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REVISTA INCLUSIONES ISSN 0719-4706 VOLUMEN 8 – NÚMERO 2 – ABRIL/JUNIO 2021

DR. VADIM KABANOV / DR. ARKADY LARIONOV

Indización, Repositorios Académicos/Universitarios y Bases de Datos Académicas Revista Inclusiones, se encuentra indizada en:

CATÁLOGO



BIBLIOTECA UNIVERSIDAD DE CONCEPCIÓN



ISSN 0719-4706 - Volumen 8 / Número 2 / Abril – Junio 2021 pp. 555-570

ASSESSMENT OF THE SOCIO-ECONOMIC SITUATION

OF REGIONS USING THE ZIPF LAW

Dr. Vadim Kabanov Volgograd State Technical University, Russia

ORCID: 0000-0001-9706-513 [email protected] Dr. Arkady Larionov

Moscow State University of Civil Engineering (National Research), Russia ORCID: 0000-0001-9706-513

[email protected]

Fecha de Recepción: 20 de noviembre de 2020 – Fecha Revisión: 02 de diciembre de 2020

Fecha de Aceptación: 09 de febrero de 2021 – Fecha de Publicación: 01 de abril de 2021

Abstract

The approval of the spatial development strategy of Russia encourages the activities of regional public authorities to develop strategic development documents. The regulations adopted by the Federal center do not define the tools necessary for scientific justification of long-term development goals of the Rus-sian Federation's subjects. This paper offers a simple tool for assessing the socio-economic develop-ment of Russian regions that can be used to determine the potential of the regional economy. Objective: to determine the dynamics of changes in the socio-economic position of regional leaders in the Russian Federation by comparing the quantitative values of 3 indicators with an inversely proportional function (Zipf’s law). The paper presents a solution to the problem of forming quantitative initial values. Statis-tical observations of Rosstat are the only source of information that has the highest level of reliability. The quantitative values of the considered indicators are published annually by the state statistics bod-ies. Standard methods of mathematical statistics were used in this work. By changing the value of the coefficient of determination for each array of quantitative values, the speed of approximation of the dis-tribution to Zipf’s law is determined. Convincing evidence is given for approximating distributions of quantitative indicators to an inversely proportional function (Zipf’s law). This conclusion can be used in the formation of long-term goals of socio-economic development in systems that are a set of territori-al entities. The results obtained coincide with the published conclusions on the feasibility of applying the Zipf’s law in the study of socio - economic situation in systems that are a set of territorial entities.

Keywords

Zipf’s law – Socio-economic development – Strategy – Gross value added – Number of jobs

Para Citar este Artículo: Kabanov, Vadim y Larionov, Arkady. Assessment of the socio-economic situation of regions using the Zipf law. Revista Inclusiones Vol: 8 num 2 (2021): 555-570.

Licencia Creative Commons Atributtion Nom-Comercial 3.0 Unported

(CC BY-NC 3.0) Licencia Internacional



Assessment of the socio-economic situation of regions using the Zipf law pág. 556

Introduction

The strategy approval of the spatial development of the Russian Federation for the

period up to 2025 (hereinafter referred to as the Strategy) simulates the study of the current situation of the constitu-ent entities of the Russian Federation to achieve long-term goals, described by the Government of the Russian Federation. The strategy approved by the order of the Government of the Russian Federation No. 207-r dated February 13, 2019.

In this document, Appendix 3, 'List of promising centers of economic growth', seems to be very interesting. The centers of economic growth are presented (Appendix 3 of the Strategy) in the form of the following classification:

Cities (largest and large agglomerations);

Centers of economic growth of the RF subjects;

Mineral resources and agro-industrial centers;

World-class scientific and educational centers.

It is important to note that as a quantitative criterion, according to the value of which various ter-ritories of the Russian Federation were classified, the authors of the Strategy used the contribution of an economic entity (territory or social-territorial association of the population) to the economic growth of the Russian Federation. Economic growth refers to the volume of gross value added produced at compa-rable prices.

