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4Stavropol Region
= № 1(21)/1 Supplement, 2016
UDK 338.48(439)
Zoltan Bujdoso, Janos Pénzes
TOURISM COMPETITIVENESS AND TOURISM DEVELOPMENT IN THE BORDER REGIONS OF HUNGARY
Abstract
Following the changes of regimes in Central Europe, research into border regions has been increasingly adverted. On the estimation and development of borders and border regions were impacted to the highest degree. In our research, we intended to explore, by applying statistical indicators, to what extend the situation of border micro-regions is different from other micro-regions and the national average. As a next objective, our research focused on how, from the point of view of tourism, the micro-regions studied can be distinguished beyond the significant spatial differences represented above as well as on to define the most relevant groups and the differences among them. In this paper, on the one hand, by applying the approach by this latter author
and, on the other, similarly by applying the method of desaggregation, the authors intended to study tourism competitiveness and its components in the tourism regions of Hungary. According to the results of our surveys, countries wiling to gain access were not blocked from each other by Schengen borders as they received facilitations in cross-border tourism. In the field of cross-border cooperation, within the tourism industry, a west-to-east and north-to-south gradient can be detected that, by the present logic, can be explained by the changes of economic circumstances and the succession of European Union accession.
Key words: border; tourism; regional development; theories; cluster analysis.
Dr. Zoltân Bujdoso -
PhD, Director of listitution, college professor, Karoly Robert College, Institution for Tourism, Regional Development and Foreign Language, Matrai üt 36, Gyöngyös, 3200 Tel.: +3637/518-374 E-mail: zbujdoso@karolyrobert.hu
Dr. Janos Penzes -
PhD, assistant professor, University of Debrecen,
Department of Social Geography and Regional Development Planning, Egyetem ter 1, Debrecen, 4010 Tel.: +3670/2915951 E-mail: penzesjani@yahoo.co.uk
Discussion
Almost one-third of the territory and 21.9 percent of the population of Hungary could be regarded as borderland in January 2009 (Figure 1). In general, these LAU-1 (former NUTS-4) microregions are
backward areas in the light of the most important statistical indicators, because they are characterised by low population density and low level of enterprising spirit, significant out-migration and unfavourable income situation (Kozma 1995, Bujdoso et al. 2011).
Figure 1 - Border microregions in Hungary
(Source: edited by Bujdoso et al. 2011)
Lots of ideas came to light in order to resolve the peripheral situation, but most of them remained unsuccessful. At the same time, tourism and tourism development were regarded as a possibility to break
out in every concept (Kozma, G. - Asworth G, 1993, Suli-Zakar, I. et al. 1999, David, L. - Baros, Z. 2007, Kozma, G. 2007, Kozma, G. 2008, Kozma, G. 2009). The Regional Operational Programmes (ROP) might
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be viable from the concepts due to the financial resources added to them. Two operational objectives were outlined within the priority of tourism; on the one hand, the prolongation of the touristic season, and on the other hand, the extension of the target areas of tourism (with the involvement of the less preferred settlements).
The following preconceptions were composed on the basis of the previous statements: the touristic supports - according to the ROP objectives - concentrated on two fields, namely on the frequented touristic target areas and the less preferred territories.
We had a threefold goal, as the analysis of the per capita touristic supports of the border microregions were aimed from territorial point of view besides the investigation of touristic competitiveness and the calculation of correlation between the touristic competitiveness and the distribution of touristic supports.
The investigation of static and dynamic competitiveness was carried out (for the year 2008 and for the interval 2000-2008) using the Hoover index and correlation calculations. The database is from the HCSO TSTAR and the EMIR (HSCO - Hungarian Central Statistical Office; EMIR (Unified Monitoring and Information System) database.
RESULTS
Competitiveness in the border microregions
The international literature of regional competitiveness is expanded as a result of Michael Porter's activity (see Porter, M. 1996; 1998; 1999). In the recent years, articles were published about touristic competitiveness (Schroeder, T. 1996; Enright, M. J. -Newton, J. 2004) but in the current study - in contradiction to their work - we mainly focus on the possibilities of measuring.
