Digital Divide of Regions: Possible Growth Points for Their Digital Maturity

The study was conducted by the Department of State and Municipal Administration of Kazan Federal University in 2022, its purpose is to investigate the process of digitalization of the regional economy and society in order to identify the root causes for the disproportions in their progress and determine the main directions for improving regional management systems to reduce their digital inequality. The objects of the study were 83 regions of Russia, with the exception of the Jewish and Chukotka Autonomous Regions due to the lack of up-to-date statistical information on a number of indicators. The analysis was carried out for 7 years from 2015 to 2021.

Authors used publicly available data from the Federal State Statistics Service of Russia and the Ministry of Economic Development of the Russian Federation, as well as other sources such as the World Bank and the International Telecommunication Union.

Various methods to assess digitalization of a territory enabled to generalize and conclude that it can be considered as a trend for the effective development of regions only if:

• digital transformation will cover everything - business, science, social sphere and ordinary life of citizens, it will be accompanied by the effective use of its results,
• its results will be used both by specialists and by ordinary citizens who will have access to technology and skills to work with it.

A similar approach has been used by the European Commission since 2014 when compiling the DESI (Digital Economy and Society Index), which summarizes the indicators of Europe’s digital efficiency and tracks the progress of the EU countries. This methodology includes 32 assessment criteria in 4 key areas of DESI: 1) Human capital includes Internet user skills (At least basic digital skills, Above basic digital skills, At least basic digital content creation skills) and Advanced skills and development ( ICT specialists, Female ICT specialists, Enterprises providing ICT training, ICT graduates); 2) Connectivity involves the analysis of Fixed broadband take-up (Overall fixed broadband take-up, At least 100 Mbps fixed broadband take-up, At least 1 Gbps take-up); Fixed broadband coverage (Fast broadband (NGA) coverage, Fixed Very High Capacity Network (VHCN) coverage); Mobile broadband (5G spectrum, 5G coverage, Mobile broad-band take-up) and Broadband prices - Broadband price index; 3) Integration of digital technology includes the analysis of the following Digital intensity parameters (SMEs with at least a basic level of digital intensity); Digital technologies for businesses (Electronic information sharing, Social media, Big data); Cloud (AI, ICT for environmental sustainability, e-Invoices), e-Commerce (SMEs selling online, e-Commerce turnover, Selling online cross-border); 4) Digital public services involves the analysis of the following data: e-Government (e-Government users, Pre-filled forms, Digital public services for citizens) and Digital public services for businesses - Open data.

As a part of our study, the methodology for assessing the digitalization of the economy and society in European countries was transformed and adapted taking into account the specifics of statistical accounting in the Russian Federation. As a result, 27 criteria were identified for assessing the digitalization of Russian regions, which were grouped into five blocks: Innovative potential, Digital potential of organizations, Household access to digital technologies, Digitalization of social institutions, Digitalization of the public services.

We collected statistical data on the selected criteria in dynamics over 7 years (2015-2021) and calculated indices for each parameter, taking into account a direct connection with the level of digital development of the region according to the following formula:

$I=\frac{x i-x \min }{x \max -x \min }\quad,$                             (1)

where, Xmax is the maximum value among all analyzed regions, Xi is the value of the region for which the calculation is made, Xmin is the minimum value among all analyzed regions.

After calculating the indices for each year, the final index for each year was calculated, and then the composite index was calculated for each of the five blocks using the simple arithmetic mean formula:

$i=\frac{\sum x}{n}$,                              (2)

where, x1, x2, x3, xn are the individual values of the attribute, and n is the number of units in the aggregate.

Index 1 Innovative potential, includes human resources, costs of innovation, the actual level of innovation activity in the region (see Table 1).

Table 1. Index 1 Innovative potential

Index 2 Digital potential of organizations covers the technical equipment of small, medium and large businesses and the share of various digital technology in them (see Table 2).

Table 2. Index 2 Digital potential of organizations

Index 3 Access of households to digital technology reveals the technical feasibility and the actual level of consumption of modern technologies by the population of the regions (see Table 3).

Table 3. Index 3 Access of households to digital technology

Index 4 Digitalization of social institutions includes educational institutions, healthcare organizations, cultural institutions and libraries (see Table 4).

Table 4. Index 4 Digitalization of social institutions

Index 5 is highlighted as a separate block (see Table 5).

After calculating all indices for each region over 7 years, the ranking method was used. Further clustering of the regions was carried out based on the following logic: regions with indices in the range from 0.80 to 1 were classified as “leaders”, from 0.61 to 0.79 - as “average performers”, and “outsiders” were regions with indices in the range from 0.25 to 0.60.

Table 5. Index 5 Digitalization of the public services

Currently, there are two ratings in Russia that measure the digital transformation of regions. The first one was prepared in 2021 by the RANEPA together with the Ministry for Digital Development, Communications and Mass Media of the Russian Federation. The rating includes 84 Russian regions, Moscow is not included in it because it is several steps ahead of other regions. The main parameters for calculating the rating are indicators of information security, regional measures of financial support for IT, and the transfer of mass socially significant services to electronic form and import substitution. Since our methodology is focused on calculating other parameters, it is inappropriate to compare the results of this rating with ours.

