Human Capital Assessment in Indigenous Regions to Enable Sustainable Futures

Abstract

This study is dedicated to the economic valuation of human capital (HC) in the regions where the Indigenous peoples of Kamchatka reside. The entire Kamchatksky Krai is considered an Indigenous people’s ancestral territory. However, in this study, only municipalities whose population is more than one-third Indigenous were chosen for the assessment. This includes six municipal districts and one urban district (Aleytsky, Bystrinsky, Karaginsky, Olyutorsky, Penzhinsky, and Tigilsky municipal districts and Palana urban district). The study employs statistical analysis and integrated, situational approaches: the investment approach was deemed the most appropriate tool for the economic assessment of HC in Indigenous territories. The document analysis was based on a content analysis of open-access government documents and included legal acts, resolutions, orders, and other documents issued by federal, regional, and local authorities pertaining to the economic activities of Kamchatka Indigenous peoples. Based on the proposed calculation algorithm, the assessed combined monetized value of HC for the Kamchatka territories with large Indigenous populations was RUB 38.8 billion (approximately USD 520 million) in 2021. The growth of HC in the Indigenous areas of Kamchatka was observed between the years 2017 and 2021. The mean regional value of HC per capita increased more than twofold during this period. The accurate and precise assessment of HC in the Kamchatka Indigenous homelands provides baseline information necessary for economic and sustainable policy decision-making, and the approach proposed in this paper can be applied to Indigenous communities worldwide.

1. Introduction

The Arctic economy rests on three pillars: extensive resource production for the global market, small-scale traditional production for local consumption, and transfers from higher tiers of government [1]. Hunting, reindeer herding, and fishing continue to play vital roles in the economy of the northern regions, including the Arctic and Sub-Arctic. The significance of the traditional sector’s contribution to the northern economy is undeniable, but there are limited means for measuring its economic valuation within the public sector. Developing methods for valuation and measurement of the extent and dynamics of the traditional sector would provide a more comprehensive understanding of the northern economy and the economic activities of Indigenous communities [1,2]. Shifting the northern regions away from dependence on exchanging non-renewable resources with the public sector to a more diversified, knowledge-based economy is a critical aspect of sustainable development in the Arctic. Creative, cultural, and knowledge-based economies rely heavily on internal community capacities and intangible competitive advantages. Measuring HC is crucial in regional economic growth development and is integral to research. Furthermore, local communities could benefit from leveraging Indigenous knowledge and traditions to aid in building institutions and developing civil society [3,4].

The northern territories present a complex picture in terms of social and economic development. Nominal monetary incomes are high, with relatively even distribution, and there is a high level of employment with low unemployment. However, the northern regions suffer from a scarcity of specialists across various professional skill levels required for their economy [5]. Other demographic challenges in the Russian Arctic are characterized by harsh living conditions and low development indices. Increasing the economic development of HC is a strategic priority to enhance the northern economy’s competitiveness in the face of globalization [6,7].

HC encompasses not only economic productivity but also personal growth, social responsibility, and the overall advancement of a nation. N. Biddle expands the definitions of cost and benefit to include health and well-being improvements, as well as social costs associated with discrimination and unfair treatment in schools [8]. This comprehensive perspective acknowledges that an individual’s skills, learning, talents, and attributes are valuable assets that can positively affect various aspects of life, both at the individual and social levels [9].

Preserving HC of northern and Arctic territories is recognized as one of the most important tasks of sustainable development, and this issue is especially important for Indigenous people of the Arctic and the north [10,11]. Researchers consider HC and its relation to Indigenous peoples of the north from different angles: from the perspective of the labor market [12] and as a socio-cultural phenomenon [13]. Several studies analyze individual components of HC: education capital [14] and health capital [15].

The founders of the modern theory of HC are Th. Schultz [16,17,18] and G. Becker [19,20,21]. Significant contributions were also made by J. Kendrick [22], D. Jorgenson, and B. Fraumeni [23,24]. Among Russian researchers, the works of R.I. Kapelyushnikov [25,26], M.M. Kritskij [27], and S.A. Dyatlov [28] are particularly important. The approach used in this paper is based on the ideas outlined by K.N. Chigoryaev and co-authors [29].

Estimating HC using the investment approach accounts for the governmental funding in the following areas: health capital and intellectual capital. Funding for each of these components stems from health care, physical training and sports, social policy, education, and culture. Therefore, our estimation of HC is the sum of all types of funding, accounting for certain losses and depreciation.

The assessment of HC for Indigenous peoples of the north, in conjunction with natural and physical capital, is vital for sustainable development of the traditional homelands and enhancing quality of life. It is only when HC is preserved and expanded that such development becomes possible at both the national and local levels. Thus, HC valuation is a fundamental prerequisite for identifying potential avenues for transitioning toward sustainable development.

This assessment is imperative not only for the development of HC among Indigenous peoples but also because it is critical to their survival. Nevertheless, to date, only a limited number of such assessments have been conducted, and none exist for the Indigenous peoples of Kamchatksky Krai.

The process of transition to sustainable development for Indigenous peoples hinges upon the preservation of Indigenous ways of life and economic activities. The prevalence of the traditional economy can have a significant impact on the qualitative and quantitative characteristics of HC among Indigenous peoples. An economic assessment must include the above-stated characteristics to accurately account for HC, which is a challenging task.

