Abstract
The Europe 2020 Strategy has formulated key policy objectives or so-called “headline targets†which the European Union as a whole and Member States are individually committed to achieving by 2020. One of the five headline targets is directly related to the key quality aspects of life, namely social inclusion; within these targets, the European Union Statistics on Income and Living Condition (EU-SILC) headline indicators at risk-of-poverty or social exclusion and its components will be included in the budgeting of structural funds, one of the main instruments through which policy targets are attained. For this purpose, Directorate-General Regional Policy of the European Commission is aiming to use sub-national/regional level data (NUTS 2). Starting from this, the focus of the present paper is on the “regional dimension†of well-being. We propose to adopt a methodology based on the Empirical Best Linear Unbiased Predictor (EBLUP) with an extension to the spatial dimension (SEBLUP); moreover, we compare this small area technique with the cumulation method. The application is conducted on the basis of EU-SILC data from Austria and Spain. Results report that, in general, estimates computed with the cumulation method show standard errors which are smaller than those computed with EBLUP or SEBLUP. The gain of pooling SILC data over three years is, therefore, relevant, and may allow researchers to prefer this method.
Highlights
In the last two decades, there has been the increased interest in the comparative analysis of poverty and social exclusion in the European Union
One of the five headline targets is directly related to the key quality aspects of life, namely social inclusion; within these targets, the EU-SILC headline indicators at-risk-of-poverty or social exclusion (AROPE, which is known as Head Count Ratio (HCR) and FGT(0) in the family of [2]) and its components will be included in the budgeting of structural funds, one of the main instruments through which policy targets are attained
In this paper we have addressed the problem of estimating measures of well-being on their “regional dimension”; if fact, we have presented and compared two small area techniques, namely the cumulation and the spatial Empirical Best Linear Unbiased Predictor (EBLUP) (SEBLUP), on the basis of EU-SILC data from Austria and Spain
Summary
A limited breakdown by major socio-demographic subgroups of the population. The Europe 2020 Strategy 2 has formulated key policy objectives or so-called “headline targets” which the EU as a whole and Member States individually are committed to achieving by 2020. One class of techniques aims at making the best use of available data from national sample surveys, such as by cumulating and consolidating the information to obtain more robust measures which permit greater spatial disaggregation; this class is described, where the particular method of cumulating three-years of the EUSILC survey is described and applied. The design yields each year a cross-sectional sample, as well as longitudinal samples of various durations
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