The practical application of the gross value-added indicator is very often criticized. The work of J. Stiglitz1 is most often cited as such criticism. However, the laureate of the. A. Nobel criticizes the use of this indicator for those economies in which a significant share of value added is produced by the stock market (that is, the non-production sector of the economy or the financial sector). In Russia, most of the value-added (GDP) produced reflects the volume of products produced by the real sector of the economy (production of goods and services in the non-financial sec-tor). In this regard, the use of the value of GDP in the study of the socio-economic system of the Rus-sian Federation seems to be reasonable and relevant.

Most often, gross domestic product (GDP produced on the territory of the constituent

entities of the Russian Federation - GRP) is found in studies:

When studying the structure of the regional economy and methods for measuring the value-added2,;

1 J. Stiglitz y J.-P. Fitoussi Sen, Misjudging our lives: why GDP doesn't make sense. Report of the Commission on measuring economic performance and social progress (Мoskau: The Gaidar Institute, 2016), 102. 2 L. A. Kormushkina, y D. A. Koloskov, “Innovative approaches to the formation of investment policy instruments from the perspective of the neo-industrial development paradigm”, Economic and social changes: facts, trends, forecast num 6 (2017): 218-233; A. G. Aganbegyan, “Investment in fixed capital and investment in human capital are two interrelated sources of socio-economic growth”, Problems of forecasting num 4 (2017): 17-20; E. A. Hanushek; J. Ruhose y L. Woessmann, “Knowledge Capital and Aggregate Income Differences: Development Accounting for US States”, American Economic Journal: Macroeconomics num 9(4) (2017): 184-224 y V. A. Korshunov y R. O. Reinhardt, “Estimation of the Solow balance for real and potential GDP: a practical calculation for OECD member countries”, Bulletin of the Institute of Economics of the Russian Academy of Sciences num 3 (2017): 137-149.




Dependence of the value of GDP (GRP) on production and consumption factors3;

Methods for assessing the socio-economic situation4.

Thus, a conclusion should be drawn about the relevance of research and the practical application of the GDP (GRP) indicator in the study of the spatial economy of Russia. Note that the author does not belong to the advocates of using the ratio of GDP to population (GDP per capita) for scientific analysis. This choice is justified by not the same structure of regional economies in the constituent entities of the Russian Federation, as well as by a significant difference in the age and sex population in the regions of Russia. The quantity and quality of jobs form the value of the added value produced. In this regard, the indicator of the number of jobs is no less relevant than the GDP. The use of the size of the employed population (employment) to study the spatial economy is not unique. This indicator has been used for a long time when comparing the socio-economic position of territorial entities in the modern domestic5 and foreign6 practice. Finally, the traditional metric for measurements in spatial economics is population size. Ultimately, the volume of the consumer market depends on the consumer demand generated by the residents of the socio-economic system of the municipality or region. To study the distribution of the population over the territory of a public-territorial association (municipality or region) in domestic7 and foreign8 studies traditionally use a method based on determining deviations from an inversely proportional function (y = 1 / x, - Zipf’s law).

Based on the above arguments to obtain an objective picture describing the

dynamics of changes in the socio-economic situation of the country's territory in the context of the constituent entities of the federation (that is, its constituent parts), it is advisable to analyze at least 3 indicators: the volume of production of gross value added, the number of

