1 Introduction
One of the main generally accepted indicators of enterprise performance is profitability. It is of interest to owners of the enterprise and investors as an indicator of the increase in business value and income generation, for managers of the enterprise in terms of the development of the enterprise and its technical modernization, and for the state insofar as the profit is subject to taxation. As the main indicator of the enterprise’s activity, it is influenced by many factors that reflect both the production efficiency within the company and the influence of the resource and good markets.
We can assume the influence of two groups of factors affecting profitability:
internal factors of the enterprise, reflecting the structure of assets and liabilities, technological features of production, the level of activity of production processes, etc.;
environmental factors, including both the market conditions for resources and goods, as well as the circumstances prevailing in the economic system at the higher levels (meso-, macro- and megatrends).
Regularities related to the profitability of enterprises are identified in various industries in both developed and developing countries, and extant research takes a special interest in the dynamics of profitability indicators in unstable economic conditions. As in a number of studies (Ahn
2008; Tang
2015; Jeanneret
2015; Griffin
2015; Ahmad et al.
2016; Spitsin et al.
2018), we use the term “economic instability” to refer to the volatility of such external factors as the ruble exchange rate and interest rates (Vukovic et al.
2019; Vukovic et al.
2020). We scrutinize the extent to which the outcome varies depending on the form of ownership, be that domestic enterprises, foreign enterprises or enterprises in the joint form of ownership.
A large number of economic studies are devoted to the analysis of factors affecting the efficiency (profitability) of enterprises. At the same time, economists identify and investigate various groups of factors affecting profitability. For example, in a classic study (Capon et al.
1990), the researchers provided the results of a meta-analysis of 320 studies that looked at 227 variables affecting the financial situation of an enterprise. According to the study’s results, indicators such as industry concentration, market share, growth size, capital investment intensity, and advertising intensity emerged as the main explanatory factors of financial performance.
A theoretical approach (the structure–conduct–performance paradigm) assumes a direct impact of industry structure on profitability (Tirole
1988). A different approach (the market-based paradigm) adds into consideration the strategic position of firms within the industry (Welge and Al-Laham
2008). In addition, firm size, market share, growth, age, advertising, R&D, patents and financial risk have been identified as empirical firm-specific determinants by the previous literature (e.g., Yurtoglu
2004; Chaddad and Mondelli
2013). Firm-specific drivers of profitability are size and financial risk (Gschwandtner and Hirsch
2018).
Very often the study of profitability considers 4 effects: firm, industry, country and period effects. For example, the analysis may suggest that firm effects on profitability for the 2005–2011 periods on the data of the international database of firms are stronger under adversity, whereas industry effects become weaker. Similarly, country main and interaction effects may be considered, with particular attention paid to the emerging economies (Bamiatzi et al.
2016).
The profitability of the company is also associated with the share of the borrowed capital. There are a number of theoretical approaches to regulating the share of borrowed capital in the capital structure of the enterprise:
Static Trade-off Theory (Kraus and Litzenberger
1973) explains that the share of debt depends on the balance of the costs and benefits. Unfortunately, there is no consensus among researchers in understanding the content of costs and benefits. For instance, it may be seen as a balance between the dead-weight costs of bankruptcy and the tax saving benefits of debt. The theory also allows considering agency problems and debt agency conflicts, but does not take into account the information asymmetry and the role of distribution of information in conflicts between insiders and outsiders (Yapa Abeywardhana
2017).
Pecking Order Theory (Myers and Majluf
1984) suggests that the cost of financing increases with asymmetric information, which results in prioritizing the company’s sources of financing from internal financing, to debt, to raising equity. Debt issue is a signal of successful management of a company that can cope with debt pressures and can overcome the agency conflict between managers and owners (Yapa Abeywardhana
2017). Empirical tests of the Pecking Order Theory indicate that it receives less support than the Static Trade-off Theory (Shyam-Sunder and Myers
1999).
Market Timing Hypothesis (Baker and Wurgler
2002), in contrast to the previously mentioned theories, suggests that the firm tends to issue equity when its market value is rising and vice versa. The capital structure develops under the influence of market conditions, and the capital market does not move to target leverage. It should be noted, however, that a number of researchers have shown the impact of market timing on the firms’ capital structure to disappear in the long run (Hovakimian
2006; Alti
2006).
