Wiesława Gierańczyk, Ph.D.

Department of Economic Geography and Regional Studies

Institute of Geography

Nicolaus Copernicus University

Torun, 87-100
Gagarina Str. 9
POLAND
e-mail: wiesia@geo.uni.torun.pl

Taxonomic analysis of the transformation progress in the countries of Central and Eastern Europe and the Commonwealth of Independent States

The paper aims at presenting socio-economic diversity of the selected countries of Central and Eastern Europe, the Caucasus and Central Asia in 2004 based on the taxonomic analysis. The selected analytical tool enabled the author to differentiate clusters of similar units, which are significantly different from other clusters. The study involved 21 states, including 13 European and 8 Asiatic ones. The researched countries include 15 states which were created after the Soviet Union collapse, 12 of which belong to the Commonwealth of Independent States presently (CIS). In the past the study area followed the same socio-political rules of the redistribution economy; since 1989-1991 it has been undergoing major political changes aiming at building democracy and competitive market economy integrated with the world economy.

Table 1. Indicators and indexes used in the study

Indicators		Index
x1	Share of agriculture (% of GDP)	y1	Assessment of transformation progress
x2	GDP per capita (USD at PPP)	y2	Reform of infrastructure
x3	Foreign trade per capita ($)	y3	Human Development Index
x4	Average annual rate of inflation in 1990–2003	y4	Poverty rate (%)
x5	Foreign investment per capita in 1993-2004 ($)	y5	Inequalities of income (Gini coefficient)
x6	Foreign debt (% of GDP)	y6	Index of Economic Freedom acc. to Heritage Foundation
x7	Population with income below 2 USD per diem (%)	y7	Index of Perception of Corruption acc. to Transparency International
x8	Gross domestic expenditure in R&D (% of GDP)	y8	Growth of markets and competition
x9	Share of private sector (% of GDP)	y9	Large Privatisation Index of EBRD
x10	Life expectancy (years)	y10	Infant mortality per 1000 live births

Source: Compiled by the author

Two groups of numerous diagnostic measures were selected for the study. The first one included relative measures, indicators, which show the intensity of all the phenomena of transformation. They were mainly based on demographic potential and gross domestic product. Moreover, the indexes of dynamics as well as other socio-economic measures were used, such as life expectancy and percentage of population below the poverty line. The second group included the synthetic indexes designed by specialised research institutions. They show the way the effects of the socio-economic transformation are viewed from the outside of a given country. Diagnostic features selected for the study were analysed in terms of their interrelations so as to exclude the ones which are closely related to one another. As a result, the following stages of the research were based on a set of 10 diagnostic features in each of the groups (Table 1). According to the basic measures which characterise the distribution of the variables (Table 2), the analysed area shows significant diversity of the studied phenomenon within the research units, which has been proved by relatively high and high values of the classic variability coefficient (V_s).

Table 2. Selected parameters of the analysed variables

Measures	Indicators
	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10
xmin	1119,00	141,00	6,10	40,76	6,10	2,00	0,00	25,00	66,00	3,16
xmax	16448,00	6742,09	226,60	7085,50	93,70	78,00	1,25	80,00	75,00	37,68
xsr	7366,47	1818,53	65,98	1625,99	47,8	22,29	0,54	64,52	70,85	15,02
s	4616,76	1980,26	63,82	2020,89	22,11	22,16	0,38	16,24	2,43	10,82
Vs	62,67	108,89	96,72	124,29	46,27	99,46	70,29	25,18	3,44	72,01
Measures	Index
	y1	y2	y3	y4	y5	y6	y7	y8	y9	y10
ymin	1,30	1,00	0,67	2,00	25,00	1,76	1,90	1,00	1,00	4,00
ymax	3,90	3,70	0,87	78,00	45,00	4,31	6,00	3,70	4,00	76,00
scale	1-4	1-4,3	0-1	0-100	0-100	1-5	1-10	1-4,3	1-4,3	0-1 000
xsr	3,06	2,41	0,78	22,29	33,33	3,16	3,16	2,37	3,12	28,38
s	0,68	0,76	0,06	22,17	4,93	0,71	1,12	0,75	0,88	22,43
Vs	22,34	31,53	7,40	99,46	14,79	22,52	35,47	31,63	28,10	79,04

Source: Compiled by the author

The following stages of the multidimensional analysis required standardizing the level of the variability of the diagnostic features; the synthetic indicators were also transformed (unified). In order to delimit the groups of the countries which show similarities in terms of their political changes leading to introduction of the market economy mechanisms, a hierarchic Ward agglomerative method based on Euclid distance was used. Among other researched agglomerative methods, this method allows the authors to clearly delimit a relatively small number of homogenous clusters. A hierarchy tree was used to present the results of the grouping of the studied countries based on two sets of features (Figures 1 and 2).

Figure 1. Agglomeration of the countries of Central and Eastern Europe and the Commonwealth of Independent States based on relative indicators (variable x)

Figure 2. Agglomeration of the countries of Central and Eastern Europe and the Commonwealth of Independent States based on synthetic indexes (variable y)

According to the research, geographical location is of great importance in terms of the progress of transformation. The clusters which are visible among both relative indicators and synthetic indexes make it possible to draw conclusions that in terms of the above diagnostic features European states show similarity. They create three separate clusters: the Baltic States, the states of Central Europe and the states of the Black Sea area (including Russia); Kazakhstan is also similar to the latter cluster. Large differences in these states’ gravitation are visible at the higher levels of aggregation. For instance, in terms of agglomeration of the variable x Lithuania, Latvia and Poland gravitate towards cluster 3 (Figure 1), while Hungary, Estonia, Czech Rep. and Slovakia differ from the other clusters. If the variable y is considered, however, the above countries gravitate towards one another and thus make one cluster (cluster 1, Figure 2) which varies from the other states of the researched region. A bit diverse gravity layout of the studied index groups is presented by the Asiatic countries. If the relative indicators are considered, they are similar to the Caucasus and Central Asiatic States. According to the synthetic indexes, however, the above states do not show such a clear regional similarity (Figure 2).

