Classification of Russian regions taking into account the structure of informal employment and the level of socio-economic development
https://doi.org/10.21686/2500-3925-2020-6-31-43
Abstract
Purpose of the study. The possibilities for the implementation of national and regional strategic objectives depend on the compliance of the measures taken with the chosen development path. The set of measures includes managerial decisions in the field of labor market regulation and concerning the legalization of informal employment. To make managerial decisions on the regulation of the labor market at the regional level, an objective statistical assessment of the relationship between informal employment and indicators of socio-economic development is required.
The information basis for the regulation of informal employment should be quantitatively assessed stable regularities of the relationship between the parameters of informal employment and the structural and dynamic characteristics of economic growth and development. To identify and evaluate these statistical patterns, based on data from the Federal State Statistics Service in a regional context, a system of statistical indicators has been developed and significant factors of informal employment and socio-economic development have been identified. Given the heterogeneity of the constituent entities of the Russian Federation in terms of the scale and structure of informal employment, the distribution of regions into homogeneous groups is required. The article describes the approaches to the classification of Russian regions, taking into account the level of socio-economic development and the structure of informal employment.
Materials and methods. The article examines an approach to the distribution of regions into homogeneous groups using the methods of cluster analysis based on a group of indicators characterizing those employed in the informal sector, which is based on the assumption that the indicators of informal employment are causally related to indicators of socio-economic development.
Results. Five groups of regions are obtained, homogeneous in terms of the structural characteristics of informal employment and generalized factors of socio-economic development. For the purposes of further interpretation, the selected groups are assessed and ranked relative to the average Russian level of socio-economic development: low level (8 regions), below average (26 regions), average (41 regions), above average (8 regions), high level (2 regions) ...
Conclusion. The resulting classification of Russian regions is a transitional stage to the construction of an econometric model of the relationship between informal employment and indicators of socio-economic development. Further analysis will allow us to assess which indicators have the greatest multiplier effect on the regional economy and to obtain a quantitative assessment of this impact on its growth.
About the Author
E. I. DubravskayaRussian Federation
Elvira I. Dubravskaya - Senior Analyst
Moscow
References
1. Radermacher W.J. Official Statistics in the context of the COVID-19 crisis [Interner]. Available from: https://officialstatistics.com/news-blog/crisespolitics-and-statistics (cited 19.11.2020).
2. ILO. Krizis COVID-19 i neformal'naya ekonomika =ILO. The COVID-19 crisis and the informal economy [Internet]. Available from: https://www.ilo.org/wcmsp5/groups/public/-- -ed_protect/---protrav/---travail/documents/ briefingnote/wcms_745853.pdf. (In Russ.)
3. Mireia J Informal employment in highincome countries for a health inequalities research: A scoping review. Work. Informal employment in high-income countries for a health inequalities research. 2016; 53; 2: 347-356.
4. Bernabè S. Measuring informal employment in transition countries. Note prepared for the WIEGO meeting on «Measuring Informal Employment in Developed Countries. 2008; 31.
5. Gimpel'son V.Ye. "Fighters of the invisible front": who are they and how many are there? History based on Omsk Refinery data. V teni regulirovaniya: neformal'nost' na rossiyskom rynke truda. Pod obshch. red. V.E. Gimpel'son, R.I. Kapelyushnikov = In the shadow of regulation: informality in the Russian labor market. Under total. ed. V.E. Gimpelson, R.I. Kapelyushnikov. Moscow: HSE Publishing House; 2014. (In Russ.)
6. Biryukova S.S., Sinyavskaya O.V. Sinyavskaya O.V. Possible measures to reduce informal employment and hidden wages. Zhurnal Novoy Ekonomicheskoy Assotsiatsii = Journal of the New Economic Association. 2018; 1(37): 193-203. (In Russ.)
7. Binelli C. Wage inequality and informality: evidence from Mexico. IZA Journal of Labor & Development. 2016; 5; 1: 5.
8. Yeliseyeva I., Yuzbashev M.M. Obshchaya teoriya statistiki = General theory of statistics. Moscow: Federal State Unitary Enterprise "Publishing House"; 2004. (In Russ.)
9. Vlasov M.P. Modelirovaniye ekonomicheskikh sistem i protsessov = Modeling of economic systems and processes. Moscow: Publishing House "Infra-M"; 2011. 311 p. (In Russ.)
10. Kaiser H.F. The application of electronic computers to factor analysis. Educational and psychological measurement. 1960; 20; 1: 141–151. Экономическая статистика Т. 17. № 6. 202042 Статистика и экономика
11. Dubrov A.M., Mkhitaryan V.S., Troshin L.I. Mnogomernyye statisticheskiye metody = Multivariate statistical methods. Moscow: Finance and Statistics; 2011. 352 p. (In Russ.)
12. Rezaee M.R., Lelieveldt B.P., Reiber J.H. A new cluster validity index for the fuzzy c-mean. Pattern recognition letters. 1998; 19; 3-4: 237–246.
13. Zhao F., Yang Y., Zhao W. Adaptive clustering algorithm based on max-min distance and bayesian decision theory. IAENG Int. J. Comp. Sci. IJCS. 2017; 44; 2: 24.
14. Ward Jr J.H. Hierarchical grouping to optimize an objective function. Journal of the American statistical association. 1963; 58; 301: 236–244.
15. Tinsley H.E., Brown S.D. Handbook of applied multivariate statistics and mathematical modeling. Academic press; 2000.
16. Oldenderfer M.S., Bleshfild R.K. Faktornyy, diskriminantnyy i klasternyy analiz = Factor, discriminant and cluster analysis. Moscow: Finance and Statistics; 1989. 215 p. (In Russ.)
Review
For citations:
Dubravskaya E.I. Classification of Russian regions taking into account the structure of informal employment and the level of socio-economic development. Statistics and Economics. 2020;17(6):31-43. (In Russ.) https://doi.org/10.21686/2500-3925-2020-6-31-43