Analysing Rural Development Models Based on Intangible Assets and Socio-Economic Development

Abstract

Despite the existence of a variety of conceptual approaches to rural development, there is a lack of methods that take into account intangible assets, such as, for example, social capital, leadership, and local identity. A more effective design of the rural development strategy may be achieved by uncovering knowledge regarding the manifestation of various intangible resources. Territorial development policies, both in terms of the level of socio-economic development and the presence of intangible resources in rural areas, are investigated in this study. The main objective is to determine how intangible resources manifest in specific empirical models of development policy for rural settlements. A novel ensemble of indices and indicators of socio-economic development and the manifestation of intangible resources, calculated based on the method of analytical hierarchies and frequency analysis, are provided. These allow for a comprehensive study of the development of rural areas by clustering settlements with a similar level of development. Patterns and deficits of resources in rural settlements are analysed according to empirical models. Verification of the empirical models is carried out by assessing the level of socio-economic development and indicators of intangible resources for 12 rural settlements in the south of Russia. Therefore, several groups of factors of intangible resources splitting the factors related (reflect the current state) and unrelated (reflect the development potential) to the socio-economic development of rural settlements have been specified.

1. Introduction

Currently, there are various conceptual, theoretical, and practical approaches to understanding rural development. Rural development policy concepts are dynamic in nature. They change both over time (transformation of theoretical ideas) and in space (adaptation to different countries and regions). Modern scientific discourse has overcome the major limitations of the “development dichotomy”, in which exogenous models aimed at maximising productivity and based on “top-down” strategies are opposed to endogenous models aimed at community development and based on “bottom-up” strategies [1]. One might argue that a general consensus has been reached regarding the transition from an exclusively exogenous model of rural development to the endogenous one, including its neoendogenous version. The neoendogenous paradigm posits that exogenous resources are “reconstructed” in accordance with local conditions. The neoendogenous “hybrid” thus exploits the synergy between endogenous resources and exogenous processes. The conceptualization of neoendogenous development is based on the valorization of the material and cultural potential of the territory, the development of social capital, and the organization of social networks stimulated by local communities, providing a “socially constructed” perspective on rural development [2]. Meanwhile, the efficacy of the development process is contingent upon the combination and interaction of material and intangible resources, which cannot be reproduced elsewhere [3,4]. Neoendogenous models, initially formulated in Western Europe [5,6], have subsequently become the basis of rural development policy in other countries, including China [7], Australia and the United States.

The academic literature presents a multitude of empirical evidence and theoretical interpretations of such initiatives. These initiatives are primarily associated with the implementation of rural development within national strategies. Thus, in the European Union, the strategic aspects of rural development are implemented within the framework of the LEADER programme (Liaison Entre Actions de Développement de l’Économie Rurale) with rural development financing in such a way that local communities become subjects rather than beneficiaries of development policy. The key characteristics of the programme are a bottom-up approach to the design and implementation of development strategies, intersectoral and networking, and a focus on social innovation. The expansion of the LEADER programme to CLLD—Community-led Local Development—has extended local development approaches and diversified funding sources [8]. In 2017, the Chinese government initiated the Rural Revitalisation Strategy. Subsequently, a significant number of policies and programmes with network functions have been adopted to involve local communities and other relevant actors in the implementation of rural policies [9,10]. In Russia, the “Strategy for Sustainable Development of Rural Territories of the Russian Federation for the Period up to 2030” is currently in effect, approved by the Order of the Government of Russia dated 02.02.2015 No. 151-r (Strategy-2030).

A significant number of researchers point to the need for a comprehensive assessment of rural development. It should take into account a range of factors, including economic, social, cultural, political and managerial, geospatial, and other aspects. In the study [11], the viability of traditional villages in China was assessed using a multidimensional index system including natural–ecological, cultural, and economic factors. The implementation of the classified rural settlement management policy is contingent upon an assessment of the sustainability of rural systems. The latter was calculated based on industrial, infrastructure, natural and climatic, socio-economic, and geospatial data [12]. The impact on the development of the regional agricultural sector was analysed in terms of economic, agricultural, demographic factors, and geospatial distribution [13].

The majority of studies rely on the development of material resources. However, in the context of global scarcity, the question arises as to the potential of intangible resources. The efficiency analysis of the implemented national initiatives should be based on a combination of quantitative and qualitative strategies. The development of methodological approaches that allow the integration of intangible resources into the management system is more in line with the essence of the neoendogenous approach and is an important research problem.

Intangible resources possess a distinct nature from the structural properties of society as a whole. This indicator systematically characterises the quality of social subjects and institutions in terms of their ability to effectively develop themselves and compete successfully. Consequently, it is possible to consider the growth of intangible resources as the most fundamental, strategic, and terminal resource of the development policy. Intangible resources or assets are defined in [14] as a non-physical source of value that will provide benefits in the future created by innovation or discovery, unique organisational design, or human resource practices. This definition has been supported in [15], which argues that intangible assets are immaterial resources that can be used to create value for the company based on the skills of employees, organizational resources, the company’s operating methods and its relationship with its shareholders. Teece defines intangible resources as stocks of strategic information and intangible assets that an organisation can draw on as needed to achieve its objectives [16]. Summarising the scientific concepts regarding intangible resources, the authors define them as a set of multilevel, multicomponent, and multifunctional components of an intangible nature, which create opportunities for the development of territorial communities (local and regional) [17]. The key intangible resources of development are human potential, local identity, network resources as social capital, social and psychological resources of local communities, and territorial development institutions providing their aggregation.

A participatory approach assigns a significant role in maintaining the sustainability of rural areas to such an intangible resource as social capital [18,19]. The active participation of local communities in the decision-making process is conducive to the successful implementation of development plans that are oriented towards the cultural characteristics and unique needs of local residents, as well as the creation of a sense of local identity [20,21,22,23]. The resilience of local communities with developed social ties and an increased sense of community and trust leads to the success of initiatives to develop rural settlements with reduced economic resources [24]. The process of counter-urbanisation leads to result in the growth of a form of intangible resource, namely, human capital, which in turn has the potential to contribute to the sustainability of local communities [25]. Furthermore, this resource is reinforced by a decline in the exodus of the younger generation. In their study, Te$\breve{s}$in et al. found that young people’s attachment to their rural home is most strongly shaped by the local identity factor [26].