After 14 years of transformation, these are predominantly Moldavia and Kazakhstan which differ from the other countries of the region in terms of socio-economic conditions. Considering agglomerative clusters based on relative indicators, Moldavia shows similarities with the states of Central Asia; considering agglomerative clusters based on synthetic indexes it is close to ‘the mixed group’ (cluster 4, Figure 2). Kazakhstan described by both the variable x (indicators) and variable y (index) shows great similarities with Bulgaria, Romania and Russia. In terms of the selected diagnostic measures the similarity of Ukraine and Belarus is under-defined, as they show large disproportions in realizing various levels of transformation.

According to the analysis of the hierarchy trees, at the 17^th level of aggregation and at the similar level of the linkage distances, four groups of clusters can be separated. It can be observed, however, that the agglomeration of the similar units takes a slightly different course if based on subjectively selected relative features (Figure 1) and on synthetic indexes, which refer to the international opinion on the level of progress in reforming the studied states (Figure 2).

In order to analyze the relations between clusters the method of arithmetic mean values was used. Cluster 1, delimitation of which was based on variable x, generally shows higher values of group means for stimulants and lower for destimulants (excluding the over-average share of the foreign debts in the GDP). A similar model of the group means is found for cluster 2, but the group means outnumber only slightly the mean of the entire study area. In this cluster the means for destimulants are closer to the mean of the studied population (excluding variable x₅).

A diverse distribution of the arithmetic means is found in cluster 4, where group means for stimulants are lower than the average for the studied area, while for destimulants they are much higher. In cluster 3, however, the value of arithmetic mean is close to 1, which shows an average scale of the studied parameters. Moreover, this cluster presents a higher level of investment into B&R (x₇), the lowest level of the GDP loaded with the foreign debt (x₅) as well as a high annual average inflation rate (x₃) (Table 3).

Table 3. The model of the arithmetic means for the clusters delimited with the use of agglomeration of the objects defined with the relative indicators

Grupa	x1	x2	x3	x4	x5	x6	x7	x8	x9	x10
1	1,96	2,99	0,25	3,28	1,23	0,13	1,65	1,24	1,03	0,28
2	1,49	1,31	0,23	1,11	1,15	0,25	1,03	1,14	1,03	0,33
3	0,93	0,62	1,66	0,41	0,78	0,99	1,11	0,87	0,97	0,91
4	0,31	0,12	1,10	0,24	1,02	1,82	0,50	0,93	0,998	1,78

Source: Compiled by the author

The clusters delimitation, based on agglomeration of the objects described with the synthetic indexes, is presented in Table 4. Cluster 1 is distinguished by a highly advanced level of transformation of the economy. Values of group means for stimulants are over 1, while for destimulants are much more advantageous than the mean for the entire studied area. The opposite model of the arithmetic means is found in clusters 2 and 4, while in group 3 the means oscillate around 1, which shows an average level of the considered measures in the researched area.

Table 4. The model of the arithmetic means for the clusters delimited with the use of agglomeration of the objects defined with the synthetic indexes

Cluster	y1	y2	y3	y4	y5	y6	y7	y8	y9	y10
1	1,22	1,30	1,08	0,19	0,92	0,75	1,41	1,37	1,20	0,32
2	0,68	0,55	0,95	1,10	1,06	1,26	0,73	0,60	0,50	1,94
3	1,03	1,06	0,98	0,78	0,99	1,08	0,85	0,92	1,12	1,10
4	0,88	0,83	0,94	2,66	1,10	1,07	0,78	0,88	0,96	1,10

Source: Compiled by the author

To sum up, 14 years into transformation of the former COMECON states, the data shows areas of highly diverse level of progress in terms of market mechanisms. In accordance with the two-directional analysis of the clusters, the division into two categories of the states is well established. The first one includes the states of Central and Eastern Europe, where parliamentary democracies as well as liberal market economy are developing. The second category, however, includes most of the former Soviet Union states, where presidential systems are strengthening and the economy is highly statist. Moreover, socio-economic disproportion between the above areas is deepening, which is expressed by various indicators, such as the level of the GDP per capita. At the beginning of the transformation in 1990 the relation between the richest and the poorest state within the researched area in term of the GDP per capita was 7:1 (Russia – Tajikistan), while by 2004 it had grown to nearly 15:1 (the Czech Rep. – Tajikistan). The growing gap stems from the fact that the Baltic States and Central European countries introduced open economy, i.e. liberal economic policy, progress in terms of structural reform as well as higher level of market institutions. The European Union played an important role in the above transformation, as it has been the main source of direct foreign investment and EU funds flowing into the countries of Central and Eastern Europe.

The course of the transformation in Central Europe and in the states of the former Soviet Union seems to support the ‘core-periphery’ model, according to which Western Europe, being an attractive economic and political ‘core’, is pulling ‘peripheries’, i.e. less developed countries neighbouring it. The first states ‘gravitating’ towards Western Europe were those located in Central Europe, followed by the Baltic States. Recently, there have been symptoms of the pro-western orientation observed in both internal and foreign policy of the countries of the Commonwealth of Independent States, such as Ukraine or Georgia.

(Translated by Aleksandra Zaparucha )