Synthetic Measures of Multidimensional Well-Being: How to Aggregate?
Several subjective indicators have already been proposed and evaluated in European and International reports during the last decade to assess societal well-being as a reaction to objective indicators. To capture the multidimensional aspect of well-being through a reliable operationalization, several challenges come up: the definition of relevant dimensions of well-being (which depends on the theoretical framework), the quality of the indicators chosen (which depends on the available data) and how indicators are combined into a synthetic measure (which depends on the methodological approach). This paper aims to focus on the methodological approach by comparing three methodologies in order to construct synthetic measures of multidimensional well-being and thus show their advantages and shortcomings.
Different methodological approaches are proposed in the literature to construct synthetic measures, notably measures based on structural equation modeling (SEM) and on counting approach, two approaches that aggregate information. A more recent approach, the posetic approach (based on the partially ordered set theory) developed by Maggino and Fattore (2011), proposed not to aggregate ordinal information, but to create a synthetic measure providing different profiles from large data sets and to assess profiles by comparing them with benchmarks.
The main contribution of this paper is to compare three different synthetic indicators measuring multidimensional well-being and discussing the impact of aggregating. Each methodology will be implemented using the Swiss Household Panel (SHP) data, a longitudinal annual panel survey available since 1999.