The weighting role of 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 European regions between 2004 and 2012, we adopt a CES aggregation framework and compare two alternative normalisation strategies: a data-driven min-max function, where the parameters depends solely on the available data, and an expert-based function where 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 and trade-offs. The data-driven strategy produces trade-offs that are hard to interpret in economic terms and debatable from a social desirability perspective, thus generating an aggregate measure with a �positive� interpretation. 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 has strikingly different parameters 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. As a result, numerous rank-reversals occur between regions when switching the normalisation methods.