Different approaches for regional analyses

amount and quality of local resources, experience of the local leadership, the geographical endowments or the accessibility of a region. It has been proven that there are significant differences between the regions countries all over the world [11], and these disparities are cause by, for instance, the unequal distribution of the abovementioned factors. To counter increasing regional differences the first task is to measure them. For this issue two topic are highlighted in this paper: the challenge of measuring the role of accessibility and the importance of defining borders according to development levels.

Accessibility and proximity, as crucial factors. The main purpose of regional development is to increase the welfare of people in a sustainable way. This goal, however, cannot be achieved by applying simple solutions – there are many aspects of regional development worth investigating. One of the most important factors of regional development is transport infrastructure. No matter how well-endowed a region with resources, if it cannot be accessed externally via roads, waterways or railways, it is doomed to fail. The more developed a country is, the higher its infrastructural level is. It can be observed all around the world that successful countries try to invest in their infrastructure to achieve economic development, and those who cannot afford investments try to find external investors to develop their infrastructure. For instance, there is a huge competition between the United States, Western-European countries, China and Japan related to African investments [9; 16].

It is no wonder, since infrastructure – including transport infrastructure – is one of the basic factors of competitiveness (as it is also one of the pillars of competitiveness of the Competitiveness Report of the World Economic Forum). The reason behind this is that people need to travel to access to goods, services and jobs. Investments aiming to improve transportation potentially open up markets and create conditions, in the context of spatial agglomerations and technical change and diffusion, which influence economic structure and performance [12]. Similarly to any types of infrastructure, transport infrastructure is not the only necessary factor for socio-economic activities; however, it is a necessary one, since it provides a basis for such activities. Although nowadays – due to technological advancement – the role of proximity has been decreasing compared to some other factors in economic activities, such as industrial production [4; 21], it is still of key importance. For instance, it has been found that there is a strong correlation between the motorways and high development level of the centre areas in Hungary [19].

We can observe that the highest motorway density is generally in the economically most advanced regions. This is true in the case of Eastern European countries as well, as we can see that motorway density tends to be the highest in capital city regions (for instance, Budapest, Bratislava and Praha).

Competitiveness and motorways seem to be closely related to each other. However, there are important questions that need answering:

• 1.    Are the regions competitive due to their high level of transport and other type of infrastructure, or do the regions invest much in their infrastructure, because they are advanced already?

• 2.    What would have happened to the surrounding regions if we did not invest into (build) specific roads (the anti-mode, that is) [2].

• 3.    What sort of indicators should be used to measure the impact of such investments?

These questions have yet to be answered; however, there are some clues that could help clearing the picture. Beside the figures demonstrated above, academics managed to prove the necessity of transport infrastructure in many cases all around the world. One of the examples is China, the second largest economy in the world, and it has been able to produce a robust growth in the last decades. However, it is still lacking some important factors to develop further. One of these factors is the motorways, which should be expanded towards the rural areas to create a link between the capital and the countryside [22]. Similar development could be observed in the Philippines in the case of the Famy – Infanta road project, which resulted improved accessibility and caused considerable indirect development effects was considerable. Once the west and east coasts were linked by the road built during the project the complementarity between the resource market (Manila) and the resource area (Pacific) could exert its full force, as Dinahican’s trade costs declined due to its improved road accessibility [15]. The case of Hungarian thermal baths and spas could be mentioned as well. It has been investigated the role of accessibility in regard to the abovementioned tourist attraction via a questionnaire (reaching 1 000 respondents), and concluded that – unlike some other sectors in the economy – for tourism accessibility and proximity are still of key importance [3].

The conclusion could be this: transport infrastructure is a key element of regional development. However, it is still not certain how much socio-economic impact it has on its surrounding regions. The most serious problem is probably that there is a lack of a unique methodology in academic literature. Therefore it is difficult to evaluate the infrastructure investments’ impact on social and economic development. Also, several variables are not enough to evaluate the impact of infrastructure on development [20].

The question is: which variables should be used? Where should the emphasis be? More economic or more social indicators? Statistical data or empirical? How many variables are truly needed? How to interpret the results of an investigation properly? These are the most important questions related to measuring the impact of transport infrastructure investments (and any sort of investment aiming to increase welfare) nowadays, and maybe in the near-future, too.

