s2fieldsofstudy: Economics Business

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Correlation-and-regression analysis of the influence of macroeconomic factors on capital structure of Russian corporations under crisis conditions

The study reveals the influence of macroeconomic factors on decisions about the optimal capital structure formation under financial globalization, in view of everchanging factors of the external economic and geopolitical environment. The study is aimed at empirical testing of hypotheses on how the level of financial leverage of corporations depends on traditional determinants during and after the financial crisis under the emerging market conditions in Russia. The study deals with a large data set of 49 public joint stock companies from 7 leading Russian economic sectors for the period from 2011 to 2017. According to the correlation-and-regression analysis results (1) the use of traditional theories of capital structure under the conditions of current financial globalization in a country with a developing economy proves to be ineffective for the optimal capital structure formation (2) the corporate capital structure formation is strongly influenced by macroeconomic factors, which is most evidently manifested during and after the crisis (3) the financial crisis exerts a strong influence on the corporate capital structure (4) the determinant of stock market development has a significant influence on leverage and plays a prominent role in making financial decisions after the financial crisis.

Introduction
Under financial globalization, in view of ever-changing factors of the external economic and geopolitical environment, the modern economy dictates new rules of conduct at all levels, which results in the revision of conventional financial theories. The growing contradictions in economic, political, and social spheres in the course of development and intensification of the financialization process provoke new economic downturns and financial crises. As a result, the decisions on the choice of corporate financing should be based not solely on traditional approaches to the capital structure formation, but also on the exact reasons and key macroeconomic factors that influence the capital structure.
There are several theories of capital structure that explain the preferences and conduct of companies depending on the choice of corporate financing. The main scientific theories of capital structure are as follows: trade-off theory and pecking order theory. The authors of the first theory that emerged during the discussion of the Modigliani-Miller theorem Modigliani and Miller (1963) claim that in the formation of equity and debt capital, the balance shall be achieved between the tax shield benefits, that ensure tax saving in the case of raising loans, and bankruptcy risks resulting from excessive debt burden (Adair & Adaskou, 2015;Bradley et al., 1984;Duan et al., 2012;Jensen, 1986;Jensen & Meckling, 1976;Köksal et al., 2013;Kraus & Litzenberger, 1973;Kuhnhausen & Stieber, 2014;Matemilola et al., 2012;Morellec, 2003;Myers, 1977;Serrasqueiro & Caetano, 2015). Thus, the trade-off theory suggests that a company exchanges its benefits and costs for the debt and equity financing and finds an optimal capital structure, considering the tax benefits, bankruptcy, and agency costs. The second theory, which is based on the information asymmetry between investors of the corporation and its managers, deals with the distribution of financing sources in a hierarchical order from the least to the most attractive for the company, provided that internal sources are more preferable than external ones, while among external sources, debt is more preferable than the issuance of shares. Therefore, it is postulated that decisions on the capital structure are made at the expense of an unpromising choice between the firm and external investors. (Donaldson, 1981;Myers & Majluf, 1984;Myers, 1984;Beattie et al., 2006;Camara, 2012;Dincergok & Yalciner, 2011;Hackbarth, 2008;Park & Kim, 2009;Fedyk, 2014;Rihab & Lotfi, 2016).
These theories help to understand the corporate capital structure, as well as to reveal potential internal and external factors; however, they do not consider various companyspecific factors, which in turn lead to contradictory forecasts and ambiguous results under conditions of financial crises. Many researchers try to elucidate the relationship between capital structure and its determinants, substantiating the reasons and regular patterns of occurrence and influence of various internal and external factors, which can have impact on the choice of corporate financing (Antoniou et al., 2008;Belkhir et al., 2016;Booth et al., 2001;Deesomsak et al., 2004;Fan et al., 2012;Rajan & Zingales, 1995;Sbeiti, 2010). Furthermore, the identification of general reasons and macroeconomic consequences for capital structure in the conditions of a highly volatile financial market and, consequently, periodic financial crises, appears to be a little-investigated issue. In several existing studies on the specified issues the authors reach a consensus for the first time that economic downturns exert a significant influence on the company's financial behavior (Akbar et al., 2013;Duchin et al., 2010;Harrison & Widjaja, 2014;Iqbal & Kume, 2014;Judge & Korzhenitskaya, 2012).
The present study, which is based on the existing scientific descriptions and explanations of the factors that determine the choice of an optimal capital structure, as well as on earlier hypotheses and assumptions, contributes to the analysis of this important scientific issue. To date, there is an obvious lack of theoretical and mainly applied results of empirical research in this area, especially for domestic companies. In the domestic literature, the main attention is paid to the development of ideas on the procedure for choosing sources of financing and options for the theory of behavioral financial structures (Blank, 2017;Bocharov, 2009;Lisitsa, 2002;Stoyanova, 2010;Volkov & Nikulin, 2012); the study of the choice of the capital structure as a stage of strategic planning (Gubanov, 2007). This study tries for the first time, by means of correlation-andregression analysis, to identify any regular patterns of a financial crisis in the conditions when the forecasts arising from traditional theories of capital structure may fall short of expectations. Besides, the results of such analysis can be immediately put into practice by Russian corporations.
On the whole, the results of this study may be interesting for several reasons. Firstly, the Russian economy is an ideal environment to explore how determinants affect the corporate capital structure during the crisis, because the Russian economy is developing and therefore it is substantially affected by various macroeconomic factors and significantly dependent on the development of the global financial market and global corporate capital. Secondly, based on the findings, it will be possible to identify, whether the effects of such determinants increase or decrease before, during, and after the crisis. The comprehension of such complex relationships is crucial for analysis and neutralization of unfavorable consequences of the financial crisis when making decisions on the corporate capital structure formation. Thirdly, the issues on the optimal capital structure formation become highly relevant in the context of the global financial crisis, which made many companies and even entire industries struggle for survival. It is worth noting that this issue is practically not covered in studies. Most studies (Deloof & Su, 2003;Dong, 2010;Eljelly, 2004;Volkov & Nikulin, 2012) were based on data samples, not including crisis periods. However, it is deemed that the following main problem will be solved with the formation of companies' financial management policy: what influence does the financial crisis exert on the leverage of companies in order to ensure their effective performance.
This study was aimed at the empirical testing of hypotheses on how the level of corporate financial leverage depends on traditional determinants, based on the multipleregression model with panel data. Based on this study, it is possible to reveal the influence of a financial crisis on the relationship between company-specific factors, macroeconomic factors, determinants of stock market development, and corporate capital structure. In view of the dynamic relationship between leverage and its determinants, a target adjustment model is used in the study, which admits the deviation of companies from their optimal leverage so as to perform another adjustment in the course of time. GMM (generalized method of moments) is used to evaluate the dynamic model. This study uses a large data set from 49 companies, whose securities are admitted to the stock exchange trading, from 7 Russian economic sectors for the period from 2011 to 2017. The separation of data among industries will reveal significant differences between economic sectors depending on their future prospects.
The article has the following structure. The second part presents a summary review of the literature about the influence of external and internal factors on leverage within the framework of the main capital structure theories. The third part deals with the planning of study methodology and selection of variables. The fourth part presents the empirical finding of the study, including correlation-andregression analysis between variables. Finally, the last part summarizes and evaluates the findings.

Theoretical justification
Studies on the capital structure are based on two main competing theories, which try to explain the reasons for using leverage. Trade-off theory considers a combination of factors that collectively determine an optimal capital structure. Through constant maintenance of the company's assets and investment plans, the company optimizes its debt ratio, with account of the trade-off between costs and benefits of borrowing (debt financing) (DeAngelo & Masulis, 1980;Myers, 1984). The theory is based on the tax advantages of debt (income tax shield), which are balanced with financial costs arising from bankruptcy (Myers, 2003).
According to the authors of the second theory, it is necessary to choose a particular means for company's capital mobilization, based on the hierarchy of preferences ("pecking order"), according to which sources of financing are distributed in a hierarchical order from the least to the most attractive for the company, provided that internal sources are more preferable than external ones, while among external sources, debt is more preferable than the issuance of shares (Donaldson, 1981). According to the concept of asymmetric information (Myers & Majluf, 1984), if insiders are better informed about the company's affairs than external investors, it is most likely that the company's shares will be underestimated by the market. As a result, financing of investment projects through the issuance of shares may turn out to be so expensive for the company that winning over new investors (and simultaneously losing old ones) may exceed the project's net present value (NPV) due to the underestimation of shares. For this reason, the issuance of shares is considered to be the least attractive source of financing. On the contrary, internal sources of financing are not affected by information asymmetry and should be used in the first place for the financing of company projects. The pecking order theorists suggest that one of the reasons why company's managers choose internal financing as a priority is their subjective motivations to follow the path of least resistance, evading the stock market discipline, as well as a relief from the costs related to the issuance of new securities (Brealey & Myers, 2003).
Thereafter, the hypothesis about the impact of information asymmetry on the choice of financing source was transformed into a signaling theory, according to which not objective, but subjective factors determine the choice of capital structure due to the unequal availability of information about the market situation for market participants (Brigham & Ehrhardt, 2008). It is assumed that managers tend to maximize the value of shares owned by current shareholders at any given moment, rather than prospective shareholders. Therefore, if a firm has good prospects, its managers will not undertake the issuance of new shares, preferring debt financing, but in the case of poor prospects it will be more profitable for current shareholders to finance at the expense of the issuance of new shares. As a result, the issuance of new shares will be regarded by investors as an alarm signal, and the share prices usually drop after the announcement of a new issuance.

