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ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.19 No.2 pp.460-475
DOI : https://doi.org/10.7232/iems.2020.19.2.460

# An Econometric Model for Assessing the Asymmetry of Urban Development as a Factor of Regional Economic Growth: The Case of Kazakhstan

Svetlana O. Mukhametzhan*, Gulsara A. Junusbekova, Marat Ye. Daueshov
Ph.D student, Master of Global Political Economy, Master of Public Administration, The Academy of Public Administration under the President of the Republic of Kazakhstan
Ph.D in Economics, Professor of the Institute of Management, The Academy of Public Administration, under the President of the Republic of Kazakhstan
Ph.D in Economics, Deputy Chairman of Agency of the Republic of Kazakhstan for Public Service Affairs, Republic of Kazakhstan
*Corresponding Author, E-mail: svetlana.mukhametzhan@yandex.ru
March 17, 2020 April 4, 2020 April 20, 2020

## ABSTRACT

Given the trend of increasing imbalance in the economic development of administrative-territorial units and ensuring their innovative development, the issue of upgrading the effectiveness of the anti-crisis regional policy is becoming urgent. The research aim is to develop an econometric model for assessing the impact of asymmetries in the development of urban economies on the regional socio-economic development based on the case of Kazakhstan. The article establishes a sample of indicators of socio-economic development of cities and regions in Kazakhstan in 2014-2018. The governance indicators of the influence of the asymmetry in urban development on the regional socio-economic development in Kazakhstan have been determined based on the Granger causality test, the creation of a cognitive map and the identification of hierarchy levels. The asymmetry in regional development has been estimated applying the coefficient of variation of the relevant indicators of socio-economic development of cities and regions, taking into account the lagged variables. The developed model allows determining the convergence model of regional development, the nature of the influence of asymmetries in the development of cities on the regional economic development in the context of economic growth and recession, as well as identifying the key factors in the asymmetry of regional development. The results are based on reliable data; they are of practical importance and can serve as the basis for the elaboration of effective preventive measures for anti-crisis regional development policy, not only to neutralize asymmetries, but also to achieve economic growth under the conditions of unbalanced regional development.

## 1. INTRODUCTION

A specific feature of economic phenomena and processes at the regional level is their asymmetry, characterized by the lack of proportionality, invariance, and invariability due to the widespread and continuous, both in space and in time, transformation of economic indicators of economic entities. A certain unevenness of regional development is an objective phenomenon with the adopted innovative- oriented development model and strategic management, the existence of innovative monopoly, and unequal distribution of the resource potential of regional systems (Misakov et al., 2017;Antonov, 2018;Rzayeva, 2017;Balynskaya and Ponomarev, 2018;Rukayah and Abdullah, 2019). Asymmetry of the economic system development, being currently the dominant form of economic growth of administrative-territorial units (ATU), in particular cities and regions, at the same time is not absolute (Shahbaz et al., 2017;Konstantaras et al., 2018).

Asymmetry in regional economic development reflects the manifestations of the so-called “market failures” in the following aspects: first, market mechanisms cannot ensure the equal and effective realization of all participating interests in the market process under its influence (Orekhova and Kislitsyn, 2019), but under certain conditions it can serve as an incentive for intensive development and, secondly, asymmetry cannot be resolved only through the market mechanism, without institutional interference (Canale and Mirdala, 2019;Surya et al., 2019). In this regard, the regulation of the asymmetry in the development of the economic system should be based on the assessment of its positive and negative role in the life of society and the regional economy, the development of innovative management approaches and the formation of adequate, effective and accurate (regarding that the superstructure is one of the asymmetry factors) instruments of such regulation. From a policy perspective, analysis of the links between regional integration and territorial development is a necessary condition for formulating sound and sustainable policy tools aimed at correcting the spatial asymmetries that may arise in the process of economic development (Giordano et al., 2005;Abid et al., 2019).

A large number of studies on this issue indicates its relevance (Konstantaras et al., 2018;Canale and Mirdala, 2019;Cole and Palmer, 2008;Gubanova and Kleshch, 2017). In modern economic literature, in most cases, the study of asymmetry is limited to assessing the nature of its influence on economic development in geo-economic relationships (Konstantaras et al., 2018;Canale and Mirdala, 2019;Cole and Palmer, 2008). At the same time, the study of the problems of regional imbalances and state mechanisms for their regulation within one country are not properly analyzed. The latter include the methodology for assessing the level of asymmetry, its impact on the regional socio-economic development. Numerous scientific discussions are held, in particular, regarding the choice of factors in determining the convergence in economic growth (Lopez-Rodriguez, 2008;Otoiu and Titan, 2015;Ahmad and Hall, 2017;Haupt et al., 2018), without taking into account the possibility of convergence of negative dynamics of regional development.

