THEORETICAL AND METHODOLOGICAL APPROACHES TO ASSESSING HUMAN POTENTIAL

The concept of human potential in Russia has firmly entered scientific circulation, along with its synonym human development. Approaches to studying their content are sometimes explained by borrowing foreign achievements during the period of market reforms, when Russia became a member of the United Nations Development Program (UNDP). In the authors’ opinion, this is not entirely correct. Domestic science, long before these reforms, was prepared for the creative perception and use of these concepts.

Among the prerequisites, one should name studies of the same problems the analysis of which abroad led to the category of human potential. This pertains to substantiation of the insufficient characteristics of growth rates for assessing economic and social progress (Anchishkin, 1973), as well as the concept of labor potential developed during the planned economy years, including the system of its qualitative parameters (Maslova, 1987).

A more serious problem should be considered: overcoming the paradigm that considers a person mainly as a production resource. This paradigm, either explicitly or implicitly, dominated the theory of both planned and market economies. An alternative human-developing paradigm puts a person at the center of the reproductive process, in which the possibilities of self-realization expand, the content of labor is enriched, and production itself produces human development (Soboleva, 2006). In domestic research, the resource approach to a person was revised when, based on the generalized achievements of domestic and foreign science, human potential was acknowledged by scientists (unlike practitioners) as the goal and criterion of social progress, and its research fit the global trajectory (Rimashevskaya, 2009).

A question was further raised as to whether human potential and human development are synonymous. Specialists who prefer the term human development rely on A. Sen’s concept of development as the empowerment of a person and interpret it not as well-being, but as the freedom people have due to a particular set of available choices (Chelovecheskoe …, 2008). Accordingly, the difference between human development and human potential is that the first is not a means (resource) of socioeconomic progress, but its goal. This understanding is reflected in the 2015 Human Development Report, subtitled “Work for Human Development” (Human …, 2015).

The term human potential is used by specialists who consider it both as a goal and a means (not contradicting the goal) of social progress. This dialectical understanding corresponds to the following definition of human potential: “The accumulated stock of physical and moral health, general cultural and professional competence, creative, entrepreneurial and civic activity accumulated by the population, implemented in various fields of activity, as well as in the level and structure of needs” (Soboleva, 2007, p. 12). Clearly, with a fairly complete set of features, the emphasis is on the realization of human potential, but its area is not limited to labor, which is characteristic of production resources, while human needs go beyond economic needs. Therefore, its study requires interdisciplinary research, which in the future can become organic parts of integral scientific ideas about a person (Martsinkevich and Soboleva, 1995).

In addition, there is a long tradition of uncertainty about these concepts, going back to the UNDP, in the framework of which the integral human development index (HDI) is determined, which is used for intercountry comparisons. The comparative approach is due not only to the UN’s mission to identify countries in need of development assistance, but also because absolute estimates (whatever indicators are used) are not easy to interpret. Therefore, comparison is used, which makes it possible you to determine which countries lead or lag behind others and due to which basic components. The rating approach is also used for intracountry comparisons.

Currently, the basic components of the HDI are reflected through indicators of such human development components as health (life expectancy), education (number of years of education), and material well-being (GDP per capita, and since 2011, GNI per capita). Whereas life expectancy can be considered an indicator of human development in its target understanding, then other indicators—education and material well-being—instead characterize the means of ensuring it. In addition, the inclusion of indicators in the HDI composition as alternatives to the indicators of well-being made it possible to obtain country ranks that differ from the ranks in terms of GDP per capita and thereby refute the persistent notion that a person’s capabilities are mainly determined by income (Chelovecheskoe …, 2008).

This study considers the qualitative characteristics of human potential within the human development paradigm, abstracting from resource aspects, and compares federal subjects according to their indicators. The regional perspective is important for such a spatially large-scale country as Russia. In territory and population, many federal subjects outrank a large number of sovereign states. Moreover, differentiation of regions in terms of socioeconomic parameters has acquired a scope that sometimes exceeds the differences between individual countries.

The authors have set the task of a comparative interregional analysis of the quality of the human potential of federal subjects in terms of dynamics. First, we proceeded from the fact that the object of analysis should be precisely the population, not its access to certain benefits. Then, the main (basic) components of human potential were determined, based on available developments and regional statistics. Among them the following should be highlighted: the studies of the Institute of Socioeconomic Problems of the Population, Russian Academy of Sciences, where an expanded set of basic components was presented: health, intellectual potential, and sociocultural activity (Rimashevskaya, 2009). Then, the demographic component was included in their composition (Rimashevskaya et al., 2013). Many specialists also include the migration component in their analysis (Tyulicheva, 2007).

Whereas health, intellectual, and partially, sociocultural components are taken into account in most interregional studies of human potential, a more skeptical attitude has developed towards the demographic component, which is estimated by population indicators. Many works consider it a quantitative characteristic and, in this aspect, include it in studies of both labor and human potential. However, in our opinion, the demographic component contains significant qualitative aspects. Thus, humanitarian value is not only the duration of earthly existence, but also the constant renewal of life, which can be interpreted as the right of a person to be born due to the human need for procreation. As a result, reproductive ability is inherent to a person in order to replenish and augment his/her own kind, so there are grounds to attribute this ability as a qualitative characteristic of human potential.

Thus, the following basic components of the human potential of the population are included in the analysis: the demographic component, health, education, and sociocultural behavior.

The basic components of the quality of human potential were operationalized, i.e., conversion to statistics. When selecting them, problems were solved that are well known to specialists in similar subjects. First, it is almost impossible to find one universal statistical indicator that characterizes a particular component. Therefore, several indicators were used. Second, some statistical problems (regional in particular) limit the choice of suitable indicators.

For the demographic component, characterizing the ability of the population to reproduce, the main indicator is natural increase. However, this ability is influenced not only by the processes of natural reproduction (birth and death rates), but also by external and internal migration flows, which affect both the number of inhabitants and their qualitative characteristics. These processes are widespread in Russia. Due to large-scale external migration, Russia is a net importer of human resources, while external flows are many times inferior to movements within regions, which in a number of federal subjects have become the main cause of population decrease. Therefore, the indicator of natural increase/decrease is supplemented by total migration increase/decrease.

The health component, just like in the HDI, is estimated using the indicator of life expectancy at birth (LE). A more correct assessment could be yielded by the indicator of healthy life expectancy proposed by experts of the World Health Organization (WHO), with correction of life expectancy for a period of ill health. However, in Russia this is not calculated in official statistics. Therefore, this study also included indicators reflecting health problems: disability and general primary morbidity (the number of newly registered diseases in patients).

Education is included in the intellectual component, but due to the lack of statistics, as in other studies, it is reflected only in education indicators and is therefore called education. Study of the quality of human potential does not use the indicator of availability of education (as in the HDI), but more adequate indicators of the proportion of the population with a particular education level. Many experts prefer to calculate them for people 15 years and older with higher and secondary vocational education, since schooling (at least at the basic general level) in Russia has long been compulsory.

This analysis uses an alternative approach based on education system scores. Each of its levels—three levels of complete general (school) and three levels of professional—receives a certain rank (the higher the education level, the higher the rank). Expert assessments assign a certain number of points to each rank. Their average value for a region is calculated from the proportion of people aged 15 and older with a particular education level (Rimashevskaya et al., 2013). Let us note right away that both of these approaches, first, do not “capture” the qualitative aspects of the education received by the population (and its quality does not remain unchanged), and second, the use of one or another method does not make a significant difference in the final result.

For operationalization sociocultural component two groups of indicators have been selected that characterize the consumption of services provided by cultural institutions and social behavior, which is assessed by the method “from the contrary”, namely, indicators of asocial (deviant) behavior. Due to their substantive differences, they are presented as two components: cultural activity of the population and his social behavior. For a more complete assessment of the sociocultural component, it should be supplemented with a sociopolitical characteristic (participation in elections, parties and other public organizations, etc.); however, it was not possible to find such information for all Russian regions.

The cultural activity of the population is expressed through the attendance rates of museums and theaters and the number of users of public libraries; social behavior, through the number of men and women who have committed crimes and the number of people registered in medical institutions for alcoholism, drug addiction, and other types of substance abuse.

The information base of the study was Rosstat data for 83 federal subjects for 2010 and 85 for 2015.Footnote 1 The index method was used to include individual indicators in the integral assessment of human potential, in which statistical indicators are converted into indices, which were calculated by the minimax normalization procedure, often used to assess social indicators (Aivazyan, 2012; Modelirovanie …, 2001).

