Multidimensional well-being: A Bayesian Networks approach

Lidia Ceriani () and Chiara Gigliarano ()
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Chiara Gigliarano: Università degli Studi dell'Insubria, Italy

No 399, Working Papers from ECINEQ, Society for the Study of Economic Inequality

Abstract: In the multidimensional well-being literature, it has been long advocated that it is important to consider how the different well-being domains interact. Nevertheless, none of the existing approaches is useful to tackle this issue. In this paper, we show that the statistical technique of Bayesian Networks is an intuitive and powerful instrument that allows to graphically model the dependence structure among the different dimension of well-being. Moreover, Bayesian Networks can be used to understand the effectiveness of given interventions addressed to one or more dimensions, as well as to design more effective policies to reach the desired outcome. The new approach is illustrated with an empirical application based on data for a selection of Western and Eastern European countries.

Keywords: Multivariate analysis; directed acyclic graphs; probabilistic inference; well-being (search for similar items in EconPapers)
Pages: 33 pages
Date: 2016-04
New Economics Papers: this item is included in nep-hap
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Related works:
Journal Article: Multidimensional Well-Being: A Bayesian Networks Approach (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:inq:inqwps:ecineq2016-399

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