3 V. V. Ivanter, “Strategy for transition to economic growth”, Problems of forecasting num 1 (2016): 3-7; V. I. Antipov y I. B. Kolmakov, “Estimation of the multiplier effect of targeted financial expenditures of households”, Bulletin of the Plekhanov Russian University of Economics num 2 (2016): 130-139 y R. Capello y A. Caragliu, “After crisis scenarios for Europe: alternative evolutions of structural adjustments”, Cambridge Journal of Regions, Economy and Society num 1(2016): 81-101. 4 M. I. Geraskin y P. V. Porubova, “Trend analysis of the dynamics of macroeconomic indicators of the Russian Federation in 1956-2014”, Bulletin of Samara State University of Economics num 4 (2017): 5-18; V. Mykhnenko y M. Wolff, “State rescaling and economic convergence”, Regional Studies num 4 (2018): 1-16 y V. I. Ponomarev, “On gross national capital, GDP, and the law of preserving economic potential (essay in terms of anthropological political economy)”. Sciences of Europe num 7(2) (2016): 90-101. 5 V. A. Rusanovsky y V. A. Markov, “Employment and labor productivity in Russia's macro-regions: spatial interdependencies”, Problems of forecasting num 2 (2018): 36-48 y A. M. Kublanov; A. V. Borshchev y M. S. Santalova, “Employment and wages of the population as indicators of social quality of life”, The scientific journal NRU ITMO num 1 (2018): 33-44. 6 B. Ingelaere; L. Christiaensen; J. De Weerdt y R. Kanbur, “Why secondary towns can be important for poverty reduction. A migrant perspective”, World Development num 105 (2018): 273-282 y A. Lavopa y A. Szirmai, “Structural modernisation and development traps. An empirical approach”, World Development num 112 (2018): 59-73. 7 T. L. Borodina, “Regional features of population dynamics in Russia in the post-Soviet period”, Proceedings of the Russian Academy of Sciences. Geographical series num 1 (2017): 47-61 y L. B. Karachurina, “Population Dynamics of Centers and Secondary Cities of Russia’s Regions: Trends towards Polycentricity”, Regional Research of Russia num 8 (2018): 308-321. 8 P. Aroca y M. Atienza, “Spatial concentration in Latin America and the role of institutions”, Investigaciones Regionales – Journal of Regional Research num 36 (2016): 233-253 y A. Tikunov y A. Boldak, “Indices of development and their practical application”, Geography, environment, sustainability num 3 (2010): 68-100




jobs (number of employed population) and population size. The results of the study of the dynamics of the listed quantitative indicators for 20 years (from 1995 to 2015) in the context of the constituent entities of the Russian Federation can confirm or question the conclusions given in the Strategy. A change like distribution of the value of indicators can serve as a basis for forecasting the spatial socio-economic development of each region and the country as a whole. Theory

The foundations of the theory of spatial socio-economic development of the Russian Federation were laid in the works of A.G. Granberg9. It should be emphasized that to compare the regions of Russia, the input-output model was used as the main instrument, which showed not only the value of output but also provided its comparison with the consumption of added value. The input-output balance model10 has not lost its relevance in modern conditions. Using this model, it seems possible to explain the rank value obtained according to Zipf’s law and to estimate the potential for growth or decline of a region among other subjects of the Russian Federation.

For a market spatial economy, it is customary to assess the potential of the territory

for profit. Foreign researchers agree that P. Krugman’s11 ‘new economic geography’ formalizes the modern principles of spatial and strategic development of territorial entities, based on the theory of central places by V. Christaller12. Here, the volume of the goods market is used as the main quantitative indicator, for which it is proposed to find the dependence on the population density, as well as on the purchasing power determined by the level of economic development (in particular, the cost of labor). At the same time, stimulating the volume of consumption presupposes the highest intensity in territories with a high population density. High population density is still associated with the availability of jobs as a function of the concentration of production capacities.

Objective

Compare the distribution of values of gross value added produced by each

constituent entity of the Russian Federation (GRP), the number of jobs in regional economies, the population of constituent entities of the Russian Federation with the Zipf’s distribution (or with the distribution described by the inverse proportional function, y = 1 / x).

To achieve the goal, the following tasks have been set:

To generate initial data, which are annual statistical values of the studied indicators and are officially published by the state statistics bodies of the Russian Federation;

To compare the distributions of quantitative values of indicators with the distribution specified by the inversely proportional function (Zipf’s curve).

9 A. G. Granberg y Yu. S. Zaytseva, Gross regional product: interregional comparisons and dynamics (Мoskau: Goskomstat of Russia, 2003), 78. 10 A. G. Granberg, “Study of Russia's productive forces”, Bulletin of the Russian Academy of Sciences num 7 (1996): 579-584. 11 P. Krugman y M. Fujita, “When is the economy monocentric: von Thunen and Chambertin unifed”, Regional Science and Urban Economics num 254 (1995): 505-528. 12 W. Christaller, Die zentralen Orte in Süddeutschland. Jena. 1993.