To reflect the dynamics of statistical indicators, when calculating the indices, the same minimum and maximum values of indicators for 2010 and 2015 were used. A composite index was calculated for each component, then, on their basis, an integral human potential index was determined as the arithmetic mean of five components. The additive formula for calculating the integral index was chosen mainly because it can be used to evaluate the contribution of each component to the overall characteristic. In addition, not all regions have the necessary indicators, and the use of a multiplicative formula yields a zero value of the component. For example, in the cultural activity of the population in the Nenets, Yamalo-Nenets, and Chukotka autonomous okrugs, there is no indicator of theater attendance due to the lack of theaters.

DYNAMICS OF THE MAIN CHARACTERISTICS OF THE HUMAN POTENTIAL OF THE RUSSIAN REGIONS IN 2010–2015

Demographic Component

The dynamics of the population is, as noted, represents not only its quantitative characteristics, but also its qualitative characteristics—the ability of the population to reproduce—and depends on the magnitude of natural and migration increase/decrease.

Natural population increase depends on the birth/death ratio. In the past five years, the pace of increase in the birth rate has slowed sharply, which is explained, among other things, by the fact that payment of maternity capital incentivized planned childbirth. Once this need is satisfied, the birth rate ceases to increase and the natural dynamics of demographic processes resumes. In addition, the proportion of women of reproductive age (15–49 years) has decreased. According to Rosstat, at the beginning of 2010, the proportion of women of this age in the country was 49.3% of the total number of women, while as of January 1, 2015, it was 45.3%.

The birth rate (the number of births per 1000 people) on average in the country in 2010 was 12.5‰; in 2015, 13.3‰. Over the years, the number of regions with a birth rate above the national average (42) has not changed. The overall mortality rate (the number of deaths per 1000 population) decreased from 14.2‰ in 2010 to 13‰ in 2015. In 2010, in 35 Russian regions, mortality was below the national average. In 2015, the number of such regions decreased to 32.

In 2010, the negative natural increase, or natural population decreaase, was 1.7‰ for the entire country. At the same time, positive population increase was recorded in 24 federal subjects: the birth rate exceeded the national average (12.5‰), and mortality rate was lower (14.2‰) than the average Russian level. The exception was Irkutsk oblast, where the mortality rate was 14.4‰.

In 2015, a slight increase was recorded (0.3‰), and the number of federal subjects with increase rose to 41. However, in 4 of these 41 regions (Moscow, Murmansk oblast, and Kamchatka and Stavropol krais) the birth rate was lower, and in 5 (the Republic of Mari El, Perm krai, and Sakhalin, Sverdlovsk and Orenburg oblasts), the mortality rate was higher than the national average. In addition, zero increaase in 2015 was recorded in Chelyabinsk and Magadan oblasts.

In the ten leading regions, natural population increase in 2010 exceeded 4.7‰, and in 2015, 5.8‰. The composition of the group characterized by a high birth rate and relatively low mortality rate remained 90% unchanged during these years: the republics of Chechnya, Ingushetia, Tyva, Dagestan, Sakha (Yakutia), Altai; the Yamalo-Nenets, Khanty-Mansi, and Nenets autonomous okrugs; Kabardino-Balkaria replaced Buryatia.

In 2010, in 11 federal subjects, the natural population decrease was more than 7‰, and in 2015, 4.6‰. They included mainly the regions of the Central Federal District and the Northwestern Federal District with a high mortality rate. In 2015, Nizhny Novgorod oblast left this group, while Kursk oblast entered. Regional values of natural population increase varied in 2010 from 24.3 (Chechen Republic) to –10.7‰0 (Pskov oblast); in 2015, from 18.2 to –7.2‰, respectively.

Migration flows of the population. Migration of the population outside a federal subject, including travel abroad and entry for permanent residence from other regions and countries, is characterized by the migration rate (persons per 10 000, %оо). Its dynamics depends on many factors, including the level of socioeconomic development of regions, the situation in the labor markets, the migration policy of the federal center and regional administration, the mentality of the population, etc. In Russia, with the underdevelopment of its rental housing market, internal migration is significantly constrained.

Migration outflow decreases and inflow increases the demographic potential of the population. In 2010, the national average migration increase rate was 19%оо. It had a positive value in 26 federal subjects, in 14 of which, it was above the average level, and in Moscow and Leningrad oblasts and St. Petersburg and Moscow it exceeded 100%оо mainly due to migrants from other regions of the country. In regions such as the republics of Komi, Tyva, and Ingushetia; Magadan oblast; and the Chukotka Autonomous Okrug, the negative rate was –100%оо or more.

In 2015, the migration increase rate for the entire country fell to 17%оо, while in 32 federal subjects, it was positive, and in 19 it exceeded the average Russian level. The leaders were Sevastopol (439%оо), as well as Tyumen (without autonomous okrugs) and Moscow oblasts and Krasnodar krai.

Compared to 2010, the composition of the group with the highest population outflow (–100%оо) also changed significantly. The Komi Republic, Chukotka Autonomous Okrug, and Magadan oblast remained in it; the republics of Ingushetia and Tyva left it; the Republic of Kalmykia, the Jewish Autonomous Oblast, and Yamalo-Nenets Autonomous Okrug entered it.

Population Change. To assess population change in regions, the specific indicators of natural and migration increase were brought to a single basis, per 1000 people (‰). The recalculation clearly showed that migration movements in the country as a whole played a more prominent role in population change than natural reproduction. In regions, these processes took place in different directions and with different intensity, changing the ranks (places) of regions in the hierarchy according to the value of the given indicator (Table 1).

Table 1.   Increase/decrease in population in federal subjects and their ranks in 2015 and 2010
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In 2010, population increase in the entire country was only 0.2‰, varying from 19.9‰ in the Chechen Republic to 16.5‰ in the Chukotka Autonomous Okrug. Population increase was observed in 25 regions and was zero in Voronezh oblast; 61.4 million people, or 43% of the total population of the country, lived in these regions. Decrease was recorded in 57 federal subjects; in 25, the decrease was above 7‰; in 10, it exceeded 10‰.

In 2015, the total population increase (2‰) was also accompanied by significant regional differences: from 42.5‰ in Sevastopol to –13.4‰ in the Jewish Autonomous Oblast. Increase was recorded in 33 Russian regions, including 23 regions where it was higher than the average Russian level. In 12 regions, the number of inhabitants increased due to natural and migration increase; in 11, only due to migration increase; and in 10, to natural increase. In total, about 77 mln people lived in these regions, or 52.6% of the total population.

Population decrease, as in 2010, was observed in most regions (52). In 19, a slight natural increase was noted (with the exception of the Yamalo-Nenets Autonomous Okrug, where it amounted to 11.3‰). In Magadan oblast, the reduction in numbers occurred due to a negative migration balance, while in Ryazan oblast, mainly due to natural population decrease. In eight regions, the natural decrease was partially offset by a positive migration balance. In the remaining 23 federal subjects, it was accompanied by migration to other regions and abroad. In seven regions where the population decreased, the value was more than 7‰, and in four regions, more than 10‰.

Health of the Population

The main indicator reflecting the state of health of the population is life expectancy at birth (LE).

At the end of the 20th century, to assess the state of health of the population, WHO experts proposed an indicator of healthy life expectancy (HLE), which in Russia is not calculated in the official statistics. In this study, health of the population is assessed with three indicators: LE (years), disability ( number of disabled people per 1000), and overall morbidity (number of registered diseases in patients with diagnosis established for the first time, per 1000 people).

Life Expectancy. In 1990, i.e., on the eve of radical economic reforms, the LE of the Russian population was 69.2 years. Four years later (1994), the average LE for the population decreased to 63.8 years. Such dynamics is the result of ongoing market reforms without the necessary social shock absorbers.

By 1998, the situation had somewhat stabilized: The average LE in the country increased to 67.1 years, but the financial crisis of the same year again changed the dynamics of this indicator, which by 2003 had dropped to 64.8 years. Since 2004, an increase in LE again began in Russia, and in 2010, it averaged 68.9 years; a year later, it reached the 1990 level of 69.8 years.