Assessing the HC of Indigenous-inhabited territories is a novel undertaking in Russia. There are no existing data on this subject, and thus this assessment represents a new contribution to the field. Within the last decade, the remote regions of Russia have been somewhat understudied and underrepresented in Western academia. This is likely due to their remote and isolated location, with much of the scientific data being written in Russian and remaining within Russia.

Focusing on the investment (cost) valuation of HC in the regions where the Indigenous peoples of Kamchatka reside, this study aims to implement a cost assessment to inform development policies in Indigenous communities. This assessment provides results in value terms for the entire region (municipality) and per capita. To assess the cost value of HC within this paper, we rely on an open-access information base, using indicators of population income and governmental budget expenditure on education, health care, etc. The questions this research examines are as follows: (1) How can we use HC estimation to examine socio-economic development trends and dependencies in these regions? (2) What are the potential applications of our results for future research in northern territories and Indigenous regions? In this regard, this work analyzed available socio-economic information to obtain accurate valuation of HC for sustainable economic development policies in the Indigenous homelands. Using an investment approach, this study assesses HC in the compact settlements of Indigenous peoples in the northern districts of Kamchatka to secure sustainable futures. This evaluation is crucial for determining the economic potential Indigenous peoples’ traditional activities and their role in regional economics.

2. Related Works

The models of HC assessment at the regional level present different options in terms of both the choice of assessment tools and the scale of analysis. Two main directions are distinguished in the literature: index- or rating-based integrated indicators and valuation-based assessments (see

Table 1. A review of the research on quantitative HC assessments.

Authors and Year of Publication	Object, Year of the Assessments	Indicators
Index-based Assessments
Koloskova Yu., I., 2016 [30]	Rural areas of Krasnoyarsk Krai, 2010–2014.	Modification of HCI: life expectancy, completeness of education coverage, level of income, and level of entrepreneurial initiative in rural areas.
Grachev S.A. and co-authors, 2016 [31]	Regions of the Central Federal Districts in 2010–2013.	Integral index of HC by elements: health, labor, intellectual, cultural, and moral.
Vlasyuk L.I., Stroev P.V. 2017 [32]	Russia as a whole for the period 2002–2015, for the constituent entities of the Russian Federation for 2015.	HC includes types of capital: demographic, educational, labor, research and development, and socio-cultural.
Kappusheva A.P. 2017 [33]	Federal districts of the Russian Federation and regions of the North Caucasus Federal District, 2000–2015.	Components of HC: health, education, culture, and innovative activity.
Mazelis L.S., Lavrenyuk K.I., 2017 [34]	The 27 regions of the Far Eastern, Siberian, and Ural Federal Districts, 2014–2015.	HC includes six extended groups: Level of professionalism, education, scientific development, innovative development, health care, and culture.
Grachev S.A., 2018 [35]	Central Federal District regions, 2012–2016.	Integral indicator according to three criteria of quantitative (number of labor force, number of personnel engaged in research and development, and unemployment rate), qualitative (average per capita monetary income of the population, average monthly nominal accrued wages, and average size of allocated pensions), and the resulting assessment (funded labor force, number of personnel engaged in research and development, and GRP per capita).
Michalkina E.V., Kryachko V.I., 2019 [36].	Regions of southern Russia	Indices on socio-economic indicators: population size, natural growth rate, migration growth rate, graduation of specialists, bachelor’s and master’s degrees, share of youth in the total population, life expectancy (quality of life indicator), and morbidity (health quality indicator).
Serebryakova N.A. and co-authors, 2019 [37]	Voronezh, Lipetsk, and Belgorod regions and RF, 2018.	Integral assessment of the HC by the components: demographic, work and education, research and development, and socio-cultural.
Ketova K.V., Vavilova D.D., 2020 [38]	Udmurt Republic, 2000–2018 and its forecast for the period 2019–2023.	Qualitative components of HC: health, education, and culture.
Shulgin S.G., Zinkina Y.V. [39]	Federal districts of the Russian Federation, 1990–2018.	Estimates for the federal districts of the Russian Federation are compared using two methods: the Human Development Index and the Human Life Indicator. The Human Life Indicator, as a complement to the HDI, considers inequality in life expectancy.
Moroshkina M.V., 2022 [40]	Territorial entities of the Karelian Arctic, 2020.	Ranking assessment by indicators: population, average number of employees, number of people unemployed, migration growth, average monthly wages, and investment in fixed capital.
Saveleva M.V., Orekhov V.D., 2016 [41]	Federal districts of the Russian Federation, 1990, 1995, 2000, 2005, 2010, and 2015.	Modification of the HDI by including the indicator of human life and workers with tertiary education (the share of professionals with higher education plus the share of professionals with specialized secondary education for middle-level professionals).
Valuation Estimates
Muchametova A.D., 2016 [42]	Regions of the Volga Federal District, 2014.	The cost of the consumer basket and spending on health care, education and science, housing and utilities, roads, infrastructure, social policy, and culture (data from the consolidated budgets of the constituent territories of the Russian Federation).
Anichin V.L., Vasheikin Yu. Yu., 2017 [43]	RF, Central Federal District and regions of the Central Federal District, comparison of 2010 and 2015.	The wages in the region, the expected period of income per employee, and the annual interest rate.
Minaev N.N., Zharova E.A. 2021 [44], 2022 [45]	All regions of the Russian Federation, 2019.	Cost approach—based on the costs associated with HC formation: the cost of education (general and vocational) and the opportunity cost of students receiving education.
Dyakov M.Yu., 2022 [46]	Kamchatsky Krai, 2011–2018	Cost approach: the sum of the intellectual capital and the health capital forms the fixed human capital, while the cash income forms the circulating human capital.
Gevrasyova A.P., 2022 [47]	Gomel region in the Republic of Belarus, 2015 and 2020.	Net income of the population and budgetary funds of the state allocated for implementation of social policy measures in the sphere of education, health care, physical education and sports, culture, mass media, and strengthening of social protection of certain categories of citizens.

