Subject. Digital inequality in Russian regions. Objectives. To identify the key socioeconomic factors influencing the digital development of territories, to assess the digital inequality of the subjects of the Russian Federation. Methods. To identify the factors influencing digitalization and assess the level of digitalization of regions, a MIMIC model based on panel data for 85 regions of Russia for 2021–2023 was used. Prior to the calculations, data standardization was carried out. Next, statistical methods and the Gini Index were applied to assess the presence of digital inequality and its level in Russian regions. Results. The main socioeconomic factors influencing digitalization in the regions of Russia have been identified: the income of the population, the share of civil servants in the number of employed, higher education, the level of innovation in organizations and urbanization. The greatest impact on the level of digitalization in the subjects of the Russian Federation is exerted by such factors as the income of the population (direct dependence) and the share of civil servants in the number of employed (inverse dependence). The regions that have been the leading regions (14 regions of Russia) and lagging regions (10 regions of Russia) in terms of digitalization for three years have been identified. The Gini Index from 2021 to 2023 took the values: 0.2655; 0.2738 and 0.3049, respectively. Conclusions. Calculations have shown that there is an annual increase in digital inequality in the subjects of the Russian Federation and it is possible to record the lag of weak regions, which is becoming more noticeable from year to year. In order to reduce digital inequality in the constituent entities of the Russian Federation, it is important for government authorities to take into account the socioeconomic indicators of the regions and take measures to improve them.
Keywords: digital inequality, digitalization, MIMIC model, digitalization factors, Gini index
References:
Cruz-Jesus F., Oliveira T., Bacao F. Digital divide across the European Union. Information & Management, 2012, vol. 49, iss. 6, pp. 278–291. DOI: 10.1016/j.im.2012.09.003
Kazanbieva A.Kh. [Assessment of the level of digitalization of Russian regions]. Innovatsii i investitsii, 2023, no. 4, pp. 369–375. (In Russ.) EDN: TYZSZA
Billon M., Marco R., Lera-Lopez F. Disparities in ICT adoption: A multidimensional approach to study the cross-country digital divide. Telecommunications Policy, 2009, vol. 33, iss. 10–11, pp. 596–610. DOI: 10.1016/j.telpol.2009.08.006
Vehovar V., Sicherl P., Hüsing T., Dolnicar V. Methodological challenges of digital divide measurements. The Information Society, 2006, vol. 22, iss. 5, pp. 279–290. DOI: 10.1080/01972240600904076
Pick J.B., Tetsushi Nishida. Digital divides in the world and its regions: A spatial and multivariate analysis of technological utilization. Technological Forecasting and Social Change, 2015, vol. 91, pp. 1–17. DOI: 10.1016/j.techfore.2013.12.026
Mia M.A., Hossain M.I., Sangwan S. Determinants of digitalization: Evidence from Asia and the Pacific countries. Digital Transformation and Society, 2024, vol. 3, iss. 4, pp. 340–358. DOI: 10.1108/DTS-10-2023-0097
Nishijima M., Ivanauskas T.M., Sarti F.M. Evolution and determinants of digital divide in Brazil (2005–2013). Telecommunications Policy, 2017, vol. 41, iss. 1, pp. 12–24. DOI: 10.1016/j.telpol.2016.10.004
Ivanova A.I. [Determinants of the digital divide in Russian regions]. Mir ekonomiki i upravleniya, 2023, vol. 23, no. 4, pp. 104–120. (In Russ.) DOI: 10.25205/2542-0429-2023-23-4-104-120 EDN: XVRGSP
Myovella G., Karacuka M., Haucap J. Determinants of digitalization and digital divide in Sub-Saharan African economies: A spatial Durbin analysis. Telecommunications Policy, 2021, vol. 45, iss. 10, no. 102224. DOI: 10.1016/j.telpol.2021.102224
Zhouying Song, Chen Wang, Bergmann L. China’s prefectural digital divide: Spatial analysis and multivariate determinants of ICT diffusion. International Journal of Information Management, 2020, vol. 52, no. 102072. DOI: 10.1016/j.ijinfomgt.2020.102072
Çilan Ç.A., Özdemir M. Measuring domestic digital divide by using latent class analysis: A case study of Turkey. Istanbul University Journal of the School of Business, 2013, vol. 42, iss. 2, pp. 219–234. URL: Link
Sadyrtdinov R.R. [The level of digitalization of the regions of Russia]. Vestnik Chelyabinskogo gosudarstvennogo universiteta, 2020, no. 10, pp. 230–235. (In Russ.) DOI: 10.47475/1994-2796-2020-11029 EDN: LBISHF
Krasnykh S.S. [Measuring the level of digitalisation of Russian regions from the position of inter-regional interaction]. Informatsionnoe obshchestvo, 2023, no. 3, pp. 120–128. (In Russ.) EDN: TRCRIS
Andreeva E.L., Glukhikh P.L., Krasnykh S.S. [Assessing the impact of the digitalization processes on technological export in the Russian regions]. Ekonomika regiona, 2020, vol. 16, no. 2, pp. 612–624. (In Russ.) DOI: 10.17059/2020-2-21 EDN: NNHFJX
Brichka E.I., Evlakhova Yu.S. [Three levels of digital inequality of the Russian population in the financial sphere]. Finansovyi zhurnal, 2023, vol. 15, no. 6, pp. 93–109. (In Russ.) DOI: 10.31107/2075-1990-2023-6-93-109 EDN: YANUMM
Schneider F. Shadow economies around the world: What do we really know? European Journal of Political Economy, 2005, vol. 21, iss. 3, pp. 598–642. DOI: 10.1016/j.ejpoleco.2004.10.002
Kireenko A.P., Nevzorova E.N., Orlova E.N., Polyakova O.Yu. [Shadow economy in Russian regions: an estimation with the mimic model]. Region: Ekonomika i Sotsiologiya, 2017, no. 1, pp. 164–189. (In Russ.) DOI: 10.15372/REG20170108 EDN: VVEZQM
Di Wang, Tao Zhou, Mengmeng Wang. Information and communication technology (ICT), digital divide and urbanization: Evidence from Chinese cities. Technology in Society, 2021, vol. 64, no. 101516. DOI: 10.1016/j.techsoc.2020.101516
Bouwman H., Nikou S., de Reuver M. Digitalization, business models, and SMEs: How do business model innovation practices improve performance of digitalizing SMEs? Telecommunications Policy, 2019, vol. 43, iss. 9, no. 101828. DOI: 10.1016/j.telpol.2019.101828
Jewczak M., Korczak K. Measuring welfare with the use of the MIMIC approach: Evidence for Poland. GeoJournal, 2023, vol. 88, pp. 5109–5127. DOI: 10.1007/s10708-023-10910-w
Darda M.A., Bhuiyan M.A.H. A structural equation model (SEM) for the socio-economic impacts of ecotourism development in Malaysia. PLoS One, 2022, vol. 17, iss. 8. DOI: 10.1371/journal.pone.0273294
Haight M., Quan-Haase A., Corbett B.A. Revisiting the digital divide in Canada: The impact of demographic factors on access to the internet, level of online activity, and social networking site usage. Information, Communication & Society, 2014, vol. 17, iss. 4, pp. 503–519. DOI: 10.1080/1369118X.2014.891633
