Available online at www.HighTechJournal.org HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 653 ISSN: 2723-9535 Innovative Strategy for Selecting Industries for Program-Target Stimulation of Regional Economic Diversification Alan K. Karaev 1 , Oksana S. Gorlova 2, Vadim V. Ponkratov 1* , Margarita L. Vasyunina 2, Andrey I. Masterov 1, Marina L. Sedova 2, Elena V. Mikhina 1 1 Institute for Research on Socio-Economic Transformation and Financial Policy, Financial University under the Government of the Russian Federation, Russia. 2 Department of Public Finance, Faculty of Finance, Financial University under the Government of the Russian Federation, Russia. Received 20 May 2023; Revised 11 August 2023; Accepted 23 August 2023; Published 01 September 2023 Abstract The purpose of this study is to substantiate the approach to the selection of industries for program-target stimulation of regional economy diversification, focusing on developing new strong industries and increasing the economic complexity of the regional economy. The research methodology is based on the application of the concept of revealed comparative advantages and an assessment of the economic complexity of industries and regions of Russia (the Udmurt Republic, Republic of Mordovia, Kaliningrad Region, and Trans-Baikal Territory) using data on tax revenues by economic sectors. The novelty of this research lies in demonstrating the effectiveness of applying the revealed comparative advantage concept, an approach to assessing economic complexity based on the use of tax revenue data by economic sectors, and a strategy for modernizing intermediate opportunities when selecting industries for program-target stimulation of regional economy diversification. The practical significance of the results is determined by the possibilities of their use in the application of program-target mechanisms to solve problems of stimulating the development of individual sectors of the regional economy. Selecting priority areas for diversification based on economic complexity methods can contribute to the improvement of budget balancing, economic growth and sustainable development, and mitigation of interregional inequality. Keywords: Innovation; Budget of a Federal Subject; Tax Revenues by Economic Sectors; Balassa Indicator of Revealed Comparative Advantage; Economic Complexity; Economic Diversification. 1. Introduction Diversification and structural transformation play a significant role in the process of economic development of a country and its regions, contributing to the growth of per capita income, especially in the early stages of development. They are often accompanied by a structural transformation of production and exports, diversification through the production of new products, and improvements in the quality of existing manufactured products [1]. Recently, the COVID-19 pandemic has become another incentive to diversify the regional economy and facilitate the redistribution of resources from less viable to more viable economic sectors. Numerous studies have shown that a country’s product structure predetermines its level of economic growth, future areas for economic diversification, and the degree of income inequality [2–13]. Countries that produce and export a diverse set of complex products, such as automobiles or medical equipment, tend to have significantly lower levels of income inequality and a higher GDP per capita than countries that depend on some products from resource-based industries, such as crude oil [5, 8]. * Corresponding author: ponkratovvadim@yandex.ru http://dx.doi.org/10.28991/HIJ-2023-04-03-013  This is an open access article under the CC-BY license (https://creativecommons.org/licenses/by/4.0/). © Authors retain all copyrights. https://creativecommons.org/licenses/by/4.0/ https://orcid.org/0000-0002-5120-7816 https://orcid.org/0000-0001-7706-5011 HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 654 Russia is a typical example of a developing country whose export basket is dominated by low-value-added products mainly based on crude oil. In contrast, more complex industrial and chemical goods prevail among the main imported products to Russia, such as cars and other vehicles; transmitting equipment for radio broadcasting or television, including receiving equipment; medicines; and machines for automatic data processing and their components, including magnetic or optical readers, etc. The discrepancy between simple exports and complex imports suggests that Russia should diversify its economy by manufacturing more complex products and identifying specific industries for diversification. Without a diversified and complex industrial structure, countries find it difficult to achieve a high standard of living and create well-paid jobs [11, 14, 15]. Natural resource or raw commodity income may temporarily allow the generation or loss of rent income, but such a country is vulnerable to price fluctuations and external shocks. In addition, the country’s long-term economic development prospects are limited due to the lack of technologies to facilitate recombinant growth processes [5, 14, 16]. Therefore, state bodies, especially in developing and emerging countries, strive to promote economic diversification and development. The related question of whether states or markets should be the key agents of structural transformations and economic development is a hotly debated topic. In recent decades, a consensus has emerged: the golden mean between an emphasis on market forces and reasonable government intervention may be necessary to overcome both market upsets and failures, as well as government failures [17–19]. It is necessary to provide incentives to facilitate the processes of development and growth for new types of activities, such as technologies or products that are novel to the domestic economy [14, 20]. There should also be clear criteria and the possibility of restricting the time limit for supporting these new activities if they do not become competitive [17]. The most significant arguments in favor of government intervention to stimulate diversification processes are based on the fact that the private sector tends to focus on the development of its economic activities and some of its areas, which in turn deepens regional specialization. Accordingly, if government economic policy is not oriented toward increasing the diversity of economic activities, this can lead to structural development traps, i.e., to a specialization that is difficult to change [21, 22]. Therefore, from the standpoint of the state’s economic policy, an important issue is how to launch and expand the economic diversification process and ensure the creation and development of sectors that are technologically distant but still connected to the established strengths of the region or country, taking advantage of existing knowledge and competencies. However, the above understanding of the need for economic diversification and sound industrial policy is insufficient to determine the specific type of economic activity or sector that needs to be supported. To solve this problem, actively developing methods for studying networks and economic complexity can be applied, enabling the determination of priority areas for diversification and the production of novel products for each country/region [2, 5]. Moreover, these methods can link a country’s food space with the expected level of income of the population, the economic complexity of production, and income inequality [23, 24]. The vast majority of studies rely on these new empirical methods to identify, in particular, areas for the diversification of regional economies, aimed at increasing their economic complexity [25–28]. At the regional level, diversification may be associated with the emergence of new economic sectors. At the same time, sectors whose development contributes to increasing the economic complexity of the region can be considered priority areas for diversification [29]. Kudrov & Afanasyev [29] conducted an analysis to select a priority area for diversifying the economy of Russian regions on the basis of tax revenue data by economic sectors in the regions, which enables characterizing the structures of regional economies, considering sectors oriented to the foreign and domestic markets, and thereby makes it possible to approximate the assessment of the region’s economic complexity after the emergence of a newly developed sector. As for the diversification of Russia’s regional economy, the state is largely involved in this issue within the framework of state programs implemented in the regions. Grebennikov & Magomedov [30] and Grebennikov et al. [31] noted an important fact indicating that the set of program measures taken within the framework of state programs for the implementation of social projects (providing quality services to the population in the fields of social protection, healthcare, education, public safety, etc.) and commercial projects (initiated by regional business entities) differently impact the balance of regional budgets [30–32]. A hypothesis has been formulated and proven regarding positive feedback between the variable characterizing the share of financing market activities in the expenditures of the consolidated budget of the Russian region and the variable characterizing the share of economic activity taxes in the revenues of the consolidated budget of the Russian region. Karaev et al. [33] analyzed the impact of the share of program expenses in the consolidated budget of the region on reducing the share of gratuitous revenues and increasing the share of economic activity taxes in the income of its consolidated budget, as exemplified by federal subjects such as the Republic of Mordovia, Udmurt Republic, Trans- Baikal Territory, and Kaliningrad Region for the period from 2001 to 2021. It was found that program expenditures of regional budgets for the development of the real sector have a significant impact on ensuring the budget balance of the Republic of Mordovia, Udmurt Republic, and Trans-Baikal Territory on certain time scales. HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 655 Thus, as follows from the research results of [30–33], state intervention in the regions to diversify the regional economy, maintain the balance of regional budgets, reduce interregional inequality, and stimulate the development of certain industries through the mechanism of state programs in the form of financing market activities leads to an increase in the tax base of the regions. Therefore, a further increase in the effectiveness of this intervention requires the identification of priority areas for the diversification of those sectors of the regional economy, the development of which contributes to a targeted increase in the tax base and economic complexity of the region [29]. It is worth highlighting the significance of the research by Hartmann et al. [34], who developed an analytical approach based on economic complexity methods to identify smart strategies for economic diversification and inclusive growth, applied to the Paraguayan economy. This approach reveals the real opportunities for each country/region to diversify its production structures and considers the weight that each country/region places on different