ISSN 0798 1015

Vol. 40 (Number 6) Year 2019. Page 6

Influence of Power Asymmetry on Economic Growth of Industry Markets: A Russian Case

Influencia de la asimetría de poder en el crecimiento económico de los mercados industriales: Un caso ruso

Svetlana V. OREKHOVA 1; Evgeniy V. KISLITSYN 2

Received: 04/08/2018 • Approved:22/12/2018 • Published 18/02/2019

Contents

1. Introduction

2. Methodology

3. Results

4. Conclusions

Bibliographic references

1. Introduction

Searching for new sources of economic growth, such as support for competition, is among the central tasks of the state economic policy. A number of research studies (Bucci, 2014; Carlin, Schaffer, Seabright, 2014; Latynina, 2013) illustrate that market inequality produces specific institutional interactions between participants and, consequently, affect their performance.

Development of effective competition policy in Russia is one of the most urgent issues. Traditionally, antitrust prohibitions serve as the core tool of competition policy. On the other hand, economists argue that state intervention negatively affects market processes. For example, R. W. Crandall (Crandall, 2001) found that in practice it was rather difficult to distinguish healthy competition from anticompetitive behavior. At the same time, G. A. Hay and G. Werden (Hay, Werden, 1993) assert that “genuine” influence of competition policy on public well-being is positive. Competition policy enhances social protection of the population and encourages companies to abandon anticompetitive behavior without any obvious interference, i.e. constrains them.

This ambiguity in opinions of theorists and practitioners underlies the purpose of the paper – to investigate the influence of market inequality parameters on economic growth of industry markets. To achieve the stated goal, we have to fulfill the following tasks:

1-To perform a theoretical analysis of the approaches to establishing the correlation between the competition’s level and economic growth of an industry market;

2-To develop a methodical toolkit for assessing the power asymmetry level as the central characteristic of competitive inequality in an industry market;

3-To devise and test a model of influence of the power asymmetry level on economic growth of an industrial market.

Proponents of classical economic theory, who examined the basic approaches to studying behavior under competition and equilibrium dynamics, laid the foundations for modern economic growth theory (for more details, see (Malthus, 1986; Ricardo, 1951; Smith, 1937)). In addition, the studies determined the role of monopolistic power as a stimulus to technological progress.

R. F. Harrod and E. D. Domar (Domar, 1946; Harrod, 1942) continued exploring the problems of economic development and combined Keynesian analysis with elements of the concept of economic analysis. R. M. Solow and T. W. Swan developed the neoclassical form of the production function (Solow, 1956; Swan 1956). Having introduced endogenous saving rates, D. Cass finalized the development of the basic neoclassical growth model (Cass, 1956).

Currently, economic growth is interpreted as:

1. quantitative and qualitative improvement of the social (cumulative) product produced in a certain period of time (Nikipelov, 2013);

2. a process that should be considered in the context of the three aspects: qualitative, quantitative and structural (Babaev, Dubrovsky, 2015);

3. long-term trends towards an increase and qualitative improvement of the national product and factors of its production (Dumnaya, 2010);

4. an increase in real income, including income per capita achieved by changing the quantity and quality of the resources used (Tenyakov, 2016).

All theories of economic growth pay attention to factors that affect its rates and quality. Direct driving forces of economic growth include the quantity and quality of labor (Todorov et al., 2018) and natural resources, the amount of fixed capital, technology and organization of production (Kubeš & Rančák, 2018; Prause & Atari, 2017) and the level of entrepreneurial abilities’ development. Indirect factors usually encompass the tax system, the level of efficiency of the monetary and banking systems, growth of consumer, investment and government spending, expansion of exports, opportunities for redistribution of production resources in the economy and the current income distribution system (Latynina, 2013). Indirect driving forces also embrace the level of market competition which allows market participants to assess the existing alignment of forces, the correctness of their actions and to orient themselves in choosing a development strategy (the fundamental approaches to competition as a driver of economic growth are given in Table 1).

