Adv Syst Sci Appl 2019; 04; 45-57 Published online at http://ijassa.ipu.ru/index.php/ijassa/article/view/748 Evaluating Comparative Total Factor Productivity in the Russian Industry Svetlana V. Orekhova1*, Evgeniy V. Kislitsyn2 1) Business Economics Dept., Ural State University of Economics, Yekaterinburg, Russia E-mail: bentarask@list.ru 2) Information Technology and Statistics Dept., Ural State University of Economics, Yekaterinburg, Russia E-mail: kev@usue.ru Received May 14, 2019; Revised November 28, 2019; Published December 31, 2019 Abstract: Searching for domestic reserves of economic growth has lately become one of the central problems in Russia. The paper examines the role of small industrial enterprises in stimulating economic growth. In contrast to the stereotype that small business serves as a driving force for the economic development and encourages innovation, the authors hypothesizes that in Russia this statement is false. The conceptual and methodological framework of the study rests upon neo- classical models of economic growth. The authors investigate the existing approaches to the analysis of factors influencing economic growth of the state and choose the tools of total factor productivity analysis. Total factor productivity is calculated using the translog production function, which allows determining the effect of the technological level on value added of the object under study. The choice of the type’s function is due to the low elasticity between the factors of production, as well as the imperfect competition in the industrial markets under review. The information base of the research includes the data of small, medium-sized and large enterprises of 10 industrial macro-sectors of the Russian economy for 2013–2017. We use SPARK-Interfax database to assess production functions. The results of the analysis prove that small enterprises operating in the Russian industry demonstrate much lower values of average and weighted average total factor productivity than medium-sized and large enterprises. The general trend for such businesses is a decline in total factor productivity. Only single leading companies produce a gain in value added in small entrepreneurship. Thus, the economic situation in Russia rejects the hypothesis about a higher entrepreneurial potential of small businesses, business models and technological innovations emerging on its basis. Our further studies are assessing institutional traps and general context in the development of small business. Keywords: total factor productivity, small enterprises, Russian industry, economic development. 1. INTRODUCTION Harrod [20] and Domar [12], who described a one-factor model for determining growth rates, provided fundamentals of the theory of economic growth. Since the paradigmatic works by Solow [30; 31] and Swan [33], who proposed endogenous growth model, contemporary economic thought has been developing towards substantiating sources and refining growth models. The basic exogenous growth factors include scientific and technological progress and labor [27]. Arrow [4] и Uzawa [35] examine the role of knowledge and human capital in economic growth. The mechanisms of innovation growth are clarified in the Schumpeterian growth model [1; 18] For Russian economic development strategy, the key issue is the effective economic structure (and also institutional environment). This aspect “solves the question of the number of types of activities, markets, the size of the general welfare and its distribution (income * Corresponding author: bentarask@list.ru mailto:kev@usue.ru 46 S.V. OREKHOVA, E.V. KISLITSYN Copyright ©2019 ASSA Adv. in Systems Science and Appl. (2019) inequality)” [32, p.26]. Equally important is the question of the contribution of different types of enterprises to economic growth. The history and practices of small business have been among the most controversial issues in economic development in different countries. Science postulate that small business is an important object of economic growth [15; 17; 38; 39]. Job creation, production of innovations and technologies, and finally, profits are the outcome of small businesses activity due to their entrepreneurial potential, flexibility and maneuverability of managerial decisions, optimization and red