In recent years, the research team of the authors has carried out a series of research of intangible resources, using case study methodologies [17,22]. The results of the researches characterise the role of individual intangible resources in the development policies of specific territories. However, these studies did not provide a holistic picture of the complex of actual and potential development resources, their inter-relationship, and their role in the development of territories and communities. This study has theoretical and instrumental–empirical aspects. Theoretically, the authors have for the first time to their knowledge in the social sciences conceptualised the notion of a complex of territorial development resources and given it operational characteristics. From an empirical point of view, qualitative methods have been confirmed by the quantitative assessment of intangible resources.

This study examined rural development policies both in terms of the level of socio-economic development and the availability of intangible resources. The objective of the study was to ascertain the potential or manifestation of intangible resources in specific empirical models of rural development policy.

Our research questions are the following:

• What integral indicators can be employed to evaluate the efficacy of disparate development policy models that diverge in terms of both contextual (geographical, socio-economic, and socio-cultural features) and substantive (subjects and their activities in development policy) characteristics?
• What is the prominence of intangible resources in specific empirical models of rural development policy?

The solution to the stated research problem has both scientific and practical significance. The scientific significance of this research lies in the development of analytical tools based on the mathematical apparatus for assessing the potential (manifestation) of intangible resources in specific empirical models of territorial development policy. The success of implementing such models is measured by the integral SED index. This research approach and the developed analytical tools (SED index and assessment of intangible resources in rural development policy) may be of interest to a wide range of researchers, including economists, sociologists, and political scientists. In practical terms, the results obtained provide public administration entities with new opportunities to design more productive development strategies for rural areas, taking into account the existing architecture of intangible resources. This will lead to balanced territorial development and improved quality of life in the context of limited economic, financial, or infrastructural resources.

2. Materials and Methods

2.1. Research Design

We selected several areas varying in nature and climate, agricultural production and SED. The selection of 6 rural districts was based on two criteria reflected in the long-term socio-economic development strategy of the Krasnodar Krai: territorial zoning and sectoral specialisation. The binary method was used to analyse the SED level, as well as the manifestation of intangible resources within the framework of the development models. According to this method, in each of the studied areas, two rural settlements (RSs) were selected with diametrically opposed SED indicators—the leading settlements and the outsider ones. The selection was based on metric ranking obtained by weighting coefficients using the Analytic Hierarchy Process. Statistical data for 2019–2020 and expert assessments were used to assess the contribution of each statistical factor to the overall assessment or SED of rural settlements. Also, using the weights of criteria in the Analytic Hierarchy Process, the SED index was calculated both in the selected settlements and in the districts in which they are located. As a result of the assessment of the SED in the selected districts, rural settlements were identified as the objects of the empirical study, which occupied leading or outsider positions in the overall ranking (see

Table 1. Districts and rural settlements selected for the study.

District	Characteristics of the District	Rural Settlement
Apsheronsk	Territories of industrial and tourist specialisation in the Piedmont Economic Zone	Nizhegorodskoye (NZHG)—leader
		Novopolyanskoye (NVP)—outsider
Belorechensk	Territories of industrial and tourist specialisation in the Piedmont Economic Zone	Pervomayskoye (PRV)—leader
		Ryazanskoye (RZN)—outsider
Kanevskoy	Traditional agricultural production territories of the Northern Economic Zone	Chelbasskoye (CHLB)—leader
		Staroderevyanskoye (STRD)—outsider
Krymsk	Territories of knowledge-intensive agricultural production, plant breeding, and pedigree livestock farming of the Central Economic Zone	Prigorodnoye (PRG)—leader
		Moldovanovskoye (MLD)—outsider
Temryuk	Territories with specialization in transport, logistics, and health resorts of the Black Sea Economic Zone	Tamanskoye (TMN)—leader
		Fantolovskoye (FNT)—outsider
Tikhoretsk	Territories with agro-processing production of the Eastern Economic Zone	Fastovetskoye (FST)—leader
		Khoperskoye (KHPR)—outsider

). In 2023, a field study was conducted combining qualitative and quantitative strategies. Intangible resource assessments were obtained. Intangible resources were divided into first-, second-, and third-order resources. It should be noted that due to the objective delay in the publication of official statistics, there is a time lag between the assessment, the RS selection procedure for the study, and the planning/conducting of experiments. The lack of sustainable dynamics of the socio-economic development of individual RSs, associated, among other things, with the COVID-19 pandemic, determined the need to identify a group of RS with unstable dynamics.

2.2. Area of Investigation

Krasnodar Krai, informally referred to as Kuban, is located in Southern Russia. It consists of 37 rural municipalities and 7 urban municipalities, covering an area of 75,485 ${\mathrm{km}}^2$. To select municipalities for the study, a number of factors influencing the RS differentiation in Krasnodar Krai were analysed. The main historical reason for the heterogeneity of the social space of Kuban is the gradual development of the territory, as a result of which three internal regions were formed (steppe, foothill, and Black Sea). These districts are located in different zones of the SED of Krasnodar Krai and are characterised by different natural, industrial, infrastructural, and socio-cultural conditions. Consequently, the following districts were identified as the most appropriate for consideration, and their statistical indicators are presented in Figure 1:

• Apsheronsk district (Foothill Economic Zone) is located in the southern foothill part of Krasnodar Krai. It is characterised by the unique natural resources (forests cover more than 80% of the district; there are more than 50 sources of thermal and mineral waters). This in turn determines the development of the health resort and tourist complex, forestry and woodworking industry, and the production of building materials. The structure of industrial production is dominated by enterprises of the forestry complex (about 70%). At the same time, the district has a high share of specially protected natural areas of various statuses, which limits certain types of economic activity. The district includes 3 urban and 9 RSs.
• Belorechensk district (Foothill Economic Zone) is located in the southeastern foothill part of Krasnodar Krai. It is characterised by a fairly diversified economy. There is a health resort complex, the development of which was stimulated by the presence of natural mineral springs. Mainly manufacturing industry (chemical and food) contributes to 60% of economic activity. It accounts for more than 90% of industrial output. Non-metallic minerals are mined. Various types of agricultural production, represented by crop production, vegetable growing, gardening, livestock farming, and beekeeping, make up about 10%. One town and ten RSs are under the district’s jurisdiction.
• Kanevskoy district (Northern Economic Zone) is located in the northwestern part of Krai. Its administrative center is the rural locality (a stanitsa) of Kanevskaya. It is the largest stanitsa in Krasnodar Krai, with a population of 50 thousand people. It is a historical place of residence of the Kuban Cossacks. The basis of the economy is the agro-industrial complex, including multisectoral agricultural production (crop and livestock). Agricultural land is more than 70%. Most of it is arable land. In addition to the food industry, which accounts for about 90% of industrial production, among the fairly developed industries one can highlight the production of building materials, mechanical engineering, and gas industry. The district consists of 9 RSs with 33 settlements.
• Krymsk district (Central Economic Zone) is located in the southwestern part of Krai. It is close both to the administrative center—the city of Krasnodar, to the ports of the city of Novorossiysk, and the resorts of Gelendzhik, Anapa, and the Azov coast. The district is characterised by a high population density. Geography favors the inflow of investments. It is an industrial district; its share in the economy is about 40%. It is represented by metallurgical and textile production, production of roofing materials, ceramic bricks, etc. The favourable climate for viticulture contributed to wine-making complexes with the corresponding infrastructure and related industries. The district includes 1 urban and 10 RSs.
• Temryuk district (Black Sea Economic Zone) is located on the Taman Peninsula in the northwestern part of Krasnodar Krai, washed by the Black and Azov Seas, as well as the waters of the Kerch Strait. The natural and geographical resources of the district contributed to the development of various types of tourism, agriculture and the transport and logistics industry. The total length of the coastline, represented by sandy beaches, is 250 km. The number of sunny days is 235 per year. This, together with moderate humidity, creates favourable conditions for the development of the recreational industry and agriculture. A significant part of the district has picturesque estuaries with salt and fresh water, ponds, lakes, and floodplains. Natural healing resources, in addition to climate, also include deposits of peloids (silt hydrogen sulphide mud and pseudo-volcanic mud), which are used in mud therapy clinics at resorts in the Krasnodar Krai. One of the priority areas of the district economy is agriculture, in particular industrial viticulture. More than 75% of Kuban vineyards are located in the district on an area of 18 thousand hectares. The wine industry is represented by full-cycle enterprises. In addition, the agricultural sector includes rice cultivation, fish harvesting, and processing. The basis of the transport and logistics specialisation of the district are the seaports of Taman, Temryuk, and Kavkaz, as well as a developed network of automobile and railway lines. Under the district’s jurisdiction are 1 urban settlement and 11 RSs.
• Tikhoretsk district (Eastern Economic Zone) is located in the northeastern part of Krasnodar Krai and has agro-industrial profile. The centre of the district—the city of Tikhoretsk—is located at the intersection of two railway lines and a federal highway, forming a large transport hub that provides the main railway and road connections. The district is a steppe plain and is largely occupied by agricultural land, which provides a significant portion of the local population’s income and employment. The main areas of agriculture are crop production, including the cultivation of grain and leguminous crops, as well as livestock farming. Under the district’s jurisdiction are 1 urban settlement and 11 RSs.

A map of Krasnodar Krai with the study areas and the distribution of administrative units within them is shown in Figure 2.

2.3. Data Sources

The main data sources used in this article are listed below:

• Statistical data from the official website of the Office of the Federal State Statistics Service for Krasnodar Krai and the Republic of Adygea “https://23.rosstat.gov.ru (accessed on 21 October 2024)”, reflecting economics, investments, demographics, and other factors. For the analysis, indicators available for all RSs were selected, and the list of indicators is presented in

  Table 2. Statistical indicators of rural socio-economic status.

  Dimension	Description	Factors
  Rural population	Population	$V_0$
  	Natural increase (decrease)	$V_6$
  	Migration increase	$V_7$
  Living condition	Commissioning of individual residential buildings	$V_1$
  	Number of non-gasified settlements	$V_8$
  Development conditions	Share of profitable organisations	$V_2$
  	Surplus (+), deficit (−) of the budget of the municipality (local budget)	$V_5$
  Development potential	Investments in fixed capital at the expense of the budget of the municipality	$V_3$
  	Investments in fixed capital by private companies	$V_4$

  .
• Results of a survey of 31 experts on paired comparisons of indicators of socio-economic development of settlements. The expert group included 15 representatives of the public sector and 16 representatives of the scientific community.
• Data from focus group interviews with representatives of local communities conducted in 12 RS in March–June 2023 and an individual expert survey of representatives of municipal government agencies. The point of entry to the administrative units was the deputy heads of the district administration, who arranged interaction with local community representatives to conduct focus group interviews (one in each rural settlement) and with experts at the level of both rural settlements and the district that includes rural settlements (a total of 60 experts). The sample of the expert survey in the context of rural settlements included the head of the rural settlement and heads of enterprises, large farms, and clergymen; experts in the context of districts were deputy regional head for internal policy, social issues, and economy; there were heads of major regional mass media, heads of the regional Cossack society, heads of large economic units, enterprises, etc. All local community representatives who volunteered to take part in the research were informed of the project objectives, planned results, and developed the ways of their publication.
• A questionnaire survey of 762 rural residents from 12 RSs. The survey included 32 questions to identify key intangible resources, such as territorial development institutions, network resources, socio-psychological resources of local identity, social solidarity, etc. The study was conducted using a representative sample (90% confidence probability, sampling error or 10% confidence interval). The study used probability (simple random) multistage sampling based on the differentiation of inhabitants of rural settlements by gender and age. The survey was conducted using a combined method of face-to-face contact with respondents via a paper or electronic questionnaire in Google Forms. Descriptive analysis of direct (linear) and cross-sectional distributions, as well as correlation analysis, were applied to the dataset.

Indicators $V_1,\cdots ,V_8$, except for $V_2$, were normalised to $V_0$; thus, statistical indicators given per inhabitant of the settlement are considered.

2.4. Calculating an Integral Indicator of Rural Socio-Economic Development

Constructing an integral SED indicator is a multicriteria problem, the solution of which requires the determination of the relative importance or weights assigned to each criterion. Among the various Multicriteria Decision-Making (MCDM) methods, the Analytic Hierarchy Process (AHP) has the significant property of preservation property [27]. To apply the AHP method, a series of pairwise comparisons between various criteria must be performed. In this study, the pairwise comparisons were conducted by a panel of experts, and the resulting ranking was determined using the Kemeny median.

