The Impact Of The Oil Market On The Socio-Economic Development Of Russia

Abstract

Currently, the oil and gas complex is a determining factor in any state's economic development. Oil and gas are employed in nearly every area of the economy, distributing economic advantages and serving as the foundation for commercial products. In this context, strong control of the oil and gas complex, particularly state regulation, is required. For competitive environment study, the market concentration indices CR-3, CR-4, Herfindahl-Hirschman index, Hall-Teidman index, and Lind index were produced. After studying the competitive landscape of the Russian oil sector from 2015 to 2019, it was discovered that the Russian oil market was somewhat concentrated in 2015-2016, and highly concentrated in 2017-2019; the Russian oil market was a monopolistic competition market for the whole time under study. The influence of integration procedures on indices of the country's socioeconomic progress was also examined in the research. GDP, economic growth, consolidated budget revenues, international trade balance, and oil and gas condensate production were all identified as factors impacting the country's socio-economic development. Integration procedures on the oil market have a fairly significant impact on GDP and consolidated budget revenues, according to the research, while they have a modest impact on the volume of oil production with gas condensate. At the same time, oil market integration has no major influence on economic growth or the international trade balance.

Keywords: oil and gas complex, state regulation, competitive environment analysis, correlation and regression analysis, pricing

Introduction

As of January 1, 2020, 292 organizations with underground soil use legal licenses are producing oil and gas condensate reservoirs of petroleum and raw material in the territory of the Russian Federation. Including:

1) 105 organizations included in the structure of 11 giant companies (VIOC), so that is stated to the year results, accounted for a total of 84.7% of all national oil production;

2) 184 independent production companies which are not part of vertically integrated companies VIOC;

3) 3 companies functioning under production joint sales.

The level of national crude oil output grew by 5.2 million tons in 2019 as compared to 2018 (+0.9%) and totaled 561.2 million tons in absolute terms. It is worth noting that today's Russian oil sector is dominated by vertically integrated oil corporations (VIOCs). They are in charge of 90% of oil production and refining, as well as providing petroleum products to end users. VIOC is a conglomerate of big energy businesses with subsidiaries and direct control of oil refineries. At the same time, the subsidiaries are effectively self-contained businesses with their own management center, resource base, and so on. The oil and gas sector is not only the biggest sector, but also plays an important role in the socio-economic development of the state, employing more than two million people. Moreover, the oil and gas sector is still the main source of foreign currency and tax revenues of the country. At the same time, it accounts for about 12 percent of the total industrial production. This is due to the fact that the oil industry is the largest taxpayer in the Russian Federation. Thus, due to the importance and dependence of certain social and economic indicators on the results of the oil and gas complex, there is a need for its effective regulation, in particular, state regulation.

During the state adjustment process of the oil and gas complex, the following operations will be performed:

- management of activities in the areas of protection and utilization of underground soil, waste mining and utilization, mineral resources, and groundwater (Liu & Ramirez, 2017; Klychova et al., 2019a);

- management of the package of shares owned by the state (Khalfaoui et al., 2019; Jiang & Liu, 2021);

- ecological and geological control over the safe conduct of works, rational use of oil and gas (Schimmel et al., 2019; Esen et al., 2020);

- tax, credit and pricing policies (Leontieva et al., 2018; Ederington et al., 2021; Alao & Payaslioglu, 2021; Castillo et al., 2020);

- licensing of activities;

- antimonopoly regulation (Talipova et al., 2019).

The state regulation of the oil and gas complex has the following goals:

- to establish world-class firms in the key areas of the oil and gas industry;

- to control the implementation of key decisions of oil and gas companies, including investment decisions regarding the establishment of production facilities (Klychova et al., 2019b);

- to ensure the country’s energy security (Klychova et al., 2019a; Axon & Darton, 2021; Rajavuori & Huhta, 2020; Karatayev & Hall, 2020);

- improving the associated evaluated standard legal regulation;

- creation of effective transport and logistics system (Rahman et al., 2019; Tang & Veelenturf, 2019);

- control of unreasonable overpricing of oil and gas products sold in the domestic market;

- regulation of monetary and credit relations, providing the enterprises of oil and gas complex with free financial resources;

- providing innovation and investment development of oil and gas companies;

- improvement of the tax and budget policy in order to develop the productive potential of the oil and gas sector enterprises.

