Non-parametric coefficients of Chuprovsimilarity were calculated for all pairs of indicators across the whole set.The set of indicators was>
The share of mortality due to infectious diseases (total malepopulation);
The share of mortality due to injuries, poisoning, murders (totalmale population);
Private cars per 1,000 inhabitants;
The share of mortality due to blood circulation diseases (totalmale population);
The share of mortality due to neoplasm (males, rural areas);
Indicators determined by climate (sum oftemperatures registered over the vegetation period (centigrade); number of dayswith temperatures below zero; length of motor roads km per 1,000 sq. km;average temperature of a cold month (centigrade); difference in temperatures ofwarm and cold months; annual rainfall (mm); employment in manufacturingindustries (per cent); employment in pasture cattle breeding (per cent);employment in agriculture, pen cattle breeding (per cent); employment inforestry and hunting (per cent);
Indicators determined by nutrition structure (share of animal fats in nutrition intake (per cent); share ofmeat, egg, milk proteins in nutrition intake (per cent); share of potato,bread, sugar carbohydrates (per cent); share of vegetable proteins in nutritionintake (per cent); share of vegetable and fruit carbohydrates in nutritionintake (per cent); outflow of native-born individuals (in per cent of currentnumber);
Indicators determined by regional infrastructure (share of housing with conveniences (per cent); share of urbanpopulation in the total population (per cent); cost of non-productive fundsRub. mil. per capita); share of individual residential housing (per cent);number of inhabitants per 1,000 ha of populated area; ratio between thesubsistence level and aggregate incomes (per cent); Pb pollution per 1,000 haof populated area (kg); mortality due to respiratory diseases (total malepopulation, in per cent of the natural mortality); share of vegetable fats innutrition intake (per cent);
Indicators related to the regional developmentspecifics (employment in fuel industry, mining (percent); share of urban population living in the regional center (per cent);employment in non-productive sphere (per cent); fuel consumption, seasonadjusted (metric ton per capita);
Indicators related to settled population and ethnicspecifics (share of settled population, in per centof current number; share of mobile in-migrants in per cent of current number;share of non-Russians in the population composition (per cent); share of fishproteins in nutrition intake (per cent); average calorie intake (kilocaloriesper day); standardized mortality (total male population); employment intransport, construction, etc. (per cent); mortality due to cancer (males,urban, in per cent of natural mortality); employment of economically activepopulation (per cent); mortality due to digestive organs diseases (total malepopulation, in per cent of natural mortality).
By the method of building multifactorregression models each group of indicators was reduced to a single parameter,which most precisely corresponded to the total set of indicators included inthe group. Therefore, the general set of primary factors was “compressed” into10 indicators giving quantitative evaluation for each of 89 regions in thecountry.
The outcome was the followingclassification of Russia’s regions into groups and subgroups:
Group 1,comprising only one subgroup 1.1., includes northern okrugs, excluding key oil and natural gasproducers. The difference in indicators is apparent – severe climate, underdevelopedinfrastructure, higher share of animal foods, etc. However, the mostcharacteristic feature is higher than in two other groups rate of mortality dueto causes related both to severe living conditions, and high mobility andrelated aggressiveness of population. The group comprises Taymyr, Nenetsian,Chukotka, Koryakian, and Evenk AOs.
Group 2. The keydistinctive feature of this group is the large number of private cars, i.e. itis the zone of most prosperous population living in areas with developedtransport infrastructure.
Subgroup 2.1. comprises regions of FarEast and most industrially developed regions of Eastern and Central Siberia, aswell as two mostly industrial, narrow specialized regions – Kemerovo oblast and Republic ofKarelia. Although the subgroup has the shortest length of roads, in these areaswas registered the largest number of cars as a result of Soviet “northernbenefits” and massive import of used cars from countries outside the formerSoviet Union occurring in the beginning of the period of reforms.
