A balanced development? The novel σ-μ efficiency of Italian regions

The use of Composite Indicators (CIs) experienced significant momentum in recent years within academia, policy makers, practitioners, and global institutions. Despite the burst of popularity, an ongoing criticism has been developed around their computation with respect to both weighting and aggregation. In brief, weighting involves the relative importance of the attributes chosen, whereas the latter involves the process that transforms them into a single value. The use of CIs for the analysis of spatial differences in levels of development and well-being shares the above criticism, therefore, encouraging initiatives (e.g. OECD, Better Life Index (BLI)) to somewhat overcome the weighting issue. Nonetheless, initiatives like the BLI come at the price of missing a single overall measure of spatial performance. This study, instead, in the attempt to contribute to the ‘beyond GDP (Gross Domestic Product)’ international trend, builds upon a very recent methodological contribution—the σ–μ efficiency (Greco et al. 2019)—that takes into account both the aforementioned issues by combining the use of Stochastic Multi-attribute Acceptability Analysis with an approach which accounts for both the average performance and its spread according to different weighting vectors. More in detail, the analysis aims to measure spatial socio-economic differences at the regional level by using the wide set of indicators developed within the BES (Benessere Equo e Sostenibile) initiative from the Italian national statistical institute (ISTAT). When applied to the Italian regional case study, the σ–μ efficiency while confirming the overall North-South divide, shows a more nuanced picture with interesting differences with respect to well-established alternative aggregation techniques such as equal weight. Considering three macro areas (North, Centre, and South), the analysis confirms the trend already observed GDP-wise and shows that the divide is moving from the tripartite North–Centre–South division to a bipartite Centre/North–South one, according to a much broader set of socio-economic indicators. Hence, it shows that the divide is much deeper and generalised than the extent to which it is captured by spatial differences in GDP alone. At a more granular level, with particular regard to the Southern macro area, by disentangling the overall performance (μ) from the balance between the considered components (σ) the analysis does show potential for an important contribution in the field of spatial analysis at least to the extent that it allows unveiling complex patterns of uneven socio-economic performance. For instance, both Puglia and Sardegna, sharing the same (higher) frontier, register a better performance than geographically confining regions such as Basilicata, Calabria, and Campania and the other big island Sicilia, respectively. Furthermore, moving from the consideration that such a pattern is not fully confirmed in terms of GDP, the current σ–μ analysis methodologically confirms the existence of trade-offs and synergies between GDP and overall spatial disparities.