socio-economic objectives [35]. Simultaneously, this approach does not ignore the structural constraints imposed by each country’s production structure and capabilities, which helps assess the probable directions for development and the consequences of different diversification strategies. The approach of Hartmann et al. [34] considers and discusses four (of many possible) diversification strategies. The first strategy focuses only on diversification into the most related products/industries. The second strategy focuses on products/industries that already have intermediate levels of revealed comparative advantages (RCA). The third strategy is aimed at relevant products/industries associated with the high income levels of exporting countries. The fourth strategy sets minimum standards for all feasibility and desirability criteria, including income, complexity, technology, and equity. From the viewpoint of the possible use of program-target mechanisms (state programs, national projects) to solve problems in supporting the balance of regional budgets, reducing interregional inequality, and stimulating the development of individual industries, the approach of Hartmann et al. [34] to identifying structural opportunities for smart and inclusive growth is particularly relevant for economies whose production structure is highly dependent on commodities and raw commodity producers, as is the case in Russia and its regions, and provides valuable information on what specific products/industries may be feasible and desirable for the country/region. It is quite obvious that a reasonable combination of industrial, innovation, and social policies and interactive learning between different segments of society is necessary to successfully enter certain industries. In addition, research results on innovation systems in developing countries have shown that economically less developed countries/regions may require a simultaneous policy focus on human development and innovation to create high-performance and effective systems for enhancing competence and innovation and entering new industries successfully [14, 36, 37]. Moreover, examples from high-performing East Asian countries have shown that successful technological upgrading and economic development may require a reasonable combination of industrial and social policies [38, 39]. This assumes a rational combination of policy incentives in new industries and investments in skills training and research in these industries [40- 42]. It should be noted that there are limits to diversification, which are constrained by the country’s level of technological capabilities. In this regard, in the process of diversification, a rapid transition to technologically complex activities is unlikely. Rather, a strategy of gradual diversification should be pursued, with moves into more complex sectors linked to existing strong sectors as technological capabilities and opportunities accumulate over time. Therefore, this research proposes a second strategy [34], which involves the modernization of intermediate capabilities, within which industries with an intermediate level (0.51). The ability to produce and sell a significant number of products/services and, accordingly, achieve an intermediate RCA demonstrates the actual ability to promote the products of this industry in the relevant country/region. A country/region may decide to further promote its existing but still ineffective product/industry to achieve competitiveness in this product/industry in the form of targeted development assistance programs, while each region must be considered unique, the specifics of which do not allow standard management decisions. Thus, the choice of a priority direction for diversifying the regional economy is associated with the choice of an industry/sector for its strong development in the region. The emergence of such a new strong sector, which leads to an increase in the production and tax base of the region and its economic complexity and thereby maintains long-term prospects for economic development, can be considered a priority. In this regard, this research solves two problems: 1) establishing strong industries in the regional economy; 2) selecting priority sectors for diversification of the regional economy with intermediate levels of revealed comparative advantages with the aim of developing them to the level of a strong industry in the regional economy through program- target incentives. HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 656 2. Research Methodology This research uses an approach based on the concept of revealed comparative advantage [43] and the concept of economic complexity assessment developed in [26, 44, 45], based on the use of tax revenue data by economic sectors [46], to establish strong sectors in the economy of the constituent entities of the Russian Federation and select priority industries for their development to the level of a strong industry through program-target stimulation of regional economy diversification. It should be noted that according to Lyubimov et al. [47], the level of export complexity of the economy and the potential for expanding and complicating the export of Russian regions are assessed using data on the export of goods from 80 Russian regions at the level of revealed comparative advantages, along with exports from 148 countries of the world, which allows us to obtain significantly more information on goods exported by Russian regions at the level of revealed comparative advantage. In this research, to avoid some of the shortcomings of the approach to assessing complexity based on data on the volume of exports of products [47], the volume of production of the regional economy, as in Afanasyev & Kudrov [44], is estimated based on tax revenue data by economic sectors, since data on tax revenues reflect the proportions of production volumes of economic sectors in value terms. This approach assumes that the number of strong sectors is considered as an assessment of the region’s economy diversification. Thus, diversification is associated with the emergence of a new strong sector, and the task of setting priorities for developing sectors to the level of strong ones is considered. For clarity, a general diagram of the modeling stages is presented in Figure 1. Figure 1. Simulation Process Flow Chat As shown in Figure 1, at the first stage, a region is selected, and tax revenue data by industry are loaded on the basis of form nom 010122. In the next stage, the index of economic complexity of the region and industry and the indicator of revealed comparative advantages of industries in the selected region are calculated using tax revenue data for all regions. Next, for the selected region, industries with different levels of revealed comparative advantage are distinguished by selecting strong industries in the region and industries with an intermediate level of revealed comparative advantage. Let us consider the methodology for identifying strong sectors of the regional economy in more detail. On the basis of the concept of the revealed comparative advantage [43], we calculate the index of the revealed comparative advantage 𝑅𝐶𝐴𝑐𝑝: RCAcp = (ycp/ ∑ ycp)p / (∑ ycp/ ∑ ycpcpc ) (1) where ycp is the volume of tax revenues from sector p of the economy of region c. Next, a matrix, 𝐴 = (𝑎𝑐,𝑝), is formed containing data on economic sectors and describing the structures of strong sectors/industries of regional economies: 𝑎𝑐,𝑝 = { 1, if 𝑅𝐶𝐴𝑐𝑝 ≥ 1; 0, if 𝑅𝐶𝐴𝑐𝑝 < 1. (2) As it follows from expression (1), 𝑅𝐶𝐴𝑐𝑝 is the ratio of the share of tax revenues from sector p in the total volume of tax revenues from all sectors of the economy of region c to the share of tax revenues from sector p by all regions in the volume of tax revenues from all sectors of the economy of all regions [44]. Select a region of the Russian Federation, download tax revenues data by sectors based on nom 010122 form Based on nom 010122 data, calculate ECI, RCA values for industries in the selected region RCA< 𝟎.𝟓 Lagging industries in the region 0.5𝟏 𝑆𝑡𝑟𝑜𝑛𝑔 𝑖𝑛𝑑𝑢𝑠𝑡𝑟𝑖𝑒𝑠 𝑜𝑓 𝑡ℎ𝑒 𝑟𝑒𝑔𝑖𝑜𝑛 HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 657 If the value of 𝑅𝐶𝐴𝑐𝑝 exceeds the threshold value of unity, then, regarding expression (1), we can assume that the economy of region c has comparative advantages in the output of sector p. Otherwise, the revealed comparative advantages are considered to not exist. Using 𝑅𝐶𝐴𝑐𝑝, matrix A is compiled, which contains data on economic sectors developed in different regions at the level of revealed comparative advantages, defined using expression (1). The rows of this matrix correspond to regions, and the columns present economic sectors. Element aс,p of matrix A is equal to 0 if region c has no revealed comparative advantages in the production of sector p products, determined using expression (1), and otherwise it equals 1 [44]. In accordance with the standard approach to assessing economic complexity [44], based on the description of the structures of strong industries, matrices are formed to determine the economic complexity of regions and industries, which are calculated as eigenvalues and eigenvectors of these matrices. As a result, estimates of economic complexity 𝐸𝐶𝐼𝑐, and 𝐸𝐶𝐼𝑝 are known for each region and industry, respectively. Simultaneously, the economic complexity of a region is proportional to the average level of economic complexity of strong sectors in the structure of its economy: 𝐸𝐶𝐼𝑐 = 𝑎1 ∑ 𝑟𝑐,𝑝𝐸𝐶𝐼𝑝𝑝 , 𝑟𝑐,𝑝 = 𝑎𝑐,𝑝/𝑘𝑐,0, 𝑘𝑐,0 = ∑ 𝑎𝑐,𝑝𝑝 , (3) where 𝑎1 is a positive constant, and the economic complexity of the sector is proportional to the average level of economic complexity of the regions in which this sector has a strong economic structure: 𝐸𝐶𝐼𝑝 = 𝑎2 ∑ 𝑟𝑝,𝑐 ∗ 𝐸𝐶𝐼𝑐𝑐 , 𝑟𝑝,𝑐 ∗ = 𝑎𝑐,𝑝/𝑘𝑝,0, 𝑘𝑝,0 = ∑ 𝑎𝑐,𝑝𝑐 , (4) where 𝑎2 is a positive constant. If we denote 𝑐 = (𝐸𝐶𝐼𝑐1 , 𝐸𝐶𝐼𝑐2 , ⋯ ) 𝑇 as a column vector of economic complexity values for regions; 𝑝 = (𝐸𝐶𝐼𝑝1 , 𝐸𝐶𝐼𝑝2 , ⋯ ) 𝑇 as a column vector of economic complexity values for sectors; 𝑅1 = (𝑟𝑐,𝑝), 𝑅2 = (𝑟𝑝,𝑐 ∗ ) as weight matrices, the economic complexity of the region is determined as the eigenvector of 𝑅1𝑅2 matrix and the economic complexity of the sector is the eigenvector of 𝑅2𝑅1 matrix [43]. Thus, matrix = (𝑎𝑐,𝑝) makes it possible to calculate the characteristics of the level of the region’s economy diversification, identifying strong sectors whose products the region produces at the level of revealed comparative advantages. In this research, matrix = (𝑎𝑐,𝑝), containing data on strong economic sectors, is constructed on the basis of tax revenue data for 85 sectors in 85 regions of Russia for 2021*, and constants in Equations 3 and 4 are equal 𝑎1= 1.9305; 𝑎2= 1.9756, respectively [44]. The following constituent entities of the Russian Federation are considered as regions for which initial data are generated and the problem of choosing priority directions for economic diversification is solved: the Udmurt Republic, the Republic of Mordovia, the Kaliningrad Region, and the Trans-Baikal Territory†. 