Trade-off theory
According to the trade-off theory, company growth potential is negatively correlated with leverage, because companies with better future prospects, as a rule, face greater financial difficulties and agency costs between shareholders and bondholders due to the risk shifting (Jensen & Meckling, 1976;Titman & Wessels, 1988) or underinvestment (Myers, 1977). Consequently, companies tend to underuse the debt financing when building an optimal capital structure. The hypothesis about the sources of financing at different life-cycle stages of a company was made by Scott and Bruce (1987), who believe that at the initial development stage a company arranges financing with its own resources until a steady cash flow is generated from operating activities. During the adolescence-stage, a company can already use bank loans as debt financing. The development of credit history takes place during this period, with a focus on further increase of the amount of available financing, extension of loan terms, and lowering of rates. At the next growth stage, a company can use bonded loans, which will attract additional financial resources, as well as will help the company in improving its internal processes and establishing cooperation with the investment-banking community. Further development of a company is characterized by the opportunity to use the advantages of foreign capital markets for long-term fund raising through the issuance of various corporate securities.
According to the trade-off theory, the company's tangible assets have an extremely controversial influence on leverage: companies with large tangible assets may have a higher leverage due to lower agency costs and less financial difficulties, which is expressed by a positive correlation between materiality and leverage (Frank & Goyal, 2009;Rajan & Zingales, 1995;Titman & Wessels, 1988). However, the disciplinary role of debt becomes less important in companies with large tangible assets, which implies a negative correlation between materiality and debt (Grossman Sanford & Hart, 1982;Titman & Wessels, 1988).
The trade-off theory suggests that profitability has a positive impact on leverage, because profitable companies with large cash holdings usually use debt financing to ensure the optimal use of free cash flow (Fama & French, 2002). This is explained by low bankruptcy risks, tax saving and low agency costs.
The company size is directly correlated with leverage, because large companies, as opposed to small-and medium-sized enterprises, usually have a low probability of default and, thus, low financial costs, which makes it possible for large companies to maintain high leverage ratio. (Ang et al., 1982;Bevan & Danbolt, 2002;Chkir & Cosset, 2001;Titman & Wessels, 1988;Warner, 1977). In confirmation of the above statement, Marsh (1982), Bennett and Donnelly (1993), making a study on how company size influences capital structure, revealed that large companies have a quite significant amount of debt obligations. Consequently, following this logic, company size must have a positive correlation with leverage.
Supporters of the trade-off theory think that risk has a negative impact on capital structure. This is conditioned by the fact that companies with higher income volatility carry higher bankruptcy risks (Bancel & Mittoo, 2004;Brierly & Bunn, 2005;Frank & Goyal, 2009;Harris & Raviv, 1991). According to some researchers, the companies' liquidity ratio is inversely proportional to financial leverage, which means that companies with high liquidity tend to use less debt (Singhania & Seth, 2010).
Macroeconomic factors (particularly, inflation and GDP growth) can also have a significant impact on the company's leverage ratio. Another important macroeconomic factor that influences the corporate structure is capital market development. Thus, inflation has a profound positive impact on capital structure, because high inflation reduces the real value of debt, prompting companies to take on more debts (Frank & Goyal, 2009;Hanousek et al., 2011;Köksal et al., 2013;Kuhnhausen & Stieber, 2014;Sett & Sarkhel, 2010). The researchers believe that GDP growth has an ambiguous influence on capital structure. Countries with high GDP growth rates have better opportunities for company growth (which has a negative impact on leverage), but at the same time they are more profitable (which has a positive impact on leverage). For example, Gajurel (2006) holds that there is a negative correlation with total debt ratio and shortterm debt ratio, but this has a positive impact on long-term debt ratio. Finally, the stock market development may influence the capital structure. According to the tradeoff theory of capital structure, the influence of stock market development is ambiguous. According to Bokpin, these variables are not correlated (Bokpin, 2009). However, Sett and Sarkhel (2010) show that there is a negative correlation between capital structure and stock market development. Moreover, according to Duan et al. (2012), financial market index has negative correlation with debt ratio. Companies choose long-term loans in the case of higher percentage of non-state economy and greater development of a financial sector.

Pecking order theory
Firstly, the pecking order theory postulates that there is a positive correlation between growth potential and leverage, arguing that additional resources are required for company growth in order to finance future investments (Frank & Goyal, 2009;Myers & Majluf, 1984). Some researchers believe that the company's future growth prospects have a positive correlation with debt obligations, especially with short-term ones (Chen, 2003;Hall et al., 2000). Moreover, agency problems and the resulting costs of financing may decrease if a company issues short-term rather than long-term obligations. Thus, company growth should lead to the upswing of demand for additional sources of financing, which in turn will necessitate the use of debt financing sources.
Secondly, materiality has a negative impact on capital structure, because companies with a high ratio of tangible assets, as a rule, have less information asymmetry that reduces the capital value (Frank & Goyal, 2009). In general, it might be expected that tangible assets would have less information asymmetry than intangible assets. However, this might not happen-therefore, it is impossible to make an accurate forecast whether information asymmetry will always be lower, thus assuming an inverse correlation between materiality and leverage.
Thirdly, the pecking order theory supposes that profitability has a negative impact on leverage. This is conditioned by the fact that high profit is generated by a larger amount of internal resources for project financing and, consequently, with a lesser demand for debt financing (Myers & Majluf, 1984). Profitable firms with a certain level of profit can rely primarily on this profit, rather than on such external financing sources as credit, while companies with low profit margin are more dependent on credit resources due to the lack of own funds (Gaud et al., 2005). To confirm this standpoint, Cassar and Holmes (2003) put forward an assumption that profitability has a negative impact on the short-term and long-term obligations, and therefore there must be a negative correlation between profitability and leverage.
Fourthly, the pecking order theorists believe that company size has a negative impact on capital structure, because large companies, as a rule, experience less information asymmetry, reducing the capital value (Fama & Jensen, 1983;Rajan & Zingales, 1995). As a result, companies will issue more shares for the financing of their long-term capital requirements. But as for small-and medium-sized enterprises, there is empirical confirmation of negative correlation between company size and short-term borrowing, but a positive correlation between company size and long-term borrowing (Cassar & Holmes, 2003;Esperanca & Mohamed, 2003;Hall et al., 2000).
Fifthly, risk is positively correlated with leverage, because companies with volatile income suffer more from an ill-chosen source of financing (Frank & Goyal, 2009).
Sixthly, according to the pecking order theory, liquidity has a negative impact on the company's capital structure. More liquid companies may use internal resources as a potential tool for capital mobilization, and therefore they will depend on debt financing to a lesser extent.
It is important to note the following macroeconomic factors that have a significant impact on leverage in terms of the pecking order theory. Inflation rate is negatively correlated with overall leverage and the short-term debt ratio, but it has a positive impact on the long-term debt ratio (Gajurel, 2006). However, empirical findings are different: according to Bastos et al. (2009), inflation does not influence capital structure; according to Frank and Goyal (2009), inflation is correlated with market leverage, but it does not influence leverage. Camara (2012) argues that macroeconomic conditions, including inflation rate, have a significant correlation with capital structure. Some authors believe that there is a profound negative correlation between capital structure and GDP, as well as GDP growth (Bastos et al., 2009;Bokpin, 2009;Camara, 2012;Dincergok & Yalciner, 2011). As for correlation between stock market development and leverage, some researchers believe that stock market development is positively correlated with capital structure (Dincergok & Yalciner, 2011). Moreover, market capitalization as indicator of stock market development has a positive impact on capital structure (Gajurel, 2006).
Within the framework of each mentioned theory of capital structure, the specified factors are presented in the Table 1.

