Chiara Bocci

Ranked set sampling allocation models for multiple skewed variables: an application to agricultural data.

The mean of a balanced ranked set sample is more efficient than the mean of a simple random sample of equal size and the precision of ranked set sampling may be increased by using an unbalanced allocation when the population distribution is highly skewed. The aim of this paper is to show the practical benefits of the unequal allocation in estimating simultaneously the means of more skewed variables through real data. In particular, the allocation rule suggested in the literature for a single skewed distribution may be easily applied when more than one skewed variable are of interest and an auxiliary variable correlated with them is available. This method can lead to substantial gains in precision for all the study variables with respect to the simple random sampling, and to the balanced ranked set sampling too.

First- and Second-Tier Cities in Regional Agglomeration Models

Abstract This work has the purpose of inquiring into the presence of an urban hierarchy within second-tier city areas and alternative agglomeration models differing in their self-propelling ability and territorial sustainability. To this aim we confront regional polycentric areas, by going inside the traditional agglomeration and variety economies and the land settlement model of small–medium urban poles. In particular, the present work compares four Italian regions characterized by a territorial development driven by second-tier cities. The first two sections of the paper evaluate the functional pattern of the different urban systems and subsequently measure their rank in terms of extra-regional attractiveness on demand, which is expressed by rare services (Sections 2 and 3). Sections 4 and 5 tackle the issue of sustainability of settlements by taking into account land consumption and the degree of territorial fragmentation caused by different urbanization models. We discovered good urban performances and settlement sustainability of the second-tier cities agglomeration model in Italian regions, which is stronger when based on the co-presence of specialized small cities (which can assure a minimum amount of local demand for advanced services) and a multifunctional medium urban centre (which can ensure rarer functions). These findings bring strong recommendations on urban policies.

L’approccio delle funzioni dose-risposta per la valutazione di trattamenti continui nei sussidi alla r&s

Un recente filone nella letteratura di program evaluation riguarda la stima di effetti causali in presenza di trattamenti continui. Allo scopo possono essere impiegate, sotto ipotesi di non confondimento, delle funzioni dose-risposta basate sulla metodologia dei propensity scores. Un interessante ambito di applicazione e quello dei programmi di sussidiazione alla r&s, dove ancora poco si sa su quale sia la giusta dimensione dei sussidi o degli investimenti privati da sussidiare. Analizzando un programma per la r&s delle pmi attuato in Toscana, troviamo che la relazione tra sussidio e investimento futuro in r&s, rappresentata dalla funzione dose-risposta, assume una forma, approssimativamente, U-rovesciata.

Modelling the location decisions of manufacturing firms with a spatial point process approach

The paper is devoted to explore how the increasing availability of spatial micro-data, jointly with the diffusion of GIS software, allows to exploit micro-econometric methods based on stochastic spatial point processes in order to understand the factors that may influence the location decisions of new firms. By using the knowledge of the geographical coordinates of the newborn firms, their spatial distribution is treated as a realization of an inhomogeneous marked point process in the continuous space and the effect of spatial-varying factors on the location decisions is evaluated by parametrically modelling the intensity of the process. The study is motivated by the real issue of analysing the birth process of small and medium manufacturing firms in Tuscany, an Italian region, and it shows that the location choices of the new Tuscan firms is influenced on the one hand by the availability of infrastructures and the level of accessibility, and on the other by the presence and the characteristics of the existing firms. Moreover, the effect of these factors varies with the size and the level of technology of the new firms. Besides the specific Tuscan result, the study shows the potentiality of the described micro-econometric approach for the analysis of the spatial dynamics of firms.

Spatial Data Mining for Clustering: An Application to the Florentine Metropolitan Area Using RedCap

The paper presents an original application of the recently proposed RedCap method of spatial clustering and regionalization on the Florentine Metropolitan Area (FMA). Demographic indicators are used as the input of a spatial clustering and regionalization model in order to classify the FMA’s municipalities into a number of demographically homogeneous as well as spatially contiguous zones. In the context of a gradual decentralization of governance activities we believe the FMA is a representative case of study and that the individuation of new spatial areas built considering both the demographic characteristics of the resident population and the spatial dimension of the territory where this population insists could become a useful tool for local governance.

Estimates for geographical domains through geoadditive models in presence of incomplete geographical information

The paper deals with the matter of producing geographical domains estimates for a variable with a spatial pattern in presence of incomplete information about the population units location. The spatial distribution of the study variable and its eventual relations with other covariates are modeled by a geoadditive regression. The use of such a model to produce model-based estimates for some geographical domains requires all the population units to be referenced at point locations, however typically the spatial coordinates are known only for the sampled units. An approach to treat the lack of geographical information for non-sampled units is suggested: it is proposed to impose a distribution on the spatial locations inside each domain. This is realized through a hierarchical Bayesian formulation of the geoadditive model in which a prior distribution on the spatial coordinates is defined. The performance of the proposed imputation approach is evaluated through various Markov Chain Monte Carlo experiments implemented under different scenarios.

A Two-Part Geoadditive Small Area Model for Geographical Domain Estimation

We are interested in estimating small domain means of a response variable that shows a spatial trend and has a continuous skewed distribution with a large number of values clustered at zero. This kind of variable can occur in many surveys, like business or agricultural surveys: examples are the quantity of crops produced or the amount of land allocated for their production collected by the Farm Structure Survey driven by the Italian Statistical Institute. The small sample size within the areas requires the use of small area model dependent methods to increase the effective area sample size by using census and administrative auxiliary data. To account simultaneously for the excess of zeros, the skewness of the distribution and the possible spatial trend of the data, we present a two-part geoadditive small area model. An application to the estimation of the per-farm average grapevine production in Tuscany at Agrarian Region level shows the satisfactory performance of the model.