CC BY
82
Elena B. DVORYADKINA
Dr. Sc. (Econ.), Prof. of Regional, Municipal Economics & Governance Dept.
Ural State University of Economics
62/45 8 Marta/Narodnoy Voli St., Yekaterinburg, Russia, 620144 Phone:(343)221-17-55 e-mail: dvoryadkina@usue.ru
Elizaveta A. BELOUSOVA
Engineer of Scientific Research Dept.
Ural State University of Economics
62/45 8 Marta/Narodnoy Voli St., Yekaterinburg, Russia, 620144 Phone:(343)221-26-33 e-mail: belousova.elizaveta@inbox.ru
Municipal Districts in Economic Space of a Region: Constructive and Destructive Trends
The research aims to design a method for identifying trends in municipal districts' economic development in economic space of Russian regions. Municipal districts are a type of rural municipalities in Russia, therefore methodologically the paper relies on the theoretical propositions of both spatial and rural economics. The authors systematise Russian scholars' approaches to the analysis of the municipalities' socioeconomic development and based on this suggest their method for identifying constructive and destructive trends in economic development of municipal districts. The method encompasses four stages: assessment of scales of municipal districts' presence in economic space of a region, assessment of municipal districts' own economic dynamics, generalisation of these assessments and final identification of type of trends with the ultimate conclusions about municipal districts' impact on the economic space of a region. The authors test the method at the example of Sverdlovsk oblast. According to the research findings, four out of five municipal districts in the oblast develop constructively. The only municipal district that has a destructive effect on economic space of the region is Slobodo-Turinsky municipal district. At this, the authors consider a trend constructive if it contributes to the development of economic space of a region, and destructive if it leads to its destruction. Theoretical and practical significance of the work is that the designed method allows not just assessing the economic dynamics of municipal districts but also linking it to the regional spatial economic development.
K JEL classification: P25
° Keywords: rural municipality; rural area; saturation of economic space; spatial basis; coher-< ence of economic space; constructive trends; destructive trends.
£
s Introduction
g
m An extension of regional economics into spatial economics in the late 20th century logics #\cally resulted in the discussion about the research subject of the latter [14; 17]. Another ^ inevitable problem appeared to be the methodology of the new subfield of economic theory. 2 As P. A. Minakir puts it, despite significant progress in the field of spatial econometric mod-si els for analysing regional economic systems of different scale and interactions between them, £ the principal task - a complete and consistent description of spatial behaviour of economic © agents, interactions between them, as well as mutual reactions of economic behaviour and
other forms of social behaviour in space - has not been fulfilled as yet [17. P. 18]. Thus, methodological issues of spatial economic analysis are a relevant scientific problem.
Municipal districts (MD) are the second by number type of municipalities in the Russian Federation. Local self-government in municipal districts needs tools that allow analysing and assessing the municipalities' economic development with a possibility to correct it. According to Ye. M. Bukhvald and N. V. Voroshilov, absence of complete and reliable information about socioeconomic development of municipalities and their tax potential in Russia remains one of the main problems hampering efficient management of municipal development [2. P. 138]. Therefore, the present study aims to suggest a method for identifying trends in economic development of municipal districts, which does not only allow assessing the dynamics of municipal districts' economic indicators, but also linking it to the transformation of regional economic space. The objective of the study required fulfilling a number of tasks:
• to critically review theoretical approaches to studying socioeconomic development of municipal districts;
• to develop a system of indicators and an algorithm for investigating trends in municipal districts' economic development in accordance with their spatial economic characteristics;
• to analyse and identify trends in economic development of municipal districts considering the case of Sverdlovsk oblast.
Theoretical approaches to studying socioeconomic development of municipal districts
The analysis of the scholarly literature revealed that in the studies of the socioeconomic development of municipal districts Russian economists use several groups of indicators. These groups of indicators characterise municipal districts either as a complex object of assessment, or as an industry-specific object of assessment. Table 1 presents a review of the methodologies for evaluating socioeconomic development of municipal districts and briefly describes their content.
Table 1
The review of the methodologies for evaluating socioeconomic development of municipal districts
Author, year Methodology content Approach
S. V. Gritsenko, 2009 [4] Methodology is based on an integrated indicator, which consists of three indices: municipal product index, economic development index, and social development index. The results of the assessment are used in classification of municipal districts by means of cluster analysis. The municipal product index is calculated by multiplying gross regional product (GRP) divided by regional number of employed, number of employed in a municipal district, and a ratio of average wages in a municipal district to average wages in a region. The indices of economic and social development are calculated as aggregated values of selected indicators reflecting the state of industrial development, retail trade, construction and investment, agriculture, municipal budget, education, healthcare, employment, incomes, and some others Complex
Ye. A. Gutnikova, A. N. Chekavinsky, N. V. Voroshilov, 2012 [5] The calculated final indicator is a sum of four integrated indicators, each reflecting different aspects of socioeconomic development. Thus, there are four blocks of indicators, each comprising from 4 to 7 indicators: demographic indicators (natural population increase, migration increase, population density, share of urban population), indicators of utility infrastructure and housing, indicators of standard of living, indicators of economic development (industrial production, agricultural production, fixed capital investment, budget sufficiency) Complex
Table (concluded)
Author, year Methodology content Approach
N. T. Avramchikova, A. I. Frolova, 2012 [1] Gross municipal product (GMP) is considered to be a single mac-roeconomic criterion, which allows a possibility to provide a generalised integrated assessment of economic activities and socioeconomic processes in the territory. For municipal districts, the authors suggest calculating GMP as a sum of municipality's own produced goods, provided services and performed works; municipal retail trade turnover; public catering turnover; volume of paid-for services to inhabitants; production of agricultural organisations Agriculture-oriented
A. V Shevandrin, 2012 [28] GMP is regarded as the central indicator in the methodology. It is believed to be capable of systemically integrate social, economic and environment components of socioeconomic development of a territory. GMP is calculated as a multiplication of GRP divided by regional number of employed; number of employed in a municipal district; regional average wages divided by municipal district's average wages. Other indicators used by the author for the comprehensive positioning of municipal districts by the level of socioeconomic development through multidimensional classification based on hierarchical and iterative methods of cluster analysis include per capita agricultural output, per capita investment in fixed capital, per capita volume of paid-for services, per capita retail trade turnover, the share of profitable organisations in the total number of organisations, the number of small and medium enterprises per 10 thousand people, per capita revenues of local budgets Complex
D. A. Syusyura, 2012 [23] The economy of a municipal district is viewed as a rural economy. Socioeconomic development of municipal districts is determined by the socioeconomic situation of the rural population, which in turn is evaluated through the aggregate indicators of living standards, the availability and condition of social and engineering infrastructures, and the extent to which they are used, and the indicator of the results of socioeconomic processes Agriculture-oriented
I. V. Ilyina, O. V. Sidorenko, 2016 [9] To monitor the state of municipal districts, the authors use an integrated rating assessment of the state of agricultural production by the method of the sum of ranks. The assessment is done on the basis of four blocks of economic indicators: "financial resources", "production resources", "production efficiency", "trends in agricultural production" Agriculture-oriented
O. V. Shumakova, M. V. Kutuzova, 2016 [30] The methodology is based on two indices: the socioeconomic development index (objective), determined by evaluating the blocks "demographic component and level of regional healthcare development", "level of economic development, employment, and incomes of the population", "social sphere quality and social and environmental safety" and the socioeconomic development index (subjective), determined on the basis of sociological surveys Complex
Table 1 allows dividing the developed methodologies into two groups: a. complex methodologies; b. agriculture-oriented methodologies. The first group of methodologies is based on the indicators (groups of indicators) that assess, except for the condition of municipal economy and social sphere, the important (according to its author(s)) components of socioeconomic development of municipal districts, such as demographic processes, standard of living, healthcare, environment, infrastructure sufficiency. The second group of methodologies starts from the assumption that agriculture is of fundamental importance for the development of municipal districts. Thus, when assessing the state of the economy of these municipalities,
the authors mostly rely on the assessment of agricultural production and the living standards of the rural population.