All ATU in Kazakhstan are characterized by a fairly high level of both regional and sub-regional asymmetries in socio-economic development according to the findings of this research. The values of the coefficients of variation of indicators of socio-economic development in 2014- 2018 indicate an asymmetry in the regional development in the Republic of Kazakhstan and cities within the regions, since the values of the coefficient of variation are mainly greater than 0.20 (20%). This level is characterized by a high degree of variability (Rousseau et al., 2018). Given the objectivity of this process, due to the inevitable asymmetry of the regional potential, it should be noted that the process of strengthening the asymmetry in socio-economic development must be constantly monitored and evaluated; and measures should be taken to prevent its strengthening to such a level that will have a negative impact on the socio-economic situation in the region as a result of the degradation of part of the ATU development, in particular cities in Kazakhstan.

This is especially relevant in view of the introduction of a long-term development strategy for Kazakhstan until 2050, where one of the main priorities is determined by the regional and urban economic growth in the republic (Nazarbayev, 2012), based on an open market economy with high levels of foreign investment and domestic savings. At the same time, it is expected to achieve real, sustainable and increasing economic growth rates. Therefore, in the framework of this article, the influence of the asymmetry in regional development and the identification of factors for ensuring the economic growth of regions is studied based on the urbanized territories in Kazakhstan. The paper is divided into several sections as follows: Section 2 reviews the literature, followed by an outline of the factors and hypotheses of this study. Section 3 presents the research methodology. Section 4 describes the data collection and the data analysis, and the findings are discussed in Section 5. Lastly, Section 6 summarizes the conclusions of this study, followed by recommendations in Section 7.

## 2. LITERATURE REVIEW

Territorial asymmetry developments are hardly reported on in academic literature. Nevertheless, different approaches to the concept have been put forward. Scholars such as Cole and Palmer (2008), Reuchamps et al. (2009), Béland and Lecours (2012) or Loughlin et al. (2013) use it to refer to differential treatments adopted in regions or federated states when it comes to administra tive and sector-based policies. Furthermore, Perlik (2011) employs the same terminology to address disparities between peri-alpine urban centres and alpine residential areas, exposing new functional and spatial divisions that amplify inequalities in relation to access to development and identity divides. Adopting a different register, Davezies (2012) uses the notion to underline heterogeneous impacts of the economic crisis on different areas. Thus, some authors’ idea of territorial asymmetry reflects a spatial division between areas and/or communities, while others associate it with a difference in the treatment of distinct administrative regions and areas.

Nevertheless, it should be noted that the asymmetry in regional development reflects not only unevenness, both positive and negative, but also its sustainable manifestation in time and space (Gubanova and Kleshch, 2017). Asymmetry is a source of differentiation of territorial development. Given these aspects, the asymmetry of territorial development can be characterized as a specific organization of the system (region, city, etc.), in which both the original objects (elements of the system) and the results of their transformations are not identical, since the processes of transformations (development) do not equally affect the main features of objects related to development, causing a decrease or increase in the differences between them (DFID, 2008). Among the manifestations of asymmetry, the most important is socio-economic, which is due to the main goal of the regional development – a qualitative improvement in the standard of living, and the condition for its achievement – economic growth. According to scientists, the key factor in the asymmetry in economic development is the production factor, which determines the differentiation in the level of profitability of capital used in this region (Béland and Lecours, 2012;Ostry et al., 2014;Perlik, 2011).

The level of return on invested capital in the regional economy determines the state and development prospects of the most important sectors of its national economic specialization, which, in turn, affects the formation of aggregate demand in the region, and, consequently, the development of supporting and service industries (Haupt et al., 2018). In this case, the modern interregional economic differentiation in the country would be explained mainly by particular historical events and trends. But it should be noted that the production factor as the cause of the asymmetry in economic development, reflects primarily industry differentiation, but does not allow understanding the internal mechanism of its reproduction. Therefore, it can be defined as a necessary, but not sufficient reason.

Taking into account the impact factors of foreign economic (international) relations of the region in the analysis, as well as inter-regional exchange in the service sector, does not fundamentally change the situation, bearing in mind the need to maintain a general balance of its economic ties in terms of ensuring the most complete realization of its competitive advantages (Antonov, 2018).

The values of asymmetry in the theory of economic development are rather ambiguous. A significant part of economic theories focused on the study of equilibrium systems (Williamson, 1965;Solow, 1957;Lopez-Rodriguez, 2008;Otoiu and Titan, 2015;Ahmad and Hall, 2017;Haupt et al., 2018;Marouani, 2018). Therefore, the opinions of most researchers coincide in two aspects: firstly, the violation of proportions, balance, and uniformity are temporary, transient (or even random) in nature (Williamson, 1965;Solow, 1957;Lopez-Rodriguez, 2008;Otoiu and Titan, 2015;Ahmad and Hall, 2017;Haupt et al., 2018). The English economist J. Williamson found that national development contributes to an increase in regional disparities in the early stages. But on the other hand, in the development process at a later stage, economic growth creates a rapprochement between regional (territorial) levels of development, i.e. regional convergence, leading to an inverted U-shaped curve (Williamson, 1965). The scientist explained this point of view by the fact that at the first stages the region has several growth poles in which assets and skilled workers are concentrated.

