The Choice of Dilemmas in Hungary's Transition: Westernization or Latin Americanization?
In the Copenhagen declaration of June 1993, the European Union (EU) offered membership to those former communist European countries that wished to join and that fulfilled certain political and economic criteria. This enlargement is the biggest challenge facing both the European Union and countries of central and eastern Europe. The goal of integrating the associated countries is widely shared. The question is when and how. Accession conditions should ensure that candidates share the same objectives as EU members, have similar democratic and market-oriented institutions, and converge to the western European income level [Crombrugghe et al., 1996, pp. 21].
The conditionality can introduce some competition between candidates on how best to satisfy these conditions.  The EU will review fulfillment of the criteria before accession by the first group of countries. It seems to be vitally important for countries in transition to measure their performance and compare themselves to EU members and other candidates.
Hungary became a member of the Organization for Economic Cooperation and Development (OECD) in 1995 and intends to join the EU. This decision underlines comparison with well developed European countries. At the same time, the Hungarian GNP per capita income level fits the upper-middle income group. This group contains several countries from Latin America (such as Mexico) and Korea (Republic) and is at the same income level [World Bank, 1996, pp. 188-9].
The process of transition means development in certain aspects. There are positive results in stabilizing the economy, significantly reducing the government deficit, and so on [World Bank, 1995, p. 136]. On the other hand, there is a widely used cliche in Hungary which states, "our country faces the danger of Latin-Americanization." This stresses the coexistence of a dual and wide black economy, deep social deviancy, corruption, extreme differences in income, and the possibility of peripherization [Kovacs-Kollar, 1997b, p. 23].
There are numerous aspects in analyzing the Hungarian transition process. Instead of comparing the situation with the previous one, comparing results to those of EU members is preferred. While previous research focused on taxation and government expenditure structure [Kovacs and Kollar, 1997a, p. 42], this paper deals with other selected characteristics concerning accession conditions. In most cases, there are no exact criteria that must be fulfilled. Therefore, the difference between the real and expected levels of performance cannot be measured. Instead of ranking countries according to one basic indicator, the existing relationship between the economic and social variables are taken into account. Based on the statistical similarity of EU members and new applicants, structural similarity and closeness can statistically prove economic success or failure.
Data and Methods
To meet the overall reliability, consistency, and comparability of data, reports from the OECD , United Nations , and World Bank [1994; 1995; 1996] are used as sources of data for multivariate statistical comparison based on the results of principal component analysis and hierarchical and nonhierarchical clustering. International comparison is frequently conducted on the basis of general economic indicators such as the GDP per capita. In addition to explicitly measurable GDP per capita, the purchasing power of GDP and changes in consumer prices are also taken into account. The labor market is characterized using the unemployment rate along with self-employment and the female participation rate. Health expenditure, in-patient care days, and life expectancy of men and women are taken into account to describe those differences which are influenced by economic indicators.
The data base here contains a limited amount of data for other transition countries. Comparing transition countries would be interesting, but data from the years of transition are extremely variable and, in some cases, are missing. The speed of transition and the pattern followed by different central European economies are not the same. As changes are introduced in different years, the result would be influenced by the time period and not only by the measures. There are no clear-cut solutions to many of the data problems, so time series data are not used for comparison. The working hours necessary to buy goods and services ["Prices and Wages...," 1998, pp. 23-4] are analyzed and compared to classify nine countries from central and eastern Europe.
Some Aspects of Transition
An earlier international comparison [Kollar, 1996] indicates that the Hungarian economy is similar to Latin American countries and several new EU member countries and faces a dilemma. Does it choose Westernization or Latin Americanization? Hungarian economic policy has special functions at this stage of economic and political transition. The new system must support economic stabilization and growth, and European impulses must be adapted to form and orient Hungary's modernization and reintegration into European society. Contradictions in Hungarian transition must be lightened to liquidate occasional tendencies of Latin Americanization. These two aspects are discussed in details using statistical results.