).

In the initial, more prolific set of works, HC assessment is calculated using individual indicators of socio-economic development at the regional level. This body of research characterizes various elements of HC. Both the elements of HC and the indicators describing them vary markedly in the publications [31,32,33,34,35,36]. As a result, a generalized indicator is constructed, which allows ranking of the regions. This group includes studies whose authors use the Human Development Index (HDI) to quantitatively assess the level of regional HC [30,39,40,41]. The index approach does not permit the estimation of the HC value, which restricts the applicability of multi-factor analysis.

The second group of studies utilizes an innovative valuation method, allowing us to obtain a value both in absolute terms (for the whole territory) and in relative terms (per capita), which opens more applications for analysis and strategic planning [42,43,44,45,46,47]. Most often, the scale of assessment was a group of regions, such as federal districts [33], regions in separate federal districts [34,42], and separate territories; for example, regions of the Karelian Arctic [40], the south of Russia [36], rural areas of regions [30], or a separate region—Kamchatsky Krai [46]. Most commonly, the object of assessment was groups of regions, such as federal districts [33], regions in individual federal districts [34,42], individual territories, such as regions of the Karelian Arctic [40], the south of Russia [36], rural areas of regions [30], or a separate region—Kamchatsky Krai [46]. Additionally, there are studies with estimates for all regions of the Russian Federation [44,45]. For the first time, estimates were presented for 2014 and 2019 using the valuation method. The methodological guidelines and the necessary data for the application of this approach are described in detail in the Guide on Measuring Human Capital prepared by the United Nations (UN) [48]. This approach estimates the costs of formal education (general and vocational) and opportunity costs of students receiving education. The employed population was considered an indicator of the number of individuals who have accumulated HC through labor activity. The depreciation of professional education was also considered, with the linear method used for assessment [44,45].

In general, despite some differences in methodology, the authors employ open-access data on income and budgetary allocations for education, health care, etc.

3. Materials and Methods

The data in this paper were gathered in different municipal districts of Kamchatka from 2017 to 2021. The data comprise regulatory documents articulating the economic activities of Indigenous peoples of the north, materials of the Government of Kamchatsky Krai and the reports on the execution of the budgets of the municipalities for the years 2017–2021, a database of indicators of municipalities of the Federal State Statistic Service, and reports on the Observance of the Rights and Legal Interests of Indigenous peoples of Kamchatsky Krai and the Activities of the Commissioner for the Rights of Numerically Small Indigenous Peoples of the North in Kamchatsky Krai in 2014–2020 [49,50,51,52,53,54,55].

In the context of this study, the concept of HC is operationalized through the cost approach [17,22]. According to this approach, “human capital of a region is a fund of abilities, knowledge, skills, health, moral values and cultural competencies accumulated in it as a result of investments, which is an integral factor of regional social reproduction” ([22], p. 560). This definition can be fully applied to regions with a predominantly Indigenous population. The first estimate of the value of HC was conducted for the entire Kamchatka Territory by M. Yu. Dyakov [46].

Under the investment (cost) approach to HC valuation, the volume (size) of HC is assessed through investments in its components. In this case, these components include health capital and education (intellectual) capital. The concept of health capital encompasses physical education and sports, health care, and social policy. Intellectual capital, on the other hand, encompasses regional budget expenditures on “Education” and “Culture and Cinematography.” In this context, the term “investments” refers to expenditure on items relevant to the subject matter, as indicated by the budgetary expenditures.

Furthermore, HC is divided into two categories: current and fixed. This division is necessary due to objective economic differences, such as the reproduction of its various components. While the labor remuneration fund and other current payments are fully reproduced within a relatively short period, education capital and health capital require a longer accumulation period and are spent for a longer period as well. Consequently, fixed HC is defined as the capital invested in health and education, while the current capital is constituted by the labor remuneration fund and other current payments.

In our view, the investment (cost) approach is the optimal methodology for valuation of HC at the regional level. The alternative rent (income) approach based on future income assessment [56] is not useful in this case since accurate estimates of future income are difficult to establish due to the lack of data and long-term population projections.

We found the investment (cost) approach to be more applicable than the rent (income) valuation for two primary reasons: First, estimates based on the present future income (rent) are relevant only for education capital, since both the educational process itself and its costs are sufficiently formalized. Furthermore, it is impractical to give a clear assessment of individual health capital due to its informal nature of investments. These investments include not only direct treatment in medical institutions and the purchase of medicines but also physical training, health-improving activities, and maintaining a healthy lifestyle. In the composition of future income (rent) from HC, it is implausible to accurately allocate the results of individual investments in health. Thus, under the rent approach, it is impossible to determine either its base value or the amount of rent received from health capital.