Because of a more rapid increase in labor productivity, economic growth accelerates at these poles and leads to an increase in regional differences (divergence) in development. At later stages of development, with increasing costs in the growth pole areas, assets are redistributed to other regions with lower labor costs. At later stages of development, with the process of increasing costs in areas of the growth pole, there is a redistribution of assets to other regions with lower labor costs. As a result, there is a redistribution of production factors between sectors, and, consequently, between regions, which ultimately determines the approximation of their regional development level (Williamson, 1965). The starting point for alignment analysis is the “β-convergence” model based on the neoclassical growth theory by Solow (1957), Voronov et al. (2014).

Secondly, asymmetry has a negative impact on the process of economic development (Galor et al., 2009;Ostry et al., 2014;Galazova and Tkhostova, 2007;Piskun and Kudrevich, 2016). According to scientists, asymmetry in economic development is accompanied by a search for growth centers, and accordingly a polar accumulation of resources with their further spontaneous individual redistribution between the peripheries. Unequal conditions arise for the competitive movement of production factors (Galor et al., 2009;Ostry et al., 2014). This, in turn, leads to a dual effect (Sannikova and Rudakova, 2018;Galazova and Tkhostova, 2007). On the one hand, due to the processes of market integration, the regional economic field is expanding, and on the other hand, there is the effect of “folding” their economic space and the formation of “market voids” (Sannikova and Rudakova, 2018;Galazova and Tkhostova, 2007). In addition, the existing economic imbalances are not direct threats to maintaining integrity, but their strengthening leads to a significant reduction in the stress tolerance of the regional economy, weakening of its immune system, and increasing its exposure to various external “shocks” (Piskun and Kudrevich, 2016).

The main dilemma is that the asymmetry in the development of the economic system has a reverse side: subjects of the other pole demonstrate high rates of growth, economic efficiency, the introduction of progressive forms of management, and, in fact, are the locomotives of the development of the entire economic system as a whole (De Neve et al., 2015;Orekhova and Kislitsyn, 2019). A. Hirschman (Hirschman, 1958) proposed the concept of unbalanced growth, based on the fact that the imbalance in the economic system is an incentive for investment. To some extent, the factor of uneven development has a positive impact on the competitive situation, contributes to the more efficient use of limited resources and is an objective characteristic of market mechanisms for regulating economic processes (Hirschman, 1958). However, a distinctive tendency to increase uneven development, exceeding imbalances (differences) in the regional development levels of a certain safe threshold, leads to the formation of crisis phenomena (Orekhova and Kislitsyn, 2019). Decrease in asymmetry in the development levels of ATU or the use of its positive properties (the emergence of growth points and poles) will create more favorable conditions for the overall regional development, harmonization of socio-economic transformations, and ensure the creation of self-sufficiency in the region (De Neve et al., 2015).

We emphasize that the harmony of the asymmetric form of territorial development consists of diverse relations and certain patterns of composition of territorial development, in which the elements are not connected by the axis of symmetry. In this context, it is logical that symmetry itself in the form of a policy of “equalization” does not yet guarantee the proportionality of the development of the economic system, just as asymmetry in this process cannot be unequivocally equated with imbalance, or disproportionality (Orekhova and Kislitsyn, 2019). Historical experience confirms that the asymmetric development of territories in terms of functional relationships and management levels is much more complicated, due to the need to build structural equilibrium and take into account sensitivity to changes in these proportions. In general, the problem of the asymmetry of territorial economic development, its nature, and the inconsistency of implementation require a more in-depth study.

The analysis of scientific knowledge in the field of assessing the uneven development of regions deserves special attention in the framework of this subject. As the review of modern economic literature has shown, the asymmetry in territorial development is assessed primarily by analyzing the variation of individual indicators of the socio-economic development of ATU and further expert assessment of their values (Pike et al., 2017;Misakov et al., 2017). A common approach is also to assess the level of socio-economic development of territories, and the degree of gap at this level is estimated by expert opinion (Orekhova and Kislitsyn, 2019;Giordano et al., 2005). Research on regional competitiveness has been developed to the greatest extent in the framework of assessing the asymmetry in the regional economic development. Ronald Martin’s work A Study on the Factors of Regional Competitiveness (Martin, 2003) can be considered the largest research on this subject. In addition, the issue of regional competitiveness has recently been highlighted in Eurostat yearbooks on regional statistical information (European Commission, 2019). However, the above publications also lack a description of a scientifically based approach to determining the level of differentiation of territorial units.