In addition to the above-mentioned methodologies, it is worth noting that there are works on socioeconomic development of municipal districts where they are examined as a part of a research object, for instance, as a type of a municipality [19], or part of a rural area [12; 22], or as a municipality of a non-urbanised territory [29], rural municipality [20; 6].
However, none of these methodologies comprehensively takes into account the spatial factor. The studies to this or that degree devoted to municipal districts in region's economic space focus on polarisation of economic space [11], inequality and asymmetry of economic development [13; 15; 25] or just evaluate spatial structure of a region [8; 10] and deal with economic space of concrete territories [26; 27].
A method for identifying trends in municipal districts' economic development in regional economic space
To reach the stated objective it is vitally important to answer two questions: what the transformations of regional economic space are and how to measure them by economic indicators of municipal districts.
The answers to these questions bring us back to the understanding of the essence of economic space. Russian academician A. G. Granberg wrote about economic space as a saturated territory (with various objects and ties between them: settlements, industrial enterprises, economically utilised and recreational places, transport and utility networks) [3. P. 25]. While characterising the quality of economic space, he attached particular significance to the parameters of density (indicators of population size, volume of gross regional product, natural resources per unit of area), location (indicators of uniformity, differentiation, concentration, distribution of population and economic activities across territory, including the existence of economically developed and undeveloped lands) and coherence (intensity of economic ties between parts and elements of space, mobility of goods, services, capital, people determined by the level of development of transport and communication networks) [3. P. 25]. The said parameters may help characterising the state and changes of regional economic space. Yet for the reason that the methodology being designed does not aim to study the processes of balanced distribution of population and economic activities in economic space, we believe another treatment given by academician P. A. Minakir and his colleague A. N. Demyanenko more relevant. According to them, economic space is a multitude of economic agents distributed within a certain geographic space and interacting with each other in accordance with the economic institutions that are common within this geographic space [18. P. 43]. In line with this definition the task of measuring spatial processes should be fulfilled through description of spatial behaviour and interaction of economic agents. The structure of the definition allows identifying several elements reflecting the state of economic space and changes in it:
• economic agents - an individual or a group of individuals, who participate in at least one of the processes: production, exchange, consumption [17. P 19];
• spatial basis, which determines spatial behaviour of economic agents and sets limits for geographic space, within which interactions occur;
• economic institutions, which are an infrastructural element, ensuring and supporting interactions between economic agents.
Therefore, transformations of regional economic space can be mirrored using the dynamics of these three elements.
The choice of economic indicators should not only allow assessing the impact of municipal districts on regional economic space, but also satisfy several criteria, in particular:
• reflect the development of municipal districts being a specific object in regional economic space possessing certain spatial economic characteristics;
• mirror the development of municipal districts being a part of a rural area, i.e. the set of indicators is adjusted taking into account the characteristics of municipal districts as rural territories [7];
• provide a possibility to conclude about trends contributing to the development of regional economic space and trends resulting in its destruction, i.e. identify these trends as constructive and destructive respectively.
The last criterion needs some explanation. Sharing the opinion of a Russian researcher V. N. Lazhentsev that spatial development is agreed progressive changes in exploitation and reproduction of natural resources, location and inner content of productive forces, resettlement of the population and arrangement of the environment for living activities [16. P. 97], we can conclude that economic space develops through:
• emergence of new settlements;
• increase in economic activities;
• increase in economically significant results (output/revenue);
• infrastructure development.
The opposite process (destruction of economic space) is characterised by disappearance of settlements, decline in economic activities, decrease in economically significant results, infrastructure degradation.
The synthesis of the presented viewpoints and formulated criteria allowed us to form three groups of indicators satisfying the aims of the method being developed: indicators characterising saturation of economic space with economic agents' activities; indicators characterising the development of the spatial (physical) basis; indicators characterising coherence of economic space.
Let us present the description of each group.
1. Indicators characterising saturation of economic space with activities of economic agents.
Economic agents participate in and interact at different stages of reproduction process taking place in municipal districts' economic space. The stages of reproduction process, which are reflected by the System of National Accounts, include production, distribution and consumption stages. Therefore, to assess saturation of economic space of a municipal district with the activities of economic agents we can use the following indicators:
• agricultural production as an indicator of production, output;
• investment in fixed capital (made by organisations located in the territory of municipality (without data for small entrepreneurship) as an indicator of distribution (of funds into accumulation of fixed capital);
• retail turnover as an indicator of consumption.
This group of indicators offers an opportunity conclude about the presence and changes in scales of economic activities occurring in economic space of a municipal district, which is an inherent part of regional economic space
2. Indicators characterising the development of the spatial (physical) basis of economic space.
This group characterises changes in utilisation of economic space within municipal district's territory. In terms of analysis, the indicators of this group permit to conclude about physical shrinkage / expansion of economic space. The group encompasses the following indicators:
• number of municipalities (municipal districts, rural and urban settlements) and settlements (cities, towns, villages, etc.);
• population density;
• area under crops (all crops in all types of farms);
• area under perennials;
• area of land allocated for housing construction, individual housing construction and integrated development for housing construction per 10, 000 people.