3. Results and Discussion The calculation results for assessing possible areas for diversifying the economy of the analyzed regions are presented in Tables 1-8. Table 1 presents the results of calculating the identified strong sectors of the economy of the Udmurt Republic with the indicator of revealed comparative advantages 𝑅𝐶𝐴𝑐𝑝 > 1. Table 2 shows the economic sectors of the Udmurt Republic with intermediate values of the indicator of revealed comparative advantages 0.5 < 𝑅𝐶𝐴𝑐𝑝 < 1. In Tables 1 and 2, the first column reflects the industry line code, in accordance with form nom 010122, the second column presents the assessment of the economic complexity of the industry (ECIp), and the third column contains decoding of the industry. In Table 2, the fourth column reflects the assessment of the indicator of the revealed comparative advantage 𝑅𝐶𝐴𝑐𝑝. Similar results for the economy of the Republic of Mordovia are presented in Tables 3 and 4; for the Kaliningrad Region in Tables 5 and 6; and for the Trans-Baikal Territory in Tables 7 and 8. The research results can become the basis for choosing priority industries in the regional economy (within the framework of the second strategy [34] – modernization of intermediate opportunities) for their development to the level of strong industries through program-target incentives of regional economy diversification. Let us consider in more detail the results obtained for the constituent entities of the Russian Federation selected in this study: the Udmurt Republic, the Republic of Mordovia, the Kaliningrad Region, and the Trans-Baikal Territory. * At the time of this scientific research, (2022), these were available tax revenue data for various sectors of the regional economy. † The choice of these regions of Russia was initiated by the customer, in whose interests the research was conducted: the Kaliningrad Region is a western subject of the Russian Federation; the Trans-Baikal Territory is a region from the eastern part of Russia; and the Udmurt Republic and the Republic of Mordovia are regions of the Volga (Privolzhsky) Federal District in the central part of Russia. HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 658 3.1. The Udmurt Republic The results of calculating the indicators of revealed comparative advantages and economic complexity of industries in the Udmurt Republic (Tables 1 and 2) made it possible to identify strong industries (RCA>1) and industries with intermediate levels of revealed comparative advantages (0.5 < RCA <1) in the descending order of RCA values. Table 1. Strong industries in the Udmurt Republic with RCA>1 in 2021 Line code 𝑬𝑪𝑰𝒑 (non-standardized) Industry 1025 -0.0327 Mixed farming 1036 -0.0724 Mining and quarrying 1050 -0.2840 Extraction of crude petroleum and natural gas 1055 -0.3892 Extraction of crude petroleum and associated petroleum gas 1084 -0.3560 Mining support service activities 1100 0.0345 Manufacture of dairy products 1110 0.0218 Manufacture of beverages 1125 0.0740 Manufacture of wearing apparel 1130 -0.0078 From line 1129: dressing and dyeing of fur 1133 0.0056 Manufacture of wood and products of wood and cork, except furniture, manufacture of articles of straw and plaiting materials 1177 0.0290 Manufacture of basic metals and fabricated metal products, except machinery and equipment 1190 -0.1120 Manufacture of basic precious and other non-ferrous metals 1211 0.0038 Manufacture of computer, electronic, and optical products 1220 -0.0042 Manufacture of electrical equipment 1227 0.0140 Manufacture of machinery and equipment N.E.C. 1257 0.0268 Manufacture of gas and distribution of gaseous fuels through mains 1261 0.0168 Water collection, treatment, and supply Table 2. Industries in the Udmurt Republic with intermediate RCA (0.51) and industries with intermediate levels of revealed comparative advantages (0.51 in 2021 Line code 𝑬𝑪𝑰𝒑 (non-standardized) Industry 1015 0.0276 Agriculture, forestry, hunting, and fishing 1020 0.0420 Crop and animal production, hunting, and related service activities 1025 -0.0327 Mixed farming 1087 0.0540 Manufacturing 1090 0.0460 Manufacture of food products 1100 0.0345 Manufacture of dairy products 1105 0.0480 Manufacture of sugar 1110 0.0230 Manufacture of beverages 1133 0.0050 Manufacture of wood and products of wood and cork, except furniture, manufacture of articles of straw and plaiting materials 1162 0.0520 Manufacture of basic pharmaceutical products and preparations 1165 0.0530 Manufacture of rubber and plastic products 1168 0.0330 Manufacture of other non-metallic mineral products 1177 0.0290 Manufacture of basic metals and fabricated metal products, except machinery and equipment 1178 0.0560 Manufacture of basic metals 1190 -0.1120 Manufacture of basic precious and other non-ferrous metals 1200 0.0360 Casting of metals 1211 0.0960 Manufacture of computer, electronic, and optical products 1220 -0.0040 Manufacture of electrical equipment 1259 0.0110 Water supply, sewerage, waste management, and remediation activities 1261 0.0168 Water collection, treatment, and supply 1263 0.0140 Waste collection, treatment, and disposal activities, materials recovery, remediation activities, and other waste management services 1270 0.0090 Construction 1295 0.0070 Wholesale and retail trade and repair of motor vehicles and motorcycles 1301 -0.0270 Wholesale trade, except for motor vehicles and motorcycles 1320 -0.0840 Transportation and storage 1321 -0.0780 Land transport and transport via pipelines 1327 -0.0820 Freight transport by road and removal services 1328 -0.1200 Transport via a pipeline HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 660 Table 4. Industries in the Republic of Mordovia with intermediate RCA (0.51) and industries with intermediate levels of revealed comparative advantages (0.51 in 2021 Line code 𝑬𝑪𝑰𝒑 (non-standardized) Industry 1015 0.0276 Agriculture, forestry, hunting, and fishing 1033 