2.4.1. Median Kemeny

At the initial stage, a quantitative expert assessment was conducted to evaluate the relative superiority of the statistical indicator $V_i$ over $V_j$ in determining socio-economic development of the RS. Expert panel members compared all criteria in pairs according to the relative importance scale proposed in the [28]. Following the processing of the expert questionnaires, matrices ${\mathbf{P}}^{\left(1\right)},{\mathbf{P}}^{\left(2\right)},\cdots ,{\mathbf{P}}^{\left(m\right)}$ ($m=31$) of multiplicative metric relations of linear order were constructed in accordance with the following rule:

$$p_{ij}^{\left(\nu \right)}=\left\{\begin{array}{cc}{\omega }_{ij}^{\left(\nu \right)},\hfill & \mathrm{if}{V}_i\mathrm{is}{\omega }_{ij}^{\left(\nu \right)}\mathrm{times\; important\; than}{V}_j,\hfill \\ 1,\hfill & \mathrm{if}{V}_i\mathrm{and}{V}_j\mathrm{are\; equally\; relevant},\hfill \\ {\displaystyle \frac{1}{{\omega }_{ij}^{\left(\nu \right)}}},\hfill & \mathrm{if}{V}_j\mathrm{is}{\omega }_{ij}^{\left(\nu \right)}\mathrm{times\; important\; than}{V}_i,\hfill \end{array}\right.\nu =\overline{1,m}.$$

(1)

The Kemeny median was chosen as the rule for aggregating the preferences of all experts. It satisfies the axioms of the ensemble supermajority efficiency, the reinforcement, and the continuity [29], and it is weakly susceptible to bias from individual outliers in the assessed opinions [30]. The median rule is designed to minimise the average distance between expert opinions, whereby the distance between two opinions is calculated by the issues on which they diverge. Accordingly, the Kemeny median is the resulting ranking as close as possible to the rankings ${\mathbf{P}}^{\left(1\right)},{\mathbf{P}}^{\left(2\right)},\cdots ,{\mathbf{P}}^{\left(m\right)}$:

$$\begin{array}{c}{\mathbf{P}}^{\ast }={\mathbf{M}}^{\ast }({\mathbf{P}}^{\left(1\right)},{\mathbf{P}}^{\left(2\right)},\cdots ,{\mathbf{P}}^{\left(m\right)})=\arg \underset{\mathrm{P}}{min}{\displaystyle \sum _{\nu =1}^m}d(\mathbf{P},{\mathbf{P}}^{\left(\nu \right)}),\\ d({\mathbf{P}}^{\left(k\right)},{\mathbf{P}}^{\left(l\right)})={\displaystyle \sum _{i<j}}|p_{ij}^{\left(k\right)}-p_{ij}^{\left(l\right)}|.\end{array}$$

(2)

The solution to the optimisation problem (2) in the case of a multiplicative metric relation can be expressed as follows:

$$ln{p}_{ij}^{\ast }={\mathbf{W}}_i^{\ast }-{\mathbf{W}}_j^{\ast },\mathrm{where}{\mathbf{W}}_k^{\ast }=\sum _{j=1}^n\sum _{\nu =1}^m{\displaystyle \frac{ln{p}_{kj}^{\left(\nu \right)}}{mn}},$$

(3)

where n is the number of compared indicators ($n=8$).

2.4.2. Analytic Hierarchy Process (AHP)

AHP is based on the following principles:

• The decomposition principle involves structuring a multicriteria choice problem in the form of a hierarchy, the simplest of which has three levels: objective, criteria, and alternatives. The objective of the AHP in this study is a comparative RS characteristic by SED within a district, as well as ranking of the selected districts. The SED indicators presented in Table 2 are considered as criteria; the districts of Krasnodar Krai or RS of one district are considered as alternatives.
• The principle of comparative judgement for prioritising criteria is based on the method of pairwise comparisons. As indicated in Section 2.4.1, experts were involved in the metric ranking of the criteria, and the Kemeny median was considered as the resulting matrix of pairwise comparisons. When constructing similar matrices for alternatives, the normalised values of the indicators from Table 2 were compared and converted into a similar scale of relative importance using threshold values.
  Thus, at the criteria level of the hierarchy, one matrix of pairwise comparisons ${\mathbf{P}}^{\ast }=\left[p_{ij}^{\ast }\right]$ of dimension $n\times n$ is defined according to the Formula (3), where $p_{ij}^{\ast }$ is a pairwise comparison of criteria $V_i$ and $V_j$ criteria, and $n=8$ is the number of criteria. On the other hand, it is assumed that $p_{ij}^{\ast }$ approximately corresponds to the ratio of the weights of the hierarchy elements, which will be determined within the framework of the algorithm, i.e., $p_{ij}^{\ast }\approx {\displaystyle \frac{{\alpha }_i}{{\alpha }_j}}$, and ${\alpha }_i$ is the weight of the i-th hierarchy element. The vector of priorities or weights $\mathit{\alpha }=\{{\alpha }_1,\cdots ,{\alpha }_n\}$ is calculated as the eigenvector corresponding to the principal eigenvalue ${\lambda }_{max}$.
  At the next level, the alternative level, as many matrices of pairwise comparisons ${\mathbf{B}}^{\left(k\right)},k=\overline{1,n}$ are constructed, and there are elements at the criteria level. The components of the matrices are determined by Formula (1) by a pairwise comparison of the normalised values of socio-economic indicators. The priority vectors ${\beta }^{\left(k\right)}=\{{\beta }_1^{\left(k\right)},\cdots ,{\beta }_n^{\left(k\right)}\}$ for each matrix ${\mathbf{B}}^{\left(k\right)}$ are also found as eigenvectors corresponding to the principal eigenvalue.
• The principle of synthesis of priorities. The final assessment of an alternative in the pairwise comparison method is the alternative weight. It is calculated as a convolution of the weight coefficients of the criteria at all levels of the hierarchy. In the case of a three-layer hierarchy, the resulting expression for the weights of the alternatives can be written in the following form:

  $$\mathit{c}=\left[{\mathit{\beta }}^{\left(1\right)},\cdots ,{\mathit{\beta }}^{\left(n\right)}\right]·\mathit{\alpha },$$

  (4)

  where all vectors are column vectors.

The reliability of the results obtained using the AHP method depends on the consistency of the pairwise comparison matrices. The judgement consistency index (CI) for each matrix according to [31] is calculated as follows:

$$CI={\displaystyle \frac{{\lambda }_{max}-n}{n-1}}$$

Furthermore, the coherence ratio (CR) is calculated by comparing it with an average random consistency index (RI), which is derived from randomly generated reciprocal matrices:

$$CR={\displaystyle \frac{CI}{RI}}$$

(5)

If the CR is less than 0.1, the pairwise comparison matrix is deemed to be coherent.