Algorithm for identification of constructive and destructive trends in economic development of municipal districts in regional economic space
3. Indicators characterising coherence of economic space.
This group characterises intensity of economic ties in municipal districts resulting from the level of development of transport and communications infrastructure that supports interaction of economic agents. Thus, the group includes:
• share of the population living in settlements that do not have regular bus and / or railway communication with the administrative centre of the municipal district, in the total population of the municipal district;
• share of the length of local public roads that do not meet regulatory requirements in the total length of local public roads;
• number of rural settlements served by postal service;
• number of rural settlements provided with telephone services.
A municipal district is a part of regional economic space, one of the types of spatial economic formations at the level of a region. Hence, the methodology should evaluate, on the one hand, independent economic development of municipal districts, but on the other hand, it should pay attention to the scales of municipal districts' influence on regional economic space.
The algorithm for identification of trends in economic development of municipal districts in regional economic space is presented in the figure.
The first group of indicators is analysed at two levels: regional and local, because the indicators of the group reflect reproduction process taking place in the territory of municipal district, which is a component of regional reproduction process.
In the second group, part of indicators is compared with regional values (population density, area under crops, changes in number of municipalities and settlements), the other part
is examined only at municipal level (area under perennials, area of land allocated for housing construction, individual housing construction and integrated development for housing construction per 10, 000 people).
The third group helps assess the integrity of economic space within municipal districts, and, simultaneously, points to the degree of its integration into regional economic space.
Analysis of economic development of municipal districts in economic space of a region
The method is tested at the example of Sverdlovsk oblast. The choice of the region was conditioned by a number of circumstances. First, the region is a classic example of traditional industrial region, what implies a certain structure of its economy, where agriculture definitely is not the dominating industry. Second, the region is characterised by a complicated situation with the environment (according to the data of the Russian public organisation "Green patrol")1, which is an important factor in the development of municipal districts being a rural municipality. The third circumstance is related to the municipal division of the region: a proportion of municipal districts to urban districts. In Sverdlovsk oblast, there are 5 municipal districts and 68 urban districts, which is a unique combination for the Russian Federation. In other regions of such type municipal districts dominate or urban districts dominate insignificantly.
The analysis of the statistics as well as calculation of indices of presence on their basis is limited by the period of 2011-2016, particularly because the year 2011 is the first year of implementation of the Concept of sustainable development of rural territories in the Russian Federation until 20202.
Assessment of scales of municipal districts' presence in the regional economic space. On
the basis of indicators from the first group we calculated indices of presence as a ratio between share of municipal district's indicator in the regional indicator and municipal district's share in regional population. Index of presence as an indicator balanced by population size allows assessing how proportional the participation of municipal districts in regional reproduction process is according to the number of economic agents. The index value lower than one means other territories dominate, equal to one - distribution of the indicator is proportional, higher than one - municipal districts are actively involved in creating value of a regional indicator.
Index ofpresence is based on population size. Consequently, first we need to lookat the changes in the share of municipal districts' population in total population of Sverdlovsk oblast (table 2).
Cumulative presence of municipal districts of Sverdlovsk oblast in regional population decreased from 2.45 to 2.33 % in the considered period. Yet negative rates of growth tend to fall: if at the beginning of the period the rate was -1.5 %, by the end of the period the rate of decrease in population share amounted to -0.5 %. Population decline in four out of five municipal districts is accompanied by falling scales of presence (their share) in economic space of Sverdlovsk oblast in terms of population. Kamyshlovsky municipal district is the only municipality, where both population size and share in regional population improved.
Calculated indices of municipal districts' presence in Sverdlovsk oblast for the indicator "agricultural production (all types of farms)" are given in table 3.
Dynamics of the index of municipal districts' presence in terms of agricultural production allows concluding that total presence of municipal districts in economic space of Sverdlovsk oblast is growing (the index increased from 2.89 to 4.75 (by 64 %), despite the fact that the presence of Kamyshlovsky municipal district in regional indicator started to decrease, and positive dynamics of Taborinsky and Slobodo-Turinsky municipal district was interrupted by a decline in 2012, and of the last one - also in 2015.
1 The 2017 Environmental Rating of the Subjects of the Russian Federation. Available at: http:// greenpatrol.ru/ru/stranica-dlya-obshchego-reytinga/ekologicheskiy-reyting-subektov-rf?tid=338 (in. Russ.)
2 Concept of sustainable development of rural territories in the Russian Federation until 2020 (approved by the Decree of the Government of the Russian Federation no. 2136-r on November 30, 2010)
Table 2
Share of municipal districts' population in total population of Sverdlovsk oblast by the end of the year, %
Municipality 2011 2012 2013 2014 2015 2016
Baikalovsky MD 0.38 0.36 0.36 0.35 0.35 0.35
Kamyshlovsky MD 0.66 0.66 0.66 0.66 0.66 0.67
Nizhneserginsky MD 1.0 0.98 0.97 0.96 0.95 0.94
Slobodo-Turinsky MD 0.34 0.33 0.32 0.32 0.31 0.31
Taborinsky MD 0.08 0.08 0.08 0.08 0.07 0.07
Totally 2.45 2.42 2.39 2.37 2.35 2.33
Note. Calculated using the data from Municipalities' Database ofthe Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi; Russian regions. Socioeconomic indicators. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/ rosstat/ru/statistics/publications/catalog/doc_1138623506156.
Table 3
Indices of municipal districts' presence in economic space of Sverdlovsk oblast in terms of agricultural production
Municipality 2011 2012 2013 2014 2015 2016
Baikalovsky MD 5.11 5.51 5.90 6.27 6.27 6.95
Kamyshlovsky MD 3.33 6.65 6.75 8.29 8.39 7.80
Nizhneserginsky MD 1.58 1.69 1.83 1.88 1.86 2.03
Slobodo-Turinsky MD 3.54 3.26 3.87 3.83 3.81 4.08
Taborinsky MD 2.66 2.64 2.82 3.55 3.62 3.73
Total index 2.89 3.86 4.10 4.64 4.68 4.75
Note. Calculated using the data from Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet. cgi; Russian regions. Socioeconomic indicators. Available at: http://www.gks.ru/wps/wcm/connect/ rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156; Agriculture, hunting and forestry in Sverdlovsk oblast: Statistical yearbook. Territorial office of the Federal State Statistics Service in Sverdlovsk oblast. Yekaterinburg, 2016.