## 3. DATA

The statistical base of the study was the values of indicators of socio-economic development of ATU in the Republic of Kazakhstan in 2014-2018. Such indicators as below formed the original data array:

• Х1 – volume of GRP per capita, thousand tenge;

• Х2 – volume of GRP created in agriculture, forestry and fisheries per capita, thousand tenge;

• Х3 – volume of GRP created in industry per capita, thousand tenge;

• Х4 – volume of GRP created in construction, thousand tenge;

• Х5 – volume of GRP created in wholesale and retail trade per capita, thousand tenge;

• Х6 – volume of GRP created in transport per capita, thousand tenge;

• Х7 – volume of GRP created in the information and communications industry per capita, thousand tenge;

• Х8 – retail sales per capita, thousand tenge;

• Х9 – specified sown area under agricultural crops, in all categories of farms per capita, ha;

• Х10 – cargo transportation by all means of transport per capita, thousand tons;

• Х11 – profit (loss) of enterprises before tax per capita, thousand tenge;

• Х12 – fixed asset investment per capita, thousand tenge;

• Х13 – unemployment rate as a percentage of the economically active population, %;

• Х14 – price index for transportation of goods, %;

• Х15 – food price index, %;

• Х16 – consumer price index, %;

• Х17 – price index for paid services rendered to population, %;

• Х18 – tariff index for cargo transportation by all means of transport, %;

• Х19 – agricultural producer price index, %;

• Х20 – construction price index, %;

• Х21 – industrial producer price index, %;

• Х22 – nominal cash income of the population, per capita, tenge;

• Х23 – nominal income per capita, tenge;

• Х24 – average monthly nominal wage of one employee recorded in agriculture, forestry and fisheries, tenge;

• Х25 – average monthly nominal wage of one employee recorded in industry and construction, tenge;

• Х26 – the average monthly nominal wage of one employee recorded in service industry, tenge;

• Х27 – annual household expenditure per capita, tenge;

• Х28 – total newly commissioned living space per capita, sq. m;

• Х29 – the proportion of population with incomes below subsistence minimum, %;

• Х30 – the number of people employed in agriculture, forestry and fisheries, thousand people;

• Х31 – the number of people employed in industry, thousand people;

• Х32 – the number of people employed in construction, thousand people;

• Х33 – the number of employees in wholesale and retail trade, thousand people;

• Х34 – the number of people employed in the service sector, thousand people;

• Х35 – fixed assets at the end of the year, taken at its depreciated value, per capita, mln tenge;

• Х36 – fixed asset renewal ratios, %;

• Х37 – fixed assets depreciation, %;

• Х38 – internal and external R&D expenditures per capita, thousand tenge;

• Х39 – profitability (loss ratio) of enterprises, %;

• Х40 – agricultural enterprises’ profitability level, %;

• Х41 – industrial enterprises’ profitability level, %;

• Х42 – volume of inventory turnover per capita, thousand tenge.

The representativeness of the proposed list of indicators for assessing the socio-economic development of the ATU in the Republic of Kazakhstan is explained by the fact that these indicators characterize the level of development of all areas in the economy (agriculture, industry, construction, wholesale and retail trade, services) based on the volume of GRP created in relevant industries; price level: in transportation, industrial and consumer prices with details; volume of inventory turnover; enterprises’ profitability level; state of fixed assets and housing funds; innovation and investment activity; and standards of living. If possible, per capita indicators were taken into consideration. Statistically, the representativeness of indicators X1-X42 is confirmed by the results of multivariate factor analysis conducted based on the values of these indicators in 2014-2018 in view of:

• 1) Regions and districts: Akmola, Aktobe, Almaty, Atyrau, West Kazakhstan, Zhambyl, Karaganda, Kostanay, Kyzylorda, Mangystau, Pavlodar, North Kazakhstan, Turkistan, East Kazakhstan – the first data array;

• 2) Cities: Nur-Sultan, Almaty, Shymkent (cities of national status), Kokshetau, Stepnogorsk, Aktobe, Taldykorgan, Қapshagai, Tekeli, Atyrau, Oral, Taraz, Karagandy, Balhash, Jezkazgan, Karazhal, Saran, Sotbayev, Temirtau, Shakhtinsk, Қostanay, Arkalyқ, Lisakovsk, Rudnyj, Қyzylorda, Aktau, Janaozen, Pavlodar, Aksu, Ekibastuz, Petropavlovsk, Turkistan, Arys', Kentau, Ust'-Kamenogorsk, Kurchatov, Ridder, Semey (regional subordination) – the second data array.

