R-ECONOMY, 2020, 6(2), 65–73 doi: 10.15826/recon.2020.6.2.006 65 https://journals.urfu.ru/index.php/r-economy Online ISSN 2412-0731 Original Paper © Ignatieva, E.D., Mariev, O.S., Serkova, A.Ye., 2020 doi 10.15826/recon.2020.6.2.006 Impact of infrastructure on socio-economic development of Russian regions: methodology and analysis E.D. Ignatieva1 , O.S. Mariev1, 2, A.Ye. Serkova1 1 Institute of Economics of the Ural Branch of the Russian Academy of Sciences, Yekaterinburg, Russia; e-mail: elen_i99@mail.ru 2 Ural Federal University, Yekaterinburg, Russia ABSTRACT Relevance. Regional infrastructure development directly affects econom- ic growth, social development and the quality of life. To identify the key areas of infrastructure development in Russian regions, it is necessary to develop a methodological approach to the analysis of the impact of infrastructure on so- cio-economic development, which determines the relevance of this study. Re- search objective. This study aims to improve the methodology of assessment of the role infrastructure plays in the socio-economic development of Russian re- gions. Data and methods. The analysis relies on a system of general and integral, static and dynamic indicators used to assess the current state and dynamics of infrastructure in regions. The analysis takes into account the structural and func- tional features of infrastructure. The proposed methodology comprises meth- ods for obtaining comparative estimates of regional infrastructure development, which can be applied to compile regional rankings. The study also uses methods of econometric and K-means cluster analysis. Results. A comparative analysis of the infrastructure development of Russian regions allowed us to assess the infrastructural potential of these regions, the discrepancies in infrastructure de- velopment and compare the infrastructure-related characteristics of the leading lagging regions. The results of econometric analysis as well as cluster analysis of regions based on general and integral dynamic indicators are discussed. Con- clusions. The methodological approach proposed by the authors has been tested by using the data on Russian regions. The analysis has revealed the most typical problems faced by Russian regions. These problems should be taken into account in strategic decision- and policy-making. KEYWORDS region, infrastructure, infrastructure development, static and dynamic indicators, economy, quality of life, public-private partnership ACKNOWLEDGEMENTS This research was supported by the Institute of Economics of the Ural Branch of the Russian Academy of Sciences. Влияние инфраструктуры на социально-экономическое развитие регионов России: методология и анализ Е.Д. Игнатьева1 , О.С. Мариев1, 2, А.Е. Серкова1 1 Институт экономики Уральского отделения Российской академии наук, Екатеринбург, Россия; e-mail: elen_i99@mail.ru 2 Уральский федеральный университет, Екатеринбург, Россия АННОТАЦИЯ Актуальность. Развитие региональной инфраструктуры напрямую влия- ет на экономический рост, социальное развитие и качество жизни. Для вы- явления ключевых направлений развития инфраструктуры в российских регионах необходимо разработать методологию, анализирующую влияние инфраструктуры на социально-экономическое развитие регионов. Цель исследования. Цель данного исследования – усовершенствовать методо- логию оценки роли инфраструктуры в социально-экономическом разви- тии регионов России. Данные и методы. Анализ основан на системе общих и интегральных, статических и динамических показателей, используемых для оценки текущего состояния и динамики инфраструктуры в регионах. Анализ учитывает структурные и функциональные особенности инфра- структуры. Предлагаемая методология включает в себя методы получения сравнительных оценок развития региональной инфраструктуры, которые можно применять для составления региональных рейтингов. В исследо- вании также используются эконометрические методы и кластерный ана- лиз с помощью метода k-средних. Результаты. Сравнительный анализ развития инфраструктуры российских регионов позволил нам оценить КЛЮЧЕВЫЕ СЛОВА регион, инфраструктура, развитие инфраструктуры, статические и динамические показатели, экономика, качество жизни, государственно-частное партнерство FOR CITATION Ignatieva, E.D., Mariev, O.S., & Serkova, A.Ye. (2020) Impact of infrastructure on socio-economic development of Russian regions: methodology and analysis. R-economy, 6(2), 65–73. doi: 10.15826/recon.2020.6.2.006 БЛАГОДАРНОСТИ Работа выполнена при под- держке Института экономики Уральского отделения Россий- ской