There are some other potentially relevant brand new theories (including, e.g., credit rating–capital structure hypothesis (Kisgen
2006), but at this point it is hard to investigate them empirically in the Russian context due to data challenges.
Studies of the effect of the share of borrowed capital on the profitability of enterprises in various industries and in different countries have different results. A number of works claim a positive relationship of profitability with the level of short-term debt and in some cases with long-term debt (Negasa
2016).
At the same time, they distinguish the effect of short-term and long-term borrowed capital on ROA (return on assets). If in the short-term period the relationship is positive and significant, in the long-term period the significance of the relationship does not exist (Jain et al.
2017), or even changes the sign (Vaicondam and Ramakrishnan
2017).
Other researchers find the opposite. In the Vietnamese data (Vy and Tra
2016; Le and Phan
2017), the relationship between profitability and leverage is negative, and is robust to the inclusion of control variables as well as firm and year fixed effects. It is a very remarkable finding for Vietnamese enterprises that the smaller the firms, the more profound the relationship. Small and profitable firms tend to have higher incentive to use less debt. In contrast, large firms seem to be indifferent in their debt use due to having greater access to other sources of finance, as well as a larger base of collateral assets. The same relationship is also observed in Thailand (Vithessonthi and Tongurai
2015a,
b). Also, along with the negative relationship of debt to total assets and ROE for Vietnamese companies, there is a positive relationship with growth of sales and size of enterprises (Vu and Phan
2016).
According to the meta-analysis (Capon et al.
1990), the impact of the firm’s debt load (debt influence on the level of firm) on its financial performance is rather negative (90 out of 147 studies, yet dependencies are insufficient).
In a Chinese study (Anwar and Sun
2013), the relationship between the level of debt of local firms and the presence of foreign firms on the market was investigated. An increase in foreign presence raises the debt of domestic firms, and its impact on firm investment is also positive. Overall, the impact of foreign presence on the leverage of domestic firms in China’s manufacturing sector is negative and relatively large. If we split the firms in two groups (domestically oriented and internationally oriented) as Vithessonthi and Tongurai (
2015a,
b) did for Thailand, the effect of leverage on performance will be different. For domestically oriented firms it is negative, whereas a positive relationship exists for the internationally oriented ones.
Next, we analyze existing approaches to assessing the effects of attracting foreign investment in the host country. In a sample of Vietnamese firms (Aitken and Harrison
1999), it was found that foreign equity participation was positively correlated with plant productivity but only for small enterprises. On the other hand, for domestically owned companies foreign investment had a negative impact on their productivity. The net impact of foreign investment, taking into account these two offsetting effects, is quite small. The gains from foreign investments appear to be entirely captured by joint ventures. In the Ivory Coast setting (Harrison and McMillan
2003) domestic firms were found to be more credit constrained than foreign firms, and borrowing by foreign firms exacerbated domestic firm credit constraints.
Crisis phenomena in the economy affect foreign and local firms differently. It is natural to assume that foreign multinationals are less linked into the domestic economy, and so are more likely to leave once the economy is hit by a negative shock. But in the case of Ireland (Godart et al.
2012) it is not confirmed: international firms do not flee from Ireland in crisis.
On the contrary, import competition and foreign direct investment discourage entry and stimulate exit of domestic entrepreneurs, the phenomenon referred to as the “crowd out effect” (De Backer and Sleuwaegen
2003). However, the empirical results also suggest that this crowding out effect may be moderated or even reversed in the long run due to the long-term positive effects of FDI on domestic entrepreneurship as a result of learning, demonstration, networking and linkage effects between foreign and domestic firms. Such effects have been identified not only in developed countries (Belgium), but also in the post-socialist countries (Czech Republic) (Kosová
2010). At the same time, the impact of foreign presence on the leverage of domestic firms is negative. In China’s manufacturing, an increase in foreign presence increases the debt of value maximizing domestic firms (Anwar and Sun
2015).