Data selection and research methodology
This study is based on data from 49 corporations belonging to 7 leading Russian industries for the period from 2011 to 2017: oil and gas industry (OIGA), fuel and energy complex (FEC), chemical industry (CHEMIC), agro-industrial complex (AGRO), telecommunications industry (TELE), metallurgical industry (METALL), transportation services (TRANS) ( Table 2). The information on companies is collected from the Moscow Exchange Database and the server that discloses information published by issuers on the websites of authorized news agencies Interfax, AK&M. The main selection criteria for companies were as follows: listing of companies' securities for trading and availability of financial reports subject to mandatory disclosure for a period of at least 5 years. Analysis does not include banks, lending agencies, and financial companies, because their capital structure is incomparable with non-financial corporations.   Financial leverage ratio is usually chosen as a dependent variable of the multiple regression analysis of the company's capital structure. In this study, the ratio of total debt to total assets (TDTA) is defined as financial leverage: As independent variables of the model were chosen factors most frequently encountered in studies devoted to the current subject matter, and namely: company growth potential, share of the company's tangible assets, profitability, company size, risk, and liquidity. As macroeconomic factors used annual GDP growth and inflation rate, as well as market capitalization as indicator of stock market development.
Growth potential is calculated as annual growth of the company's total assets (gross total assets).
Materiality is evaluated as net fixed assets divided by total assets (NFATA). ROA (return on total assets) calculated as the ratio of EBIT (earnings before interest and taxes) to TA (total assets) is used as a proxy variable of profitability.
The company size is presented as net fixed assets index. Liquidity is calculated as the ratio of current assets to current liabilities (CL). Financial risk (FR) is used in this study as an indicator of company's risk, demonstrating which share of company's assets is being financed through the applied capital (equity capital and long-term liabilities).
Macroeconomic independent variables of this study are used as indicators of the dynamics of Russian stock market capitalization (SMC) to GDP, as well as indicators of annual GDP growth (GDPG), inflation rate (INFL).
The panel data set was processed with Microsoft Excel Office Suite, and the multiple regression models were developed with the software package Statistica.
Descriptive statistical samples of this study are presented in the Tables 3, 4, and 5, based on the periods before and after the crisis, as well as depending on economic sectors. A quick analysis of the tables shows clear signs of the imminent onset of the 2014 crisis and the impending recession of the Russian economy. In 2014, the Russian economy faced a serious monetary and financial crisis that escalated into a deep economic crisis. The sharp weakening of the Russian ruble against foreign currencies caused by the rapid decline in world oil prices, as well as the introduction of economic sanctions against Russia, led to many adverse consequences, in particular, an increase in inflation, a decrease in consumer demand, and an economic downturn. These factors could not but affect the activities of corporations in the leading sectors of the Russian economy.  Table 3 contains the mean values of these maximum, minimum values, as well as the average values of each variable for individual periods (pre-crisis, post-crisis, and general periods). The analysis of data given in Table 3 demonstrates that in more than half of the cases the average value of indicators is close to median. This indicates a relatively uniform sampling, which consequently demonstrates general trends in the capital structure formation of companies from various branches. The overall average value of the dependent variable TDTA (total debt to total assets) is 1.73%. According to Table 3, the crisis has a minimal impact on financial leverage, as this ratio was 1.73% before the crisis and 1.62% after the crisis. However, a close statistical analysis of the sample standard deviation shows that the variance of leverage ratio between companies became slightly larger after the financial crisis  (1.10%), than before the crisis (0.57%). But at the same time, the difference between maximum and minimum leverage values increased after the crisis, which can indicate that in the conditions of persistently high-volatile Russian economy, companies quickly recover from crisis shocks and experience a temporary revival and a certain degree of stabilization. Thus, although overall average values show a slight impact of the 2014 crisis on leverage ratios, such results may entail significant deviations, reduced through the averaging, which implies a further more extended and detailed check of the study samples depending on the period and individually for each economic sector. In spite of this deviation, quite interesting findings are presented in Table 3. Thus, after the crisis, growth potential insignificantly improved (from 0.20 to 0.09%), materiality slightly deteriorated (from 0.72 to 0.69%); liquidity increased from 2.86 to 3.88%, and net fixed assets index increased from 0.77 to 0.99%; financial risk (0.79%), as well as profitability remained at the same level. Although some of these changes forecast a lessening demand for debts, others lead to opposite effects. Thus, the influence of a crisis on financial leverage will depend on the predominance of particular factors. According to Table 3, changes in macroeconomic factors are deemed to be significant and are manifested through low value of average GDP growth (0.96%) with a relatively high standard deviation (2.66%). It may be concluded that Russia in view of its raw-material oriented economy and the lack of solid investments under economic sanctions cannot maintain a high GDP growth rate. Although this indicator shifted towards improvement before the crisis (3%), the average GDP growth became negative (− 1%) in the post-crisis period. This is confirmed by adverse consequences of the financial crisis for the Russian economy. Low GDP growth was followed by high average inflation (7.33%) and low but relatively stable values of market capitalization (37% before the crisis and 39% after the crisis). Thus, Russian corporations in the leading economic sectors may be characterized as unattractive for foreign investors, underestimated by the market with missed investment opportunities and unrealized development potential during the whole study period. Table 4 shows descriptive statistics of the study samples for independent and dependent variables, arranged by years (mean values). Although some variables underwent considerable changes during the crisis in 2014, the average leverage value did not change significantly even during this period. Crucial changes affected primarily macroeconomic indicators: dynamics of Russian stock market capitalization to GDP deteriorated significantly in 2014 and was 29%, which is 21% less than in 2011; although GDP growth rate was low in 2011, it deteriorated since 2014 and even became negative; a surge of inflation was observed in 2014 (from 6.10 in 2011 to 11.36% in 2014), and by 2017 inflation dropped sharply to 2.52%. The company growth potential in 2014 was also negative; profitability and materiality worsened. The net fixed assets index as of 2011 amounted to 0.76%; however, its value became extremely unsatisfactory in 2014 (1.09%). This means that during the crisis companies did not follow the general rule of long-term investment generation, primarily at the expense of the most reliable sources of financing, that is, at the expense of equity capital. Based on the analyzed data in Table 4, it is possible to make an assumption about the influence of the financial crisis primarily on macroeconomic indicators and the overwhelming majority of analyzed variables. Table 5 reflects the consequences of the financial crisis in 2014 on domestic corporations in the leading economic sectors. The overview of indicators shows that there are different trends depending on economic sector, but at the same time it is possible to detect some common patterns of the influence of the financial crisis on the development and functioning of public companies in most branches. Firstly, growth potential of the companies belonging to the fuel and energy complex (FEC) considerably worsened from 0.31 to − 0.15% and materiality deteriorated from 0.52 to 0.43%, while leverage ratio increased sharply after the crisis from 0.66 to 1.35%. This may be explained by the fact that in spite of poor investment prospects caused by low growth potential, companies of this branch have high operating costs that require financing through various forms of government support, in order to do away with insufficient internal resources. Russian companies in the chemical industry (CHEMIC) demonstrated a slight decrease in growth potential from 0.51 to 0.21%, and decrease in profitability from 0.15 to 0.13%. The same situation is observed in the metallurgical industry (METALL). However, as opposed to the chemical industry, the average financial leverage in the metallurgical industry surged dramatically from 2.31 to 4.07. On the contrary, the average leverage of the transportation companies (TRANS) significantly decreased from 3.69 to − 0.11. This may mean that after the crisis, transportation companies became more profitable. The key drivers of the Russian economy-the oil and gas industry (OIGA)-suffered slightly during the crisis. The average leverage remained almost unchanged after the crisis. Minor changes were observed in the average values of growth potential, materiality, ROA, net fixed assets index, liquidity, and financial risk. However, Russian companies in the agro-industrial complex (AGRO) experienced extreme consequences due to the financial crisis: the averaged liquidity ratio increased significantly from 4.59 to 7.6%, while the leverage ratio decreased from 1.61 to 0.85%. Average liquidity of companies in the telecommunications industry (TELE) also increased sharply from 1.97 to 5.2%, while ROA increased from -0.02 to 0.07%. This may be evidence of the increase in solvency of companies from the specified industries; however, a sharp increase in liquidity can also predict the formation of an irrational capital structure, which implies the insufficiently effective use of negotiable assets or short-term financing. Moreover, the agro-industrial and telecommunications industries display more caution in the use of leverages after the financial crisis. Table 5 also allows us to draw conclusions about the role of traditional determinants in each sector of the economy. For example, ROA dynamics became positive in the fuel and energy complex and the telecommunications industry after the 2014 crisis, as well as significantly improved liquidity in these industries. In addition, after the crisis in the agro-industrial sector, the difference in the liquidity indicator was 3 points. This may mean that in conditions of financial uncertainty and high volatility of the financial market, the Russian economy-using the example of the main catalysts of the domestic economy-the fuel and energy complex, and the innovative information industry, began to rely less on foreign capital due to the greater dependence on the mobilization of domestic savings for investment purposes. This means that under the current restrictions and sanctions, the Russian economy acquires hidden benefits from the high volatility of both the process of financial globalization itself and from its consequences-the financial crisis (Metel'skaya, 2018).