2.4.3. Integral Index Construction

In accordance with the study design delineated in Section 2.1, at the stage of RS selecting for analysis, they are ranked on the basis of the AHP carried out in each districts under consideration according to the values of the weights $\mathit{c}$ obtained by Formula (4). The settlements with the highest and lowest values of weight are selected for the next stage of the study. Furthermore, the districts can be ranked according to the weights $\mathit{c}$, which are calculated based on the statistical indicators of the districts. Consequently, each rural settlement under study is characterised by two indicators: the metric rank of the settlement and the metric rank of the district in which the settlement is located.

The weights identified in the AHP algorithm, utilising the matrix of the resulting ranking $P^{\ast }$ of the criteria, are employed to calculate the integral indicator. The integral indicators for both settlements and regions are calculated using the following formula:

$${\mathrm{I}}_{\tau }=\sum _{j=1}^n{\alpha }_j·X_j,\mathrm{where}{X}_j={\displaystyle \frac{{\tilde{V}}_j-{\tilde{V}}_{\min }}{{\tilde{V}}_{\max }-{\tilde{V}}_{\min }}}\mathrm{and}{\tilde{V}}_j={\displaystyle \frac{V_j}{V_0}}.$$

(6)

As to RS, the lower index $\tau$ assumes the value s in the calculation of the integral indicator; but, in the case of a district, the value assumed is r.

2.5. Indicators for Intangible Resources

2.5.1. Identification of Key Intangible Resources

In accordance with the author’s definition, the intangible resources of rural areas are conceptualised as a set of multi-level, multi-component, and multi-functional elements of different genesis, which together form a system of social relations and guarantee the sustainability of local communities. One uses a qualitative analysis (methodology of polar comparisons) to determine key intangible resources that contribute to the RS development. In the process of operationalising the indicators of intangible resources of rural development, we relied on a set of characteristics identified in the course of theoretical and methodological analysis. The approach involves the use of empirical data obtained from expert surveys and transcripts of focus group interviews in combination with traditional document analysis, in particular, the study of official data. The procedures for identifying and summarising nominations in a qualitative analysis made it possible to identify the following configuration characteristics of intangible resources for the RS development:

• Human capital, which is determined by the age, education, and health of the local population.
• Local identity, the characteristics of which are related to its temporal orientation (retrospective/prospective), population involvement (positive/negative), integrative quality (exclusive/inclusive).
• Leadership, the configuration of which depends on its subjectivity (individual/ collective), origin (local/non-local), mode of action (traditional/innovative), institutionalisation degree (formal/informal), and interaction with the local community (consolidating/autonomous).
• Social capital, which is defined depending on the types of social connections prevailing in the local community (social capital as a private/public good) and institutionalization (informal/formal).
• Development institutions, the configuration of which is determined by the following criteria: by type of institutionalisation (formal/informal); government level (local/regional/territorial); by profile of institutional development (business development institutions/NPO development institutions/support for territorial development within national projects and/or initiative budgeting).
• Social and psychological resources characterised by the level of social solidarity, trust in the current local government, and subjective well-being.

2.5.2. Pyramid of Intangible Resources

When classifying general nominations that reflect the configuration characteristics of intangible resources and their influence on RS development, an intellectual structure was constructed that can be described as a pyramid of intangible resources. At the base of the pyramid are first-order resources, which include local identity, human potential, and leadership. The second level of the pyramid is made up of second-order resources, which include social capital and development institutions. At the top of the pyramid are third-order resources, which include civic solidarity, trust, and subjective well-being.

First-order resources—human capital, local identity, and leadership—create conditions for designing and implementing territorial policies, determine their content and institutional practices. The feeling of attachment of local residents to the territory (local identity) contributes to their permanent residence in the RS, the return of young people to their native places after receiving a quality education, the formation of family dynasties in existing enterprises and in social and cultural institutions, and family business (agricultural, tourism). Leaders and/or leadership communities are formed from local residents who strongly identify themselves with a specific rural area and have a high level of education and cross-professional skills. They take responsibility for the development of the territory, preserving its natural resources and cultural and historical heritage and offering innovative ways to solve territorial problems, participating in the future of municipalities.

Intangible resources of the second order are social capital and development institutions, which are formed under the influence of resources of the first order. If local identity has a fragmentary (split on certain grounds: local residents—migrants; business—local residents; local government—local residents; older generation—younger generation, etc.) or exclusive character (excluding certain groups of residents from the local community), then social capital acquires the configuration of a private good. In this case, the local community is a divided, differentiated society of social networks, focused exclusively on their own internal interests, not ready to unite to achieve common goals. The formed inclusive local identity contributes to the formation of social capital as a public good, with a wide range of social connections that extend beyond the territory, and free access to intangible resources for their conversion into institutional development practices. Local development institutions in the form of formal and informal funds, autonomous non-profit organizations, etc. are created only in the presence of broad institutionalised social connections (social capital as a public good). The capabilities of federal and regional institutions are used by the leadership community and non-profit organizations for local development.

Social and psychological resources as third-order non-material resources are resulting markers. They determine the success of integrating non-material resources into territorial development and are determined by the levels of social solidarity, trust in the current municipal government, and the feeling of subjective well-being.

A sociological tool (questionnaire) was developed to describe the state and potential of key intangible resources in specific RSs. In this questionnaire, in accordance with the identified categories of key intangible resources, indicators were defined, and closed questions based on the Likert scale were developed.

Table 3. Intangible resources for territorial development.

Pyramid of Intangible Resources	Intangible Resource	Group of Questions in the Questionnaire (Factors of Intangible Resources)	Label
First-order resources	Human capital	prospects for youth	HC1
		impact of migration	HC2
	Local identity	commonality and unity of population	LI
	Leadership	trust in formal RS and district leaders	L
Second-order resources	Institutions for territorial development	development strategy	ITD1
		territorial branding	ITD2
	Social capital	personal engagement in territorial development	SC1
		network resources	SC2
Third-order resources	Social and psychological resources	authority confidence	SPR1
		solidarity	SPR2
		well-being	SPR3

presents the indicators of intangible resources used in the study.

2.5.3. Measure of Intangible Resources

The survey results described in Section 2.5.2 are categorical data, ranked differently in each question, and collected for analysis in the form of adjacency tables. The distribution of responses to a given question was calculated as a percentage of the total number of respondents. Only positive or negative responses were considered to calculate the measure of intangible resources for each indicator in Table 3. The partial index for each question was defined as the difference between the shares of positive and negative responses. Since one dimension of intangible resources may correspond to several questions in the questionnaire, the partial indices are then averaged, which resulted in the following calculation formula:

$$\eta ={\displaystyle \frac{1}{k}}\sum _{i=1}^k{\zeta }_i,{\zeta }_i=s_i^{+}-s_i^{-},$$

(7)

where $s_i^{+}$ is the percent of positive answers, and $s_i^{-}$ is the percent of negative answers for the i-th question. The indicator takes values from $-100$ to 100, which indicates a positive or negative state of the resource.

