02 сентября 2017г.
FORECASTING THE DYNAMICS OF ECONOMIC PERFORMANCE OF SMALL ENTERPRISES OF RUSSIA
Gorlov A.V., Grigorev P.V.
Institution of Russian Academy of Sciences Central Economics and Mathematics Institute RAS, Moscow, RussianFederation
* Статья подготовлена при финансовой поддержке РФФИ. Проект № 16-32-01004 – «Исследование тенденций и закономерностей развития малого предпринимательства в России и ее субъектах с использованием методов экономико-математического анализа».
The process of development of small business in Russia is characterized by a periodic change in stages due to recurring cycles in the economy, since the level of growth of the national economy, the effectiveness of it’s functioning and the range of business relationships between business entities can determine the degree of development of any sector, including small business. The analysis of the behavior of the influence of macroeconomic processes on the activity of small enterprises (the main subjects of small business) allows to identify and prevent various economic risks and to reduce the uncertainty factor, since dynamic fluctuations in macroeconomic processes can have a significant impact on the parameters of small firms, which in turn affects on their financial sustainability, profitability and productivity. Evaluation of the influence of external environmental criteria’s on the functioning of small enterprises is of great importance in planning and implementing state policies aimed at maintaining the sustainable growth and productivity of small enterprises, their competitiveness in the international markets and increasing the scale of economic activity.
A small enterprise can be represented as an open, dynamic, self-organizing, developing economic subsystem of small business that is distinguished by its flexibility and adaptability to the conditions of the market environment and various influences from the regulatory and supervisory institutions of the national economy. An important input characteristic of the activity of a small enterprise is interaction with the external (macroeconomic) environment, which should be perceived as an external layer of elements (macroeconomic factors), reflecting the main aspects of the functioning of the Russian economy and implying conditions for small enterprises to conduct entrepreneurial activities. Macroeconomic factors, in complex combination with production factors, directly related to the internal environment of small enterprises and related to the economic process, are able to influence to a certain extent the results of the activity of small enterprises and determine their future prospects.
In order to confirm the synthesized character of the impact of production and macroeconomic factors on the activities of small enterprises, the authors proposed and implemented a comprehensive approach aimed at identifying the most significant factors and assessing their impact on the results of economic activities of small enterprises, which includes the following methodological principles:
1. As the dependent variable, implying the main indicator of the effectiveness of small enterprises – the volume of gross output (Y ).
2. Explaining variables are understood as factors characterizing the internal environment of the economic process of small enterprises – production factors ( L , I ), and the external environment of the functioning of the Russian economy – macroeconomic factors ( X1,2,...k ).
3. A classification of factors is suggested, depending on the social and economic sphere they represent and a set of statistical indicators identifying the factors being investigated (table 1).
4. Information base of the initial statistical data comprised the main indicators of continuous monitoring of the activities of small enterprises and micro enterprises, as well as socio-economic characteristics of 79 constituent entities of the Russian Federation5, officially published by Rosstat [2-4].
5. The starting point of the time range of the study is 2000 year and the final one is 2015 year. The time period in the aggregate was 15 years.
6. The electronic statistical information bank was created in the MC Excel. Data processing and computational procedures were performed using application packages EViews 6.0 and Stata/SE 10.0.
7. As the methods of economic and mathematical tools used correlation analysis, panel analysis of data, the device of production functions, scenario calculations and forecasting.
