Human development, satisfaction with human capital and security in the Siberian and Far Eastern border regions

If we examine the distribution of the the HDI indices by border regions grouped by federal districts, we will see non-significant mean values in 6 out of 8 federal districts - from 0.848 to 0.875 (Fig. 1). In each federal district, there are leaders and outsiders according to the regional economy and possible investment in human resources. In the Siberian Federal District, the leaders are the Krasnoyarsk Region, one of the most developed export-oriented industrial regions of Russia (0.892), the Novosibirsk Region - the administrative center of the federal district and the leading scientific center with the diversified economy and a strong industrial sector (0.883), and the Omsk Region - an industrial-agrarian region (0.879). The Republics of Buryatia and Altai and the Republic of Altai with the significant share of agrarian and tourist-recreational sectors and the low level of urbanization show a moderate human development (0.826-0.838), while the Republic of Tyva has the smallest HDI in Russia (0.801). In the Far Eastern Federal District, there are three border regions with the primary-products oriented economies and developed mineral industries, whose HDI is very high and comparable with some European or SouthEastern high-income countries: Republic of Sakha (0.903), Magadan (0.897) and Sakhalin (0.896) Regions. The least fortunate regions are the Jewish Autonomous Region (0.824) and the Transbaikal Territory (0.836) - rich in mineral reserves but peripheral and the most `abandoned'. Other border regions of this district have rather modest HDI - from 0.840 in the Amur Region to 0.867 in the Chukotka Autonomous Region.

Fig. 1. Mean values of the the HDI in border regions (groups with non-significant differences are marked with the same color and letters)

Source: Analytical Center of the Government of the Russian Federation, 2019

CFD - Central Federal District, FEFD - Far-Eastern, NFD - Northwestern, SFD - Southern, SibFD --

Siberian, VFD - Volga, NCFD - North-Caucasian, UFD - Ural Federal District.

The cluster analysis with 11 integral indices of social security allowed to identify 5 main groups of border regions (Fig. 2). The first cluster consists of 3 regions - Chukotsky, Nenetsky and Yamalo-Nenetsky Autonomous Regions (AR) with the very high level of social security except for mental health and suicides. The second cluster consists of 8 the least-advantaged regions - Karachay-Cherkessia, Kabardino-Balkar, Ingush, Chechen, Kalmyk, Dagestan, Tyva and Altai Republics, in which 6 out of 11 factors indicate low standards of living, problems in the labor market, weakness of the health care system, high pressure on the social security system, and low access to information technologies. However, these regions show higher demographic security, physical and mental health, a relatively low level of crime and a favorable ecological situation. The third cluster includes regions with favorable conditions for 10 integral factors except for physical health: Arkhangelsk, Astrakhan, Magadan, Murmansk, Sakhalin, Khabarovsk, Kamchatka Regions, Republics of Karelia, Sakha, North-Ossetia. The fourth cluster was formed by the Republic of Buryatia, Kurgan, Amur, Transbaikal, Altai and Jewish Autonomous Regions, in which the main threats to social security are the problems of the social- economic development and employment, law standards of living, weaknesses in the health care system, mental health of the population, poor access to information technologies. At the same time, there is a relatively high level of physical health and a developed social infrastructure, and a better environment as a counterbalance to the social-economic difficulties. The fifth cluster consists of 21 regions in which social security is threatened by depopulation, undeveloped social infrastructure, problems with somatic health and ecological security. However, there are tendencies contributing to the positive development, such as a lower level of unemployment, a higher level of mental health and public safety, a better access to IT technologies. These are: Leningrad, Tumen, Pskov, Novosibirsk, Omsk, Smolensk, Bryansk, Rostov, Volgograd, Saratov, Belgorod, Kaliningrad, Chelyabinsk, Voronezh, Orenburg, Krasnoyarsk Regions, and the Republic of Crimea.

Thus, the Siberian and Far Eastern Federal Districts are not homogenous and differ in social security: in the latter, there is a great regional differentiation - 5 regions are in the third cluster, 3 regions - in the fourth, 1 - in the first cluster with the highest level of social security, and 1 - in the fifth cluster with social- demographic and ecological problems. In the Siberian Federal District, almost all regions are on the negative pole of the social security continuum and differ by risks and vulnerabilities: 3 border regions are in the fifth cluster, 2 regions - in the second, and 2 - in the fourth.