tape reduction of business processes. Small businesses make a huge contribution to national prosperity. On the other hand, empirical studies demonstrate that the size of enterprises can influence their growth rate differently (the positive correlation is highlighted in [6; 13; 37] and the negative one is discussed in [3; 10; 15]). The role of small business in an economy has frequently been undermined and even misinterpreted. In the past, small businesses were believed to impede economic growth by attracting scarce resources from their larger counterparts [5]. These inconsistencies between theory and practice encourage more detailed studies to be conducted on the role of small enterprises in promoting economic development of the Russian industry, and later – on the factors determining this role. The purpose of the research is to analyze total factor productivity (TFP) of small enterprises of Russia’s industrial sector in comparison with medium-sized and large companies. Such a research agenda is set for the first time and implies a consistent solution of a number of tasks. The first task is to perform a critical analysis of the existing theoretical approaches and Russian empirical studies on measuring economic growth rates taking into account various factors. The second task is to substantiate the choice of a method for calculating TFP and its empirical testing. It is reasonable to carry out a comparative analysis of data on small and other enterprises in the context of individual industries, which allows taking into account the organizational and production specifics of enterprises. The authors state that industrial segments, serving as the basis for the RF Government’s economic growth policy, are of the greatest academic interest. At the same time, regional, technologic and economic disproportions of industrial markets are very serios [2]. The third task of the research is to interpret the obtained results of calculating the total factor productivity from the standpoint of small industrial enterprises’ development in the Russian economy. 2. THEORETICAL APPROACHES AND EMPIRICAL STUDIES ON FACTOR PRODUCTIVITY Neoclassical Economics Theory showed that the level of technological development was the primary indicator of the degree to which society masters the forces of nature. The combination of productive forces and the type of production relationships constitutes a unique mode of production. Neo-institutional economics largely associates the rate of technological growth with institutional environment for business development. [11; 28] Total factor productivity is the most integrated indicator of technological progress and growing economic efficiency. It is calculated as the ratio of output to the volume of production factors used [23]. The logic of TFP calculation is as follows: an enterprise uses a certain set of factors of production (labour and capital) to produce the final product. Then the growth of the factors of production used or a change in the combination of their use (technology) causes an increase in output of the final product. The dynamics of factor productivity may indicate the degree to which the growth of an enterprise, industry or economic sector is sustainable. Measuring TFP means discovering the correlation between output (production quantity) and factors of production, such as resource EVALUATING COMPARATIVE TOTAL FACTOR PRODUCTIVITY IN THE RUSSIAN INDUSTRY 47 Copyright ©2019 ASSA. Adv. in Systems Science and Appl. (2019) costs and the level of technology. Such a quantitative dependence was dubbed “production function”. There are numerous types of production functions and the most famous of them are the models of Cobb–Douglas, Leontiev, CES, etc. The choice of these models is determined by a combination of factors that embrace data access, market specificity, a dynamic or static aspect of research. Methods for assessing TFP are widely used throughout the world, including as a way of comparing the economic state of countries [14; 16; 21; 25] and the reasons for their differentiation [19; 26; 29]. In Russia, the evaluation of the effectiveness of economic subjects are conducted on a regular basis using certain types of production functions. This fact is due to a number of reasons. First, the production function