The aim of this paper is to propose a theoretical "multi-phase" strategy for analysing in dynamic terms the territorial impact of agricultural and environmental EU policy measures. This approach should also allow to evaluate the adjustment capability of farms as a function of the characteristics of different territories. The proposed methodology is illustrated by an example using data relative to the 41 provinces of Northern Italy. In the first step, a multivariate statistical analysis (MSA) consisting in Principal Component Analysis and Cluster Analysis leads to the identification of homogeneous clusters of territorial units. The territorial mapping is conditional to a predetermined set of indicators that takes into account different aspects of agricultural development. In a second step, Positive Mathematical Programming (PMP) allows to introduce the impact of agricultural policies (compensatory payments, price changes, etc.) returning different scenarios of land use and agricultural profitability. According to the outputs of the PMP, the third step consists in a new MSA for detecting any changes in the territorial mapping. Convergence analysis can then synthesise the impact of the different policy options. © Springer-Verlag 2001.
Arfini F., Brasili C., Fanfani R., Mazzocchi M., Montresor E., Paris Q. (2001). Tools for evaluating EU agricultural policies: An integrated approach. STATISTICAL METHODS & APPLICATIONS, 10(1-3), 191-210 [10.1007/BF02511648].
Tools for evaluating EU agricultural policies: An integrated approach
Brasili C.;Fanfani R.;Mazzocchi M.;
2001
Abstract
The aim of this paper is to propose a theoretical "multi-phase" strategy for analysing in dynamic terms the territorial impact of agricultural and environmental EU policy measures. This approach should also allow to evaluate the adjustment capability of farms as a function of the characteristics of different territories. The proposed methodology is illustrated by an example using data relative to the 41 provinces of Northern Italy. In the first step, a multivariate statistical analysis (MSA) consisting in Principal Component Analysis and Cluster Analysis leads to the identification of homogeneous clusters of territorial units. The territorial mapping is conditional to a predetermined set of indicators that takes into account different aspects of agricultural development. In a second step, Positive Mathematical Programming (PMP) allows to introduce the impact of agricultural policies (compensatory payments, price changes, etc.) returning different scenarios of land use and agricultural profitability. According to the outputs of the PMP, the third step consists in a new MSA for detecting any changes in the territorial mapping. Convergence analysis can then synthesise the impact of the different policy options. © Springer-Verlag 2001.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
