The Impact of Socio-Economic Factors on the Regional Economic Security Indicator

The economic security of the region is one of the most important indicators characterizing the ability of the regional socio-economic system to achieve economic and social interests. The research takes into account one of these state-regional interests - sustainable economic growth. The aim of the study is to assess the influence of socio-economic factors based on the regression analysis method on the economic growth as an indicator of economic security of Russian Federation regions. The authors used regression modeling as the main method of analysis. The authors used regression models based on statistical data from 85 subjects of the Russian Federation for the period from 2014 to 2021. The most influential factors are the main factors of production (share of the employed population, fixed assets, and investments), foreign trade, which characterizes the openness of the region’s economy, specializing of the region in the mining industry, and the share of the employed population with higher education (human capital). The analysis confirms the possibility of using analysis and modeling tools in the practical activities of executive authorities to solve problems in the field of monitoring the economic security of the region.

Economic growth; Economic security; Regression analysis; Socio-Economic factors

In modern foreign scientific literature, the concept of "Economic Security" and "Economic Insecurity" is considered by scientists at the micro level from the standpoint of an individual's personal economic security from potential economic losses (Osberg and Sharpe, 2014) or is presented as "the degree to which people are protected from economic losses" (Hacker et al., 2014). But economic security is a complex phenomenon that can be analyzed at the level of countries and regions (Polyanin et al., 2020). Economic security is the ability of the regional socio-economic system to ensure sustainable economic growth, social development of territories, and a high quality and standard of living for the population under the negative impact of various factors (Sverdan, 2015).

    Ensuring economic security is an important task of state and regional institutions (Kahler, 2014). The ongoing processes of globalization have a significant impact on the possibility of achieving economic security in countries and regions (Kahler, 2014). Other challenges appear due to a digital transformation which effect the internal social and economic processes of regional development (Arteeva et al., 2022), including the formation of human capital (Zaborovskaia, Nadezhina, and Avduevskaya, 2020), development of various sectors of the economy such as agriculture (Eremina et al., 2022), manufacturing, mining, including gas and oil complex (Khaykin and Toechkina, 2021), and the formation of new types of economy like Digital economy and Circular economy based on new high-tech technologies (Berawi, 2020).

The stages of the study of the impact of socio-economic factors on the regional economic security indicator are shown in Figure 1. In the first stage, we determined the dependent and independent variables. We selected the dependent variable based on the premise that: firstly, this indicator should be a criterion for achieving the economic security of the region; secondly, it should correlated with other socio-economic factors; thirdly, it should be quantifiable. To assess economic security, authors usually use a system of economic and social indicators and an index method (Pak and Andronova, 2023; Akhmetshin et al., 2018). However, despite the variety of indicators used, the main indicator that has a relationship with other socio-economic factors at the regional level is the gross regional product per capita (GRP per capita) and the growth index of GRP per capita (Jones and Vollrath, 2013). We chose the growth index of GRP per capita as a dependent variable in regression models 

Figure 1 Stages of the study the impact of socio-economic factors on the regional economic security indicator

The selection of independent variables (regressors) was based on the provisions of the neoclassical theory of economic growth and the results of research assessing the impact of socio-economic factors on economic growth. We understand socio-economic factors as phenomena that influence economic growth whose nature is determined by economic processes in the region, such as economic and investment activity, foreign trade, as well as social processes, such as developing human capital.