Speaking of the relationship of capital structure of domestic, foreign and joint ownership with profitability, one can refer to the data from an Indian study (Chhibber and Majumdar
1999), in which after controlling for a variety of firm and environment-specific factors, firms display relatively superior performance only when property rights belong to foreign owners at ownership levels providing unambiguous control at 51%. Also, the relationship between foreign entry and profitability of domestic firms has an inverted U-shape (Fu and Wu
2013). Furthermore, we also find that the effect of foreign entry on domestic firm profitability varies according to the ownership structure of domestic firms and the export intensity of foreign newcomers.
An interesting correlation is observed in Canada regarding the profitability of local firms and firms owned by the US (Shapiro
1983). It is found that US-controlled firms were more profitable than either Canadian- or other foreign-controlled firms. In addition, the study suggested that the higher the degree of non-resident (presumably American) ownership, the higher the profitability of the US-controlled firms. The reverse was true of other foreign firms. These results for the US firms are consistent with the Hymer–Caves and internalization approaches to the multinational corporation.
When studying the influence of factors on profitability in countries with unstable economies, economists, among other factors, single out and investigate the influence of exchange rates.
In Korea (Ahn
2008), small and medium firms are more susceptible to the exchange rate fluctuations than large firms. More importantly, profitability of a more productive firm is found to be less sensitive to the exchange rate fluctuations than that of a less productive firm. Also, there is a relationship between firm size and exposure effects, which also shows that lagged exchange rate changes have significant exposure effects on firm returns (Tang
2015).
An analysis of 84 developed and emerging economies over the 1996–2012 period (Jeanneret
2015) describes effects of exchange rate uncertainty on foreign direct investment. Firms face a choice between participating in foreign markets through exports or investing abroad to relocate production. The most productive firms invest abroad when exchange rate volatility is low and export otherwise, whereas the least productive firms invest abroad when the volatility is high. Other authors (Ahmad et al.
2016) confirm that exchange rate depreciation affects the volume of FDI and promotes growth in the long run. Economic development and inflows are also associated with exchange rates. In Nigeria (Zakari
2017) there was a strong positive relationship between FDI and exchange rate on the one hand, and a weak positive relationship between FDI and GDP on the other hand.
In Colombia (Griffin
2015) no strong evidence was found for the conjecture that real appreciation has, on average, negatively affected the profitability of manufacturing firms. On the contrary, real appreciation may have increased firms’ profitability by reducing the cost of imported inputs as Colombian manufacturing firms become more domestically oriented.
Taken as a whole, the literature review suggests that the effects of financial and non-financial factors on firm profitability in the developing countries have not been documented sufficiently and are poorly understood. Moreover, despite the key role of foreign owned and joint enterprises in stimulating the domestic economic activity, extant research rarely distinguishes between the firms based on ownership. Jointly owned enterprises are largely ignored by the received literature. Given the increased government reliance on the technology spillover initiated through participation of foreign and jointly owned firms in the domestic economy, this is a major shortcoming that we try to address in this paper.
In this work, a comprehensive study of the influence of factors on the profitability of enterprises in countries with unstable economies is being conducted. The main division of enterprises is carried out according to the forms of ownership (enterprises in the Russian (RO), joint (JO) and foreign (FO) ownership), and it is used in all sections of the investigation. Additional divisions are carried out by the branches of the manufacturing industry and by the share of borrowed capital in the liabilities side of the balance. Additionally, the impact on the profitability of internal and external factors in an unstable economy is being tested.
The object of the research is manufacturing enterprises in Russia.
The uniqueness of the situation in Russia in 2012–2016 is that:
For the study period the Russian economy was characterized by instability and crisis manifestations: a strong depreciation of the national currency, a decline in real income of the population, etc.;
During the study period, there were political tensions, and economic sanctions were imposed on the country with respect to the export and import of high-tech goods. In response, Russia also imposed sanctions, primarily on the products of the food industry.
In this paper, it is planned to assess the impact of this unique situation on the profitability of enterprises in the RO, JO and FO in the context of manufacturing industries.
4 Results and discussion
4.1 Option 1
The study of the influence of ownership on the net profitability of assets.
The four regression models formed above are presented in Table
3. The coefficients and standard errors are given according to model 4.
Table 3Regression results (fixed effects estimates, robust estimates).