The federal regulation aims to enhance social and economic indicators, investment attractiveness, and the quality and level of life of the Russian Federation's people. The scope of government control is determined by socio-economic growth and geopolitical circumstances. We were able to identify the following steps of implementation after studying the theoretical underpinnings of oil market analysis: to study goods and services; to research supply and demand, to research the behavior of current and future customers; to research the market environment; to research pricing dynamics for the aim of maximizing product marketing in the market place (Liu, Shao & Zhang, 2020; Alamgir & Amin, 2021; Lin & Bai, 2021; Raheem et al., 2020).

As an object of the analysis of branch markets it is accepted to allocate a set of the companies which have common interests in one sector of economy and represent an economic branch. The economic industry is represented by the production, distribution and consumption of a particular type of goods and services. With the help of methods of analysis, all indicators and the market as a whole are studied in an integrated way. The methods used in market research include: statistical data processing, multivariate methods, simulation methods, regression methods, correlation methods, statistical theory, hybrid methods, deterministic methods and other methods. The choice of one or another method is influenced by the circumstances and goals of the analysis. And the objectivity of indicators is influenced by the combination of several methods.

The conjuncture of the raw materials market is understood as a specific economic situation on the market in a specific period of time. In the study of the raw materials market, the following activities are carried out: conjuncture information is collected and processed; integral and differential assessment of the market is carried out. The scale of the market is characterized; market trends are detected, analyzed and forecasted; seasonality and cyclic pattern of market development are evaluated and analyzed.

Problem Statement

Currently, the oil and gas complex is decisive in the economic development of any state. Oil and gas are used practically in all sectors of the national economy, providing the distribution of economic benefits and being the basis for the products demanded in the market. Oil and gas complex in Russia has a significant impact on the economic development of the country. In this regard, there is a need for the organization and implementation of effective and competent state management in this industry; regular analysis of the competitive environment of the oil market; conducting correlation and regression analysis of the impact of integration processes in the oil and gas complex on socio-economic indicators.

Research Questions

The subject of the study is the oil market and integration processes affecting the socio-economic development of Russia;

Purpose of the Study

The purpose of the study is to analyze the oil market and identify the impact of integration processes on the socio-economic indicators of Russia's development.

Research Methods

The following methods were used in the study: statistical data processing, comparison method, grouping method, correlation and regression analysis.

Findings

To characterize the oil market in the Russian Federation, to analyze the competitive environment and economic concentration in this market, we chose seven companies: PJSC Gazprom Neft, PJSC Lukoil, PJSC Rosneft Oil Company, PJSC Tatneft, PJSC NOVATEK, PJSC Surgutneftegaz, PJSC RussNeft. The following formula is used to analyze the competitive environment:

1) Market volume is the number of commodities that the market can absorb within a period of time under certain conditions.

Calculation of market volume:

$V_m=\underset{i=\mathrm{1}}{\overset{n}{\sum }}{V}_i$(1)

Calculation of the subject's market share:

$Q_i=\frac{V_i}{V_m}\mathrm{*}\mathrm{100}\mathrm{\%}$(2)

where Vm - total sales volume (thousand rubles units); Vi - sales volume by the i-th seller; n - the number of sellers operating within the geographical boundaries of the market.

2) Index of market concentration CR-3, CR-4.

Calculation of market concentration indices:

${CR}_n={\sum }_{\mathrm{1}}^n{Q}_i$(3)

where Qi - the share of sales of the i-th firm in the total market (in %) by the seller of the product.

3) The Herfindahl-Hirschman index (HHI).

Calculation of the Herfindahl-Hirschman index:

$HHI=\underset{i=\mathrm{1}}{\overset{n}{\sum }}{Q}_i^{\mathrm{2}}$(4)

The Herfindahl-Hirschman index is calculated taking into account data on all market participants. In contrast to CR, it takes into account both the number of enterprises and their disparity in the market.

The Herfindahl-Hirschman index allows us to divide all the markets into highly concentrated, moderately concentrated and low concentrated ones (Table 1).

4) Hall-Teidman index (HT).

