The aim of clustering the districts is to identify, and analyse specific groups of districts whose regional profiles are similar.
This process is carried out simultaneously for all indicators characterizing the socio-economic situation and the development of districts by using neural networks.
The types of regional profiles identified can be used for a variety of purposes: to identify complex positive or negative developments, to reveal and analyse the reasons due to which the different types of regional profiles have emerged, to formulate general or sectoral policies for a given type of regional profile identical for all districts in the cluster, etc.
Eight types of regional profiles have been identified. For some of these, the characteristics of the districts falling within each type are substantially similar. For others, such similarity is not as clearly outlined, and in this sense they do not form natural clusters, but they are characterized in the analysis for the purpose of completeness.
The district in which the capital city and its adjacent residential areas are situated has been separated into its own cluster and occupies, as would be expected, the top position in socio-economic terms. Sofia (capital) only cedes its "primacy" in certain social areas, e.g. in healthcare.
Sofia District (capital city) reported the largest per-capita GDP in 2010. According to this indicator, the runner-up (Stara Zagora District) reported a value that is 2.3 times lower. Sofia District also features the highest employment rate and highest income per household member.
Against the background of such positive economic performance there is the strongly contrasting assessment that Sofia (capital city) is one of two districts with the worst business environment; this is mainly due to the high local tax rates. The tax rate for the annual retail license tax per 100 square metres of net shopping area is the highest in Sofia (capital city): BGN 20 in 2013, i.e. double the average rate for the country. The waste collection charge is also among the highest in the country. This district also receives one of the worst scores awarded by the business community on the level of unofficial payments made.
The capital city is no exception to the negative trends for the entire country in the demographic and economic spheres; here, however, they are relatively less pronounced than in other areas. Sofia (capital city) is the most attractive to migrants, and has reported the highest positive net migration rate of 5.4‰. This district is the only one among all districts in the country with a positive growth of population density.
Developments in the healthcare field in this district are the most negative when compared to other districts. Sofia (capital city) has the largest decline in the number of health-insured residents per 100 people of the population and ranks second in the increase of cases of hospitalisation in multi-profile hospitals for active treatment per 1,000 people of the population.
The five districts in the cluster enjoy a favourable demographic situation in comparison to other districts in the country. Only Sofia (capital city) has a relative advantage over them in this respect. In two of the districts in the cluster (Varna and Bourgas) the general negative demographic trends valid for the whole country are less pronounced. Sofia (capital city), Blagoevgrad, Varna, and Bourgas have the most favourable age structure of the population measured by the age dependency ratios 65+ to 15-64 year-olds, 22.7%, 24.0%, 24.4%, and 24.5% respectively, against the country average of 28.5%.
The economic situation of the cluster ranks it among the top performing districts. It ranks second only after the Sofia (capital city) cluster. From among the districts in this cluster, Bourgas occupies the position immediately below Sofia (capital city). The districts of Blagoevgrad, Plovdiv, and Bourgas benefit from a better employment rate than the country average, with the employment rate in the district of Blagoevgrad being 6.5 percentage points higher than the national average of 46.6%.
The dynamics of economic development for the cluster, however, is not so pronounced. All five districts feature negative development trends, with the district of Varna suffering from the most negative economic developments over the period as compared to all other districts in the country.
All districts from the cluster, with the exception of Bourgas, report a better condition of the infrastructure compared to the national average.
The districts in this cluster are characterized by contrasts in their socio-economic situation.
The infrastructure in all four districts is in better condition than the country average. The density of the road network in Pernik and Sofia is 23.7 and 21.2 km/sq. km respectively, against a national average of 17.6 km/sq. km.
The degree of development of the economy and the educational environment of Sofia District is higher than the national average. The unemployment rate in the district is 6.4% (the lowest value for all districts), against a country average of 12.3%. The relative share of "fail" scores from the matriculation exam in Bulgarian language and literature for the district is 2.20%, with only Sofia (capital city) reporting an insignificantly lower share (2.19%).
Yambol District features the best dynamics of the economy compared to all districts in the country, while Sofia District has the most thriving business environment. Yambol ranks second in terms of increased employment rates and the most intense increase in the number of enterprises per 1,000 of the population: 2.1% against a national average decrease of 1.1%.
Trends in healthcare are positive. Infant mortality in the districts of Stara Zagora, Sofia, and Yambol is decreasing at a rate that is two to three times higher than the rate of decrease for the entire country over the period of concern.
In spite of the positive trends of development, the condition of certain environmental components in some of the districts in the cluster is not good. The districts of Stara Zagora and Pernik are the two districts with the most strongly deteriorating environment in the country. The emissions of carbon dioxide into the atmosphere in Stara Zagora District are 2.8 times higher than the district ranking second according to this indicator (Varna), and 12 times higher than the country average.
Disparities in development are characteristic of this cluster. With regard to certain aspects of the socio-economic development, this cluster is among the leaders in the country, while for others it is at the very bottom of the ranking.
This cluster shows the best development of the educational system (Kardzhali District ranks first in the country and the remaining two districts are immediately below it). The average matriculation grades in Bulgarian language and literature in the district are the highest (4.60), the country average being 4.26.
Figure 4: Please see image 4 in the Pictures folder
Kardzhali District is the national leader in infrastructure development. The relative share of households with Internet access in the district has increased a stunning 24 percentage points (from 29.7% to 54%).
