Giorgia Capacci, Istituto Nazionale di Statistica (ISTAT)
Mauro Albani, Istituto Nazionale di Statistica (ISTAT)
Antonella Guarneri, Istituto Nazionale di Statistica (ISTAT)
Matteo Mazziotta, Istituto Nazionale di Statistica (ISTAT)
With respect to demography and labour market situation, as well as for many other characteristics, the alpine territory is a kaleidoscope of much differentiated realities. This paper has been realized in the framework of the Alpine Convention that is an international treaty between the Alpine countries (Austria, France, Germany, Italy, Liechtenstein, Monaco, Slovenia and Switzerland) and the EU, aimed at promoting sustainable development in the Alpine area and at protecting the needs of the people living within it. As of 2013, the Alps were inhabited by 14,232,088 people on a 190,717 km2 territory, with an average population density of 75 inhabitants per km2, that makes the Alps one of the less populated areas in central Europe but also one of the most dense mountain areas worldwide. To provide an easy-to understand overview of the Alpine complex and colorful picture it can be useful to apply methods and tools such as synthetic indexes, which are able to summarize in a single average value the multiplicity of characteristic values of each different micro territorial area. In particular, we applied a generalized composite index, denoted as MPI (Mazziotta-Pareto Index), suitable in the case where the components are non-substitutable, i.e., they have all the same weight (importance) and a compensation among them is not allowed. In order to rank the 4,700 Alpine municipalities taking into account their level of demographic and labour market’s situation in the Alps the MPI has been applied to a set of eight demographic and labour market’s indicators. The chosen indicators are: Foreign resident population (per 1,000 residents), Population density, Crude birth rate (per 1000 residents), Population growth rate (per 100 residents); Working-age total resident population (per 100 residents), Employment rate (per 100), Unemployment rate (per 100), Variation in employment rate. All the indicators calculated on the last data available.
Presented in Session P1. Poster Session 1