Study methods

The choice of the method for studying the distributions of GRP, the number of employed, and the population were carried out taking into account the published results of studies carried out using the Zipf curve:

In the traditional study of the spatial differentiation of the population13;

When studying the distribution of values of non-demographic indicators used to describe the socio-economic situation of territories14.

For the study, three key indicators have been established GRP, number of jobs

(number of employed population), and population. For each indicator, quantitative values have been established for the period from 2000 to 2015 based on officially published statistical information (GRP, number of employed, population) in the context of subjects of the Russian Federation. A maximum was found for each of the statistical values of the corresponding year. When constructing the Zipf’s curve, all values were lined up in descending order relative to the maximum value.

The well-known criticism of the practical application of Zipf’s dependence in socio-

economic research15 cannot influence the conclusions drawn from comparing the regular deviations of the distributions of the studied variables. If mathematics has not found an explanation why in competitive socio-economic systems the distribution of values of quantitative indicators tends to an inverse function, then this is not a reason to abandon the practical use of Zipf’s law in the study of regional socio-economic systems.

Taking into account that the graphical representation of the results of comparing the

actual distribution with the Zipf’s curve does not always allow measuring the dynamics of trends in changes, the classical criterion of determinism (R2) was used, showing the degree

of correlation of the actual values (deviation) from the theoretical function (inversely proportional to the dependence of the Zipf’s curve). By approaching the value of R2 to unity, one can judge how accurately the Zipf’s curve describes the distribution of the values of the studied indicators. It is important to emphasize that there are several other criteria (for example, the Kolmogorov criterion or the Gibson criterion, and others). However, in the domestic practice of studying statistical samples, the criterion of determinism (R2) is the most widespread. In this regard, this work shows the quantitative values of R2. To be fair, it should

be noted that the values of other criteria show similar results.

13 M. N. Makarova, “Small cities in the spatial structure of the region's population distribution”, Economic and social changes: facts, trends, forecast num 2 (2017): 181-194; R. González-Val y L. Lanaspa, “Patterns in US Urban Growth, 1790–2000”, Regional Studies num 50 (2016): 289-309; S. N. Rastvortseva y V. Manaeva, “Analysis of Zipf's law in Russian cities”, Economic analysis: theory and practice num 46 (2015): 56-66 y O. A. Kozlova y O. N. Soskova “Factors of spatial differentiation of urban settlement of industrial regions of the Urals and TRANS-Urals”, Ars Administrandi num 1 (2018): 64-79. 14 P. Veresh, “Percentage and non-Gaussian distribution-Zipf of creativity and IQ of the population”, Society and power num 4 (2016): 29-36; L. V. Shamrai-Kurbatova y M. V. Filippov, “The main provisions of the mechanism for remuneration of employees, taking into account the cost of electronic information used in the organization”, Business. Education. Law num 4 (2017): 207-212 y J. Glückler, “Knowledge, Networks and Space: Connectivity and the Problem of Non-Interactive Learning”, Regional Studies num 47 (2013): 880-894. 15 F. Nota y S. Song, “Further Analysis of the Zipf's Law: Does the Rank-Size Rule Really Exist”, Journal of Urban Management num 1 (2012): 19-31.




Results

A graphical representation of the distribution of GRP values employed population (number of jobs) and population relative to the Zipf’s curve (inversely proportional to the function, y = 1 / x) is shown in Figure 1 (a - c). Distribution curves are shown on the graph with an interval of 5 years. Such a time interval was chosen based on the clarity of the presentation of the results. If the GRP distribution in 1995 was far enough from the theoretical Zipf’s line, then, starting from 2000, it practically coincides with the graph of the inversely proportional function (Figure 1c). The GRP curve for 1995 shows the remnants of a socialist economy, which is characterized by a uniform or planned distribution of production capacities over the territory of the country. The deviation of the distribution of GRP values from the inversely proportional function in 1995 is usually explained by the influence of the ‘planned economy16’. It is difficult to disagree with this conclusion since the absence of competition left the state the right to centrally plan the allocation of production facilities (jobs) in the territory of the country.