Table 1
Classification, description and statistical identification of factors
|
Classification factors
|
Description of factors, their statistical identification and symbols
|
|
Production factors
|
Determine the completeness and level of efficiency of use by small enterprises of labor and financial resources:
average number of employees of small enterprises, thousands of people – L
investment in fixed assets of small enterprises, million rubles – I
|
|
External economic factors
|
They reflect the conditions for the entry of small enterprises into foreign markets:
import from the CIS countries and other countries, million rubles – X1
export from the CIS countries and other countries, million rubles – X 2
|
|
Institutional factors
|
Include the norms and conditions for the interaction of small firms with large and medium-sized companies, as well as the exploitation of the available material resources:
number of officially registered enterprises and organizations, units – X 3
availability of fixed assets at full cost, million rubles – X 4
|
|
Innovative factors
|
Provide access to innovation, equipment and technologies, form the basis for the introduction of new technological developments and the development of innovative- oriented entrepreneurship:
used of advanced production technologies, units – X 5
|
|
Financial factors
|
Reflect the openness for small firms to products offered by financial and credit institutions, as well as the implementation by the state of measures of fiscal policy in relation to subject of the small businesses:
deposits of legal entities in rubles, attracted by credit institutions, million rubles – X 6
tax on profits paid by organizations to the budget, million rubles – X 7
|
|
Educational factors
|
Imply accessibility and professional level of cadres for small business, and the availability of special programs for its development:
graduates of qualified specialists with higher professional education, thousands of people – X 8
|
|
Social factors
|
Characterize the level of criminal situation in the country and the number of crimes that directly or indirectly affect the functioning of small businesses:
the number of registered crimes against someone property (robbery, theft), units – X 9
number of registered crimes in the sphere of economy, units – X10
|
|
Infrastructural factors
|
Include the availability, accessibility and quality of transport and logistics facilities for small businesses:
specific gravity of highways with improved coverage in the length of public roads with hard cover, % – X11
|
Based on the use of the correlation analysis, the fact of the presence (absence) of the relationships between the variables studied was established. The obtained values of the correlation coefficients are ranked according to the degree of influence on the observed variable and are represented in the form of a diagonally symmetric correlation matrix in table 2.
Table 2
Matrix of correlation interrelations between factors
|
Y
|
X 6
|
X 4
|
X1
|
X 7
|
X 2
|
X 3
|
X 8
|
L
|
X 5
|
X 9
|
I
|
X 10
|
X11
|
|
1.000
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
0.860
|
1.000
|
|
0.849
|
0.812
|
1.000
|
|
0.836
|
0.891
|
0.773
|
1.000
|
|
0.824
|
0.823
|
0.819
|
0.903
|
1.000
|
|
0.816
|
0.899
|
0.866
|
0.924
|
0.924
|
1.000
|
|
0.790
|
0.737
|
0.705
|
0.863
|
0.909
|
0.835
|
1.000
|
|
0.789
|
0.736
|
0.718
|
0.848
|
0.895
|
0.836
|
0.969
|
1.000
|
|
0.682
|
0.562
|
0.615
|
0.761
|
0.830
|
0.731
|
0.949
|
0.934
|
1.000
|
|
0.602
|
0.439
|
0.569
|
0.534
|
0.578
|
0.507
|
0.606
|
0.633
|
0.655
|
1.000
|
|
0.555
|
0.457
|
0.547
|
0.566
|
0.674
|
0.593
|
0.738
|
0.759
|
0.754
|
0.653
|
1.000
|
|
0.404
|
0.202
|
0.350
|
0.316
|
0.338
|
0.292
|
0.340
|
0.415
|
0.461
|
0.501
|
0.360
|
1.000
|
|
0.274
|
0.189
|
0.269
|
0.317
|
0.435
|
0.307
|
0.559
|
0.550
|
0.630
|
0.546
|
0.763
|
0.256
|
1.000
|
|
0.166
|
0.120
|
0.150
|
0.178
|
0.187
|
0.151
|
0.239
|
0.265
|
0.282
|
0.302
|
0.229
|
0.165
|
0.325
|
1.000
|
The analysis of the correlation matrix indicates the presence of variables of positive relationships of varying degrees of tightness. High communication (|± 0.7| - |± 0.9|) can be traced with the deposits of legal entities, the availability of fixed assets, the volumes of imports and exports, the taxes on profit, the number of enterprises and organizations, the graduates of qualified specialists and the average number of employees of small enterprises. Moderate communication (|± 0.3| - |± 0.7|) is fixed with the used of advanced production technologies, investments in fixed assets of small enterprises and the number of crimes against someone property and in the economic sphere. Weak influence (|± 0.1| - |± 0.3|) is observed in the infrastructure factor (specific gravity of highways with improved coverage).
The results of the correlation analysis made it possible to establish that factors of the external environment are of primary importance for the functioning of small enterprises. Concerning production factors, it should be noted that the growth of volume of output of small enterprises is largely due to the attraction of labor, while investment plays a less prominent role.
The next stage of the research consisted in constructing the production function, combining production (labor and investment) and macroeconomic factors, with the aim of assessing the degree of influence of the analyzed indicators and forecasting the rates of output of small enterprises. The production function in question will have a
multifactor structure 
, where: Y – volume of output of small enterprises, million rubles; A– aggregate factor productivity; L – average number of employees of small enterprises, thousand people; I – investment in fixed assets of small enterprises, million rubles; … k – estimated elasticity coefficients.
