Hierarchical Pareto classification of the Russian regions by the population's quality of life indicators

Автор: Mironenkov Alexey A.

Журнал: Economic and Social Changes: Facts, Trends, Forecast @volnc-esc-en

Рубрика: Social development

Статья в выпуске: 2 т.13, 2020 года.

Бесплатный доступ

Improving population’s quality of life is a key goal of the state. In this regard, it is very important to correctly measure its level and, accordingly, classify the country’s regions by quality of life indicators. Most research in this area involves dividing variables into groups, unifying variables in each group and building an integral indicator, grouping or clustering objects as a linear convolution of variables with weights. Such approaches have their drawbacks due to the subjectivity of expert estimates, instability of the coefficients of the main component, inability to work with ordinal data, etc. Thus, the purpose of this study is to build a methodology for classifying the regions of the Russian Federation by quality of life indicators devoid of the above disadvantages. The proposed method is based on the concept of Pareto optimality well-known in Economics according to which all the regions are divided into disjoint classes. After dividing variables into groups we recommend using Pareto class as a representative of the category instead of the traditional unification and construction of intra-group convolutions, which is obtained after the intra-group Pareto classification, and building the final Pareto classification of the regions of the Russian Federation on the basis of the obtained intra-group Pareto classes. The advantage of the proposed approach is that it can be applied on the ordinal data, that is, when some variables are characterized only by their order and there are no exact values for each region. In addition, the algorithm is undemanding for computing power and does not use expert estimates, except for the selection of research variables. The main results of the study are the construction of a classification of the Russian Federation regions by quality of life indicators, comparison with traditional approaches and analysis of the features of the proposed methodology.

Еще

Regional ranking, population's quality of life indicators, stratification, pareto ratio, pareto dominance, pareto classification, pareto optimum, quality of life

Короткий адрес: https://sciup.org/147225448

IDR: 147225448   |   DOI: 10.15838/esc.2020.2.68.11

Список литературы Hierarchical Pareto classification of the Russian regions by the population's quality of life indicators