академии наук. http://doi.org/10.15826/recon.2020.6.2.006 http://doi.org/10.15826/recon.2020.6.2.006 66 https://journals.urfu.ru/index.php/r-economy R-ECONOMY, 2020, 6(2), 65–73 doi: 10.15826/recon.2020.6.2.006 Online ISSN 2412-0731 Introduction Comprehensive modernization of productive forces at the regional and macro-regional levels is impossible without infrastructure development. In order to monitor infrastructure development in Russian regions and adjust it to the needs of economic growth and to improve the quality of life in these regions, it is necessary to design the appropriate methodology. The purpose of this article is to describe and substantiate the methodological approach to as- sessing the impact of regional infrastructure de- velopment on the socio-economic situation in the country. We are also going to describe the corre- sponding methodological principles and tools. Infrastructure influences all socio-economic pro- cesses in regions, creates conditions for the deve- lopment of the real sector, helps improve the quality of life and provides opportunities for peo- ple’s individual growth. Our study focuses on the infrastructure in Russian regions and their socio-economic sys- tems. A comprehensive approach should be ap- plied to address the problems of infrastructure development in Russian regions, because the de- velopment of some types of infrastructure is as- sociated with the development of other types of infrastructure. The high level of infrastructure de- velopment ensures the comparative advantages of regions in their interactions with each other and on a global scale. Literature review There are various approaches to analyzing and evaluating the impact of certain types of in- frastructure on socio-economic development. Efimova (2009) identifies four main approaches to assessing the role of transport in regional de- velopment: by focusing on the availability of the market of resources and sales; by analyzing transport costs; by analyzing investment activity in the region; and by looking at the role of infra- structure factors in production and location de- cisions (the fourth approach also implies the use of entrepreneur surveys). Kazakova & Pospelova (2017) compared the transport infrastructure in Russia with that of other countries and found its qualitative characteristics to be among the most significant limitations of economic growth. Wang et al. (2020) investigated the impact of transport infrastructure (railway and road) on the econo- mic growth in the countries of the Belt and Road Initiative in 2007–2016. Maliy & Gusev (2010) assess the impact of energy enterprises on regional development by focusing on the case of Saratov region in Russia. A number of studies consider the impact of so- cial infrastructure on the reproduction of human capital and the implementation of social proj- ects (Tikhonovich, 2012; Roskruge, 2011; Wai et al., 2013). Tiwari (2008) discusses the impact of economic infrastructure on agricultural develop- ment, and Owualah (1987) on the development of small businesses. Chen & Fang (2018) exami- ned the relationships between economic growth, industrial electricity consumption and human capital by using a panel of 210 Chinese prefecture cities in 2003–2012. Some studies apply instrumental methods and modeling: for example, Shvetsov et al. (2018) analyze the impact of infrastructure on regio- nal socio-economic development (the case of the Nenets Autonomous Okrug) with the help of an autoregressive model with equations reflecting the dependence of endogenous indicators (GRP, R&D costs, per capita income and volume of polluting emissions) on exogenous variables – factors of in- frastructure. Cantos et al. (2005) use the production function to study the dependence of regional out- put on capital investment in transport infrastruc- ture in Spanish regions. Kiselev & Tkachev (2015) propose an economic and mathematical model for assessing the impact of social infrastructure on re- gional development. Malafeev & Baskakova (2017) investigate the significance of infrastructure capital for the gross output of the material production sec- tor by applying econometric analysis of panel data using Cobb-Douglas production function. инфраструктурный потенциал этих регионов, различия в развитии ин- фраструктуры и сравнить инфраструктурные характеристики регионов. Проинтерпретированы результаты эконометрического анализа, а также кластерного анализа регионов на основе общих и интегральных динами- ческих показателей. Выводы. Предложенный авторами методологиче- ский подход