In the last few years, remarkable studies appeared about the measuring possibilities of the concept of regional competitiveness in Hungary as well and we tried to utilize the results of these (Kozma,G 2002, Penzes, et al., 2008). These studies represented the quantitative decomposition of the relative personal incomes into the adaptable and clear social-economic factors (Lengyel, I. 2000; Nemes Nagy, J. 2004). The method of decomposition was carried out by the study of Jozsef Nemes Nagy in order to investigate the competitiveness and its components of the border microregions. The multiplication became more easily treatable summary after the logarithmic transformation, using the formula below:
Income from accom mod ation fee„ Jncome from accom mod ation fee„ , Tourism night „ Capacity
log(-—-=-——) = log(-—-=-——) + log(-- 6 ) + log(---—)
Population Tourism _ night Capacity Population
In our study, the total income from accommodation fee, the number of tourism nights and the capacity of the public accommodation establishments, and the number of population for the microregions were applied. The total income from accommodation fee per capita expresses the tourism development of microregions, the income from accommodation fee per tourism night refers to the effectiveness, the number of tourism nights per one bed of accommodation establishments means the occupancy rate of capacity and the number of accommodation establishments could provide reasonable estimation about the importance of tourism in the microregions.
The current typology was based on the relative values of microregions compared to the national average in the case of the specific income from accommodation fee and its three components. According to the definition of competitiveness, the microregions with above average income per capita level were regarded as advantageous and those with below average were classified as disadvantageous. If a given microregion represented an above average level by three of the income components then it was labelled with complex competitiveness. In the case of two or two components with above average, multi-factored advantage and single-factored advantage was pointed out. The concept of disadvantageousness was created by similar analogy.
The map of border microregions represents the categories separated by the static analysis of competitiveness (Figure 2). Six microregions could be regarded as competitive in Hungarian comparison by the tourism, however five microregions from this group are located in the western part of Hungary - the Gyula microregion constituted an exception. Complex touristic advantage could not be found in any of the border
microregions, multi-factored advantage appeared in four cases and single-factored advantage was detected in two cases. Most of the microregions (43 microregions) were disadvantageous in this respect, complex disadvantage could be found in 29 microregions and multi-factored disadvantage was observed in 14 ones (Bujdoso et al., 2011).
Dynamic analysis was carried out in order to investigate the changes between 2000 and 2008. (This definition was applied by Jozsef Nemes Nagy in his study -Nemes Nagy, J. 2004) However, this kind of analysis cannot be regarded as dynamic in its terms, as only the data for the first and the last years are compared to each other instead of the investigation of the whole period.
It is clearly seen that the situation of the border microregions is not so unfavourable at all by the dynamic investigation, as it was discovered by the static analysis previously (Figure 3). More than half of the microregions represented better dynamism than the national average and these 27 microregions can be regarded as competitive. Complex advantage appeared in the case of five microregions and moreover only one is located in the western part of Hungary. Multi-factored advantage could be detected in 21 microregions and single-factored in one. Five microregions out of the 22 units with disadvantage were characterised by single-factored, 12 by multi-factored and five by complex disadvantage in the touristic competitiveness (Bujdoso et al., 2011).
The competitiveness of the Hungarian microregions and the spatial distribution of the touristic supports1
1 This paper was supported by the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences.
Figure 2 - Types of tourism competitiveness in the border microregions of Hungary, 2008.
(Source: edited by Bujdoso et. al. 2011)
Figure 3 - Types of tourism competitiveness in the border microregions of Hungary, 2000-2008.
(Source: edited by Bujdoso et. al. 2011)
Correlation calculation was the second phase of the current investigation between the competitiveness of the microregions and the distribution of the touristic supports. The aim of this survey was to discover the statistical relationship between these indicators. First of all, the distribution of the touristic supports was completed. The database of this analysis was based on the EMIR that contained the accepted touristic development supports of the NFT (National Development Plan), the UMFT (New Hungary National Development Plan) and the USZT (New Szechenyi Development Plan).2
The Gyula microregion - and the touristic developments of the town Gyula - received the largest
amount of development support (more than one billion HUF) from the NFT between 2004 and 2006. More than half billion HUF financial support was approved in the case of the microregions of Csurgo, Tata, Baja, Szob, Siklos and Esztergom. 12 border microregions did not receive any support during the period of NFT.
Eleven microregions were missing on^ the list of the supported microregions during the ÜMFT and the highest total amount of developments reached 7 billion HUF. Each of the Szegedi, the Sopron-Fertödi, the Siklosi, the Edelényi, the Nyfrbatori and the Mo-hâcsi microregions received more than 2 billion HUF supports.
