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Multidimensional Well-Being: A Bayesian Networks Approach

Author

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  • Lidia Ceriani

    (Georgetown University)

  • Chiara Gigliarano

    (Università degli Studi dell’Insubria)

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.

Suggested Citation

  • Lidia Ceriani & Chiara Gigliarano, 2020. "Multidimensional Well-Being: A Bayesian Networks Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 152(1), pages 237-263, November.
  • Handle: RePEc:spr:soinre:v:152:y:2020:i:1:d:10.1007_s11205-020-02432-6
    DOI: 10.1007/s11205-020-02432-6
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    4. Hamed Khalili, 2024. "Can Data and Machine Learning Change the Future of Basic Income Models? A Bayesian Belief Networks Approach," Data, MDPI, vol. 9(2), pages 1-18, January.
    5. Omar A. Guerrero & Gonzalo Casta~neda, 2019. "Quantifying the Coherence of Development Policy Priorities," Papers 1902.00430, arXiv.org.
    6. 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.
    7. Gonzalo Castañeda & Omar A. Guerrero, 2018. "The Resilience of Public Policies in Economic Development," Complexity, Hindawi, vol. 2018, pages 1-15, October.
    8. Rodrigo García Arancibia & Ignacio Girela, 2023. "Graphical Representation of Multidimensional Poverty: Insights for Index Construction and Policy Making," Working Papers 233, Red Nacional de Investigadores en Economía (RedNIE).
    9. Federica Cugnata & Silvia Salini & Elena Siletti, 2021. "Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach," IJERPH, MDPI, vol. 18(15), pages 1-10, July.

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