Data Versus Survey-based Normalisation in a Multidimensional Analysis of Social Inclusion

In the context of the multidimensional measurement of complex phenomena, the major focus of the recent literature has been on the choice of the dimensions' weights and the shape of the aggregation function, while few studies have concentrated on how normalisation influences the results. With the aim of building a measure of Social Inclusion for 63 European regions between 2004 and 2012, we adopt a standard linear aggregation framework and compare two alternative normalisation approaches: a data-driven min-max function, whose parameters depend solely on the available data, and an expert-based function, whose parameters are elicited through a survey at the University of Venice Ca' Foscari. Regardless of the adopted strategy, we show that normalisation plays a crucial part in defining variables' weighting. The data-driven strategy allocates a large relative weight to the longevity dimension, whereas the survey-driven results in a rather equal distribution of weights. The data-driven approach produces trade-offs that are hard to interpret in economic terms and debatable from a social desirability perspective, thus constituting a positive analysis of Social Inclusion. Moreover, it softens the aftermaths of the recent economic crisis on Social Inclusion, by putting a consistent weight on the longevity variable. Conversely, the expert-based normalisation is heavily affected by elicitation techniques, and allows for a normative interpretation of the resulting index. Furthermore, it emphasizes the worsening trends in long-term unemployment and the relevance of early school leaving in the Social Inclusion measure. The two strategies lead to substantially different conclusions in terms of levels (both between and within countries) and distribution of Inclusion: numerous rank-reversals occur when switching the normalisation methods.