апробирован с использованием данных по регионам России. Анализ выявил наиболее типичные проблемы, с которыми сталкиваются российские регионы. Эти проблемы должны учитываться при принятии стратегических решений и разработке государственной политики. ДЛЯ ЦИТИРОВАНИЯ Ignatieva, E.D., Mariev, O.S., & Serkova, A.Ye. (2020) Impact of infrastructure on socio-economic development of Russian regions: methodology and analysis. R-economy, 6(2), 65–73. doi: 10.15826/recon.2020.6.2.006 http://doi.org/10.15826/recon.2020.6.2.006 R-ECONOMY, 2020, 6(2), 65–73 doi: 10.15826/recon.2020.6.2.006 67 https://journals.urfu.ru/index.php/r-economy Online ISSN 2412-0731 Types of infrastructure and their functions Our study aims to provide a comprehensive assessment of the impact that various types of infrastructure have on the socio-economic deve- lopment of Russian regions. Such assessment can then be used for devising infrastructure deve- lopment policies. The methodology of this study relies on the structural and functional aspects of infrastructure. Infrastructure has a fairly complex structure, including various industries, structures, facilities, and institutions. Infrastructure per- forms various economic, financial, demographic, social, environmental, and other functions. In- frastructure is a major factor that determines the economic and social prosperity of a region. For a more in-depth understanding of the impact of infrastructure, we should consider how specific types and element of infrastructure affect regional development (Ignatyeva et al., 2018). There are three main types of regional in- frastructure depending on its functions: pro- ductive-economic, financial and social. Pro- ductive-economic infrastructure, whose main function is to provide conditions for social pro- duction, includes transport, communications, electricity, and construction. Transport infrastructure is an enabler for economic activity, an essential part of economic relations at the regional, interregional and global levels. According to Pchelintsev (2004), transport and communications contribute to the intensi- fication and improvement of the quality of eco- nomic relations and ensure the mobility of pro- duction factors and the availability of production results. As the world practice shows, the presence of new infrastructural networks, including mo- tor transport, is the most important competitive advantage of regions, a factor that is crucial for regions’ general development and specialization, formation of local-scale territorial and industrial complexes and effective inter-regional interaction (Melnikovet et al., 2019). Transport accounts for 8% of the industry structure of GRP. According to the data of the Federal State Statistic Service (Rosstat), fixed as- sets of transport at the end of 2018 accounted for 22.4% of the total volume of fixed assets of Rus- sia; transport accounted for 18.7% of total invest- ment. The main documents regulating the key strategic areas of transport development in Russia are the ‘Transport Strategy of the Russian Fede- ration for the period until 2030’, federal program ‘Development of the Transport System of Russia (2010–2021)’, ‘Development Strategy of the Russian Railways until 2030’, program ‘Russian Automobile Roads in the Long Term (2010–2020)’, and so on. Regional transport infrastructure, ensuring the territorial integrity of the region, is an impor- tant element of the system of national economy and economic security (Lyutov, 2017). Transport is one of the largest basic sectors of the economy and an important component of infrastructure, which provides conditions for economic growth and contributes to national and regional pros- perity. A region with developed transport infra- structure is in a relatively better position than its less successful counterparts. Moreover, deve- loped transport infrastructure also facilitates the region’s achievement of its strategic and tactical goals and allows it to optimize the use of all types of resources (Kudryavtsev & Tarasenko, 2014). The fuel and energy infrastructure performs such functions as providing people and enterpris- es with electricity and fuel and ensuring the ener- gy-related and economic security of the country and regions. It also serves as a source of revenue for state and regional budgets. Moreover, its posi- tive impact on regional growth is achieved due to the spatial effects that go beyond the boundaries of individual regions (Maliy & Gusev, 2010). The construction infrastructure participates in the creation of fixed assets and their expanded reproduction, implementation of housing con- struction programs, creating conditions for the development of the production and non-produc- tion sphere of the region. The financial infrastructure, in its turn, en- sures the consolidation of financial resources, their rational allocation and use, creating a favo- rable investment climate in the region. The main function of the social infrastructure is to satisfy people’s needs and create conditions for expanded reproduction of labor and creative potential of the region’s population. This function largely depends on the quality of life in the region as well as the development of the real sector, since social infrastructure is ‘the main factor in the for- mation of human capital that creates labor pro- ducts’ (Tikhonovich, 2012). The social infrastruc- ture of a region includes health care, education and culture, public catering and consumer ser- vices, housing and communal services, and so on. For optimal decision- and policy-making in a region, it is necessary to assess the actual state of http://doi.org/10.15826/recon.2020.6.2.006 68 https://journals.urfu.ru/index.php/r-economy R-ECONOMY, 2020, 6(2), 65–73 doi: 10.15826/recon.2020.6.2.006 Online ISSN 2412-0731 its infrastructure against other regions (Kudryavt- sev & Tarasenko, 2014), which requires a set of appropriate indicators. Depending on the goal, objectives, and time interval of the study, partial, general and integral, static and dynamic indica- tors are calculated. There are particular indicators reflecting the availability of individual elements of infrastruc- ture in a region, while there are also consolidat- ed indicators that characterize specific types of infrastructure and integrated indicators that re- flect the state of infrastructure as a whole. Thus, in accordance with the types of infrastructure, it is possible to identify general indicators characte- rizing economic, financial, social and other types of infrastructure in a region. General indicators are based on particular indicators that characte- rize the availability of elements of specific types of infrastructure in the region. Static indicators are used to assess the state of infrastructure in the current period, while dynamic indicators reflect its changes in time. Both static and dynamic indi- cators can be partial, general or integral. Methodology and data To calculate indicators and assess their im- pact on economic growth and the quality of life in a region, we have formulated the following methodological principles: – substantiation of hypotheses about the na- ture and aspects of the infrastructure’s impact on regional development, regarding specific types of infrastructure and in general; – selection of baseline indicators, their nor- malization and grouping by infrastructure type, formation of a data base; – calculation of static and dynamic indicators characterizing regional infrastructure; – comparative analysis and rating of regions regarding their infrastructure in general and its specific types; – econometric analysis of the impact of infra- structure-related factors on economic growth and quality of life in a region; – cluster analysis of the regions based on the calculated indicators; – identification of the most typical problems of infrastructure development and the corre- sponding priority areas of strategic development. In our study, the choice of initial (particu- lar) indicators was determined, as already noted above, by the principles of complexity, consisten- cy, representativeness, reliability and comparabil- ity. The indicators also corresponded to specific types of infrastructure. Aggregated, static indicators are formed by using normalized particular indicators that char- acterize elements of a specific type of infrastruc- ture. Normalization of individual indicators is done by using the following formula: − = − min max min ,ij iRij i i N N N N N where RijN is the normalized estimation of the i-th indicator for the j-th region; Nij is the value of the i-th particular indicator for the j-th region; min max,i iN N are the lowest and highest values of the i-th indicator for all regions; i, j are the sequential numbers of the indicator and the region, respectively. Aggregate static indicators are calculated as arithmetic means of normalized indicators, while aggregate dynamic indicators are calculated as geometric means of indexes of private indicators (Iij) for each type of infrastructure. Similarly, inte- gral static indicators are calculated as arithmetic means and integral dynamic indicators are calcu- lated as geometric means of aggregate indicators for all types of infrastructure. The k-means method was used to classify Russian regions depending on the values of ag- gregate and integral indicators of specific types of infrastructure. Cluster analysis is necessary to identify problems of infrastructure develop- ment that are characteristic of certain groups of regions, assess the development of different types of infrastructure in each region, and assess whe- ther the existing infrastructure meets the needs of the real sector and the population. The use of this method makes it possible to identify the weak- nesses and comparative advantages of regions in socio-economic development regarding their in- frastructure, which is necessary for strategic deci- sion-making. The methodological recommendations were tested by the authors in relation to Russian re- gions. To calculate the indicators, a data base was created drawing from the official Rosstat data. The initial data for the econometric analysis is presented as a panel for Russian regions for 1999– 2015. To calculate static and dynamic indicators, we used the data from official statistics for 5 years (2012–2016). http://doi.org/10.15826/recon.2020.6.2.006 R-ECONOMY, 2020, 6(2), 65–73 doi: 10.15826/recon.2020.6.2.006 69 https://journals.urfu.ru/index.php/r-economy Online ISSN 2412-0731 Results The results of the regression analysis (Igna- tyeva et al., 2018) have shown that the most sig- nificant factors that positively affect GRP per capita are fixed capital investment and the share of university graduates in the total population. This conclusion confirms the importance of in- vestment and education as factors of regional so- cio-economic development. The road density index had a very significant impact on GRP per capita, in contrast to the rail- way density index. This can be explained by the following features of rail transport – less flexi- ble schedule of cargo delivery compared to road transport, possible distance of the tracks from the points of cargo delivery, longer transportation times, and “bottlenecks”. Intervals of values of static indicators of infra- structure development for 2012–2016 are shown in Table 1. Our calculations of statistic indicators have shown that Moscow is the leader in terms of in- frastructure development among Russian regions. In all aggregate indicators, as well as in the inte- gral indicator and the volume of GRP per capita, it surpasses other Russian regions. Moscow is also the absolute leader in terms of railways and roads with solid cover, which provided a relatively high level of this region in the aggregate indicator of productive-economic infrastructure. Relatively high levels in the indicators characterizing pro- ductive-economic, economic and financial infra- structure and in the integral indicator are typical of the Khanty-Mansiysk, Yamalo-Nenets and Ne- nets Autonomous okrugs, Kamchatka and other regions rich in natural and mineral resources. The group of high achieving regions also includes Sverdlovsk region, St. Petersburg, Moscow region, Leningrad region, the Republic of Tatarstan, Ka- liningrad region and some others. The medium level in aggregate indicators of infrastructure development is characteristic of Belgorod, Lipetsk, Vologda, Murmansk, Perm and Novgorod regions. The lowest levels in all the indicators are found in Altai, Adygea, Kalmykia, Astrakhan region, Dagestan, Ingushetia, the Ka- bardino-Balkar, Karachay-Cherkess and Chechen Republic. The relatively low level of infrastructure development in these regions impedes the devel- opment of the real economy and improvement of the quality of life. These regions are also charac- terized by the lowest levels of GRP per capita. We used the indicators of productive-eco- nomic, financial and social infrastructure for cluster analysis and grouping of Russian regions. In this article, we discuss primarily the results of cluster analysis based on dynamic indicators (for more on typological groupings of Russian regions based on static indicators, see Ignatieva et al, 2019) As a result of cluster analysis, 6 typological groups (clusters) of Russian regions were identi- fied. Table 2 illustrates the comparative characteris- tics of these groups based on the aggregate dynamic indicators for three types of infrastructure – indus- trial, financial and social. The table also shows in- tegrated static indicators to show the dynamics of infrastructure development in regions. The figure shows significant discrepancies in regional infrastructure development in Russia. The first group constitutes the largest share (45%), the fourth group accounts for 24%, and the fifth, for 15%. The shares of the second, third and sixth groups were 7%, 2% and 7%, respectively. Let’s look at some characteristics of each cluster. Group 1 45% Group 2 7% Group 3 2% Group 4 24% Group 5 15% Group 6 7% Figure 1. Distribution of Russian regions based on dynamic indicators of infrastructure development Table 1 Intervals of values of static indicators of infrastructure development in Russian regions № Value of indicators Type of infrastructure Integrated indicator GRP per capita, RUBProduction-economic Social Financial 1 Maximum value 0,027 0,128 0,003 0,088 106 756,6 2 Minimal value 0,664 0,610 0,686 0,595 5 821 559,8 Source: the authors’ calculations are based on statistical data (Rosstat). http://doi.org/10.15826/recon.2020.6.2.006 70 https://journals.urfu.ru/index.php/r-economy R-ECONOMY, 2020, 6(2), 65–73 doi: 10.15826/recon.2020.6.2.006 Online ISSN 2412-0731 As the Table illustrates, the highest values in dynamic indicators are found in the third group of regions (especially in financial indicators), al- though their infrastructure development is at the medium level (Bashkortostan) or somewhat lower (Mari El). The negative dynamics of infrastructur- al development (or the lowest figures in dynamic indicators) is typical of the sixth group, which includes both regions with a high level of infra- structure development (Tatarstan, Perm Krai) and regions with a relatively low level of infrastructure development (Karachay-Cherkess Republic, As- trakhan region). The relatively stable dynamics of infrastruc- ture development was observed in groups 1, 2, 4, and 5, but each of these groups had its own characteristics. The financial infrastructure in the regions of the second and fifth groups developed relatively dynamically at relatively low starting levels. The stable dynamics of productive-eco- nomic and financial infrastructure development is characteristic of the regions of the first group, which is also the largest, including regions whose current level of development for these types of in- frastructure is quite high (Moscow, Saint Peters- burg, Khanty-Mansi Autonomous Okrug). Such regions as Moscow, Tatarstan, and the Yamalo-Nenets Autonomous Okrug with high levels of static indicators have demonstrated low levels in the dynamic indicators of social infra- structure development. In a number of regions, the opposite picture was observed – social infra- Table 2 Comparative characteristics of groups (clusters) of Russian regions based on aggregate dynamic indicators Values General dynamic indicators by type of infrastructure Integral static indicatorProductive-economic Social Financial Group 1 (37): Belgorod region, Vladimir region, Voronezh region, Ivanovo region, Kaluga region, Kostroma region, Kursk region, Moscow region, Orel region, Smolensk region, Tver region, Yaroslavl region, Moscow, Nenets Autonomous Okrug, Murmansk region, Novgorod region, Saint Petersburg, Stavropol territory, Republic of Mordovia, Chuvash Republic, Kurgan oblast, Khanty-Mansi Autonomous Okrug – Yugra, Yamalo-Nenets Autonomous Okrug, Tyumen region, Chelyabinsk region, Republic of Altai, Republic of Buryatia, Republic of Tuva, Republic of Khakassia, Altai Krai, Zabaykalsky Krai, Krasnoyarsk Krai, Novosibirsk region, Tomsk region, Republic of Sakha (Yakutia), Kamchatka territory, Sakhalin region. Maximum 1,464 1,417 0,979 0,595 Minimum 0,826 1,122 0,701 0,157 Mean 1,034 1,251 0,877 0,256 Group 2 (6): Krasnodar territory, Volgograd region, Rostov region, Udmurt Republic, Kirov region, Ulyanovsk region Maximum 1,479 2,799 1,081 0,350 Minimum 1,278 2,040 0,934 0,195 Mean 1,375 2,246 1,004 0,253 Group 3 (2): Republic of Bashkortostan, Republic of Mari El Maximum 2,326 4,652 1,132 0,233 Minimum 2,071 4,101 1,106 0,182 Mean 2,199 4,377 1,119 0,207 Group 4 (20): Ryazan region, Tula region, Republic of Karelia, Komi Republic, Arkhangelsk region , Vologda region, Kaliningrad region, Leningrad region, Pskov region, Republic of Adygea, Republic of Kalmykia, Republic of Ingushetia, Republic of North Ossetia, Kabardino-Balkar Republic, Sverdlovsk region, Kemerovo region, Omsk region, Primorye Krai, Jewish Autonomous Okrug, Chukotka Autonomous Okrug Maximum 1,142 1,143 1,163 0,357 Minimum 0,782 0,913 0,816 0,088 Mean 0,976 1,062 0,944 0,217 Group 5 (12): Bryansk region, Lipetsk region, Tambov region, Republic of Dagestan, Chechen Republic, Orenburg region, Penza region, Saratov region, Irkutsk region, Khabarovsk Krai, Amur region, Magadan