Individual indices of presence for agricultural production are higher than one. This indicates that the share of municipal districts in the regional agricultural production exceeds their share in population, as well as confirms active participation of municipal districts in creating regional agricultural output.
Indices of municipal districts' presence in Sverdlovsk oblast for the indicator "investment in fixed capital" are given in table 4.
The scales of municipal districts' presence in economic space of Sverdlovsk oblast in terms of investment in fixed capital changed unevenly comparing both territories and time periods. Generally, the scales of presence were decreasing (from 0.43 in 2011 to 0.19 in 2016) and did not exceed one, what was linked with relatively higher investments in urban districts' economies under the impact of new industrialisation. The only municipal district that demonstrated growing scales of its presence in Sverdlovsk oblast's economic space in terms of investment in fixed capital is Baikalovsky municipal district, though the improvement was not significant (from 0.35 to 0.37). Besides, the changes of its index were also erratic.
Indices of municipal districts' presence in Sverdlovsk oblast for the indicator "retail turnover" are given in table 5.
The scales of cumulative presence of municipal districts in Sverdlovsk oblast's economic space in terms of retail trade turnover, which characterises participation in regional
Table 4
Indices of municipal districts' presence in economic space of Sverdlovsk oblast in terms of investment in fixed capital
Municipality 2011 2012 2013 2014 2015 2016
Baikalovsky MD 0.35 0.24 0.39 0.26 0.31 0.37
Kamyshlovsky MD 0.23 0.32 0.13 0.17 0.18 0.19
Nizhneserginsky MD 0.71 0.41 0.22 0.18 0.46 0.16
Slobodo-Turinsky MD 0.10 0.19 0.09 0.10 0.07 0.07
Taborinsky MD 0.29 1.26 0.65 0.14 0.35 0.13
Total index 0.43 0.36 0.22 0.18 0.30 0.19
Note. Calculated using the data from Municipalities' Database ofthe Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi; Russian regions. Socioeconomic indicators. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/ rosstat/ru/statistics/publications/catalog/doc_1138623506156.
Table 5
Indices of municipal districts' presence in economic space of Sverdlovsk oblast in terms of retail turnover
Municipality 2011 2012 2013 2014 2015 2016
Baikalovsky MD 0.24 0.24 0.22 0.23 0.23 0.23
Kamyshlovsky MD 0.16 0.16 0.16 0.16 0.14 0.14
Nizhneserginsky MD 0.24 0.24 0.27 0.27 0.29 0.29
Slobodo-Turinsky MD 0.17 0.17 0.18 0.20 0.22 0.23
Taborinsky MD 0.17 0.16 0.16 0.16 0.10 0.10
Total index 0.21 0.20 0.21 0.22 0.22 0.22
Note. Calculated using the data from Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet. cgi; Russian regions. Socioeconomic indicators. Available at: http://www.gks.ru/wps/wcm/connect/ rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1138623506156; Socioeconomic situation in urban districts and municipal districts of Sverdlovsk oblast in 2012-2016: Statistical yearbook. Parts 1,2. Territorial office of the Federal State Statistics Service in Sverdlovsk oblast. Yekaterinburg, 2017.
consumption, practically did not change (compare 0.21 in 2011 and 0.22 in 2016). The values of municipal districts' individual indices signify that consumption is mainly concentrated in urban districts. In other words, municipal districts' share in regional population exceeds their share in regional consumption.
Considering municipal districts of Sverdlovsk oblast individually, we can point to that two of them demonstrated positive dynamics (Nizhneserginsky and Slobodo-Turinsky), whereas in the rest the scales of presence in regional retail turnover were declining.
The overall picture seems to be that the scales of municipal districts' presence in Sverdlovsk oblast's economic space are shrinking in terms of population and investment in fixed capital, what, taking into account the changes in regional values of these indicators, shows that labour resources and capital move to urban districts, which appear to be more attractive being urban territories. Nonetheless, in terms of what refers to the economic specialisation of municipal districts - to agriculture - the scales of municipal districts' presence are going up. This gives us grounds to argue that development of characteristic economic activities in municipal districts' territories balances and diversifies regional economic space of a traditional industrial region, offering an opportunity to maintain relatively stable scales of municipal districts' presence in the region in terms of consumption.
Assessment of municipal districts' economic performance. The next stage of the method for identification of trends in economic development of municipal districts in regional economic space is the assessment of municipal districts' own economic performance using three groups of indicators: indicators of saturation of economic space with economic agents' activities, indicators of development of spatial basis and indicators of coherence of economic space.
The analysis of saturation of municipal districts' economic space with economic agents' activities is presented from the standpoint of reproduction process occurring in municipal districts' territories by characterising production (agricultural production), distribution (investment in fixed capital) and consumption (retail turnover).
Agricultural production of municipal districts in Sverdlovsk oblast is presented in table 6.
Table 6
Agricultural production of municipal districts in Sverdlovsk oblast, thousand rub.
Territory 2011 2012 2013 2014 2015 2016
Baikalovsky MD 1,069,771 1,031,082 1,244,712 1,461,401 1,669,985 1,814,579
Kamyshlovsky MD 1,250,059 2,249,651 2,593,145 3,594,982 4,201,669 3,871,642
Nizhneserginsky MD 893,097 856,792 1,044,052 1,182,447 1,333,981 1,409,351
Slobodo-Turinsky MD 681,033 552,482 731,411 794,372 890,794 924,380
Taborinsky MD 121,359 107,843 128,107 175,916 201,506 199,687
Sverdlovsk oblast 56,587,000 51,374,000 58,576,000 65,686,000 75,605,000 74,209,000
Note. Compiled using Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi; Russian regions. Socioeconomic indicators. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ ru/statistics/publications/catalog/doc_1138623506156; Agriculture, hunting and forestry in Sverdlovsk oblast: Statistical yearbook. Territorial office of the Federal State Statistics Service in Sverdlovsk oblast. Yekaterinburg, 2016.