Calculation of the Hall-Teidman index:

$HT=\frac{\mathrm{1}}{\left(-\mathrm{1}\right)+\mathrm{2}\underset{i=\mathrm{1}}{\overset{n}{\sum }}{Q_{i}R}_i}$(5)

where Qi - the share of sales of the i-th firm in the total market (in fractions); Ri - the rank of the i-th company in the market (in descending order, the largest company has a rank of 1). HT is always in the range from 0 to 1. The value of the Hall-Teidman Index allows us to determine the type of market competition (Table 2).

Using these indices, let us analyze the competitive environment in the Russian oil market in the period 2015-2019. First of all, let us find out what were the indicators of oil production with gas condensate in the 7 largest oil companies of the Russian Federation during this period (Table 3). As shown in Table 3, the volume of oil production with gas condensate increased by 4.9% from 2015 to 2019.

The analysis of Table 4 shows that in 2015 the aggregate market share of oil occupied by the selected companies was 85.32%. In 2016, this figure decreased by 0.97 p.p. and reached 84.35%. In 2017 and 2018, the aggregate oil market share held by the companies was 88.92% and 88.11%, respectively. As for the aggregate oil market share occupied by companies in 2019, this figure decreased by 0.5 p.p. compared to the previous year and reached 87.67%. For each year, the market share of each of the 7 companies under consideration was determined. In accordance with the values of the market share indicator, each company for all 5 years received a certain rank, which is required for calculation of the Hall-Teidman index. For example, Rosneft had the first rank for all five years, Lukoil had the second, Tatneft had the fifth, NOVATEK had the sixth, and RussNeft had the seventh. In 2015, Surgutneftegaz had the third rank, while Gazprom Neft had the fourth, and then in 2016-2019, Gazprom Neft had the third rank, while Surgutneftegaz had the fourth (Table 4).

By calculating the Herfindahl-Hirschman Index (HHI), we can conclude that in 2015-2016 the oil market was a moderately concentrated market because 1000<HHI<2000, and in 2017-2019, it was a highly concentrated market because 2000<HHI<10000. The CR-3 and CR-4 market concentration indices support the 2015-2019 oil market conclusions of the Herfindahl-Hirschman Index (HHI). The Hall-Teidman Index (HT) was 0.34 in 2015, 2016, and 2019, and 0.33 in 2017 and 2018. This means that between 2015 and 2019, the oil market was a monopolistic competition market (Table 5).

Thus, we considered the features of the competitive environment analysis of the industry market with the help of market concentration indices CR-3, CR-4, Herfindahl-Hirschman index, Hall-Teidman index. Having analyzed the competitive environment of the Russian oil market in 2015-2019, it was possible to find out that the Russian oil market in 2015-2016 was moderately concentrated, in 2017-2019 - highly concentrated; during the whole period under consideration the Russian oil market was a monopolistic competition market; 4 of 7 companies under consideration (Rosneft, Lukoil, Gazprom Neft, Surgutneftegas) had a significant influence. In the period 2015-2019, the Russian oil market was not dominated by any of the companies in question. In 2018, the closest to the mark of more than 50% of the oil market share was Rosneft. Its share was equal to 41,41%.

In addition to analyzing the competitive environment in the oil market of Russia, it is important to analyze the impact of integration processes in this market on indicators of socio-economic development of the country. The indicators of integration processes will be: the number of mergers and acquisitions (X1), the total industry volume of mergers and acquisitions (X2), the total volume of all mergers and acquisitions (X3), the industry volume of all domestic transactions (X4). The indicators of socio-economic development of the country include gross domestic product (GDP) (R1), economic growth (R2), consolidated budget revenue (R3), foreign trade balance (R4), oil and gas condensate production (R5).

A specialized computer program Statistics was used for the correlation and regression analysis. The analysis was conducted for the period from 2001 to 2019 inclusively. During the analysis, we selected significant factors (explanatory variables; indicators of integration processes - X) for each indicator of socio-economic development of the country (explanatory variable - Y). For this purpose, we checked the level of multi-collaboration between the explanatory variables and tested the explanatory variables by p-value criterion (criterion of significance level of explanatory variables). We calculated coefficient of determination, which allowed to determine the strength of influence of each of the selected explanatory variables on the explanatory variable in question. We also constructed regression equation for each Y.

