Accuracy Evaluation of LFS-BES Indicators: A Regional Assessment
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Accuracy Evaluation of LFS‑BES Indicators: A Regional Assessment Claudio Ceccarelli1 · Alessio Guandalini1 · Alessandro Martini1 · Maria Elena Pontecorvo1 Accepted: 26 October 2020 © Springer Nature B.V. 2020
Abstract Equitable and sustainable well-being (in Italian, “BES”) has become an integral part of the decision-making process of economic and financial planning. By now, the needs of up-to-date and accurate measures of BES at local level is certain. Among BES indicators, several are obtained from Labour Force Survey (LFS) data. LFS provided estimates keeping with the highest quality and methodology standards required by the new Integrated European Social Statistics (IESS) framework regulation. The aim of this paper is to extend recent improvements in LFS variance estimation methodology also to BES indicators computed on LFS data. The direct consequence is that, besides estimates, accuracy measures can be provided. This can help researchers and decision makers to analyze the performance among the Italian regions and their evolution over time. Keywords Well-being · Territory · Accuracy · Changes · Linearization
1 Introduction It is by now well-established that well-being is a multidimensional concept. A well-being measure should catch the economic component of progress, of course, but also social, environmental, equity and sustainability aspects (Hall et al. 2010; OECD 2008, 2011, 2014). In this perspective, the GDP—that has been for long time the main measure of wellbeing—is by now overcome. Therefore, for capturing simultaneously all the aspects of well-being, several alternatives or complementary indicators have been proposed in the literature. Two main approaches can be distinguished: a dashboard of indicators or a * Alessio Guandalini [email protected] Claudio Ceccarelli [email protected] Alessandro Martini [email protected] Maria Elena Pontecorvo [email protected] 1
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composite index. The former provides a very detailed picture, with several indicators for different dimensions of well-being (see, e.g. Stiglitz et al. 2009.). However, it is lacking of a summary and it does not allow for easy comparisons across geographical areas and over time. On the other hand, the latter enables comparisons over time and space, but it collapses all the information in just a single value (for further details see, e.g., Massoli et al. 2014; Mazziotta and Pareto 2016, 2013; Ciommi et al. 2017). Therefore it provides an extreme summary and a lot of aspects might be hidden (Bleys 2012). In 2011, the Organization for Economic Co-operation and Development, OECD, published a report to measure the well-being in 34 countries through a dashboard of indicators grouped in 11 dimensions (income and wealth, jobs and earnings, housing conditions, health status, work-life balance, education and skills, social connections, civic engagement and governance, environmental quality, personal security, and subjective wel
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