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Multidimensional Well-Being: A Bayesian Networks Approach

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Abstract

The study of multidimensional well-being has long recognized the importance of formalizing the interaction between dimensions, but came short of treating this formally. In this paper, we show that the statistical technique of Bayesian Networks is an intuitive and powerful instrument that allows to model the dependence structure among the different dimension of well-being. Moreover, Bayesian Networks are useful to understand the effectiveness of policies directed to one or more dimensions, as well as to design more effective interventions to improve well-being. The new approach is illustrated with an empirical application for a selection of Western and Eastern European countries.

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Fig. 1

Source: Authors’ elaboration on LITS II dataset

Fig. 2

Source: Authors’ elaboration on LITS II dataset

Fig. 3

Source: Authors’ elaboration on LITS II dataset

Fig. 4

Source Authors’ elaboration on LITS II dataset

Fig. 5

Source: Authors’ elaboration on LITS II dataset

Fig. 6

Source: Authors’ elaboration on LITS II dataset

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Notes

  1. In particular, the logarithm of the Dirichlet posterior density score assumes that the Bayesian network (BN) is a stochastic variable. Prior knowledge of BN is combined with sample data to estimate a posterior probability distribution for BN. Usually, conjugate prior and posterior probabilities are chosen, such as the family of Dirichlet distributions for multinomial sampling. Bayes theorem is used to update the Dirichlet prior distribution of BN, given sample data, to a Dirichlet posterior distribution of BN. Hence, the logarithm of the posterior probability is used as the Bayesian network score, which will always be negative since it is the logarithm of a probability; thus the smallest negative score among a group of BNs indicates the most probable BN.

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Authors and Affiliations

  1. School of Foreign Service, Georgetown University, ICC 394, 37th St NW & O St NW, Washington, DC, 20057, USA

    Lidia Ceriani

  2. Department of Economics, Università degli Studi dell’Insubria, Via Monte Generoso, 71, 21100, Varese, VA, Italy

    Chiara Gigliarano

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  1. Lidia Ceriani
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  2. Chiara Gigliarano
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Correspondence to Chiara Gigliarano.

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Ceriani, L., Gigliarano, C. Multidimensional Well-Being: A Bayesian Networks Approach. Soc Indic Res 152, 237–263 (2020). https://doi.org/10.1007/s11205-020-02432-6

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