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Spatial Distribution of Multidimensional Educational Poverty in Italy using Small Area Estimation

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Abstract

Inclusive and equitable education and the promotion of lifelong learning opportunities for all are important targets in the 2030 Agenda for Sustainable Development. Deprivation in education, read also as deprivation of opportunities and rights i.e. health, culture, participation, social relations, referred to as educational poverty (EP), has attracted interest of researchers, which highlighted its complexities and consequences, such as being excluded from acquiring the skills needed to live in a world characterized by knowledge-based economy, rapidity and innovation. In the last few years, the Italian National Statistical Institute started to measure it by a multidimensional index, the composite educational poverty index (EPI). The index is based on survey direct estimates, which are reliable only at regional (NUTS 2) level, while to monitor and contrast the phenomenon it is important to obtain information at a finer geographical level. In this paper small area estimation models are applied to the unidimensional indicators which compose the multidimensional EPI. The aim is to enhance the knowledge of the spatial distribution of EP at local level in Italy, separating urban and non urban areas and focusing on peripheries in Italian Regions, using DEGURBA classification in order to help the policy maker to address resources towards the areas where the phenomenon is strongly present.

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Acknowledgements

The work of Bertarelli, Giusti and Pratesi has been carried out with the support of the Project InGRID-2, European Project G.A. No. 730998.

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Authors and Affiliations

  1. Department of Economics and Management, University of Pisa, Pisa, Italy

    Monica Pratesi, Gaia Bertarelli & Caterina Giusti

  2. Centro Camilo Dagum, Tuscan Universities Research Centre, Pisa-Tuscany, Italy

    Monica Pratesi, Gaia Bertarelli, Alessandro Gemignani & Caterina Giusti

  3. Italian National Institute of Statistics, Rome, Italy

    Luciana Quattrociocchi

Authors
  1. Monica Pratesi
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  2. Luciana Quattrociocchi
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  3. Gaia Bertarelli
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  4. Alessandro Gemignani
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  5. Caterina Giusti
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Correspondence to Gaia Bertarelli.

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Appendices

Appendix 1: Auxiliary Variables from “A misura di comune” (ISTAT) Considered in the Fay and Herriot Models

See Tables 2 and 3.

Table 2 List of considered auxiliary variables from “A misura di comune” (ISTAT)
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Table 3 FH models: significant auxiliary variables for each FH model for each indicators
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Appendix 2: B Maps of Regions by DEGURBA Areas (Ranking by Quintile) for adjMPI of Each EP Domain: adjMPI is Constructed Using the FH Estimates of Single Indicators

See Figs. 8 and 9.

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Region by DEGURBA areas (ranking by quintile) for adjMPI in each domain

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Fig. 9
figure 9

Regions by DEGURBA areas (ranking by quintile) for adjMPI in each domain

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Pratesi, M., Quattrociocchi, L., Bertarelli, G. et al. Spatial Distribution of Multidimensional Educational Poverty in Italy using Small Area Estimation. Soc Indic Res 156, 563–586 (2021). https://doi.org/10.1007/s11205-020-02328-5

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