The factoring rate was 91.14% and 89.73% respectively, which indicates the representativeness of set of the proposed indicators X1-X42. The appropriateness of factor analysis is confirmed by the sufficiency of the sample: N = 70 and N = 173 observations for the first and second arrays, respectively. The initial list of indicators of socio-economic development of regions and cities is composed of 70 indicators, which, in addition to the indicated X1-X42, included indicators of financial stability of enterprises, the banking system, income and expenses of local budgets, balances, percentage of completion, debt of local budgets, foreign direct investment in region. An expert assessment was used to justify the optimal number of indicators and statistical confirmation of the representativeness of each individual indicator. The expert group is composed of 40 experts, representatives of local authorities: administrations of the Akmola and Karaganda regions, who have at least 5 years of experience in the specialty and directly specialize in ensuring the socio-economic development of the region. Experts were asked on a 10-point scale to evaluate the significance of indicators in assessing the socio- economic development of regions and cities, where a rating of “0” corresponds to the insignificance of the indicator, “10” to the maximum value. Evaluation was anonymous. Significant indicators were considered, the sum of points for which exceeds 50% of the maximum possible number of points (200 points). The consistency of experts is evidenced by the value of the concordance coefficient 0.86. Thus, a list of indicators for analysis is formed - indicators X1-X42.The need to use two arrays is explained by the necessity to determine the indicators of socio-economic development of the regions (the first array) and cities (the second array) separately.

The initial step in building a model of convergence of the regional economic development in the Republic of Kazakhstan was to check for the presence of cause-andeffect relationships between the indicators of socioeconomic development of regions and cities (indicators X1-X42) and determine the representative. The data array has been formed in such a way as to investigate the influence of the level of urban development on the level of regional development – regions and districts, which include the corresponding cities, and to check for the inverse effect: the impact of the level of regional development on the level of urban development. At this stage of the study, the values of indicators for the following ATU groups were used (Table 1).

The data have not been used for the cities of Nur- Sultan, Almaty, Shymkent, which are the cities of national status (Official Statistics, 2020), which is not the object of research – the regions of Kazakhstan.

## 4. MATERIALS AND METHODS

The methodological basis for creating a model of the influence of asymmetries in urban development on the regional development is the autoregressive model, the resulting indicator of which is the growth rate of GRP per capita. The use of the autoregressive model is because, in addition to independent factors, the level of the regional economic development is affected by the value of the resulting indicator (GRP volume) for the previous periods. The choice of indicators for the model is based on the results of checking indicators for causality by the Granger test, constructing a cognitive map and determining the hierarchy levels of indicators by the graph method. The autoregression model is used as a basis (Ruan et al., 2018):

(1)

where Yt is the vector of the resulting variable, characterizing the level of socio-economic development of the region (district);

• A1 is coefficient matrices for lagged values of the resulting variable;

• A2 is coefficient matrices for lagged values of variables characterizing the urban socio-economic development;

• A0 is a vector of constant values;

• (Xi)t – vector of variables describing the socioeconomic development of the city;

• εt – model bug;

• tl – time lag, l = 0,3.

The choice of indicators Xi was based on compliance with the condition A2 ≠ 0.

The necessary condition for choosing the indicators Xi and Y is the accuracy to the equality (Kühn et al., 2017):

$[ S ( z i ) ∩ P ( z i ) ] j = [ P ( z i ) ] j ,$
(2)

where S(zi) is a reachability set of vertex zi, a directed graph corresponding to the i-th indicator;

• P(zi) is a set of predecessors of the directed graph;

• j – the number of iterations corresponding to the level of the indicator hierarchy.

For the indicator Xi, equality (2) holds when j = 1, for the indicator Y, when jmax. As shown by the results of constructing the graph and performing certain iterations (Figure 1, Table 3), jmax = 6.

The asymmetry in regional development of Kazakhstan is estimated using the coefficient of variation of the relevant indicators of socio-economic development of cities and regions. Coefficients of variation are calculated in order to assess the asymmetry in development of regions (based on indicators of regional socio-economic development) and cities (based on indicators of socioeconomic development of cities that are part of the corresponding region) in 2014-2018. If the region includes one city, the coefficients of variation were not calculated.

Given the fact that the ultimate goal is to build a model of the influence of the asymmetry in urban development on the regional development, model (1) has been transformed into:

$Y t ′ = A 1 Y ′ t − 1 + A 2 V ( X i ) t − l + A 0 + ε t ,$
(3)

where Yt′ is growth rate in GRP per capita by region;

• V (Xi)t−l – lagged value of the coefficient of variation of the i-th indicator by city.

## 5. RESULTS

The results of checking the Grangers test and correlation analysis are given in Table 2.

Table 2 presents the chains of cause-and-effect relationships statistically significant at p = 0.05 between indicators of socio-economic development of cities and regions in Kazakhstan. Correlation analysis allows confirming the results obtained by the Granger causality test. If Prob. <0.05, the value of r > | 0.7 |, that is, the strength of the connection is statistically significant by Student’s t-test. Besides, correlation analysis allows quantifying (by the value of the coefficient r) the strength of influence, which is not given by the Granger causality test. The use of Student’s t-test made it possible to determine that for r > | 0.2 | correlation is statistically significant at a significance level of 95%.