On the other hand, trends in healthcare are negative. The number of population per one specialist in cardiology in the districts of Kardzhali and Targovishte has increased by 19.4% and 15.6%, respectively, compared with an average decrease of 3.3% for the country. The number of population per one physician specialised in Internal Medicine in Targovishte has increased by 22.4%, against a national average decrease of 1%. Targovishte reported the worst state of healthcare: in this district, one general practitioner is in charge of an average of 2,057 local residents, compared with the country average of 1,491 people.
Developments in the business environment are also slipping in a negative direction. The Haskovo District suffers from a severely deteriorated business environment, and has reported one of the strongest negative trends in corruption perceptions according to the business community in the district: an 18% deterioration in the score given.
The socio-economic development of the cluster is negative. The only exception is the development of healthcare. The number of population served by one general practitioner in the Gabrovo District has decreased over three times compared to the country average: respectively by 13.3%, as compared to 4.7%. The infant mortality rate in the Smolyan District has halved. By comparison, the national average decrease was 9.1%.
All remaining areas of development in districts from the cluster have registered negative trends.
Figure 5: Please see image 5 in the Pictures folder
Smolyan District reported the worst trends in the development of business environment and a strongly deteriorated economic and demographic condition. The assessment of the quality of the electronic services provided by the district/municipal administration in the district has halved: from 4.8 to 2.5 on a 1 to 5 scale.
The deterioration of the age structure of the population in the two districts is the most strongly pronounced as compared to all remaining Bulgarian districts. It is manifested by an increase in the age dependency ratios (65+ to 0-14 years of age) by three and two percentage points respectively, against unchanged country-average levels.
The Gabrovo District reported a severely deteriorated demographic situation and development. The district also shows signs of negative trends in the natural environment, social environment, and business environment.
The socio-economic condition of the cluster is characterised by contrasts. On the one hand, the districts benefit from a good condition of the education system (Smolyan being the national leader), the environment, and healthcare (Gabrovo ranks first in the country). On the other hand, their demographic situation is worse than the national average (particularly Gabrovo District), with the age dependency ratio (65+ to 0-14) for the district being 1.6 times higher than that for the entire country.
The socio-economic condition of districts in this cluster gives them a position that is close to, but still below the country average.
The economic condition of the cluster is below the national average, with Montana District reporting a GDP per capita that is only half that for the country.
The same applies to education, the environment, infrastructure, and healthcare: the districts in this cluster are close to, but still below the national average. The relative share of the population aged 25-64 years with higher education in districts from this cluster is between 16.8% and 21.7%, while the national rate is 24%. The highest share of population living in settlements with public sewerage systems (70.5%) from among districts in the cluster is that of Pazardzhik District; however, this value is still lower than the country average of 74%. The relative share of households with Internet access in all six districts (from 37.3% to 49.7%) in the cluster is below the national average (50.9%).
The dynamics of socio-economic processes in the cluster is also near the average levels. The share of population living in material deprivation in three of the districts has increased slightly by between 0.3 and 2 percentage points, while for the remaining three districts it has decreased by 0.8 to 7.7 percentage points.
This cluster is characterised by the poor socio-economic condition of its constituent districts.
Virtually all districts in the cluster are in an economic and demographic condition that is worse than the country average, with Vidin District reporting the poorest economic and demographic condition from among all Bulgarian districts. The district has the third lowest GDP per capita, equal to half of that for the country, and only 20% of the GDP reported by the leading district, Sofia (capital city). This district also has the lowest employment rate compared to all other districts in the country, with an employment rate of 37%. The age structure of the population in the district is also among the most deteriorated: only Gabrovo District has reported worse results.
The average income per household member in Lovech District is 30% lower than the country average.
Infrastructure in this cluster is in poor condition. Kyustendil, Lovech, and Vidin are the three districts in the country with the lowest relative share of households with Internet access: about one-third of households, against a 50.9% national average.
All districts in the cluster have reported worse-than-average trends in the development of education and demographics. Lovech, Vidin, and Kyustendil reported a decrease in the share of the population with higher education aged 25 to 64 when compared to national trends. Lovech District reported the largest reduction according to this indicator as compared to all other districts: 5.2 percentage points.
This cluster is made up of the districts of Razgrad, Silistra, and Sliven. Razgrad District has very pronounced negative demographic trends. Only the districts of Smolyan and Gabrovo have reported worse demographic developments than Silistra District. Razgrad District has the second lowest natural population growth rate and is among the districts with the fastest deteriorating age structure of the population. Silistra District is among the four districts suffering from the fastest depopulation.
The economic situation of these three districts is also characterized by some of the most deteriorating indicators: only two other districts have less developed economies. The Sliven and Silistra districts reported the lowest GDP per capita: only half the national average.
The situation in the field of education is not very different. Sliven District not only has the worst educational environment, but also the most negative trends. On almost all indicators, Sliven District ranks either last or very close to the last position. The group net enrolment rate of the population (grades 5th through 8th) in the district is the lowest in the country. The same applies to the number of teachers per 1,000 students in primary and secondary education. In the districts of Razgrad, Silistra, and Sliven there are respectively two, three, and four university students per 1,000 of the population, while the country average is 38 per 1,000.
As a result of clustering using neural networks (Kohonen maps) certain specific types of regional profiles have been identified. The most significant profiles are, as follows:
As in 2012, the following conclusions are still valid:
Although the results of clusterisation in 2013 are methodologically not directly comparable with those from 2012, some trends in the formation of the types of regional profiles can be commented on: