CC BY
14Research article
DOI: https://doi.org/10.48554/SDEE.2021.2.3
RANKING THE REGIONS OF THE NORTHWESTERN FEDERAL DISTRICT AS A SUSTAINABLE DEVELOPMENT POLICY TOOL
Olga Zaborovskaia1, Elena Zhogova2*, Anis Alamshoev3
1 State Institute of Economics, Finance, Law and Technology, Russia, [email protected]
2 Peter the Great St. Petersburg Polytechnic University, Russia, [email protected]
3 Academy of Public Administration under the President of the Republic of Tajikistan, Republic of Tajikistan, [email protected]
* Corresponding author: [email protected]
Abstract
The study reported in this paper ranked the regions of the Northwestern Federal District according to the degree of their socio-economic development to evaluate such regions' sustainable development. It used a ranking algorithm to calculate the socio-economic development indices. The index of economic development of a region is calculated not only through the given average per capita values but also through the average values of the financial indicators, considering the number of employed individuals in the region. The index is an integral one, calculated as the sum of the partial integral indices. In this method, various difficult-to-compare indicators can be analysed to measure the degree of sustainable development achieved by the regions. The algorithm relies on balancing the importance of social and economic development. Striking such balance is essential for maintaining sustainable development in the long term. Thus, to calculate the final index of socio-economic development considering both social and economic development, equal shares are assigned to the components of the index, such as the index of the maturity level of the region's economy and the index of the maturity level of the social sphere. However, if each index is calculated separately, a different set of indicators with various specific weights is used. For example, to measure the index of economic maturity, the index of the industrialisation level in the region and the index of enterprise financial status are considered, and in this case, a greater specific weight is assigned to industrialisation. To calculate the index of the social sphere, the following indicators are considered: the index of the monetary income of the population in the region and the index of the quality of the social situation in the region. All these indices have an additional division according to the method described in the book Investment Potential of the Russian Economy by Bard, Buzulukov, Drogobytsky and Shchepetova (2003). Using the algorithm for calculating the indices of socio-economic development, we can judge the state of the economy and the social sphere at a specific time (in our case, we studied the 2018 data) and their potential for achieving sustainable economic development in the end. The result of the study is the ranking of the regions of the Northwestern Federal District, which considers their socio-economic development and how favourable their climate is for achieving the sustainable development of the territories. The regions that received above-average index values were St. Petersburg, Leningrad Region, Kaliningrad Region and Murmansk Region.
Keywords: region, socio-economic development index, ranking, algorithm for the regional ranking of the socio-economic indices, sustainable economic development.
Citation: Zaborovskaia, O, Zhogova, E., Alamshoev, A., 2021. Ranking of the regions of the Northwestern Federal District as a Sustainable Development Policy Tool. Sustainable Development and Engineering Economics 2, 3. https://doi.org/10.48554/SDEE.2021.2.3 '
" I This work is licensed under a CC BY-NC 4.0
© Zaborovskaia, O., Zhogova, E., Alamshoev, A., 2021. Published by Peter the Great St. Petersburg Polytechnic University
Sustainable development of regional infrastructure 41
Научная статья УДК 332.14
DOI: https://doi.org/10.48554/SDEE.2021.2.3
РАНЖИРОВАНИЕ РЕГИОНОВ СЕВЕРО-ЗАПАДНОГО ФЕДЕРАЛЬНОГО ОКРУГА КАК ИНСТРУМЕНТ ПОЛИТИКИ УСТОЙЧИВОГО РАЗВИТИЯ
Ольга Заборовская1, Елена Жогова2*, Анис Аламшоев3
1 Государственный институт экономики, финансов, права и технологий, Россия, [email protected]
2 Санкт-Петербургский политехнический университет Петра Великого, Россия, [email protected]
3 Академия государственного управления при Президенте Республики Таджикистан, Республика Таджикистан, [email protected]
* Автор, ответственный за переписку: [email protected]
Аннотация
Статья посвящена ранжированию регионов СЗФО по их социально-экономическому развитию.
Новизна данного исследования заключается в том, что для ранжирования применен алгоритм
расчета индексов социально-экономического развития, при этом индекс развития экономики региона рассчитывается с учетом не только среднедушевых значений, но и средних значений финансовых показателей с учетом числа трудоустроенных в регионе. Рассчитываемый индекс является интегральным и строится как сумма частных интегральных индексов. Метод позволяет использовать различные трудносопоставимые показатели. Данный алгоритм построен на сбалансированной важности как социального, так и экономического развития, в связи с этим при расчете итогового индекса социально-экономического развития, объединяющего обе эти сферы, его составным частям, а именно индексу уровня развития экономики региона и индексу уровня развития социальной сферы присвоены равный удельный вес. Однако для расчета каждого индекса в отдельности применены различный набор показателей с различными удельными весами, такими как для индексу уровня развития экономики: индекс уровня развития производства в регионе и индекс финансового состояния предприятий, в данном случае больший удельный вес присвоен уровню производства, для расчета индекса социальной сферы рассматриваются показатели: индекс уровня денежных доходов населения в регионе и индекс качества социальной обстановки, все указанные выше индексы имеют дополнительное подразделение согласно методики описанной в книге «Инвестиционный потенциал Российской экономики» Бард В.С., Бузулу-ков С.Н., Дрогобыцкий И.Н., Щепетова С.Е. Алгоритм расчета индексов социально-экономического развития позволяет выявить уровень состояния экономики и социальной сферы на конкретный момент времени (в нашем случае изучались данные 2018 года), рассчитанных на основе формализованных показателей. Результатом исследования стал рейтинг регионов СЗФО по их социально-экономическому развитию, а именно регионами, получившими значения индекса выше среднего, стали Санкт-Петербург, Ленинградская область, Калининградская область, Мурманская область.
Ключевые слова: регион, индекс социально-экономического развития, ранжирование.
Цитирование: Заборовская, О., Жогова, Е., Аламшоев, А., 2021. Ранжирование регионов Северо-Западного федерального округа как инструмент политики устойчивого развития. Sustainable Development and Engineering Economics 2, 3. https://doi.org/10.48554/SDEE.2021.2.3
к-атжш Эта работа распространяется под лицензией ^ BY-NC 4.0
© Заборовская, О., Жогова, Е., Аламшоев, А., 2021. Издатель: Санкт-Петербургский политехнический университет Петра Великого
42
Устойчивое развитие региональной инфраструктуры
1. Introduction
Today, to encourage the sustainable economic development of a territory, many factors that interact with each other and are simultaneously influenced by the global and virtual environment have to be considered. Accordingly, the parameters that reflect these changes and also the weaknesses and strengths of territories need to be identified.
The study reported in this paper (our study) was about measuring the sustainable development of a region and was based on the research on the region's social and economic spheres. In particular, it focused on deciding how to choose the factors to be included in the analysis of the statistical data of the region in terms of their validity and specific weights. Accordingly, the object of our study was the analysis of the possibility of achieving sustainable socio-economic development in a region using an algorithm for calculating integral indices.
Extensive statistical data are used to study the dynamics of change in the state of the socio-economic environment of a region, considering the relationships existing between them. The results obtained can be matched with the global economic changes, and the possibility of implementing government programs aimed at sustainable socio-economic development can be explored. However, the data array as such is neutral, and it is the research method that determines the results it can demonstrate. Hence, a problem arises regarding how to select a specific range of indicators that will reflect the changes taking place in the economic and social spheres and that can be used to balance the economically and socially significant factors with specific weights attached to them.
Foreign and national studies suggest that regional government bodies should track a region's rank in the national and world economy (Yakimchuk et al., 2021) in accordance with the available resources. The quantitative estimates obtained should be used for making informed management decisions, reducing the negative consequences and maximising the opportunities for the region's sustainable development (Andra-Madalina, 2014). Furthermore, the focus in studying regional inequality shifts from the economic factors to the social ones and to the ways of more effectively influencing the development of the regional economy through the better living standards of the population and an improved social sphere. It is essential to analyse the factors affecting the regional economy in the long term (Lvov et al., 2004). Therefore, some algorithms have to be created for the integral assessment of the socio-economic state of the region, with the indicators selected being well balanced in relation to each other so that the economic situation in the region can be realistically reflected.
2. Literature review
Investigating the socio-economic states of regions and ranking them on the basis of the results obtained is one way of deciding on the scope of sustainable development programs for a particular territory and of choosing the sustainable development policy priorities for the targeted financial support of the regions to be provided by the federal government.
Regional socio-economic systems are affected by many factors. These factors are interconnected and are influenced by both the global and virtual environments, which causes changes in the operation of the economic entities in the regions (Zuti, 2018). Accordingly, the parameters that reflect these changes must be identified and the weaknesses and strengths of regions must be assessed and considered in the formulation of the sustainable development policies of regions.
The changes occurring in the state of regional socio-economic systems can be evaluated if the dynamics of the statistical data are analysed. Such analysis can also determine the impact of the global environment and world trends on the region.
A number of research algorithms can be used to generalise and structure statistical data so that the various economic aspects of the sustainable development policies being pursued in different territories can be studied using a list of concrete indicators.
In our study, statistical indicators were the bases of the algorithm that was used for calculating the index of socio-economic development of the regions of the Northwestern Federal District (NWFD). This algorithm involves calculating the indices of maturity of the economy and social sphere of the region1.
It is important to study the socio-economic development of regions because it is the 'socio-economic modernization' of the economy that can result in an increase of some indicators in the region, such as income and employment (Obasaju et al., 2021). The sustainable socio-economic development of the region, however, is a major issue (Martin and Sunley, 2015).
A number of Russian and foreign researchers have brought up the question of what the drivers of regional development2 are in the context of sustainable development policy. One of such drivers can be an agglomeration effect (Wu et al., 2014) and the cluster approach (Kourtit and Gordon, 2019). In addition, we agree that the key driver can be the possibilities of human capital (Gordon and Kourtit, 2020; McCool and Kruger, 2003). Human capital is also quite important in the transformation processes occurring in a country (and in a region as its integral part) caused by new economic realities and innovations (Sm^tkowski, 2015).
In addition, it is highlighted that there is a need to separate the power and responsibilities for socio-economic development and the priority areas of the region's sustainable development policy not only at the federal level but also at the regional and municipal levels (Yakimchuk et al., 2021; Tatarkin, 2016). It is important to decide on the priorities for the economic development of the country as a whole and of a concrete territory therein in particular, according to the priorities that have been chosen.2
The aforementioned hypothesis is confirmed in the book Strategic Management: Region, City, Enterprise written by Lvov, Granberg et al. (2004). According to the said book, it is reasonable to relate global problems with local ones, such as problems arising in the social sphere of a city (Lvov et al., 2004). Thus, if local problems are investigated, the economic problems existing in a particular territory and in a specific industry can be studied in a more in-depth way.
Some national studies have pointed out that it is hard to analyse the state of the regions because of various factors, which are multidimensional and multidirectional. Thus, regional statistics should be considered to identify and evaluate the scales and directions of internal processes. After that, priority areas should be chosen by comparing, ranking and grouping the development levels of the region according to the characteristics selected (Pascariu et al., 2020). The method suggested in this study relies on the synthesis of the results of the analysis of objective (regional) factors (the geography and history of the region) and the specific development conditions in the region (economic, social, financial and other components) to carry out a summary assessment of the state of the region.
Some authors highlight the need for sustainable development policies to be based on the predicted general economic, sectoral and regional proportions. For example, when the country's development is projected, an indicator that characterises the standard of living of the population in each specific region,
1 Bard, V.S., Buzulukov, S.N., Drogobytsky, I.N., Schepova, S.E., 2003. The Investment Potential of the
Russian Economy. Moscow: Examen. URL: https://search.rsl.ru/ru/record/01002355429
2 Chernyak, V.Z., Chernyak, A.V., Dovdienko, I.V., 2010. The Economy of a City. Moscow: KNORUS.
etc., should be considered to a certain degree (Tyutin et al., 2019). In addition, the development forecast should take into account the micro and macro constraints, such as inflation, unemployment, poverty and social factors. It is pointed out that the factors of social and individual development have a great impact on the level of the country's economic potential and on the degree of sustainability of its development in the long run. Regional government bodies need to objectively evaluate the region's position vis-à-vis other territories and given the available resources. They should use objective quantitative data to manage the risks and to minimise the effects of negative factors.
Some studies have acknowledged the need for a region to choose its own model of economic behaviour given its competitive advantages. At the same time, the federal centre should aim at minimising the differences in the living standards of the people in various regions, taking into account their economic development level and unique natural resources.
Some foreign studies have focused on studying regional inequality, its causes and the factors leading to its growth. Sustainable development of territories is the key to achieving high living standards. This opinion is widespread both in Russia and abroad (Yakimchuk et al., 2021). It is stated in particular that the state needs to monitor the dynamics of the changes in the causes of these differences and to assess the influence of political institutions (distribution of power among the regional government bodies and tax deductions among the regions), world trade and the ability of economic entities to make a profit in a particular region, depending on the economic conditions in such region (Kim, 2008).
The review of foreign studies in our study was partially based on classical models used to evaluate the causes of regional inequality. The key factors are the geographic location of the region, the maturity of its infrastructure and the production costs. Today, these ideas are supplemented by the investigation of the social indicators of the people's living standards because these can significantly expand the production potential of the region due to the growth of the quality of human capital (Capello and Nijkamp, 2009).
Previous studies have described the methods used for assessing the statistical indicators and investigating the dynamics and structure of the gross regional product (GRP), consumption indicators, unemployment rate, labour migration and availability of social services (Zhogova et al., 2020). We should clearly highlight the statements about the different interpretations of the values of the indicators and their dynamics in the long and short run (Zhalsaraeva and Dugarzhapova, 2020). Thus, in the short run, the dynamics can be interpreted as negative (Song et al., 2019). However, if we assess, for example, the ratio of labour productivity, the development of education and the introduction of new technologies (Yu et al.), the results in the long term can be interpreted as diametrically opposite (Magomedov et al., 2020).
The significance of the social factors of regional development can be evaluated on the basis of a range of indices, such as the Labor Force Survey (LFS), the Statistics on Income and Living Conditions, the Inclusive Society Index and other indices aimed at measuring the population's income level, healthcare level, environmental indicators, and other factors. These factors must be considered when assessing regional development because the people's high standard of living is a catalyst for economic growth (Medgyesi et al., 2017).
3. Methodology
The algorithm for calculating the indices of socio-economic development can be used to identify the levels of the economy and the social sphere at a specific time (in our case, the 2018 data were studied). The indices are calculated using formalised indicators, and help us assess the chances that a sustainable development policy will be pursued in a given territory. The index to be calculated is integral and represents the sum of partial integral indices. Various hard-to-compare indicators can be used in this method.
The indices consider the development level of the region and that of the national economy as a whole (this value is considered equal to 1).1
The aforementioned method uses the indices demonstrating the level of socio-economic development of regions (IEj); in this study, the regions of the NWFD in Russia. The index is the sum of the indices indicating the development levels of the social sphere and the economy of a particular region. Each of the indices is assigned an equal specific weight amounting to 0.5.
In turn, each of the sub-indices, which are used to calculate the indices of the social sphere and economic development, is the sum of the relative values estimated from the statistics of the region and has a certain specific weight. For example, to calculate the Economic Development Index, we have to count the Production Index (Iij) and the Enterprise Financial Status Index (If). The Production Index consists of the sum of the following indicators per capita: industrial production of region (I ), construction (C), agricultural produce (AX.), and retail goods turnover (T). The Financial Status Index consists of the volume of indicators, such as the amounts of profit (loss) made by the enterprises (P.) in the region and the amounts of overdue receivables and payables (Z.) per employed person. The initial method used indicators per employable person, but we believe that if the indices are calculated given the number of employed people, it can paint a better picture of the region being studied because the values obtained pertain to the people engaged in the economy of the region (e.g. production of goods, work and services), and correspondingly, we can indirectly measure how effectively the human resources are used in the region's economy.
All the above values were calculated in this study as a ratio of the regional values to the all-Russian ones. The following specific weights were assigned: 0.33 for the Financial State Index and 0.67 for the Industrialization Index. The index values were calculated according to the basic method, and were not changed.1
The levels of economic development of the Russian regions as of year 2018 are presented in Table 1.
To compute the Social Sphere Index (Is), we calculated the Social Environment Index (Ie j) and the Population Monetary Income Index (Imj). The prevailing specific weight (0.65) was assigned to the Monetary Income Index while the other index was appraised as 0.35.
The indices are calculated given the deviations of the regional values along the country as a whole. Income (Id.) and average salary (Sc.) are calculated given the values per capita and the size of the regional minimum wages. The Social Environment Index consists of the indicators of housing provision H unemployment rate U , and crime rate C . Here, the specific weights remain unchanged according to the basic method, and are evenly distributed among the indicators.
4. Results
We chose the NWFD in the context of its regions as the object of our study. The range of the main indicators used in the analysis included the volumes of industrial production, construction, production of the main types of agricultural products and retail goods turnover; the indicators of companies' financial performance and the size of overdue receivables and payables. The indicators were calculated given the population size of every region while some indicators considered the size of the employed population in the region. Then the indicators of the NWFD regions were compared to the data for Russia as a whole3.
According to the above indicators, the constituent indicators of the Industrialization Index and Financial Status Index of enterprises in the NWFD were calculated (Figure 1).
3 Committee for Economic Development and Investment Activities in Leningrad Region URL: https://lenobl.ru
Indicators of the industrialization level and financial status of enterprises in the NWFD
8.000 7.000
Figure 1. Industrialisation level and financialstatusofenterprises in theregions of the Northwestern Federal District. Source: compiled by the authors.
Regional development index of the NWFD
Saint-Petersburg iii:::: :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: llill
Pskov Region iii :"
Novgorod Region :::::::::::::::::::::::::::: :::::|j||
Murmansk Region ll]]lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllil]]]]ll
Leningrad Region ]]|]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]||;
Komi Republic ll]]llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllill]]]]]|]l
Ka I in ing ra d R eg ion ]] 11]]] 11111]]]]]]]]]]]] 1111mmmmmmmmmimmmmmmmmmmmiiiimiiiiiiiiiiiiiiiiintfmmmmiiimi 1111]]||
Vologda Region ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::: :j|
Arkhangelsk Region lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllli
Republic of Karelia llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllilllllllllllllllllll
0.000 0.200 0.400 0.600 0.800 1.000 1.200 1.400
Figure2.Regional Development Index of theNorthwesternFederalDistrict. Source: compiled fy the authors
The indices were interpreted according to the 'Investment Potential of the Russian Economy' method worked out by Bard, Buzulukov, Drogobytsky and Schepetova (2003). All the indicators were based on the calculation of the ratio between the regional and Russian indicators: I was the volume of industrial production per capita, C the volume of construction per capita, Ax the volume of agricultural produce per capita, T the volume of retail goods turnover, P the amount of profits to losses from all types of economic activity per employed person and Z the amount of overdue receivables and payables calculated per employed person.1
Then, according to the calculation algorithm, the values of the Industrialization Index (Iij), Enterprise Financial Status Index (If) and Final Regional Development Index (I ); this unites the data of the first two aforementioned indices) indicators were given. The intermediate values of indices (Iij) and (If) were united in the bar chart of the ranked NWFD regions according to the indicators of the Regional Development Index (I ) presented in Figure 2.
Thus, according to the above data, St. Petersburg is in the first place, followed by Leningrad Region in the second place and Vologda Region in the third place.
Leningrad Region has higher indicators, but given the larger size of its population (1 867 000) against Vologda Region's 1 167 700, the gap turned out to be insignificant because the indicators were relative rather than absolute.
Then, to compute the development level of the NWFD regions' social sphere, the indicators shown in Figure 3 werecalculated.
Indicators of the population monetory income and quality of the social environment in the NWFD regions
-2.000
-4.000
£
ao C
s?
5?
£P n
¡2
(L
t) Q-
BMdj Scj :Hj Uj acj
Figure 3. Population monetoy income acdeualityofsocial environmentinlhe Northwestern Federal
District regions. Source: compiled by the authors
Social Sphere Development Index
Saint-Petersburg
Pskov Region
Novgorod Region
Murmansk Region
Leningrad Region
Komi Republic
Kaliningrad Region
Vologda Region
Arkhangelsk Region
Republic of Karelia
0,000 0,100 0.200 0.300 0,400 0,500 0,600 0,700 0,800 0,900
Figure4. Soccalspheredevelopsoentindex oftheNoahwestemFedrral Dietrictrepions.
Source :compiled bythaautnors
As mtntioned earlier, the indices were interpreted according to the 'Investment Potential of the Ruts'ao Econom1' method wa>fkoa oue ey Barr, Buzu^'^ Drogobdiena knd ^hepntova (2003). Wall regarsl to the >Viic^\a^ng; indieutors^htva^esof the region deviated from the Russian values: housing ptovisinn jeoe ca°ithL ((n.X nndmplonmunt tait (err, ce(me ante 1)°, , ratin o' monularuliesn^e^ek^^r^aki^^ra mmimum wage (Mdj) and ratio of average salary (given the debt) to minimum wagr (5(g).>
Then t^^i^^di^n ^lteg^^^rc^^ta, we calculated the Social Sphere Development Index of regions Is TorankthgeweгFDrkgionebo rheinOigotortoS the eon^trS^^l^t^re Devulopment Index, the results obtained were given in the form shown in Figure 4.