Empirical findings: results and discussion
Pearson's correlation analysis was carried out as the first step of this study, which helped to detect the most interconnected indicators, to estimate trends of the analyzed correlations, as well as to determine the observations, the elimination of which from the population of analysis leads to significant changes in the ratio of estimated correlations. The findings differ depending on the study periods (general period from 2011 to 2017; pre-crisis period from 2011 to 2013 and post-crisis period from 2015 to 2017). Based on the matrix of paired correlation coefficients (Table 6), no strong dependences of macroeconomic indicators were revealed for the general period.
In the post-crisis period, the most significant indicators of correlation are macroeconomic factors, which show a strong correlation with each other, in particular, the GDP growth rate has a significant positive correlation with SMC (0.81), while inflation has a strong opposite impact on SMC (− 0.93) and the GDP growth (− 0.53). It may be noted that before the crisis in 2014, the company size significantly influenced its growth potential, as well as the company's choice of assets that are financed through the applied capital (equity capital and long-term liabilities). As opposed to the entire period, macroeconomic factors were distinguished by strong and different mutual influence.
According to the observations made since 2014, inflation has a strong negative impact on the GDP growth (− 0.98) against the background of an obviously weakening correlation between the GDP growth, inflation and the dynamics of Russian stock market to GDP (0.50 and − 0.65, respectively).
The findings show the importance of the capital structure formation in the conditions of a financial crisis and other external unfavorable effects for the national economy. The findings also indicate a certain importance of the national economy's development insofar as it represents an emerging financial market with capability for rapid adaptation to external shocks or without such a property. Macroeconomic factors, determining the capital structure, play a prominent role in the making of financial decisions, particularly during the period when the functioning of the Russian stock market is actually restricted due to the financial crisis of 2014 and the imposition of sanctions. Proceeding from the correlation matrix, it may also be concluded that after the crisis, methods and strategies of the optimal capital structure formation were significantly revaluated by many companies, which resulted in the dramatic change of the nature of impact of financial risk on profitability (from − 0.15 to 0.15), which suggests some caution when making a choice concerning the potential loss of financial resources and debt financing. Moreover, the financial crisis made an impact on the companies' ability to settle their obligations fully and within the time limit, depending on their overall performance and competitive capacity. If during the pre-crisis period, a steady decline of the companies' current solvency (CL) was observed under the competitive growth (Return on Assets), after the crisis this ratio changed and lost significant value. Thus, the analysis let us identify the priority areas of financial management within the specified time period. A company has a low liquidity in the case of positive dynamics of profitability indicators, which makes it necessary to take managerial decisions aimed at the balancing of these indicators. In view of the post-crisis situation, the issues connected with the achievement of an optimal level of liquidity and profitability ceased to be the financial management priorities.
As the second step of this study, the regression analysis was performed to define the correlation ratio between variables by constructing a matrix of multiple correlation coefficients (Table 6), according to which throughout the entire study period, there is a statistical dependence between variables, and the indicators of the correlation ratio according to the Chedoke scale are predominantly high (0.7-0.9) and very high (0.9-0.99). In particular, a strong close relationship is observed between FR and NFATA indicators (0.99) in the general period (2011-2017); ROA and IPA (0.99) in the pre-crisis period (2011-2013), FR and GTA (0.99) in the post-crisis period (2015)(2016)(2017). The strong correlation between these variables means the following. The higher the risk of the company's activities, the greater the increase in net assets, which testifies to the increase in own funds in the process of carrying out financial and economic activities during the entire study period. A similar process can be traced in the post-crisis period. Thus, when the financial risk increases, companies have better growth opportunities. At the same time, in the pre-crisis period, the growth rate of profitability significantly influenced the increase in company size. These facts may be due to the following. Most companies consider the main negative consequences of the crisis to be a decrease in demand and an increase in production costs, which, in turn, put pressure on profitability. In addition, the lack of funding for new projects is cited as one of the most serious problems. This circumstance, given its negative impact on profitability, as well as the short-term orientation of a large number of shareholders, may have an extremely negative impact on the long-term growth opportunities of companies. At the same time, some companies, especially manufacturers of consumer goods operating in Russia, note the positive impact of devaluation on their prices relative to those companies that have a large share of imports in their purchases. In addition, the acceleration of inflation could threaten with a decrease in the parameters of the indexation of tariffs for electricity and gas for the population and industrial consumers. This could have a negative impact on the prospects for energy and gas companies, but for most sectors of the economy, it could mean additional cost reductions, which are so necessary in a crisis. Thus, under the current circumstances, despite the growing financial risk, most companies did not lose their growth prospects and increase their assets.
The regression models developed under such conditions are of high practical importance. The study gives an estimation of four variants of the model: the first variant includes only specific indicators for public companies, which usually influence the capital structure decisions (growth potential, materiality, profitability, company size, liquidity, and financial risk) (model 1); the second variant, along with the company-specific characteristics, includes also main macroeconomic factors (GDP growth and inflation), in order to determine the influence of the general economic environment on leverage ratios (model 2); the third model makes an emphasis on the application of borrowed funds, debt in the conditions of supervision over the stock market development (model 3); the fourth variant covers all listed variables (model 4). All 4 models are constructed under 3 scenarios: from 2011 to 2017, from 2011 to 2013, and from 2015 to 2017, which correspond to the periods before and after the financial crisis of 2014 and the entire study period.
Let us proceed to a more detailed analysis of the findings with regard to the hypothesis on the influence of a financial crisis on the capital structure formation.  Table 7 shows that for the entire period, the growth opportunity factor (GTA) in all models and for each period is negative and significant. This means that the crisis has had a negative impact on corporate leverage. The regression coefficient for GTA is negative and means that the more corporations increase their growth rate, the lower the financial leverage ratio becomes. Such signs are more consistent with the theory of compromise. However, in addition to the possibility of growth, profitability is also statistically significant (in Table 7, in bold). The profitability ratio for the financial leverage variable is positive: the higher the percentage of profitability of the corporation, the higher the level of financial leverage. These features are also characteristic of the theory of compromise and contradict the theory of hierarchy. It is interesting to note that profitability and growth opportunities lead to opposite effects on leverage. Thus, when building the optimal capital structure of a corporation, it is necessary to proceed from the prevailing factor. Probably, in this case, such variables compensate for each other, which may result in a minor impact on leverage.