In the period under consideration municipal districts in Sverdlovsk oblast demonstrated a twofold increase in agricultural production. On average, agricultural output in municipal districts went up from 37 933 rubles per capita in 2011 to 81 367 rubles in 2016. The growth in agricultural production in all municipal districts of Sverdlovsk oblast was higher than the regional growth (31 % to the level of 2011), though the dynamics of the districts differed subtly. Agricultural production in Kamyshlovsky municipal district had improved more than three times by 2016. In other municipal districts the growth rates were more modest and varied between 36 % in Slobodo-Turinsky to 70 % in Baikalovsky municipal district and were interrupted by a decline in 2012.
Investment in fixed capital in municipal districts in Sverdlovsk oblast is presented in table 7.
All together municipal districts in Sverdlovsk region receive less than 1 % of investments and their share in regional investment process keeps on decreasing. Baikalovsky municipal district is the only one that increased the volume of investment in fixed capital. The fall in investments in other municipal districts varied from 13 % in Kamyshovsky to 78 % in Nizh-neseginsky.
Retail turnover in municipal districts in Sverdlovsk oblast is presented in table 8.
Positive changes in the indicator of consumption - a retail turnover - were observed in three out of five municipal districts (Baikalovsky, Nizhneserginsky, Slobodo-Turinsky) during the whole reviewed period. At this, the dynamics of retail turnover and corresponding indices (exclusively positive) coincides only in Slobodo-Turinsky and Nizhneserginsky municipal districts. The increase in consumption in these two districts exceeded regional rates (38 %) and amounted to 70 and 56 % respectively. On the contrary, in Taborinsky municipal district, consumption decreased substantially: in 2016 the retail turnover was lower by 26 % than in 2011, despite quite a stable growth in 2011-2014.
Table 7
Investment in fixed capital in municipal districts in Sverdlovsk oblast, thousand rub.
Territory 2011 2012 2013 2014 2015 2016
Baikalovsky MD 429,704 301,500 493,660 347,426 386,498 444,233
Kamyshlovsky MD 514,718 748,100 293,564 406,922 411,040 448,124
Nizhneserginsky MD 2,383,184 1,430,800 769,859 630,338 1,510,289 532,547
Slobodo-Turinsky MD 110,555 221,800 107,225 119,835 77,864 74,049
Taborinsky MD 78,828 350,600 178,628 39,731 90,352 33,500
Sverdlovsk oblast 333,451,000 351,637,000 352,916,000 371,631,000 349,964,000 345,812,000
Note. Compiled using Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi; Russian regions. Socioeconomic indicators. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/ statistics/publications/catalog/doc_1138623506156.
Table 8
Retail turnover in municipal districts in Sverdlovsk oblast, thousand rub.
Territory 2011 2012 2013 2014 2015 2016
Baikalovsky MD 679,200 736,700 770,200 828,100 850,200 869,100
Kamyshlovsky MD 825,700 892,500 985,500 1,056,100 937,100 970,500
Nizhneserginsky MD 1,832,700 1,988,100 2,463,300 2,542,900 2,825,200 2,867,800
Slobodo-Turinsky MD 434,300 481,300 558,400 632,700 707,000 736,800
Taborinsky MD 103,500 107,900 119,400 120,200 76,600 77,100
Sverdlovsk oblast 764,558,000 858,801,000 953,973,000 998,643,000 1,035,793,000 1,054,177,000
Note. Compiled using Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi; Russian regions. Socioeconomic indicators. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ ru/statistics/publications/catalog/doc_1138623506156; Socioeconomic situation in urban districts and municipal districts of Sverdlovsk oblast in 2012-2016: Statistical yearbook. Parts 1,2. Territorial office of the Federal State Statistics Service in Sverdlovsk oblast. Yekaterinburg, 2017.
Thus, municipal districts of Sverdlovsk oblast demonstrated growth in production (agricultural production had increased in all municipal districts by the end of the period) and consumption (retail turnover grew everywhere except for Taborinsky municipal district). The indicator of distribution, on the contrary, had negative dynamics almost in all municipal districts except for Baikalovsky.
Analysis of indicators characterising spatial (physical) basis of economic space allows assessing changes in exploitation and development of economic space within municipal districts' territories. This section of analysis considers changes in economic agents' density, municipal division, specialised (crops, perennials) and general (housing construction) land use.
The first indicator to be considered is economic agents' density as the one showing economic agents' distribution across space. The calculated density of economic agents for Sverdlovsk oblast is given in table 9.
Table 9
Economic agents' density in municipal districts of Sverdlovsk oblast, people per sq. km
Territory 2011 2012 2013 2014 2015 2016
Baikalovsky MD 6.94 6.86 6.79 6.69 6.65 6.64
Kamyshlovsky MD 12.89 12.83 12.79 12.90 12.95 13.08
Nizhneserginsky MD 15.07 14.87 11.09 11.21 11.08 10.94
Slobodo-Turinsky MD 5.44 5.28 5.17 5.07 4.97 4.90
Taborinsky MD 0.31 0.30 0.29 0.29 0.28 0.27
Sverdlovsk oblast 22.17 22.21 22.24 22.27 22.29 22.28
Note. Calculated using the data from Municipalities' Database ofthe Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi; Russian regions. Socioeconomic indicators. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/ rosstat/ru/statistics/publications/catalog/doc_1138623506156.
In all municipal districts, population density is lower than average population density in the region. Kamyshlovsky and Nizhneserginsky districts have the highest indicators, 13 and 11 people per sq. km respectively. Taborinsky has the lowest (0.27 people per sq. km). This results from considerable (the greatest among municipal districts) land area of the municipality - 11 367 sq. km and very modest population (just over 3,000 people). Population density is falling in all municipal districts of Sverdlovsk oblast, by 2016 economic agents' density had improved only in Kamyshlovsky municipal district. A dramatic decrease in Nizhneserginsky municipal district in 2013 compared to 2011-2012 was also a result of an increase in the municipality's territory. Falling density of economic agents seems to be a rather negative trend, because economic agents are involved in creation and development of economics space being the bearers of economic actions. Among other things, economic agents form labour potential of territories, which is the main driver of social and economic progress in territories. Reduction of economic agents' density appears to be even more critical for the reason that labour potential can decrease at higher rates than population generally (and, consequently, population density), as it happens, for instance, in municipal districts and urban districts of Volgograd oblast [25. P. 176].
The next stage in the spatial basis analysis is examination of changes in municipal and territorial division of the region. As we have already pointed out, development of economic space is manifested in emergence of new settlements, whereas its destruction is expressed in their disappearance. We will consider the concept "settlement" both in geographical sense (settlement), and in terms of municipal division (municipality).