Table 1
Approaches to competition as a driving force of economic growth

In modern studies, empirical analysis is regarded as the most relevant method for assessing the relationship between competition and economic growth. According to (Shastitko, Avdasheva, Golovanova et al., 2012, p. 7), annually GDP in Russia decreases by at least several points due to a low competition level. At the same time, W. Carlin et al. (Carlin, Schafer, Seabright, 2014) have found that the correlation between a competition level and economic growth is of non-linear character, but has a U-shape, i.e. to a certain critical point, intensification of competition causes an increase in output, but after that, it leads to negative results. Moreover, as indicated in the World Bank Report, the impact of competition on economic growth will be differentiated depending on the stage of market development in a particular country. For developed nations, a decrease in state regulation of competitive processes is positive, whereas for emerging markets, there should be a balance between competition and state regulation (Huggins et al., 2014).

Generalizing the approaches to determining the relationship between competition and economic growth, we draw the two main conclusions:

1. there is a link between the level of competitive inequality and economic growth;

2. empirical research results are always different and determined by the specifics of economic relationships in the market (country), therefore, it is important to take into account the specific features of the research object.

Inequality in the industry market can be best reflected in an integrated approach to assessing the level of power asymmetry. In our opinion, power asymmetry refers to a state of an industry market where some economic agents are mighty enough to influence the decisions of other market agents (including the state) and can directly or indirectly establish the terms of contracts (institutional agreements) (Orekhova, Kislitsyn, 2017).

Synthesis of the existing approaches to assessing market inequality (for more details, see (Orekhova, Kislitsyn, 2017)) and the clarified term “power asymmetry” allows us to identify its three main elements: Structural – market inequality among firms of the same industry market; Interactional – market inequality among firms in related industry markets (organizational fields); Institutional – the degree to which the needs and the trajectory of market development correspond to the institutional environment (needs and the path of the state development).

Based on these elements, we have developed a method for assessing the power asymmetry level. The first step of the algorithm is to measure the level of structural asymmetry. It takes account of direct (the Bain index) and indirect (the Hall-Tideman and Herfindahl-Hirschman indices) indicators of market inequality, which guarantees a comprehensive assessment. The resulting coefficient of structural asymmetry is calculated by formula (1):

The third step of the algorithm is to assess the degree to which the needs and the path of the market development correspond to the institutional environment, the so-called institutional asymmetry. The logic of this indicator is as follows: the more stimulating factors there are in the industry market and the more prioritized the industry is, the more actively enterprises of this market can advance their interests and the bigger influence is exerted on the formation of the institutional environment. Assessment indicators of the institutional asymmetry level are the volume of investment in fixed capital of the enterprises within the industry out of public funds, as well as the volume of publicly funded loans received by the enterprises operating in the industry market. To compare the data, the indicators’ values were normalized using a scale ranging from 1-5, where “5” is the largest volumes of public investment.

Based on the analysis of the state programs and general state industry strategies, we used an expert way to determine the importance of the industry market for the purposes of current industrial policy.

The coefficient of institutional asymmetry is calculated on the basis of scalar indices by formula (3):

3. Results

To establish the influence of the power asymmetry level on economic growth of the industry market, we use the tools of multiple regression.

Theoretical analysis of the discussion about the competition level and economic growth allows us to put forward a number of hypotheses:

H1 – the lower the level of structural asymmetry (equality of competition in the industry), the higher the economic growth rate of the industrial market;

H2 – the lower the level of interactional asymmetry (equal conditions in the industry and related markets), the higher the economic growth rate of the industry market (since in the absence of asymmetry, the distribution of rents between the markets is approximately the same, which generates greater opportunities for the development of markets);

H3 – the more institutional incentives there are for market development (the higher level of institutional asymmetry), the higher the rate of economic growth.

In the process of developing the model, we have studied the data from 40555 enterprises in 42 industry markets of the extractive and manufacturing industry of the Russian Federation. The data for enterprises were retrieved from SPARK-Interfax Database for 2016. To assess the factors of institutional power asymmetry, the data of Russian statistics were used.

The basic econometric model is as follows (4):

Т = f (SA, IA, IE),                                                                                                (4)

where T is the industry market growth rate; SA is the level of structural asymmetry in the market; IA is the level of interactional asymmetry; IE is the level of institutional asymmetry.

