IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i15p8110-d605707.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/15/8110/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/15/8110/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Scutari, Marco, 2010. "Learning Bayesian Networks with the bnlearn R Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 35(i03).
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    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. S. M. Iacus & G. Porro & S. Salini & E. Siletti, 2022. "An Italian Composite Subjective Well-Being Index: The Voice of Twitter Users from 2012 to 2017," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 471-489, June.
    3. Akay, Alpaslan & Bargain, Olivier & Elsayed, Ahmed, 2020. "Global terror, well-being and political attitudes," European Economic Review, Elsevier, vol. 123(C).
    4. Yonas Alem & Jonathan Colmer, 2015. "Consumption smoothing and the welfare cost of uncertainty," GRI Working Papers 118b, Grantham Research Institute on Climate Change and the Environment.
    5. Yonas Alem & Jonathan Colmer, 2015. "Consumption Smoothing and the Welfare Cost of Uncertainty," CEP Discussion Papers dp1369, Centre for Economic Performance, LSE.
    6. Dietrich, Stephan & Nichols, Stafford, 2023. "More than a feeling," MERIT Working Papers 2023-005, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    7. Yukun Zhao & Feng Yu & Bo Jing & Xiaomeng Hu & Ang Luo & Kaiping Peng, 2019. "An Analysis of Well-Being Determinants at the City Level in China Using Big Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 143(3), pages 973-994, June.
    8. Clément S. Bellet & Jan-Emmanuel De Neve & George Ward, 2019. "Does employee happiness have an impact on productivity?," CEP Discussion Papers dp1655, Centre for Economic Performance, LSE.
    9. Akay, Alpaslan & Bargain, Olivier B. & Elsayed, Ahmed, 2018. "Everybody's a Victim? Global Terror, Well-Being and Political Attitudes," IZA Discussion Papers 11597, Institute of Labor Economics (IZA).
    10. Silvia Facchinetti & Elena Siletti, 2022. "Well-being Indicators: a Review and Comparison in the Context of Italy," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 159(2), pages 523-547, January.
    11. Knight, S.J; Howley, P.;, 2017. "Can clean air make you happy? Examining the effect of nitrogen dioxide (NO2) on life satisfaction," Health, Econometrics and Data Group (HEDG) Working Papers 17/08, HEDG, c/o Department of Economics, University of York.
    12. Yonas Alem & Jonathan Colmer, 2015. "Consumption Smoothing and the Welfare Cost of Uncertainty," STICERD - Economic Organisation and Public Policy Discussion Papers Series 059, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    13. Gallardo, Mauricio, 2022. "Measuring vulnerability to multidimensional poverty with Bayesian network classifiers," Economic Analysis and Policy, Elsevier, vol. 73(C), pages 492-512.
    14. Alem, Yonas & Colmer, Jonathan, 2015. "Consumption smoothing and the welfare cost of uncertainty," LSE Research Online Documents on Economics 63816, London School of Economics and Political Science, LSE Library.
    15. Degli Antoni, Giacomo & Vittucci Marzetti, Giuseppe, 2022. "Estimating the effect on happiness through question randomization: An application to blood donation," Social Science & Medicine, Elsevier, vol. 309(C).
    16. Giacomo Degli Antoni & Chiara Franco, 2022. "The effect of technological behaviour and beliefs on subjective well-being: the role of technological infrastructure," Journal of Evolutionary Economics, Springer, vol. 32(2), pages 553-590, April.
    17. Abel Brodeur, 2012. "Smoking, Income and Subjective Well-Being: Evidence from Smoking Bans," Working Papers halshs-00664269, HAL.
    18. Senik, Claudia, 2009. "Direct evidence on income comparisons and their welfare effects," Journal of Economic Behavior & Organization, Elsevier, vol. 72(1), pages 408-424, October.
    19. Foliano, Francesca & Tonei, Valentina & Sevilla, Almudena, 2024. "Social restrictions, leisure and well-being," Labour Economics, Elsevier, vol. 87(C).
    20. Guven, Cahit & Senik, Claudia & Stichnoth, Holger, 2012. "You can’t be happier than your wife. Happiness gaps and divorce," Journal of Economic Behavior & Organization, Elsevier, vol. 82(1), pages 110-130.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:15:p:8110-:d:605707. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.