Municipal division of Sverdlovsk oblast did not change in 2011-2016. The number of municipal districts and rural settlements included into them remained the same1. However, if looking at changes in the number of settlements, we will see that in the analysed period in Taborinsky municipal district one rural settlement disappeared. The Law of Sverdlovsk oblast of November 14, 2016 no. 105-0Z abolished Posolka settlement there2. At the same time, in Sverdlovsk oblast in the reviewed period 45 rural settlements disappeared, though the number of urban settlements (cities, towns and urban-type settlements) remained unchanged. Municipal and territorial division of Sverdlovsk oblast as of the end of 2011 and 2016 in terms of rural and urban settlements is presented in table 10.
1 Bulletin of the Federal State Statistics Service of the Russian Federation "Formation of local self-government in the Russian Federation as of January 1, 2017" Available at: http://www.gks.ru/wps/wcm/ connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1244553308453 (in Russ.)
2 Law of Sverdlovsk oblast of November 14, 2016 no. 105-0Z "On the abolition of the settlement of Posolka, located in the territory of the administrative and territorial unit of the Sverdlovsk region
"Taborinsky district", and on amending Appendix 92 to the law of the Sverdlovsk region "On the borders of municipalities located in the Sverdlovsk Region. ".
Table 10
Municipal and territorial division of Sverdlovsk oblast as of the end of 2011 and 2016
Territory 2011 2016
Cities, towns Urban-type settlements Rural settlements Cities, towns Urban-type settlements Rural settlements
Baikalovsky MD - - 68 - - 68
Kamyshlovsky MD - - 54 - - 54
Nizhneserginsky MD 2 3 33 2 3 33
Slobodo-Turinsky MD - - 48 - - 48
Taborinsky MD - - 34 - - 33
Municipal districts totally 2 3 237 2 3 236
Sverdlovsk oblast 47 27 1 840 47 27 1 795
Note. Compiled using data from: Population change in municipalities of Sverdlovsk oblast in 2011: Statistical yearbook. Territorial office of the Federal State Statistics Service in Sverdlovsk oblast. Yekaterinburg, 2012; Population change in municipalities of Sverdlovsk oblast in 2017: Statistical yearbook. Territorial office of the Federal State Statistics Service in Sverdlovsk oblast. Yekaterinburg, 2018.
The analysis of general land use is based on the indicator of land plots allocated for housing construction, individual housing construction and integrated development for housing construction per 10, 000 people. Time series for this indicator in municipal districts of Sverdlovsk oblast are presented in table 11.
Table 11
Land area allocated for housing construction, individual housing construction and integrated development for housing construction per 10, 000 people in municipal districts of Sverdlovsk oblast
Municipality 2011 2012 2013 2014 2015 2016
Baikalovsky MD 3.79 4.9 4.2 4.6 5.6
Kamyshlovsky MD 25.6 4.9 4.43 5.2 5.32 8.27
Nizhneserginsky MD 7.5 3.9 19.9 29.88 6.2 5.5
Slobodo-Turinsky MD 6.8 8.58 9.5 9.7 6.63 18.23
Taborinsky MD 7.5 45.5 14.9 20
Note. Compiled using the data from Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi
Changes in the volume of allocation of land plots for housing construction are unstable and largely uneven, what results from dependence of demand for housing on general socioeconomic situation. Despite this, volume of allocation of land plots for housing construction per 10, 000 people had increased in Baikalovsky, Slobodo-Turinsky and Taborinsky municipal districts by the end of the period, what indicates that the municipal districts' territories are still undergoing some economic development.
The analysis of specialised land use is based on the indicators of area under all crops in all types of farms and area under perennial fruits and berries. Using these indicators, we can assess the degree of exploitation of physical basis of economic space in accordance with the characteristics of municipal districts as rural municipalities. Time series for area under all crops in municipal districts of Sverdlovsk oblast are presented in table 12.
Total area under all crops of Sverdlovsk oblast did not change in 2016 compared to 2011, at this, in the same period municipal districts increased their area under crops (the growth varied from 26 % in Baikalovsky municipal district to 74 % in Nizhneserginsky municipal
Table 12
Area under all crops in all types of farms in municipal districts of Sverdlovsk oblast, ha
Municipality 2011 2012 2013 2014 2015 2016
Baikalovsky MD 37,755 38,347 44,884.8 47,116.9 46,519.6 47,473.1
Kamyshlovsky MD 23,372 23,273 40,200.1 36,633.6 36,490.9 38,834.0
Nizhneserginsky MD 12,334 12,464 21,594.1 21,886.0 21,489.1 21,512.9
Slobodo-Turinsky MD 12,251 12,825 17,599.9 17,370.0 19,415.3 20,583.9
Taborinsky MD - - - - - 3.3
Sverdlovsk oblast 847,100 839,200 856,800 862,400 866,400 847,100
Note. Compiled using the data from Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi; Russian regions. Socioeconomic indicators. Available at: http://www.gks.ru/wps/wcm/connect/rosstat_main/ rosstat/ru/statistics/publications/catalog/doc_1138623506156.
district), what resulted in improvement of municipal districts' share in regional area under crops. Accounting for 11.5 % of the region's territory, municipal districts managed to increase their share in the region's area under crops from 10.1 to 15.2 % in the considered period, which reflects the process of expanding the economic oecumene.
In Taborinsky municipal district, there were no area under crops accounted by statistics until 2016, and in 2016 it amounted to 3.3 ha. Taking into consideration the land area of the municipal district the value is not significant; however, it speaks in favour of the expansion of economically developed space.
Time series for the area under perennial fruits and berries in municipal districts of Sverdlovsk oblast are presented in table 13.
Table 13
Area under perennial fruits and berries in municipal districts of Sverdlovsk oblast, ha
Municipality 2011 2012 2013 2014 2015 2016
Baikalovsky MD 79.0 158.0 82.7 81.6 85.9 87.7
Kamyshlovsky MD 54.0 98.0 67.7 66.9 64.5 58.7
Nizhneserginsky MD 341.0 682.0 398.1 392.7 449.3 458.5
Slobodo-Turinsky MD 260.0 520.0 275.1 271.4 295.7 301.8
Taborinsky MD 18.0 36.0 31.8 31.4 28.4 29.0
Note. Compiled using the data from Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi.