A generalization of the results obtained is presented as a cognitive map of the influence of the level of socioeconomic urban development on the level of socioeconomic regional development (Figure 1). The data of the cognitive map testify to the statistically significant strength of the influence of the level of urban development on the development of the region (district) by the Student’s t-test. The time lag of response to changes in indicators is defined at the level of L = 0 and L = 1. Indicators of GRP volumes by industry, profit of enterprises, incomes and expenses of households affect other indicators mainly without lag (L = 0); volumes of investments, expenses on innovations, volumes of goods turnover, price index for transportation of goods – with a lag of 1 year (L = 1). GRP indicators by industry, profit of enterprises, incomes and expenses of households affect other indicators mainly without lag (L = 0); investments, expenses on innovations, goods turnover, and price index for transportation of goods – with a lag of 1 year (L = 1). Based on the relationships shown on the cognitive map, a hierarchy of indicators is constructed using the graph method. The cognitive map acts as a direct graph, reflecting the direction of influence of some indicators on others. The sequence of iterations to determine the hierarchy levels of indicators of socio-economic development of cities and regions of the Republic of Kazakhstan is given in Table 3.

Applying the graph method allowed identifying that the resulting indicator of socio-economic development of the ATU system in Kazakhstan is the indicator X1 (p) – the volume of GRP per capita in the region, which is at the 6th hierarchy level. The governance indicators are X12 (c), X12 (r), X13 (c), X14 (c), X38 (c), X38 (r), X42 (c), X42 (r) – indicators of the 1st hierarchy level, which are basic and affecting all other indicators. The influence of these indicators on the resulting indicator – urban and regional GRP (Prob. <0.05) – is statistically significant.

As the research purpose is to assess the influence of the asymmetry of urban development on the regional development, the list of governance indicators is narrowed to indicators X12 (c), X13 (c), X14 (c), X38 (c), X42 (c), characterizing the socio-economic status of the city. At the same time, the use of regional development indicators in the study, which did not form the resulting and governance indicators, was necessary to reflect the chain of influence of the level of urban development on the regional development and building up a hierarchy.

The values of the coefficients of variation of indicators of socio-economic development in 2014-2018 reveal a significant asymmetry in the economic development of the regions in Kazakhstan and cities within the regions. Despite the fact that over the period under study, the coefficient of variation for some indicators has a negative tendency, its level remains fairly high. This is evidenced by the values of the coefficient of variation in the values of governance indicators X12 (c), X13 (c), X14 (c), X38 (c), X42 (c), which is more than 0.20 (20%) (Table 4). An autoregression is used to create an econometric model for assessing the influence of asymmetries of economic development of cities on the regional development:

(4)

where Х1′ (r) is the growth rate of the indicator X1 in the regions, a list of which is given in Table 1;

• Х1′ (r)(−1) is the growth rate of indicator X1 by regions for the previous period (L=1);

• V[Хi(c)](−1) – coefficient of variation of the i-th indicator for the cities within the corresponding region (Table 1) in the previous period.

The statistical significance of the created model is evidenced by the deviation of the predicted values of the resulting indicators from the actual ones no more than by 5%.

The use of the lagged variable in the model is because these indicators affect the level of economic development of the city and region with a lag of 1 period, which is confirmed with the data in Table 4. The growth rate indicator was applied as a dependent variable to determine the dynamics of the regional socio-economic development; independent variables were variation coefficients in order to assess the asymmetry of urban development by key socio-economic indicators affecting the level of regional economic development.

Based on the model for assessing the asymmetry in the economic development of cities on the regional development in Kazakhstan, one can determine:

• • Negative values of the coefficients near the indicators V [X12 (c)] (- 1), V [X13 (c)] (- 1), V [X14 (c)] (- 1), V [X38 (c)] (- 1), V [X42 (c)] (- 1), therefore, the higher the values of the coefficient of variation for all indicators, the lower the growth rate of GRP ((X1 (r));

• • The negative value of the coefficient at the indicator (X1 (r)) ́ (-1), therefore, the higher the growth rate of the regional economy in the previous period, the lower the growth rate currently ((X1 (r)) ́).

While using the proposed model, it can be stated that an increase in the coefficient of variation of the indicator X12 (c) by 1% will lead to a decrease in the growth rate of the regional GRP in the period ahead by 0.09%; an increase in the coefficient of variation of the indicator X13 (c) by 1% – to a decrease in the regional GRP by 0.03%; an increase in the coefficient of variation of the indicator X14 (c) by 1% –to a decrease in the regional GRP by 0.05%; an increase in the coefficient of variation of the indicator X38 (c) by 1% – to a decrease in the regional GRP by 0.02%; an increase in the coefficient of variation of the indicator X42 (g) by 1% – to a decrease in the regional GRP by 0.04%.