Source: calculated by the authors according to data (SPARK: Information system. Interfax (Russia)
2018)
Size of the enterprise | 6.80 (0.29)b | 7.40 (1.43)b | 5.48 (1.48)b | 6.90 (0.28)b |
Fixed assets share | − 1.25 (0.14)b | − 2.25 (0.77)a | − 1.38 (0.73) | − 1.17 (0.15)b |
Current liquidity ratio | − 0.10 (0.09) | − 3.64 (1.58) | − 0.53 (1.61) | − 0.09 (0.09) |
Gross profitability of sales | 5.17 (0.22)b | 7.21 (1.02)b | 4.02 (0.99)b | 5.09 (0.22)b |
Share of borrowed capital | − 6.44 (0.23)b | − 8.30 (0.88)b | − 8.64 (1.30)b | − 5.96 (0.24)b |
Average interest rates | − 0.64 (0.05)b | − 3.11 (0.28)b | − 1.58 (0.26)b | − 0.37 (0.04)b |
Average annual exchange rate | 0.04 (0.05) | 1.49 (0.30)b | 0.91 (0.29)a | − 0.14 (0.05)a |
Intercept | 5.89 (0.00)b | − 1.88 (1.04) | 2.19 (0.74)a | 6.75(0.03) [p < 0.001] |
Model 1 R2 | 0.070 | 0.051 | 0.077 | 0.077 |
Model 2 R2 | 0.165 | 0.149 | 0.140 | 0.178 |
Model 3 R2 | 0.266 | 0.277 | 0.240 | 0.273 |
Model 4 R2 | 0.272 | 0.330 | 0.262 | 0.276 |
∆R2 of production efficiencya | 0.095 | 0.098 | 0.063 | 0.101 |
∆R2 of financial factorsb | 0.107 | 0.181 | 0.122 | 0.098 |
Including—internal | 0.101 | 0.128 | 0.100 | 0.095 |
External | 0.006 | 0.053 | 0.022 | 0.003 |
Number of enterprises in the sample | 6134 | 470 | 294 | 5370 |
For all four cases (full sample, enterprises in FO, enterprises in JO, enterprises in RO), a highly significant impact on the net return on assets of control variables was revealed:
positive impact—enterprise size;
negative impact—the share of fixed assets in assets.
Among the tested variables, we identified:
highly significant positive impact of gross profitability of sales;
highly significant negative impact of the share of borrowed capital;
highly significant negative impact of the average interest rates on bank loans;
the average annual exchange rate of the ruble to the dollar has a positive effect on the net profitability of enterprises in FO and JO (i.e., when the ruble exchange rate falls, their profitability increases) and negatively on enterprises in the RO (i.e., when the ruble exchange rate falls, their profitability decreases).
For enterprises in FO and JO, the main contribution to R2 growth is provided by financial factors: internal and external for enterprises in FO, mainly internal—for enterprises in JO. This confirms hypothesis 1.1.
For enterprises in RO, the contribution to the increase in R2 (to the increase in the explanatory power of the model) is comparable (approximately the same) to the production efficiency and domestic financial factors. At the same time, the contribution of external financial factors is minimal, although they are significant. We cannot say that the production efficiency gives a greater R2 spillage of enterprises in the RO, since this increase is comparable (approximately equal) to the growth of enterprises in the FO. Thus, hypothesis 1.2. is not confirmed. Also, production efficiency has a much smaller impact on R2 growth at enterprises in JO.
4.2 Option 2
Study of the effect of manufacturing industries on profitability.
The regression models for 6 branches of the manufacturing industry (types of economic activities) are presented in Table
4. The number of enterprises of each form of ownership for each type of economic activity is indicated at the bottom of the table. The coefficients and standard errors are given according to model 7.
Table 4Net profitability of assets.