Fig. 2. Typology of border regions by factors of social security

AL - Altai Region, AM - Amur, AR - Arkhangelsk, AS - Astrakhan, BL - Belgorod, BR - Bryansk Region, BU -of Buryatia, CN - Chechen Republic, CL - Chelyabinsk, CK - Chukotka, DA - Dagestan, GA - Altai, IN - Ingushetia, KB - Kabardino-Balkaria, KN - Kaliningrad, KL - Kalmykia, KQ - Kamchatka,

KC - Karachay-Cherkessia, KI - Karelia, KH - Khabarovsk Region, KD - Krasnodar, KX - Krasnoyarsk, KU - Kurgan, KS - Kursk, LN - Leningrad, MG - Magadan, MM - Murmansk,

NN - Nenets Autonomous Region, NO - North-Ossetia, NS - Novosibirsk, OM - Omsk, OB - Orenburg, PR - Primorsky, PS - Pskov, RO - Rostov, SK - Sakha (Yakutia), SL - Sakhalin, SA - Samara,

SR - Saratov, SM - Smolensk, TU - Tyva, TY - Tyumen, VG - Volgograd, VR - Voronezh,

YN - Yamalo-Nenetsky Autonomous, YV - Jewish Autonomous, ZB - Transbaikal Region,

KR - Republic of Crimea

The comparison of the human development (HDI) with 11 factors of social security (Table 1) showed the most important and confident relationship between the labor market security and employment (r = 0.677, p < 0.01), which links human capital to the level of employment and a share of active population, and between the economic well-being and pressure on the social security system (r = 0.672, p < 0.01), which relates the human development to the low share of wages below the poverty line and a greater share of decent incomes. There are moderate but significant correlations between the HDI and the social infrastructure development as ensuring sport and pre-school activities (r = 0.480, p < 0.01), access to the quick and safe Internet (r = 0.459, p < 0.01), ecological security (r = 0.386, p < 0.01), economic sustainability (r = 0.378, p < 0.01), and demographic security r = 0.310, p < 0.01).

Table 1

Correlation between the HDI and factors of social security in border regions

* Correlation is significant at the 0.05 level (two-sided) ** Correlation is significant at the 0.01 level (two-sided)

In the Far-Eastern Federal District, the human development has a stronger correlation with the capability to develop economy and ensure necessary investments (r = 0.822, p < 0.01) and health-care infrastructure (r = 0.639, p < 0.05). In the Siberian Federal District, there is a strong negative correlation between the HDI and demographic security (r = -0.828, p < 0.01). The level of public health is a significant indicator of social security and an intrinsic component of human capital; it positively correlates with the HDI (r = 0.883, p < 0.01), but this positive relation, due to the inversion, represents an opposite tendency: in regions with the high HDI, the level of health measured with mortality from cardio-vascular and oncologic diseases is lower (Novossibirsk, Omsk, Krasnoyarsk and Altai Regions), while in regions with the relatively low HDI (republics Tyva or Altai), the level of health is higher.

An even more important social security factor relevant for the Siberian borderland--employment (r=0.909, p < 0.01) measured with the level of unemployment and a share of working population. This relationship confirms that the human development in Siberia is impossible without institutional conditions ensuring the stability of the labor market. The relationship between the factor of well-being and pressure on the social security system and the HDI (r = 0.787, p < 0.05) is more complex and non-linear despite a significant and strong correlation (Fig. 3): several regions have similar shares with the income below subsistence level but very different HDI (Novosibirsk, Krasnoyarsk and Altai Regions, Republic of Buryatia). The factor of public order shows a negative correlation with the HDI (r = -0.786, p < 0.05), i.e., the higher human capital is associated with the low level of crime including committed by the youth, under alcohol or drugs. The positive correlation of the HDI with the factor of ecological security, which is also inverse (high values with greater ecological risks and pollution (r = 0.836, p < 0.05, Fig. 4), mean that the border regions with stronger industrial sectors and higher human development (Krasnoyarsk and Novosibirsk Regions) have numerous sources of air pollution and greater volumes of pollutants, whereas in the agrarian, ecologically safe regions (Republics of Altai, Buryatia and Tyva), the human development is not high.

