В статті досліджено інвестиційну привабливість Рівненської області порівняно з іншими областями України для визначення реального інвестиційного потенціалу області.
Ключові слова: інвестиційна привабливість, кластер ний аналіз, фактори, k–means, дендрограма.
In this article is investigated the investment attractiveness of the Rivne region compared to other regions of Ukraine to determine the real investment potential of the region.
Keywords: investment attractiveness ing cluster analysis, factors, k-means, dendrohram.
Problem setting. The complexity of investment regulation is the fact that it covers diverse areas of economic life – the field of scientific and technological progress, government management of the economy, finance and banking, commercial calculation enterprises pricing. Therefore, the relevance of the study is to evaluate the investment attractiveness with the help of tools, which will take into account all these factors.
Last scientific researches and publications analysis. The issue of investment attractiveness of regions and factors that affect them quite substantially explored in the works of foreign and domestic scientists. Theoretical principles and aspects of investment in general and investment attractiveness, in particular, studied by such scholars as form I. A. Blank, V. I. Tolstonoh, K. V. Romanov, L. V. Tsedylyn.
Formulation of the article’s purpose. The purpose is to determine the investment attractiveness of regions using cluster analysis; compare the potential of Rivne region compared to other regions.
The statement of basic material of research. Investment attractiveness of the area that eventually manifested in the ability of the region to attract investment funds formed under the influence of a number of factors. Some of these factors can be described by statistical indicators.
To building mathematical and statistical models of economic development of various regions is to identify regions of homogeneous populations presented a system of economic indicators. An effective method that allows you to group together into homogeneous regions using a wide range of indicators, cluster analysis.
Problem cluster analysis lies in the fact that on the basis of data that contain many X, split into a set of objects G m (m – integer) clusters (subsets) Q1, Q2, …, Qm, so that each object Gj belong to one and only one subset of breakdown and that objects belonging to the same cluster are similar, while as objects belonging to different clusters are dissimilar.
Cluster analysis was carried out to trace the position of Rivne region relative to other regions. Attention was taken five groups of factors are included in the fairly common in domestic practice is a technique developed I. Blank. He offered to carry out the evaluation of the investment climate and investment attractiveness of regions on the basis of all the following data:
1) the level of economic development of the region (35% significance);
2) the level of infrastructure investment (15%);
3) the demographic characteristics of the region (15%);
4) the level of development of market relations and market infrastructure (25%);
5) the presence of environmental, investment, political, credit,
commercial, foreign exchange risk (10%).
The level of economic development of the region includes the following factors: income, million, net exports and imports, mln. Gross regional product by 1 person, hrn., The number taken into operation housing sq.m., number of firms tys.od. The level of infrastructure investment is described by means of capital investment, mln. and foreign direct investment, mln., the number of companies that have implemented innovative projects thousand units. The level of development of market relations and market infrastructure described data on the average wage in the region, hrn., Unemployment, the level of the economically active population, thousand, the number of people enrolled in higher education, thousand people Environmental risks described in emissions of pollutants and carbon dioxide into the atmosphere by regions, thousand tons.
At the first stage of the cluster analysis was performed hierarchical model building (dendrogram) sample in 27 regions in 14 factors. We observe regional grouping in 4 sets:
Group 1: Volyn, Kirovograd, Kherson, Sumy, Chernivtsi, Transcarpathian, Zhytomyr, Cherkasy, Rivne, Mykolaiv, Khmelnytsky, Ivano-Frankivsk region;
Group 2: Lviv, Odessa, Poltava, Zaporozhye, Lugansk region;
Group 3: Crimea, Sevastopol, Kiev and Kharkiv regions;
Group 4: Dnipropetrovsk, Donetsk Oblast and Kyiv.
In group 4 is apparent anomalous phenomenon of Kyiv as a highly attractive investment focus.
The performed cluster analysis using k-means (all regions are divided into 4 clusters): 1 cluster – a very high level of investment attractiveness; Cluster 2 – high investment attractiveness; 3-cluster – medium level of investment attractiveness; Cluster 4 – low level of attractiveness, argues leading position in Kyiv, 2 group with high potential include: Dnipropetrovsk, Donetsk region. Rivne region is included in group 3, ie, a medium attracts. For a detailed observation of conduct cluster analysis by k-means; was removed in one case, Kyiv, in the second case 4 group as a whole, since there is a clear advantage of this group: Dnipropetrovsk, Donetsk Oblast and Kyiv. This may be caused by the following factors:
– High sophistication of industry and infrastructure;
– A significant amount of capital and attract foreign direct investment;
– Powerful natural resource endowment;
– Qualitatively developed consumer segment;
For the next cluster analysis by k-means taken 26 regions, 13 factors and grouping into 4 clusters: Rivne region went into the 3 groups – with an average level of attractiveness. This is due to the fact that in this area there are industries that are clearly dominant and competitive, but at the same time, there are industries that require immediate attention and improvement. F-statistics show that the most significant factors are: pollution and emissions into the atmosphere, income, realized the amount of goods and services, the level of the economically active population. Graph shows the mean values of clusters difference one group from another by means of the, for example cluster number 2 is characterized by the highest level of pay and the number of people enrolled, compared to other clusters.
Analyzing the Rivne region on these indicators can be said that the region is a significant need for education (qualification), as this is the largest area of a number of young people. Emissions of pollutants and carbon dioxide into the atmosphere by region are mean because in our region Rivne nuclear power plant and chemical industry Rivneazot. Consider creating more industrial zones that will help attract additional investment, human resources, but it generates additional threat of pollution and aging assets. The level of the economically active population of the region is rather high, since the business segment developed quite well, although in Rivne region recorded a small number of companies. Quite a vulnerable side to the area is the presence of small amount of available lands it reduces the chances of attracting investment through the creation of new facilities management, agro.
Conclusion. Overall, the clustering analysis is very effective for determining the investment attractiveness of the region may include a set of indicators, as most factors are correlated and are closely related, for example: the level of the economically active population and unemployment, as further possible exception of one of these factors Replacement and other. It is important indicators to describe the attractiveness of the various parties: Economic: income, inflation, unemployment, domestic regional product; Social: education; innovation, as innovation activities is one of the key factors that attract investment; Political: corruption, open government.
Rivne region has huge potential for foreign direct investment in the future, but is vital sectors that need above all government intervention – for example, education. High levels of corruption – is not stimulating factor, this is a significant barrier towards foreign investors. The business sector and business climate tends to improve as the number of firms will increase.
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