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 21 42 63 84

Ципфа 1995

2000 2005

2010 2015

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 21 42 63 84

Ципфа 1995

2000 2005

2010 2015

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1

0 21 42 63 84

Ципфа 1995

2000 2005

2010 2015

a) b) c)

Figure 1 Distribution of values of indicators of subjects of the Russian Federation relative

to the Zipf curve: a) population size; b) the number of jobs; c) produced regional gross value added. (GRP)

Ципфа - Zipf

Source: table 2.2, pp. 39 - 41, table 3.5, pp. 110 - 112, table 10.1, pp. 514 - 516

Regions of Russia. Socio-economic indicators. 2016. Р32 Statistical collection of Rosstat. Moscow, 2016. 1326 p.

16 M. N. Makarova, “Small cities in the spatial structure of the region's population distribution”, Economic and social changes: facts, trends, forecast num 2 (2017): 181-194.




There remains a debatable question about which distribution of production capacities

is most preferable for the spatial economy of Russia. An even distribution of production capacities over an area equal to 1/6 of the land area will require colossal government spending on the creation of transport and social infrastructure.

Transport and social infrastructure, in addition to state capital investments for the

creation, require significant budgetary expenditures for maintenance. The trend in the distribution of the subjects of the Russian Federation in terms of GRP indicates that the existing uneven economic development will persist.

They are approaching the graph of the inversely proportional function of the

distribution of the population and the number of jobs in the regions of Russia somewhat slower (Figures 1a and 1b, respectively). The disproportionate reduction of jobs in relation to GRP is explained by the high share of employment of the population in the public sector (public administration, education, and health care).

The most significant consequence of the reduction in GRP is a decrease in the

number of jobs in the non-state sector of goods production. The reduction in jobs in the real sector of the economy (production of goods) was somewhat offset by an increase in the volume of the consumption market (production of services).

It is important to emphasize the role of the state in maintaining jobs in the service

production sector. For example, half of the jobs in the service production sector are in health care and education, which is financed from the budget17 for at least 90%. Thus, the budgetary financing of education and health care creates conditions for lower, in comparison with GRP, rates of job loss in the regional economy as a whole (Figure 1b).

The population indicator is the slowest to respond to a reduction in production

capacity in the real sector of the economy. First, the population who has not reached working age cannot independently make a decision to move. Secondly, able-bodied citizens are forced to agree to work on a rotational basis, which formally leaves them among the inhabitants of the settlement.

Thirdly, the inertia of the older generation is associated with attachment to the place

of residence, especially for pensioners who have a source of income guaranteed by the state.

The age and sex composition have a no less significant impact on the dynamics of

population change. However, this is a topic for deeper and more specialized demographic research. In this work, only the relationship of GRP indicators - jobs – the population is considered.

The dynamics of the approximation of the three considered indicators (GRP, number

of jobs, population) to the graph of the Zipf’s function is most conveniently quantified by the value of the determinism criterion (R2), the values of which are given in Table 1.

17 V. N. Kabanov y E. V. Mikhailova, “Agglomeration of rural settlements as a strategy for socio-economic development of territories”, Regional economy: theory and practice num 11 (2018): 2092-2107.




Year GRP Number of workplaces Population

1995 0,759 0,549 0,432

1999 0,966 0,596 0,480

2000 0,964 0,622 0,494

2001 0,968 0,625 0,506

2002 0,969 0,632 0,517

2003 0,969 0,649 0,521

2004 0,971 0,657 0,525

2005 0,961 0,663 0,530

2006 0,962 0,668 0,534

2007 0,959 0,680 0,538

2008 0,952 0,697 0,541

2009 0,968 0,686 0,545

2010 0,968 0,695 0,603

2011 0,974 0,696 0,608

2012 0,978 0,701 0,625

2013 0,975 0,713 0,632

2014 0,974 0,727 0,630

2015 0,982 0,836 0,637

Source: compiled by the author based on the data: table 2.2, 3.5, 10.1 Regions of Russia. Socio-economic indicators. 2016. P32 Statistical collection of Rosstat. Moscow, 2016. 1326 p.

