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Deepening Well-Being Evaluation with Different Data Sources: A Bayesian Networks Approach

Author

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  • Federica Cugnata

    (University Centre of Statistics for Biomedical Sciences (CUSSB), Vita-Salute San Raffaele University, 20132 Milano, Italy)

  • Silvia Salini

    (Department of Economics, Management and Quantitative Methods, Università degli Studi di Milano, 20122 Milano, Italy)

  • Elena Siletti

    (Department of Economic and Political Sciences, Università della Valle d’Aosta, 11020 Saint-Christophe, Italy)

Abstract

In this paper, we focus on a Bayesian network s approach to combine traditional survey and social network data and official statistics to evaluate well-being. Bayesian networks permit the use of data with different geographical levels (provincial and regional) and time frequencies (daily, quarterly, and annual). The aim of this study was twofold: to describe the relationship between survey and social network data and to investigate the link between social network data and official statistics. Particularly, we focused on whether the big data anticipate the information provided by the official statistics. The applications, referring to Italy from 2012 to 2017, were performed using ISTAT’s survey data, some variables related to the considered time period or geographical levels, a composite index of well-being obtained by Twitter data, and official statistics that summarize the labor market.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:15:p:8110-:d:605707
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    References listed on IDEAS

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    1. Iacus Stefano M. & Salini Silvia & Siletti Elena & Porro Giuseppe, 2020. "Controlling for Selection Bias in Social Media Indicators through Official Statistics: a Proposal," Journal of Official Statistics, Sciendo, vol. 36(2), pages 315-338, June.
    2. Daniel Kahneman & Alan B. Krueger, 2006. "Developments in the Measurement of Subjective Well-Being," Journal of Economic Perspectives, American Economic Association, vol. 20(1), pages 3-24, Winter.
    3. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    4. 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.
    5. John Feddersen & Robert Metcalfe & Mark Wooden, 2016. "Subjective wellbeing: why weather matters," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(1), pages 203-228, January.
    6. Stefano Maria IACUS & Giuseppe PORRO & Silvia SALINI & Elena SILETTI, 2015. "Social Networks, Happiness and Health: From Sentiment Analysis to a Multidimensional Indicator of Subjective Well-Being," Departmental Working Papers 2015-20, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
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    Cited by:

    1. Aurea Grané & Irene Albarrán, 2022. "Editorial on S.I. “Advances in Measuring Health and Wellbeing” in the International Journal of Environmental Research and Public Health," IJERPH, MDPI, vol. 19(9), pages 1-3, April.
    2. Massimo Arnone & Angelo Leogrande & Carlo Drago & Alberto Costantiello, 2024. "Social Trust and Support Networks: A Regional Analysis of Italy," Working Papers hal-04698270, HAL.

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