1) Consider the indicator U1 of GDP (Table 6).

When analyzing the multicollinearity of the explanatory variables (X), it was found that X1 and X3 were multicollinear (0.77; greater than or equal to 0.7 - strong multicollinearity of the explanatory variables). It was necessary to decide which of these two factors should be excluded (Table 7).

According to the multicollinearity coefficient b* we excluded factor X1 - the number of M&A deals (in this case the explanatory variable that has a smaller value of b* is excluded) (Table 8).

By the p-value criterion (level of significance), we excluded the X2 factor - the total industry volume of (M&A) mergers and acquisitions deals. In this case, we exclude those explanatory variables that do not meet the p-value criterion, that is, greater than 0.05. Explanatory variables are excluded sequentially, i.e. first those X's with p-value greater than other variables are excluded. Then the remaining explanatory variables that do not meet the p-value criterion are excluded in descending order by the same principle (Table 9).

The following results were obtained.

The coefficient of determination R^2 = 0.62 (0,6<= R^2<0,7), that is, in this model moderately strong relationship, factors X3 - total volume of all mergers and acquisitions and X4 - industry volume of all domestic transactions moderately strong impact on Y1 - GDP. Sixty-two percent of the changes in Y1 are due to the effects of X3 and X4.

The regression equation is as follows:

Y = 9.085083 + 0.000008×X3 – 0.000011×X4.

Thus, the total volume of all mergers and acquisitions and the industry volume of all domestic transactions have a moderately large impact on GDP.

2) Consider Y2 economic growth (Table 10).

When analyzing the multicollinearity of the explanatory variables (X), it was found that the indicators X1 and X3 were multicollinear. It was necessary to decide which of these two factors should be excluded (Table 11).

According to the multicollinearity coefficient b* we excluded the factor X1 - the number of M&A deals (Table 12).

According to the p-value criterion, we saw that all three of the remaining factors do not meet its condition at this stage, so we began to sequentially exclude them depending on the value of p-value in descending order.

The first factor that was excluded was factor X3 - the total volume of all mergers and acquisitions (Table 13).

Then we excluded the factor X4 - the industry volume of all domestic transactions (Table 14).

The following results were obtained. Determination coefficient R^2 = 0.036575 (R^2<0.4), that is, there is no strong relationship in this model. By default, we can conclude that the selected X2 - total industry M&A (mergers and acquisitions) has no significant effect on Y2 - economic growth. It is not possible to construct a regression equation using this model. All of the factors considered do not significantly affect the change in economic growth.

3) Let us consider the indicator Y3 - revenues of the consolidated budget of the Russian Federation (Table 15).

When analyzing the multicollinearity of the explanatory variables (X), it was found that the indicators X1 and X3 were multicollinear. It was necessary to decide which of these two factors should be excluded (Table 16).

According to the multicollinearity coefficient b* we excluded the factor X1 - the number of M&A(mergers and acquisitions) deals (Table 17).

According to the p-value criterion, we saw that factors X2 and X4 do not satisfy its condition at this stage, so we began to sequentially exclude them depending on the value of p-value in descending order. The first factor that was excluded was factor X2 - total industry M&A (mergers and acquisitions) volume (Table 18).

After excluding the factor X2, it turned out that X4 satisfies the condition of the p-value criterion.

The following results were obtained. The coefficient of determination R^2 = 0,6 (0,6<= R^2<0,7), that is, in this model is moderately strong relationship, factors X3 - total volume of all M&A (mergers and acquisitions) deals and X4 - industry volume of all domestic deals moderately strong impact on Y3 - consolidated budget revenues. In 60% of the cases, changes in Y3 are caused by the impact of X3 and X4.

The regression equation has the following form:

Y = 3.1527 + 0.000003×X3 – 0.000005×X4.

Thus, the total volume of all mergers and acquisitions and the industry volume of all domestic transactions have a moderately strong impact on the revenues of the consolidated budget of the Russian Federation.

4) Consider the Y4 indicator of the foreign trade balance (Table 19).

When analyzing the multicollinearity of the explanatory variables (X), it was found that the indicators X1 and X3 were multicollinear. It was necessary to decide which of these two factors should be discarded (Table 20).