Source: calculated by the authors according to data (SPARK: Information system. Interfax (Russia)
2018)
Size of the enterprise | 6.94 (1.22)b | 8.14 (0.61)b | 7.20 (0.94)b | 6.35 (0.59)b | 7.44 (0.97)b | 5.69 (0.47)b |
Fixed assets share | − 0.56 (0.65) | − 1.31 (0.37)b | − 0.90 (0.39) | − 1.54 (0.32)b | − 2.12 (0.50)b | − 1.10 (0.22)b |
Current liquidity ratio | 3.56 (3.63) | − 0.12 (1.46) | − 0.09 (0.09) | − 1.62 (0.70) | − 0.22 (0.11) | 0.16 (0.20) |
Gross profitability of sales | 6.65 (1.10)b | 5.70 (0.39)b | 4.70 (0.53)b | 5.12 (0.55)b | 4.74 (0.51)b | 4.91 (0.37)b |
Share of borrowed capital | − 4.59 (1.06)b | − 5.95 (0.55)b | − 5.84 (0.58)b | − 5.59 (0.48)b | − 6.20 (0.56)b | − 6.76 (0.42)b |
Average interest rates | − 1.36 (0.21)b | − 0.58 (0.10)b | − 0.32 (0.13) | − 0.77 (0.11)b | − 0.48 (0.12)b | − 0.44 (0.08)b |
Average annual exchange rate | 0.71 (0.23)a | − 0.04 (0.11) | 0.26 (0.15) | 0.08 (0.12) | − 0.23 (0.13) | − 0.12 (0.09) |
Gross profitability of sales * ShareFO | − 1.22 (0.26)b | − 0.39 (0.14)a | − 0.46 (0.16)a | − 0.61 (0.14)b | − 0.43 (0.17) | − 0.49 (0.10)b |
Share of borrowed capital * ShareFO | 0.05 (0.60) | − 1.64 (0.44)b | − 2.09 (0.71)a | − 0.65 (0.46) | 0.84 (0.44) | − 0.61 (0.38) |
Share of borrowed capital * ShareFO * Ruble’s depreciation | − 1.47 (0.22)b | − 0..80 (0.23)b | − 0.94 (0.18)b | − 0.85 (0.15)b | − 1.14 (0.26)b | − 0.43 (0.17) |
Average interest rates * ShareFO | 2.61 (0.85)a | − 0.01 (0.48) | − 0.63 (0.66) | − 0.42 (0.46) | 1.84 (0.49)b | − 0.42 (0.34) |
Average annual exchange rate * ShareFO | 1.00 (0.25)b | 0.58(0.15)b | 0.49 (0.16)a | 0.38 (0.15) | 0.30 (0.19) | 0.08 (0.12) |
Intercept | 4.39 (0.64)b | 7.49 (0.21)b | 5.01 (0.35)b | 6.93 (0.20)b | 6.17 (0.22)b | 5.41 (0.10)b |
Model 1 R2 | 0.097 | 0.120 | 0.062 | 0.080 | 0.084 | 0.040 |
Model 2 R2 | 0.277 | 0.238 | 0.146 | 0.198 | 0.158 | 0.111 |
Model 3 R2 | 0.330 | 0.327 | 0.248 | 0.284 | 0.2.44 | 0.234 |
Model 4 R2 | 0.369 | 0.332 | 0.252 | 0.292 | 0.251 | 0.238 |
Model 5 R2 | 0.403 | 0.334 | 0.256 | 0.297 | 0.253 | 0.243 |
Model 6 R2 | 0.468 | 0.354 | 0.292 | 0.313 | 0.281 | 0.247 |
Model 7 R2 | 0.508 | 0.358 | 0.298 | 0.315 | 0.288 | 0.247 |
∆R2 of production efficiencya | 0.214 | 0.12 | 0.088 | 0.123 | 0.076 | 0.076 |
∆R2 of financial factorsb | 0.197 | 0.118 | 0.148 | 0.112 | 0.128 | 0.131 |
Including—internal | 0.118 | 0.109 | 0.138 | 0.102 | 0.114 | 0.127 |
External | 0.079 | 0.009 | 0.01 | 0.01 | 0.014 | 0.004 |
Number of enterprises |
Total | 303 | 1070 | 728 | 1113 | 869 | 2051 |
FO | 56 | 78 | 87 | 73 | 52 | 124 |
JO | 25 | 51 | 44 | 58 | 43 | 73 |
RO | 222 | 941 | 597 | 982 | 774 | 1854 |
Common to all the studied industries are the following consistent patterns on the influence of factors on the profitability of assets:
high significant positive impact of size of the enterprise;
highly significant positive impact of gross profitability of sales (a significant increase in the explained variation R2);
highly significant negative impact of the share of borrowed capital (a significant increase in the explained variation R2).
