The Efficiency of the Italian Judicial System: A Two Stage Data Envelopment Analysis Approach

Social Indicators Research - During the last two decades there was great attention for improved performance in the public sector. We know how important a good judicial system is: it...     

A model for estimating point sources in spatial data

We introduce a point source model which may be useful for estimating point sources in spatial data. It may also be useful for modelling general spatial data, and providing a simple explanatory model for some data, whilst in other cases it may give a parsimonious representation. The model assumes that there are point sources (or sinks), usually at unknown positions, and that the mean value at a site depends on the distance from these sources. We discuss the general form of the model, and some methods for estimating the sources and the regression parameters. We demonstrate the methodology using a simulation study, and apply the model to two real data sets. Some possibilities for further research are outlined.     

A simulation study on the hybrid nature of Tango's index

Since the early 1990s, there has been an increasing interest in statistical methods for detecting global spatial clustering in data sets. Tango's index is one of the most widely used spatial statistics for assessing whether spatially distributed disease rates are independent or clustered. Interestingly, this statistic can be partitioned into the sum of two terms: one term is similar to the usual chi-square statistic, being based on deviation patterns between the observed and expected values, and the other term, similar to Moran's I, is able to detect the proximity of similar values. In this paper, we examine this hybrid nature of Tango's index. The goal is to evaluate the possibility of distinguishing the spatial sources of clustering: lack of fit or spatial autocorrelation. To comply with the aims of the work, a simulation study is performed, by which examples of patterns driving the goodness-of-fit and spatial autocorrelation components of the statistic are provided. As for the latter aspect, it is worth noting that inducing spatial association among count data without adding lack of fit is not an easy task. In this respect, the overlapping sums method is adopted. The main findings of the simulation experiment are illustrated and a comparison with a previous research on this topic is also highlighted.     

A Measure of Well-Being Across the Italian Urban Areas: An Integrated DEA-Entropy Approach

In recent years, there has been an increasing proliferation of initiatives focusing on the concept of quality of life and well-being. At the centre of these studies there is the recognizing that the GDP offers only a partial perspective of factors affecting people’s lives. Following this line of the research, this paper is aimed at computing the well-being efficiencies of a sample of Italian Province capital cities, using a methodological approach that combines data envelopment analysis (DEA) with Shannon’s entropy formula. To avoid subjectivity in choosing a representative set of variables that proxy the phenomenon under study, we rely on the theoretical framework adopted by the Italian National Institute of Statistics (ISTAT) within the equitable and sustainable well-being (BES) project. The dashboard of indicators included in the analysis are related to the Ur-BES initiative, promoted by ISTAT to implement the BES framework at cities level. In a first step of the analysis, an immediate focus on separate dimensions of urban well-being is obtained by summarizing the plurality of available indicators through the building of composite indices. Next, the adopted integrated DEA–Shannon entropy approach has permitted to increase the discriminatory power of DEA procedure and attain a more reliable profiling of Italian Province capital cities well-being efficiencies. The results show a marked duality between the Northern and Southern cities, highlighting important differences in many aspects of human and ecosystem well-being.