0.0140 Fishing and aquaculture 1081 -0.0840 Other mining and quarrying 1090 0.0460 Manufacture of food products 1095 0.0510 Processing and preservation of meat and production of meat products 1100 0.0345 Manufacture of dairy products 1105 0.0480 Manufacture of sugar 1120 0.0710 Manufacture of textiles 1125 0.0810 Manufacture of wearing apparel 1129 -0.0050 Manufacture of leather and related products 1130 -0.0060 Dressing and dyeing of the fur 1140 0.0610 Printing and reproduction of the recorded media 1165 0.0520 Manufacture of rubber and plastic products 1211 0.0960 Manufacture of computer, electronic, and optical products 1233 0.0350 Manufacture of motor vehicles, trailers, and semi-trailers 1237 0.0270 Manufacture of other transport equipment 1238 0.1400 Building of ships and boats 1243 0.0790 Other manufacturing 1257 0.0280 Manufacture of gas and distribution of gaseous fuels through mains 1258 0.0240 Steam and air conditioning supply 1261 0.0180 Water collection, treatment, and supply 1270 0.0070 Construction 1295 -0.0840 Wholesale and retail trade and repair of motor vehicles and motorcycles 1301 -0.0270 Wholesale trade, except for motor vehicles and motorcycles 1320 -0.0840 Transportation and storage 1321 -0.0780 Land transport and transport via pipelines 1326 -0.0680 Taxi operation. This class also includes: – other renting of private cars with driver 1350 0.0040 Accommodation and food service activities 1364 0.0230 Publishing activities 1373 -0.0720 Telecommunications 1388 -0.0170 Insurance, reinsurance, and pension funding, except compulsory social security The calculation results, presented in Table 5, show that in the Kaliningrad Region, according to tax revenue data for 2021, 8 economic sectors with an intermediate level (0.51) and industries with intermediate levels of revealed comparative advantages (0.51 in 2021 Line code 𝑬𝑪𝑰𝒑 (non-standardized) Industry 1025 -0.0327 Mixed farming 1045 -0.1160 Mining of coal and lignite 1047 -0.1770 Mining of lignite. This class includes washing, dehydrating, pulverizing, and compressing of lignite to improve quality 1065. -0.1810 Mining of metal ores 1080 -0.2240 Mining of non-ferrous metal ores 1081. -0.0840 Other mining and quarrying 1084 -0.3200 Mining support service activities 1095 0.0510 Processing and preservation of meat and production of meat products 1100 0.0345 Manufacture of dairy products 1105 0.0620 Manufacture of sugar 1162 0.1200 Manufacture of basic pharmaceutical products and preparations 1243 0.1080 Other manufacturing 1255 0.0320 Electricity, gas, steam, and air conditioning supply 1256 0.0280 Electric power generation, transmission, and distribution 1258 0.0240 Steam and air conditioning supply 1259 0.0110 Water supply, sewerage, waste management, and remediation activities 1261 0.0180 Water collection, treatment, and supply 1270 0.0100 Construction 1320 -0.0030 Transportation and storage 1321 -0.0780 Land transport and transport via pipelines 1326 -0.0020 Taxi operation. This class also includes: – other renting of private cars with driver 1327 -0.0820 Freight transport by road and removal services 1350 0.0060 Accommodation and food service activities 1355 0.0030 Hotels and similar accommodation 1364 0.0040 Publishing activities 1373 0.0240 Telecommunications Table 8. Industries in the Trans-Baikal Territory with intermediate RCA (0.51, and economic sectors with intermediate levels of revealed comparative advantages (0.51) are necessarily desirable options from the income/tax revenue perspective, complexity, and reducing interregional inequalities. 4.1. Limitations and Future Research Some limitations of the approach based on the strategy of modernizing intermediate capabilities need to be regarded. Although product/industry structure is a significant factor, it is not the only factor explaining income, complexity, and income inequality. Other crucial factors such as institutions, demand structure, geography, technological changes, and innovation capabilities should be considered and studied in more detail. HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 664 Despite all the limitations, the approach considered in this work, based on government support (including through program-target mechanisms) for industries that already have intermediate levels of revealed comparative advantages (RCA), in some constituent entities of the Russian Federation (the Republic of Mordovia, Udmurt Republic, Trans- Baikal Territory, and Kaliningrad Region), provides up-to-date information on structural constraints and opportunities for reasonable and inclusive diversification of the economies in these regions. Since this study was conducted on the basis of tax revenue data from the budgets of constituent entities of the Russian Federation for 2021, further research will consider data on norm 010123 and norm 010124 forms for 2022 and 2023 to analyze the dynamics of changes in revealed comparative advantages of the regions for the period of 2021-2023. In addition, further research is required to study areas for diversifying the economy of the constituent entities of the Russian Federation, the production structure of which is highly dependent on raw commodities and producers, related to the identification of structural opportunities for reasonable and inclusive growth. 5. Declarations 5.1. Author Contributions Conceptualization, A.K.K. and O.S.G.; methodology, A.K.K.; software, A.K.K.; validation, V.V.P., M.L.V., and A.I.M.; formal analysis, V.V.P.; investigation, O.S.G. and M.L.V.; resources, E.V.M.; data curation, M.L.S.; writing— original draft preparation, A.K.K., V.V.P., and E.V.M.; writing—review and editing, O.S.G. and M.L.S.; visualization, A.I.M.; supervision, M.L.V.; project administration, V.V.P.; funding acquisition, V.V.P. All authors have read and agreed to the published version of the manuscript. 