In this paper we want to provide a contribution to the analysis of territorial disparities considering Italian Local Labour Systems as basic geographic units. Local Labour Systems (LLS) represent the result of the spatial aggregation of neighbouring municipalities based on the daily commuting flows of local population owing to work reasons. Thus LLS can be described as self-contained economic-territorial spaces, constituted by two or more municipalities, which provide a basis to sub-provincial analysis, being a useful tool for the implementation of local development policies without relations with administrative zones. Having recourse to value added per capita at this particular disaggregation level of the national production apparatus, we aimed to point out the particular structure of income differentials, beyond the traditional dichotomy North-South characterizing our Country.Although this measure has been criticised in literature when used to evaluate the population well-being, the income per capita continues to represent, in the words of Mamalakis, “the best available indicator of levels, growth rates and differences of SNA welfare between and within nations”.Also considering that the spatial arrangement of the data is scarce, we have used/considered the data provided by ISTAT at SLL level (value added, employment and population) to build a convenient indicator of the economic performance at such a detailed geographic/territorial partition.So, despite being aware that other indicators have been privileged in the analysis carried out in recent studies, for the reasons above mentioned we have realized a suited modification of the selected indicator. Through the use of this new version we considered the patterns of spatial association among the 784 LLS in the period 1996-2000 and for the 686 LLS in the interval 2001-2005 by means the Exploratory Spatial Data Analysis (ESDA) developed by Haining (2003). ESDA allows summarizing properties of data, detecting their spatial patterns and identifying cases,or subsets of cases, that are unusual given their location on the map. We remember that Exploratory Spatial Data Analysis has some key characteristics: - Methods are descriptive rather than confirmatory. - Aims are to formulate hypotheses and to assess spatial models. - Techniques are visual and resistant to unusual data values. - Techniques employ few data transformations. We have used the two classes of ESDA statistics: global statistics, which treat all the cases for one, or more, attributes and local statistics, which treat subsets of data one at time and may involve a sweep through the data looking for evidence of smooth and rough elements of the mapped data consisting of a set of techniques useful to describe and visualize spatial distributions. Actually, our analysis allowed to take into account the particular configuration of income inequalities across the SLL and, above all, the changes undergone in the periods examined. In fact, the persistence of a marked North-South dualism has been accompanied by growing economic disparities among the LLS which are limited local realities characterized by a particular production specialization inside of the larger administrative regions they belong to.
|Numero di pagine||9|
|Stato di pubblicazione||Published - 2009|