is an adequate tool for measuring the influence of various factors on economic growth. Second, this tool allows analyzing intra-industry, cross- industry and inter-regional differentiation. Third, in the context of uncertainty and volatility of incomes and expenditures of enterprises, such an assessment allows performing a real-time monitoring of the reasons behind the rise or decline in economic development and determining its trend. For example, Bessonov [7] show that industries with relatively safe output dynamics and lacking sufficient incentives to increase productivity demonstrated the worst TFP dynamics in Russia in 1989–2002. The Tornqvist index was also calculated by Orekhova [24] to analyze the growth sustainability in metallurgy. Tornqvist index formula allows characterizing the change in the efficiency of resource use by factors. The Tornqvist index deals with the change in the volume of two resources – labor (data on the average number of employees) and capital (data on the nominal value of fixed assets). The results of the analysis illustrate the overall technological and technical underdevelopment of the industry. Timmer and Voskoboynikov [34] prove that the growing role of capital expenditures in the growth of value added in Russia resulted in the use of an extensive model of economic growth, which is typical of rental economies. Bessonova [9] demonstrates that there is a significant gap in the total factor productivity of enterprises within the industry. Such a gap may arise due to both organizational-efficiency-based reasons and resource or institutional constraints of the Russian market. All evaluations of the Russian industry’s effectiveness illustrate spasmodic and chaotic shifts, but also display a declining total factor productivity. At the same time, such assessments are more integrated and not explaining the behavior of individual firms, their contribution to the total efficiency of the industry or the entire economy. On the basis of the recent research [10; 39], we assume that small entrepreneurship, a priori not so rich in resources and power in comparison with large business, will be less efficient. 3. TFP RESEARCH METHOD When it comes to the method for calculating total factor productivity, our study rests upon a series of works by Bessonova [8; 9]. Total factor productivity is value added of the final product minus changes in labour and capital costs. Empirically TFP growth can be calculated as an unexplained residue of the final product’s growth. This residue encompasses the effects from technological or organizational innovations that determine technological progress in the industry and influence the shift of the production function [9, p. 99]. However, such a calculation method is possible to be applied only on the basis of correct data on the share of costs of production factors by industry. The previous approach to the evaluation of TFP within the framework of the Solow Growth Model [30] makes an assumption about competitive factors of production. But nowadays we knows that industrial markets are characterized by a hybrid form of organization, which obliges a researcher to first evaluate their production functions, and only after that calculate indicators of production factors and growth of TFP. 48 S.V. OREKHOVA, E.V. KISLITSYN Copyright ©2019 ASSA Adv. in Systems Science and Appl. (2019) This fact rationalizes the choice of the translog production function for calculating TFP. The function allows one not to make prerequisites about absolute elasticity of substitution between factors of production and perfect competition in the markets of these factors [22]: ln 𝑌𝑖𝑡 = 𝛼0 + 𝛼𝐿 ln 𝐿𝑖𝑡 + 𝛼𝐾 ln 𝐾𝑖𝑡 + 𝛼𝑡𝑡 + 𝛼𝐿𝐿(ln 𝐿)2 + 𝛼𝐾𝐾(ln 𝐾)2 + 𝛼𝑡𝑡𝑡2 + 𝛼𝐿𝐾 ln 𝐿𝑖𝑡 ln 𝐾𝑖𝑡 + 𝛼𝐿𝑡 ln 𝐿𝑖𝑡𝑡 +𝛼𝐾𝑡 ln 𝐾𝑖𝑡𝑡 + 𝜀𝑖𝑡 (3.1) where Yit denotes value added at the enterprise i for the period t; Kit is fixed assets of the enterprise i for the period t; Lit is wage at the enterprise i for the period t; t denotes a time factor ranging from 1 to N (where N is the number of observed periods). Based on the proposed production function, the growth of TFP is calculated by formula: 𝐴𝑖,𝑡 = ln ( 𝑌𝑖,𝑡 𝑌𝑖,𝑡−1 ) − �̅�𝐿 ln ( 𝐿𝑖,𝑡 𝐿𝑖,𝑡−1 ) − �̅�𝐾 ln ( 𝐾𝑖,𝑡 𝐾𝑖,𝑡−1 ) (3.2) where A denotes a total factor productivity; is average elasticity of value added