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.

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

Bertarelli, Gaia
;
2021-01-01

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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Descrizione: Spatial Distribution of Multidimensional Educational Poverty in Italy using Small Area Estimation
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11382/538310
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