2.6. Empirical Models of Development Policy

As a result of clustering as a procedure for qualitative analysis of empirical data (transcripts of focus group and expert interviews with representatives of local communities), the authors developed an analytical tool for classifying empirical models of municipal development policy based on an assessment of the influence of a complex of intangible resources: a responsible development model, a fragmented development model, a distant development model, and a stagnant model.

The model of responsible development as a process of developing strategic priorities and securing institutional mechanisms has been considered successful among the empirical models identified. It allows local communities, interacting with authorities at various levels, business structures and civil society institutions, to create, reproduce, and use various resources, including intangible ones, to achieve a qualitatively new standard of living for the population.

The list of unifying characteristics of intangible resources in the model of responsible rural development includes able-bodied healthy population with a high level of education and meta-competences, reproduced through natural growth and migration flows; formed positive inclusive local identity with a forward-looking orientation (aimed at achieving an image of the future); formed individual (heads of settlements) and collective (management team, uniting representatives of government, business, and the public in settlements) leadership, focused on local community development, using innovative approaches, consolidating different groups of residents; social capital as a public good, reflected in institutional practices of intersectoral partnerships when activated by the local community through the activities of change teams and non-profit organisations of a wide range of development institutions at the federal and regional levels. The fundamental difference of this model is the presence of a socio-cultural mechanism for integrating traditions and innovations in the process of developing and implementing development policy.

The fragmented development model and the distant development model are deficient in terms of the formation of first- and second-order intangible resources. The fragmented development model is determined, first of all, by the configuration characteristics of local identity, which acquires an exclusive character for certain groups of the local population. This entails deep social cleavages and divisions (local residents—migrants; youth—older generation; supporters of traditions and supporters of innovations), which hinder the formation of a common territorial development policy model. Exclusive configurations of local identity, given a certain level of human potential, do not allow for the full formation of leadership communities capable of consolidating the local population, as well as a wide network of institutionalised social connections.

The distance model is characterised by an exclusive local identity of local authorities and the business community, which are united by a common socio-economic strategy of a distance nature. In this model, local residents form enclave communities that are not connected by common interests and development goals. The overall result for the fragmented and distance models is that the population demonstrates low levels of social solidarity, trust in the current municipal government, and subjective well-being. At the same time, the presence of basic intangible resources in these models determines the potential for designing successful models of development policy using the proposed scenarios.

The stagnant model is characterised by a negative local identity, which hinders the formation of human potential and leadership in the studied RS, and, accordingly, intangible resources of the second and third order. Among the factors that determine the formation of this model, we can highlight the following: the absence of a historically established social space; numerous transitions of a settlement from one jurisdiction to another; the predominance of older residents; and the absence of a common image of the future that would unite residents. The RSs under consideration relate to the following models of municipal development policy:

• Responsible Development Model (RS Tamanskoye (TMN), Temryuk district, RS Fastovetskoye (FST), Tikhoretsk district; RS Prigorodnoye (PRG), and Moldovanovskoye (MLD), Krymsk district).
• Fragmented Development Model (RS Ryazanskoye (RZN), Belorechensk district; RS Novopolyanskoye (NVP), Apsheronsk district; RS Chelbasskoye (CHLB), and Staroderevyanskoye (STRD), Kanevskoy district).
• Stagnant Model (RS Khoperskoye (KHPR), Tikhoretsk district; RS Fantolovskoye (FNT), Temryuk district, RS Pervomayskoye (PRV), and Belorechensk district).
• Distant Development Model (RS Nizhegorodskoye (NZHG) and Apsheronsk district).

3. Results

3.1. Integral Indicators of the Socio-Economic Development of Rural Settlements

3.1.1. Intra-District Ranking of Rural Settlements by Level of Socio-Economic Development

The AHP algorithm allowed for ranking the RS within each district using weights calculated by Formula (4). These weights can be considered as the share of each RS contribution to the overall SED of the district. According to Section 2.1, based on the 2019–2020 data, within each district, the RS with the highest weight as the leading settlement and the lowest weight as the outsider settlement were selected. Figure 3 shows the dynamics of each RS contribution to the development of its district for 2019–2022. The upper half of the diagram depicts the RS leaders, while the lower half depicts the outsiders. Let us note that the contribution of outsider settlements in 2019–2020 was less than 10%. The exception was the STRD of the Kanevskoy district. The contribution of RS leaders in at least one year of this period was above 10%. The graph also demonstrates that the contribution share is not a stable value for all settlements. It can be seen that some settlements showed a stable contribution to the development of the region. For example, the contribution of the TMN of the Temryuk district was high at 15–21%; and the contribution of the RZN of the Belorechensk district was at a low level of 5–6%. At the same time, some settlements showed relatively high values in 2019–2020, but in 2021–2022, their indicators decreased, such as FST of Tikhoretsk district. Therefore, for the purpose of further analysis, RSs were classified according to their SED level as high, low, and unstable settlements.

Figure 4 shows a comparative characteristic within the group of settlements selected for analysis. Here, it is clear that the leading settlements of some districts have a lower SED level than others. For example, the TMN and CHLB settlements can be compared. This is primarily due to the overall different development levels of the district in which these settlements are located.

3.1.2. Analysis of Criteria Determining the SED of Settlements

As a result of processing the questionnaires of 31 experts, the resulting metric ranking of statistical indicators of the socio-economics in rural areas was calculated using the Formula (3). Figure 5 shows the calculation of the weighting coefficients of the criteria priority in the AHP algorithm based on the Kemeny median as a matrix of pairwise comparisons of criteria. The aggregated expert opinion suggests that the natural population growth indicator $V_6$ has the greatest weight, which, together with the migration indicator $V_7$, provides a joint contribution of 34% to the SED of the RSs. It can be concluded that the main determining factor is human resources. The next most important are the financial solvency of commercial organisations and their contribution to RSs (indicators $V_4$ and $V_2$), which illustrate the business climate. Their combined contribution is more than 30%. The RS budget and its investment capacity (indicators $V_3$ and $V_5$), which are related to budgetary and financial indicators, together contributed just over 20% to the SED. And lastly, the development of RS was determined by the volumes of housing construction and gasification (indicators $V_1$ and $V_8$), which are infrastructural indicators of housing and communal development. Their combined contribution was about 15%.