At the same time, other factors affect the profitability of the assets of the studied industries in different ways. In particular, the share of fixed assets in assets has a highly significant negative impact on the profitability of the DK, DJ, DL, DA subsections. The value of average interest rates on loans to legal entities has a highly significant negative effect on the profitability of enterprises of all industries except DG, but its effect does not lead to a significant increase in the explained variation (R2) except for the automotive industry (DM).
In all sectors, except the food industry, a highly significant negative impact on enterprises with foreign participation has been revealed on the factor “Share of borrowed capital * ShareFO * Ruble’s depreciation”, which leads to a significant increase in the explained variation (R2). This fact suggests the presence of foreign currency loans or borrowed funds at enterprises in FO and JO of all sectors, except for the food industry.
We identified significant differences in the DM subsection (automobile industry) from other industries both in terms of the share of explained variations (R2 = 50.8%) and in terms of the influence of variables, including those associated with the share of foreign owners, and their contribution to the increase in R2. In this subsection, a significant increase in the explained variation (R2) is provided by external factors (dynamics of interest rates on loans), as well as factors related to enterprises in foreign and joint ownership. It should be noted that the crisis was the most acute for enterprises in FO and JO of this subsection, and most of them showed losses in 2014–2015.
The findings partially confirm hypothesis No. 2 in relation to 5 branches: DM (automobile industry), DK, DL, DJ, DG. For these branches, enterprises with the participation of foreign capital are characterized by increasing negative impact on the profitability of the share of borrowed capital but weakening the positive impact of gross profitability of sales. In the branch DA (food industry) the share of borrowed capital (share of borrowed capital * ShareFO * Ruble’s depreciation) has a significant negative impact (− 0.43 *), but it is significantly weaker than that of other foreign trade activities.
We also found that production efficiency provides a significant increase in R2, comparable to the increase in R2 from financial factors in the following industries: DM, DK, DJ. Thus, in a crisis, production efficiency is important in these industries. On the contrary, the main contribution to the growth of R2 comes from internal financial factors in the DG, DL, DA industries. The contribution of production efficiency in these industries is significantly lower. External financial factors provide a significant increase in R2 only in the automotive industry (DM). Perhaps this is because in this industry the share of enterprises in FO and JO in the total value of production is high (about 63%). In other industries, the contribution of external financial factors to R2 growth is minimal.
4.3 Option 3
4.3.1 Study of the impact of the share of borrowed capital on profitability
The regression models for the samples of enterprises with different shares of borrowed capital are presented in Table
5. The number of enterprises for each sample is indicated at the bottom of the table. The coefficients and standard errors are given according to model 7.
Table 5Regression results (fixed effects estimates, robust estimates).
Source: calculated by the authors according to data (SPARK: Information system. Interfax (Russia)
2018)
Size of the enterprise | 6.84 (0.30)b | 13.77 (0.67)b | 3.24 (0.33)b |
Fixed assets share | − 1.25 (0.14)b | − 1.97 (0.31)b | − 1.06 (0.19)b |
Current liquidity ratio | − 0.10 (0.10) | − 0.21 (0.06)b | 0.58 (0.45) |
Gross profitability of sales | 5.14 (0.22)b | 5.78 (0.48)b | 3.47 (0.28)b |
Share of borrowed capital | − 6.10 (0.23)b | − 5.59 (0.52)b | − 9.46 (0.52)b |
Average interest rates | − 0.61 (0.05)b | − 0.19 (0.08) | − 0.74 (0.06)b |
Average annual exchange rate | 0.03 (0.05) | − 0.63 (0.09)b | 0.49 (0.07)b |
Gross profitability of sales * ShareFO | − 0.64 (0.06)b | − 0.16 (0.19) | 0.20 (0.13) |
Share of borrowed capital * ShareFO | − 0.66 (0.22)a | 0.70 (0.43) | − 0.55 (0.34) |
Share of borrowed capital * ShareFO * Ruble’s depreciation | − 0.87 (0.09)b | 0.06 (0.14) | − 1.39 (0.17)b |
Average interest rates * ShareFO | 0.34 (0.28) | 1.21 (0.69) | − 0.07 (0.29) |
Average annual exchange rate * ShareFO | 0.47 (0.07)b | − 0.03 (0.14) | 0.34 (0.10)b |