Territorial cohesion. The example of transport infrastructure demonstrated that the accessibility of regions and their proximity from economic centres (cities, the capital) is still a crucial factor to be considered during regional development activities. Similarly to that, we can observe that the situation of borders (administrative and geographical) also results territorial differences in many countries, such as in Hungary.

Two micro-region level investigations were carried out in recent years to measure territorial differences – or territorial cohesion – in Hungary (one in 2007, the next in 2013), in which the microregions were categorised according to their development levels, and later on they received financial support based on their respective levels. The reason why this paper deals with this specific topic is that the two investigations attempted to analyse regional cohesion twice with relatively different methodology and results, demonstrating how different approaches can bring different results.

From 2007 the categories created by the government to investigate the development level of microregions (especially in the case of the most disadvantageous micro-regions and micro-regions supported by complex programme) served a good field of research. In the 1st of January, 2013, by the creation of districts the methods had to be changed, and in the beginning of 2015 the list of beneficiary districts was unveiled. The situation of the country had not improved much between 2007 and 2015; therefore the number of disadvantageous areas exceeded the number from 8 years ago. Furthermore, there were new disadvantageous areas in previously more developed territories.

During this topic we investigated if there is an obligatory methodology to create categories, and if so, what elements it consists of, what factors does it take into account. We examined if there is a difference between the ways of defining micro-regions and districts.

Defining the beneficiary micro-regions, 2007. The socio-economic development in certain regions in Hungary is on varying development levels, due to their different geographical positions, internal resources and external endowments. Certain regions, settlements are not able to tackle with the challenges of globalisation and territorial competition by themselves. There are a significant number of regions which, without government intervention, would lag even further behind. The task of determining the principals of regional development subsidies and decentralisation, and establishing the criteria-system for classifying beneficiary regions belongs to the Parliament. By law, categorising beneficiary regions is the task of the Government [1].

In 2007, a regulation made it compulsory to establish the development level of micro-regions as well. During the categorisation of micro-regions according to their development levels – with the exception of micro-regions where cities with county rights can be found – a complex index based on economic, infrastructural, employment and two kind of social indicators (five index groups) has to be used [17].

Beneficiary micro-regions consist of disadvantageous micro-regions, including the most disadvantageous micro-regions.

Micro-regions are disadvantageous microregions, if their complex index values are lower than the average of all the micro-regions’ complex index value. In this category, we call the most disadvantageous micro-regions those areas, which have the lowest values and contain 15 % of the population the country.

A complex programme – based mainly on European Union funds – is needed to be created to support and promote those most disadvantageous micro-regions, which have the lowest complex index values and where 10 % of the population of Hungary lives, and the implementation should be supported with special consideration [7].

In the case of dynamic indicators data from the last available years are used to define beneficiary micro-regions. In the case of static indicators, the newest available indicators are used.

In order for the complex indicator system to take the changes of socio-economic situation into special consideration, and to ensure that the actual needs are included into the system, besides the economic, infrastructural and employment indicators, two groups of social indicators are used, too. In the new system the social factors are more highlighted than before, but in the same time the economic, infrastructural and employment factors become less important. The four-index consisting of 19 indicators will be changed to a system with five groups (31 indicators), by which the situation of micro-regions can be observed in a wider spectrum [6].

The data and the reference years for calculating the complex indicator system for measuring the development level of micro-regions are the following [18]:

• I.    Economic indicators:

• 1.    The number of operating business organisations per 1 000 inhabitants, number, 2004.

• 2.    Number of guest nights spent in business and private accommodations per 1 000 inhabitants, nights, 2005.

• 3.    Number of retail establishments per 1 000 inhabitants, number, 2005.

• 4.    The rate of people working in agriculture within the employment structure, %, 2001.

• 5.    The rate of people working in the service sector within the employment structure, %, 2001.

• 6.    The change of the number of operating business organisation, %, 1999–2004.

• 7.    Local tax revenues of local governments per one inhabitant, Ft, 2005.