In accordance with the prerequisites of the basic Solow model, economic growth is set using a production function described by three groups of factors: physical capital, labor, and the total productivity of factors characterizing scientific and technological progress, given exogenously (Solow, 2016). The studies of N.G. Mankiw, D. Romer, D.N. Weil, P. Romer, R.J. Barro, J.W. Lee and other researchers expand the theory of economic growth by including factors of scientific and technological progress and human capital to the list of the main production factors (Wilson and Briscoe, 2004). Based on the results of this researches, we identified the main factors of production, which include the value of fixed assets per capita, investments in fixed assets per capita, and share of the employed population. As a factor of scientific and technological development, we used the indicator of internal research and development per capita. This indicator is used to study the development of regional innovation systems (Rudskaya et al., 2022). As a factor of human capital, we used the share of the employed population with higher education and the share of the employed population with secondary specialized education (Bilan et al. 2020; Cuaresma, Doppelhofer, and Feldkircher, 2014). We selected economic structure factors characterizing the openness of the economy and the involvement of the region in foreign trade activities (Rahman and Alam, 2021), as well as factors characterizing the industry specialization of the region (agriculture, industry, and mining). We also used GRP per capita in period t-1 as an in independent variable to assess how the value of GRP per capita in the past affects the increase in the current period (Mudronja, Jugovic, and Skalamera-Alilovic, 2020). The list of variables is presented in Table 1.

We used the growth index of these variables  calculated through the ratio of the values of indicators in period t to the value of the indicator in period t-1 and values of variables for the previous period  In order to ensure the linearity of the models, we chose the logarithmic form (ln) as the main functional form of variables.

Thus, within the framework of the study, we tested the hypothesis that the positive increase in socio-economic factors and past values of variables leads to a positive increase in GRP per capita.

Table 1 Variables for modeling

3.1. Results of a preliminary study of factors

Figure 2 contributes a cartogram of the GRP per capita distribution by subjects of the Russian Federation for 2014 (a) and 2021 (b). The highest values of the GRP per capita in 2014 and 2021 were achieved in economically developed regions of the country as well as in the regions specializing in the mining industry.

The leaders in terms of GRP per capita among regions specializing in the mining industry are Nenets JSC (6.553 million rubles, the share of revenue of mining enterprises in the total revenue of organizations in the region 75%), Yamalo-Nenets JSC (5.585 million rubles, the share of the mining industry 75%), Khanty-Mansiysk JSC-Yugra (2.298 million rubles, the share of the mining industry - 75%), Chukotka JSC (1.927 million rubles, the share of the mining industry 68%). Among the economically developed regions of the country, the leaders are Moscow (1.284 million rubles) and St. Petersburg (1.168 million rubles). These regions also have the highest values of the cost of fixed assets per capita and the volume of investments in fixed assets per capita.

There is a positive trend in the number of regions with GRP per capita: if in 2014 the median value of the indicator was 0.291 million rubles, then in 2021 – 0,388 million rubles. The largest increase is observed in the Magadan region (+136.43%), Sevastopol city (+149.88%), Murmansk region (+134.01%) and St. Petersburg (+126.59%). A large increase in the value of GRP per capita may be associated with a decrease in the population rather than economic growth, which is due to the calculation of this indicator. For example, in the Magadan region, the population in 2021 decreased by 7% compared to 2014, and in the Murmansk region by 5%. However, in regions such as St. Petersburg, there is an increase in population by 5%, and in Sevastopol, the increase was 34%, which suggests that the increase in the value of GRP per capita in these regions attributable to the economic growth. The lowest values of GRP per capita are mainly in the regions of the Southern and Caucasian Federal Districts (such regions as the Republic of Ingushetia, the Chechen Republic, the Kabardino-Balkarian Republic, the Republic of Dagestan, the Republic of Crimea), as well as the Republic of Tyva.

In the regions with the highest GRP per capita values, the highest values of the share of the employed population are observed. The average growth rate of the employed population share in the country for the period from 2014 to 2021 amounted to 4.9%. A slight increase may be attributed to a decrease in the share of the employed population in 52 subjects of the Russian Federation in 2021 compared to 2014.

The largest share of the employed population is in the service sector (58.4%), contributing approximately 52.9% to the country's GDP. The smallest share of the employed is in the mining industry (1.6%), yet this sector accumulates 10.1% of the country's GDP. The least productive industry is agriculture, with 6.3% of the employed population, contributing 4.7% to the country's GDP.