Table 1 Changing the criterion of determinism (R2) for the distribution regional indicators

When analyzing the obtained distributions, the most interesting is the location of the

first 20 regions.

(Table 2 - 4) in terms of population size, number of employed and produced value added (GRP).

№ p/p

1995 2000 2005 2010 2015

1. Moscow Moscow Moscow Moscow Moscow

2. Moscow region

Moscow region

Moscow region

Moscow region

Moscow region

3. Krasnodar region

Krasnodar region

Krasnodar region

Krasnodar region

Krasnodar region

4. St. Petersburg St. Petersburg St. Petersburg St. Petersburg St. Petersburg

5. Sverdlovsk region

Sverdlovsk region

Sverdlovsk region

Sverdlovsk region

Sverdlovsk region

6. Rostov region Rostov region Rostov region Rostov region Rostov region

7. Republic of Bashkortostan

Republic of Bashkortostan




8. Republic of Tatarstan

Republic of Tatarstan




9. Nizhny Novgorod Region

Chelyabinsk region

Chelyabinsk region

Chelyabinsk region Tyumen

region



10. Chelyabinsk region

Nizhny Novgorod Region


Tyumen region Челябинская

область

11. Samara Region

Samara Region

Tyumen region



12. Tyumen region

Tyumen region

Samara Region

Samara Region

Samara Region

13. Krasnoyarsk region

Krasnoyarsk region

Krasnoyarsk region

The Republic of Dagestan


14. Kemerovo region

Kemerovo region

Kemerovo region

Krasnoyarsk region

Krasnoyarsk region

15. Perm region Perm region Perm region Stavropol region

Stavropol region

16. Volgograd region

Stavropol region

Stavropol region

Kemerovo region

Novosibirsk region

17. Saratov region Volgograd region

Novosibirsk region

Novosibirsk region

Kemerovo region

18. Novosibirsk region

Novosibirsk region


Perm region Perm region

19. Irkutsk region Saratov region Volgograd region

Volgograd region

Volgograd region

20. Stavropol region

Altai region Saratov region Saratov region Saratov region

Source: compiled by the author from the data: table 2.2, pp. 39 - 41, Regions of Russia. Socio-economic indicators. 2016. P32 Statistical collection of Rosstat. Moscow, 2016. 1326 p.

Table 2 The first 20 subjects of the Russian Federation ranking according to the ‘population size’

criterion in the period from 1995 to 2015

№ p/p

1995 2000 2005 2010 2015


2. Moscow region

Moscow region

Moscow region

Moscow region

Moscow region

3. St. Petersburg St. Petersburg St. Petersburg St. Petersburg St. Petersburg

4. Krasnodar region

Krasnodar region

Krasnodar region

Krasnodar region

Krasnodar region


Sverdlovsk region

Sverdlovsk region

Sverdlovsk region

Tyumen region

6. Rostov region Rostov region Rostov region Tyumen region

Sverdlovsk region



Tyumen region

Rostov region Republic of Tatarstan


Tyumen region



Rostov region

9. Tyumen region











Samara Region

11. Samara Region

Chelyabinsk region

Chelyabinsk region

Chelyabinsk region

Chelyabinsk region


Samara Region

Samara Region

Samara Region



Krasnoyarsk region

Krasnoyarsk region

Krasnoyarsk region

Krasnoyarsk region

14. Perm region Perm region Perm region Perm region Novosibirsk region

15. Kemerovo region

Kemerovo region

Kemerovo region

Kemerovo region

Kemerovo region

16. Volgograd region

Samara Region

Volgograd region

Novosibirsk region

Stavropol region

17. Samara Region

Volgograd region

Novosibirsk region

Stavropol region

Perm region


Novosibirsk region

Samara Region

Volgograd region

Volgograd region

19. Irkutsk region Irkutsk region Stavropol region

Samara Region

Irkutsk region

20. Altai region Altai region Irkutsk region Irkutsk region Samara Region

Source: compiled by the author based on: table 3.5, pp. 110 - 112, Regions of Russia. Socio-economic indicators. 2016. P32 Statistical collection of Rosstat. Moscow, 2016. 1326 p.