Second, the region’s (or territory’s) HC is notable for its high variability, both in terms of quantity and quality. Migration processes exert a profound influence on the demographic composition of a population, affecting the number, sex, and age structure, as well as the educational and professional levels of individuals. In this case, it is not possible to make long-term assessments based on the current characteristics of the population.

In a qualitative approach, various indicators and indices such as average years of schooling, life expectancy, etc., are used for valuation. Such methods can be effectively employed for comparative chronological assessments and geographical aspects between countries and regions; however, they are not designed for cost valuation.

In contrast, the cost (investment) approach applied by the authors in this study does not have these disadvantages. The same approach was followed in the classic studies by T. Schultz [16] and J. Kendrick [22].

Specifically, this paper employs the methodology proposed by K.N. Chigoryaev and co-authors for the assessment of HC [29], which was subsequently refined by M. Yu. Dyakov for the evaluation of HC at the regional level [46]. This approach differs from the prevailing international literature on this subject, as well as from the UN guidelines for measuring HC. Nevertheless, this method is most acceptable on the grounds of its informational and statistical support.

The method of K.N. Chigoryaev and co-authors is founded upon a comprehensive understanding of HC, encompassing the following three components: wage funds, intellectual capital, and health capital. The method was initially developed for HC assessment of an enterprise. Consequently, the total HC of an enterprise is equivalent to the sum of its wage fund and its expenditure on intellectual capital and health capital. In order to account for the return on each investment, weighted coefficients were introduced. These parameters consider the correlation between efficiency of investment in intellectual capital and the average level of education, and between investment of health capital and the average age of employees. This method presents several limitations. First, it fails to account for inflationary impacts. Second, it does not consider the depreciation of HC. Third, it does not acknowledge the accumulation of HC. In essence, the size of HC is assumed to be equivalent to the current level of investment in it. All forms of HC, including wage funds, health capital, and intellectual capital, are typically regarded as working capital. This implies that it is reproduced in full in each period. If this assumption is valid for the wage fund, it cannot be extended to intellectual capital and health capital, which are accumulated and depleted over extended periods of time. The advantages of the method include comparative simplicity of calculations and undemanding information and statistical base. Due to this advantage, this method has been improved and adapted to solve the cost estimation of HC in the region. In order not to reduce the assessment of HC exclusively to current investments, and to gauge accumulation and depreciation, changes were implemented. Thus, fundamental distinctions such as current and fixed HC were introduced into the classification of HC. Such a distinction also gives the possibility of a more accurate, methodological approach to cost estimation. The modified method of Chigoryaev K.N. and co-authors was applied to assess the HC of Kamchatksky Krai for the period 2011–2018 [46].

Within the framework of this method, the value of HC of the territory is determined by Formula (1):

$$HC={\beta }_1{A}_n+\sum _{t=1}^n({\beta }_2{\alpha }_{1}B+{\beta }_3{\alpha }_{2}C)\times (1-AQ)$$

(1)

where:

HC—human capital;

A_n—payroll fund (working human capital) in the n-th period;

B—intellectual capital cost;

C—health capital cost;

α_1,α₂—coefficients, considering the age and sex structure of the population;

β₁, β₂, β₃—coefficients of health contribution;

AQ—amortization quota;

t = 1,…n—account periods.

The coefficients β₁, β₂, and β₃ reflect the returns of each component for total HC. The coefficients α₁ and α₂ reflect the effect of education level on intellectual capital and age on health capital, respectively.

We use the value of 4.7 for coefficient α₂, calculated by M. Yu. Dyakov for Kamchatsky Krai [46]. The value of the coefficient β is taken to be 0.1, according to [57], based on the materials of the World Health Organization. Assuming full depreciation of basic HC over a 50-year working life and using the straight-line method, we calculate a depreciation rate of 2%. The values of α₁, β₁, and β₂ are taken as one, as there are currently insufficient data to determine them. This assumption introduces a certain degree of imprecision into the estimation process, potentially leading to an overestimation of the values in question ([46], p. 562).

4. Study Area

The study regions are the homelands of the Indigenous peoples of Kamchatka. Kamchatsky Krai is part of the Far Eastern Federal District and covers the Kamchatka Peninsula, as well as the nearby mainland, the Commander Islands, and Karaginsky Island. The region borders Magadan Oblast to the northwest, Chukotka Autonomous Okrug to the north, and Sakhalin Oblast to the south. Kamchatka is bounded by the Pacific Ocean to the east, the Bering Sea to the northeast, and the Sea of Okhotsk to the west (Figure 1).

The Government of the Russian Federation Resolution established the List of Areas in the Far North and Localities Equal to the Far North in 2021. There are 24 constituent entities categorized within the Far North and equivalent areas. Among these entities, 13 are fully situated in the north zone including Arhangelsk, Murmansk, and Sakhalin Regions; the Republics of Sakha (Yakutia), Karelia, Tuva, and Komi; and Kamchatsky Krai, Chukotka, Nenets, Yamalo-Nenets, and Khanty-Mansiysk Autonomous Okrugs. In contrast, the other 11 are partially situated in the north zone, and these are Amur, Irkutsk, Tomsk, and Tyumen Regions; the Republics of Altai and Buryatia; and Zabaikalsky, Permsky, Primorsky, Krasnoayrsky, and Khabarovsky Krais [58].