The indicators for constructing the multiple regression model are summarized in Appendix.

During the development of the econometric model by means of the construction of correlation fields, a nonlinear form of the dependence of the resultant variable on the factors was identified. The results of the regression analysis are presented in Tables 2 and 3.

Table 2
Dispersion analysis of the multiple regression model

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Table 3
Results of regression analysis of the multiple regression model

Prior to interpretation of the obtained results, the assessments were checked in terms of unbiasedness, effectiveness and consistency. When analyzing the data in the model, insignificance of the factor of institutional asymmetry was revealed, which disproves the hypothesis H3. Significance F in the regression model is lower than 5%, hence it can be considered reliable and the number of observations is sufficient. P-Value for each coefficient is also not more than 5%. The correlation coefficient of 0.998 indicates a strong correlation between the factors and the dependent variable. Thus, the regression equation taking into account the factors’ significance is as follows (5):

We identified a direct correlation between economic growth of the industry market and the indicators of power asymmetry. The magnitude of the effect of structural asymmetry is 0.69, and that of the interactional asymmetry is 0.47.

The research results demonstrate that the dependence of the industry market’s growth rate on the indicators of power asymmetry is not linear, but takes a power form (see Figure 1).

Fig. 1
The graph of the dependence of economic growth rate on the indicators
of structural and interactional asymmetry

Thus, if the indicators of structural and interactional asymmetry increase in the interval below 50% (as compared to the interval above 50%), this entails a greater increase in growth rate. This means that for markets with a low power asymmetry level, its increase entails a greater increment in economic growth than for markets with a high level of power asymmetry.

Modeling the effect of the power asymmetry level on economic growth of the industry market demonstrates non-trivial empirical results and allows us to arrive at interesting, and new to economic science, conclusions.

The level of power asymmetry has a positive effect on the performance indicators of the Russian industry markets under consideration. In other words, the higher the level of power asymmetry, the faster the economic growth in the industry market.

At the same time, the degree of influence of structural asymmetry is much higher than that of interactional one. The influence of institutional asymmetry within the framework of this model is not established. In addition, it is worth mentioning that we have identified a power form of dependence of economic growth rates in the indicators of structural and interactional asymmetry.

The current research has allowed expanding representations about influence of market inequality on economic growth of the industry market. In our opinion, the research is also valuable due to the development of the methodical tools that make it possible to formulate more effective proposals on competition policy.

Babaev B.D., Dubrovsky S.P. Economic Growth: An Expanded Interpretation. Economic Growth Quality. Economics of Education, 2015, no. 1, pp. 33–38.

Boboev G.G. The Role of Institutions in Economic Growth Theories. Bulletin of TSULBP, 2013, no. 3(55), pp. 131–138.

Bucci A. Population, competition, innovation, and economic growth with and without human capital investment. International Review of Economics, 2014, vol. 61, no. 1, pp. 61–84. https://doi.org/10.1007/s12232-013-0192-2

Buccirossi P., Ciari L., Duso T., Spagnolo G. and Vitale C. Competition Policy and Productivity Growth: An Empirical Assessment. The Review of Economic and Statistics, 2013, vol. 95(4), pp. 1324–1336. https://doi.org/10.1162/REST_a_00304

Carlin W., Schaffer M., Seabright P. A Minimum of Rivalry: Evidence from Transition Economies on the Importance of Competition for Innovation and Growth. Journal of Economic Literature, 2014, vol. 2, no. 1, pp. 23–35.

Cass D. Optimum Growth in an Aggregative Model of Capital Accumulation. Review of Economic Studies, 1965, no. 32, July, pp. 233–240. https://doi.org/10.2307/2295827

Crandall R.W. The Failure of Structural Remedies in Sherman Act Monopolization Cases. Oregon Law Review, 2001, Spring, 80, pp. 109–98. https://doi.org/10.2139/ssrn.286357

Domar E. D. Capital Expansion, Rate of Growth, and Employment. Econometrica, 1946, April, pp. 137–147. https://doi.org/10.2307/1905364

Dumnaya N.N. Synergy of Market Economy and State Policy. Society. The State. Policy, 2010, no. 1, pp. 23–34.