• 8.    The number of academic researchers and developers per 1 000 inhabitants, number, 2005.

• II.    Infrastructural indicators:

• 1.    The percentage of houses connected to the water system, %, 2005.

• 2.    The length of sewage system per one km of water pipeline system, meter, 2005.

• 3.    The percentage of households consuming gas from the gas network within the whole house stock, %, 2005.

• 4.    The percentage of houses included in regular waste collection, %, 2005.

• 5.    The indicator of everyday accessibility, minute, 2007.

• 6.    Number of telephone stations (including ISDN) per 1 000 inhabitants, number, 2005.

• 7.    The number of cable television subscribers per 1 000 inhabitants, number, 2005.

• 8.    The number of broadband internet subscribers per 1 000 inhabitants, number, 2006.

• 9.    Accessibility indicator of high-speed roads, minute, 2007.

• III.    Social indicators/1:

• 1.    The percentage of flats with 3 or more rooms from the house stock by the end of the observed time period, %, 2000–2005.

• 2.    The number of cars per 1 000 inhabitants, weighted by the cars’ age, number, 2005.

• 3.    Balance of migration at the middle of the observed time period, annual average per 1 000 inhabitants, number of people, 2000–2005.

• 4.    Mortality rate (the number of deaths per one thousand inhabitants), number, 2005.

• 5.    Income (after which the person pays personal income tax) per one inhabitant, Ft, 2005.

• 6.    The index of urbanity/rurality (what percentage of the micro region’s population lives in a settlement with a population density of 120 people/km², %, 2007.

• IV.    Social indicators/2:

• 1.    Rejuvenation index (the percentage of inhabitants below the age of 15 per the inhabitants above the age of 60), %, 2005.

• 2.    The percentage of households without employed people, %, 2001.

• 3.    The percentage of people in the age of 18 or above with secondary education, %, 2001.

• 4.    The annual average number of inhabitants receiving regular social support from the local government per 1 000 inhabitants, number of people, 2005.

• 5.    The percentage of inhabitants receiving regular child protection support from the age group between 0–24 years, %, 2005.

• V.    Employment indicators:

• 1.    The percentage of registered job seekers from the working age population, %, the average of 2006.

• 2.    The percentage of long-term – for at least 12 months – registered job seekers from the workingage population, %, the average of 2006.

• 3.    Activity rate, %, 2001.

The average value of the indicator groups provided the index of the economic, social, infrastructural and employment situation. The average of the five indicator groups became the common index of underdevelopment, the so-called complex development index in those micro-regions, where there are no cities with county rights. In the case of microregions with cities with county rights the complex indicator calculated with and without the seat of the micro region was the basis of classification [14].

94 micro-regions were below the average (2.90) value; these are considered to be lagging behind microregions. The population number of the 94 lagging behind micro-regions in the 1st of January, 2007 was 3.152 million, which is 31.3 % of the population of Hungary.

Defining the beneficiary districts, 2015. During the categorisation of districts complex a complex index consisting of social, demographic, housing and life circumstances, local economic and employment, infrastructural and environmental indicators was taken into account.

According to the regulation [5]:

– Districts in need for development: districts characterised by the lowest complex indicator values with 15 % of the population number of the country.

– Beneficiary districts: those districts, which have lower complex indicator value than the average of all the districts.

– Districts in need for complex development programs: these areas are the lowest performers with 10 % of the population number of the country.

• I.    Indicators for social and demographic situation:

• 1.    The index of urbanity/rurality (what percentage of the micro-region’s population lives in a settlement with a population density of 120 people/km², %.

• 2.    Mortality rate (the number of deaths per one thousand inhabitants) (the average value of the last five years), ‰.

• 3.    Balance of migration per one thousand inhabitants (the average value of the last five years), number of people.

• 4.    The number of places in day care centres per ten thousand 0–2 years old, number of places.

• 5.    Percentage of inhabitants receiving regular child protection support per the age group of 0– 24 years old, %.

• 6.    The number of inhabitants receiving social support per one thousand inhabitants, number of people.

• II.    Indicators of housing and life circumstances:

• 1.    Average prices of used flats, Ft.

