SMEs in Regions of Russia: Workers Salary
DOI:
https://doi.org/10.7596/taksad.v7i2.1587Keywords:
Small enterprises, Medium enterprises, Wages, Regression model, Function of normal distribution, Unemployment, Subsistence minimum, Incomes of inhabitants, Budget expenditures, Regions.Abstract
This paper aims to explore the impact of factors socioeconomic position of Russian regions on the average wage of employees in small and medium enterprises (SMEs). The present study uses empirical data to obtain new knowledge and estimate the current wages of employees of SMEs in the regions of Russia. It was the data from the official statistical observation of activities of all Russian SMEs for 2015 year. Authors estimated multiple nonlinear regression model on the spatial data. The ratios between wage of employees in SMEs and the subsistence minimum were measured with the use of the function of normal distribution. Studies showed great differentiation of wages of SMEs employees in the regions of Russia. Elaborated instruments for measurement and analysis wages can ensure the development of SMEs in perspective. In order to increase wages, it is necessary to take steps to develop regions and reduce unemployment. Such activities must be carried out by the authorities at the federal and regional levels and included into the development plans of the Russian economy.
References
Acs, Z. J.; Desai, S. & Hessels, J. (2008). Entrepreneurship, economic development and institutions, Small Business Economics, 31: 219-234.
Allanson, P. (1992). Farm size structure in England and Wales, Journal of Agricultural Economics, 43: 137-148.
Anker, R. (2006). Living Wages around the World: A New Methodology and Internationally Comparable Estimates, International Labour Review, 145(4): 309-338.
Arunagiri, S.; Kalaippiriya, K.; Lenggesh Krishna, R.; Maha Vithya, J. & Kalaivani, K. (2015). A Study on Small and Medium Enterprises (SME’S) Growth Domestic Product (GDP) Contribution From Malaysian Economic Perspective, Journal of Business and Management, 17(6III): 62-69.
Balaev, A. I. (2014). Modelling Financial Returns and Portfolio Construction for the Russian Stock Market, International Journal of Computational Economics and Econometrics, 1/2(4): 32–81.
Basareva, Е. G. (2010). Institutional features of the formation of the small business sector in the period of transformational change, Region: Economics and sociology, 3: 249-266.
Baumol, W. J. (2004). Entrepreneurial enterprises, large established firms and other components of the free-market growth machine, Small Business Economics, 23: 9-21.
Betcherman, G. (2013). Labor Market Institutions: A Review of the Literature. Background paper for the world development report. University of Ottawa.
Bolshev, L. N. & Smirnov, N. V. (1983). Tables of mathematical statistic. Moscow. Science, Head redaction of physics-mathematical literature.
Charoenrat, T. & Harvie, C. (2013). Technical Efficiency of Thai Manufacturing SMEs: A Stochastic Frontier Analysis, Australasian Accounting, Business and Finance Journal, 7(1): 97-122.
Decker, R.; Haltiwanger, J.; Jarmin, R. & Miranda, J. (2014). The Role of Entrepreneurship in US Job Creation and Economic Dynamism, Journal of Economic Perspectives, 28(3): 3-24.
Demidenko, E. Z. (1981). Linear and nonlinear regression. Moscow. Finance and statistic.
Development of small and average business. Foreign experience. (2015). Moscow. SME Bank.
Draper, N. & Smith, H. (1998). Applied regression analysis, New York, John Wiley & Sons.
Dumitrescu, L.; Stanciu, O.; Tichindelean, M. & Vinerean, S. (2012). The use of regression analysis in marketing, Research Studies in Business and Economics. 7(2): 94-109.
Dung, P. K. (2017). The effects of minimum wage hikes on employment and wages in Viet Nam’s micro, small, and medium enterprises, Wider Working Paper 2017/95. April. United Nations University (UNU), World Institute for Development Economics Research (WIDER).
Edoho, F. M. (2016). Entrepreneurship paradigm in the new millennium: A critique of public policy on entrepreneurship, Journal of Entrepreneurship in Emerging Economies, 8(2): 279-294.
Federal State Statistics Service of the Russian Federation (2017a). (in Russian). Available from http://www.gks.ru/
Federal State Statistics Service of the Russian Federation (2017b). Rossiiskii statisticheskii ezhegodnik-2017 [The annual statistical abstract of the Russian Federation] (p. 686). (in Russian). Available from http://www.gks.ru/wps/wcm/connect/rosstat_main/rosstat/ru/statistics/publications/catalog/doc_1135087342078
Filatov, S. V. (2008). Some questions of perfection methods of complex evaluation of condition enterprise, Science-practical journal «Economy, statistic and informatics. Journal UMO», MESI, 3: 56-62.
Gmurman, V. Е. (2003). Theory of probability and mathematical statistic, Moscow, High school.
Granberg, A. G. (1988). Modeling of the socialist economy. Moscow. Economics.
Grigore, A. -M. A. & Dragan, I. -M. B. (2015). Entrepreneurship and its economical value in a very dynamic business environment, Amfiteatru Economic, 17(38): 120-132.