Fig. 3. Relationship between the HDI and the factor of well-being and pressure on the social protection system in the border regions of the Siberian Federal District

Fig. 4. Relationship between the HDI and the factor of ecological security and environment in the border regions of the Siberian Federal District

Subjective estimates of human capital and their relation with social security

The sociological analysis focused on the subjective characteristics of human capital and their determinants including institutional environment and social relations. The respondents' evaluations of their income and job, career development and upward social mobility were combined in an integral index of personal human capital satisfaction which showed significant regional differences. The highest level of satisfaction - in the Khabarovsk Region (m = 0.26), compared with the mean value in the Omsk Region (m = 0.05), while the least satisfied people lived in the Altai Region (m = -0.21) and the Republic of Altai (m = -0.14). The Amur Region has mean values close to the Omsk and Khabarovsk Regions (m = 0.15). Thus, the results reflect general differences in the human development of border regions as described in the previous section, which confirms the validity of our scale and its non-intangible dimensions (the subjective perception of personal achievements and perspectives).

Fig. 5. Mean values of the human capital evaluations in five regions (significant differences at the 5 % level at least are marked with the same color and letter)

AK - Altai Region, AO - Amur, KH - Khabarovsk Region, RA - Republic of Altai, OO - Omsk Region

In addition to the regional specifics explained by institutional features of the regional economy and management, there are other social-structural factors reflecting a significant gap in social conditions and opportunities. Thus, the difference between urban and rural areas is significant in all border regions except for the Republic of Altai with the low density of population and low urbanization (only 29.3 % lived in the city - the administrative center of the Republic - Gorno-Altaysk) combined with the poor standards of living and traditional (agrarian) activities, which results in people's dissatisfaction with their human capital regardless their location (m = -0.11 in the city, m = -0.16 in the village, p = 0.57). The urban- rural difference is more evident in the Altai Region - its rural inhabitants are unhappy (m = -0.37) compared with inhabitants of cities (m = 0.02, p < 0.0005). In the Amur Region, rural residents are also frustrated with their social-economic position (m = -0.21), while the urban population is in a more favorable situation than in the Altai Region (m = 0.19).

Gender differences have marginal significance (m = 0.084 for men and m = -0.0016 for women, p = 0,053), and in regional samples, are statistically significant only in the Amur Region (m_men = 0.43, m_women = 0.15, p < 0,05): women are generally less satisfied with their job, career and finances. The gender comparison of subjective evaluations in this region show that women more often report a moderate/middle level of income (74.2 %) and rarely admit belonging to the wealthy class (19.6 %); while the corresponding shares of men are different - 59.8 %o and (29.5 %, p < 0.05).

Age is not an important factor for the general satisfaction with human capital, but in some regions differences are statistically significant. Thus, in the Amur Region, people under 30 show higher levels of satisfaction (m = 0.422) than people over 50 (m =0.13). In the contrary, in the Omsk Region, younger people are less satisfied with their human capital (m = -0.19) compared with the middle-aged (m = 0.17) and old-aged (m = 0.17, p < 0.05). The factor of ethnicity is insignificant for all regions except for the Omsk Region, in which the Russian majority is more satisfied (m = 0.12) than other ethnic groups (m = -0.05, p < 0.01).

Despite pessimistic statements about the non-effective and low-quality education [10; 22], our research shows that the more educated people are more satisfied with their human capital, at least at the all-regional level (p < 0.0005). This tendency is true for 3 regions - the Altai, Amur and Omsk Regions (p < 0.05) (Fig. 6). In the Khabarovsk Region, both groups (with and without higher education) are equally satisfied (m = 0.26 and 0.27 correspondingly), which indicates comparable opportunities for people of working and highly-qualified professions. In the Republic of Altai, the situation is opposite: both groups are dissatisfied with their financial and professional situation and perspectives (m = -0.07 and m = -0.19).