of labour; is average elasticity of value added of capital. Average elasticity is calculated as average value of the elasticities of the added value of labour and capital for the periods (t – 1) and t, which in turn are measured as a partial derivative of the corresponding factor: �̅�𝐿,𝑡 = 𝜕 ln 𝑌𝑖𝑡 𝜕 ln 𝐿𝑖𝑡 = �̂�𝐿 + 2�̂�𝐿𝐿 ln 𝐿𝑖𝑡 + �̂�𝐿𝐾 ln 𝐾𝑖𝑡 + �̂�𝐿𝑡𝑡, (3.3) �̅�𝐾,𝑡 = 𝜕 ln 𝑌𝑖𝑡 𝜕 ln 𝐾𝑖𝑡 = �̂�𝐾 + 2�̂�𝐾𝐾 ln 𝐾𝑖𝑡 + �̂�𝐿𝐾 ln 𝐿𝑖𝑡 + �̂�𝐾𝑡𝑡, (3.4) To estimate value added and factors of labour and capital, it is required to use the following indicators: fixed assets, the revenue volume, total costs and wage. Value added was calculated using formula: Yit = Volit-(TCit-Wageit) (3.5) where Volit is the revenue volume of the enterprise i for the period t; TCit is total costs of the enterprise i for the period t; Wageit is labour cost borne by the enterprise i for the period t. The amount of capital was estimated as the average annual value of fixed assets, and labour – as the enterprise’s costs incurred in labour remuneration. 4. EVALUATING TFP OF INDUSTRIAL ENTERPRISES IN RUSSIA: SMALL VS. LARGE ENTERPRISES The study aims at primarily identifying the contribution of small enterprises to the economic growth of the Russian industry. In accordance with the Federal law no. 209-FZ “On the development of small and medium-sized entrepreneurship in the Russian Federation” of July 24, 2007, a small enterprise is that which employs no more than 100 people and its revenue is below 800 million rubles. The empirical testing of the proposed method for calculating TFP was divided into several stages (Fig. 4.1). EVALUATING COMPARATIVE TOTAL FACTOR PRODUCTIVITY IN THE RUSSIAN INDUSTRY 49 Copyright ©2019 ASSA. Adv. in Systems Science and Appl. (2019) Stage 1. Description of the research object Stage 2. Constructing production functions Stage 3. Evaluating total factor productivity Stage 4. Analysis and interpretation of results Choosing the industries for investigation Descriptive statistics Data upload Calculating value added Forming panel data Estimating production function s coefficients Checking coefficients for reliability, unbiasedness and consistency Calculating elasticities of value added of labour and capital for each period Calculating total factor productivity for each period Calculating average TFP of industry for each period Calculating TFP of small enterprises for each period Calculating average TFP of small enterprises in the industry for each period Fig. 1. Algorithm for comparative evaluation of TFP of small and large businesses Hence, the authors use SPARK-Interfax database to assess production functions; the period under study was from 2013 to 2017. The research object was industrial sectors of the Russian Federation. The entire set of industries is combined into 10 industrial sectors (tab. 4.1). This grouping is based on similar industry characteristics within groups. The group “small enterprises” embraces all the companies meeting the aforementioned criteria (micro-enterprises included). Since TFP is an average of the indicators’ values for two years, TFP was calculated for 4 periods. Table 4.1. Number of enterprises in the study sample Industry 2014 2015 2016 2017 Enterprises Large and medium -sized Smal l and micr o Large and medium -sized Smal l and micr o Large and medium -sized Smal l and micr o Large and medium -sized Small and micro 50 S.V. OREKHOVA, E.V. KISLITSYN Copyright ©2019 ASSA Adv. in Systems Science and Appl. (2019) Mining 501 432 537 518 502 608 510 666 Food industry 886 995 933 1223 1012 1489 1064 1817 Light industry 77 317 83 373 87 428 94 497 Woodworking , pulp and paper 240 464 242 574 260 664 264 817 Petrochemical industry 525 570 569 742 591 913 697 1033 Mineral industry 266 618 288 736 308 840 306 998 Metallurgy 365 475 395 633 384 798 391 993 Mechanical engineering 693 912 725 1099 749 1304 790 1622 Industrial service 79 321 102 435 100 537 99 662 Energy industry 631 2167 360 1927 850 1696 1296 1502 Total 4263 7271 4234 8260 4843 9277 5511 1060 7 At Stage 2, production functions were constructed for each enterprise belonging to the 10 industrial sectors. The data were retrieved from SPARK-Interfax database and included the following details: company size, fixed assets, revenue, total costs and wages for 