Aleuts, Olyutortsy, Itelmen, Kamchadals, Koryaks, Chukchi, Evenks, Siberian Yupik, and representatives of other Numerically Small Indigenous Peoples of the North live in Kamchatka [59].

The definition of “Numerically Small Indigenous Peoples of the North, Siberia, and the Far East” is defined by Russian legislation. According to the legislation, they must meet several qualities and requirements: “Numerically Small Indigenous Peoples” must have less than 50,000 persons, must live on the territories of their ancestors’ traditional settlements, preserve their traditional way of life, economy, and crafts, and consider themselves as separate ethnic communities [60]. Further, in this paper, we refer to the “Numerically Small Indigenous Peoples of the North” as the Indigenous peoples (of the north), recognizing that this definition may exclude certain groups that are not officially identified as Indigenous by Russia’s laws. The distribution of the Numerically Small Indigenous Peoples of the North in Kamchatsky Krai is presented in

Table 2. Average share of Numerically Small Indigenous Peoples of the North out of the total population of Kamchatsky Krai by municipality.

Municipal Districts of Kamchatka Krai	Population on 1 January 2010	Indigenous Residents, 2010	Indigenous, Residents, %	Population on 1 January 2020	Indigenous Residents, 2020	Indigenous Residents, %	Average Indigenous Resident 2010–2020, %
Petropavlovsk-Kamchatsky urban district	202,865	1203	0.6	179,586	271	0.2	0.4
Penzinsky municipal district	2340	1449	61.9	2009	1508	75.1	68.5
Olyutorsky municipal district	5036	2545	50.5	3732	2265	60.7	55.6
Bystrinsky municipal district	2560	1104	43.1	2416	1333	55.2	49.1
Tigilsky municipal district	4152	2010	48.4	3494	1689	48.3	48.4
Aleytsky municipal okrug	676	261	38.6	676	342	50.6	44.6
Karaginsky municipal district	4076	1216	29.8	3555	1489	41.9	35.9
Palana urban district	3155	1428	45.3	2915	566	19.4	32.3
Elizovsky municipal district	64,135	1068	1.7	64,346	742	1.2	1.4
Milkovsky municipal district	10,585	1515	14.3	9258	1772	19.1	16.7
Sobolevsky municipal district	2604	107	4.1	2484	282	11.4	7.7
Ust-Bolsheretsky municipal district	8331	98	1.2	7256	144	2.0	1.6
Ust-Kamchatsky municipal district	11,744	203	1.7	9066	238	2.6	2.2
Kamchatsky Krai	322,079	14,207	4.4	313,016	12,784	4.1	4.3

.

In this study, municipalities where Indigenous peoples comprised one-third or more of the total population were chosen to assess the value of HC. They include six municipal districts and one urban district (see Table 2) [61,62]. The largest proportion of the Indigenous peoples of the north remains in the Koryak okrug, which includes Karaginsky, Tigilsky, Olyutorsky, and Penzinsky municipal districts, as well as the Palana urban district.

The calculations were made based on the reports on budget spending for 2017–2021, which are publicly available on the websites of Bystrinsky, Aleytsky, Karaginsky, Tigilsky, Olyutorsky, and Penzinsky municipalities and Palana urban district [63,64,65,66,67,68,69].

5. Results and Discussion

5.1. Demographic and Socio-Economic Background in Indigenous Regions in Kamchatka and Indigenous Communities

The entire territory of Kamchatsky Krai is referred to as a place of traditional residence and traditional economic activities of Indigenous peoples [59,70]. The areas of concentrated residence of the Indigenous peoples of the north include Koryak okrug, the Aleuts (Commander Islands), and Bystrinsky district. In 2020, there were 12,784 Indigenous people in Kamchatsky Krai (Table 2). Of these, 2396 people have not documented their affiliation with Numerically Small Indigenous Peoples of the North. A decline in the number of Indigenous people was observed in all municipal districts of Kamchatsky Krai in 2020 [61]. Compared to 2002, by 2010, the number of Indigenous people in Kamchatka had decreased by 5.7%. According to the results of the All-Russian Population Census of 2010 [71], the number of Kamchadals and Aleuts decreased and the number of Itelmen, Evens, and Chukchi increased insignificantly. The number of Indigenous people of the north is decreasing faster than the total population. From 2010 to 2020, the population of Russia increased by 3%, while in Kamchatsky Krai it decreased by 8%, and the number of Indigenous residents in Kamchatka decreased by 11% [62].