LE indicators vary markedly by federal subjects. In 2010, in 25 regions, LE was higher than the average Russian indicator, of which in 7 regions it exceeded 72 years (the republics of Ingushetia, Dagestan, Karachay-Cherkessia, North Ossetia, Kabardino-Balkaria, and Moscow and St. Petersburg) and in 3 regions, more than 71 years (Chechen Republic, Belgorod oblast, and Stavropol krai). In half of federal subjects, LE was less than 68.2 years, of which in ten regions it was 65.1 or less (Pskov, Novgorod, and Magadan oblasts; Zabaykalsky krai; Amur oblast; Jewish Autonomous Oblast; Nenets Autonomous Okrug; Sakhalin oblast; Chukotka Autonomous Okrug; the Tyva Republic). The maximum regional differences in LE in 2010 were 1.3 times, between the Republic of Ingushetia (74.7 years) and Chukotka Autonomous Okrug (57.5 years).

In 2015, LE in all federal subjects increased and the national average was 71.4 years. In 25 federal subjects, it was higher than the average Russian level; in eight of them (the North Caucasian republics, Moscow and St. Petersburg) it exceeded 74 years. Whereas in the republics of the North Caucasus, in addition to the genetic factor, the LE indicator can also be influenced by the statistical factor (reliability of data); in the two Russian capitals, social living conditions and, primarily, the availability of qualified medical care for the population play an important role.

In half the regions, LE was below 70.5 years; in ten, less than 68.5. The composition of this outsider group remained 70% unchanged. Pskov and Novgorod oblasts and the Nenets Autonomous Okrug left this group, while Kemerovo and Irkutsk oblasts and the Republic of Altai entered. The reasons for the low LE in these regions differ: harsh natural and climatic conditions in Magadan oblast and in the Chukotka Autonomous Okrug, environmental problems in the Kemerovo oblast, Chukotka Autonomous Okrug, Zabaykalsky krai, low availability of qualified medical care (high infant mortality serves as an indicator) in Chukotka Autonomous Okrug, Jewish Autonomous Oblast, Republics of Tuva and Altai. The maximum regional differences in LE in 2015 somewhat decreased, 1.25 times (between the republics of Ingushetia (80 years) and Tyva (63.5 years)).

Number of disabled among the population. After a significant increase in the number of disabled people, recorded in 2005–2006 in connection with monetization of benefits, their number continued to increase until the end of 2010 (13 209 000 people). Since 2012, it began to gradually decrease, and by the end of 2015, it amounted to 12 751 000 people. There is no information in the statistics for Moscow and Leningrad oblasts; the number of disabled people living in these regions is included in the figures for Moscow and St. Petersburg. Therefore, in this study, the published number of disabled people in both capitals is distributed between them and adjacent regions in proportion to the number of residents.

The average annual specific number of disabled people in the Russian Federation in 2010 (per 1000 people) was 92.5. In 48 regions, it was lower than the average Russian indicator, and in the 10 leading regions (with a low proportion of disabled people), it was less than 56 people (Yamalo-Nenets and Khanty-Mansi Autonomous Okrugs, Moscow and Leningrad oblasts, Chukotka Autonomous Okrug, Magadan, Murmansk, and Astrakhan oblasts, Kamchatka krai, and Moscow). The group of 10 regions with the highest specific number of disabled (more than 117 people) in 2010 included Orenburg oblast; the Republic of Karelia; Tula, Kostroma, Novgorod, Lipetsk, Tambov, Ryazan, and Belgorod oblasts; and the Altai Republic—regions with a high proportion of people older than working age. The maximum regional differences were 6.4 times between Belgorod oblast (181.9) and the Yamalo-Nenets Autonomous Okrug (28.6).

In 2015, the average annual number of persons with disabilities decreased to 87.7. However, in 26 federal subjects, an increase in the number of disabled people was recorded. In half the regions, it was below the national average. The composition of the ten leading regions (53 or fewer people) remained 90% the same; Kamchatka krai left this group, while Sevastopol krai entered.

The group of ten federal subjects with the highest proportion of disabled people (over 115 people) has changed more in terms of composition compared to 2010. Orenburg, Tula, and Kostroma oblasts left the group, while Kursk oblast and the republics of Chechnya and Ingushetia entered. The maximum regional differences in the proportion of persons with disabilities in 2015 compared to 2010 decreased, amounting to 5.2 times between Belgorod (159.8) and Moscow (30.8) oblasts.

Overall morbidity of the population it is represented in statistics by the number of registered diseases in patients with a diagnosis established for the first time, per 1000 people. The dynamics of this indicator on average countrywide in recent years demonstrated no clear trend: by 2013 it increased to 799.4 versus 780 in 2010, then gradually decreased, and in 2015 amounted to 778.2.

In 2010, the overall primary morbidity in 36 federal subjects was below the national average. This group was headed by the ten leading regions (with a specific number of diseases up to 650). Half of them are the territories of the North Caucasus Federal District, as well as Leningrad, Voronezh and Kursk oblasts, Krasnodar krai, and the Tyva Republic. This composition indicates a certain dependence of the state of health on the ethnic factor, the effectiveness of which was manifested in regions of the North Caucasus and Tyva. The 10 outsider regions (with the highest specific morbidity) included 11 federal subjects (the Nenets, Chukotka and Yamalo-Nenets autonomous okrugs; the republics of Karelia, Komi, Udmurtia, and Sakha (Yakutia); Altai krai; Arkhangelsk and Samara oblasts). Most of them belong to the northern regions, which indicates another factor of ill health—living in difficult natural and climatic conditions. This is primarily reflected by the high morbidity of respiratory organs, as well as diseases of the nervous and endocrine systems and digestive organs. In the Nenets, Yamalo-Nenets, and Chukotka autonomous okrugs, and the Republic of Karelia, the morbidity rate of these diseases is highest.

The maximum regional differences in the specific primary morbidity of the population in 2010 were 4.5 times between the Nenets Autonomous Okrug (1813.8) and Kabardino-Balkaria (399.8).

In 2015, as noted above, the specific indicator of primary morbidity on average in the country decreased and in half of federal subjects it became less than the national average. For 5 years, the composition of the ten leading regions has changed significantly (with a low indicator, up to 621 diseases) due to the replacement of Karachay-Cherkessia, North Ossetia, and Tyva with Sevastopol and the Republic of CrimeaFootnote 2 and Buryatia.

At the same time, an increase in overall morbidity was revealed in 36 regions. In the composition of ten federal subjects with the highest specific morbidity (over 980 diseases), the changes are minimal: Udmurtia replaced Chuvashia. The maximum regional differences in the specific morbidity of the population decreased up to three times between the same regions with extreme values as in 2010: the Nenets Autonomous Okrug (1421.5) and Kabardino-Balkaria (466.2).

The composite health index is calculated based on three indices determined by normalizing each of the three considered indicators. The LE index in assessing health was given a more important role than others characterizing “ill health” in a given period of time: disability and primary morbidity of the population, which only slightly correct the LE value.

The composite health index (Ih) is carried out according to the following formula:

Ih = [ILE +(Id + Ipm)/4]/2,

where ILE is the LE index; Id is disability index; and Ipm is the primary morbidity index.

The distribution of federal subjects by value of the composite health index in 2015 is presented in Table 2, which also shows the rank of region for this indicator in 2010, which makes it possible to see the shifts that have taken place over this period.

Table 2.   Composite index of population health in federal subjects in 2015 and their ranks in 2015 and 2010
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In 2010, the country’s average composite health index was 0.41503, and the LE index was 0.49786. Muscovites have the highest health index, 0.54321, and Chukotka residents, the lowest, 0.21452. The group with relatively good health included 31 federal subjects with an index above the national average. In addition to Moscow, the top ten federal subjects with the highest health scores included six North Caucasian republics, Krasnodar and Stavropol krais, and the Khanty-Mansi Autonomous Okrug. Most had the highest LE in the country, with the exception of Krasnodar krai and Khanty-Mansi Autonomous Okrug.

In half the regions, the composite health index was less than 0.392, of which the group of ten outsider regions (index below 0.345) included regions with the lowest LE: Pskov, Novgorod, and Amur oblasts; the Jewish Autonomous Oblast; the Tyva Republic; and the Nenets and Chukotka autonomous okrugs. In the republics of Karelia and Altai, health assessment was affected by high disability of the population; in Irkutsk oblast, by primary morbidity.

Five years later, in 2015, the composite health index on average for the country increased to 0.46259. A similar trend was observed in all federal subjects. The group with relatively good health (composite index above the national average) included 30 regions. At the same time, the composition of the ten leading regions changed by a third: Sevastopol, Moscow, and Leningrad oblasts entered the group. Chechnya, Krasnodar krai, and the Republic of North Ossetia lost their positions, where morbidity increased markedly; in addition, in Chechnya and Krasnodar krai, disability of the population increased.