Stabilization and Growth. Measurable Performance Indicators
The accession conditions include explicitly measurable performance conditions like GDP per capita. Three countries in transition, the Czech Republic, Hungary, and Poland, are members of the OECD, so their figures can be easily compared to the other 25 member states' data. The average GDP per capita level for the 28 members was $21,314, and the range is wide enough at $40,486.
According to the GDP per capita at the current exchange rate, in 1995, Turkey had the lowest level of income ($2,747) of the OECD members. Mexico was next ($2,946), and transition countries followed them. With $3,057, Poland reached 14 percent of the average, Hungary had $4,273 (20 percent) and the Czech Republic showed $4,420 (21 percent). One widely cited accession condition is that the living standard among newcomers should be equal to at least 75 percent of the EU average, which is higher than the OECD level. Eligibility for structural funds is based on per capita income below 75 percent of the EU average. On this basis, all the new central European members would be eligible. After these five countries, a big income gap is noticed, and Portugal and Greece had higher than $10,000 GDP per capita ($10,060 and $10,936, respectively).
The tendency toward convergence in the living standard can be completed with structural convergence in other aspects of the economy and society. Consumption is determined not only by the income level, but by the purchasing power parity (PPP) and changes in consumer prices. The average GDP per capita using current PPP is only $18,893, and the difference between the maximum and the minimum is $25,612. Therefore, it is only 63 percent of the range calculated at the current exchange rate. People from eight countries are in better financial position on the basis of the PPP. Four of them (Greece, Mexico, Portugal, and Turkey) could significantly increase their relative position.
The PPP was not published for new OECD members in 1995. Using the proportion of the PPP estimates and dollar amounts of GNP per capita [World Bank, 1996, pp. 1889], rough estimation can be calculated for the Czech Republic ($12,287), Poland ($6,951), and Hungary ($6,751). In Hungary's case, this means 36 percent of the OECD average.
According to previous results concerning taxation and government expenditures [Kovacs and Kollar, 1997a], Portugal is very similar to Hungary in a structural sense. In 1986, Portugal entered the EU with a GDP per capita equal to 32 percent of the EU average, reached 47 percent of the OECD average in 1995, then 66 percent according to the GDP using PPP. Membership in the EU underpins very rapid economic growth as it is experienced in Portugal. The expected membership could give strong impetus to Hungary's economic stabilization and growth.
According to changes in consumer prices (from December 1995 to December 1996), when combining the comparison of the OECD countries with the unemployment rate, this gives a two-dimensional classification of members concerning the stability of their economy (Table 1). These two indicators are not linearly correlated. There is no typical combination with the higher rate on one side and the lower rate on the other side. Some countries with stable economies could preserve a low unemployment rate with limited changes in consumer prices. Twenty-three countries had less than a 5 percent increase in consumer prices, but their unemployment rate varied between 2 and 24 percent. Six countries with smaller unemployment rates (3 to 13 percent) presented a higher increase in prices. The average consumer price index was 7.5 percent with a high standard deviation (15.7) because of Turkey's extreme value (79.8). The average unemployment rate was 8.5 percent (standard 4.9), and the maximum was reached by Spain (23.8 percent) f ollowed by Finland and Ireland in the same cluster. In this context, the Czech Republic, Hungary, and Poland are not similar. Further analysis of this classification seems to be necessary to identify those economic efforts which could improve the position of Hungary in reducing the unemployment rate and changes in consumer prices.
As expected, significant differences can be noticed between Hungary and other OECD members. Hungary and Poland seem to be similar, but the Czech Republic is in a unique position. This similarity that classifies nine countries from central and eastern Europe is worth verifying on the basis of the differences in working hours necessary to buy goods and services. Transition countries are compared to the geographically close EU member country, Austria.
The working time for 14 selected items were compared, and the total working hours for goods and services was estimated by stepwise regression. In the presence of strong multicollinearity, only one variable entered into the regression model. The total working time for goods and services can be estimated as a linear function of minutes for bread in December 1997.  Social policy can be mentioned among other effects.
