germany.

    More about this item

    Keywords

    Composite Indicator; Food Security; Data Aggregation; Data Imputation; Normalization; Weighting; FAO; Development; Economics;

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • F63 - International Economics - - Economic Impacts of Globalization - - - Economic Development
    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture
    • Q19 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Other

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