2 These development plans fitted to the principles of the European supports in different financial periods (the New Szechenyi Development Plan launched by the Orban Government in 2011).
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The summarized supports per capita values of the two periods are illustrated by Figure 4. Polarized distribution of the resources can be seen that tends to represent significant spatial differences. Four border microregions had no kind of supports from these applications. Most of them are backward along
the eastern border of Hungary (the Csengeri, the Hajduhadhazi and the Sarkadi microregions). However, at the same time, the largest values of support can be found in this part of the borderland (the Sato-raljaujhelyi, the Edelenyi, the Gyulai, the Morahalomi and the Szobi microregions).
IOO Kilometers
Categories 0
□ < 20000 20000 - 39999
■ 40000 - 59999
■ > 60000
Figure 4 - The total value of the touristic supports per capita in the border microregions of Hungary,
2011, HUF
(Source: edited by the authors)
The correlation calculation might discover the relationship between the types of competitiveness and the approved supports. The high level of correlation coefficient would primarily represent the dominance of developed microregions that might strengthen their touristic profile even more. This fact might mean the further increase of the signifi-
cant level of inequalities. Weak-medium - and significant - correlation was calculated in the case of the touristic supports per capita and the categories of competitiveness (correlation coefficient = -0.42). The other indicators - in spite of the calculations -did not show significant coefficients.
Table 1 - Border microregions categorized by the supports per capita and the types of static competitiveness (Source: edited by the authors)
categories multi-factored advantage single-factored advantage multi-factored disadvantage complex disadvantage
without support - - Csengeri Hajduhadhazi, Sarkadi, Sellyei
<20,000 - Mosonmagyarôvâri Fehérgyarmati, Gyôri, Kiskunhalasi, Komaromi, Lenti Bajai, Balassagyarmati, Berettyôujfalui, Bodrog-kozi, Derecske-Létavér-tesi, Encsi, Kapuvar-Beledi, Kazincbarcikai, Kormendi, Letenyei, Makôi, Matészalkai, Mezôkovâcshâzai, Nagykanizsai, Ôzdi, Szentgotthardi, Szom-bathelyi, Zahonyi
20,000-40,000 Sopron-Fertôdi - Abauj-Hegykozi, Esztergomi, Szegedi, Vâsârosnaményi Barcsi, Salgôtarjani, Szécsényi
40,000-60,000 Tatai - Kôszegi, Ôriszentpéteri Csurgôi, Mohacsi, Nyirbatori
>600,000 Gyulai, Szobi Siklôsi Môrahalomi, Satoraljaujhelyi Edelényi
The results of the correlation calculation between the total values of supports in each period separately tended to represent a weakening but negative correlation. The correlation between the approved supports during the NFT and the static categories of competitiveness showed a medium strong relationship (-0.53) that was weaker during the era of UMFT (-0.33). More competitive microregions received higher amount of development supports by these calculations, however the correlation became weaker between the two periods.
The investigated microregions were categorized by the approved supports per capita and by the competitiveness besides the correlation-calculation. The unfavourable situation of the microregions with com-
plex static disadvantage can be clearly seen in Table 1 as only one microregion - the Edelenyi microregion - was in the highest category of supports. And what is more, this outstanding value appeared as a result of only one large-scale investment, namely the reconstruction of the L'Huillier-Coburg castle in Edeleny (the total budget of the project amounted to 2.2 billion HUF) (http://edelenyikastelysziget. hu). 18 microregions with complex disadvantage belonged to the lowest category of per capita supports, while three similar microregions did not receive any kind of financial support. All of the four microregions with multi-factored static advantage received at least 20,000 HUF support per capita.
