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EN
An attempt has been made in this paper to analyse the spatio-temporal variations of cropping intensity and irrigation intensity, and their relationship, in North Twenty Four Parganas district in West Bengal, India from 1996/97 to 2015/16. The relationship between cropping intensity and irrigation intensity has been assessed using partial correlation, residual mapping and hierarchical cluster analysis. One-way ANOVA has been conducted for testing the equality of cluster means. Temporal analysis from 1996/97 to 2015/16 has shown a low positive correlation between cropping intensity and irrigation intensity for the entire district. Analysis at Agricultural Block level has revealed that cropping intensity decreased in many cases even after an increase in irrigation intensity. In general, cropping intensity has increased with the increase in irrigation intensity in the Coastal Saline Region and the Ichhamati Basin, whereas cropping intensity has increased even after a decrease in irrigation intensity in the Gangetic Plains Region in the district.
EN
This paper focuses on hierarchical clustering of categorical data and compares two approaches which can be used for this task. The first one, an extremely common approach, is to perform a binary transformation of the categorical variables into sets of dummy variables and then use the similarity measures suited for binary data. These similarity measures are well examined, and they occur in both commercial and non-commercial software. However, a binary transformation can possibly cause a loss of information in the data or decrease the speed of the computations. The second approach uses similarity measures developed for the categorical data. But these measures are not so well examined as the binary ones and they are not implemented in commercial software. The comparison of these two approaches is performed on generated data sets with categorical variables and the evaluation is done using both the internal and the external evaluation criteria. The purpose of this paper is to show that the binary transformation is not necessary in the process of clustering categorical data since the second approach leads to at least comparably good clustering results as the first approach.
PL
Celem artykułu jest ocena jakości instytucjonalnej w 28 państwach członkowskich UE oraz próba oceny zależności pomiędzy jakością instytucjonalną a poziomem napływu zagranicznych inwestycji bezpośrednich (ZIB). Opracowanie ma następującą strukturę. Po pierwsze, dokonaliśmy przeglądu badań poświęconych związkom między jakością instytucjonalną a atrakcyjnością inwestycyjną. Następnie omówiliśmy napływ ZIB do krajów UE i wybraliśmy zmienne diagnostyczne, które posłużyły za podstawę do dalszej analizy. W tym celu posłużyliśmy się miernikami Globalnego Indeksu Konkurencyjności. W kolejnym etapie wykorzystując rankingi i metody statystyczne podzieliliśmy państwa członkowskie UE na grupy o zbliżonym poziomie jakości instytucjonalnej. Następnie zbadaliśmy zależności między podobnymi do siebie grupami krajów oraz grupami państw uszeregowanych według wartości napływu ZIB jako % PKB. Badanie wykazało, że państwa członkowskie UE różnią się wyraźnie pod względem jakości instytucjonalnej. Wyniki analiz statystycznych dały podstawę do pozytywnej weryfikacji hipotezy o pozytywnym związku między poziomem jakości instytucjonalnej a atrakcyjnością inwestycyjną.
EN
The aim of the article is to assess institutional quality in 28 EU Member States and to examine the relationship between the quality of institutions and FDI inward stock as % of GDP. This study is structured as follows. Firstly, we reviewed studies dedicated to the relationship between institutional quality and investment attractiveness. Then, we discussed FDI inflow into the EU countries and selected diagnostic variables that later served as the basis for our research in which we used categories of the Global Competitiveness Index. Based on rankings and using statistical methods, in the next stage, we divided the EU Member States into groups representing similar institutional quality. Then we investigated the relationships between groups of countries similar to one another when it comes to institutional quality and groups of countries ranked in ascending order by the value of foreign direct investment inflow measured as FDI inward stock as % of GDP. The study demonstrated that the EU Member States differ with respect to institutional quality. The results of the statistical analysis have provided grounds to positively verify the hypothesis about a positive relationship between the level of institutional quality and investment attractiveness.
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