Thus, according to the above data, St. Petersburg is in the first place, and all the others regions are far behind. Leningrad Region is in the second place, Kaliningrad Region is in the third and Murmansk and Vologda Region are in the fourth.
Once again, it must be noted that the figures may differ from the absolute statistical values because they are based on mathematical calculations of the regions' indicators per capita and indicators per employed person.
To assess the validity of the study results obtained, the socio-economic indicators of the NWFD must be briefly reviewed.
Figure 5 shows the specific weights of the land areas of the NWFD regions.
Figure 5 presents the indicators of the distribution of the population size across the NWFD. The consolidated figure of the district's population size was used as a basis, and the specific weight of each region was calculated.
Land area of the NWFD, thousand square kilometers
11%
35%
i Republic of Karelia Arkhangelsk Region Vologda Region Kaliningrad Region i Komi Republic i Leningrad Region i Murmansk Region i Novgorod Region i Pskov Region
8%
1%
Figure 5. Land area of the Northwestern Federal District, thousand square kilometres. Source: Federal State; Statistics Service4
Population size, thousand people
5333, B9
1144,1
1167,7
1011.5
■ Republic of Kane tia Arto an gelsk Re g on
■ Voiced a Region Ka&iingrad Region
■ Komi Republic
■ Leningrad Region
■ Murmansk Region
■ Novgorod Region
■ Pskov Re g'on
■ Saint-Pet eirburg
596,17
Figceeö. PopulationoftheNorthwestern Fe de ralDistrictaso f 2018. Source:FederalStateStatistics Service.4
Stittrtdos. Fttpudtrosstat.gov.ru
4
Table 1. Socio-economic indicators for 2018, million rubles
Northwestern Federal District regions Volumes of industrial production, mln. rub. Volumes of construction, mln. rub. Volumes of major agricultural produce Volumes of retail goods turnover
Republic of Karelia 226 673.90 3 582.10 4 347.20 12 439.80
Arkhangelsk Region 668 166.00 77 910.00 12 703.30 262 434.80
Vologda Region 691 100.00 100 981.30 28 870.40 185 839.00
Kaliningrad Region 621 030.00 146 465.90 36 718.00 168 930.30
Komi Republic 527 714.00 42 461.00 11 189.80 154 311.00
Leningrad Region 1 125 100.00 159 950.00 86 800.00 387 800.00
Murmansk Region 327 370.40 32 932.20 1 860.80 169 676.60
Novgorod Region 222 080.70 23 032.90 26 935.50 115 300.00
Pskov Region 113 642.60 24 412.70 39 394.90 112 140.50
SPB 3 301 428.00 572 871.00 15 900.00 1 396 600.00
RF 69 086 000.00 8 385 700.00 5 119 800.00 31 579 000.00
Source: Federal State Statistics Service.4
In addition to the population size, we should consider the data on the major socio-economic indicators given in the statistical digest of the Russian regions' socio-economic indicators for the year 2018 (Table 1).
The statistics that do not consider the social sphere of the NWFD indicate the primacy of St.Petersburg, for example, in terms of industrial production. It is followed by Leningrad Region (second place), Vologda Region (third place), and Arkhangelsk and Kaliningrad Region (fourth and fifth places).
As for the indicators of the social sphere, the following statistics can be presented for the year 2018 (Table 2).
Table 2. Socio-economic indicators for 2018
Northwestern Federal District regions Population monetary income per capita, rub. Ratio of average salary (given the debt) to minimum wage Housing provision indicator, sq.m. per capita Unemployment rate, % Crime rate, %
Republic of Karelia 29 150.00 4.56 26.90 8.2 4%
Arkhangelsk Region 33 831.00 4.59 28.20 10.6 6%
Vologda Region 26 982.00 4.13 30.20 5.1 5%
Kaliningrad Region 27 461.00 3.72 28.20 4.7 4%
Komi Republic 30 100.00 4.74 28.70 7.4 5%
Leningrad Region 31 341.00 4.86 29.00 4.4 7%
Murmansk Region 41 564.00 4.47 25.40 6.8 4%
Novgorod Region 25 292.00 3.51 31.90 4.5 3%
Pskov Region 23 880.00 3.00 31.10 5 2%
SPB 44 999.00 6.68 25.40 1.5 16%
RF 33 178.00 5.01 25.80 4.80 1.00
Source: Federal State Statistics Service.4
4 Statistics. https://rosstat.gov.ru
According to the data in Table 7, it can be concluded that in terms of the population monetary income, for instance, St. Petersburg, Murmansk Region and Arkhangelsk Region occupy the leading positions. Correspondingly, the indicators of the social and economic spheres approximate the results of our study.