2013–2017. Calculation of value added for each enterprise was carried out according to formula (3.5). To construct the production function, the authors used panel data, which contain statistical information about the same number of enterprises for several consecutive periods of time (2013–2017). The use of panel data allowed us to enhance the size of the sample under review, which provided greater efficiency in estimating the regression model’s parameters. To construct the production function, we applied the method of least squares. The obtained results were checked for reliability, unbiasedness and consistency through calculating the expected value, the Durbin–Watson statistic, constructing the correlation matrix and holding the White test. We obtained a total of 10 production functions for various groups of industries. It is worth noting that for different groups of industries the indicators’ coefficients vary significantly. Nevertheless, we can notice that in all production functions, coefficients with cross-section indicators have a negative value, and in more than half of them – the coefficient with a logarithm of the labor factor. All the rest coefficients have a positive value. For example, for mechanical engineering enterprises, the production function is as follows: ln(𝑌) = 9.65 − 0,10 ln(𝐿) + 0,17 ln(𝐾) + 0.04(ln(𝐿))2 + 0.01(ln(𝐾))2 + 0.01𝑡2 − 0.03 ln(𝐿) ln(𝐾) − 0.01 ln(𝐿) 𝑡 + 0.01 ln(𝐾) 𝑡 (4.1) Once the production functions are constructed, we proceed to Stage 3: calculating total factor productivity. Based on the coefficients of production functions, we calculate the elasticity of value added of labour and capital for each period through identifying partial derivatives (formulas (3.2) – (3.3)). The result is presented in table 4.2. EVALUATING COMPARATIVE TOTAL FACTOR PRODUCTIVITY IN THE RUSSIAN INDUSTRY 51 Copyright ©2019 ASSA. Adv. in Systems Science and Appl. (2019) Table 4.2. Elasticity of value added of labour and capital for industrial sectors for 2013–2017 Year / Industry M in in g F o o d in d u st ry L ig h t in d u st ry W o o d w o rk i n g , p u lp an d p ap er P et ro ch em i ca l in d u st ry M in er al in d u st ry M et al lu rg y M ec h an ic al en g in ee ri n g In d u st ri al se rv ic e E n er g y in d u st ry 2013 0,605 0,916 0,848 0,846 0,912 0,889 0,854 0,884 0,844 1,04 0,222 0,134 0,132 0,143 0,1 0,112 0,099 0,036 0,03 0,008 2014 0,51 0,902 0,84 0,83 0,899 0,863 0,829 0,869 0,819 1,015 0,31 0,135 0,128 0,151 0,099 0,111 0,1 0,041 0,041 0,019 2015 0,519 0,906 0,84 0,838 0,892 0,854 0,809 0,855 0,808 1,003 0,297 0,131 0,12 0,147 0,097 0,104 0,101 0,045 0,047 0,025 2016 0,522 0,904 0,852 0,839 0,89 0,836 0,798 0,845 0,794 0,998 0,294 0,13 0,108 0,148 0,095 0,099 0,101 0,048 0,053 0,028 2017 0,523 0,886 0,794 0,798 0,852 0,787 0,742 0,792 0,702 0,993 0,29 0,118 0,13 0,153 0,092 0,078 0,103 0,063 0,093 0,027 Over the entire period, the greatest elasticity of value added of capital was typical of the industrial sectors, such as energy, food and petrochemical industries. For the mining industry, on the contrary, this indicator varies between 51–60%. Most industries exhibit the same level of the elasticity of labor value added – 9–15%. However, in 2013, this indicator for the mechanical engineering and industrial service was quite low; but by 2017, there was a positive trend, i.e. an increase of 6% and 9% respectively. The lowest level of labor elasticity was observed in the energy sector. Taking into account the elasticity values, we used formula (3.1) to calculate a rise in TFP for the industrial sector as a whole, as well as for large/medium-sized enterprises and small enterprises taken separately. TFP growth rate was analyzed using the indicator of simple average TFP growth rate. The method developed by Bessonova [9] also implies the calculation of another indicator, i.e. weighted average TFP growth rate by the volume of value added. However, within the scope of the present research, the calculation of this indicator does not make sense. One of the study’s tasks is to compare TFP growth rates for large and small industrial enterprises. At the same time, the volume of value added of large and medium-sized enterprises will be greater, which means a strong preponderance of such businesses and, as a result, a distortion of the TFP indicator. For this reason, we deliberately exclude the calculation of this indicator from our research. Dynamics of average TFP growth rates for 2013–2017 illustrates that their values for large and medium-sized enterprises significantly exceeds similar values for small enterprises (Fig. 4.2). 