region. Maximum 1,189 1,832 1,083 0,367 Minimum 0,954 1,436 0,864 0,148 Mean 1,024 1,588 0,946 0,231 Group 6 (6): Astrakhan region, Karachay-Cherkess Republic, Republic of Tatarstan, Perm region, Nizhny Novgorod region, Samara region. Maximum 1,041 0,854 0,784 0,284 Minimum 0,495 0,403 0,663 0,111 Mean 0,694 0,630 0,733 0,217 Source: the authors’ calculations are based on statistical data (Rosstat). http://doi.org/10.15826/recon.2020.6.2.006 R-ECONOMY, 2020, 6(2), 65–73 doi: 10.15826/recon.2020.6.2.006 71 https://journals.urfu.ru/index.php/r-economy Online ISSN 2412-0731 structure developed more dynamically in regions with low initial levels of development (Chechnya, North Ossetia, Mari El, and Tambov region). The only exceptions were the Chukotka Autonomous Okrug and Leningrad region, characterized by relatively high dynamic estimates with high initial levels of infrastructure development. Thus, the calculation of static and dynam- ic indicators allowed us to compare the levels and dynamics of various types of infrastructure, which is necessary to identify the threats to eco- nomic security associated with negative trends in infrastructure development and determining the prospects for infrastructure development in Rus- sian regions. According to Rosstat, depreciation of fixed assets in transport at the end of 2018 amount- ed to 39.7% and in construction, 48.9%. To modernize and update fixed assets and introduce new technologies, it is necessary to attract invest- ment, which includes investment from institu- tional investors (banks, pension funds, insurance companies) through public-private partnership (PPP) mechanisms. This opens new opportunities for investors, developers and consumers as PPP mechanisms allow to combine the potential of the state and business. Currently, the most promising PPP projects in the transport sector are imple- mented in Moscow and St. Petersburg, since these regions have most advantages in terms of road density, financial infrastructure development, and the level of income (Ofin, 2016). To expand the scope and improve the efficien- cy of PPP mechanisms in Russian regions, it may be useful to study the foreign experience of using such financing schemes in infrastructure develop- ment. For example, in Australia, 10–15% of total investment in infrastructure by the mid-2000s was carried out at the expense of public-private partnerships (Gilmour et al., 2010). The bulk of this investment was primarily in- tended for the creation of economic infrastruc- ture, although in some cases social infrastructure was also created. There is a foreign practice of at- tracting investment to create and develop infra- structure facilities by issuing infrastructure bonds. In the United States, pension funds are invested in infrastructure bonds to finance infrastructure projects, with preference given to municipal loans. The main issuers of infrastructure bonds in India are banks, and in Chile – concessionaires, and concession projects using infrastructure bonds enjoy the highest credit ratings (Ivanov, 2011). In Russia, infrastructure bonds worth about 50 billion rubles were issued by JSC ‘Western High-Speed Diameter’, JSC ‘Main Road’, and LLC ‘North-Western Concession Company’. The po- tential amount of funds of the Russian pension system invested in infrastructure development may be from $ 3.5 to $7.0 billion in the short term, with the possibility of an almost two-fold increase in the medium term. The participation of the state as an issuer of infrastructure bonds guarantees a reduction in risk and an increase in the invest- ment attractiveness of regional and municipal in- frastructure projects (Stuart, 2017). As we have noted above, the development of some types of infrastructure leads to the develop- ment of other types of infrastructure, i.e. balanced development of infrastructure is an important condition for its effective functioning. One of the key factors of infrastructure development in re- gions is the efficient performance of the financial infrastructure, since an important area for imple- menting investment projects in the field of pro- ductive economic and social infrastructure is to attract funds from institutional investors (banks, pension funds, insurance companies) to finance the creation of new and improve the already existing facilities. Conclusions Problems of infrastructure development in Russian regions should be solved comprehensive- ly as the development of infrastructure affects all social and economic processes and is necessary to create conditions for the