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Measuring and monitoring multidimensional wellbeing have become central issues in international policy debates in recent years. Interpreting heterogeneity in achievement levels across dimensions in terms of individual freedom of choice, and arguing that the degree of substitutability should not only depend on this variation, but also on the typology of dimensions, we propose a Generalized Multidimensional Synthetic index that introduces several layers of flexibility in the aggregation methodology. We classify dimensions into different groups, for example basic and non-basic, and allow for varying substitutability rates within as well as between groups. After examining the theoretical properties of the new index, we perform simulation experiments which highlight the salient features of our index compared to other frequently used indices.

The estimation of Sub-national purchasing power parities (PPPs) for countries where the regions and provinces have different level of development is fundamental for income, consumption, standard of living real term comparisons, as well as for measuring cross-region welfare inequality. This is even truer for large countries like China, where the above aggregates exhibit great variability among provinces. The aim of this paper is to compute the price level differences, measured by the PPPs, for 31 Chinese Provinces and Municipal Cities, based on a sample of 62 goods and services for the year 2014. To our knowledge, this is the first attempt to do it since many years. After a short review of previous studies on China cross-province and municipal cities price level differences measurement and the illustration of methodology and data used, the results of our elaborations are presented and discussed. Taking Beijing as the base area, there is evidence that the PPP max/min ratio is 1.74, confirming the common belief that China cross-province and municipal cities price levels are significantly different.

The Italian National Statistical Office is implementing a new project for computing sub-national Purchasing Power Parities (PPPs) on a regular basis, which is based on an appropriate use of existing Consumer Price Index (CPI) data and new sources of data. Concerning the use of CPI data, in this paper the role of the Country Product Dummy (CPD) method for compiling sub-national PPPs at Basic–Heading (BH) level is analysed together with the specific issues that arise in this context, such as the need to take spatial autocorrelation among price relatives into account. The results of various experiments based on CPI data concerning 7 BHs and 19 Italian regional chief towns are presented and discussed with the aim of exploring the performance of various CPD models and analysing to what extent the type and characteristics of the data affect the estimates obtained. The statistical uncertainty associated with the ranks derived from the regional PPPs was then calculated by means of a simulation procedure. Our findings prove to be interesting and confirm that methods and CPI data for spatial comparisons are reciprocally influenced.