Economically developed regions have high values of the indicator of foreign economic activity. According to the results of 2021, the largest volumes of foreign trade turnover were observed in such regions as Moscow (42.6% of the total foreign trade turnover), St. Petersburg (7.2%), Moscow Region (5.7%), Khanty-Mansiysk Autonomous Okrug (2.3%) and the Republic of Tatarstan (2.2%), close the list of regions by volume of foreign trade turnover, the Republic of Ingushetia, Sevastopol and the Republic of Kalmykia – these regions account for less than 0.001% of the total volume of foreign trade turnover.

The largest share of employed in the Russian Federation have secondary vocational education (45.2%) and higher education (34%). According to the results of 2021, the highest concentration of employed with higher education was recorded in the Central Federal District (40.6%), with secondary vocational education – in the Ural Federal District (48.9%). There is a slight tendency to reduce the share of those employed with secondary general education in favor of higher levels of education. At the same time, there are regions with positive growth rates of those employed without basic general education, mostly remote regions from the federal center.

3.2. Results of regression analysis

As part of the regression analysis, paired regression models were constructed. These models aimed to estimate the influence of the GRP per capita in the t-1 period, along with the growth indices of the main factors of production and the values of factors in the t-1 period, on the GRP per capita growth index. The results are presented in Table 2.

Table 2 Results of regression analysis of main production factors (step 1)

At the next stage, we constructed multiple regression models in which factors of the structure of the economy, innovative development, and human capital were gradually added to the listed factors. The results are presented in Table 3.

In the final step, we constructed a multiple regression model that considered all variables. The model was built using the stepwise tool of Stata, which automatically discarded insignificant variables from the model at a 10% significance level. As a result of excluding outliers from the m1_12 model, the m1_13 model was built at a 10% significance level and the m1_14 model at a 5% significance level. The results of step 3 are presented in Table 4.

Table 3 Results of regression analysis (step 2)

Table 4 Results of regression analysis (step 3)

Figure 3 shows a matrix of partial residual graphs based on the m1_13 model, visualizing the revealed linear relationships between variables. The graphs show a clear negative linear relationship between the growth index of GRP per capita and growth index of GRP per capita in the period t-1, with the growth index of the share of manufacturing and agriculture industry. There are positive linear relationships between the growth index of GRP per capita and other indicators which detailed interpretation is presented in the Discussion of Obtained Results section.

Figure 3 Matrix of partial residual graphs (model m1_13)

3.3.  Discussion of Obtained Results

The results of the research allow us to conclude the following. A positive relationship was revealed between the GRP per capita growth index and the main production factors: the index of growth in the value of fixed assets, as well as the value of fixed assets in the period t-1 at only 10% significance level; the index of investment growth in fixed assets, the value of fixed assets in the period t-1. These findings align with results from prior research (Smirnova and Listopad, 2020). The index of growth in the share of employed in the total number of the population and the share of the employed in the period t-1.

The revealed relationship between the GRP per capita growth index and the foreign trade turnover growth index, as well as the value of foreign trade in the t-1 period, correspond to the results of studies on the positive impact of economic openness on economic growth (Rahman and Alam, 2021).

A positive relationship was revealed between the GRP per capita growth index and the share of those employed with higher education in the period t-1. This relationship suggests that in those regions where the largest share of those employed with higher education was observed, economic growth was more intense. The results obtained correspond to the results of a study on the positive impact of human capital on economic growth (Bilan et al., 2020; Cuaresma, Doppelhofer, and Feldkircher, 2014).

At the same time, a negative relationship was revealed between the GRP per capita growth index and the GRP per capita in the t-1 period. The results obtained differ from the early research results (Mudronja, Jugovic, and Skalamera-Alilovic, 2020). This may indicate that regions of the Russian Federation with higher GRP per capita have lower rates of economic growth, and vice versa: regions with low GRP per capita have higher rates of economic growth. In our opinion, this trend is rather statistical in nature and is due to the effect of a «low base». For the Russian Federation, this trend is characteristic and confirmed at the macro level (Shokhin et al., 2021).