development of the real sector, rational use of material and labor resour- ces. Infrastructure is also essential to provide op- portunities for personal growth, improvement of the demographic situation and the quality of life. The methodological principles described in this article can be used to assess the impact of infra- structure on GRP per capita as a general indicator of socio-economic development, assess the com- parative advantages of regions related to the state of their infrastructure, and identify strong and weak aspects in infrastructure development. The methodological principles and tools have been tested by using the data for Russian regions, which shows the practical significance of this approach. As a result, we identified the most typical problems faced by Russian regions. These problems should be taken into account in strategic decision- and policy-making. As our regression and correlation analysis has shown, http://doi.org/10.15826/recon.2020.6.2.006 72 https://journals.urfu.ru/index.php/r-economy R-ECONOMY, 2020, 6(2), 65–73 doi: 10.15826/recon.2020.6.2.006 Online ISSN 2412-0731 References Cantos, P., Gumbau-Albert, M., & Maudos, J. (2005) Transport infrastructures and regional growth: evidence of the Spanish case: Universitat de Valencia, Instituto Valenciano de Investigaciones Economicas, Retrieved from https://mpra.ub.uni-muenchen.de/15261/1/MPRA_paper_15261.pdf Chen, Y., & Fang, Z. (2018). Industrial electricity consumption, human capital investment and economic growth in Chinese cities. Economic Modelling, 69, 205–219. Efimova, E.G. (2009) The role of transport in the economic development of the region: interna- tional aspect. Bulletin of St. Petersburg State University. Series 5. 1, 77–85. Gilmour T., Wiesel I., Pinnegar S., Loosemore M. (2010) Social infrastructure partnerships: a firm rock in a storm?. 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Economics. Ecology, 1(20), 22–28. Tiwari, A.K. (2008) Economic infrastructure and agricultural development in Himachal Pradesh: A district level analysis. Social Change, 38, 245–262. Wai, S.H., Yusof, A. Md, Ismail, S., & Ng, C.A. (2013) Exploring success factors of social in- frastructure projects in Malaysia. International Journal of Engineering Business Management, 5 doi: 10.5772/55659. Wang, C., Lim, M. K., Zhang, X., Zhao, L., & Lee, P.T.W. (2020). Railway and road infrastructure in the Belt and Road Initiative countries: Estimating the impact of transport infrastructure on eco- nomic growth. Transportation Research Part A: Policy and Practice, 134, 288–307. Information about the authors Elena D. Ignatieva – Cand.Sc. (Economics), senior researcher, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (29, Moskovskaya str., Yekaterinburg, 620014, Rus- sia); e-mail: elen_i99@mail.ru Oleg S. Mariev – Cand.Sc. (Economics), associate professor, Head of the Department of Econometrics and Statistics, Ural Federal University (19, Mira str., Yekaterinburg, 620002, Russia); senior researcher, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (29, Moskovskaya str., Yekaterinburg, 620014, Russia); e-mail: olegmariev@mail.ru Alla Ye. Serkova – leading economist, Institute of Economics of the Ural Branch of the Russian Academy of Sciences (29, Moskovskaya str., Yekaterinburg, 620014, Russia); e-mail: muccio@bk.ru ARTICLE INFO: received February 9, 2020; accepted May 10, 2020 Информация об авторах Игнатьева Елена Дмитриевна – кандидат экономических наук, старший научный сотрудник Института экономики Уральского отделения Российской академии наук (620014, г. Екатеринбург, ул. Московская, 29); e-mail: elen_i99@mail.ru Мариев Олег Святославович – кандидат экономических, доцент, заведующий кафедрой эконометрики и статистики Уральского федерального университета (620002, Россия, г. Екатеринбург, ул. Мира, 19); старший научный сотрудник Института экомоники Уральского отделения Российской академии наук (620014, г. Екатеринбург, ул. Московская, 29); e-mail: olegmariev@mail.ru Серкова Алла Евгеньевна – ведущий экономист Института экономики Уральского отделения Российской академии наук (620014, г. Екатеринбург, ул. Московская, 29); e-mail: muccio@bk.ru ИНФОРМАЦИЯ О СТАТЬЕ: дата поступления 9 февраля 2020 г.; дата принятия к печати 10 мая 2020 г. http://doi.org/10.15826/recon.2020.6.2.006 https://www.forbes.ru/finansy-i-investicii/349401-illyuziya-infrastrukturnyh-obligaciy-pochemu-fokus https://www.forbes.ru/finansy-i-investicii/349401-illyuziya-infrastrukturnyh-obligaciy-pochemu-fokus http://doi.org/10.5772/55659 mailto:muccio@bk.ru mailto:muccio@bk.ru