  • Easterlin R.A. Does Economic growth improve the human lot? Some empirical evidence. In: P.A. David, M.W. Reder. Nations and Households in Economic Growth: Essays in Honor of Moses Abramowitz. N.Y.: Academic Press, 1974, рр. 89–125. Available at: https://doi.org/10.1016/B978-0-12-205050-3.50008-7
  • Aivazyan S.A. Analiz kachestva i obraza zhizni naseleniya [Analysis of the Quality of Life and the Lifestyle of the Population]. Moscow: Nauka, 2012. 432 p.
  • Aivazyan S.A. Russia in cross-national analysis of synthetic categories of the quality of population life. Part 1. Analysis Methodology and Example of its Implementation. Mir Rossii. Sotsiologiya. Etnologiya=Universe of Russia, 2001, no. 4, pp. 59–96. (in Russian)
  • Zhgun T.V. Building an integral measure of the quality of life of constituent entities of the russian federation using the principal component analysis. Ekonomicheskie i sotsial’nye peremeny: fakty, tendentsii, prognoz=Economic and Social Changes: Facts, Trends, Forecast, 2017, vol. 10, no. 2, pp. 214–235. DOI: 10.15838/esc.2017.2.50.12 (in Russian)
  • Kislitsyna O.A. Izmereniya kachestva zhizni/blagopoluchiya: mezhdunarodnyi opyt [Measurement of the Quality of Life / Well-Being: International Experience]. Moscow: Institut ekonomiki RAN, 2016. 62 p. ISBN 978-5-9940-0541-5
  • Mkrtchyan N.V., Karachurina L.B. Migration in Russia: flows and centers of attraction. Demoskop Weekly=Demoskop Weekly, 2014, no. 595–596. Available at: http://www.demoscope.ru/weekly/2014/0595/tema01.php (in Russian)
  • Mints V. On factors of housing prices dynamics. Voprosy ekonomiki=Voprosy Ekonomiki, 2007, no. 2, pp. 111–121. (in Russian)
  • Smet Yves De, Linett Montano Guzmán. Towards multicriteria clustering: An extension of the k-means algorithm. European Journal of Operational Research, 2004, no. 158 (2), pp. 390–398. Available at: https://doi.org/10.1016/j.ejor.2003.06.012
  • Leshchaykina M.V. Econometric cross-country analysis of the living population social comfort. Prikladnaya ekonometrika=Applied Econometrics, 2014, no. 36 (4). Pp. 102–117. (in Russian)
  • Sun Y., Han Jiawei, Zhao Peixiang, Yin Zhijun, Cheng Hong, Wu Tianyi. RankClus: integrating clustering with ranking for heterogeneous information network analysis. Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, 2009, pр. 565–576.
  • Aleskerov F., Ersel H., Yolalan R. Multicriterial ranking approach for evaluatingbank branch performance. International Journal of Information Technology & Decision Making, 2004, vol. 3, no. 2, рр. 321–335. Available at: https://doi.org/10.1142/S021962200400101X
  • Ramanathan R. ABC inventory classification with multiple-criteria using weighted linear optimization. Computers & Operations Research, 2006, vol. 33, no. 3, рр. 695–700. Available at: https://doi.org/10.1016/j.cor.2004.07.014
  • Douissa M.R., Jabeur K. A New model for multi-criteria ABC inventory classification: PROAFTN method. Procedia Computer Science, 2016, no. 96, pp. 550–559. Available at: https://doi.org/10.1016/j.procs.2016.08.233
  • Orlov M.A. An algorithm for multicriteria stratification. Biznes-informatika=Business Informatics, 2014, no. 4 (30), pp. 24–35. (in Russian)
  • Buchanan J.M. The relevance of Pareto optimality. Journal of conflict resolution, 1962, vol. 6, no. 4, pp. 341–354.
  • Rodríguez J.D., Lozano J.A. Multi-objective learning of multi-dimensional Bayesian classifiers. Eighth International Conference on Hybrid Intelligent Systems, 2008, рр. 501–506. DOI: 10.1109/HIS.2008.143
  • Satchidananda Dehuri, Sung Bae Cho. Multi-objective classification rule mining using gene expression programming. 2008 Third International Conference on Convergence and Hybrid Information Technology. Busan, 2008, pp. 754–760. DOI: 10.1109/ICCIT.2008.27
  • Talukder A.K.M., Deb K., Blank J. Visualization of the boundary solutions of high dimensional pareto front from a decision maker’s perspective. Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2018, July, pp. 201–202. DOI: 10.1145/3205651.3205782
  • Désilles A., Zidani H. Pareto front characterization for multiobjective optimal control problems using Hamilton-Jacobi approach. SIAM Journal on Control and Optimization, 2019, no. 57(6), рр. 3884–3910. doi.org/10.1137/18M1176993.
  • Shakin V.V. Pareto classification of the finite sample sets, applications of multivariate statistical analysis in economics and assessment of production quality. Proceedings of V Scientific Conference of CIS States, RAS CEMI, 1993.
  • Kolenikov S. The Methods of the Quality of Life Assessment. NES, 1999.
  • Grinchel’ B.M., Nazarova E.A. Typology of regions by level and dynamics of the quality of life. Ekonomicheskie i sotsial’nye peremeny: fakty, tendentsii, prognoz=Economic and Social Changes: Facts, Trends, Forecast, 2015, no. 3, pp. 111–125. DOI: 10.15838/esc/2015.3.39.9 (in Russian)
  • Polynev A.O., Grishina I.V., Timonin S.A. Quality of life of Russian regions’ population: Research methodology and results of comprehensive evaluation. Sovremennye proizvoditel’nye sily. Ot dogonyayushchego k operezhayushchemu razvitiyu=Modern Productive Forces. From Catching-up Development to Advanced Development, 2012., no. 1, pp. 70–84. (in Russian)
Еще
Статья научная