According to the multicollinearity coefficient b*, we discarded the factor X3 - the total volume of all M&A (mergers and acquisitions) deals (Table 21).

According to the p-value criterion, we saw that all three of the remaining factors do not meet its condition at this stage, so we began to sequentially exclude them depending on the value of p-value in descending order. The first factor X1 was excluded - the number of mergers and acquisitions (Table 22).

Then we excluded the factor X4 - the industry volume of all domestic transactions (Table 23).

The following results were obtained. Determination coefficient R^2 = 0.02195 (R^2<0.4), that is, there is no strong relationship in this model. By default, we can conclude that the selected X2 - total industry M&A has no significant effect on Y4 - foreign trade balance. It is impossible to construct a regression equation using this model. All of the considered factors do not significantly affect the change in the balance of foreign trade.

5) Let us consider the indicator Y5 - production of oil with gas condensate (Table 24).

When analyzing the multicollinearity of the explanatory variables (X), it was found that the indicators X1 and X3 were multicollinear. It was necessary to decide which of these two factors should be excluded (Table 25).

According to the multicollinearity coefficient b*, we discarded factor X1 - the number of M&A(mergers and acquisitions) deals (Table 26).

For the p-value criterion, we excluded the factor X2 - total industry M&A(mergers and acquisitions) volume. The remaining factors satisfy the p-value criterion (Table 27).

The following results were obtained. Determination coefficient R^2 = 0.51 (0.3< R^2<0,6), that is, in this model a moderate relationship, factors X3 - total volume of all M&A deals and X4 - industry volume of all domestic deals moderately strongly affect Y5 - oil production with gas condensate. In 51% of cases, changes in Y5 are due to the impact of X3 and X4.

The regression equation is as follows:

Y = 399.9470 + 0.0002×X3 – 0.0002×X4.

So, the total volume of all mergers and acquisitions and the industry volume of all domestic deals are moderately strongly influenced by gas condensate oil production.

Thus, a correlation and regression analysis of the impact of integration processes in the oil market on the indicators of socio-economic development of the country was conducted. GDP and consolidated budget revenues are moderately strongly affected by the integration processes in the oil market. At the same time, integration processes at the oil market do not have a significant impact on economic growth and foreign trade balance.

Oil and gas are and will be an important source of energy in the near future. A special impact on the world market of oil and gas products, its structure, the degree of liberalization, pricing had a global financial crisis caused by the impact of the COVID-19 pandemic.

The basic principles of pricing in oil production are:

- the Brent crude oil benchmark is recognized as a nominal value. Brent oil is an indicator of quality and, at the same time, an object of economic relations.

- the pricing system established in the country depends on changes in Brent prices;

- the price fixed in the sales contracts is determined by the following principle: a premium in American dollars is added or subtracted to the price of Brent oil. In this case, the difference is determined with the help of oil quality indicators.

Conclusion

Thus, the results of the Russian oil and gas complex have a significant impact on the economic development of the country, so it is important to organize and implement an effective and competent state management in this industry; regularly analyze the competitive environment of the oil market; conduct a correlation and regression analysis of the impact of integration processes in the oil and gas complex on socio-economic indicators.

The analysis of the competitive environment of the oil market is carried out with the help of market concentration indices CR-3, CR-4, Herfindahl-Hirschman index, Hall-Teidman index. Having analyzed the competitive environment of the Russian oil market in 2015-2019, it was possible to find out that the Russian oil market in 2015-2016 was moderately concentrated, in 2017-2019 - highly concentrated; during the whole period under consideration the Russian oil market was a monopolistic competition market; 4 of 7 companies under consideration («Rosneft», «Lukoil», «Gazprom Neft», «Surgutneftegas») had a significant influence.

As a result, it was found that consolidated budget revenues integration processes in the oil market had a moderately strong impact on GDP and a moderate impact on the volume of oil production with gas condensate. At the same time, integration processes on the oil market had no significant impact on economic growth and foreign trade balance.

References

• Alamgir, F., & Amin, S. B. (2021).The nexus between oil price and stock market: Evidence from South Asia. Energy Reports, 7, 693-703.