X1,2,...k – a set of macroeconomic factors; α , β , ε
Since the study used statistical information on subjects of the Russian Federation, which are characterized by a variety of specific individual characteristics that are not taken into account in the framework of standard regressions, the development of the production function was based on the application of the panel data analysis. Were creating two production functions: with fixed effects and with random effects.
Econometric estimation of the parameters of production functions was carried out according to the logarithms of the initial statistical data. When testing the hypothesis of adequacy, the values of Fisher statistics (for a model with fixed effects) and the Wald’s criterion (for a model with random effects). The main condition for the selection of factors was the avoidance of multicollinearity, and in connection with this, factors were taken into account in constructing production functions, the correlation level of which did not exceed the threshold value |± 0.7|. The performed calculations made it possible to establish that, from the point of view of the proposed limitation, the process of functioning of small enterprises at the regional level adequately describes the following production functions:
Estimates of the parameters of production functions6 and the results of the Hausman’s test, which makes it possible to check the degree of difference between the estimates of models with fixed and random effects for choosing the most suitable, are presented in table 3.
Table 3 Estimates of the parameters of production functions with fixed effects and with random effects and the results of checking their quality
|
Function
|
t-statistics
|
Criterion of adequacy
|
Individual effects
|
Hausman test
|
|
const
|
t1
|
t2
|
t3
|
|
(1)
|
-16.736
(0.000)
|
5.5357
(0.000)
|
11.512
(0.000)
|
41.872
(0.000)
|
2851.90
|
0.68184455
|
144.77
(0.000)
|
|
(2)
|
-16.231
(0.000)
|
1.3818
(0.167)
|
18.374
(0.000)
|
39.542
(0.000)
|
9096.71
|
0.27021045
|
P-values of t-statistic of the estimated coefficients can be considered acceptable, confirming the statistical reliability of the factors considered in the functions, and the high F-statistics and the Wald test indicate the adequacy of the production functions obtained. The share of individual effects was 68% (in the FE-model) and 27% (in the RE-model), which means a predominant influence on the volume of gross output of small enterprises of fixed characteristics, rather than
random ones. Thus, interindividual differences are more pronounced than dynamic ones. This is to be expected, since the sample of the study is made up of territorial entities that practically do not change from year to year, the dynamics of their development occur evenly, without sharp forcing jumps. The results of the Hausman’s test indicate that the best correlation between the volume of gross output of small enterprises and the factors included approximates the production function with fixed effects that reflect the influence of individual factors for each subject of the Russian Federation: the greater the value of specific effects in the region, the higher the probability of an increase of the volume of gross output.
The coefficients of elasticity, reflecting the share of labor resources ( α = 0.244), investments in fixed assets ( β = 0.155) and fixed assets put into operation ( ε = 1.106), allow one to estimate the percentage change of the results
of business activity of
small
firms from the costs of each
factor. A significant excess of the coefficient ε over the rest indicates that the economic performance of small enterprises is characterized by a marginal return on assets (the efficiency of fixed assets), and not the marginal productivity of labor and investments.
To determine the accuracy and suitability of the production function for forecasting, an average relative error was calculated (indicating the correspondence of the model trend to the actual data), which for the period under study was 0.96%, indicating good predictive properties of the created production function and adequacy to the real data.
Forecasting the dynamics of the volume of gross output of small enterprises was carried
out for three years (2016, 2017 and 2018), with using the production function (1). To make predictive calculations, on the basis of chain indices of the growth rates of factors included in the production function, were formulated two possible scenarios – an pessimistic and optimistic, as well as an expected scenario, determined using Hurwitz’s pessimism- optimism
criterion. The development of the proposed scenarios was based on the following methodological principles:
•
When implementing to pessimistic scenario, should expect a decrease in the dynamics of the average number of employees of small enterprises, investment in fixed assets of small enterprises and commissioned fixed assets. To display such a course of events, the average minimum growth rates of factors over the interval studied are taken into account.
•
According to optimistic scenario, an active increase in the average number of employees of small enterprises, investments in fixed assets of small enterprises and commissioned fixed assets is planned for several years.
To
reproduce
the
proposed situation, the
average maximum growth rates of factors for the
period under study were used.
•
To implement the expected scenario, in
accordance with the theory of
pessimism-optimism, a result is selected that occupies an intermediate
position between the pessimistic and
optimistic values. The
expected result is achieved by multiplying the minimum and maximum index by the weighting factors (the authors took the following weights: 0.6 for the pessimistic variant, 0.4 for the optimistic variant) and their summation.