Table 3 The first 20 constituent entities of the Russian Federation ranking according to the criterion

‘the number of employed population’ in the period from 1995 to 2015

№ p/p

1995 2000 2005 2010 2015


2. Tyumen region

Tyumen region

Tyumen region

Tyumen region

Tyumen region


Krasnoyarsk region

Moscow region

Moscow region

Moscow region

4. Moscow region

St. Petersburg St. Petersburg St. Petersburg St. Petersburg

5. St. Petersburg Republic of Tatarstan


Krasnoyarsk region

Krasnodar region

6. Samara Region

Moscow region

Sverdlovsk region

Sverdlovsk region



Sverdlovsk region

Krasnoyarsk region

Krasnodar region

Sverdlovsk region



Samara Region


Krasnoyarsk region


Samara Region






10. Perm region Krasnodar region

Krasnodar region

Samara Region

Samara Region

11. Kemerovo region

Chelyabinsk region

Chelyabinsk region

Rostov region Rostov region


Perm region Kirov region Chelyabinsk region

Chelyabinsk region

13. Irkutsk region Nizhny Novgorod Region

Orenburg region

Orenburg region

Orenburg region


Irkutsk region Kemerovo region

Kemerovo region

Kirov region

15. Krasnodar region

Rostov region Rostov region Kirov region Irkutsk region

16. Rostov region Kemerovo region

Irkutsk region Irkutsk region Novosibirsk region


The Republic of Sakha (Yakutia)

Novosibirsk region

Leningrad region

Leningrad region

18. Omsk region Orenburg region

Omsk region Sakhalin Region

Kemerovo region

19. Saratov region Novosibirsk region

Penza region Novosibirsk region

Sakhalin Region

20. The Republic of Sakha (Yakutia)

Vologda Region

Leningrad region

Primorsky Krai Voronezh region

Source: compiled by the author according to: table 10.1, pp. 514 - 516, Regions of Russia. Socio-economic indicators. 2016. P32 Statistical collection of Rosstat. Moscow, 2016. 1326 p.

Table 4 The first 20 subjects of the Russian Federation ranking according to the criterion ‘value

added (or GRP)’ in the period from 1995 to 2015

The change in the population in the first eight constituent entities of the Russian Federation does not lead to a change in the position of the region, that is, during this period, the places of the territories did not change (Table 2). In the lower part of the table, one can observe significant changes, for example, the movement of the Tyumen region from 12th to 9th place against the background of the fall of the Nizhny Novgorod region from 9th to 11th place. From the author's point of view, the dynamics of population change is one of the most essential criteria that should be used when assessing the socio-economic situation or the comfort of living. For the majority of the population of working age, perhaps the only and main criterion used to choose a place of residence has been formed: the possibility of employment with decent wages. Of course, one cannot ignore the birth rate, for example, the appearance of the first 20 subjects in 2005 of the Republic of Dagestan, probably, should be associated with this factor, the importance of which has significantly increased in connection with government measures to stimulate natural population growth.

The above analysis according to three criteria can be applied in the sequence

‘population - employment - produced GRP’, as well as in the reverse order ‘GRP - employment - population’. For example, not getting into the top 20 in terms of the GRP produced in the Volgograd and Saratov regions means a reduction in jobs in the economy, as a result, a decrease in the population.




To stabilize the socio-economic situation in these territories, it is necessary to

immediately locate new production facilities of the real sector, preferably related to the types of economic activities listed in the Strategy. If the strategies for the socio-economic development of these constituent entities of the Russian Federation do not contain measures to increase production capacities (jobs in the real sector), an exodus of the population is inevitable, primarily in the working-age18.