Table 2 - Border microregions categorized by the supports per capita and the types of dynamic competitiveness (Source: edited by the authors)
catego-ries complex advantage multi-factored advantage single-factored advantage single-factored disadvantage multi-factored disadvantage complex disadvantage
without support - - - Csengeri Sellyei Hajdühadhazi, Sarkadi
<20,000 Encsi Berettyoüj-falui, Fehérgyar-mati, Kiskunhalasi, Komaromi, Kör-mendi, Makoi, Matészalkai, Mosonma-gyarovari, Lenti, Szombathelyi Kazincbarci-kai Balassagyar-mati, Özdi, Zahonyi Bacsalmasi, Bajai, Györi, Kapuvar-Bel-edi, Letenyei, Mezökovacs- hazai, Nagykanizsai, Szentgotthardi Bodrogközi, Derecske-Létavértesi,
20,000-40,000 Abaüj-Hegykö-zi, Vasaro-snamé-nyi Esztergomi, Sopron-Fertödi, Szegedi - Szécsényi Barcsi, Salgotarjani -
40,000-60,000 Csurgoi Nyirbatori, Ôriszentpéte-ri, Tatai - - Köszegi Mohacsi
>600,000 Morahalomi Edelényi, Gyulai, Satoraljaüj-helyi, Siklosi, Szobi - - - -
The dynamical categories of competitiveness provide a more mosaic-like pattern than the previous categorisation (Table 2). Microregions with competitive advantage received financial support for their touristic developments. It is an interesting fact that most of the microregions with complex or multi-factored advantage were in a backward situation. The touristic dynamism of these microregions arose from the low level of basic data in 2000 however the developments of the touristic indicators by 2008 were not significant which is reflected by their moderate positions of static competitiveness. On the other hand, the tourism of these peripheral territories can be characterised by the higher participation of inland tourists that are less sensitive to the economic recession than the foreign visitors. Tourism is highly responsive to the changes of the mac-roeconomic environment because the effect of the narrowing income of individuals and companies can be especially destructive on the touristic expenditures. The result of this negative process mainly affected the territories with developed tourism (e.g. by the absence of orders from the business sector) (ASZ 2010).
The current investigation contained the analysis of spatial inequalities of the approved supports by the Hoover-index. The Hoover-index is one of the most frequently applied methods to measure inequalities (for the detailed description of the method see Nemes Nagy, J. 2005)
In order to calculate the index, the distribution of the summarised accepted supports (and personal incomes3) in the microregions and the population number were compared to each other. The results of the Hoover-index was extremely high - iinft= 57.7 % -for the period of NFT. This value decreased in the next - UMFT - period (hUMFT=45.3 %), however it is many times higher than the income inequality among the border microregions in 2010 (hINCOME=11.6). The results proved the more unequal and concentrated distribution of touristic supports, however the process of convergence tends to appear in time.
3 The concept of income means the gross incomes confessed in the personal income tax which has been published by the PM-APEH (Ministry of Finance - Hungarian Tax and Financial Control Administration) (National Tax and Customs Administration from 2010) and the HCSO (Hungarian Central Statistical Office) since 1988.
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These results are not in contradiction with the objectives of the Regional Operational Programme, but the flowing of the largest touristic supports into the most developed and competitive touristic microregions fulfilled only one part of the previously aimed principles. This process was more spectacular in the case of the NFT (this statement was confirmed by the study of the National Audit Office) (ASZ 2010).
Conclusions
The border microregions of Hungary can be regarded as heterogeneous from a touristic aspect and can be characterised by significant spatial disparities. These specific features were represented quantitatively by our static competitive analysis for 49 microregions and the characteristics became more detailed by the dynamic analysis for the period between 2000 and 2008. The macroeconomic impacts affecting the touristic trends (terror attack in 2001, financial cutting downs in 2006, and the global economic recession from 2008) had negative influence mainly on the territories with developed tourism. Underdeveloped areas are primarily orientated towards the inland tourism and this fact with the low level of basic data resulted in larger dynamism in their case.
REFERENCES
1. Számvevoszék Á., Intézete K. A turisztikai fejlesztések állami támogatása térségi és nemzetgazdasági szintu hatékonyságának vizsgálata. Budapest: Tanulmány, 2010. 138 p.
2. Bujdosó Z., Remenyik B., Dávid L., Tóth G. Connection between tourism and regional development on the Hungarian-Croatian border // Central European Regional Policy and Human Geography. 2011. №. 1 (2). PP. 27-40.
3. Dávid L., Baros Z. A Possible Use of Indicators for Sustainable Development in Tourism // Anatolia. 2007. №. 18 (2). PP. 349-355.
4. Enright M. J., Newton J. Tourism destination competitiveness: a quantitative approach // Tourism Management. 2004. №. 25 (6). PP. 777-788.