Results and discussion for the full period
It should be noted that NFATA, IPA, CL, and FR are not significant for the total period. Similar results are shown by macroeconomic indicators (GDP growth, inflation growth rate, and financial market development growth rate). Thus, from a complete sample for the entire period, it is impossible to give a clear answer whether macroeconomic factors and financial markets benefit both debt and equity, in particular, and leverage, in general.  According to Tables 10, 11, 12, and 13, R2 is above 0.74, showing that 74% of the calculated parameters of the model explain the dependence and changes in the studied parameter-TDTA on the studied factors-GTA, NFATA, ROA, IPA, CL, and FR. The table shows that the adjusted coefficient of determination did not increase when a new explanatory variable was added.
From the analysis of models 1-4 for the period 2011-2017, the coefficient of determination (R2) shows how the conditional variance of the model differs from the variance of the real values of Y. Since R2 exceeds 0.5 and tends to 1, the conditional variance of the model is quite small and very likely that the model describes the data well.
In almost each of the four proposed model scenarios, the regression model explains 74% of the fluctuations in the dependent variable in the sample. This level is high enough for a model describing financial and economic processes, especially in a transitional economy.
Based on the complete sample of model no. 4 (Table 13) for the entire study period, the regression model is presented as an equation: where Y is the financial leverage ratio (TDTA); X2 is the growth potential (GTA); X3 is the return on assets (ROA). The inclusion of only these variables in the right part is due to the fact that only these features have a probability value p less than 0.05.
The significance test of the model coefficients based on t-statistics showed statistically significant variables of growth potential and profitability. The significance of the regression equation was evaluated with F-test, which showed that the model's quality is principally good. The adjusted R 2 = 0.735 is quite high. The model explains the behavior of the Y-index by 74%. The standard error amounts to just 3.037. Testing for the residual autocorrelation with 5% critical values of the Durbin-Watson statistics showed that there is no residual autocorrelation.   Based on the presented model, a direct correlation is established between leverage and profitability, which is inconsistent with the pecking order theory, but corresponds to the trade-off theory. The detected inverse correlation between leverage and growth potential determinants corresponds to the trade-off theory and is inconsistent with the pecking order theory.
Thus, it is possible to model a general policy for companies operating in crisis conditions: with an increase in the level of profitability by 1 point, an increase in the leverage ratio follows. On the contrary, the negative impact on the leverage is a decrease in the possibility of companies' growth.
In favorable conditions for the functioning of the Russian economy, companies should build their policies based on their size, taking into account the conflicting impact of macroeconomic factors on the capital structure. In particular, if the company size ratio increases by 1 point, there will be a proportional increase in leverage.
Results and discussion for the pre-crisis period According to Table 8, before the 2014 crisis, the statistically significant variables for leverage are NFATA, ROA, and IPA for models 1-3 (model 4 is not applicable, since the multicollinear SMC indicator was used in the construction). The share of company's tangible assets, as well as profitability is positively correlated with leverage. Liquidity has a negative and insignificant influence on leverage in all four models. Thus, the determinants of company's solvency before the financial crisis are more associated with the character of corporate assets and long-term savings, than short-term profit fluctuations.
Models 1 and 3 (Tables 14 and 15) show a strong negative influence of company's financial risk on leverage. Moreover, the financial leverage ratio is very high and negative. This significant reduction of financial leverage may be conditioned by the fact that public companies used more debt capital before the crisis, which in turn forced creditors to increase lending rate, considering that an additional risk premium was built into it. At the same time, return on equity decreased, because some part of the profit generated by equity capital went for the repayment of high loan interest rates. Macroeconomic variables (inflation and GDP growth) are significantly correlated with leverage during the pre-crisis period, which is consistent with the complete sample results for the entire period (model no. 2 according to Table  14). Besides, during the pre-crisis period, there was a strong positive influence of inflation on leverage. Such high inflation during the pre-crisis period can be explained by high collective consumption and relative certainty of loan offer. The development of the stock market (model no. 3, Table 15) is not significant and has a negative impact on leverage. So this does not support the hierarchy theory. This means that, as in Table 7 for the entire period (Table 7), and before the crisis, the development of stock markets does not benefit both debt obligations and equity capital, since equity markets are not able to fully collect information about issuing companies for banks and investors and, therefore, do not make external financing less risky.
It may be concluded that during the pre-crisis period, the capital structure decisions were more dependent on macroeconomic factors, and predominantly on specific internal factors that determined the demand for debts.
Since the variable SMC showed a high degree of multicollinearity, the values of model 4 to Tables 8 and 16 cannot be correct and cannot be applied. Based on the model no. 2 (Table 17)  where Y is the financial leverage ratio (TDTA); X3 is the return on assets (ROA); X4 is the company size (IPA); X6 is the financial risk (FR); X9 is the inflation rate (INFL). The significance test of the model coefficients based on t-statistics showed that the variables of return on assets, company size, financial risk, and all macroeconomic factors (GDP growth, inflation) are significant. The significance of the regression equation was evaluated with F-test, which showed that the model's quality is principally good. The adjusted R 2 = 0.89 is quite high. The model explains the behavior of the Y-index by 89%. As compared with the previous model, the standard error decreased and amounts to 2.677. Testing for the residual autocorrelation with 5% critical values of the Durbin-Watson statistics showed that there is no residual autocorrelation. Based on this model, a positive correlation is established between leverage and company size before the financial crisis, which corresponds to the trade-off theory, but is inconsistent with the pecking order theory.
Macroeconomic factors are extremely important for the determination of capital structure and show various degree and nature of influence on financial leverage. After the crisis, companies should be guided by the following scenario for building a capital structure: reducing the company size by 1 point has a positive effect on leverage. It is also necessary to pay attention to macroeconomic indicators.

Results and discussion for the post-crisis period
After the financial crisis of 2014, all models demonstrated that company growth potential does not have a significant influence on leverage, because the annual growth of company's total assets was not recorded both before and after the crisis, which is not consistent with the pecking order theory (Tables 9,18,19,20,21,22,23). In view of low profitability in the post-crisis period, the only alternative for companies is to borrow funds from creditors for the support of their business, rather than for the financing of their growth.
Profitability is still the most significant variable, but in the post-crisis period the positive influence on leverage significantly increased, as compared with the precrisis period (Tables 9,18,19,20,21,22,23). This correlation is inconsistent with the pecking order theory, suggesting that in the post-crisis period profitable companies with a certain level of profit cannot rely primarily and solely on this profit, and that they accumulate debt liabilities.
The company size in the pre-crisis period was more significant, as compared with the post-crisis period. Moreover, the materiality coefficient before 2014 was positive, in spite of some statistical insignificance demonstrated in some models. This indicator generally decreased in the post-crisis period and even exerted a significant negative influence on leverage in the complete sample according to Table 22 (model no. 4). Consequently, after the economic slowdown, large companies in the leading economic sectors were confronted with difficulties in raising funds      from banks due to the absence of proven solvency. Moreover, precisely big companies with a large share of tangible assets fall into the group, against which economic and financial restrictions (sanctions) were imposed during the crisis. This is consistent with the pecking order theory. The same as in the pre-crisis period, the variable of financial risk is still negative and has a significant influence on leverage. This is conditioned by the fact that since 2014 companies with higher income volatility carry higher bankruptcy risks.
Macroeconomic factors in the post-crisis period are as significant as in the pre-crisis period and have a prominent impact on the capital structure formation. But as opposed to the negative impact of GDP growth in the pre-crisis period, an obvious growth and a strong positive correlation between GDP growth and financial leverage were observed since 2014 (model no. 4 according to Table 22). According to the trade-off theory and the pecking order theory, large growth leads to adverse effects that can be mutually compensated, which eventually will exert an insignificant influence on leverage. Based on this analysis, in accordance with main theories of capital structure the specified position is not confirmed. However, it should be noted that these observations may be incorrect due to the use of a multicollinear variable of the GDP growth rate in the regression model 4. Therefore, this factor was excluded when constructing model 4 (Table 23). The estimation of the parameters according to the constructed model 2 (Table 19) is unbiased and effective.
The influence of inflation on leverage diminished after the crisis. As a rule, inflation in the post-crisis period is uncertain due to the low collective consumption and the uncertain magnitude and duration of the crisis. It should not be forgotten that some restrictions (sanctions) were imposed on a number of Russian companies. Under such conditions, the lowering of inflation leads to low short-term interest rates and the increase of demand for credit. Thus, following these consequences, it is implied that not only geopolitical and macroeconomic environment, but also microeconomic factors should be considered when making capital structure decisions.
From the analysis of model 3 (Table 21), the dynamics of the stock market capitalization remains insignificant and negatively affect the leverage, which is generally consistent with the results of the study of all periods. Taking into account the consequences of the financial crisis and the restrictions imposed, the issuing companies probably revised their capital structure formation strategies in terms of external investment on the basis of increased stock market volatility, lower trading volumes on the stock exchange, deterioration in capitalization indicators and weakening of the securitization of the economy. Thus, it should be concluded that geopolitical conditions create the background, the atmosphere of the market, or, in other words, the stock markets change only in the direction that the macroeconomic indicators set. In general, it can be stated that the impact of the macroeconomic environment after the crisis is increasing.
Based on the complete sample of model no. 2 (Table 19) for the period from 2015 to 2017, the regression model is presented as an equation.
The significance test of the model coefficients based on t-statistics showed that all variables are significant, except for company growth potential, profitability, and liquidity. The significance of the regression equation was evaluated with F-test, which showed that the model's quality is principally good. The adjusted R 2 = 0.85 is quite high. The model explains the behavior of the Y-index by 85%. As compared with the previous model, the standard error amounts to 3.249. Testing for the residual autocorrelation with 5% critical values of the Durbin-Watson statistics showed that there is no residual autocorrelation.
After the financial crisis, the inverse correlation was established between leverage and company size variable, which is inconsistent with the trade-off theory and corresponds to the pecking order theory. Macroeconomic factors are also significant, but the variable of stock market development increased significantly as compared with the precrisis period.