It is important to note that if the Zipf’s curve is divided according to the Pareto

criterion, then for the RF it is possible to objectively obtain 4 or 5 groups of regions. Each of these groups with a different level of confidence will obey Zipf's law. The research results obtained by the author show that the greatest discrepancies are observed in the second and third groups of regions of the Russian Federation. Currently, work continues to find an acceptable explanation for this phenomenon.

The practical application of Zipf’s law in the development of strategic development

documents justifies the long-term goals of socio-economic development. The uniform development of the territories of the constituent entities of the Russian Federation cannot be ensured in the conditions of market relations. With the planned allocation of production facilities, the unevenness of socio-economic development can be significantly reduced. This is evidenced by the experience of the deployment of productive forces in the Soviet Union19. However, this experience did not always ensure the high efficiency of capital investments.

The results of the study of the distribution of the main quantitative indicators

describing the dynamics of changes in the relative quantitative values of the volume of production (GRP), the number of jobs and the population showed that the unevenness of the socio-economic situation of territorial entities tends to an inversely proportional function. When performing long-term forecasts, taking into account the existing degree of state participation in the economy, it is necessary to provide that a change in the volume of value-added production will inevitably lead to a corresponding change in the number of jobs, and population. In this case, the direction of the vectors of these indicators coincide.

Conclusion

The given distributions of socio-economic indicators allow us to draw the following conclusions:

1. A reduction in the level of interregional differentiation without direct government

investment in production capacity is hardly possible given the observed distribution of the constituent entities of the Russian Federation in terms of value-added (GRP). The driver of spatial socio-economic development can be such types of economic activity, the products or services of which are focused on external consumer markets to a specific constituent entity of the Russian Federation. Small and medium-sized businesses are not capable of creating and developing such production facilities. New enterprises will face big problems in entering the consumer market. The most rational, in this regard, is to attract large companies that have a high level of competitiveness, as well as proven technologies for promoting their products in new markets.

18 V. N. Kabanov, “Territorial planning and the Zipf law”, Economic and social changes: facts, trends, forecast num 2 (2019): 103-114. 19 A. G. Granberg, “Study of Russia's productive forces”, Bulletin of the Russian Academy of Sciences num 7 (1996): 579-584. P. 579.




An alternative to attracting large companies is state capital investments in the

creation of production facilities for which demand in the national market is ensured and / or there is a program of state protectionism in foreign markets.

2. Socio-economic development without a program to increase jobs in sectors of the

economy whose products or services are focused on external markets, concerning a particular region, is impossible. The exception is the types of economic activity whose products are purchased by the state in full (for example, for the needs of the army or the military-industrial complex). It is important to emphasize that by socio-economic development, the author understands the growth of the quality of life of the population, as well as the availability of services provided by the state in full (the full volume represents the volume of public services to the population, provided, for example, in Moscow).

3. The improvement of the territorial organization of the provision of public services

is likely to be achieved through the concentration of the population. Such concentration can be stimulated by the creation or consolidation of production capacities in a large industrial complex and, above all, in agriculture. Receiving public services in full is possible when the population density in the territory of an urban or rural settlement is at least 50 people per square meter20. In this regard, it can be concluded that it is necessary to provide for the creation of new jobs at large enterprises that can compete not only in the domestic but also in foreign markets. This conclusion is based on the nature of the given distributions of GRP, population size, number of jobs and is supported by published findings on the impact of competition on the level of inequality21.

4. In the absence of state capital investments in the creation of production capacities,

the distribution of territorial entities in terms of the added value produced is subject to Zipf’s law (has the form of an inversely proportional function). In this case, one should agree with the conclusions22 on the correctness of the application of Zipf’s law to assess the initial position, as well as for long-term planning of spatial socio-economic development.

5. The above results rather confirm than refute the conclusion23 that in a competitive

economy the idea of equalizing the socio-economic status of the population is hardly feasible. Bibliographic References

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