In half the federal subjects, the composite health index was less than 0.442, and in the ten outsider regions, it was below 0.4. The outsider regions were basically the same as in 2010, except for Pskov oblast and the Nenets Autonomous Okrug. They were replaced by the Zabaykalsky krai and Kemerovo oblast. Thus, with overall improvement in the health of the population in all regions, the rank of 31 federal subjects in the distribution of the composite health index decreased versus 2010, including due to the inclusion of Crimea in the calculations (Sevastopol occupied 10th place; the Republic of Crimea, 19th).

Education of the Population

As a result of reform of the education system, its usual classification, primarily vocational education, changed. Two new levels appeared in higher education: bachelor and master degrees. According to the 2010 All-Russian census (VPN-2010), the proportion of people with these new forms of education was insignificant (bachelor, 1%; master, 0.5%). Together with specialists, the share of all persons with higher education was 22.8%, and taking into account postgraduate education, 23.4%. In the 2015 microcensus (MPN-2015), new forms of higher education, such as postgraduate education, were not covered. Primary and secondary vocational education were also reformed; they were merged into secondary vocational education, and its institutions began to train both mid-level specialists and skilled workers (employees). Despite these changes, the data of VPN-2010 and MPN-2015 for secondary vocational (in 2010 together with primary) and higher education are comparable.

Analysis of the distribution of the population at least 15 years old in 2010 and 2015 showed that the proportion of people with higher education (including postgraduate) slightly increased, from 23.4 to 25.8%, and the proportion of those trained as mid-level specialists over 5 years remained almost the same (31.2 and 31.3%, respectively). Only the proportion of those trained as skilled workers changed more significantly, from 5.6 to 9.2%. For other education levels, the share of those who completed training decreased: with incomplete higher education, from 4.6 to 2.8%; with complete general education, from 18.2 to 17.9%; with basic general education, from 11 to 9.7%; and those without a general or primary education, from 6 to 3.5%.

It should be noted that Rosstat’s current education statistics (Labor Force Survey) cover only the population of working age (15–72 years). According to this survey for 2015, the proportion of people with higher education increased to 26.4 versus 22.3% in 2010; it remained virtually unchanged, with secondary vocational education (40.1 and 40.5%), respectively; shares with other education levels slightly decreased.

To assess the education level of the population as a whole and in the regions, a point-based method was used, when each level is assigned a certain score: 1, initial; basic, 3; complete basic, 4; primary vocational, 4.5; secondary vocational, 5.5; incomplete higher, 6; higher, 7.

The average assessment of education level depends on gender, age, and social composition (employed, unemployed, pensioners), and place of residence (urban, rural). A detailed analysis of education level assessments for different categories of the population in 2010 for the Russian Federation as a whole is presented in (Rimashevskaya et al., 2013).

The average score for the education of Russia’s entire population aged 15 years and over was, according to the MPN-2010 data, 4.994' according to the MPN-2015 data, 5.138 points; i.e., it increased by 2.7%. However, only the Republic of Crimea is included in the MPN-2015 materials; there are no data for Sevastopol nor the Nenets Autonomous Okrug (like in the VPN-2010 data). However, the missing information for Sevastopol and the Nenets Autonomous Okrug in this study was covered by Labor Force Survey data. Recall that the average estimates of education level, calculated from the current statistics, are slightly lower than according to census data for the age group 15–72 years. Thus, the average assessment of education of the population of the Russian Federation at the same age according to MPN-2015 was 4.758, and according to the Labor Force Survey, 4.553.

The average education level of the population in all federal subjects also increased over the course of 5 years, but the rates varied significantly from 100.6% in Karelia to 112.1% in Chechnya, which affected the rank of most regions (Table 3)

Table 3.   Education level (average score) of population in federal subjects and their ranks in 2015 and 2010
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In 2010, in 18 regions, the average assessment of education was higher than the average for the Russian Federation, and after 5 years, this increased to 25 regions. In half the federal subjects in 2010, this estimate was below 4.867, and in 2015, 5.062. The composition of the ten leading regions remained 70% unchanged. In 2015, this group included the city of Sevastopol, the Republic of Crimea, and Tomsk oblast with growth rates of this indicator above the average Russian level of 104.2% The group of leaders saw the departure of Kamchatka krai and Kaliningrad and Murmansk oblasts, where the average estimate for education increased by only 1%.

The composition of the ten federal subjects with the lowest average estimates in education (below 4.927) did change much either. In 2015, having increased their rank, Tyva, Tambov oblast, and Ingushetia left the group. In the first two regions, the average estimate increased by 7%, and in Ingushetia, by 9.6%. In the Chechen Republic, with growth of this indicator by 12.1% as a result of a twofold increase in the proportion of people with higher education (from 11.8% to 23.1%), the rank did not increase. In addition, Altai krai, the Jewish Autonomous Oblast, Zabaykalsky krai, Kurgan and Kirov oblasts, and Dagestan remained in this group. Only in Zabaykalsky krai was growth of the average estimate somewhat inferior to the national average, 2.5%, while in other regions, it varied from 3.2% in Kirov oblast to 5.2% in the Jewish Autonomous Oblast. Among the new members of the group, Karelia stands out, the rating of which fell from 36 to 77 as a result of the lowest growth rate of this indicator among all regions, mainly due to a decrease in the share of people with higher and postgraduate education, from 19.7 to 18.4% .

In total, in 2015, in addition to Karelia, 14 federal subjects decreased in rank by ten or more positions (in Table 3 they are highlighted in color). Among them are such regions with well-known scientific centers as Kaluga, Novosibirsk, Sverdlovsk, and Voronezh oblasts, the Republic of Tatarstan, and a number of others. The low growth rates of the average estimate in these regions, despite the increase in the share of those who received higher education, are mainly explained by an increase (by 1.5–2 times or more) in the share of people trained in programs for skilled workers and a decrease in programs for training secondary specialists.

The distribution of regions according to the average assessment of education of the population, in comparison with other qualitative characteristics, is characterized by low interregional differentiation. The maximum differences in 2010 were 1.37 times (between Moscow and the Chechen Republic), and in 2015, 1.27 times. The main reason for the low regional inequality is compulsory secondary (school) education for younger generations back in the Soviet period, as well as the availability of vocational education.

Cultural Activity of the Population

The cultural activity of the population, which characterizes its level of cultural development, can be assessed by statistical indicators: attendance at theaters and museums (number of visits per 1000 people) and the number of readers of public libraries (thous. people). The latter is most important for federal subjects in which the number of theaters and museums is limited. Libraries in district centers are cultural centers where art exhibitions, festive events, meetings with writers, children’s circles, etc., are held.

Attendance at theaters and museums. In general, in the Russian Federation, the number of professional theaters increased by 10% within 5 years (from 604 to 665), and the specific attendance, by 20.3% (from 217 to 261 visits per 1000 people). In most regions (68), there was a positive dynamics in theater attendance, and it grew at the highest rate in Chechnya (5.5 times), Ingushetia (2.9 times), Altai (2.2 times), Tyva (2 times). This indicator slightly decreased in 11 federal subjects: Oryol, Pskov, Leningrad, Murmansk, Kirov, Penza, Saratov, Novosibirsk, Omsk, and Sakhalin oblasts and the Republic of Kalmykia. During the period under review, there were no theaters in the Chukotka, Nenets and Yamalo-Nenets okrugs.

The largest number of theatrical spectators in 2010 and 2015 was observed in the cultural capitals: Moscow (respectively, 527 and 612 visits) and St. Petersburg (517 and 780). In third place in 2010 was Omsk oblast (360), and in 2015, the Republic of Mari El (374). The top ten regions in theater attendance in 2010 and 2015 included Magadan and Novosibirsk oblasts, in addition to the two capitals, the Republic of Mari El, and Omsk oblast. In these regions, the specific attendance of theaters increased, with the exception of Omsk and Novosibirsk oblasts, but even in them, it remained at a relatively high level (352 and 328). In 2015, this group included Sevastopol, Astrakhan and Kostroma oblasts, Perm krai; departures were Krasnoyarsk krai, the Chuvash Republic, Tomsk and Saratov oblasts, and only in the latter was there a decrease in attendance (from 264 to 239).

In 2010, in 11 federal subjects, the specific theater attendance was less than 100, and the minimum level was observed in Tyva (48), Altai (39), and Chechnya (28). After 5 years, only five such federal subjects remained: Tyva (98), Stavropol krai (86), Altai (84), and the Jewish Autonomous Oblast (73).

