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Are they Twins or Only Friends? The Redundancy and Complementarity of Energy Poverty Indicators in Italy

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  • Rossella Bardazzi

    (University of Florence)

  • Luca Bortolotti

    (University of Bologna)

  • Maria Grazia Pazienza

    (University of Florence)

Abstract

A large body of literature has developed numerous unidimensional and multidimensional indicators to identify energy-poor households, also with the aim of targeting public policies. They all have pros and cons and may identify different populations. How do they relate to each other? How should they be used in a country such as Italy where energy poverty has not yet been officially defined? We frame these research questions in a context of general social vulnerability in which several dimensions of poverty intersect. Using SILC data (EU and IT modules) for the period 2004–2015 we compare four widely used unidimensional indicators and provide evidence—for the Italian case—that complementarity among the indicators prevails since the complexity of energy poverty requires a combination of metrics to capture various vulnerabilities. Our results confirm the set of relevant factors affecting the probability of being energy poor which are indicated in the related economics literature, regardless of which indicator is used. They may help tailor policies to tackle the phenomenon. Based on these findings, we strongly support the idea of using a dashboard of different metrics to measure energy poverty to design effective policies.

Suggested Citation

  • Rossella Bardazzi & Luca Bortolotti & Maria Grazia Pazienza, 2024. "Are they Twins or Only Friends? The Redundancy and Complementarity of Energy Poverty Indicators in Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 10(2), pages 585-623, July.
  • Handle: RePEc:spr:italej:v:10:y:2024:i:2:d:10.1007_s40797-023-00246-2
    DOI: 10.1007/s40797-023-00246-2
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    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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