Russia has special institutions, Obshichiny collectives, created to support traditional economic activities and traditional subsistence. Obshichiny collectives are voluntary associations of citizens belonging to small-numbered Indigenous peoples in Russia and can be either kinship-based (rodovie obshichiny) or residence-based (territorialno-sosedskie), according to the Civil Code of the Russian Federation [72]. Obshichiny collectives aim to protect traditional livelihoods, preserve cultural heritage, crafts, and traditions through organized efforts [73]. Fishing, reindeer husbandry, hunting, sea mammal hunting, gathering and processing of wild plants, etc., form the basis of the traditional way of life and traditional economic activities of the Indigenous peoples of the north in Kamchatka. In the study area, there are 156 registered Indigenous obshichiny, 14 hunting grounds, and 142 fishing sites (

Table 3. Demographic and features of traditional economic activities by municipalities.

Municipal Districts	Indigenous Residents, 2020	Number of Rodovie Obshichiny	Number of Fishing Sites	Number of Hunting Grounds	Hunting Grounds Area (Thousand Hectares)	Predominant Traditional Economic Activity and Subsistence
Aleytsky municipal okrug	342	7	7	-	-	Fishing/sea mammal hunting
Bystrinsky municipal district	1333	15	-	-	-	Reindeer herding/gathering
Palana urban district	566	16	-	-	-	Fishing
Karaginsky municipal district	1489	43	28	3	551.81	Fishing/reindeer herding
Olyutorsky municipal district	2265	24	22	1	540.77	Fishing/reindeer herding
Penzinsky municipal district	1508	23	19	1	402.23	Fishing/reindeer herding
Tigilsky municipal district	1689	28	66	9	924.51	Fishing/reindeer herding
Total	9192	156	142	14	2419.32

).

The current nature management among Indigenous communities in Kamchatka can be categorized as ‘complex nature management’, with fishing serving as the primary pursuit. Though traditionally reindeer herding has been the primary occupation in Koryak okrug (Karaginsky, Olyutorsky, Penzhinsky, and Tigilsky districts) and Bystrinsky district, presently only a small number of Indigenous people are involved in this activity. The majority of Indigenous families rely on wage-based employment for their livelihood. Indigenous residents devote considerable time to subsistence fishing, hunting, gathering, and gardening. This underlines its importance in their traditional mixed economy and as an important source of subsistence.

5.2. HC in the Indigenous Regions in Kamchatka

Initially, the HC for each municipal district was calculated according to Formula (1).

Table 4. HC dynamic of Indigenous peoples of the north in Karaginsky municipal district.

Index	2017	2018	2019	2020	2021
1. Budget expenditure on education, mln. RUB	436	454	469	483	523
2. Budget expenditure on culture and cinematography, mln. RUB	33	95	140	128	124
3. Investments in intellectual capital, mln. RUB (row 1 + row 2)	468.8	549.3	609.0	611.2	647.0
4. Budget expenditure on physical culture and sports, mln. RUB	4.6	9.6	19.0	4.1	24.9
5. Budget expenditure on social policy, mln. RUB	39.5	42.2	52.6	44.9	44.2
6. Investments in health capital, mln. RUB	44.1	51.8	71.5	49.0	69.1
Coefficient β	0.1	0.1	0.1	0.1	0.1
Coefficient α	4.7	4.7	4.7	4.7	4.7
7. Investments in health capital including coefficients, mln. RUB	20.7	24.3	33.6	23.0	32.5
8. Fixed human capital in current prices, mln. RUB	489.6	573.6	642.7	634.2	679.4
Depreciation rate of investments in fixed capital	0.02	0.02	0.02	0.02	0.02
9. Accumulated fixed human capital, mln. RUB	479.8	1041.9	1671.7	2293.3	2959.1
10. Working human capital (taxable income of population), mln. RUB	4562	7637	10071	9734	10193
11. Human capital in current prices, mln. RUB	5041.3	8679.4	11,742.9	12,027.3	13,152.6
Index-deflator to previous year, % [75]	105.3	110	103.3	100.9	119
Index-deflator to 2017	1	1.1	1.14	1.15	1.36
12. Fixed human capital in 2017 prices, mln. RUB	479.8	947.2	1466.4	1994.1	2175.8
13. Working human capital in 2017 prices, mln. RUB	4561.6	6943.1	8834.4	8464.4	7495.2
14. Human capital in 2017 prices, mln. RUB	5041.3	7890.3	10,300.8	10,458.5	9671.0

provides an illustrative example of the calculation of HC for the Karaginsky municipal district. To calculate the cost of basic HC, data from the reports on the execution of the local budget, published on the websites of municipalities, were utilized [64,65,66,67,68,69]. To estimate the circulating human resources, we employed the volume of social payments to the population and the taxable cash income of the population [74]. To eliminate the impact of inflation on the cost estimates, the results were brought to the initial period of calculation, namely 2017, using the deflator index [75].

Over the period under review, the nominal (in current prices) volume of HC increased markedly (see Figure 2).

It is more accurate to describe the cost of HC in terms of the impact of inflation. By means of deflation, we provided general estimates of HC in the context of districts of Kamchatsky Krai with concentrated residence of Indigenous peoples (see

Table 5. Dynamic of the cost of HC for districts of Kamchatsky Krai with concentrated residence of Indigenous peoples of the north in base or 2017 prices, millions of rubles.