It should be noted that, as in the study (Smirnova and Listopad, 2020), the relationship between GRP per capita and R&D expenditures turned out to be insignificant. There is a negative relationship between the growth index GRP per capita and the share of revenue of organizations in the agricultural sector, which suggests that economic growth is declining in regions where agriculture dominates the economy. Similar results were obtained in a study that found that in agricultural regions, the availability of human capital contradictorily reduces economic growth (Cadil, Petkovova, and Blatna, 2014). In contrast to the results of the study (Smirnova and Listopad, 2020),  our models showed a negative relationship between economic growth and the manufacturing industry. This result may be related to the problems of socio-economic development of Russian regions specializing in the agro-industrial complex described in the literature, as well as the problems of «old industrial regions» reflected in the research of Russian scientists (Sorokina and Latov, 2018).

In our study, we combine the theory of economic security with the theory of neoclassical economic growth in terms of using factor analysis methods to study the influence of socio-economic factors on the indicator of economic security of the region. The conducted factor econometric analysis on the example of the subjects of the Russian Federation based on data from 2014 to 2021 confirms the possibility of using these tools in the practical activities of executive authorities in terms of monitoring the economic security of the region. The proposed and tested approach to the study of the socio-economic factors that influence on the indicator of economic security is universal and can be applied to other countries and regions if there is a sufficient amount of statistical information and software. The methods used to identify the correlations between the factors of socio-economic development and the indicator of economic security (GRP per capita), can be used for classifying and identification of destabilizing and stimulating factors of economic security. This allows the justification for the preventive measures development to respond to changes in the intensity of the impact of destabilizing factors (threats) economic security and scientifically substantiates adjusting the state policy in the field of industrial development, investment, foreign economic activity, as well as human capital development. The limited set of social indicators presented in the form of human capital did not allow us to fully study the impact of the social sphere on economic growth and, consequently, to assess the contribution to ensuring economic security. The most important further direction will be the improvement of the indicator set. It is so important to study the impact of factors such as the quality of life in the region, the presence of informal institutions such as corruption and bureaucracy, and the level of criminality of society on the economic security of the region.

The research was financed as part of the project «Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization» (FSEG-2023-0008).

Akhmetshin, E.M., 2018. Assessment of the Economic Security of the Region (on the Example of Chelyabinsk Region). Journal of Applied Economic Sciences, Volume 13(8), pp. 2309–2322

Antamoshkina, E.N., Rogachev, A.F., 2020. Economic and Mathematical Modelling of Food Security of the Subjects of the Russian Federation. In: Proceedings of the International Scientific Conference ‘Far East Con’ (ISCFEC 2020), pp. 1–7

Arteeva, V., Sokol, I., Asanova, E., Ushakov, D., 2022. The Impact of Digitalization and Infrastructure Development on Domestic Tourism in Russia. International Journal of Technology. Volume 13(7), pp. 1495–1504

Berawi, M.A., 2020. Managing Nature 5.0: The Role of Digital Technologies in the Circular Economy. International Journal of Technology, Volume 11(4), pp. 652-655

Bilan, Y., Mishchuk, H., Roshchyk, I., Kmecova, I., 2020. An Analysis of Intellectual Potential and Its Impact on The Social and Economic Development of European Countries. Journal of Competitiveness, Volume 12(1), p. 22

Cadil, J., Petkovova, L. Blatna, D., 2014. Human Capital, Economic Structure and Growth. Procedia Economics and Finance, Volume 12, pp. 85–92

Cuaresma, J.C., Doppelhofer, G., Feldkircher, M., 2014. The Determinants of Economic Growth in European Regions. Regional Studies, Volume 48(1), pp. 44–67

Eremina, I., Yudin, A., Tarabukina, T., Oblizov, A., 2022. The Use of Digital Technologies to Improve the Information Support of Agricultural Enterprises. International Journal of Technology. Volume 13(7), pp. 1393–1402