Let us now analyse the GRP of the NWFD regions and rank them according to this indicator (Figure 7).
According to the data presented in Figure 7, in terms of GRP, St. Petersburg and Leningrad Region are in the first place in the NWFD. Correspondingly, the results of our study are quite realistic, except for an error in the relative indicators and the human factor, and can serve as the bases for learning about the development trends in the territory being studied.
The results of our study include the calculated index of the level of socio-economic development of the NWFD regions. In essence, this index is a comprehensive indicator consisting of indices that take into account the level of economic development and the index of development of the social sphere in the regions. The indicators of the indices can be used to rank the regions, comparable to the ranking built according to the indicators of socio-economic analysis using non-aggregated statistics.
Which weights to assigntothe indicators ateach stagewhenthis algorithm is calculated, however, aoe debatoblo. Aseiening e specific weigbt of 0.5 for the aocio-economic developmenO indices oOtia rogiona to expleinable for the finat stage tOthe holculatlon ieocust mosb rtuhies aim ta feO the goldtn mean botween tncesOlno in tOe oconomy and investiuo in seeiol programs. Hotn-ever,the othprwcights In Ohe ulfooithm, which we took entirely from the basic method, without any chang c ftO eral, Beieulokov, DrogoP .Osky, & Shchep etova, 20eet, nee hoidoov ceaial ani h aae to it fubatanhibted ic some way depending on the priority level of a particular indicator.
Gross regional product of the NWFD by regions for 2018
■ Republic of Kane I is Arkh 3n gel sk Re gj on Vologda Region Kaliningrad Region
■ Komi Republic
■ Leningrad Region
■ Murmansk Region
■ Novgorod Region
■ Pskov Region
■ Saint-Petersburg
2%
Figure 7. Gross regional product of the Northwestern Federal District by region for 2018.
Source:FederalStateStatisticsService.4
4 Statistics, https://rosstat.gov.ru
Socio-economic development index of the NWFD regions
Saint-Petersburg ■ C.S21
Pskov Regjon ■ 0.16
Novgorod Region H 0.139
Murmansk Region 0.359
Leningrad Region ■ 0.519
Komi Republic 0.083
Kaliningrad Region 0.445
Vologda Region ^^^m 0.346
Arkhangelsk Region ^^^m 0.348
Republic of Kane I ia 0.294
0
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Figure 8. Socio-economic development index oftheNorthwestern Federal District regions.
Source: compiled by the authors
In our analyeis, wx loft all the iodicos unchangeO. Tfv onty chancx thvl was intsodu ced wa s than seme inhixa.ors were d^^e^te^^d cot axe empleyuNlx peescx iut xer empioyfd persxe, wC-IcIi sl^glsS:lv changed sho s^l^uNp'n finet results. NevertPalesr, ct lie end of the anrlyrisc the voclo-eco-nomlc inlyx oC eavh NWFD region hU) sans r^^l^k^i^V, to ie is Rosslble i^sc rrvlc iCr
regions bo iheir toiiooervuomix I^u^i^I xnd to drxw deiXsln oonclnslcxt ubnui ^uxh
regins' potentivl ^c^ira^x^e^ld^^ny sc siamable Uxvelxprnvnt scid -d^x workingynthepriyrity areas of the sustainaxlc devdspment; poH c° wrihm Ac fedvaa1 c^,xte ^o^am (Figure x).