Municipal Districts	2017	2018	2019	2020	2021
Aleytsky municipal okrug	513.9	620.1	774.3	993.6	936.4
Bystrinsky municipal district	1350.8	2107.4	2529.5	2988.1	3186.5
Karaginsky municipal district	5041.3	7890.3	10,300.8	10,458.5	9671.0
Olyutorsky municipal district	4102.0	3905.8	4775.5	5424.3	5246.3
Penzinsky municipal district	2701.2	2950.4	3573.9	4400.6	4221.7
Tigilsky municipal district	2285.5	2596.9	3182.8	3724.2	3720.8
Palana urban district	1897.0	2176.3	2403.3	2714.7	2603.1
Total	17,891.7	22,247.2	27,540.1	30,704.0	29,585.8

). The general trend of HC cost growth by districts over the whole period is noticeable, but the high level of the deflator index in 2021 had a significant impact on the fact that HC cost in 2021 decreased in all districts, except for Bystrinsky municipal district, where HC growth amounted to 126% in nominal prices, which is higher than the deflator index. The deflator index for 2021 amounted to 119%. One of the significant reasons for such price growth is the impact of the COVID-19 pandemic on economic activity.

For comparison purposes of HC dynamics, the total HC value was divided by the average population of each district (see Figure 3). Karaginsky district is the leader in terms of HC. However, Aleytsky municipality is approaching it in 2021, showing rapid growth since 2020. This can be attributed to the fact that in 2020, the cash income of the population increased considerably by 40% due to the merger with Nikolsky rural settlement. In other districts, the value of HC is also increasing, but at a lesser rate.

In all districts, between 2017 and 2021, HC increased by 65% or 1.65 times. The highest growth was in Bystrynsky district (2.4 times). The lowest was in Olyutorsky district (1.3 times), but this municipal district had the highest level of HC in 2017.

In 2021, Karaginsky district had the greatest HC value in both absolute and relative terms, accounting for one-third of the total HC in the study area. In Tigilsky district, which has a comparable population, the HC is noticeably lower. In 2017, the value of HC in Karaginsky and Olyutorsky districts was close. The most significant factor influencing the value of HC is the monetary income of the population. Therefore, the greater the economic development and the more opportunities for well-paying jobs available in an area, the higher the level of HC will be.

The growth rates of HC differed markedly across the regions. Bystrinsky district exhibited an increase in its share from 8% to 11%, whereas Palana urban district demonstrated a decrease from 11% to 9%. In Bystrinsky district, there was an increase in funding for social projects. Palana urban district exhibited a decline in both economic activity and population.

To calculate the proportion of HC attributable to the Indigenous peoples of the north, we employed the mean ratio of the number of Indigenous people in the total population by municipality for 2017–2021 (

Table 6. HC of the Indigenous peoples by municipal districts of Kamchatka Krai in 2017 prices, millions of rubles.

Municipal Districts	2017	2018	2019	2020	2021
Aleytsky municipal okrug	229.2	276.6	345.3	443.1	417.6
Bystrinsky municipal district	663.2	1034.7	1242.0	1467.2	1564.6
Karaginsky municipal district	1628.3	2548.6	3327.2	3378.1	3123.7
Olyutorsky municipal district	2280.7	2171.6	2655.2	3015.9	2916.9
Penzinsky municipal district	1850.3	2021.0	2448.1	3014.4	2891.9
Tigilsky municipal district	1106.2	1256.9	1540.5	1802.5	1800.9
Palana urban district	681.0	781.3	862.8	974.6	934.5
Total	8439.0	10,090.7	12,421.0	14,095.8	13,650.1

).

The absolute value of HC is greatly influenced by the size of the population, and it is more appropriate to compare per capita value. As can be seen in Figure 3, for the whole period, there is no district where HC would be lower than RUB 0.5 mln/person.

On average, for all districts, HC per capita was RUB 0.93 million in 2017 and RUB 1.58 million in 2021. Karaginsky district had the highest level of HC per capita, slightly higher than Penzhinsky district. Bystrinsky district had the highest per capita growth of 242% between 2017 and 2021, and thus overtook Palana urban district and Tigilsky district by 2021. Olyutorsky district had a below-average HC per capita growth rate: over 5 years, it increased by only 38%, i.e., it had an annual growth rate of 7%, while the average growth rate for all districts was 11%.

Aleytsky district has the smallest share in HC, only 3%, and it was stable over the period under review. The district’s population was also the smallest in the study area, with 676 people in total, of which 342 were Indigenous. The share of Penzhinsky district out of the total regional HC was also stable, fluctuating between 13 and 15%.

In general, the HC in the study area has been growing, although the growth rate varied across different districts. The leader not only in terms of total HC but also per capita is Karaginsky municipal district. The HC growth is associated with investment and an increase in incomes in the district.

Our results can be compared with similar cost estimates in other regions of Russia. In one study [38] in 2018, the size of HC per resident of the Udmur Republic amounted to about RUB 0.4 million; this is less than the estimates we obtained, which varied by region in the range of 0.73–2.18 million RUB/person. This discrepancy can be explained by the large share of income in the total value of HC, which is not considered in the Udmurt study methodology [38].

Another study [43] determined the value of HC involved in organizations per employee, and in 2015, it amounted to 3.99 million RUB/person in the Russian Federation, 4.92 million RUB/person in the Central Federal District, and the maximum level—Moscow—was 7.5 million RUB/person, while the minimum level—Ivanovskya region—was 2.48 million RUB/person. Even though the income approach was used, the results obtained are quite close and of the same order.