Conclusions
The main findings of this study, which are of applied relevance for Russian public companies in the leading economic sectors, are as follows: Based on the established direct correlation between leverage and company size before the crisis (2011-2013), it may be concluded that large companies had more opportunities for obtaining a loan in the conditions of relative certainty. But since 2014, the inverse correlation between debt load and company size proves that larger companies, whose securities were admitted to the stock exchange trading, have difficulties in increasing the debt load under harsh economic restrictions (sanctions) and highly volatile domestic stock market. This conclusion may serve as a recommendation on the business scale reduction for companies that plan to implement investment decisions mainly through the debt financing. In other terms, large companies probably pursue a less aggressive market policy after the financial crisis and therefore need less debt funds.
The significant correlation between leverage and company growth potential proves that the financial crisis does exert a considerable impact on the corporate financing policy aimed at the expansion of their business, growth of prospects, and improvement of competitiveness. The companies' policies are based predominantly on the fixed asset financing from their own sources, which is explained by potentially high agency costs and corporate management standards of the analyzed joint stock companies.
Based on the established concept of positive impact of leverage on ROA (return on assets) and the significant positive correlation between leverage and ROA established during testing the model, the following conclusion may be drawn up. Public joint stock companies prefer to build up ROA by increasing the financial leverage effect. Moreover, the financial crisis had a significant impact on the ROA growth predominantly used by the analyzed companies.
It is established that the dynamics of financial risk indicators has a significant negative impact on leverage both before the crisis and after the crisis. Thus, due to the continuity of most industrial technologies and aggressive nature of the environment where the fixed assets of joint stock companies from key industries circulate, it would be logical to recommend such industrial companies to use high depreciation rates for objects that are being operated in an aggressive environment or in the conditions of increased shift system. This would significantly reduce the risks of financial difficulties.
As follows from the conducted analysis, debt is mainly conditioned by the pre-crisis demand, when it is assumed that supply has the greatest elasticity and can satisfy any potential demand. Since 2014, supply and demand are also very important for the determination of financial leverage ratio. This is explained by the fact that in the postcrisis period, creditors began to focus more on company size in the light of high concern over bankruptcy risks.
All models demonstrated the increase of financial leverage ratio in the postcrisis period, which proves the influence of the crisis on the corporate capital structure. It is supposed that, as a rule, companies have to borrow more funds during and after crises, as their lower rate of return does not generate sufficient amount of internal resources for the maintenance of their long-term growth potential.
The detected significant influence of macroeconomic factors on the capital structure formation before, during and after the crisis proves that managers make financial decisions according to the source of financing, based on macroeconomic conditions and the specific character of their industry. The findings indicate a certain importance of the national stock market development insofar as it represents a developing and open market for external investors. The inflation rate has a positive influence on the developing market. The influence of GDP growth on leverage is strong and negative in the pre-crisis period, but significant and positive in the post-crisis period. Following the presented analysis, the traditional theories of capital structure do not correlate with empirical values.
External factors (SMC) determining the capital structure are not very important for financial decisions. This is confirmed by the fact that the dynamics of the stock market capitalization after the financial crisis has insignificant negative impact on leverage. With the help of information on the power and direction of such influence, managers can effectively and accurately choose a source of financing for stable and successful development of the company. Under economic restrictions and sanctions imposed on a number of large public joint stock companies from the leading Russian economic sectors in 2014, as well as under the conditions of highly volatile domestic stock market after the financial crisis, companies began place more emphasis on the macroeconomic environment characteristics during the formation of an optimal capital structure.
Author's contributions VVM independently collected, analyzed and interpreted case study data, developed and prepared the work, and contributed to the concept. The author(s) read and approved the final manuscript.

Funding
No funding has been received by the author for this research.

Competing interests
The author states that there are no competing interests.


extfieldsofstudy: Physics Geography

text:
Price transmission of Arabica coffee (Coffea Arabica) between Sumatera Utara and major export destination countries in Asia and Australia

Increasing global coffee consumption is driving the increasing need for world coffee. Indonesia is a coffee producing country in the world where one of Indonesia’s coffee production is produced in Sumatera Utara Province. This research was conducted to analyse the pattern of price transmission between the Sumatera Utara coffee export market and the Japanese, Singapore, Malaysia, Taiwan, Australia markets and to analyse whether there is an effect of coffee prices on other exporters’ markets, importer’s markets, import volumes and Indonesian Rupiah exchange rates against the US Dollar in the formation of coffee export prices on the Sumatera Utara exporter market. Based on statistical analysis using the Asymmetric Error Correction Model and Error Correction Model, it is known that the transmission pattern of coffee prices occurs symmetrically in the long run, whereas in the short term the price transmission occurs asymmetries. The formation of coffee export prices in the Sumatera Utara market in the long run is influenced by import prices in the Japanese and Malaysian markets, and the volume of exports, while in the short term is influenced by import prices in the Australian, Japanese, Malaysian markets, export volumes, and exchange rates Indonesian Rupiah against US Dollar.

Introduction
The growing coffee consumption phenomenon is driving an increase in world coffee needs. Consumption of beverages made from coffee bean extract worldwide reaches 2.25 billion cups per day [1]. Meanwhile according to the International Coffee Organization [2], world coffee needs in 2013 were in the range of 8.77 million tons. Coffee is a popular drink due to the unique and unique taste of coffee, besides the influence of traditional, historical, social and economic factors [3].
Indonesia is listed as one of the largest coffees producing countries in the world besides Brazil and Vietnam according to FAO data in 2014. This encourages Indonesia to place coffee as part of the main commodity of estate crops besides palm oil, rubber and cocoa. Noted the value of Indonesia's coffee export trade in 2016 through the 1.01 billion USD [4].
Sumatera Utara Province as one of the provinces in Indonesia, places coffee as one of the mainstay export commodities besides palm oil and rubber. This is supported by the potential of suitable and suitable areas for the development of coffee cultivation. The type of coffee that is widely cultivated in Sumatera Utara Province is a type of Arabica coffee. Based on data Table 1 from the Secretariat General of the Ministry of Agriculture [5] it is stated that during the period 2013 -2017 Sumatera Utara Province ranked first in Arabica coffee production with an average production of 49,698 tons per year and contributed 30.90% of the total national Arabica coffee production. The production capability of Sumatera Utara Province far exceeds the production of other provinces of Arabica coffee, making Sumatera Utara Province the largest producer of Arabica coffee in Indonesia.
The volume and value of Sumatera Utara Arabica coffee exports each year varies according to world market demand. Fluctuations in the volume and value of Arabica coffee exports during the period 2012 -2017 in more detail are presented in Table 2 below: Data in Table 2 proves the highest export volume in 2015 and the lowest in 2012, but the fluctuations formed in export volumes do not reflect the same pattern as the fluctuations in export values. Based on data from Table 2 and Table 3 it can be seen that there is a close relationship between the export value of Sumatera Utara Arabica coffee with the price of Arabica coffee on the world market, where in the comparison of export values in 2012 with 2013 it can be seen that with higher world coffee prices in 2012 compared to 2013 made the export value in 2012 higher than the export value in 2013 although in 2013 the export volume was greater than the export volume in 2012.
There is a significant difference between the price of coffee in the Sumatera Utara Province market compared to the price in the market of the export destination country. This reflects the influence of the strengths of each market and the influence of the length of the marketing chain. Price fluctuations that occur are a picture of conditions of supply and demand movements that have an impact on prices on world markets both in terms of exporters and importers. The direct relationship between the two markets will form perfect integration, making symmetry transmission of prices possible. The asymmetries in the transmission of prices between the two markets indicate the possibility of differences in market power. This study aims to analyse the price transmission between the export prices of the Sumatera Utara market coffee with prices in several markets of the export destination countries, namely Japan, Singapore, Malaysia, Taiwan, Australia and to analyse whether there is an effect of coffee prices on exporters' markets and in the importer's markets, the volume of imports and the value of the rupiah against foreign currencies in the formation of coffee export prices in the Sumatera Utara exporter market.

Location and sample
This research was carried out deliberately where the research area was chosen based on consideration of its potential. Sumatera Utara Province in this case was chosen with consideration of its potential as the largest Arabica coffee producer area in Indonesia. This study uses secondary data based on time series (time series) over a period of 5 years, from 2014 to 2018. The data examined are monthly data concerning the volume of Sumatera Utara coffee exports, the export price of Sumatera Utara coffee, the price of imported coffee in the markets of Japan, Singapore, Malaysia, Taiwan and Australia, and the exchange rate of the Indonesia Rupiah against the US Dollar which will be collected from the Indonesian Coffee Exporters Association (AEKI) of Sumatera Utara, International Coffee Organization (ICO), Directorate General of Plantations, the Ministry of Trade, and other accountable sources..

Data analysis
Analysis of the data in this study is divided into two analyses, namely: 1) analysing the transmission patterns of prices that occur between the Sumatera Utara market and the markets of the export destination countries, and 2) testing the factors forming coffee prices on the export market in Sumatera Utara.

Analysis of price transmission patterns of Sumatera Utara market and export destination country markets.
This analysis was carried out using the Asymmetric Error Correction Model (AECM) with the following model approach : Where: PES = export price of Sumatera Utara coffee (USD/Kg) PESt-1 = previous export price of Sumatera Utara coffee (USD/Kg) PIT = import price of main market destination country coffee (USD/Kg) ECT = Error Correction Term This method will describe the price fluctuations that occur in response to the price of the Sumatera Utara export market for price changes in the markets of the coffee export destination countries. Analysis through this model will separate the asymmetry patterns between long-term and short-term periods. Pattern symmetry or price asymmetry will illustrate the factors that influence price transmission

Analysis of the effects of several factors in formation of Sumatera
Utara coffee export price. The second analysis was performed using the Error Correction Model (ECM) with the long-term and shortterm models as follows : Long Where : PESt = export price of Sumatera Utara coffee (USD/Kg) PESt-1 = previous export price of Sumatera Utara coffee (USD/Kg) PEBt = export price of Brazilian coffee (USD/Kg) PEVt = export price of Vietnamese coffee (USD/Kg) PIAUt = import price of Australian coffee (USD/Kg) PISt = import price of Singapore coffee (USD/Kg) PIJt = import price of Japanese coffee (USD/Kg) PITt = import price of Taiwanese coffee (USD/Kg) Vit = export volume of Sumatera Utara coffee (Kg) NTt = exchange rate of Indonesian Rupiah against the United States Dollar (IDR/USD) ECT = Error Correction Term To find out the validity of the ECM model specifications, an Error Correction Term (ECT) coefficient test is performed. ECM model specifications are said to be valid if the ECT coefficient test results show a significant value.