In thissubgroup there was registered the highest rate of mortality due to causesrelated to severe living conditions, high mobility of population and relatedaggressiveness of population (unnatural mortality). The subgroup comprises:Primorsky, Krasnoyarsk, Khabarovsk krais, Kamchatka, Kaliningrad, Sakhalin,Irkutsk, Amur, Kemerovo oblasts, Republics of Khakasia and Karelia.
Subgroup 2.2. A specific feature of thissubgroup is the fact that its population is the least prosperous as compared toother regions belonging to this group. In spite of the most lengthy roadnetwork in this subgroup there is registered the smallest number of cars. Thesubgroup includes a motley set of regions belonging to European Russia,Caucasus, and Western Siberia. A distinctive feature of this subgroup is theall-Russian minimal rate of mortality due to unnatural causes and to bloodcirculation diseases, least developed infrastructure, the largest share ofrural and settled population – i.e. the most tranquil way of life and as a result – the most healthy population inthis group. The subgroup comprises: Yaroslavl, Belgorod, Orenburg, Samara,Tula, Saratov, Volgograd, Novosibirsk, Moscow, Voronezh, Tomsk, Chelyabinsk,Rostov, Kurgan oblasts, Stavropol, Altai krais, Republics of Adygea, NorthOsetia, and Karach-Cherkesian Republic.
Subgroup 2.3. includes the zone ofsettlement of the most prosperous populations. Accordingly, in this subgroup isregistered the group highest rate of mortality due to blood circulation anddigestive organs diseases, most unfavorable environmental parameters, maximaldensity of urban populations, maximal consumption of meat products and animalfats, etc. The subgroup comprises port northern regions, oil okrugs of WesternSiberia and both capital cities. Cities of Moscow and St. Petersburg, Murmansk,Magadan oblasts, Khanty-Mansi and Yamal-Nenetsian AOs.
Group 3 comprisesthe rest of Russia’sregions with populations poorer than in the second group. Alongside with smallnumber of cars, regions of this group demonstrate the all-Russian minimumindicators of meat product consumption and maximum indicators of vegetableproduct consumption, and the minimum mobility of population.
Subgroup 3.1. includes a number ofindustrially developed regions of the European part, Ural, and Western Siberia.The subgroup comprises more developed than in subgroup 2.2 regions, however,they have experienced more severe depression over the period of reform. Aspecific feature of this subgroup is highest rate of mortality due to bloodcirculation diseases and worst environmental parameters in the group. Thesubgroup includes: Vologda, Vladimir, Nizhny Novgorod, Tyumen, Kirov, Lipetsk,Ryazan, Sverdlovsk, Tver, Novgorod, Arkhangelsk, Leningrad, Perm oblasts,Republics of Komi and Tatarstan.
Subgroup 3.2 comprises the mostindustrially backward regions across the country. It shall be noted that thenumber of cars in this group is at the maximum as compared with other regionsof the same type, since local populations needed cars most in the Soviet time.Other parameters are at the group’s average levels. The subgroup includes: Smolensk, Omsk,Ulyanovsk, Penza, Oryol, Kostroma, Astrakhan, Tambov, Chita, Pskov, Kalugaoblasts, Udmurtian Republic, Republics of Buryatia, Bashkortostan, Sakha(Yakutia), Yevreyskaya Autonomous Oblast.
Subgroup 3.3. comprises the leastindustrially developed autonomies of Russia. Main specific features of theseregions are a very low (all-Russian minimum) availability of housing providedwith conveniences, low mobility of population, all-Russian minimal ratiobetween household incomes and the subsistence level. Therefore, these regionsare extremely poor. Here is registered higher rates of mortality due to socalled unnatural causes, diseases of digestive organs, cancer (males, rural),infectious diseases. Persistence of traditional economy and way of life undersevere conditions in mountains and steppes is a factor behind thesedevelopments. The subgroup comprises: Republic of Ingushetia, ChechenRepublic, Republics of Kalmykia, Tyva, Dagestan, Altai, Ust'-Orda AO,Aguinsky Buryat AO.