Area under perennial fruits and berries in municipal districts generally increased in 2016 compared to 2011, the growth varied from 9 % in Kamyshlovsky municipal district to 61 % in Taborinsky. Changes in the area under perennial fruits and berries were uneven, the highest growth was in 2012 - almost twofold in all municipal districts except for Kamyshlovsky municipal district (by 81 % to the level of 2011). The sharp increase in 2012 was followed by a decline, however, by the end of the period positive dynamics resumed in all municipal districts except for Kamyshlovsky. There the growth rate was negative (- 9 % to the level of 2015).
The analysis of indicators characterising coherence of economic space allows evaluating infrastructure that supports interface between economic agents. At this stage of analysis of municipalities' own economic performance, we consider four indicators reflecting the development of road and rail network, postal and telephone services.
Changes in the share of the population living in settlements that do not have regular bus and / or railway communication with the administrative centre of the municipal district, in the total population of the municipal district are given in table 14.
Table 14
Share of the population living in settlements that do not have regular bus and / or railway communication with the administrative centre of the municipal district, in the total population of the municipal districts of Sverdlovsk oblast, %
Municipality 2011 2012 2013 2014 2015 2016
Baikalovsky MD 0.91 0.87 0.87 0.99 0.95 0.40
Kamyshlovsky MD 2.13 1.72 1.62 1.62 1.60 1.36
Nizhneserginsky MD 0.80 0.70 0.70 0.70 0.70 0.70
Slobodo-Turinsky MD 0.40 0.40 0.35 0.32 0.45 0.50
Taborinsky MD 19.80 20.30 14.00 14.00 5.00
Note. Compiled using the data from Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi.
In three municipal districts of Sverdlovsk oblast the share of the population living in settlements that do not have regular bus and / or railway communication with the administrative centre of the municipal district did not exceed one percent during the whole period under review (Baikalovsky, Nizhneserginsky, Slobodo-Turinsky). In two remaining municipal districts the share decreased: in Kamyshlovsky municipal district from 2.13 to 1.36 %, in Taborinsky municipal district - even more substantially - from 19.8 to 5 %. Given the low population density in the latter, in absolute numbers such population amount to less than 156 people, what is lower than the number of such population in Kamyshlovsky and Nizhneserginsky municipal districts. On the whole, we can conclude that the share of the population living in settlements that do not have regular bus and / or railway communication with the administrative centre of the municipal district is not significant and the level of coherence of economic space within the municipal districts' territories ensured by rail and bus transportation is high.
Changes in the share of the length of local public roads that do not meet regulatory requirements in the total length of local public roads for municipal districts in Sverdlovsk oblast is given in table 15.
Table 15
Share of the length of local public roads that do not meet regulatory requirements in the total length of local public roads for municipal districts in Sverdlovsk oblast, %
Municipality 2011 2012 2013 2014 2015 2016
Baikalovsky MD 86.29 59.16 58.77 59.50 58.30
Kamyshlovsky MD 52.32 48.97 48.16 37.62 37.41
Nizhneserginsky MD 53.54 54.01 70.89 80.36 80.36
Slobodo-Turinsky MD 8.56 35.34 49.29 51.78 51.78
Taborinsky MD 53.73 0.90 9.58 10.15 10.45
Note. Compiled using the data from Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi.
Situation with the quality of pavement in local public roads is much more dramatic. The research by P. A. Pykhov and T. O. Kashina [21. P. 71-72] demonstrated that the sufficiency with roads in Sverdlovsk oblast is generally lower that sufficiency with railroads. From 10% (in Taborinsky municipal district) to 80 % (in Nizhneserginsky municipal district) of local public roads do not meet regulatory requirements; on average, in municipal districts of Sverdlovsk oblast 47 % of roads are not in order. In two municipal districts the situation was worsening during the whole period under review (in Nizhneserginsky and Slobodo-Turinsky municipal
districts). In both municipal districts the scales of degradation of pavement look critical: in the first one the share of local public roads that do not meet regulatory requirements increased from 53.5 to 80.4 %, in the second one - from 8.6 % to 51.8 %. Interestingly, the condition of pavement in the municipal district with the lowest population density - in Taborinsky - is comparatively better that in other municipal districts. We also should point to the positive changes in Kamyshlovsky municipal district, where the indicator was gradually improving.
Statistical data for the provision of postal and telephone services are available only from 2014 on. Time series for the number of rural settlements served by postal service in municipal districts of Sverdlovsk oblast are given in table 16.
Table 16
Number of rural settlements served by postal service in municipal districts of Sverdlovsk oblast, units
Municipality 2014 2015 2016
Baikalovsky MD 65 65 65
Kamyshlovsky MD 51 51 51
Nizhneserginsky MD 31 31 31
Slobodo-Turinsky MD 44 44 44
Taborinsky MD 16 23 23
Note. Compiled using the data from Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi.
The number of rural settlements1 served by postal service remained unchanged in all municipal districts except for Taborinsky, where the number of such settlements increased. It is necessary to report the fact that currently not all rural settlements in municipal districts are served by postal service. Postal service is not provided in 3 settlements of Baikalovsky municipal district, in 3 settlements of Kamyshlovsky municipal district, in 2 settlements of Nizhneserginsky municipal district, in 4 settlements of Slobodo-Turinsky municipal district and in 11 settlements of Taborinsky municipal district.
Changes in the number of rural settlements provided with telephone services in municipal districts of Sverdlovsk oblast are given in table 17.
Table 17
Number of rural settlements provided with telephone services in municipal districts of Sverdlovsk oblast, units
Municipality 2014 2015 2016
Baikalovsky MD 67 67 67
Kamyshlovsky MD 53 53 53
Nizhneserginsky MD 30 31 31
Slobodo-Turinsky MD 44 44 44
Taborinsky MD 20 24 24
Note. Compiled using the data from Municipalities' Database of the Federal State Statistics Service of the Russian Federation. Available at: http://www.gks.ru/dbscripts/munst/munst65/DBInet.cgi.
Scales of telephone penetration in municipal districts of Sverdlovsk oblast also did not change substantially in 2014-2016. The changes were only noticed in Nizhneserginsky municipal district (+1 settlement) and Taborinsky municipal district (+4 settlements). At the same time, telephone services are not provided in 1 settlement of both Baikalovsky and Kamysh-lovsky municipal districts, in 4 settlements of Slobodo-Turinsky, in 2 settlements of Nizhneserginsky and in 10 settlements of Taborinsky municipal district.