The second national rating of digital maturity of regions was developed by the Ministry for Digital Development, Communications and Mass Media of the Russian Federation [19]. The federal entities included in it were divided into three groups: high (with values over 50% of digital maturity); average (from 25% to 50%); low value of digital maturity (less than 25%). The evaluation criteria were the number of regional specialists using information and communication technologies, the costs of implementing and using digital solutions in industry, agriculture, construction, energy, financial services, healthcare and public administration. The ranking results for 2021 are as follows: nine advanced regions are Moscow, St. Petersburg, the Belgorod, Lipetsk, Moscow, Nizhny Novgorod regions, Tatarstan, Khanty-Mansi Autonomous Okrug - Yugra and Yamalo-Nenets Autonomous Okrug. The group with an average level of “digital maturity” included 62 entities. Low-level digital maturity characterized 14 regions. These are Adygea, Dagestan, Jewish Autonomous region, Ingushetia, Kabardino-Balkaria, Kalmykia, Crimea, the Omsk region, North Ossetia, the Sverdlovsk region, the Stavropol territory, Udmurtia, Chechnya and the Chukotka Autonomous District.

Comparing the results of the rating of the Ministry for Digital Development and our calculations, we see that most of our results coincide: in both ratings, Moscow, St. Petersburg, the Belgorod and Nizhny Novgorod regions, Khanty-Mansi Autonomous Okrug - Yugra and Yamalo–Nenets Autonomous Okrug are among the leaders. The outsiders include traditionally depressed regions of Russia: The Republics of Dagestan, North Ossetia, Kalmykia, Kabardino-Balkaria. In the ranking of the Ministry for Digital Development, the Jewish and Chukotka Autonomous regions also have a low level of digital maturity, and they are excluded in our analysis due to a lack of statistical data. Our calculations coincided with the rating for the “average level of digital maturity” cluster, which included the Altai territory, the Penza, Kirov and Volgograd regions, the Republic of Bashkortostan, Khabarovsk and Krasnoyarsk Territories.

Large-scale programs for the development of the digital economy are being implemented almost all over the world: in the USA, Asian countries and countries of the European Union. In Russia, the topic of developing the digital economy is also being actively discussed, both at the federal and regional levels. Big data serves the basis for making better decisions, the optimal functioning of administrative structures and the efficient allocation of resources within the framework of the implementation of public administration processes, such as revenue management, procurement and interaction with citizens [20, 21]. Digitalization enables governments to provide better quality services with greater efficiency and at lower costs, increases the accountability and transparency of the state, and reduces corruption [22-27].

The potential of modern digital technology suggests possible directions for their use in the cycle of effective public administration. At the stage of results planning, predictive analytics technologies based on “big data” can be used. For example, based on 15 years of historical data, the Pennsylvania Child Protection Bureau developed a solvency calculator using smart modeling. This calculator allows bureau employees to predict in advance the behavior of parents who pay alimony and prevent them from breaking the law. As a result, the level of protection of children rights in Pennsylvania is one of the highest in the United States [28]. Big data can be used for tactical planning and decision making. Thus, the German Federal Labor Agency analyzed the historical data of its clients, including data on unemployed citizens who applied for a job, the measures that the agency took to find them, as well as data on the final results, reflecting how long the unemployed searched for the job timing. As part of the analysis, groups of the unemployed were identified, each having various measures developed to promote their employment. Because of this decision, 10 million euros budgetary savings for the maintenance of the agency were obtained annually, the period for finding a job by the unemployed was reduced, and the level of their satisfaction with the quality of public services in the field of employment increased [29].

Digital technology can significantly transform the processes of monitoring and evaluating the results achieved in the public administration system. In this sense, international initiatives to use “big data” for the purposes of official statistics (including as an alternative to traditionally used methods) are of interest. Such price statistics projects have been implemented in Austria, Belgium, Denmark, Italy, China, Canada, the Netherlands, Norway, Korea, Czech Republic, Hungary, Switzerland, USA and other countries, as well as at the Eurostat level; the main sources of these projects were data from scanners in supermarkets on actual prices for consumer goods, data on product prices published on the Internet.

Thus, the best world practices in digitalization of public administration enable to formulate the key steps that need to be implemented in Russia to expand the use of digital technologies in public administration and reduce the digital divide at the regional level:

1. Setting the task for key information systems. Despite a number of government decisions aimed at coordinating measures for informatization and digital transformation of the public administration system, regional departments continue to be autonomous customers of IT solutions when automating their own activities. The absence of a supra-departmental federal structure that has the appropriate competencies and powers to set system tasks to develop the architecture of IT solutions, plan budget expenditures on information systems and components of the ICT infrastructure, and coordinate informatization in regional departments leads to a further increase in the inefficiency of spending budget funds and lobbying specific IT systems.

2. Auditing the quality of primary data. Making high-quality and timely management decisions is impossible without an adequate assessment of the current situation. Modern challenges of the global digital transformation of the economy are accelerating the pace of managerial decision-making. However, to collect information on basic economic and social processes and indicators, the following outdated hierarchical model is still employed: federation-federal entities-municipalities-primary management bodies. Therefore, very often, the data collected for making managerial decisions become obsolete faster than they are at the disposal of decision makers.

3. Pursuing an evidence-based policy and ensuring the achievement of results. The digitalization of the social life, the emergence of a huge array of “big data” requires a new look at the information basis for making managerial decisions. Without this, the cost of creating digital infrastructure, banks and data centers can be largely inefficient. Taking into account the accumulated problems with the implementation of the plans adopted and decisions made, it is advisable to strengthen the system to ensure the achievement of the results by establishing clear reporting and progress indicators; assistance in drawing up a realistic budget and plans; objective monitoring of the process of achieving results and possible corrective actions, accounting and risk management.

4. Upgrading the strategic planning system. Regional strategies include a significant amount of repetitive analytical materials, the rationale for development scenarios, as a rule, is formal. Dynamic changes in the economy and social sphere devalue the significance of long-term strategic documents. The main attention in strategic planning should be shifted from the analysis of existing trends to the justification and description of the reform, innovation and development.