Fig. 6. Differences in the level of satisfaction with human capital by education

There are differences between three social categories: managers and entrepreneurs with the highest values of satisfaction (m = 0.52), specialists with higher education (m = 0.06) and the most dissatisfied categories - low-qualified workers, specialists with vocational training and unemployed (mean values vary from -0.14 to -0.36 with non-significant differences). More prosperous regions show less significant differences between social-professional groups compared with the regions with social-economic problems. For instance, in the Altai Region, specialists with higher education and managers (m = 0.13 and m = 0.07) have smaller but at least positive scores compared to other groups (differences significant - from -0.27 to - 0.46, p < 0.05). In the Republic of Altai, there is a huge gap between the highly satisfied (managers, entrepreneurs, m = 0.54) and all other groups. Specialists with higher and vocational education have similar scores (m =-0.39 and m = -0.29), which indicates a low demand for qualified skills. Moreover, unskilled workers show insignificantly higher levels of satisfaction (-0.03), while the unemployed are the most frustrated (m = -0.74).

In the Omsk Region, the main differences are observed between the unemployed and specialists with intermediate professional education (m = -0.55 and m = -0.40), on the one side, and managers and businessmen (m = 0.71), on the other (p < 0.05). In the Khabarovsk and Amur Regions, differences are insignificant due to their similar and rather high scores compared with other regions - correspondingly, 0.65 and 0.43 in the group of managers and businessmen, 0.50 and 0.03 - in the group of unskilled workers, 0.24 and 0.22 - in the group with vocational training, 0.16 and 0.20 - in the group with higher education, -0.06 and 0.11 in the unemployed group. Despite the statistical insignificance, educated people are in a less favorable position than groups of the lower educational status, which indicates that the human development in these regions do not follow an innovative path of knowledge-based technologies that need a high specialization of workers, but rather an extensive and conservative path.

Finally, we tested hypotheses whether human capital was an important factor affecting the character of social relations with other people and institutions and the level of social security. According to the statistical data, security dimensions have different relationships with the human development in different border regions. We conducted a regression analysis of the sociological data to prove our conclusions. The results showed significant and reliable relationships between the human capital evaluations and all other dependent variables: the former are associated with the institutional trust (P = 0.37 - all regions, P = 0.45 - AK, P = 0.32 - AO, P = 0.27 - KH, P = 0.42 - RA, P = 0.42 - OO, p < 0.001), which confirms that more educated and experienced people have a more respectful attitude towards the state and its institutions, and that the institutional regime favors and relies on people with higher education, experience, income and social status, while vulnerable categories (low access to education and `social lifts') are usually socially excluded and do not find enough institutional support. Human capital is associated with trust in other people in general (P = 0.28 - all regions, P = 0.37 - AK, P = 0.24 - AO, P = 0.15 - KH, P = 0.28 - RA, P = 0.3 - OO, p < 0.001); and the economic wealth is rather mediated by the human capital it contributes to create.

Human capital allows to live in a safer environment and defend personal interests, it is an important indicator of social anxiety and fear which are more expressed by people with the low human capital outcomes. Corresponded coefficients are as follows: perceived discrimination: p = 0.21 - all regions, P = 0.19 - AK, p = 0.22 - AO, P = 0.16--KH, p = 0.36--RA, p = 0.23 - OO (p < 0.001); social feelings (P = -0.41 - all regions, P = -0.42 - AK, P = -0.45 - AO, P = -0.31 - KH, P = -0.45 - RA, P = -0.48 - OO (p < 0.001); personal security: P = -0.34 - all regions, P = -0.24 - AK, p = -0.39 - AO, P = -0.28 - KH, p = -0.37 - RA, P = -0.38 - OO (p < 0.001). At the same time, the `weight' of human capital differs by region, i.e., its accumulation and functioning depend on various contexts and conditions, especially concerning the social security indicators.

The proposed research methodology is complex and versatile, it allows to consider human capital in different perspectives - social-statistical and subjective. When focusing on human capital in the border regions of Russia, one should be aware that there is a lack of official data on many important characteristics of human capital; that international comparisons based on global indices usually use the national-level data representing the whole country but not its regional differentiation; that differences in approaches and conceptual grounds make human capital a very blurred concept with unclear operationalization patterns. Due to the non-experimental design of our research, we cannot make conclusions about causal relationships; therefore, we chose the path of hypotheses - considering human capital a determinant of trust and social security, which is logically and theoretically grounded. However, there is rather an interdependence. i.e., more cohesive-trustful relations and safer social conditions lead to the human capital development and improvement.

References