Fraymovich, D.Y., Konovalova, M.E., Roshchektaeva, U.Y., Karpunina, E.K., Avagyan, G.L., 2021. Designing Mechanisms for Ensuring The Economic Security of Regions: Countering The Challenges of Instability. In: Institute of Scientific Communications Conference, pp. 569–581

German, O.I., Bobrovskaya, T.V., 2019. Development of Human Capital as a Factor of Economic Security. The Eurasian Scientific Journal, Volume 11(2), pp. 1–7

Hacker, J.S., Huber, G.A., Nichols, A., Rehm, P., Schlesinger, M., Valletta, R., Craig, S. 2014. The Economic Security Index: A New Measure for Research and Policy Analysis. Review of Income and Wealth, Volume 60, pp. S5–S32

Jones, C.I., Vollrath, D., 2013. Introduction to Economic Growth. W. W. Norton

Kahler, M., 2004. Economic Security in an Era of Globalization: Definition and Provision. The Pacific Review, Volume 17 (4), pp. 485–502

Khaykin, M., Toechkina, O., 2021. Service Capital as a Condition for the Sustainable Development of Society. International Journal of Technology. Volume 12(7), pp. 1458–1467

Mudronja, G., Jugovic, A., Skalamera-Alilovic, D., 2020. Seaports and Economic Growth: Panel Data Analysis of EU Port Regions. Journal of Marine Science and Engineering, Volume 8(12), p. 1017

Osberg, L., Sharpe, A., 2014. Measuring Economic Insecurity in Rich and Poor Nations. Review of Income and Wealth, Volume 60, pp. S53–S76

Pak, A.Y., Andronova, I.V., 2023. Assessment of the State of Economic Security of Regional Integration Associations on the Example of the Eurasian Economic Union. Studies on Russian Economic Development, Volume 34(3), pp. 329–334

Polyanin, A., Pronyaeva, L., Pavlova, A., Fedotenkova, O., Rodionov, D., 2020. Integrated Approach for Assessing the Economic Security of a Cluster. International Journal of Technology, Volume 11(6), pp. 1148–1160

Rahman, M.M., Alam, K., 2021. Exploring The Driving Factors of Economic Growth In The World’s Largest Economies. Heliyon, Volume 7(5), p. e07109

Rudskaya, I., Kryzhko, D., Shvediani, A., Missler-Behr, M., 2022. Regional Open Innovation Systems in A Transition Economy: A Two-Stage DEA Model to Estimate Effectiveness. Journal of Open Innovation: Technology, Market, and Complexity, Volume 8(1), p. 41

Shokhin, A.N., Akindinova, N.V., Astrov, V.Y., Gurvich, E.T., Zamulin, O.A., Klepach, A.N., Mau, V.A., Orlova, N.V., 2021. Macroeconomic Effects of The Pandemic and Prospects For Economic Recovery. Voprosy Ekonomiki, Volume 7, pp. 5–30

Smirnova, G.I., Listopad, M.E., 2020. Economic and Mathematical Modeling of Russia’s Economic Security in the Period Under Sanction. Economic Strategies, Volume 2, pp. 32–39

Solow, R.M., 2016. Resources and Economic Growth. The American Economist, Volume 61 (1), pp. 52–60

Sorokina, N.Y., Latov, Y.V., 2018. Evolution of Old Industrial Regions in The Economy of Russia. Journal of Economic Regulation, Volume 9(1), pp. 7–21

Sverdan, M.M., 2015. Regional Economy and Economic Security Of Region. Uzhgorod National University Herald, Volume 2, pp. 92–99

Wilson, R.A., Briscoe, G., 2004. The Impact of Human Capital on Economic Growth: A Review. Impact of Education and Training. Third Report on Vocational Training Research In Europe: Background Report. Luxembourg: EUR-OP

Zaborovskaia, O., Nadezhina, O., Avduevskaya, E., 2020. The Impact of Digitalization on the Formation of Human Capital at the Regional Level. Journal of Open Innovation: Technology, Market, and Complexity, Volume 6(4), p. 184