|Generate QR code
Open Access
Subscription Access
Endogenous Household Classification: Russian Regions
https://doi.org/10.32609/0042-8736-2021-5-107-128
Abstract
In order to study the structure of society, sociologists usually distinguish several homogeneous social groups, or classes. The most common division consists of three groups: upper, middle and lower classes. Such a partition is traditionally based on a subjective (exogenous) criteria adopted by a particular researcher. In this paper, the distribution of households in Russian federal districts is modeled as a mixture of three lognormal distributions. The mixing proportions (probabilities) of the mixture components and the corresponding distribution parameters are modeled as functions of the individual characteristics of households. The result is an endogenous decomposition of household sample into three clusters (lower, middle, upper). This classification allows analyzing the difference between regions and the patterns of intergroup dynamics in the period 2014—2018. The approach used in this work has demonstrated great flexibility in analyzing the distribution of income, the dynamics of this distribution over time, as well as a migration between relatively homogeneous clusters. The use of mixture density function with endogenously determined probabilities allows for precise detection of the effects of the income heterogeneity determinants within each cluster.
Supporting agencies: The reported study was funded by RFBR, project number 20-310-90064
About the Authors
A. R. Nartikoev
HSE University
Russian Federation
Russian Federation
Alan R. Nartikoev
Moscow
A. A. Peresetsky
HSE University
Russian Federation
Russian Federation
Anatoly A. Peresetsky
Moscow
References
1. Aivazian S. A. (2012). Analysis of population life quality and lifestyle. Moscow: Nauka. (In Russian).
2. Anikin V. A. (2020). Social classes of the new Russia: Unequal and different. Sotsiologicheskie Issledovaniya, No. 2, pp. 31—42. (In Russian).] https://doi.org/10.31857/S013216250008492-4
3. World Bank (2014). Russian economic report No. 31: Confidence crisis exposes economic weakness. Moscow: The World Bank in the Russian Federation.
4. Diday E. (1985). Methods of data analysis. Approach based on dynamic clusters. Moscow: Finansy i Statistika. (In Russian).
5. ISRAS (2014). Middle class in modern Russia: 10 years later. Analytical report. Moscow: Institute of Sociology of the Russian Academy of Sciences. (In Russian).
6. Maleva T. M. (2008). Forming mass middle class: Expectations and reality. Paper presented at the 3rd Russian sociological congress, Moscow, HSE, October 22. (In Russian).
7. Nartikoev A. R., Peresetsky A. A. (2019). Modeling the dynamics of income distribution in Russia. Applied Econometrics, No. 54, pp. 105—126. (In Russian).
8. Tikhonova N. E. (2016). The impact of crisis on the life of Russian middle class. Obshchestvennye Nauki i Sovremennost, No. 4, pp. 48—64. (In Russian).
9. Tikhоnova N. E. (2020). The worldviews and identities of the mass strata of modern Russian society. Universe of Russia, Vol. 29, No. 1, pp. 6—30. (In Russian). https://doi.org/10.17323/1811-038X-2020-29-1-6-30
10. Tikhоnova N. E., Mareeva S. V. (2009). Middle class: Theory and reality. Moscow: Alpha-M. (In Russian).
11. Chotikapanich D., Griffiths W. (2008). Estimating income distributions using a mixture of gamma densities. In: D. Chotikapanich (ed.). Modelling income distributions and Lorenz curves. New York: Springer, pp. 285—302.
12. Cowell F. A., Flachaire E. (2015). Statistical methods for distributional analysis. In: A. B. Atkinson, F. Bourguignon (eds.). Handbook of income distribution, Vol. 2A. North-Holland: Elsevier, pp. 359—465. https://doi.org/10.1016/B978-0-444-59428-0.00007-2
13. Cowell F. A., van Kerm P. (2015). Wealth inequality: A survey. Journal of Economic Surveys, Vol. 29, No. 4, pp. 671—710. https://doi.org/10.1111/joes.12114
14. Flachaire E., Nunez O. (2007). Estimation of income distribution and detection of sub-populations: An explanatory model. Computational Statistics and Data Analysis, Vol. 51, No. 7, pp. 3368—3380. https://doi.org/10.1016/j.csda.2006.07.004
15. Fourrier-Nicolai E., Lubrano M. (2020). Bayesian inference for TIP curves: An application to child poverty in Germany. Journal of Economic Inequality, Vol. 18, No. 1, pp. 91—111. https://doi.org/10.1007/s10888-019-09426-6
16. García-Fernández R. M., Gottlieb G., Palacios-González F. (2013). Polarization, growth and social policy in the case of Israel, 1997—2008. Economics: The Open-Access, Open-Assessment E-Journal, Vol. 7, No. 2013-1, pp. 1—40. https://doi.org/10.5018/economics-ejournal.ja.2013-15
17. Gontmakher E., Ross C. (2015). The middle class and democratisation in Russia. Europe-Asia Studies, Vol. 67, No. 2, pp. 269—284. https://doi.org/10.1080/09668136. 2014.1001578
18. Hagenaars A. J. M., de Vos K., Zaidi M. A. (1994), Poverty statistics in the late 1980s: Research based on micro-data. Luxembourg: Office for official publications of the European Communities.
19. López Rodríguez M. I., Barac M. (2019). Inequality of Spanish household expenditure for the 2006—2016 period — Are we converging? Ekonomika Regiona, Vol. 15, No. 3, pp. 780—790. https://doi.org/10.17059/2019-3-12
20. Lubrano M., Ndoye A. A. J. (2016). Income inequality decomposition using a finite mixture of log-normal distributions: A Bayesian approach. Computational Statistics and Data Analysis, Vol. 100, pp. 830—846. https://doi.org/10.1016/j.csda.2014.10.009
21. Pittau M. G., Zelli R. (2006). Empirical evidence of income dynamics across EU regions. Journal of Applied Econometrics, Vol. 21, No. 5, pp. 605—628. https://doi.org/10.1002/jae.855
22. Pittau M. G., Zelli R., Johnson P. A. (2010). Mixture models, convergence clubs and polarization. Review of Income Wealth, Vol. 56, No. 1, pp. 102—122. https://doi.org/10.1111/j.1475-4991.2009.00365.x
23. Pittau M. G., Zelli R., Massari R. (2016). Evidence of convergence clubs using mixture models. Econometric Reviews, Vol. 35, No. 7, pp. 1317—1342. https://doi.org/10.1080/07474938.2014.977062
24. Winkelried D., Torres J. (2019). Economic mobility along the business cycle. The case of Peru. Applied Economics, Vol. 51, No. 18, pp. 1894—1906. https://doi.org/10.1080/00036846.2018.1529401
Supplementary files
Review
For citations:
Nartikoev A.R., Peresetsky A.A. Endogenous Household Classification: Russian Regions. Voprosy Ekonomiki. 2021;(5):107-128. (In Russ.) https://doi.org/10.32609/0042-8736-2021-5-107-128
Views:
1095
Email this article 
