Statistical Analysis of Uneven Digitalization Across Russian Regions and Its Impact on the Total Fertility Rate
DOI:
https://doi.org/10.17059/ekon.reg.2024%20-1-7Keywords:
digitalisation, information and communication technologies, digital employment, Russian regions, regional differentiation, total fertility rate, cluster analysis, reproductive behaviour, multivariate statistical analysis, fertility factorsAbstract
Russia has been historically characterised by a high regional socio-economic differentiation, including in the sphere of population. Nowadays, information and communication technologies are spreading at different speeds in various regions. Since the impact of digitalisation on fertility is understudied, it is necessary to find methods for identifying connections between them. The paper assesses the development of Russian regions in terms of the total fertility rate (TFR) in regions differently using information and communication technologies. To this end, the study obtained data from the Federal State Statistics Service, namely, from the section “Information and communication technologies” of reports “Regions of Russia: socio-economic indicators”. Univariate and multivariate statistical methods were applied. Russian regions were clustered according to 16 indicators characterising their digital development. Data for 2014 and 2019 were analysed. Three clusters — «best», «average» and «worst» — were identified. The higher polarisation was observed in 2014: 4 regions were included in the “average” cluster, 29 in the “best” cluster, and 46 in the “worst” cluster. In 2019, the polarisation diminished: 45 regions belonged to the “average” cluster, 33 to the “best” cluster, only 4 to the “worst” cluster (Republics of Dagestan, North Ossetia-Alania, Tyva, Chechen Republic). The results show that the total fertility rate is lower in clusters with higher values of digital development. In 2014–2019, TFR decreased by 31.1% in the “best” and by 47.7% in the “average” clusters; on the other hand, this indicator increased by 37.7 % in the “worst” cluster. However, it is difficult to assess the exact effect of specific digitalisation factors on fertility due to their complexity and interdependence. Further studies can focus on statistical evaluation of the impact of employment on reproductive behaviour.
References
Arkhangelskiy, V. N. (2006). Fertility Factors . Moscow, Russia: TEIS, 399. (In Russ.)
Billari, F., Giuntella, O., & Stella, L. (2019). Does broadband Internet affect fertility? Population Studies, 73 (3), 297–316. https://doi.org/10.1080/00324728.2019.1584327
Chernenko, I. M., Kelchevskaya, N. R., Pelymskaya, I. S., & Almusaedi, H. K. A. (2021). Opportunities and threats of digitalisation for human capital development at the individual and regional levels. Ekonomika regiona [Economy of Region], 17 (4), 1239–1255. https://doi.org/10.17059/ekon.reg.2021-4-14 (In Russ.)
Chung, H., & van der Horst, M. (2018). Women’s employment patterns after childbirth and the perceived access to and use of flexitime and teleworking. Human relations, 71 (1), 47–72. https://doi.org/10.1177/0018726717713828
Fedorova, A., Chudinivskikh, M., & Polents, I. (2022). Legal regulation of work in the digital economy: protecting employees from psychosocial risks. In Zaramenskikh, E., Fedorova, A. (Eds.). Digitalization of Society, Economics and Management. Lecture Notes in Information Systems and Organisation, 53 (pp. 269–277). Cham: Springer. https://doi.org/10.1007/978-3-030-94252-6_20
Friedman, D., Hechter, M., & Kanazawa, S. (1994). A theory of the value of children. Demography, 31 (3), 375–401. https://doi.org/10.2307/2061749
Guldi, M., & Herbst, C. M. (2017). Offline effects of online connecting: the impact of broadband diffusion on teen fertility decisions. Journal of Population Economics, 30 ,69–91. https://doi.org/10.1007/s00148-016-0605-0
Gurova, I. M. (2020). Remote work as a trend of time: results of mass testing. MIR (Modernizatsiya. Innovatsii. Razvitie) [MIR (Modernization. Innovation. Research)], 11(2), 128–147, https://doi.org/10.18184/2079-4665.2020.11.2.128-147 (In Russ.)
Kalabikhina, I. E. (2019). Demographic Reflections on the Digital Economy. Vestnik Moskovskogo universiteta. Seriya 6. Ekonomika [Moscow University Economic Bulletin], 6 , 147–166. https://doi.org/10.38050/013001052019611 (In Russ.)
Kalabikhina, I. E., Abduselimova, I. A., & Klimenko, G. A. (2020). The impact of high speed internet on reproductive behavior in Russia. Vestnik Moskovskogo universiteta. Seriya 6: Ekonomika [Moscow University Economics Bulletin], 6 , 90–103, https://doi.org/10.38050/01300105202065 (In Russ.)