52 S.V. OREKHOVA, E.V. KISLITSYN Copyright ©2019 ASSA Adv. in Systems Science and Appl. (2019) Note: based on the authors’ calculations. Fig. 4.2. Average TFP growth rates by size of enterprises In comparison with 2013, large and medium-sized enterprises’ TFP growth rates in 2014 was 4.4%, whereas small companies demonstrated a 1.4% drop in growth. In the post-crisis period (after 2014), small industrial enterprises experienced an increase of up to 1.2%, but it was still significantly lower than that of other enterprises. Let us look at the dynamics of changes in TFP for the 10 industries. Figure 4.3 shows the values of TFP average growth in 2014. Note: based on the authors’ calculations. Fig. 4.3. TFP average growth in 2014 The period under consideration is characterized by both the decline in the global economic situation and the worsening national institutional environment in Russia. This results in the fact that more than 50% of the industries experience a negative TFP growth rate, regardless of the size of enterprises. However, for the mining, food, woodworking, pulp and paper industries and metallurgy, TFP growth of large and medium-sized industrial enterprises significantly exceeds that of small businesses. It is also noteworthy that TFP decline rate of large enterprises 0,044 -0,014 0,069 -0,023 -0,030 -0,020 -0,010 0,000 0,010 0,020 0,030 0,040 0,050 0,060 0,070 2017 2016 2015 2014 -8,0% -6,0% -4,0% -2,0% 0,0% 2,0% 4,0% 6,0% 8,0% Large and medium-sized enterprises Small enterprises Mining Food industry Light industry Woodworking, pulp and paper Petrochemical industry Mineral industry Metallurgy Mechanical engineering Industrial service Energy industry EVALUATING COMPARATIVE TOTAL FACTOR PRODUCTIVITY IN THE RUSSIAN INDUSTRY 53 Copyright ©2019 ASSA. Adv. in Systems Science and Appl. (2019) operating in industrial service and petrochemical industry are much lower than that of small ones. In general, in 2014, small enterprises of two industries only had a positive dynamic of TFP growth. Note: based on the authors’ calculations. Fig. 4.4. TFP average growth in 2015 Figure 4.4 demonstrates that, compared with the previous period, TFP growth rate in 2015 changed dramatically. For all the industries, with the exception of energy production, TFP growth rate of large and medium-sized enterprises significantly exceeded that of small enterprises. The biggest gap – from 6 to 14% – was characteristic of mining, food, light and petrochemical industries. In 2014–2015, the strongest TFP growth was achieved by small enterprises engaged in woodworking, pulp and paper industry – 3.6%, petrochemical industry – 3.5% and light industry – 3.2%. The same indicators for large and medium-sized businesses equaled 6.1%, 9.1% and 8.6% respectively. Note: based on the authors’ calculations. Fig. 4.5. TFP average growth in 2016 -8,0% -6,0% -4,0% -2,0% 0,0% 2,0% 4,0% 6,0% 8,0% Large and medium-sized enterprises Small enterprises Mining Food industry Light industry Woodworking, pulp and paper Petrochemical industry Mineral industry Metallurgy Mechanical engineering Industrial service Energy industry -8,0% -6,0% -4,0% -2,0% 0,0% 2,0% 4,0% 6,0% 8,0% Large and medium-sized enterprises Small enterprises Mining Food industry Light industry Woodworking, pulp and paper Petrochemical industry Mineral industry Metallurgy Mechanical engineering Industrial service Energy industry 54 S.V. OREKHOVA, E.V. KISLITSYN Copyright ©2019 ASSA Adv. in Systems Science and Appl. (2019) In 2016, the absolute growth of average factor productivity of large and medium-sized enterprises was typical of only 6 out of 10 industries under study (Fig. 4.5): mining, woodworking, pulp and paper, mineral industry, mechanical engineering, industrial service and energy industry. Moreover, in all sectors, excluding industrial service, the growth exceeded that of small and micro-enterprises. Other industries experienced a decline in TFP, but only in light industry this decline was greater