## 6. DISCUSSION

Within the framework of this study, an econometric model is proposed for assessing the influence of the asymmetry in the urban economic development on the regional socio-economic development. The advantage of this model is that on the example of the regional economic development in Kazakhstan it is proved that asymmetry with a coefficient of variation of 0.20 and higher has a negative effect on regional development. The findings confirm the conclusions made by other scientists that asymmetry reduces the level of regional competitiveness (Galor et al., 2009;Ostry et al., 2014). Based on the fact that the most significant decrease in the rate of regional GDP is caused by the asymmetry in investments in the fixed assets per capita, it can be argued that the key factor in the asymmetry of regional development in Kazakhstan is the rate of return on invested capital.

Consequently, the dominant rate of return on capital investment in the city’s economy (as a reflection of asymmetry in production factor) determines the state and prospects of development of the most important branches of its national economic specialization and ensures the pace of regional economic growth. It also confirms the conclusions made earlier, but in terms of achieving a balanced regional development to ensure the conditions for the most complete realization of its competitive advantages (Béland and Lecours, 2012;Ostry et al., 2014;Perlik, 2011;Piskun and Kudrevich, 2016). It should be noted that in the framework of the proposed model, the purpose was to ensure the general growth rates in the region, and not the balanced development of ATU.

The significant influence of an indicator such as regional trade turnover also confirms the scientists’ point of view on the importance of this factor in achieving a balanced regional development (Piskun and Kudrevich, 2016). Regarding this aspect, it should be noted that the achievement of a balanced exchange of goods in cities cannot ensure conditions for achieving faster growth of the economy of the region as a whole and the city in particular. Examples include maintaining a balanced regional development against the background of crisis processes in the economy and social sphere of the regions, an absolute decrease in GRP, investments, and the rate of return on capital and real household income. Under the given conditions, anti-crisis measures include the strengthening of the subsidized nature of the economy in such regions, which objectively leads to a relative increase in the import of goods and services, and, therefore, even greater asymmetry in inter-regional commodity exchange.

Therefore, based on the findings regarding the importance of trade as a factor ensuring the growth rate of the regional economy, it should be noted that it implies, first of all, the factor of qualitative characteristics of trade exchanges, reflecting not so much the general scale of inter-regional commodity flows as the comparative production efficiency of exchanged goods and services, as well as the efficiency of transportation of exported and imported products. Obviously, a higher level of GVA is materialized in a unit of exported products with the given labor costs for its production and transportation, which provides a higher level of income from its sale, and, therefore, contributes to an increase in the level of GRP per capita and the growth rate of the regional economy as a whole. At the same time, the existing gap in the level and dynamics of development of various cities and regions also depends on the level and dynamics of changes in the quality parameters of the total volume of imported products, reflecting the actual effectiveness of its production in the domestic market.

Therefore, it is precisely the ratio of the qualitative characteristics of interregional commodity exchange, reflecting comparative levels of GVA in the corresponding volumes of import and export of products, that underlies the understanding of the internal sources of differentiation of regional development, which ensure the reduction of the negative impact of the asymmetry in their development, achievement of convergence and sustainable economic growth. The conclusion obtained in the framework of this study determines the need for innovative reform of economic development and inter-regional commodity exchange of urbanized territories of Kazakhstan, based on increasing the quality of manufactured products, in particular, ensuring its innovative component. In view of this, it seems appropriate to create and develop innovation centers in the regions, the main task of which is to upgrade innovation by the implementation of a technology transfer mechanism through the development of high-tech entrepreneurship and the expansion of international scientific and technological cooperation. This will facilitate the cooperation of educational, scientific and entrepreneurial structures for R&D in priority areas of research; providing the consulting, information and practical support to small and medium-sized enterprises; accelerating the implementation of the transfer of the latest developments in production; and venture capital business development.

The development of Smart-city technologies is also necessary in the framework of the regional management strategy. The creation of such structures will facilitate the efficient distribution and application of scientific and technological resources, as well as the development of small businesses in regions, improving the quality, productivity and interactivity of urban services, reducing costs and resource consumption, and improving communication between urban residents and the state.

In view of the reduction of regional asymmetry in Kazakhstan, in order to achieve these regional policy goals in the long term, it also seems necessary to solve the following important tasks: optimal allocation of state, regional and public infrastructure, development and practical implementation of government support measures for the development of the initial conditions of private investment of priority economic specializations of each territory. For this, it is advisable to identify priority economic specializations of urbanized territories and centers of economic growth in accordance with the objectives of the Development Strategy of Kazakhstan until 2030. An important aspect in reducing regional asymmetry is the transition to new principles of fiscal instruments in regional policy. First, it is planned to reduce the amount of budgetary support for local budgets in order to stimulate them to intensive socio-economic development. Moreover, the last one of the most important tasks in the authors’ opinion is to ensure the necessary financial independence of local governments, especially in terms of increasing the share of tax revenues in territorial budgets: reducing and eliminating tax benefits, improving taxation of vertical integrated structures, etc.

