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The Analysis and the Measurement of Poverty: An Interval-Based Composite Indicator

Author

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  • Carlo Drago

    (University “Niccolò Cusano”, NCI University in London)

Abstract

The analysis and measurement of poverty is a crucial and unsolved issue in the field of social science. This work aims to measure poverty as a multidimensional notion using a new composite indicator. However, subjective choices as different weighting schemes on the indicator's construction could affect their interpretation and policy. It is necessary to consider the possible weighting configurations randomly to overcome this problem, and it is proposed in this work as interval-based composite indicators based on the results. This work aims to obtain robust and reliable measures based on a relevant conceptual model of poverty we have identified, considering various factors as weightings. Methodologically speaking, it is proposed an original procedure for measuring poverty in which it is computed a different composite indicator for each simulated weighting scheme of the identified factors. The weighting scheme in the Monte-Carlo simulation randomly creates an interval-based composite indicator based on the results. The different intervals are compared using different criteria (upper bound, center, and lower bound), and various rankings help analyze extreme scenarios and policy hypotheses. Critical situations are identified in Sicilia, Calabria, Campania and Puglia. The results demonstrate a relevant and consistent indicator measurement and the shadow sector's relevant impact on the final measures.

Suggested Citation

  • Carlo Drago, 2021. "The Analysis and the Measurement of Poverty: An Interval-Based Composite Indicator," Working Papers 2021.21, Fondazione Eni Enrico Mattei.
    • Handle: RePEc:fem:femwpa:2021.21
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    Citations

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    Cited by:

    1. Matheus Pereira Libório & Alexandre Magno Alves Diniz & Hamidreza Rabiei-Dastjerd & Oseias da Silva Martinuci & Carlos Augusto Paiva da Silva Martins & Petr Iakovlevitch Ekel, 2023. "A Decision Framework for Identifying Methods to Construct Stable Composite Indicators That Capture the Concept of Multidimensional Social Phenomena: The Case of Social Exclusion," Sustainability, MDPI, vol. 15(7), pages 1-17, April.
    2. Xiaoning Li & Lingling Li, 2021. "Evaluation of China’s Targeted Poverty Alleviation Policies: A Decomposition Analysis Based on the Poverty Reduction Effects," Sustainability, MDPI, vol. 13(21), pages 1-17, October.
    3. Yizhong Huan & Lingqing Wang & Mark Burgman & Haitao Li & Yurong Yu & Jianpeng Zhang & Tao Liang, 2022. "A multi‐perspective composite assessment framework for prioritizing targets of sustainable development goals," Sustainable Development, John Wiley & Sons, Ltd., vol. 30(5), pages 833-847, October.
    4. Carlo Drago & Loris Di Nallo & Maria Lucetta Russotto, 2023. "Social Sustainability in European Banks: A Machine Learning Approach using Interval- Based Composite Indicators," Working Papers 2023.13, Fondazione Eni Enrico Mattei.
    5. 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.
    6. 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).
    7. Marek Walesiak & Grażyna Dehnel, 2023. "A Measurement of Social Cohesion in Poland’s NUTS2 Regions in the Period 2010–2019 by Applying Dynamic Relative Taxonomy to Interval-Valued Data," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
    8. Yue Pan & Ying Wang & Yingxue Wang & Yanling Xie & Junmei Dong & Min Liu, 2023. "Spatiotemporal Dynamics of the Suitability for Ecological Livability of Green Spaces in the Central Yunnan Urban Agglomeration," Sustainability, MDPI, vol. 15(22), pages 1-18, November.

    More about this item

    Keywords

    Poverty; Composite Indicators; Interval Data; Interval-based Composite Indicators; Symbolic Data;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • I3 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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