As a result, the maximum regional differences in specific theater attendance over 5 years decreased from 18.8 times (between Moscow and Chechnya) to 10.7 times (between St. Petersburg and the Jewish Autonomous Oblast). If we exclude the two Russian capitals, which made a significant contribution, including the tourism factor, then the maximum regional difference in 2010 was 12.9, and in 2015, 5.1 times.

The specific attendance of museums in the entire Russian Federation over 5 years increased even more (by 43.4%): from 567 to 813, including as a result of an increase in the number of museums from 2578 to 2758 (7%). In the vast majority of federal subjects (75), a similar trend was observed, and the leaders in growth of museum attendance were Chechnya (14.2 times), Altai (by 9 times), Tatarstan (2.5 times), and the Jewish Autonomous Oblast (3 times). The largest decrease in museum attendance in 2015 was recorded in Leningrad oblast: by more than 40% (from 731 to 429), as a result of the annexation of Peterhof to St. Petersburg. Similar trends were observed in Primorsky and Altai krais (a decrease by 25 and 19%, respectively), Ingushetia (by 20%), Astrakhan oblast (by 16%), Buryatia (less than 5%), Saratov oblast, and Perm krai (about 1%).

In 2010, the group of leading regions with a high proportion of museum attendance (more than 1200) was headed by St. Petersburg (3669) and Yaroslavl and Vladimir oblasts (1402 and 1372, respectively). In 2015, St. Petersburg, the most attractive to tourists, retained its lead (4860); Sevastopol took second place (3468), and Moscow, third (more than 2100). In addition, the Republic of Crimea (1383) entered the top ten. As a result, Vologda and Bryansk oblasts left this group. It still has regions with cities that are part of the touristic Golden Ring of Russia (Yaroslavl and Vladimir oblasts), as well as Novgorod, Pskov, Volgograd, and Kaliningrad oblasts with a large number of historical sites.

In half the federal subjects, the specific attendance of museums in 2010 was below 389, and in 2015, below 454. The group of ten regions with the lowest attendance (less than 150) was headed by Adygea; Chechnya was last (13). In 2015, this group (less than 200) still included Adygea, Chechnya, North Ossetia, Karachay-Cherkessia, Kalmykia, and Magadan oblast. Bashkortostan, Kabardino-Balkaria, and Altai left the group. They were replaced by Altai krai and the republics of Dagestan and Tyva. In the regions that remained in or entered the group, the specific attendance of museums increased.

The tourist factor in museum attendance plays a larger role than in theater attendance, so regional differences are also higher. In 2010, the maximum gap in museum attendance was 282.2 times (between St. Petersburg and the Chechen Republic), and after 5 years, it decreased, but remained still high, 86.8 times (between St. Petersburg and Karachay-Cherkessia). With the exception of St. Petersburg in 2010, and in 2015 also Sevastopol, the differences decreased to 107.8 and 38 times, respectively.

Number of Users of Public Libraries. In the last decade, the number of users of public libraries has been steadily declining, also due to the intensive development of the Internet, which has led to the closure of libraries and reduction of library fund. These processes occurred almost at the same rates in cities and rural areas. The number of public libraries within 5 years decreased from 46 200 to 39 000, and the library fund has decreased from 6459 to 5726 copies per 1000 people.

In 2010, the total number of users of public libraries was about 56 mln, and in 2015, 52 mln (392 and 355, respectively, per 1000 people). However, in 17 federal subjects, there was an increase in the specific number of users. Small (from 0.1 to 5%) growth was noted in the Belgorod, Kemerovo, Penza, and Chelyabinsk oblasts, and in Tyva; it somewhat higher (6–7%) in Sakhalin and Novosibirsk oblasts and in Kabardino-Balkaria. In Khabarovsk krai, Sverdlovsk and Yaroslavl oblasts, and Chechnya, the specific indicator increased by more than 10%; in the Nenets Autonomous Okrug and Leningrad and Vologda oblasts, by more than 20%. In Novgorod oblast, the growth was 30.8%, and in the Yamalo-Nenets Autonomous Okrug, 93.3%. In seven federal subjects, the positive dynamics of this indicator is partly due to a decrease in population (Kemerovo, Penza, Novgorod, Sakhalin, and Vologda oblasts, Khabarovsk krai, and the Yamalo-Nenets Autonomous Okrug).

Despite the reduction in the specific number of library users, in 2015, the Chukotka Autonomous Okrug; Smolensk, Murmansk, and Magadan oblasts; and the republics of Mordovia, Chuvashia, and Mari El remained in the 11 leading regions; Novgorod, Sakhalin, and Vologda oblasts were included, where an increase in users was recorded libraries, and Mari El, which retained a relatively high number. In 2010, the leaders were federal subjects with a specific number of users greater than 552; in 2015, greater than 535. This indicator was higher than the average Russian level in 55 regions in 2010 and in 54 regions in 2015.

The group of ten regions with the lowest specific number of library users (less than 300 in 2010 and 260 in 2015) remained 60% unchanged. The regions where the attendance of libraries has decreased—the Khanty-Mansi Autonomous Okrug, St. Petersburg, Moscow oblast, and Primorsky krai, as well as Leningrad oblast and Chechnya—have retained their positions, where this indicator has grown by 21 and 12%, respectively. In 2015, Sverdlovsk oblast, the Yamalo-Nenets Autonomous Okrug, Kabardino-Balkaria, and North Ossetia left the group, while Moscow, Volgograd and Voronezh oblasts, and Ingushetia entered, each demonstrating a decrease in this number by 30–80%.

As a result of multidirectional dynamics of the specific number of users of public libraries, the maximum regional differences between the Chukotka Autonomous Okrug and Chechen Republic decreased from 6 times in 2010 to 3.4 times in 2015.

The composite index of cultural activity of the population was calculated as the average of three indices: specific theater attendance (It), museums (Im) and library users (Il ): Ic = (It +Im +Il )/3 (Table 4).

Table 4.   Composite index of cultural activity of population in federal subjects in 2015 and their ranks in 2015 and 2010
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For five years, the composite index of cultural activity on average in Russia increased by 9.3%, from 0.21115 to 0.23087. A similar positive trend was observed in 53 regions. In half the federal subjects in 2010, this index was more than 0.207, and in 2015, 0.214, but the number of regions with an index higher than the Russian average decreased from 39 to 28. The composition of the leading regions also remained 70% unchanged. In 2010, the Chukotka Autonomous Okrug and Karelia and Omsk oblast were among the leaders, but in 2015, they were replaced by Sevastopol, Novgorod and Yaroslavl oblasts. The composition of the regions with the lowest cultural activity of the population, as well as the group of leaders, did not change significantly over five years. The group of outsiders included Dagestan, Kabardino-Balkaria, and Chechnya (despite the fact that in these republics, growth was recorded in all three cultural development indicators, and in Chechnya, it was significant); Krasnodar krai, Moscow oblast, and the Khanty-Mansi Autonomous Okrug (where the number of library users decreased); and Leningrad oblast, where the attendance of theaters and museums decreased, which affected their rating (in theaters it decreased from 55 to 73 and in museums from 11 to 44). The low cultural activity of the inhabitants of Leningrad and Moscow oblasts is largely due to the proximity of metropolitan cities.

Social Behavior of the Population

The social behavior of the population was estimated by the number of citizens with antisocial (deviant) behavior. These entail not only persons who have committed crimes, but also those registered at medical institutions (MI)—patients suffering from drug addiction, alcoholism, and other types of substance abuse. In health care, these deviations are considered diseases, but the state of health of these citizens is associated with an antisocial lifestyle.

Crime. In official statistics, the main indicator making it possible to judge the scale and dynamics of crime is the total number of reported crimes per 100 000 people. In addition, there are Rosstat data on the total number of women and men who have committed crimes (thous. people). The first indicator is significantly higher than the second, which is apparently because the same person simultaneously commits different types of crimes. Both indicators, according to official statistics, have shown a positive trend in recent years: the number of crimes has decreased.

In 2015, in the Russian Federation, the number of persons (women and men per 100 000 people) who committed crimes was 734. However, this indicator is published in a regional context for odd years. Therefore, to assess the scale of antisocial behavior associated with crime, the study used the indicator of the total number of people who committed a crime per 100 000 people in 2009 and 2015.

The total number of persons who committed crimes in Russia in 2009 was 1 220 000; in 2015, 1 075 000; per 100 000 people, 860 and 734 people, respectively. Whereas the country’s average specific indicator of persons who committed crimes decreased by 14.6%, a multidirectional dynamics was recorded in the regions. In most federal subjects (62), this indicator decreased: in 24 regions, the decrease was greater than in the country as a whole, and Moscow, the Nenets Autonomous Okrug, and Astrakhan and Novosibirsk oblasts were the leaders (a decrease of more than a third).