5. Kozma G., Ashworth G. J. Projected urban images: A comparison of Groningen and Debrecen // Groningen studies. 1993. № 55. PP. 1-32.
6. Kozma, G. A debreceni onkormányzat elsö lépései a városmarketing területén // Comi-tatus: onkormányzati szemle. 1995. № 5(5). PP. 15-21.
7. Kozma G. The Difficulties of the Self-government Economic Management Systems in the Border Regions of Hajdú-Bihar and Sz-abolcs-Szatmár-Bereg counties In: // Borders and Cross-Border Co-operation in the Central European Transformation Countries. Debrecen: Kossuth Egyetemi Kiadó, 2002. PP. 225-232.
The investigations carried out represented the concentration of supports during the NFT period with the flowing of resources into the microregions with developed tourism. This process caused pushing one of the operational objectives - the development of areas with underdeveloped tourism - into the background and the increase of inequalities. The homogeneity of supports did not decrease effectively by the end of the investigated UMFT period, but the concentration of resources preferring the developed areas partly melted. However, it has not still accomplished the determined objectives of the regional operational programme. The resource-absorption capacity of the underdeveloped microregions is much lower than in the developed ones and most part of the backward territories primarily concentrate on the development of basic physical and human infrastructure (Radics, Zs. - Penzes, J. - Molnar, E. 2011).
Calls for applications and the system of supports should be smaller scaled, should take the local characteristics into consideration and should compensate for the lack of local additional financial resources in order to effectively decrease the significant disparities among the microregions.
8. Kozma G. A határmenti onkormányzatok gazdálkodásának legfontosabb jellemzoi // A határok és a határon átnyúló (CBC) kapc-solatok szerepe a kibovült Európai Unió keleti perifériáján. Debrecen: Debreceni Egyetem Kossuth Egyetemi Kiadó. 2007. PP. 63-71.
9. Kozma G. Characteristic Features of the Economic Management of local Authorities in the Western and the Eastern Border Areas of Hungary // Neighbours and partners: on the two sides of the border. Debrecen: Süli-Za-kar I. 2008. PP. 19-26.
10. Kozma G. Changes in the income conditions in the border area of the North Great Plain Region // Regional Development in the Romanian - Hungarian Cross-Border Space: From National to European Perspective. Debrecen: Süli-Zakar I, Horga I. 2009. PP. 307-316.
11. Lengyel I. Aregionális versenyképességrol // Kozgazdasági Szemle. 2000. № 12. PP. 962-987.
12. Nemes-Nagy J. Új kistérségek, új városok. Új versenyzok? // Regionális Tudományi Tan-ulmányok. 2004. № 9. PP. 5-42.
13. Nemes-Nagy J. Regionális elemzési módsz-erek. Budapest: ELTE Regionális Foldrajzi Tanszék, 2005. 284 p.
14. Pénzes J., Tagai G., Molnár E. Effects of unifying economic space on the border areas of Hungary // Dimensions and trends in Hungarian geography : 31st International Geographical Congress (Tunis, 12-15 August 2008) / Hungarian Academy of Sciences Geographical Research Institute. Budapest. 2008. PP. 223-238.
15. Porter M. E. Competitive Advantage, Agglom- 19. eration Economics, and Regional Policy // International Regional Science Review. 1996.
№ 1-2. PP. 85-94.
16. Porter M. E. On Competition. // Harvard Busi- 20. ness Review Book, Boston : Harvard Business School Press, 1998.
17. Porter M. E. Microeconomics competitiveness: Findings from the 1999. Executive sur- 21. vey. // The Global Competitiveness Report. (Geneva, Switzerland). 1999. PP. 30-53.
18. Radics Zs., Pénzes J., Molnár E. The spatial aspects of the resource-allocation of the Regional Operational Programmes' development resources // New results of cross-border Cooperation. Debrecen : Kozma G., 2011. PP. 119-126.
Schroeder T. The relationship of residents' image of their state as a tourist destination and their support for tourism // Journal of Travel Research, 1996. № 34(4). PP. 71-74. Suli-Zakar I., Czimre K., Teperics K. Regionalism in Central-Europe: The study of the Carpathian Euroregion from the aspect of human relations. Bilbao : RSA. 1999. PP. 139-140. Edelényi kastélyziget. URL: http://ede-lenyikastelysziget.hu (download time: 14.07.2015)