Koropets, O. A., & Tukhtarova, E. Kh. (2021). The impact of advanced industry 4.0 technologies on unemployment in Russian regions. Ekonomika regiona [Economy of Region], 17 (1), 182–196. https://doi.org/10.17059/ekon.reg.2021-1-14 (In Russ.)
Kulkova, I. (2020). The coronavirus pandemic influence on demographic processes in Russia. Human Progress, 6 (1). htttps://doi.org/10.34709/im.161.5 (In Russ.)
Lucia-Casademunt, A. M., García-Cabrera, A. M., Padilla-Angulo, L., & Cuéllar-Molina D. (2018). Returning to work after childbirth in Europe: well-being, work–life balance, and the interplay of supervisor support. Fronties in Psychology, 9 , 68. https://doi.org/10.3389/fpsyg.2018.00068
Madhavan, S., & Adams, A. (2004). Women’s network and the social world of fertility behavior. International family planning perspectives, 29 (2), 58–56. https://doi.org/10.1363/ifpp.29.058.03
Miller, A. R. (2010). The effects of motherhood timing on career path. Journal of population economics, 24 (3), 1071–1100. https://doi.org/10.1007/s00148-009-0296-x
Negroponte, N. (1995). Being digital . New York: Knopf, 243.
Novikova, N. V., & Strogonova, E. V. (2020). Regional aspects of studying the digital economy in the system of economic growth drivers. Journal of New Economy, 21 (2), 76–93. https://doi.org/10.29141/2658-5081-2020-21-2-5
Pisarev, I. V., Byvshev, V. I., Panteleeva, I. A., & Parfenteva, K. V. (2022). Study on readiness of Russian regions for digital transformation. π-Economy, 15 (2), 22–37. htttps://doi.org/ 10.18721/je.15202 (In Russ.)
Pishnyak, A. I., & Nadezhdina, E. V. (2020). Employment of Russian women after childbirth: incentives and barriers. Zhurnal issledovanii sotsial’noi politiki [The Journal of Social Policy Studies], 18 (2), 221–238. http://dx.doi.org/10.17323/727-0634-2020-18-2-221-238 (In Russ.)
Razumova, T. O., & Serpukhova M. A. (2022). Theoretical and methodological foundations for the formation of the work-life balance indicator. Uroven’ zhizni naseleniya regionov Rossii [Living Standards of the Population in the Regions of Russia], 18 (4), 466–476. https://doi.org/10.19181/lsprr.2022.18.4.4 (In Russ.)
Sadyrtdinov, R. R. (2020). The level of digitalization of the regions of Russia. Vestnik Chelyabinskogo gosudarstvennogo universiteta [Bulletin of Chelyabinsk State University], 10 (444), 230–235. https://doi.org/10.47475/1994-2796-2020-11029 (In Russ.)
Sizova, I. L., Karapetyan, R. V., & Orlova, N. S. (2022). Features of the Digital Work Culture of Modern Russian Workers. Monitoring obshchestvennogo mneniya: ekonomicheskie i sotsial’nye peremeny [Monitoring of Public Opinion: Economic and Social Changes] , 5, 231–256. https://doi.org/10.14515/monitoring.2022.5.2246 (In Russ.).
Smirnov, A. V., & Khramova, M. N. (2021). The Impact of the COVID-19 Pandemic on the Reproductive Attitudes of Russian Women. DEMIS. Demograficheskie issledovaniya [DEMIS. Demographic Research], 1 (4), 72–81. https://doi.org/10.19181/demis.2021.1.4.6 (In Russ.)
Stavrou, E., & Ierodiakonou, C. (2011). Flexible work arrangements and intentions of unemployed women in Cyprus: a planned behaviour model. British journal of management, 22 (1), 150–172. http://dx.doi.org/10.1111/j.1467-8551.2010.00695.x
Tonkikh, N. V. (2021). Distance employment and parenthood: women’s opinions. Narodonaselenie [Population], 24 (3), 92–104, https://doi.org/10.19181/population.2021.24.3.8 (In Russ.)
Tretyak, V. P. (2008). Numerous variances of using cluster technology. Nauka. Innovatsii. Obrazovanie [Science. Innovation. Education], 3 (4), 87–98. (In Russ.)
Ward, J. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58 (301), 236–244. https://doi.org/10.1080/01621459.1963.10500845
Yastremsky, B. (1920). The connection between the elements of the peasant economy. Vestnik statistiki [Bulletin of Statistics] , 9–12, 48–69. (In Russ.)
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Natalia V. Tonkikh , Vladislav A. Kataev , Elena M. Kochkina
This work is licensed under a Creative Commons Attribution 4.0 International License.