Among the 21 federal subjects with an increase in the specific number of persons who committed crimes, Vologda oblast, Kamchatka krai (by 13.1%), Kabardino-Balkaria (by 16.9%), and Dagestan (by 18.3%) had the highest growth rates.

In 2015, the number of federal subjects with an indicator below the Russian average remained almost unchanged: 39 versus 40 in 2009. The composition of the group of leaders—with a low number of those who committed crimes: in 2010 less than 575, and in 2015, less than 502 per 100 000—remained 70% the same. Despite the growth of this indicator in Ingushetia (from 168 to 186) and Dagestan (from 263 to 311), they retained their positions in this group: first and fourth. It also included the republics of Chechnya, Kabardino-Balkaria, and Karachay-Cherkessia, and Moscow and St. Petersburg. In 2015, the group included Tula and Belgorod oblasts and Sevastopol, which replaced Ryazan oblast, Adygea, and North Ossetia.

The group of ten outsider regions (with the highest specific number of persons who committed crimes—in 2010 more than 1280, and in 2015, 1130 per 100 000) retained, like the leader, 70% of the composition (Magadan oblast, Komi, Tyva, Khakassia, Altai, Buryatia, and Zabaykalsky krai). Whereas in Komi and Zabaykalsky krai an 5.2 and 6.8% increase in this indicator was recorded, respectively, in other regions of the group, there was a decrease from 4.7% in Buryatia to 14.7% in Altai. In 2009, it also included the Nenets Autonomous Okrug and Amur and Astrakhan oblasts, and in 2015, the Jewish Autonomous Oblast and Sakhalin and Kemerovo oblasts. The maximum regional differences decreased from 10.1 times (between the republics of Buryatia and Ingushetia) in 2009 to 7.9 times (between the republics of Altai and Ingushetia) in 2015.

The contingent of persons registered in health care facilities for alcoholism and alcoholic psychosis, drug addiction, and other types of substance abuse in the country as a whole in 2010 was 1607 per 100 000, of which 85.1% were alcoholics, 14.4% were drug addicts, and 0.5% other types of substance abusers. In 32 federal subjects, this specific indicator was below the national average.

After 5 years, the total specific number of the contingent registered in health care facilities for these reasons decreased in the country as a whole to 1278.8, or by 20.4%. Moreover, the contingent has changed little. A reduction in their number occurred in all federal subjects with the exception of Buryatia (growth by 2%) and the Jewish Autonomous Oblast (by 7.6%). The number of regions with an indicator below the national average remained almost the same, 33.

There were no significant changes in the composition of the ten regions with the lowest specific indicators: less than 1153 in 2010 and 868 in 2015. This group included five North Caucasian republics, Moscow, St. Petersburg, and Buryatia. In 2010, Belgorod and Orenburg oblasts were also among the leaders, and in 2015, they were replaced by Tomsk oblast and Krasnodar krai, where the contingent of those registered in health care facilities decreased by two times.

No major changes were recorded in the composition of the ten outsider regions (with the largest specific number of people registered in health care facilities—more than 2460 in 2010 and 1925 in 2015). In addition to the Chukotka Autonomous Okrug, the group included Kostroma, Nizhny Novgorod, Novgorod, Sakhalin, Ivanovo, and Magadan oblasts; Karelia and the Nenets Autonomous Okrug departed the group—there, the specific number of registered persons decreased by 1.5 and 3 times, respectively. They were replaced by Tambov and Bryansk oblasts, where the rate of decrease in this indicator was significantly lower. The maximum regional differences also decreased slightly: 53.4 times in 2010 between the Nenets Autonomous Okrug and Ingushetia versus 50.8 times in 2015 between the Chukotka Autonomous Okrug and Ingushetia.

The total number of persons with antisocial behavior was calculated as the sum of the absolute values of the considered indicators (Table 5).

Table 5.   Number of people with antisocial behavior (persons per 100 000) in federal subjects in 2015 and their ranks in 2015 and 2010
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The number of people with antisocial behavior in the period under review in the country as a whole decreased from 2467.2 to 2012.8 per 100 000, or by 18.4%. Similar dynamics was observed in all federal subjects, but its pace was different. As a result, most of the regions changed their rank: in 20 federal subjects, it increased, since the rate of decrease in the value of the specific population was higher. The maximum regional difference decreased from 24.7 between the Nenets Autonomous Okrug and Ingushetia in 2010 to 18 times between the Chukotka Autonomous Okrug and Ingushetia in 2015. As well, the composition of people with deviant behavior did not change as significantly. The share of those registered in health care facilities decreased from 65.1 to 63.5%, including more than half the contingent with alcoholism: in 2010 55.4%, and in 2015, 53.5%.

The number of regions with a specific number of people with antisocial behavior below the national average in 2015 decreased to 28 compared to 33 in 2010. As in the distribution of regions by level of crime and specific number of the contingent registered in health care facilities, as part of the group of ten, in addition to the two Russian capitals, the majority of the leading regions were represented by the North Caucasian republics. In 2010, it also included Tatarstan. Five years later, Karachay-Cherkessia and Tatarstan were replaced by Krasnodar krai and Leningrad oblast, where the proportion of people who committed crimes significantly decreased, and the contingent of those registered at health care facilities decreased by 50%.

As for the group of regions with the highest rates of antisocial behavior (in 2010, over 3500, in 2015, over 2900 per 100 000), the Far Eastern regions predominated. In addition, the group of outsiders included Novgorod and Ivanovo oblasts with a high number of people registered at health care facilities, and the Republic of Khakassia with a high crime rate. In 2010, this group included Perm krai, Karelia, and the Nenets Autonomous Okrug; after 5 years, Nizhny Novgorod and Bryansk oblasts, and the Jewish Autonomous Oblast, which lowered its rating compared to 2010 by 22 positions (from 54 to 76) . Significant shifts (by ten or more positions) occurred in another 15 federal subjects (Table 1; 5 highlighted in color).

To include this indicator in the integral assessment of human potential, its index was calculated. Moreover, the higher the index, the fewer the people with antisocial behavior live in a given territory; in other words, it characterizes, to a greater extent, socially normal rather than deviant behavior.

HUMAN POTENTIAL OF THE POPULATION OF RUSSIAN REGIONS

The human potential index (HPI) of Russia and its regions was calculated as the arithmetic mean of the indices of its five components. In the Russian Federation as a whole, it has increased over 5 years by 12.7%, from 0.39622 to 0.44636. All components of the HPI had a positive trend, but the growth rates varied from 109.3% in cultural activity to 114.4% in education. Therefore, the share of each of the components in the average Russian HPI did not undergo major changes over the period under review: the share of the demographic component and the health of the population decreased from 14 to 13.7% and from 20.9 to 20.7%, respectively; the components of cultural activity of the population, from 10.7% in 2010 to 10.3% in 2015; the role of education and social behavior in the formation of HPI slightly increased, from 25.1 to 25.5%, respectively, and from 29.3 to 29.7%.

In federal subjects, the HPI also improved, with the exception of two republics of the North Caucasus Federal District (the integral index decreased in Dagestan to 99.8% and in Karachay-Cherkessia to 95.5%) and the Jewish Autonomous Oblast, 99% compared to 2010. In all three regions, the demographic situation and cultural activity of the population deteriorated. In the Jewish Autonomous Oblast in 2015, in addition to the largest emigration in the country and a decrease in the number of users of public libraries to 61.7%, the share of people with antisocial behavior increased. In the remaining 80 regions (except for the Republic of Crimea and Sevastopol), the growth rate of the HPI ranged from 105.2% in North Ossetia to 192% in the Nenets Autonomous Okrug and in 47 federal subjects, it was higher than the national average.

By HPI value, all federal subjects can be divided into three types: (a) those with a relatively high human potential (the HPI is higher than the average Russian level), among which is the group of ten leading regions; (b) those with an average level of human potential (the HPI is below the Russian average, but not below the median level); (c) those with human potential below the average level (HPI is less than the median level), with indication of the ten regions with the lowest HPI (Table 6). In the number of federal subjects included in each type, there were practically no changes over 5 years, except for the fact that the median level shifted by one position in 2015 due to an increase in the number of regions from 83 to 85.