Table 2 shows the average working time for 14 goods or services. Austria reached minimum ten times, and five countries in transition have very similar minimum times in certain cases. Slovakia is the only country not mentioned in this table (no minimum and no maximum was reached).
The dendrogram gained by hierarchical clustering.  The average linkage (between groups) method separates these nine countries in transition into two extremely different groups. The first group contains the Czech Republic, Croatia, Hungary, and Poland combining Slovakia and Austria with greater distances. The second group involves Bulgaria, Romania, Russia, and Ukraine. The highest dissimilarity is measured between Austria and Bulgaria. Croatia is the closest to Austria in this comparison followed by Poland, Hungary, and the Czech Republic.
These two groups, separated by cluster analysis, underpin the final conclusion. Dissimilarities measured in working time are in accordance with those differences measured in the first part of the analysis, and geographical closeness does not mean structural similarity. There are basic differences in prices which indicate differences in consumption and in the living standard of countries in transition. This kind of inhomogeneity seems to be a consequence of different social and economic development.
Reintegration or Peripherization? Dilemmas of Hungarian Transition
There are several direct and indirect indicators of economic and social development to complete information gained from the basic performance measures. In this part of the analysis, Hungary will be compared to other OECD members on the basis of social characteristics. Twelve indicators were selected [OECD, 1997], covering demography, employment, health, and education. An agglomerative cluster analysis formed two large groups with 10 and 11 members, and other countries were separated from each other into smaller groups with 2, 3, and 3 members. Hungary, the Czech Republic, and Poland were members of the first group together with Austria, Belgium, France, Germany, Korea, Luxembourg, Portugal, and Switzerland. Which variables are responsible for this classification? Mainly, the female participation rate and in-patient care days cause significant differences among clusters. These two variables are in linear correlation with the others. To avoid interactions, a smaller number of variables were selected.
To characterize the employment structure of countries, the unemployment rate, self-employment (percent of total employment), and the female participation rate were analyzed together.
From the employment characteristics, five clusters can be identified using complete linkage clustering:
1) Greece, Mexico, and Turkey have a traditionally low rate of female participation (40 percent) with very high self-employment (30 percent). Because of these two results, their unemployment rate is close to the OECD average.
2) Spain, Italy, and Ireland have a higher unemployment rate with a higher female participation rate and lower self-employment.
3) Korea, New Zealand, Poland, and Portugal have relatively high self-employment (24 percent) and female participation (60 percent) which help to reach the lower unemployment rate (7.5 percent).
4) The relatively low unemployment rate (7 percent) and self-employment (12 percent) are associated with a 60 percent female participation rate in 10 countries. They are Hungary, the Czech Republic, Australia, Austria, Belgium, France, Germany, Japan, Luxembourg, and the Netherlands.
5) Nine countries from northern Europe, Canada, and the U.S. are in the fifth group with the highest female participation rate (71 percent) and the lowest self-employment (11 percent).
Each group contains countries from the EU. Their social development is determined by their tradition, urbanization, and agricultural structure. Neither the high rate of female participation nor the low rate of self-employment can be mentioned as a source of development. Hungary faces the problem of high unemployment. The process of privatization was quick and effective. More than 70 percent of the GDP was produced by the private sector in 1997 [Kerepesi, 1998]. The number of small businesses have increased during the privatization. Most of the small firms avoiding unemployment can be mentioned as self-employment. The changing structure of the business sector and decreasing involvement by the state in Hungary's economy are impressive results of the transition period. Hungary's position must be preserved by reducing the unemployment rate without increasing the self-employment proportion.
Demography and Health
The total fertility rate, health expenditure (as percent of GDP), in-patient care days, and men and women's life expectancy at birth are used for classifying countries. The total fertility rate does not cause significant differences among OECD countries. Differences can be noticed mainly in life expectancy and in-patient care days. The variable "in-patient care day" has an extremely high value for Japan (45 days) which is associated with a relatively high life expectancy for both men and women. The Netherlands is the only European country with 32 sick days, but the health expenditure is higher than in Japan or the European average. The other six developed countries with a high health expenditure and long life expectancy are in one cluster with 15 in-patient care days. Twenty-one countries with a low number of in-patient care days (9 days) are classified into two groups. Six of them (the Czech Republic, Hungary, Korea, Mexico, Poland, and Turkey) spend less on health, and people die 5 to 6 years sooner than in the other countries. Taking into account the demographic and health characteristics, European periphery seems to be realistic from 1995 data.