3.1.3. Index of Socio-Economic Development of Rural Settlements and Districts

The SED index of RS and districts was calculated by Formula (6) using the statistical indicators specified in

Table 4. Integral index development of rural settlements and districts in 2021–2022.

District	Integral Index of District Development	Rural Settlement	Integral Index of Settlement Development
Apsheronsk	0.306	NZHG	0.303
		NVP	0.436
Belorechensk	0.516	PRV	0.328
		RZN	0.223
Kanevskoy	0.328	CHLB	0.439
		STRD	0.344
Krymsk	0.394	PRG	0.487
		MLD	0.345
Temryuk	0.559	TMN	0.618
		FNT	0.418
Tikhoretsk	0.252	FST	0.351
		KHPR	0.244

for 2021–2022. Table 3 shows that the differentiation of RS within one district by SED level has become less pronounced. Just two settlements (TMN and PRG) retained their leading status, and two settlements (RZN and KHPR) were the outsider ones not only within their district but also in relation to all the settlements studied. In other cases, the development level of a district needed to also be taken into account. However, the ratio of more developed/less developed settlement was preserved for all pairs, except for the Apsheronsk district. In the NZHG, there was a decrease in budgetary and financial indicators, while in the NVP settlement, these indicators increased in 2021–2022. This led to a change in the order of settlements by SED level.

Figure 6 demonstrates the spatial distribution of the studied districts, indicating the average SED index. The most developed districts, with an index of about 0.55, are Belorechensk and Temryuk districts. This is due to the established industrial specialization and favourable geographic location. Next comes the Krymsk district, where the index is about 0.4. Less-developed districts with a development index of about 0.3 are the Apsheronsk, Kanevskoy, and Tikhoretsk ones, which are characterised to a greater extent by agriculture.

3.2. Analysis of Intangible Resourses

The results of the survey of 762 respondents from 12 rural settlements on 32 questions were aggregated through Formula (7) into 11 indicators of intangible resources, which are presented in Table 3. The relationship between the intangible resource indicators and the integral index of the SED (see Table 4) was examined using Pearson correlation coefficients, which are presented in

Table 5. Correlation between intangible resource indicators and the SED integral index.

Intangible Resourse	HC1	HC2	LI	L	ITD1	ITD2	SC1	SC2	SPR1	SPR2	SPR3
Correlation coefficient r	0.478 *	0.096	0.037	0.499 *	0.439 *	0.537 **	0.417 *	0.260	0.161	0.276	0.094

* Significance level 0.05; ** Significance level 0.01.

.

Among the first-order resources, a correlation with the SED of the RSs was observed for intangible resources such as “human capital” and “leadership”. The resource “local identity” appears to be unrelated to the level of development of the RS. For the intangible resource “human capital”, there was a rather high correlation with the indicator “prospects for youth” with the SED of the RSs and a statistically insignificant correlation with the indicator “impact of migration”. In other words, residents of higher socio-economic settlements have a more positive view of their settlement and district as a place for young people. Although the “increase in migration” factor ($V_7$) was higher in such settlements, residents rarely perceived the impact of migrants on life in the settlement.

Second-order intangible resources, “institutions of territorial development” and “social capital”, were statistically significantly related to the SED index of the RSs. Meanwhile, the factor “personal engagement in territorial development” was more manifest in more developed settlements, while the factor “network resources” was insignificant.

Socio-psychological resources, which are third-order resources, had a statistically insignificant relationship with the SED index.

3.3. Analysis of Empirical Models

3.3.1. Empirical Models in the SED Context of Rural Settlements

Figure 7 shows the average SED indices for 2021–2022, which were calculated using Formula (6) in the context of empirical development models.

There is a decreasing trend in the average values of the SED index in the models, the predominant value of which is in the responsible development model.

Three leading settlements in their district (TMN, PRG, and FST) belong to the responsible development model. At the same time, the SED index has a lower level. It should be noted here that this settlement is located in the district with the lowest SED index of the studied areas. MLD also belongs to this model. It, together with PRG, is located in a district with successfully developing winemaking enterprises. In 2021–2022, both budget and private investments increased in the MLD.

Two settlements of the Kanevskoy district (CHLB and STRD) belong to the model of fragmentary development. This is a historical place of residence of Kuban Cossacks. Tradition is strong here, being inferior to modern methods of doing business. However, we note that CHLB is one of the leading settlements in its district, so its SED index is comparable to the responsible development model. Also, the NVP has a relatively high index (see Figure 3). Here, in recent years, SED indicators have been growing due to external resources (increased budget investments in infrastructure and migration influx). However, local private investment is at a fairly low level. RZN is an outsider one. Although it is located in a district with a relatively high level of development, its SED indicators are consistently low. At the same time, many pending issues are being resolved in the RSs. This has been achieved through the implementation of participatory budgeting projects, which allow for the attraction of funds from the regional budget to solve the most important social problems at the local level, as well as the inclusion of the settlement in the national projects such as “Housing and Urban Environment”, “Culture”, “Ecology”, and “Education”.

The model of stagnant development includes two outsider settlements (FNT and KHPR). However, Fontalovskoe has a relatively high SED index due to its location in a socio-economically developed district. Budget security in RS is formed by tax and non-tax revenues, in which a significant share belongs to gratuitous budget revenues (subsidies, other inter-budget transfers). Demographic indicators show a high level of population over 60 years old (about 30 percent). The PRV also belongs to this type of development models. At the initial stage of the study, it was chosen as a leading settlement. However, in all subsequent years, it experienced a strong decline in the SED level (see Figure 3), mainly due to a decrease in budgetary and financial indicators. The PRV is an association of disparate small settlements, which hinders the formation of social space and SED increase.

Only the NZHG in the Apsheronsk district, selected as the leading settlement, belongs to the model of distant development (see Figure 3). In certain periods, its rating among the studied RS is quite high (see Figure 4). The tourism industry is developing here, but this development is disconnected from the needs of the settlement and does not bring the desired dividends.

3.3.2. Empirical Models in Terms of the Manifestation of Intangible Assets

The intangible resource indicators were calculated based on a questionnaire survey of local residents and represent subjective assessments. The calculations were carried out according to Formula (7) for the indicators presented in Table 3 for the resource pyramid described in Section 2.5.2. The analysis of the expression of intangible resources in the context of RS development models was carried out for resources of the first, second and third order, respectively, and the average value of the indicator is calculated for each model.

