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Multidimensional well-being: A Bayesian Networks approach


  • Lidia Ceriani

    () (The World Bank, U.S.A.)

  • Chiara Gigliarano

    () (Università degli Studi dell'Insubria, Italy)


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.

Suggested Citation

  • Lidia Ceriani & Chiara Gigliarano, 2016. "Multidimensional well-being: A Bayesian Networks approach," Working Papers 399, ECINEQ, Society for the Study of Economic Inequality.
  • Handle: RePEc:inq:inqwps:ecineq2016-399

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    References listed on IDEAS

    1. M. Pittau & Roberto Zelli & Andrew Gelman, 2010. "Economic Disparities and Life Satisfaction in European Regions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 96(2), pages 339-361, April.
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    3. Bosmans, Kristof & Decancq, Koen & Ooghe, Erwin, 2015. "What do normative indices of multidimensional inequality really measure?," Journal of Public Economics, Elsevier, vol. 130(C), pages 94-104.
    4. Koen Decancq & María Ana Lugo, 2013. "Weights in Multidimensional Indices of Wellbeing: An Overview," Econometric Reviews, Taylor & Francis Journals, vol. 32(1), pages 7-34, January.
    5. Winkelmann, Liliana & Winkelmann, Rainer, 1998. "Why Are the Unemployed So Unhappy? Evidence from Panel Data," Economica, London School of Economics and Political Science, vol. 65(257), pages 1-15, February.
    6. Silvia Salini & Ron Kenett, 2009. "Bayesian networks of customer satisfaction survey data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(11), pages 1177-1189.
    7. Rolf Aaberge & Andrea Brandolini, 2014. "Multidimensional poverty and inequality," Discussion Papers 792, Statistics Norway, Research Department.
    8. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    9. Silvia SALINI & Ron S. KENETT, 2007. "Bayesian networks of customer satisfaction survey data," Departmental Working Papers 2007-33, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    10. Ferrer-i-Carbonell, Ada, 2005. "Income and well-being: an empirical analysis of the comparison income effect," Journal of Public Economics, Elsevier, vol. 89(5-6), pages 997-1019, June.
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    Cited by:

    1. Federica Onori & Giovanna Jona Lasinio, 0. "Modeling “Equitable and Sustainable Well-being” (BES) Using Bayesian Networks: A Case Study of the Italian Regions," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 0, pages 1-35.
    2. Omar A. Guerrero & Gonzalo Casta~neda, 2019. "Quantifying the Coherence of Development Policy Priorities," Papers 1902.00430,
    3. Castañeda, Gonzalo & Chávez-Juárez, Florian & Guerrero, Omar A., 2018. "How do governments determine policy priorities? Studying development strategies through spillover networks," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 335-361.
    4. Gonzalo Castañeda & Omar A. Guerrero, 2018. "The Resilience of Public Policies in Economic Development," Complexity, Hindawi, vol. 2018, pages 1-15, October.

    More about this item


    Multivariate analysis; directed acyclic graphs; probabilistic inference; well-being;

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