• Alao, R. O., & Payaslioglu, C. (2021).Oil price uncertainty and industrial production in oil-exporting countries. Resources Policy, 70, 101957.

• Axon, C. J., & Darton, R. C. (2021).Sustainability and risk – a review of energy security. Sustainable Production and Consumption, 15.

• Castillo, P., Montoro, C., & Tuesta, V. (2020).Inflation, oil price volatility and monetary policy. Journal of Macroeconomics, 66, 103259.

• Ederington, L. H., Fernando, Ch. S., Lee, Th. K., Linn, S. C., & Zhang, H. (2021). The relation between petroleum product prices and crude oil prices.Energy Economics, 94, 105079.

• Esen, O., Özer, S. C., Soylu, A., Rend, A. R., & Fişne, A. (2020). Geological controls on gas content distribution of coal seams in the Kınık coalfield, Soma Basin, Turkey. International Journal of Coal Geology, 231, 103602.

• Jiang, W., & Liu, Y. (2021). The asymmetric effect of crude oil prices on stock prices in major international financial markets. The North American Journal of Economics and Finance, 56, 101357.

• Karatayev, M., & Hall, S. (2020). Establishing and comparing energy security trends in resource-rich exporting nations (Russia and the Caspian Sea region). Resources Policy, 68, 101746.

• Khalfaoui, R., Sarwar, S., & Tiwari, A. K. (2019).Analysing volatility spillover between the oil market and the stock market in oil-importing and oil-exporting countries: Implications on portfolio management.Resources Policy, 62, 22-32.

  Google Scholar

  Crossref

• Klychova, G., Zakirova, A., Sadrieva, E., Avkhadiev, F., & Klychova, A. (2019a). Reporting in the area of sustainable development in agribusiness.E3S Web of Conferences, 91, 06002.

• Klychova, G., Zakirova, A., Mannapova, R., Pinina, K., & Ryazanova, Y. (2019b). Assessment of the efficiency of investing activities of organizations.E3S Web of Conferences, 110, 02075.

• Klychova, G., Zakirova, A., Shadieva, M., Raheem, U., & Zakirov, A. (2021). Analysis of the oil market and the impact of integration processes on socio-economic development indicators of Russia. E3S Web of Conferences 284, 07012.

• Lin, B., & Bai, R. (2021). Oil prices and economic policy uncertainty: Evidence from global, oil importers, and exporters’ perspective. Research in International Business and Finance, 56, 101357.

• Leontieva, J., Zaugarova, E., Klychova, G., Zakirova, A., & Klychova, A. (2018). Tax system of consolidated taxpayers groups in Russia and ways of its improvement. MATEC Web of Conferences, 170, 01087.

• Liu, F., Shao, Sh., & Zhang, Ch. (2020). How do China's petrochemical markets react to oil price jumps? A comparative analysis of stocks and commodities. Energy Economics, 92, 104979.

• Liu, W., & Ramirez, A. (2017).State of the art review of the environmental assessment and risks of underground geo-energy resources exploitation. Renewable and Sustainable Energy Reviews, 76, 628-644.

• Raheem, I. D., Bello, A. K., & Agboola, Y. H. (2020).A new insight into oil price-inflation nexus. Resources Policy, 68, 101804.

• Rahman, M. S., Khan, F., Shaikh, A., Ahmed, S., & Imtiaz, S. (2019). Development of risk model for marine logistics support to offshore oil and gas operations in remote and harsh environments. Ocean Engineering, 174, 125-134.

• Rajavuori, M., & Huhta, K. (2020). Investment screening: Implications for the energy sector and energy security. Energy Policy, 144, 111646.

• Schimmel, M., Liu, W., & Worrell, E. (2019). Facilitating sustainable geo-resources exploitation: A review of environmental and geological risks of fluid injection into hydrocarbon reservoirs. Earth-Science Reviews, 194, 455-471.

• Talipova, A., Parsegov, S. G., & Tukpetov, P. (2019). Russian gas exchange: A new indicator of market efficiency and competition or the instrument of monopolist? Energy Policy, 135, 111012.

• Tang, Ch. S., & Veelenturf, L. P. (2019). The strategic role of logistics in the industry 4.0 era. Transportation Research Part E: Logistics and Transportation Review, 129, 1-11.