Analysis of price transmission patterns between the Sumatera Utara market and the markets of export destination countries
Causality Test Results to analyse the pattern of the relationship between coffee prices in the Sumatera Utara market and the markets of the export destination countries are described in the following Table : International  In Table 4 it can be seen that a one-way relationship occurs between Sumatera Utara market prices and Australian market prices, Sumatera Utara market prices with Japanese market prices, two-way relationships occur Sumatera Utara market prices with Taiwan market prices. The Wald Test results in Table 5 show that overall the pattern of coffee price transmission between the Sumatera Utara market and the markets of the export destination country occurs symmetrically in the long term, whereas in the short term the price transmission occurs asymmetry. The difference in patterns that occur can be caused by the influence of the distance between the exporting country and the importing country which will have an impact on the cost of shipping goods. Besides that Vietnam's presence as another coffee producer in the Asia and Australia region will have an influence on the Sumatera Utara market.

Analysis of factors in formation of coffee export prices in Sumatera Utara
Based on the results of statistical analysis of data using the Error Correction Model presented in Table  6, it is known that the formation of coffee export prices in the Sumatera Utara market for long-term conditions is only influenced by import prices in the Japanese and Malaysian markets, and export volumes.


Institutional foundations of the business environment in the field of transport services

The development of the services market is impossible without improving the regulation of trade in services. Trade in services has been regulated bilaterally for many years. However, as a result of the emergence of a network of services based on the use of modern technical means of communication and computers, the effectiveness of traditional regulatory methods in this area has decreased, which, in turn, has hindered the full realization of the economic potential of scientific and technological progress in the service sector. As a result, it became necessary to create a universal effective mechanism by which trade relations in this area could be coord inated. An important component of the implementation of state and private projects in the field of socially significant public interests at an effective level is the study of the functioning environment of economic entities that is adequate to modern competitive conditions for the provision of socially significant services. First, we are talking about the formation of a business environment, the functioning conditions of which are set by the state. The article substantiates the need to form a system of relations between economic entities based on the principles of public-private partnership in the field of public passenger transport services. It is proved that this system is aimed not only at increasing the number of financially independent actors and their associations, but also at transforming state institutions that become active participants in market relations, interacting, and sometimes competing with the business community, transferring more and more functions to it even in those industries for which they previously traditionally held sole responsibility.

Introduction
The Russian system of public passenger automobile transport (PPAT), like any other socially oriented sphere of the economy, is in a state of permanent changes under the conditions of modern transformation processes. Not only are approaches to the provision of socially significant services changing, but significant institutional transformations are taking place in the industry, which do not always positively affect producers and consumers. The reduction of budgetary allocations, which traditionally ensure the reproduction of PPAT enterprises, significantly complicates the adaptation of industry entities to new socio-economic conditions, as well as the implementation of long-term projects by economic actors in order to provide high-quality transport services to the population. Meanwhile, individualization and differentiation of consumer demands, growing economic activity and spatial mobility of citizens present new requirements for the production processes and the provision of socially significant services, reinforcing the trend towards their commercialization.
This leads to the need for joint activities between government and business in the provision of socially important PPAT services, since without the involvement of the private sector for regular VIII International Scientific Conference Transport of Siberia -2020 IOP Conf. Series: Materials Science and Engineering 918 (2020) 012186 IOP Publishing doi: 10.1088/1757-899X/918/1/012186 2 passenger transport it is impossible to achieve the most complete satisfaction of human needs for services and ensure high-quality and uninterrupted operation of public transport.
The need to apply an entrepreneurial approach to implementing socially significant projects has actualized the problem of searching and developing a mechanism for cooperation between government bodies and economic actors aimed at the efficient use of budget resources, improving the quality of services, achieving social stability and sustainable economic development of the region as a whole. Such a mechanism can be based on the principles of public-private partnership between government and business, which has gained widespread over the past two years in certain sectors of the social and industrial infrastructure of Russian regions (private investment in energy, transport, construction, water supply, urban improvement and other areas) However, in the field of socially significant public passenger transport services, it is at the stage of its inception.
The purpose of the article is to investigate the features of the interaction of economic entities in the field of socially significant services using the example of public transport in the framework of the modern business environment. To achieve this goal, the article discusses the nature and characteristics of the business environment as an economic category, as well as the evolution of relations between the state and economic actors in the field of socially significant public transport passenger services.
The study is based on an interdisciplinary approach using the methods of logical-structural, situational, and comparative analysis. The obtained results and recommendations can become the basis for decision making by business entities in the field of transport services.

Literature review
Various researchers pay attention to the principle of forming an entrepreneurial environmentcompetition, which in its essence is an essential element of the growth of entrepreneurial activity, an incentive to improve and sustainability of business development. Maintaining the competitive nature of relations between economic entities is possible only with equal principles of management and the division of economic power between competitors. This means that all entities operating in a specific environment can apply for certain results of their activities.
Discussion is the assertion that the business environment is a favourable, comfortable situation. Some authors rightly point out in their scientific works that there are problems of research in this category due to the differentiated nature of the impact of elements of the business environment on its formation and development: conditions and factors of the business environment, determined by the special characteristics of socially significant services, can have both positive and negative effects for business [1,2,3]. For example, a reduction in budgetary allocations that traditionally provide for the reproduction of public passenger transport enterprises (PPAT) significantly complicates the adaptation of industry entities to new socio-economic conditions, as well as the implementation of long-term projects by economic actors in order to provide high-quality public transport services [4]. Meanwhile, individualization and differentiation of consumer demands, growing economic activity and spatial mobility of citizens present new requirements for the production processes and the provision of socially significant services, reinforcing the trend towards their commercialization [5]. Thus, the level of «comfort» of carrying out activities in a socialized entrepreneurial environment is determined not only by the established «rules of the game», but also depends on the results (experience) of managing economic entities in the territory of presence in the form of external, often constraining effects, contrary to the normal practice of market relations.

Methods of research
Summarizing the points of view presented by various researchers on the essence of the business environment as an economic category, the authors conclude that most interpretations come down to a structural-level characteristic of the business environment, which implies a consistent study of nano, micro, meso and macro levels of the environment, as well as internal and external factors impacts inherent in each of the specified levels [6,7]. According to the authors, this approach not only does not reflect the multidimensional nature of state participation in the production of socially significant  [8,9], but also does not reflect the role of the entrepreneur in shaping the functioning environment in order to determine the possibility of any influence on its individual elements, and also requires adjustment in terms of systematization of processes occurring at different levels of the environment.
In the author's understanding, the business environment is interpreted taking into account the specifics of the provision of socially significant services and is characterized from the perspective of the subject-object approach, which implies a system of interconnections of its main institutions (entities) that have goals and interests aimed at the same objects. The institutes represent both individual producers, consumers, and the whole industry, state. The objects are the principles, conditions, and factors of the implementation by the institutions of their activities, including all that based on which market relations arise.
The basic (typical) principles for the formation of the institutional environment are defined in the Constitution of the Russian Federation (Article 8) and imply a guarantee of the unity of the economic space, free movement of goods, services and financial resources, support for competition, and freedom of economic activity. The set of principles is subject to correction depending on the scope or type of ongoing economic activity. So, the specific (atypical) principles of carrying out activities in the field of socially significant services include competition, market power, personal interest, economic freedom, and innovativeness. Owing to the presence of special characteristics of socially significant services and the principles of the formation of the environment caused by them, manufacturers are forced to carry out their activities in predetermined conditions, which are also differentiated by the author (typical and atypical). It should be noted that manufacturers do not directly influence the principles and conditions for the implementation of their own activities, which are «dictated» by the state.
The conditions generated in the environment, determined by the principles of formation, in turn, are transformed into factors that directly affect the efficiency of the company in the analysed space. Factors are understood to mean a system of regional, sectoral, and productive impact on the ability of economic entities to carry out their activities (including their activity) [10]. The interdependencies between the principles, conditions, factors are determined by the nature and characteristics of production in the territory of presence. After the principles of the implementation of the activity are formulated, the entrepreneur evaluates the created conditions under which it will be organized, identifies the factors of external and internal environment [11].
Thus, the subject-object structure of the institutional environment proposed by the authors in the field of socially significant services is sensitive to any changes in the economic situation at any of its levels (nano, micro, meso, macro).