5.2. Data Availability Statement The data presented in this study are available in the article. 5.3. Funding This article was prepared based on the results of research conducted at the expense of budget funds under the state assignment of the Financial University under the Government of the Russian Federation. 5.4. Institutional Review Board Statement Not applicable. 5.5. Informed Consent Statement Not applicable. 5.6. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. 6. References [1] Rigby, D. L., Roesler, C., Kogler, D., Boschma, R., & Balland, P. A. (2022). Do EU regions benefit from Smart Specialisation principles? Regional Studies, 56(12), 2058–2073. doi:10.1080/00343404.2022.2032628. [2] Hidalgo, C. A., Winger, B., Barabási, A. L., & Hausmann, R. (2007). The product space conditions the development of nations. Science, 317(5837), 482–487. doi:10.1126/science.1144581. [3] Saviotti, P. P., & Frenken, K. (2008). Export variety and the economic performance of countries. Journal of Evolutionary Economics, 18(2), 201–218. doi:10.1007/s00191-007-0081-5. [4] Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences of the United States of America, 106(26), 10570–10575. doi:10.1073/pnas.0900943106. [5] Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yildirim, M. A. (2014). The Atlas of Economic Complexity. In The Atlas of Economic Complexity. The MIT Press. doi:10.7551/mitpress/9647.001.0001. [6] Cristelli, M., Tacchella, A., & Pietronero, L. (2015). The heterogeneous dynamics of economic complexity. PLoS ONE, 10(2), 117174. doi:10.1371/journal.pone.0117174. [7] Hartmann, D., Jara-Figueroa, C., Guevara, M., Simoes, A., & Hidalgo, C. A. (2017). The structural constraints of income inequality in Latin America. Integration & Trade Journal, 40, 70–85. doi:10.48550/arXiv.1701.03770. HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 665 [8] Hartmann, D., Guevara, M. R., Jara-Figueroa, C., Aristarán, M., & Hidalgo, C. A. (2017). Linking Economic Complexity, Institutions, and Income Inequality. World Development, 93, 75–93. doi:10.1016/j.worlddev.2016.12.020. [9] Hidalgo, C., & Hartmann, D. (2017). Economic Complexity, Institutions and Income Inequality. Debate the Issues: Complexity and Policy Making, OECD Publishing, 1-3. doi:10.1787/9789264271531-en. [10] Gala, P., Camargo, J., & Freitas, E. (2018). The Economic Commission for Latin America and the Caribbean (ECLAC) was right: Scale-free complex networks and core-periphery patterns in world trade. Cambridge Journal of Economics, 42(3), 633– 651. doi:10.1093/cje/bex057. [11] Gala, P., Camargo, J., Magacho, G., & Rocha, I. (2018). Sophisticated jobs matter for economic complexity: An empirical analysis based on input-output matrices and employment data. Structural Change and Economic Dynamics, 45, 1–8. doi:10.1016/j.strueco.2017.11.005. [12] Pinheiro, F. L., Alshamsi, A., Hartmann, D., Boschma, R., & Hidalgo, C. A. (2018). Shooting High or Low: Do Countries Benefit from Entering Unrelated Activities? doi:10.48550/arXiv.1801.05352. [13] Blokhin A.A., & Likhachev A.A. (2022). The impact of institutional factors on economic dynamics in the regions. Economic and Social Changes: Facts, Trends, Forecast, 15, 60–73. doi:10.15838/esc.2022.4.82.4. [14] Hartmann, D. (2014) Economic Complexity and Human Development: How Economic Diversification and Social Networks Affect Human Agency and Welfare. Routledge, United Kingdom. doi:10.4324/9780203722084. [15] Blokhin, A. A., Golovan’, M. V., & Gridin, R. V. (2023). The Contribution of Large, Medium and Small Companies to Industry Dynamics. Studies on Russian Economic Development, 34(1), 51–58. doi:10.1134/S1075700723010033. [16] Hidalgo, C. A., Balland, P. A., Boschma, R., Delgado, M., Feldman, M., Frenken, K., Glaeser, E., He, C., Kogler, D. F., Morrison, A., Neffke, F., Rigby, D., Stern, S., Zheng, S., & Zhu, S. (2018). The Principle of Relatedness. Springer Proceedings in Complexity, 0, 451–457. doi:10.1007/978-3-319-96661-8_46. [17] Rodrik, D. (2004). Industrial policy for the twenty-first century. Harvard University, Cambridge, United States. Available online: https://drodrik.scholar.harvard.edu/files/dani-rodrik/files/industrial-policy-twenty-first-century.pdf (accessed on May 2023). [18] Anureev, S. V. Comparative analysis of budget-tax relations in the oil industry of the USA, Canada, Great Britain and Russia. ECO Journal, 5, 140–161. [19] Solyannikova, S. P. (2021). Appropriate Budgetary Policy for a Changing Economy. The World of New Economy, 15(2), 6–15. doi:10.26794/2220-6469-2021-15-2-6-15. [20] Hausmann, R., & Rodrik, D. (2003). Economic development as self-discovery. Journal of Development Economics, 72(2), 603– 633. doi:10.1016/S0304-3878(03)00124-X. [21] Afanasiev, M. (2022). New guidelines for choosing priority areas of economic diversification based on a system of situational centers. Economics and the Mathematical Methods, 58(4), 29. doi:10.31857/s042473880023017-7. [22] Ali, S. S., & Kaur, R. (2021). Effectiveness of corporate social responsibility (CSR) in implementation of social sustainability in warehousing of developing countries: A hybrid approach. Journal of Cleaner Production, 324, 129154. doi:10.1016/j.jclepro.2021.129154. [23] Rivera, B., Leon, M., Cornejo, G., & Florez, H. (2023). Analysis of the Effect of Human Capital, Institutionality and Globalization on Economic Complexity: Comparison between Latin America and Countries with Greater Economic Diversification. Economies, 