for small enterprises. The greatest TFP growth among small enterprises was observed in mining (2.6%), energy industry (2.4%) and industrial service (1.1%). Note: based on the authors’ calculations. Fig. 4.6. TFP average growth in 2017 In 2017, only 50% industries encountered an increase in TFP of large and medium-sized enterprises, which dominated over the same indicator for small businesses. Only small enterprises operating in metallurgy demonstrated a positive dynamic of TFP growth (0.9%), in contrast to total factor productivity of large businesses (–2.2%). The research results indicate that small industrial enterprises are characterized by low TFP growth rates. The general trend for such businesses is a decline in total factor productivity. In 2017, the decrease in TFP for small enterprises in the petrochemical industry was 11.3%; food industry – 4.7%; woodworking, pulp and paper industry – 2.2. However, in 2016–2017, small enterprises of some industrial sectors exhibited a slight gain in TFP (mining – 2.7%; engineering – 1.7%; industrial services – 1.3%; metallurgy – 0.9%). Most industrial sectors experienced an increase in total factor productivity of large and medium-sized enterprises. A sharp fall in TFP growth rates in 2017 was recorded only for large enterprises engaged in food and woodworking industries. Having compared the data, we found that small enterprises’ TFP growth exceeded that of the rest of the enterprises only in 2016–2017 in metallurgy (small businesses’ TFP growth was 0.4 and 0.9% respectively). In all other cases, even if TFP growth rate of small businesses in absolute value is greater than that of large and medium-sized enterprises, the value of this indicator is negative, that is, only if there is no TFP growth per se. 5. CONCLUSION The period under consideration is not long enough to project further TFP dynamics. However, it is possible to draw a number of important conclusions. -8,0% -6,0% -4,0% -2,0% 0,0% 2,0% 4,0% 6,0% 8,0% Large and medium-sized enterprises Small enterprises Mining Food industry Light industry Woodworking, pulp and paper Petrochemical industry Mineral industry Metallurgy Mechanical engineering Industrial service Energy industry EVALUATING COMPARATIVE TOTAL FACTOR PRODUCTIVITY IN THE RUSSIAN INDUSTRY 55 Copyright ©2019 ASSA. Adv. in Systems Science and Appl. (2019) Firstly, the hypothesis that large enterprises develop faster than small ones was confirmed. There are several possible explanations for this phenomenon and each of them has to be checked and verified in further studies. On the one hand, there are objective reasons behind poor efficiency of small businesses. One of them is a vague possibility to get increasing returns due to economies of scale. Since we only examine the industrial sector, this factor can be of considerable importance. In addition, industry in Russia is vertically or horizontally integrated structures, where auxiliary production is outsourced. Thus, small industrial enterprises in Russia are poorly performing divisions of large businesses. On the other hand, there are subjective factors associated with small industrial business in Russia lagging behind in terms of technology and innovation. It means that the technological factor of TFP growth is more attributed to the development of technical equipment, rather than technological innovation. Large businesses possess more advanced and productive types of fixed assets, which supports the conclusions about the rental, extensive growth of the Russian industry. Secondly, the industrial sectors under review are characterized by a significant companies differentiation, which leads to a slowdown in the economic growth of the entire industrial production, and consequently, the processes of re-innovation of the Russian economy. The results obtained are confirmed by other studies on total factor productivity of the Russian industry. However, the previous research made conclusions “about a large group of inefficient enterprises that continue functioning in the market ...” [8, p. 31], but failed to reveal what kind of enterprises they were and what features they had. We suppose that the current paper lays the foundation for the study of such enterprises’ behavior. 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