The -a ex It of vur sU^ con t^^ the foUowmg a^sv^ted ccnldsg of txr NWTD reguons w^ thegreateai;pxteotlT fotxursumgsh eoux.cmaMi development policy priorities. The regions whose indexvaluesare above the average (0.356) are considered the most successful in the context of their social ced xconomic Covvlopment l^ecs. Tcxse esx St. Peiessburg, Venlngrsd Regisn, Ka-lil^lha^xaO Rsgion rntO Murmansx Rig^n. Those tdaf Oeii Vei ow thc thxy^lussll valxe ecu can vlderx d suvceeeful rexiycv (mrkhangeltk Region enf Voloxda Regionl. All tie oiler regione ves in c less favootable posltlm and will mad more revourcxs Vo lvl Iow al^e vu-tvinaO le develnpment vTlcy b ccauuvCdeisi cVuxvalues are below the average threshold values.
The anoletls rssulls m our ttuSy vocefximfte rexllSyandcan 1-c veedVooonxider tncompcra-bIs Vnciossi vcch y- tho economic siid covlvl tnhxsev, to evaluate VOr levd fV susteldable xoxcomi c duxdo'sx . of txrcitories.
5. Conclusion
It can be concluded from our study that the causes of the inequality among regions have to be investigated further. Here, both the economic factors and the results of the analysis of the social sphere should be taken into account because it is essential to eliminate the factors leading to a decline in the economic development of a region to be able to adopt a sustainable economic development policy.
The findings of some studies conducted by scientists from various countries show that the growing social satisfaction and security of a region's population can become a more effective leverage for the productive potential of the regional economy than the singular potential for using the factors of geographical location and the calculation of the production cost level (Zhogova et al., 2020; Malgorzata et al., 2018). Moreover, a better standard of living within the context of global trade can further stabilise the socio-economic situation in the region (Medvedev, 2018) and drive its sustainable development, and can lead to progress in the innovative environment (Babskova, 2017). The outcome of this can be the higher morale of the public, economic entities and the country as a whole during times of crisis not only in the economy but also in politics.
It should also be noted that it is essential to establish some common criteria and priorities for the development not only of a region but also of the country as a whole, and to implement the sustainable development programs of a territory at the international level (Bobylev, 2017).
Some studies have shown that gross domestic product growth may not always sufficiently reflect the growth of the Russian economy. It may just be the evidence of increasing prices in the raw materials sector and may not reflect the stagnation of the regional economy (Bobylev, 2017). It is the analysis of the sustainable development of each specific region that will allow the state to choose and implement the sustainable development policy priorities at both the federal and municipal levels in the most efficient way.
The results of the ranking herein analysed according to the indicators of the socio-economic development index of the NWFD regions allowed us to highlight the regions with the greatest potential for implementing the sustainable development policy priorities. The regions with index values above the average are considered the most successful ones at the social and economic levels and include St. Petersburg, Leningrad Region, Kaliningrad Region and Murmansk Region. Those that ended up just below the threshold value, Arkhangelsk and Vologda Region, are also considered successful regions.
In general, the results we obtained in our study approximate reality. The proposed algorithm is suitable for analysing the socio-economic sphere of regions and for deciding if sustainable development can be achieved. It can also be used to consider incomparable factors such as economic and social ones.
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The article was submitted 18.05.2021, approved after reviewing 13.07.2021, accepted for publication 23.07.2021.
Статья поступила в редакцию 18.05.2021, одобрена после рецензирования 31.07.2021, принята к публикации 23.07.2021.
About the authors:
1. Olga V. Zaborovskaia, Doctor of Economics, Professor of The State Institute of Economic, Finance, Law and Technology, Gatchina, Russia, [email protected]
2. Elena V. Zhogova, Ph.D. in Economics, Teacher of Graduate School of Industrial Economics, Peter the Great St. Petersburg Polytechnic University, Russia, [email protected]
3. Anis K. Alamshoev, Candidate of Economic Sciences, Associate Professor, Head of the Department of Public Administration and National Economy of the Academy of Public Administration under the President of the Republic of Tajikistan, the Republic of Tajikistan, [email protected]
Информация об авторах:
1. Ольга Витальевна Заборовская, доктор экономических наук, профессор Государственного института экономики, финансов, права и технологий, Гатчина, Россия, [email protected]
2. Елена Вячеславовна Жогова, кандидат экономических наук, Высшая инженерно-экономическая школа, Санкт-Петербургский политехнический университет Петра Великого, Россия, [email protected]
3. Анис Курбониддинович Аламшоев, Кандидат экономических наук, доцент, заведующий кафедрой государственного управления и национальной экономики, Академия