Harrod R.F. Toward a Dynamic Economics: Some Recent Developments of Economic Theory and Their Application to Policy. London: Macmillan, 1942.

Hay G.A., Werden G. Horizontal Mergers: Law, Policy, and Economics. American Economics Review, 1993, Papers & Proceedings 83, pp. 173–77.

Huggins R., Izushi H., Prokop D., Thompson P. Regional competitiveness, economic growth and stages of development. Zb. rad. Ekon. fak. Rij, 2014, vol. 32, pp. 255–283.

Kubeš V., Rančák J. Sustainability of organization performance via management techniques. Entrepreneurship and Sustainability Issues, 2018, 5(4), pp. 1031-1042. https://doi.org/10.9770/jesi.2018.5.4(23)

Latynina N.A. Factors in Ensuring Economic Growth. Actual Problems of Economics and Law, 2013, no. 1(25), pp. 5–9.

Leibenstein H. Allocative Efficiency VS. “X-Efficiency”. American Economic Review, 1966, vol. 56, no. 3, June.

Malthus T. R. An Essay on the Principle of Population. London: W. Pickering, 1986.

Nallari R., Griffith B. Competition, Competition policy and Growth. Competiveness clusters. 2013. Pp. 105–110.

Nikipelov A.D. On Economic Growth and Economic Development. Russian Journal of Economic Theory, 2013, no. 4, pp. 1–12.

Orekhova S.V., Kislitsyn E.V. Empirical Study of the Power Asymmetry in the Natural Gas Market. Manager, 2017, no. 2(66), pp. 28–35.

Orekhova S.V., Kislitsyn E.V. Theoretical Foundation and Methods for Assessment Commodity Market Power Asymmetry. RUDN Journal of Economics, 2017, no. 25(1), pp. 74–90.

Prause G., Atari S. On sustainable production networks for Industry 4.0. Entrepreneurship and Sustainability Issues 4(4), 2017, pp. 421-431. https://doi.org/10.9770/jesi.2017.4.4(2)

Ricardo D. On the Principles of Political Economy and Taxation. Cambridge: Cambridge University Press, 1951.

Shastitko A., Avdasheva S., Golovanova S. et al. Consequences of Weak Competition: Quantitative Assessments and Conclusions for Policy (Expert-analytical report of the Russian Academy of Science and Technology). Economic Policy, 2012, no. 6, pp. 5–53.

Smith A. An Inquiry into the Nature and Causes of the Wealth of Nations. New York: Random House, 1937.

Solow R.M. A Contribution to the Theory of Economic Growth. Quarterly Journal of Economic, 1956, no. 70, February. Pp. 65–94. https://doi.org/10.2307/1905364

Swan T.W. Economic Growth and Capital Accumulation. Economic Record, 1956, no. 32, November, pp. 334–361. https://doi.org/10.1111/j.1475-4932.1956.tb00434.x

Tenyakov I.M. Contemporary Economic Growth: Sources, Factors, Quality. Moscow, Prospect Publ., 2016. Pp. 8–9.

Todorov G., Kalinina A., Rybakova A. Impact of labour migration on entrepreneurship ecosystem: case of Eurasian Economic Union. Entrepreneurship and Sustainability Issues, 2018, 5(4), pp. 992-1007. https://doi.org/10.9770/jesi.2018.5.4(20)

Zaikin N.N. The Role of Competition and Corruption in Economic Growth Model. Vestnik of Astrakhan State Technical University. Series: Economics, 2014, no. 1, pp. 14–21.

Table. Indicators for constructing a multiple regression
model of the influence of the level of power asymmetry
on economic growth of the industrial market

Source: calculated by the authors from SPARK-Interfax Database for 2016.
Note: * OKVED – Russian Standard Industrial Classification of Economic Activities.

1. Cand. Sci. (Economic), Associate Professor, Department of the Business Economics, Ural State University of Economics. E-mail: bentarask@list.ru

2. Senior Lecturer of the Statistics, Department of the Econometrics and Information Science, Ural State University of Economics. E-mail: kev@usue.ru