Besides, the developed model for assessing the influence of the asymmetry of the economic development of the city on the regional socio-economic development allows identifying the model of convergence of the economic development of urbanized territories. For the economy of Kazakhstan, a β-convergence model has been applied; according to which less developed regions are developing at a faster pace than more developed ones. In this study the β-convergence model shows the growth rate of the regional economy, rather than the rate of deviation from the equilibrium state (as in neoclassical models) (Lopez-Rodriguez, 2008;Otoiu and Titan, 2015;Ahmad and Hall, 2017;Haupt et al., 2018), which is not necessarily an uptrend. That is, the model allows assessing economic convergence in the context of economic growth and recession. In addition, in contrast to existing convergence models, besides assessing regional convergence, it permits assessing the nature of the influence of asymmetries in urban development on the regional socio-economic development.

A significant advantage of the proposed model within the study of the asymmetry in regional economic development is the consideration of not only production factors, as in the neoclassical model (Galor et al., 2009;Lopez-Rodriguez, 2008;Ostry et al., 2014;Ahmad and Hall, 2017), but a system of factors: investment, innovation, monetary, and unemployment factors, which allows a comprehensive assessment of regional socio-economic development under the impact of asymmetry. On top of that, the governance variables in the model have a time lag, which can serve as the basis for preventive measures to level the negative consequences of the asymmetry in the urban development on the regional socio-economic development when elaborating anti-crisis policy measures.

The methodological approach to determining the influence of asymmetries of the economic development of cities on the development of regions is universal, but depending on the country, the coefficients for variables in the model will change, which reflect the nature and strength of the influence of asymmetries of the economic development of cities on the development of regions. The nature and strength of influence will depend on the actual level of asymmetry in the economic development of cities: with a high level of variation of indicators (as in Kazakhstan), the effect of asymmetry is negative, with a lower level of variation, a positive effect of asymmetry or a less strong negative effect is possible. Also, the proposed methodological approach can be used to explain the influence of asymmetry not only in the city-region system, which was the subject of research in the article, but also in others, reflecting the interdependence of the development of the general and the private (for example, in the region-country, region-region, City, country). The proposed model in practice can serve not only as a tool for effective preventive measures of the anti-crisis policy of regional development in order to neutralize asymmetries, but as a tool for achieving economic growth, that is, to determine and stimulate significant factors in the development of the region.

The results obtained are based on a sample of indicators of regional socio-economic development of one country – Kazakhstan and cannot be applied to any other. In addition, due to the lack of official data related to the regional economic development in Kazakhstan, the study has not considered the levels of asymmetry at which it has a positive effect on economic development. The designated aspects are important in the development of this topic and will be taken into account for future research.

## 7. CONCLUSION

Based on the results obtained, the following conclusions can be drawn that currently, in the development of the regional economy in Kazakhstan, a high level of asymmetry in the economic development of cities is observed, which negatively affects the overall regional economic development. Farther, a β-convergence model is being implemented for the economy of Kazakhstan, according to which less developed regions are upgrading at a faster pace than more developed ones. This aspect may become an incentive to attract investment in less developed cities to neutralize the negative consequences of the asymmetry of the regional economic development. The key factors of asymmetry and neutralization of its negative consequences for the regional socio-economic development in Kazakhstan are the rate of return on capital, as well as the comparative efficiency of the production of exchanged goods and services, and transportation of exported and imported products. Therefore, to reduce the negative effects of the asymmetry in the urban economic development in Kazakhstan and to ensure the rate of economic growth for predominantly commodity regions with a restructured economy, it is of great importance to overcome the existing narrow commodity specialization and increase the degree of horizontalness of external relations, production and interregional exchange of innovative and high-tech products on this basis, and achieve a balance in production related to the aggregation of processing branches, including high-tech industries. Taking into account the features identified in the study will contribute to the development of an effective regional management strategy that provides innovative reform in the economic development and interaction of the urbanized territories in Kazakhstan.

## Figure

Cognitive map of the influence of the level of socio-economic urban development on the level of socio-economic regional development.

## Table

Pairwise ATU groups in Kazakhstan to identify cause-and-effect relationships between indicators of socio-economic development of cities and regions according to the granger causality test

Cause-and-effect relationships between indicators of socio-economic development of cities and regions in Kazakhstan

Identification of hierarchy levels of indicators of socio-economic development of cities and regions in the Republic of Kazakhstan

Coefficient of variation of indicators for regional socio-economic development in Kazakhstan

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