This analysis highlights the contradictions in Hungary's position among the upper-middle income countries. Processes in Hungary's economy for the last two years indicate the positive changes of perceptible economic growth, stabilization of market economy, moderating inflation, and the like. Analysis by several researchers and institutions (for example, the Hungarian Central Statistical Office ), show that in spite of these tendencies, there are still other alternatives to economic processes. Economic growth with increasing disequilibrium can reverse positive results, and symptoms of Latin Americanization can characterize the future. The duality of society would be strengthened with greater differences in income distribution, unbalanced regional development, and concentration of growth in foreign investments.
The other possibility of Westernization solves previous problems. This alternative means continuing positive processes and Hungary's stabilized position in developed Europe -growth and equilibrium can be in tune. However, this dilemma for Hungary's transition is still unsolved.
(1.) The accession conditions can be divided into four types. They are institutional or legal, macroeconomic and microeconomic management, and performance.
(2.) y = 3.918x + 79.5 where y is total working time for goods and services, and x is working time in minutes for bread. Student t-values are 5.443 and 4.266 (with 0.00 significance), Pearson correlation coefficient is 0.887, and the Durbin-Watson test equals 1.88.
(3.) The average linkage (between groups) method was used, and squared Euclidean distances were computed.
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Consumer Prices and Unemployment in the OECD Consumer Price Changes from December 1995 to December 1996 [*] Unemployment [*] -0.4 to 2.0 2.1 to 5.0 5.1 to 9.9 10 to 20 20 to 30 2.0 to 5.0 Japan Austria Czech Republic Mexico Luxembourg Korea Switzerland 5.1 to 7.5 Norway Iceland Netherlands Portugal U.S. 7.6 to 9.9 Australia Denmark Greece Germany New Zealand Sweden U.K. 10 to 15 France Belgium Hungary Poland Ireland Canada Italy 15 to 20 Finland 20 to 25 Spain Unemployment [*] 70[greater than] 2.0 to 5.0 5.1 to 7.5 7.6 to 9.9 Turkey 10 to 15 15 to 20 20 to 25 Notes: (*.)denotes figures in percentages. Working Time for Goods and Services Good or Service Unit Average Minutes Minimum [*] Bread kilogram 21.9 Austria, Poland Potato kilogram 11.7 Austria, Poland Beef kilogram 175.0 Austria, Croatia Milk liter 21.6 Austria, Croatia Cigarettes pack 50.6 Austria, Poland Train Ride 100 km 73.0 Romania, Austria Bus Ride local trip 9.7 Austria, Romania Petrol liter 20.3 Austria Telephone 3 minutes 3.3 Ukraine (no fee) Electricity kilowatt hour 2.2 Austria Gas meter [**] 3.7 Russia Repair hour 260.0 Russia Restaurant Meal person 505.0 Austria, Hungary Beer (Local) serving 17.9 Austria, Czech Rep. Good or Service Maximum [*] Bread Russia Potato Russia, Ukraine Beef Bulgaria Milk Ukraine Cigarettes Russia Train Ride Ukraine Bus Ride Russia Petrol Bulgaria Telephone Bulgaria Electricity Poland Gas Bulgaria Repair Ukraine Restaurant Meal Russia Beer (Local) Ukraine Notes: (*.)denotes second countryis mentioned if the values are very close to each other. (**.)denotes average linkage method was used, and squared Euclidean distances were computed.
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|Author:||KOVACS, ERZSEBET; KOLLAR [*], ZOLTAN|
|Publication:||International Advances in Economic Research|
|Date:||Feb 1, 1999|
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