Table 6.   Human potential index (HPI), growth rate of HPI (2015/2010) in federal subjects, and their ranks in 2015 and 2010
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The group with relatively high development of human potential in 2010 and 2015 included Its composition did not change significantly in 5 years. Murmansk oblast, Stavropol krai, Volgograd oblast, where the HPI increased by only 6–7%, and Karachay-Cherkessia, where the HPI decreased, entered the group with an average HPI. They were replaced by Sevastopol, the Republic of Crimea, Krasnodar krai, and Novosibirsk oblast. In the last two regions, the growth rate of the HPI significantly exceeded the average Russian level (22 and 18.7, respectively).%). More significant shifts in this type occurred in the population: in 5 years it increased from 48.1 to 54 mln people, or from 33.7 to 36.7% of Russia’s total population. This increase is not so much the result of a change in the composition of the group, but an increase in the populations of regions.

The average HPI in this group in 2010 was 0.44665; in 2015, it increased to 0.50940 (by 14%). In relation to the average Russian index, it was higher in 2010 by 12.7% and in 2015 by 14.1%, and the indices of its components, with the exception of cultural activity, were also higher than in the country as a whole (Table 7). The education component grew at the highest rate (123.6%) (from 0.51016 to 0.63055), and its share on average in the group in the HPI increased from 22.7 to 24.8%. The social behavior component continued to play the largest role in the formation of the index, despite the relatively low growth rates of its index (109.9%) and decrease in the share in the structure of the average-group HPI from 31.4 to 30.5%. The average health index grew at an even slower pace (108.5%), and its share decreased from 21.1 to 20.1%. The indices of the demographic component (113.6%) and cultural activity (118.5%) increased at a relatively high pace; however, the share of the former in the structure of the average HPI for the group decreased slightly (from 16.9 to 16.7%), and the former increased only from 8 to 8.1%.

Table 7.   Average value of human potential index (HPI), its growth rate and structure in federal subjects of various types in 2010 and 2015
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The maximum regional differences in the HPI in this group (including its leaders) did not change: by 1.5 times in 2010 between St. Petersburg and Volgograd oblast and in 2015 between Sevastopol and Leningrad oblast.

The group of ten leading regions differs by the highest values of all HPI components. Its average HPI in 2015 increased by 14.6% and was 7.6% higher than the group average (0.54836 versus 0.50940). In relation to the average Russian HPI in 2010, the average HPI of the leading regions was 120.7%, in 2015, it was 122.9% (see Table 7).

The composition of the group of leaders over 5 years changed by 40%; the republics of Dagestan, Karachay-Cherkessia, and North Ossetia–Alania and Kaliningrad oblast left it, and whereas the first two, as noted above, the HPI decreased slightly, the latter, on the contrary, witnessed growth by 5 and 9%, respectively. With the exception of Karachay-Cherkessia, all three regions in 2015 remained in the group with a relatively high HPI. The group of leaders in 2015 included Krasnodar krai and the Chechen Republic, where the HPI increased by 22 and 14%, respectively, as well as Sevastopol and the Republic of Crimea. The population in the leading regions grew at a higher rate than in the group as a whole (118 versus 112%) and increased from 31.7 to 37.4 mln people over 5 years.

Six permanent members of the group of leaders are distinguished by relatively low (below the Russian average) growth rates in HPI, with the exception of Ingushetia (19.1%) and Tyumen oblast without autonomous okrugs (13.8%). Each of the ten regions in 2015, as in 2010, was also distinguished by a number of high values of the HPI components. Thus, for all, the index of the demographic component was higher than the average Russian level, and the first three places were occupied by Sevastopol (due to migration increase), Ingushetia, and Tyumen oblast (without autonomous okrugs), where natural and migration population increase was recorded simultaneously.

The assessment of the health of the population in all federal subjects of the group of leaders is also higher than the national average, but its growth rate was very modest (108.1%), and the first three places were occupied by Moscow (due to relatively high LE and low morbidity), Ingushetia (with the highest LE), and Moscow oblast (with relatively high LE and low disability). As for education of the population, Ingushetia and Chechnya remain problematic in this group, even though the average education level (in points) increased by 10 and 12%, respectively, versus 2.7% on average in the country. The first three places not only in this group, but also among all federal subjects in terms of education level are occupied by Moscow, St. Petersburg, and Sevastopol. The same regions are also first in cultural activity of the population, while in Moscow oblast, Krasnodar krai, and the Khanty-Mansi Autonomous Okrug, this index in 2015 was two times lower than the national average. The situation is not much better in Chechnya.

The regions in the group of leaders are distinguished by a high social behavior index; only in Moscow and Tyumen oblasts is it slightly lower than the average Russian one; the top places are occupied by Ingushetia, Chechnya, and St. Petersburg.

In the structure of the average HPI of the leading regions, as well as overall for 19 federal subjects, no significant changes are observed. The difference in the role of various components in formation of the HPI decreased on average for this group. However, the maximum regional differences in the HPI slightly increased from 1.4 to 1.47 times, as well as in the group as a whole, from 1.52 to 1.54 times.

The second group (with an average level of development of human potential) in 2015 included 24 federal subjects; in 2010, 23. Its composition changed by almost a third: in addition to the four regions that departed the first group, it included Mari El, the Nenets Autonomous Okrug, and Kursk and Astrakhan oblasts. The population decreased over 5 years from 48.1 to 41.4 mln people (from 33.7 to 28.3% of the total number of Russians). The average HPI for the group increased from 0.37894 to 0.43016 (by 13.5%) and in relation to the average Russian index, in 2010 it was 95.6%, and in 2015 it was 96.4%. All components of HPI were, on average, slightly lower than the national average. The maximum regional differences in the HPI index decreased from 1.16 to 1.13 times.

The index of the education component grew at the highest rate (121.5%), and its ratio with the average Russian indicator increased from 91.7 to 97.4%, and its share in the structure of the average HPI increased from 24.1 to 25.8%. In 2015, the population of the Yamalo-Nenets Autonomous Okrug, Samara oblast, the republics of Sakha (Yakutia) and Adygea had a relatively high education level, and the population of the Stavropol krai had a low education level. Cultural activity increased at the lowest rate (104.8%). At the same time, in the level and share in the structure of the HPI index, it remained somewhat higher than the average in the group with a relatively high HPI. According to the level of cultural activity in 2015, on the one hand, Yaroslavl oblast, Krasnoyarsk krai, and Chuvashia and Mari El were distinguished, while on the other, Karachay-Cherkessia, the Nenets Autonomous Okrug, and Stavropol krai. The health of the population in the group improved, but not at a fast rate (110.4%); as a result, in 2015, its average group index became slightly lower than the average for the Russian Federation (0.45977 versus 0.46259). Good health in the group is particularly demonstrated by residents of Stavropol krai, Karachay-Cherkessia, and Astrakhan oblast; serious problems with the health of the population are mainly noted only in the Nenets Autonomous Okrug.

The demographic component on average in the group increased at a higher rate than in the Russian Federation (114.3 versus 110.5%). As a result, its lag behind the national average decreased from 14.4 to 11.4%. Problematic in this regard are still Murmansk and Volgograd oblasts and, in particular, the Yamalo-Nenets Autonomous Okrug, where the demographic component was 0.09677, and in the structure of the HPI it accounted for only 4.6%. The largest share in its structure in this type of regions, like the first, is occupied by the social behavior component, and by region in 2015 it varied from 34.6% in Karachay-Cherkessia to 26.9% in the Yamalo-Nenets Autonomous Okrug. Over 5 years, its value increased by 12.3% (from 0.57596 to 0.64687), but the ratio with the average Russian level decreased from 99.2 to 97.5%. Relatively low values (less than 0.6) of this component remain in the Yamalo-Nenets Autonomous Okrug, the Nenets Autonomous Okrug, and the republics of Adygea, Mari El, and Chuvashia.

The third type regions with development of human potential below the average level is the most numerous: in 2010 it included 41 federal subjects; in 2015, 42; and its composition has not changed much. In 2015, there was an exchange with regions of the second type: four federal subjects left, replaced by Kalmykia and Chelyabinsk, Voronezh, and Penza oblasts. In the third group, the total population increased from 46.7 to 51 mln people, or from 32.7 to 34.8% of the total number of Russians.