Subgroup 3.4.comprises most depressive regions at present time. A specific feature of theseregions is the minimal consumption of meat products (at the all-Russianminimum); a very high rate of mortality due to respiratory diseases (onlyslightly below the preceding group), is apparently related to nutritionpeculiarities and stress caused by economic depression.
The subgroup comprises:Ivanovo, Bryansk, Kursk oblasts, Republic of Mariy El, Chuvash Republic,Republic of Mordovia, Komi-Permyak AO.
Classification of Russia’s regions according to medicaland environmental indicators
Group I Group II GroupIII
Subgroup 1.1. Subgroup 2.1 Subgroup 3.1
Subgroup 2.2 Subgroup 3.2
Subgroup 2.3 Subgroup 3.3
8. A typology of European Union regions
Typologies of regions according to levelsof their social and economic development have been frequently carried out indifferent countries. The key common feature of such typologies is theirrelative simplicity, authors usually introduce only few indicators, which theythink most comprehensively reflect specialization and socioeconomic situationof regions. Below we include some examples of such studies.
9 analyzed the economic specialization and functional structure ofEU regions in 1997. 202 EU regions were selected as typology units ЕС (NUTS1and NUTS2). Five indicators were selected as criteria:
labor force participation rate;
per capita income;
proportion of persons employed in industry;
proportion of persons employed in service sector.
Basing on cluster analysis the regions wereclassified into 8 types:
Type 1.Metropolitan service regions, which comprise largest EU cities;
Type 2.Semi-peripheral service regions;
Type 3. Poorservice regions, which comprise only regions in Italy and Spain;
Type 4. Industrialcore regions;
Type 5. Industrialsemi-periphery;
Type 6. Industrialperiphery;
Type 7. Collapsedindustrial regions;
Type 8. TheMediterranean agricultural regions.
Types 1, 2 and 4 were further divided into2 subtypes each, type 5 was subdivided into 3 subtypes, since from theauthor’s point ofview they included some regions significantly differing by territory and anumber of other indicators.
9. A typology of Slovak regions
In Slovakia the Academy of Science’s Institute for Prognostication*
10*regularly compiles typologies of the country’s regions according to per capitaGDP dynamics and unemployment rates. Regions are >
Type 1. Regionsdemonstrating growth in per capita GDP and unemployment. In mid-1990s this typecomprised 20 regions.
Type 2. Regions,where unemployment decreased at the background of growing per capita GDP. Inmid-1990s this type comprised 13 regions. A key specific feature of theseregions was a growth in new spheres of the Slovak economy, first of all, theservice sector. The capital region of the country belongs to this type.
Type 3. Regions,where per capita GDP decreased at the background of growing unemployment. Inmid-1990s this type comprised 6 regions demonstrating the worstdynamics.
10. A typology of Yugoslav regions
In the late 1980s and early 1990s,Chaslav Ocic, an expert of the Belgrade Institute forEconomics*
11 published several studies dedicated to the regional problems in(socialist) Yugoslavia, in particular, the>
Type 1. Mostdeveloped regions. Slovenia.
Type 2. Developedregions. Croatia and Vojvodina.
Type 3.Underdeveloped regions. Serbia, Montenegro, Bosnia and Herzegovina, Macedonia.
Type 4. Leastdeveloped regions. Kosovo and Metohia.
The author notes a significantdifferentiation of the indicators across the territory of the SFRY. Especiallylarge gaps exist between Slovenia and regions of the second type, and regionsof the third type and Kosovo and Metohia.
11. “Strong” and “weak” towns ofRussia*
This study is based on the mostcomprehensive set of data available in 1996 (940 towns out of total 1090, 87mil. inhabitants out of total 95 mil.). The only explanation of the fact thatthese data have remained practically used over previous years is the laborcosts of processing and understandable lack of trust in statistics.
The set of indicators characterizing“strong” and “weak” towns of Russia, leaders and outsiders was limited by thecapacity of the database. There were selected seven key negative and positiveparameters ranked by primitive points (the more the number of points the betterthe situation):
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