Can a neighbouring region influence poverty? A fuzzy and longitudinal approach

This chapter provides fuzzy measures of poverty and deprivation, covering both monetary and non-monetary aspects from cross-sectional and longitudinal perspectives. It presents the most important characteristics of the spatial empirical best linear unbiased predictor (SEBLUP), which is the small area technique that people believe is the most appropriate for estimating poverty at the regional level in the European Union (EU), because it includes correlations of poverty among neighbouring regions. The chapter describes the micro-dataset used and the construction of computational units for variance estimation. It explores the EU survey on the statistics on income and living conditions (EU-SILC), a large-scale micro-database, from which people use a subset in our analysis. The primary result obtained is the extension of variance estimation to go beyond measures of monetary longitudinal poverty, specifically using a fuzzy formulation of those measures and, as a corollary, multi-dimensional measures of longitudinal deprivation, which by their nature are a matter of degree - i.e. are fuzzy.