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136 – Development of Indicators: Prediction vs. Inference

New Frontiers in Measuring the Well-Being in the Big Data Era

at Universität Göttingen

The measurement of well-being (WB) is extremely challenging especially due to the multidimensional, country-specific and latent nature of this concept. Despite these difficulties, a timely estimation of WB is essential in order to support policy makers' decision processes and to obtain reliable measurements able to assess, a-posteriori, policies' effectiveness. In the last years a lot of effort has been dedicated to develop research projects aimed at measuring citizen well-being. In the recent era of "big data", many sources of information (e.g. the web or social networks) can contribute in producing enhanced, timely and less expensive estimates and indicators of WB that could integrate or update the available official statistics. This paper represents a preliminary step towards this objective. The analysis focuses on 18 European countries, by using data from the European Social Survey (ESS) collected in 2016 and by applying SEM (Structural Equation Modelling) to variables covering the main subjective well-being (SWB) dimensions. In particular, we evaluate if a country-specific (i.e., local) model rather than a global European model is able to provide reliable estimates of the relative importance of well-being dimensions. This allows us to evaluate, by country, if and how much the main dimensions affect the SWB of citizens. Finally, we test if a "local approach" can also enhance the goodness of fit obtained estimating a global European model.