Gulin, К. А. & Khamidulina, Е. А. (2005). Problems and prospects of development of small business. Vologda. VNKC CEMI RAS.
Harris, R. J. (1985). A primer of multivariate statistics. New York, Academic Press.
Hollender, М. & Wulf, D. (1983). Nonparametric methods of statistic. Moscow. Finance and statistic.
International Labour Conference, 104th Session (2015). Report IV Small and medium-sized enterprises and decent and productive employment creation. International Labour Office (ILO). Geneva.
Jenčová, A. (2000). Linearization conditions for regression models with unknown variance parameter, Applications of Mathematics, 45(2): 145-160.
Jenish, N. & Prucha, I. R. (2009). Central limit theorems and uniform laws of large numbers for arrays of random fields, Journal of Econometrics, 150: 86-98.
Kanevskaya, I. Y. (2017). Statistical evaluation of the reliability of the correlation and regression model of production figures of agricultural enterprise, Agricultural science journal: economic science, 08: 79-84.
Khodasevich, G. B. (2017). Working with experimental data processing on computer. Part 2. Processing one-dimensional arrays. Available at: http://dvo.sut.ru/libr/opds/i130hod2/index.htm
Kramer, G. (1962). Mathematical methods of statistic. Princeton, New Jersey. Princeton University Press.
Kremer, N. S. & Putko, B. A. (2002). Econometric. Moscow. UNITI-DANA.
Lacombe, G.; Douangsavanha, S.; Vogel, R. M.; McCartneya, M.; Chemin, Y.; Rebelo, L. -M. & Sotoukee, T. (2014). Multivariate power-law models for streamflow prediction in the Mekong Basin, Journal of Hydrology: Regional Studies, 2: 35-48.
Lukyanova, A. L. (2016). The Practice of setting regional minimum wages in the constituent entities of the Russian Federation (2007-2015), Problems of state and municipal management, 1: 81-102.
Marek, L. & Vrabec, M. (2013). Model wage distribution - mixture density functions, International Journal of Economics and Statistics, 1(3): 113-121.
Musienko, S. O. (2017). Financial analysis and forecasting of the results of small businesses performance based on regression model, Actual problems of economy and law, 11(1): 18-33.
Pearson, E. S.; D’Agostino, R. B. & Bowmann, K. O. (1977). Test for departure from normality: Comparison of powers, Biometrika, 64: 231-246.
Pinkovetskaia, I. (2015a). Methodology of research indicators of work entrepreneurial structures, Works of Carel science centre RAN, 3: 83-92.
Pinkovetskaia, I. (2015b). Modeling of indicators activity small and medium entrepreneurship in the regions with the use of function density of normal distribution, Problems of development territories, 6 (80): 93-107.
Poire, M. & Sabel, C. (1984). The Second Divide: Possibilities for Prosperity. New York. Basic Books.
Qin, J.; Niu, Y. & Li, Z. (2014). A Linearization Regression Method to Determine Life Data Distribution Type, Advanced Materials Research, 933: 734-739.
Schneider, A.; Hommel, G. & Blettner, M. (2010). Lineare Regressionsanalyse, Teil 14 der Serie zur Bewertung wissenschaftlicher Publikationen. Deutsches Ärzteblatt International. 107(44): 776-782.
Schröder, C. & Yitzhak, S. (2014). Reasonable sample sizes for convergence to normality, SOEPpapers on Multidisciplinary Panel Data Research at DIW Berlin, Berlin, 714.
Shapiro, S. S. & Francia, R. S. (1972). An approximate analysis of variance test for normality, Journal of the American Statistical Association, 67: 215-216.
Shapkin, А. S. (2003). Economica and financial risks. Estimation, management, investment portfolio. Moscow. Dashkov and К.
Simon-Moya, V. A.; Revuelto-Taboada, L. A. & Ribeiro-Soriano, D. B. (2016). Influence of economic crisis on new SME survival: reality or fiction? Entrepreneurship and Regional Development, 28(1-2): 157-176.
Sjögren, A. & Vikström, J. (2013). How long and how much? Learning about the design of wage subsidies from policy discontinuities. Working Paper, IFAU -Institute for Evaluation of Labour Market and Education Policy, 2013:18.
Sollner, R. (2014). The economic importance of small and medium-sized enterprises in Germany, Wirtschaft und Statistik, January: 40-51.
Terebova, S. V. (2017). The condition and features of development of small business in Russia, Economic and social changes: facts, trends, forecast. 10(1): 178-199.
The strategy of development of small and medium business in the Russian Federation until 2030: Order of the Government of June 2, 2016, No. 1083-r/Russian Federation Code, 24, Art. 3549.
Totmianina K. M. (2011). Review of models of probability of default, Management of financial risks, 01(25): 12-24.
Uyanık, G. K. & Güler, N. (2013). A study on multiple linear regression analysis, Procedia-Social and Behavioral Sciences. 4th International Conference on new horizons in education. 106: 234-240.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. New York. John Wiley & Sons.
Downloads
Published
How to Cite
Issue
Section
License
All papers licensed under Creative Commons 4.0 CC-BY.- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Under the following terms:
Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