Intercept | 5.88 (0.04)b | 5.10 (0.65)b | 9.04 (0.38)b |
Model 1 R2 | 0.070 | 0.169 | 0.048 |
Model 2 R2 | 0.165 | 0.271 | 0.125 |
Model 3 R2 | 0.266 | 0.294 | 0.296 |
Model 4 R2 | 0.272 | 0.303 | 0.307 |
Model 5 R2 | 0.277 | 0.304 | 0.313 |
Model 6 R2 | 0.295 | 0.304 | 0.358 |
Model 7 R2 | 0.298 | 0.307 | 0.360 |
∆R2 of production efficiencya | 0.1 | 0.103 | 0.083 |
∆R2 of financial factorsb | 0.128 | 0.035 | 0.229 |
Including—internal | 0.119 | 0.023 | 0.216 |
External | 0.009 | 0.012 | 0.013 |
Number of enterprises |
Total | 6134 | 1430 | 3073 |
FO | 470 | 93 | 226 |
JO | 294 | 67 | 143 |
RO | 5370 | 1270 | 2704 |
The strength of the influence of the tested variables and the significance of the influence differ significantly depending on the amount of borrowed capital in the balance sheet.
If the share of borrowed capital does not exceed 50%, the main impact on the net return on assets is provided by the control variables (R2—16.9%) and production efficiency (R2 increase—10.2%). The remaining variables (financial factors) provide R2 growth by only 3.5%, including: internal financial factors—2.3%, external financial factors—1.2%. There are no differences in ownership of this group of enterprises. Variables with * ShareFO, associated with the share of foreign owners are insignificant. The total share of the explained variation in this case is small (R2 = 30.7%) and practically does not differ from the full sample.
If the share of borrowed capital exceeds 50%, the variables associated with the share of borrowed capital and the share of foreign owners have a major impact on the net return on assets:
Thus, in this case, financial factors provide an increase of R2 22.9%, including: internal financial factors—21.6%, external financial factors—1.3%. The total share of the explained variation in this case is significantly higher (R2 = 36%).
Thus, hypothesis No. 3 is confirmed, and the main role is played by domestic financial factors with an increase in the share of borrowed capital. It was revealed that with the deterioration of the liabilities structure of the balance sheet (increase in the share of borrowed capital) the negative influence of financial factors rises, and the share of the variation explained by them grows significantly. On the contrary, the influence of economic factors and control variables weakens significantly, and the share of the variation explained by them decreases.
We note that the share of enterprises whose share of borrowed capital is small (not exceeding 50% of liabilities annually for 2012–2016) is only 23% of the entire sample of enterprises (this share practically does not differ in ownership forms). At the same time, the share of enterprises with a large share of borrowed capital (more than 50% of liabilities annually for 2012–2016) is 50% of the total sample (differences in ownership patterns are insignificant). Finally, a high share of borrowed capital is characteristic of a significant number of manufacturing enterprises of all forms of ownership in Russia. Our results are generally consistent with existing research in this area (Myers and Majluf
1984; Anwar and Sun
2013; Vy and Tra
2016; Le and Phan
2017). In particular, we have identified the negative impact of the share of borrowed capital on the net return on assets. It is consistent with the Pecking Order Theory, which claims that companies use borrowed funds if they have problems with profitability and do not have enough of their own financial resources. This research underlines the relevance of this problem for countries with unstable economies, in particular, Russia. Analyzing the solid sample of enterprises of the main branches of the manufacturing industry in Russia, we have established their high dependence on borrowed capital. The average and median share of borrowed capital in the liabilities side of the balance are above 60% (Table
1), and only 23% of the sampling companies had a share of borrowed capital below 50% for each year of the study period. Thus, enterprises in countries with unstable economies are highly dependent on borrowed capital. The situation is aggravated by the high level of loan rates in such countries, which increases during crisis periods. There is a vicious circle when enterprises, due to the high cost of loans, cannot increase their profitability and reduce their dependence on borrowed funds. Adjusting the rates on bank loans in times of crisis and actions aimed at systematically reducing these rates are necessary conditions for improving the profitability of enterprises in developing countries with unstable economies.