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

Federica Cugnata, Silvia Salini and Elena Siletti
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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

IJERPH, 2021, vol. 18, issue 15, 1-10

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.

Keywords: Bayesian networks; big data; well-being; life satisfaction; sentiment analysis (list three to ten) (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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