Variance of factors according to the generated scenarios is given in table 4.
Table 4
Characteristics of the proposed scenarios
|
Title of the scenario
|
Variation of factors, %
|
|
L
|
I
|
X 4
|
|
Pessimistic scenario
|
95.90
|
77.33
|
97.57
|
|
Optimistic scenario
|
105.6
|
132.7
|
106.5
|
The results of the forecasted calculations of the volume of gross output
of
small enterprises for the
selected time interval and the basic growth rates
of the values obtained in relation to 2015 are presented in table 5.
Table 5 Scenario calculations and forecast values of the volume of gross output of small enterprises for 2016–2018 years
|
Title of the scenario
|
Forecast, trillion rubles
|
Growth by 2015 year, %
|
|
|
2016
|
2017
|
2018
|
2016
|
2017
|
2018
|
|
Pessimistic scenario
|
16.736
|
15.491
|
14.341
|
10.32%
|
9.55%
|
8.84%
|
|
Optimistic scenario
|
20.522
|
23.294
|
26.441
|
12.65%
|
14.36%
|
16.30%
|
|
Expected
scenario
|
18.250
|
18.612
|
19.180
|
11.02%
|
11.47%
|
11.82%
|
In accordance with the results of scenario calculations, volume of gross output of small enterprises for the forecast period will occur in the following possible variations.
The pessimistic scenario is characterized by a high probability of maintaining the existing independent development of small enterprises without serious economic support, limited access to credit resources, selective state aid, problems of selling products even in conditions of compliance with import substitution policies,
increased tax burden and toughening of registration and licensing procedures, business transfers its activity to the shadow sphere. If these trends continue, the growth in production volumes is expected to decrease by an average of 9.6%.
The realization of the optimistic scenario (in which the increase of volume of gross output of small enterprises will be 14.4% on average) is laid down (mitigating) the negative forms of the pessimistic scenario. First of all, this is the improvement of the mechanism of state support for small
businesses, which should be formed from interrelated blocks: organization-managerial, financial-economic, technical-technological, social and legal. Such a course of events should not be ruled out, despite the economic sanctions imposed on Russia by Western countries, the dynamics
of
factors taken into account in the production function from 2012 year shows a positive trend that, in the future, can positively affect the performance of small enterprises.
In accordance with the expected scenario, calculated by the Hurwitz’s criterion, in 2016-2018 years it is planned to increase of volume of gross output of small enterprises by an average of 11.4%. The realization of this scenario will largely depend
on
a number of planned in 2016-2018
years government measures aimed at developing a system for supporting
small and medium-sized enterprises [1]:
▪
Provision of financial, infrastructural, property, legal and methodological assistance to small enterprises;
▪ Organization of information, marketing, financial
and legal support of investment projects implemented by small enterprises;
▪ Organization and implementation of measures aimed at increasing the share of purchases of goods, works, services from small enterprises;
▪ Maintenance of information interaction with public authorities, local governments and other bodies (organizations) in order to support small businesses;
▪ Attraction of funds of Russian, foreign and international organizations for the purpose of supporting small enterprises;
▪ Improvement of special tax regimes, in particular the taxation system in the form of a single tax on imputed income for certain types of economic activities of small enterprises.
The sample covered data for 2 cities
of federal
significance, 20 republics, 9 regions, 46 oblasts, 1 autonomous region and 1 autonomous region. The ChechenRepublic, the Republic of Crimea and Sevastopol were not included in the sample.
2Unlike regression analysis, in the panel data analysis method, the determination coefficient is not an important component, informing about the quality of dependencies, and therefore is not given.
Bibliography
1.
Forecast of socio-economic development of the Russian Federation for 2016 and for the planned period 2017 and 2018: URL http://economy.gov.ru/
2.
The official
site of the Federal Service of State Statistics of Russia. Section: Official statistics / Entrepreneurship / Institutional transformations in the economy: URL http://www.gks.ru
3.
The official
site of the Federal Service of State Statistics of Russia. Section: Official statistics / Publications / Catalog of publications / Statistical collections / «Regions of Russia. Socio-economic indicators»: URL http://www.gks.ru
4.
The official
site of the Federal Service of State Statistics of Russia. Section: Official statistics / Publications / Catalog of publications / Statistical collections / «Small and medium-sized business in Russia»: URL http://www.gks.ru