Results and discussion
Using the example of socially significant public passenger transport services, the author identifies the following constraints (elements) of the competitive environment that do not allow actors to ensure predictability of their development results: 1. "The paradox of a monopolistic competitor." The contradiction lies between the desire of the enterprise to take a monopoly position in the market and at the same time diversify its activities to strengthen market power. In the practice of managing PPAT operators, this contradiction is resolved by splitting a large enterprise into smaller ones or distributing the market between subsidiaries of a holding type to maintain its competitive space.
2. The restrictive tariff policy of the state. In modern practice of regular passenger transportation in the Russian regions, the level of involvement of the private sector in solving socially significant problems largely depends on the formation of an effective mechanism for recovering the costs of their production, which makes it possible to make the consumption volume independent of the tariff level, resource prices and current consumer income. The basis for pricing in the field of PPAT is a mechanism for determining a rational level of tariff that takes into account the solvent demand of the population for socially significant transportation, as well as the existing system of subsidies and financial budget opportunities in the region. Calculation of the tariff for the carriage of passengers on the basis of control and regulatory indicators, the value of which, as a rule, is underestimated relative to the fact, together with the lack of budgetary sources of financing, leads to a low percentage of expenses covered by enterprises' own incomes.
3. Lack of motivation to reduce costs and develop competition. The inability of enterprises engaged in passenger transportation to influence the pricing model is the main factor holding back the development of competitive relations in the market. As a rule, competition is limited to several enterprises (sometimes it is one enterprise), which have inherited from the Soviet era a scientific and industrial-technical base that is currently able to comply with modern standards and rules for the implementation of transport activities, providing high quality services.
4. Innovative passivity of manufacturers. Weak orientation of PPAT operators to innovations is a consequence of the factors mentioned by the author above, namely, insufficient financing, restrictive tariff policies, protracted unprofitability of enterprises, as well as the prevalence of state initiatives in the form of distributing directives for manufacturers to use innovations in order to fulfil the requirements of various norms, standards, licensing requirements, etc. (for example, the installation of turnstiles, cash registers, radar communications in buses).
5. Economic fragmentation of the market. Relations based on the conditions for the provision of PPAT services, by their nature close to competitive ones and allowing enterprises to gain economic independence, are mainly formed in large cities. This affects the volume of passenger traffic and the structure of demand, which, in turn, depends on the size and gender composition of the population, which noticeably decrease with distance from regional centres.
6. Amorphous market boundaries. Socially significant services, as a rule, have the targeted nature of the provision, and their provision involves the simultaneous presence in one place of both the manufacturer and the consumer (for example, within the region). However, the area of functioning of the regional market of socially significant services does not always coincide with the territory of a given region, going beyond its administrative borders. There are contradictions not only in determining the powers and functionality of the executive authorities of the region and municipalities, but it is also difficult to distribute areas of responsibility between economic entities, often causing opportunistic behaviour of partners based on distortion and the provision of incomplete information in order to obtain sole profit (Figure 1).  Modern realities are such that a business in the field of socially significant services functions according to the principles and in the conditions of the business environment, which is uniform in terms of structural and level descriptions in comparison with the business environment in other types of economic activities, but in practiceexcellent in subject-object characteristics [12]. The insufficient elaboration of the theoretical and methodological foundations of socially significant services, the lack of a successful, widespread empirical experience of including Russian business in solving socially significant problems do not allow us to fully explore the environment for the functioning of entrepreneurship in the specific conditions for the provision of the analysed goods (goods, services). Manufacturers are forced to adapt to the established "rules of the game", being under the strong influence of the state and largely dependent on its financing [13]. As a result, the activities of business entities in this environment are vulnerable, and often impossible. The transformation processes taking place in recent years in the relationship between the state and business in the social sphere are more likely to be institutional (policy) in nature, and are based on organizational and legal changes initiated by the authorities in terms of attracting business to solve socially significant problems, which led to the absence of social basis for the development of the business environment [14]. Changing the nature of the interaction between government and business, focused on the formation of a socially oriented entrepreneurial environment should not be based on state support of producers, but should be achieved by concluding contractual relations between economic entities through the implementation of a mechanism of public-private partnership, moving from the economic socio-economic plane to the political and economic [15].
A modern understanding of public-private partnerships is based on the effective building of an alliance between the official authorities and both separate, separately functioning entrepreneurial structures and their associations, which act as coordinator of social investments of the business on a par with the state [16]. The process of forming partnerships between government and business (including integrated business structures) takes place in several stages, each of which is due to the transformation of goal setting and the motives of their activities.
The evolution of relations between the state and economic actors in the sphere of socially significant public transport passenger services was considered by the author in two planes: «powerbusiness», the space of which narrows as the interaction between state and business structures becomes closer; in the coordinates of «business-to-business», which ultimately form the region for the formation of a production alliance in this area, which further determines the nature of relations with the state. Studying the behavioural scenarios that have developed in the regional practice of public administration and management in the PPAT system from the 90s of the 20th century to the present, the author identifies the main trends in the transformation processes that were the basis for the evolution of the interaction between government and business from private to associated based on the coordination of the goals and motives of each of the participants [17].
Thus, the interaction of economic entities in the field of socially significant public passenger transport services in the space "power-business", "business-to-business" undergoes the following stages of development: 1. «Antagonism-Rivalry» is characteristic of a period of active privatization processes in a transitive economy. The model of the free market for PPAT services, applied by individual regions, has led to excessive commercialization of passenger transportation, namely, the emergence of many individual private entrepreneurs who are economically isolated and distant from each other as a result of competition for more favourable conditions of production activity and personal economic wellbeing. The indifference of business in solving socially significant problems of society inevitably gives rise to an antagonism of the relationship between power and private structures, in which the achievement of goals by some economic entities is difficult or impossible as a result of the actions of other economic entities.
2. «Solidarity-Competition» is inherent in the stage of active structural and institutional transformations in transport of the last decade, when at the federal and regional levels legal and organizational and managerial mechanisms for a model of regulated services market were created that meet the requirements of a market economy based on licensing, certification, combining administrative and economic leverage. The real factor stimulating the development of passenger transportation in the regions is competition between economic actors both within the industry itself and with enterprises of other modes of transport. A new institutionally organized and fixed form of rivalry (for example, based on the conclusion of state contracts) creates the unity of the market operators in achieving the goal of providing quality services, and is aimed at understanding the need for interaction between government and business in solving socially significant problems facing society. Despite the commonality of interests and the unity of goal setting, the actions of economic entities to implement their tasks are fragmented. The state, guided by the motive for the most efficient performance of its social functions in the face of increasing limited budgetary resources, creates an additional restrictive framework that not only disorganizes the effective interaction of economic actors among themselves, but also does not fully overcome the amorphous relations between the organizers and operators of the regional PPAT market.
3. «Synergy-Integration» as a result of effective interaction between authorities and the business community in the field of PPAT services based on partnerships. The economic actors integrated among themselves form a regional entrepreneurial network, the effect of joint activities in which exceeds the individual successes of each of them. In addition, the combined forms of business offer opportunities to influence not only socio-economic, but also political decisions in the region. In turn, the authorities are interested in expanding the scope of applying the entrepreneurial approach and building effectively functioning business alliances in the region. The high valency of the companies participating in the interaction and the state gives a synergistic effect, manifested not only in obtaining economic, but also social results from the implementation of a public-private project, thereby justifying the «hopes» of the population for high-quality and affordable services, businessfor public trust and long-term development, authoritiesfor further partnership in order to increase the sustainability of economic development and social stability of the region in the context of existing budgetary constraints, creating the prerequisites for investment in a socially significant sphere. The constructed relationship system allows the authors to form a field of interaction between economic entities based on the principles of public-private partnership and aimed not only at increasing the number of financially independent actors and their associations, but also at transforming state institutions that become active participants in market relations, interacting, and sometimes and competing with the business community, transferring more and more functions to it, even in those industries for which traditionally they were solely responsible.

Conclusion
Thus, the evolution of the system of scientific views on services in the context of the transition to postindustrialism is caused not only by the specifics of their genesis (identification in the essence of material components, the emergence of new types and groups of services), as well as by the characteristics of consumption due to the law of exaltation of needs, which determines a stable relationship between the level of social -economic development and qualitative characteristics of the needs of the individual and society. The socio-economic development of the socially significant service sector is accompanied by the features inherent in socially significant services in Russia, as well as the specifics of the formation of the institutional business environment. Firstly, the terminological instability of the definitions of «service», «service industry», as well as the tendency to diversify the products of the service sector in the world and domestic markets, determined the presence of diverse and often antinomistic classification groups. Secondly, the specific characteristics inherent in atypical types of services (social, public services), and significant external effects arising from their consumption, formed the corresponding expectations of the population regarding the free provision of services of this category, as well as business expectations regarding the formation of an effective mechanism reimbursement by the state of «falling out» income from their production (provision). As a result, the desire of economic entities in their activities to balance the desired consumption and the result actualize the problem of humanization of modern economic relations that meet the trends of socialization of the Russian economy.