• 2.    The percentage houses built in the last five years from the whole housing stock at the end of the year, %.

• 3.    The percentage of substandard occupied houses from all the occupied houses, %.

• 4.    Income (after which the person pays personal income tax) per one inhabitant, thousand Ft.

• 5.    Cars operated by natural person, weighted by age of the cars per one thousand inhabitants, number of cars.

• 6.    Life expectancy at birth, – men, years.

• 7.    Life expectancy at birth, – women, years.

• III.    Local economic and employment indicators:

• 1.    The percentage of people in the age of 18 or above with secondary education, %.

• 2.    The percentage of registered job seekers from the working age population, (annual average), %.

• 3.    The percentage of long-term – for at least 12 months – registered job seekers from the working age population, %.

• 4.    The percentage of registered job seekers with only primary education, %.

• 5.    The number of operating businesses per one thousand inhabitants, number.

• 6.    Number of retail establishments per one thousand inhabitants, number.

• 7.    The percentage of local tax revenues of the local government from the annual revenues, %.

• IV.    Infrastructural and environmental indicators:

• 1.    The percentage of houses connected to the sewage system, %.

• 2.    The percentage of houses included in regular waste collection, %.

• 3.    The number of broadband internet subscribers per 1 000 inhabitants, number.

• 4.    The percentage of built roads from all the roads within the authority of the local government, %.

The methodology of calculating the complex index does not differ from the one established in 2007 to categorise micro-regions. The basic indicators were transformed to scales of identical length by a normalisation process by the following formula:

fa, , - min fa(, ) fa, j =        ------- * 100

max( fa, , _y ) - min( fa, , j )

where fa_i,j – normalised basic indicator, min( fa_i,j ) – the minimum value of the basic indicator, max( fa _,j ) – the maximum value of the basic indicator.

The average value of the basic indicators gives the value of the group indicators. Its formula is the following:

1n fa    X fa. j n j=1

where fa – group indicator, fa _,j – normalised basic indicator, n – the number of indicators within the group.

The value of the final complex indicator, which is the value of the development level complex indicator, comes from the average values of the four group indicators by the following formula:

m fi=—X fa, m,=m where fa – group indicator, f – complex indicator, m – number of group indicators.

During the establishment and rethinking of the indicator system it was a usual problem to represent certain segments (economy, society, infrastructure and environment). By the investigation of the methodology from this direction it would be easier to control the real weight of certain groups and the indicators belonging to the categories should be selected more effectively.

Conclus ons. It was demonstrated throughout this paper that the analyzation process of a region can be very complex and it can be interpreted in differently if carried out in different year or by another researcher. There is one distinctive characteristic in every analysis, which is the need of answers to the following questions: Which variables should be used? Where should the emphasis be? More economic or more social indicators? Statistical data or empirical? How many variables are truly needed? How to interpret the results of an investigation properly? Only after answering these questions can our research become unique.

Many regional investigations tend to be rather theoretical, and have less practical results. Therefore, country-level analyses similar to the ones mentioned in this paper could be risky, since they could contribute to the unequal distribution of development funds, which can potentially cause increasing regional disparities. It is logical to question the usefulness and reliability of the indicators and methodology used for the investigation, since by using them researches will probably have an impact on the population. For instance, in the case of the Visegrad countries it was proven that their economic situation showed considerable diversity even at the time of their EU accession, which was further amplified by the use of European Union’s Structural and Cohesion funds [10].

Investigations must take the situation into account by using a well-prepared methodology based on facts. An example could be the designation process of free enterprise zones in Hungary – only those micro-regions (districts) are parts of these zones which are districts in need for complex development programs. Being part of these zones holds many advantages; for instance, the tax rates are lower inside them and businesses receive other benefits, too. Therefore, the less developed regions can revitalize their economies. However, if the methodology is failing – for example, by missing crucial indicators showing the real situation in the region – the micro-region may fall out from the category, thus losing its position in the beneficial enterprise zone.

The aim of this paper is not to criticize methodological bases, but to shed light on the fact how some seemingly small factors can affect the results of analyses. Taking them into account becomes imperative when carrying out country-level investigations.