11(8), 204. doi:10.3390/economies11080204. [24] Hidalgo, C. A. (2023). The policy implications of economic complexity. Research Policy, 52(9), 104863. doi:10.1016/j.respol.2023.104863. [25] Hausmann, R., Hwang, J., & Rodrik, D. (2011). What You Export Matters. SSRN Electronic Journal, KSG Working Paper No. RWP05-063. doi:10.2139/ssrn.896243. [26] Afanasyev, M. Y., & Kudrov, A. V. (2020). Estimates of economic complexity in the structure of the regional economy. Montenegrin Journal of Economics, 16(4), 43–53. doi:10.14254/1800-5845/2020.16-4.4. [27] Hartmann, D., Zagato, L., Gala, P., & Pinheiro, F. L. (2021). Why did some countries catch-up, while others got stuck in the middle? Stages of productive sophistication and smart industrial policies. Structural Change and Economic Dynamics, 58, 1–13. doi:10.1016/j.strueco.2021.04.007. [28] Stojkoski, V., Koch, P., & Hidalgo, C. A. (2023). Multidimensional economic complexity and inclusive green growth. Communications Earth and Environment, 4(1), 130. doi:10.1038/s43247-023-00770-0. [29] Kudrov, A. V., & Afanasiev, M. Y. (2021). Forecast of new strong sectors in the region’s economy. In Multivariate Statistical Analysis, Econometrics and Simulation of Real Processes, 69–73. doi:10.33276/978-5-8211-0797-8-69-73. http://dx.doi.org/10.1787/9789264271531-en https://drodrik.scholar.harvard.edu/files/dani-rodrik/files/industrial-policy-twenty-first-century.pdf HighTech and Innovation Journal Vol. 4, No. 3, September, 2023 666 [30] Grebennikov, V. (2019). Budgetary Self-Sufficiency as a Problem of the Governmental Programming of Regional Development. Economics and the Mathematical Methods, 55(4), 68. doi:10.31857/s042473880006774-0. [31] Grebennikov, V. (2021). Intergovernmental relations as a problem of program budgeting. Economics and the Mathematical Methods, 57(4), 40. doi:10.31857/s042473880017515-5. [32] Abdrassilov, A., Orynbassarova, Y., & Tvaronaviciene, M. (2023). Exploring Environmental Factors for the Sports Clusters Development. Journal of Environmental Management & Tourism, 14(3), 799-810. doi:10.14505/jemt.v14.3(67).19. [33] Karaev, A. K., Gorlova, O. S., Ponkratov, V. V., Vasyunina, M. L., Masterov, A. I., & Sedova, M. L. (2023). Program-Target Mechanisms to Ensure the Fiscal Balance of the Federal Constituent. Emerging Science Journal, 7(5), 1517–1533. doi:10.28991/ESJ-2023-07-05-05. [34] Hartmann, D., Bezerra, M., & Pinheiro, F. L. (2019). Identifying Smart Strategies for Economic Diversification and Inclusive Growth in Developing Economies. The Case of Paraguay. In SSRN Electronic Journal, 1-40. doi:10.2139/ssrn.3346790. [35] Sen, A. (1999). The possibility of social choice. American Economic Review, 89(3), 349–378. doi:10.1257/aer.89.3.349. [36] Bjorn, J., Edquist, C., Lundvall, B. (2003). Economic Development and the National System of Innovation Approach. In Proceedings of the Innovation Systems and Development Strategies for the Third Millenium, Rio de Janeiro, Brazil, 2–6 November 2003. [37] Lundvall, B. Å., Joseph, K. J., & Chaminade, C. (2009). Handbook of innovation systems and developing countries: Building domestic capabilities in a global setting. Handbook of Innovation Systems and Developing Countries: Building Domestic Capabilities in a Global Setting. Edward Elgar Publishing, Cheltenham, United Kingdom. [38] Ranis, G., Stewart, F., & Ramirez, A. (2000). Economic growth and human development. World Development, 28(2), 197–219. doi:10.1016/S0305-750X(99)00131-X. [39] Amsden, A. H. (2010). Say’s Law, Poverty Persistence, and Employment Neglect. Journal of Human Development and Capabilities, 11(1), 57–66. doi:10.1080/19452820903481434. [40] Hartmann, D., & Buchmann, T. (2016). The comparative technological advantages and international patent network of Turkey and Germany. In International Innovation Networks and Knowledge Migration: The German-Turkish Nexus, Routledge, 225– 245. doi:10.4324/9781315691077. [41] Pyka, A., Kuştepeli, Y., & Hartmann, D. (2016). International innovation networks and knowledge migration: The German- Turkish nexus. In International Innovation Networks and Knowledge Migration: The German-Turkish Nexus, Routledge, London, United Kingdom. doi:10.4324/9781315691077. [42] Bahar, D., & Rapoport, H. (2021). Migration, Knowledge Diffusion and the Comparative Advantage of Nations. SSRN Electronic Journal, IZA Discussion Paper No. 9788, 1-67. doi:10.2139/ssrn.2750271. [43] Balassa, B. (1965). Trade Liberalisation and “Revealed” Comparative Advantage. The Manchester School, 33(2), 99–123. doi:10.1111/j.1467-9957.1965.tb00050.x. [44] Afanasyev, M., & Kudrov, A.V. (2021). Economic complexity and embedding of regional economies’ structures. Economics and the Mathematical Methods, 57(3), 67. doi:10.31857/s042473880016410-0. [45] Afanasyev, M.Yu., & Gusev, A.A. (2022). Approximation of estimates of economic complexity when choosing priority areas for diversification. Digital Economy, 1, 52–59. [46] Federal Tax Service of Russia (2023). Tax revenue data. Form 1NOM 010122. Accrual and receipt of taxes, fees and insurance contributions to the budget system of the Russian Federation for the main types of economic activity, Russia. Available online: https://www.nalog.gov.ru/rn77/related_activities/statistics_and_analytics/forms/8826515/ (accessed on May 2023). [47] Lyubimov, I. L., Gvozdeva, M. A., Kazakova, M. V., & Nesterova, K. V. (2017). Economic complexity of Russian regions and their potential to diversify. Journal of the New Economic Association, 2(34), 94–122. doi:10.31737/2221-2264-2017-34-2-4. https://www.nalog.gov.ru/rn77/related_activities/statistics_and_analytics/forms/8826515/