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<p>This paper aims to examine the impact of the Covid-19 pandemic on multidimensional poverty in Italy and its provinces by comparing household poverty levels before and after the outbreak. To capture the multidimensionality of poverty, we analyze various dimensions, including economic well-being, health status, education, neighborhood quality, and subjective well-being. The empirical analysis relies on micro-data from Istat’s aspects of daily life (AVQ) survey, covering the years 2018–2021. As the survey’s direct estimates are reliable only at the regional level (NUTS 2), we apply small area estimation techniques to produce accurate estimates of provincial (NUTS 3) deprivation incidences. Subsequently, we aggregate the deprivation headcounts across the elementary indicators using penalized power mean composite indicators. The empirical findings indicate that overall multidimensional poverty worsened in most of the Italian provinces, particularly during the second year of the pandemic, with higher levels persisting in southern areas. The various dimensions of poverty exhibited different trends, with education, subjective well-being, and health emerging as the most negatively affected in numerous provinces.</p>

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Unveiling multidimensional poverty across Italian Provinces using small area estimation and penalized power means

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月28日

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  • Gloria Polinesi,
  • Mariateresa Ciommi,
  • Francesca Mariani,
  • Chiara Gigliarano

摘要

This paper aims to examine the impact of the Covid-19 pandemic on multidimensional poverty in Italy and its provinces by comparing household poverty levels before and after the outbreak. To capture the multidimensionality of poverty, we analyze various dimensions, including economic well-being, health status, education, neighborhood quality, and subjective well-being. The empirical analysis relies on micro-data from Istat’s aspects of daily life (AVQ) survey, covering the years 2018–2021. As the survey’s direct estimates are reliable only at the regional level (NUTS 2), we apply small area estimation techniques to produce accurate estimates of provincial (NUTS 3) deprivation incidences. Subsequently, we aggregate the deprivation headcounts across the elementary indicators using penalized power mean composite indicators. The empirical findings indicate that overall multidimensional poverty worsened in most of the Italian provinces, particularly during the second year of the pandemic, with higher levels persisting in southern areas. The various dimensions of poverty exhibited different trends, with education, subjective well-being, and health emerging as the most negatively affected in numerous provinces.