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Multidimensional well-being: A Bayesian Networks approach

Author

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  • Lidia Ceriani

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

  • Chiara Gigliarano

    (Università degli Studi dell'Insubria, Italy)

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.

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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    Cited by:

    1. Rodrigo García Arancibia & Ignacio Girela, 2024. "Graphical Representation of Multidimensional Poverty: Insights for Index Construction and Policy Making," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 172(2), pages 595-634, March.
    2. Omar A. Guerrero & Gonzalo Casta~neda, 2019. "Quantifying the Coherence of Development Policy Priorities," Papers 1902.00430, arXiv.org.
    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. Merz, Joachim & Scherg, Bettina, 2021. "Time, Income and Subjective Well-Being - 20 Years of Interdependent Multidimensional Polarization in Germany," IZA Discussion Papers 14870, Institute of Labor Economics (IZA).
    5. Gallardo, Mauricio, 2022. "Measuring vulnerability to multidimensional poverty with Bayesian network classifiers," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 492-512.
    6. 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.
    7. Gonzalo Castañeda & Omar A. Guerrero, 2018. "The Resilience of Public Policies in Economic Development," Complexity, Hindawi, vol. 2018, pages 1-15, October.
    8. Federica Onori & Giovanna Jona Lasinio, 2022. "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. 161(2), pages 1003-1037, June.
    9. 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.

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    Keywords

    Multivariate analysis; directed acyclic graphs; probabilistic inference; well-being;
    All these keywords.

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