1 Rural settlements here are considered in geographical sense.
Taborinsky MD Slobo do-Turin sky MD Nizhneserginsky MD Kamyshlovsky MD Baikalovsky MD Municipality
o o o o Share in regional population Municipal district's presence in the region
u> OJ IP in terms of agricultural production
- - - - IP in terms of investment in fixed capital
o o - IP in terms of retail turnover
OJ OJ OJ OJ U) Agricultural production Saturation of mu districts' econom with the activ of economic a
- - - - U) Investment in fixed capital
- ^ OJ ^ Retail turnover h» a cJ. o 3 3 a o S w ^ r ÛJ ^3 C S- rt 1
o O - OJ o Economic agents' density Development of physical basis of municipal districts
o K> K> K) K> Municipal and territorial division
OJ OJ - - U) Land plots for housing construction
OJ U) U) U) Area under all crops
OJ OJ U) U) U) Area under perennials
OJ - ^ U) Without buses and rail transport Coherence of municipal districts' economic space
OJ o o u> Poor quality of pavement
K> K> K) K) Postal service
K) ^ K) K) Telephone services
OJ U) K) U) ON U) VO U) VO Total
We can conclude that from the viewpoint of coherence of economic space a relatively favourable situation in municipal districts is with regular bus and rail transportation. The situation with provision of telephone and postal services is now changing, though it does not look critical except for Taborinsky municipal district. As for the quality of pavement, high percentage of local public roads that do not meet regulatory requirements is a huge obstacle to provision of internal integrity of municipal districts as well as their integration in regional economic space.
Identification of constructive and destructive trends in economic development of municipal districts
The two final stages are the generalisation of assessments (of saturation of municipal districts' economic space, exploitation of the spatial basis, development of economic space coherence, and the identification of types of trends in economic development of municipal districts to make ultimate conclusions about municipal districts' impact on regional economic space.
For the aggregate assessment we applied a scoring method. At this, the points are given to the dynamics of municipal districts in line with the following:
4 points - an indicator grew during the whole period;
3 points - uneven dynamics, by the end of the period an indicator has increased;
2 points - uneven dynamics, by the end of the period an indicator has remained unchanged or an indicator did not change during the whole period;
1 point - uneven dynamics, by the end of the period an indicator has decreased;
0 point - negative dynamics during the whole period.
The criteria are effective for all indicators except for two, which have a negative meaning (for them the criteria are applied in reverse order):
- share of the population living in settlements that do not have regular bus and / or railway communication with the administrative centre of the municipal district, in the total population of the municipal district;
- share of the length of local public roads that do not meet regulatory requirements in the total length of local public roads.
The maximum score is 64; therefore, constructive trends are the trends of municipal districts that received from 33 to 64 points, destructive - from 0 to 32 points. Aggregate assessment of economic development of municipal districts is presented in table 18.
According to the calculations, four out of five municipal districts in the oblast develop constructively. The only municipal district that has a destructive effect on economic space of the region is Slobodo-Turinsky municipal district, but its score is on the border between constructive and destructive trends.
Conclusion
To detect and assess trends in economic development of municipal districts it is vitally important to apply a method, which is based not only on the analysis of economic indicators' dynamics, but also on the characteristics of spatial transformations. Compared to existing methodological approaches, such two-in-one method construction allows detecting not just upward and downward development trends, but identifying them as constructive, i.e. fostering the development of regional economic space, and destructive, i.e. causing its destruction. Testing the designed methodological approach at the example of Sverdlovsk oblast enabled the authors to obtain corresponding analytical results, which can be applied to formulate main directions for the region's spatial development.
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Муниципальные районы в экономическом пространстве региона: конструктивные и деструктивные тенденции
Е. Б. Дворядкина, Е. А. Беяоусова
Исследование направлено на разработку методики идентификации тенденций экономического развития муниципальных районов в экономическом пространстве региона. Методологическая база исследования основывается на теоретических положениях пространственной и сельской экономики. Систематизация подходов российских ученых к анализу социально-экономического развития муниципальных образований позволила предложить авторскую методику идентификации конструктивных и деструктивных тенденций экономического развития муниципальных районов, которая включает в себя четыре этапа. На первом этапе оцениваются масштабы присутствия муниципальных районов в экономическом пространстве региона. На втором проводится оценка экономической динамики муниципальных районов. Третий этап включает обобщение данных оценок, четвертый - идентификацию типа тенденций экономического развития муниципальных районов с заключительными выводами о влиянии муниципальных районов на экономическое пространство региона. Представлена апробация методики на примере Свердловской области. Согласно результатам исследования, развитие четырех из пяти муниципальных районов Свердловской области характеризуется как конструктивное. Единственным оказывающим деструктивное воздействие на развитие экономического пространства региона является Слободо-Туринский муниципальный район. При этом конструктивные тенденции понимаются авторами как способствующие развитию экономического пространства региона, а деструктивные - как приводящие к его разрушению. Теоретическая и практическая значимость исследования заключается в том, что разработанная методика позволяет не только оценивать экономическую динамику муниципальных районов, но и увязывать ее с региональным экономико-пространственным развитием.
Ключевые слова: муниципальный район; сельская территория; насыщенность экономического пространства; пространственный каркас; связанность экономического пространства; конструктивные тенденции; деструктивные тенденции.
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Сведения об авторах:
Е. Б. Дворядкина, д-р экон. наук, профес- Уральский государственный экономический сор, профессор кафедры региональной, университет
муниципальной экономики и управления 620144, РФ, г. Екатеринбург, Контактный телефон: (343) 221-17-55 ул. 8 Марта/Народной Воли, 62/45
e-mail: dvoryadkina@usue.ru
Е. А. Белоусова, инженер управления по Уральский государственный экономический
научно-исследовательской работе университет
Контактный телефон: (343) 221-26-33 620144, РФ, г. Екатеринбург,
e-mail: belousova.elizaveta@inbox.ru ул. 8 Марта/Народной Воли, 62/45
Ссылка для цитирования: Dvoryadkina E. B., Belousova E. A. Municipal Districts in Economic Space of a Region: Constructive and Destructive Trends // Известия Уральского государственного экономического университета. 2018. Т. 19, № 5. С. 84-106. DOI: 10.29141/2073-1019-2018-19-5-7
For citation: Dvoryadkina E. B., Belousova E. A. Municipal Districts in Economic Space of a Region: Constructive and Destructive Trends. Izvestiya Uralskogo gosudarstvennogo ekonomicheskogo universiteta - Journal of the Ural State University of Economics, 2018, vol. 19, no. 5, pp. 84-106. DOI: 10.29141/2073-1019-2018-19-5-7