The average HPI increased by 18.4% over 5 years (from 0.31747 to 0.37591). In 2010, it was below 0.3 in eight federal subjects; 5 years later, only 2 remained: the Jewish Autonomous Oblast and Chukotka Autonomous Okrug. This type of regions is distinguished, first, not only by the highest growth rate of the average HPI, but also by the high average growth rates of its four components: demographic (129.6%), health (116%), education (121.4%), and social behavior (122.7%); there has also been slight decrease in the average value of the cultural activity component (by 0.5% from 0.21893 to 0.21321). Second, the average HPI index in this type of regions and the average indices of all five of its components remain, as in 2010, the lowest. The most problematic are the characteristics of demography and social behavior. The average index of the first component, even as a result of its increase in 2015, remained 1.5 times lower than the Russian average (0.20338 versus 0.30645), and social behavior, 1.2 times lower (0.53820 versus 0.66313). Whereas problems with demography are more the result of departure of residents to other regions, with social behavior, they are the result of a high proportion of deviants.

Due to the urgency of the problem of integrated spatial development and reduction of interregional differences, including in human development, the situation that has developed in the ten federal subjects with the lowest HPI (outsider regions), which are included in the third type of regions, requires special attention. Seven million people lived in these ten regions in 2010; in 2015, 6.1 mln, or 4.9 and 4.2% of the total population of the country, respectively. After 5 years, the composition of this group remained 70% unchanged; its permanent members were five regions of the Far Eastern Federal District, as well as Kurgan and Ivanovo oblasts. In 2015, Perm krai, Novgorod oblast, and the Nenets Autonomous Okrug left the group; in each, all components of the HPI demonstrated significant positive changes. The index of the demographic component in the Nenets Autonomous Okrug significantly (1.5 times) exceeded the average Russian indicator, but the main shift was recorded in the characteristics of social behavior, the index of which in 2010 was slightly higher than zero and in 2015 reached 0.563182 (outrageous specific indicators for alcohol dependence of the population decreased by 3.2 times, but at the same time, they remained 1.5 times higher than the average for the Russian Federation). Novgorod oblast reached high positive shifts in the cultural sphere.

In 2015, the group of outsiders included Zabaykalsky krai and the republics of Komi and Karelia. Only in Komi was the growth rate of the HPI higher than the national average (16.2%), while in the other two territories, the increase was less than 10%. In Komi, all components of the HPI had a positive trend; in Karelia, the index of cultural activity of the population decreased by 1.7 times. In Zabaykalsky krai, a decrease was recorded in two characteristics: demographic (the result of emigration) and cultural activity (the number of library users decreased). In 2010, only Karelia was relatively far from the majority of outsiders in HPI level (63rd place among Russian).

The average HPI among outsiders grew at the highest rate (122.2%). Among the seven permanent members of the group in 2015, the HPI decreased only in the Jewish Autonomous Oblast, while in all the rest it increased, and the highest growth rates were noted in Magadan oblast (137%), the Chukotka Autonomous Okrug (132.2%), and Amur and Sakhalin oblasts (123.6 and 121.9%, respectively). In all ten regions, there are serious problems with the demographic component due to the active departure of the population. The average index of this component in 2015 remained almost two times lower than the Russian average and varied from 0.23710 in Sakhalin oblast to 0.08387 in Magadan oblast and 0.05806 in the Jewish Autonomous Oblast. The average health index (0.39560) in 2015 was 85.5% of the average for the Russian Federation. Health problems are still most acute among residents of Amur oblast, the Jewish Autonomous Oblast, Chukotka Autonomous Okrug, and the Republic of Karelia, despite the fact that in all four regions, the health index has increased, but remains significantly below the national average (0.346–0.375 versus 0.463 for the RF). The average group value of the education component remained almost in the same ratio with the average Russian level, and in Zabaykalsky krai, and Kurgan oblast, this index was less than 0.38.

The indicator of cultural activity of the population in 2010 was 5% higher than the national average, and after 5 years, it dropped to 90.8%; the Jewish Autonomous Oblast and Kurgan and Amur oblasts were the most problematic in this area. Problems with the social behavior of the population remain particularly relevant in the Chukotka Autonomous Okrug and Magadan oblast. On the whole, in the ten regions, the growth rates of the social behavior component were the highest (157.3%); however, in relation to the average Russian level, it was 65.5% (in 2010, only 47.6%). The average structure of the integrated HPI of outsider regions in 2015 became much closer to the average structure of all regions of the third type than in 2010.

The maximum regional differences in the HPI in regions of the third type decreased over 5 years from 1.63 times (between Orenburg oblast and the Nenets Autonomous Okrug) to 1.44 times (between Kalmykia and the Jewish Autonomous Oblast), including in outsider regions: 1.38 times (between Kurgan oblast and the Nenets Autonomous Okrug) to 1.24 times (between Sakhalin oblast and the Jewish Autonomous Oblast), and in the entire Russian Federation, from 2.76 times (between St. Petersburg and the Nenets Autonomous Okrug) to 2.41 times (between Sevastopol and the Jewish Autonomous Oblast).

CONCLUSIONS

One of the factors of socioeconomic development and, at the same time, its goal is the human potential of the population of the country and its regions. Modernization of the economy is impossible without proper quality of human potential. In Russia, with its vast territory, which includes 85 federal subjects, and the multinational composition of the population, an important task is to overcome spatial differentiation, including quality of human potential. In this study, assessment of HPI included five of its components: demographic, health, education, cultural activity, and social behavior of the population. It was determined by the index method. Rosstat data for 2010 and 2015 served as the information base.

The study showed that over 5 years, the quality of human potential of the Russian population has improved. The HPI for the whole country increased by 12.7%, the value of each of its components increased from 9.3% in cultural activity to 14.4% in social behavior. The same processes were observed in federal subjects, except for the Jewish Autonomous Oblast and two republics of the North Caucasian Federal District—Dagestan and Karachay-Cherkessia—but they also did not show a significant decrease in the quality of human potential. The dynamics of the HPI and its individual components in regions differed sharply from the national average. Thus, the growth rate of HPI varied in a fairly wide range: from 105.2% in North Ossetia to 192.2% in the Nenets Autonomous Okrug; cultural activity, from 59.6% in Karelia to 770% in Chechnya.

According to the value of the integral index, all federal subjects are grouped into three types: (1) those with human potential above the average Russian level; (2) those with average human potential; and (3) those with human potential below the average level. In the two extreme groups, ten federal subjects were additionally distinguished with the highest and lowest HPI, the so-called leaders and outsiders. For five years, the composition of the three types of regions has not undergone major changes, and in terms of leaders and outsiders, 70% remained the same as in 2010. However, as a result of different growth rates of HPI, the rank of regions in the distribution of this indicator in most of them has changed. At the same time, the inequality between federal subjects in the quality of human potential has slightly decreased: from 2.76 to 2.41 times.

Since the ultimate goal of the study is to assess the readiness of federal subjects to participate in economic modernization, the average Russian level was used as a criterion by which, from this viewpoint, regional human potential and its individual characteristics were assessed.

In 2010 and 2015, the HPI was higher than the national average in only 19 regions: 48 mln people resided in this group in 2010 and 54 mln in 2015. In 2010, only two regions with a total population of 16.3 mln people (11.4%)—Moscow and St. Petersburg—met this criterion for all five HPI characteristics. In 2015, Sevastopol joined them, increasing the number to 17.9 mln people (12.2%). Another five regions—Tyumen oblast without autonomous okrugs, the republics of Crimea and Tatarstan, the Khanty-Mansi Autonomous Okrug, and Kaliningrad oblast—in 2015 did not reach the average Russian level of one characteristic. Moscow and Leningrad oblasts had two such characteristics, and one of them was cultural activity of the population. However, their proximity to two Russian capitals makes up for this shortcoming. All of the above regions, which are home to 18.8 mln people (12.9%), have a chance to rectify the situation relatively quickly.

In 2015, the regions of the first type included four North Caucasian republics. The quality of the HPI of these republics is explained by the high birth rate and LE, as well as by national sociocultural traditions. However, in Chechnya and Ingushetia, serious problems have been identified with education of the population, which does not allow us to consider them even conditionally ready for the implementation of the modernization process.

The analysis showed that in most federal subjects, demographic problems remain the most pressing, manifested as a low natural increase in and high migration decrease of the population. Their solution is one of the most difficult tasks. Improving health is particularly important for regions with harsh natural and climatic conditions, poor environmental conditions, and a high proportion of rural settlements remote from administrative centers. A number of regions lag behind the Russian average in education level of the population. The social and cultural component of the HPI also requires serious attention. And whereas cultural activity of the population living in a given territory largely depends on the presence of cultural institutions, the decrease in the number of people with antisocial behavior depends on many factors, above all, on the increase in demand for labor and its qualitative balance with labor supply.