In another paper [44], where estimates are given for all regions of the Russian Federation, it is shown that the accumulated amount of HC in 2019 for Kamchatsky Krai amounted to RUB 235.68 billion, which is close to the estimate published by Dyakov M. Yu. for 2018—RUB 191 billion [46]. The per capita value of HC amounted to RUB 1.4 million, which is only 3% less than the maximum level in the city of Moscow. This figure is also close to the 2019 estimates obtained in our study, which vary by district of Kamchatsky Krai from 0.8 million RUB/person in Palana urban district to 2.8 million RUB/person in Karaginsky municipal district.

Summarizing the results obtained, we can conclude that in 2021 the amount of HC for territories with concentrated residence of the Indigenous peoples of the north exceeded RUB 38.8 billion. At the official September 2021 exchange rate of RUB 60 to USD 1, this value is equal to USD 648 million. Our estimations revealed that the HC in Kamchatsky Krai is growing, while the number of Indigenous people is decreasing. The primary reason for HC growth can be attributed to the investments in education and health care in the study area, resulting in a significant increase in personal income in the above-mentioned districts. The substantial monetary income in the region is closely linked to the significant extraction of aquatic biological resources. Kamchatka stands out as a leader in the Pacific salmon catch and has the highest number of employees within the fishery sector in the Far East. Furthermore, mining companies play a pivotal role in enhancing income for these municipalities, demonstrating increased activity during this period. A secondary reason for the steep increase could be due to the fact that fixed HC was accounted for beginning in 2017. If HC was initially underestimated in 2017, this would contribute to the rapid growth observed in 2018.

The data support for the assessment of HC in the selected districts does not allow the construction of the time series sufficient for the identification of long-term trends and regularities. According to the methodology used, the share of working capital in the structure of HC will decrease in the future, while the share of fixed capital will increase due to accumulation. It is our position that this methodology is not comprehensive and is not without its shortcomings. The initial cost estimate of HC in Indigenous areas will serve the purpose of providing information for subsequent research endeavors in Kamchatka.

6. Conclusions

It must be noted that research in HC assessment of regions and territories, and especially of regions with a compact Indigenous population, is still in its initial stage in Russia and has broad prospects for continuation and development. Prospects for future research include the following:

• The absolute values of the volume of HC have increased in all the municipal districts that have been studied. This is attributable to the substantial expansion of the population’s income, which represents the HC of these municipalities. Concurrently, the available statistical data do not permit the tracing of the dynamics of health capital in detail. There is a dearth of data on health care at the municipal district level, and even less so in terms of year-on-year comparisons. It is challenging to conclude the development of HC in the region without conducting a multi-factorial analysis. Such an analysis is necessary to substantiate the directions of development of HC quality by municipality.
• A more complex picture emerges when HC per capita is considered. A slight decline in values can be observed in several municipal districts, including Karaginsky, Penzhinsky, and Olyutorsky municipal districts, as well as Palana urban district. This picture can be affected by the specifics of individual territories associated with the population size, level of economic activity, and the volume of investment in HC.
• Methodologically, the applied approach has demonstrated its suitability for the economic assessment of HC within Indigenous peoples’ regions. Consequently, the findings can serve as a foundation for further research in the field of sustainable development of northern territories and regions of compact settlement of Indigenous peoples. These findings can establish a foundation for research in the field of preserving and improving the quality of life of Indigenous peoples of the north. In the practical sphere, the results can be utilized in the development of regional documents of conceptual, strategic, and programmatic nature.

The quantitative assessments of HC in Indigenous regions are necessary and can be conducted with emphasis on HC dynamics and structure, as well as compared with other regions and groups. In the future, these estimates can be used to study trends and dependencies in the socio-economic development of these territories. It is also important to compare the value of HC with the results of other social studies, such as those on the quality of life and transition to sustainable development. This can become the basis for comprehensive research into the socio-economic development of Indigenous territories.

HC includes not only economic output, which is presented in this study, but also personal growth, social responsibility, and the overall development of a nation. This comprehensive perspective acknowledges that an individual’s skills, learning, talents, and qualities represent valuable assets that can positively influence various aspects of life, both at the individual and social levels [8]. The study of interdependence between value and qualitative characteristics of HC is a promising area for future research.

HC assessments of these regions and their large share of Indigenous population can be used in several ways. Firstly, they serve as an information base for the development of strategic and program-targeted documents concerning the sustainable development of the region and its particular areas. The resulting estimates may allow policymakers to consider HC accumulation as an integral component of sustainable development, to forecast and calculate the desirable rates of further HC accumulation. Secondly, the calculation method proposed and tested in this paper can be used to obtain estimates of HC not only in Kamchatka but also in other regions of Russia with a compact residence of Indigenous peoples. Thirdly, the proposed method can be further improved in terms of refining the coefficient values and estimating the depreciation rate more accurately, which will make it possible to obtain more accurate estimates.

The constructed series outlined in this paper is insufficient in order to establish trends; a holistic portrait of the emerging dynamics of HC valuation is necessary for future study. There are insufficient statistical data on municipal districts (health care), including on Indigenous peoples and their communities (sex and age structure, education level, unemployment, employment by type of activity, etc.).