3.3.3. First-Order Resources

The first-order resources include, first of all, human capital, represented in the study by the prospects of the RS and the district in which it is located for young people, and the presence of migrants and their positive impact on the RS development. Figure 8 shows the distribution of the resource “human capital” according to development models. The settlement and district are considered most promising for young people, where a responsible development model is defined (see Figure 8, prospects for youth). At the same time, the assessment in settlements with fragmented and stagnant development models is approximately the same. The lowest value of the indicator is observed in the settlement with the distant model. This is most likely due to the negative assessment of business practices in the settlement. Figure 8 (impact of migration) shows approximately the same positive value of the indicator. This denotes the average positive expression of the resource in almost all types of development models. Only the fragmented development model is different. Residents value the role of migrants in development to a lesser extent in such settlements. One can conclude that the resource “human capital” is less pronounced in the fragmented development model than in the stagnant one.

Figure 9 shows the distribution of two resources at once: “local identity” and “leadership”. Local identity is defined through a sense of unity between the residents of the settlement, their attachment to the territory. Leadership as an intangible resource is considered as a “derivative” of human potential and local identity, and it is defined in the survey as trust in formal leaders. There is consistency in the distribution of “local identity” and “leadership” resources across model types. These resources are most expressed in the responsible development model, which, together with the resource “human capital”, demonstrate the greatest development of first-order resources. The model of stagnant development is in second place. This indicates a certain inertia of processes and confirms the gradual decline in once developed settlements. These resources cannot fully form into a fragmented development model due to the peculiarities of settlement development. The weakest manifestation of first-order resources is in a settlement with a distant development model. It can be concluded that they are weakly formed in principle.

3.3.4. Second-Order Resources

The intangible resource “territorial development institutions” is represented in the questionnaire as “the presence of a known development strategy” and “the presence of a known territorial brand”. Figure 10 shows the distribution of indicators of these factors. It is worth noting right away that the branding of a territory (see Figure 10, territorial branding) is not clearly linked to territorial development models. Rather, it has a historically established reputation of certain territories as a result of both natural and climatic conditions and old brand enterprises. The well-known development strategy (see Figure 10, territorial development strategy) is most pronounced in settlements belonging to the responsible development model. In settlements with the stagnant development model, the indicator is almost twice as small. Except that FNT has a rather high indicator and is comparable in significance to the responsible development model. This is due to the location of this settlement in the most developed district with a known development strategy. In the case of settlements belonging to fragmented and distant development models, the average indicator values are very low. This indicates the absence or inadequate elaboration of development strategies in these settlements.

The intangible resource “social capital” is defined by two factors: network resources and personal engagement. Figure 11 illustrates the distribution of indicator values by development models. In the responsible development model, the highest average value of the indicator is observed as anticipated. This confirms the expressiveness of the resource “social capital” through the development of horizontal connections between people in the form of network resources and active personal engagement in the development of their RS. In the model of stagnant development, the resource “social capital” declines as a result of a decrease in personal engagement in the development of the settlement and, as a consequence, the weakening of horizontal connections. In models of fragmented and distant development, the value of the personal engagement indicator goes into the negative zone. The residents of these settlements, for certain reasons, are not motivated to engage and personal contacts between residents are either not formed or have already been broken.

3.3.5. Third-Order Resources

The “Socio-psychological resource” as a third-order non-material resource is defined through authority confidence, solidarity, and well-being of the residents. Figure 12 shows the distribution of indicator values by RS development models. The responsible development model demonstrates the highest values. The indicators’ authority confidence and solidarity are in second place in the model of stagnant development; the indicator well-being is lower. This is explained by the large proportion of the ageing population, whose life satisfaction is declining. In some settlements with fragmented development, there is an increase in “human capital”. Consequently, against the background of lower authority confidence, there is greater satisfaction with life. Distant development model demonstrates low authority confidence and solidarity; the life satisfaction indicator is negative.

4. Discussion

In addition to the “leader settlements”, the group with the responsible development model includes two settlements from the same district (Krymsky district), initially selected as diametrically opposed in terms of SED indicators. Moreover, this district has an average SED index level (0.4), and it is home to modern, successfully developing wineries. One concludes that the settlement development model is likely to be strengthened by the district development strategy in the case of the inclusion of settlement development policy in the dynamics of district development. The consistency of settlement and district development patterns can also be observed in the fragmented development model. Two settlements from one district (Kanevskoy district) belong to this model. This district is home to the Kuban Cossacks with traditional forms of agriculture, in which there is inertia in the processes of transition to modern methods of management. The indicators “territorial development”, “development of network resources” and “personal engagement” in these two settlements are the lowest in the entire group. Therefore, we can conclude that the residents have a weak initiative against the background of the problem of development policy for the entire district. The group of settlements with stagnant development includes two settlements (FNT and PRV) from the most developed districts in the studied sample (Temryuk and Belorechensk districts). Therefore, their SED indicators are the highest in the group. However, such intangible resources as “youth prospect”, “personal engagement” and “authority confidence” have rather low indicators, which reflects the pessimistic mood of the residents. Generally, it seems necessary to consider the SED level of a district and the degree to which the development of the settlement itself fits into the context of the district’s development when developing an appropriate strategy.

The impact of single intangible resources on the sustainable development of rural areas has been studied by a number of authors. In particular, “social capital” [18,19,20,24] and “human capital” [25,26] have been identified as contributing to the successful implementation of integrated rural development initiatives and creating the potential for sustainable development. The findings of this study are in alignment with the conclusions of these researchers. The model of responsible development corresponds to rural settlements with sustainable development in terms of socio-economic indicators, and it is in the settlements belonging to this model that most of the intangible resources considered are manifested. However, to the authors’ knowledge, the proposal to consider of intangible resources as a single multicomponent and multifunctional complex that influences socio-economic development, as proposed in this paper, has not been made previously. The analytical tools developed by the authors in the form of an index of socio-economic development and the methodology of empirical research, integrating qualitative and quantitative strategies, open up new possibilities for mathematical measurement and evaluation of the contribution of intangible resources.

5. Conclusions

The key to the successful implementation of target initiatives is the selection of differentiated policies and strategies for the RS development. They must consider the unique conditions and both material and non-material resources. The classification of rural settlements by development models, based on the existing complex of non-material resources, is a method for solving this problem. The results of the three-year study aimed to extend the existing explanatory models and analytical tools in rural development policy, developing an interdisciplinary method in its meta-theoretical and instrumental–empirical dimensions. This has made it possible to integrate the advantages of different approaches to the study of the influence of intangible resources in their complex interrelationships on the actualisation of rural development models in different natural–climatic, socio-economic and socio-cultural contexts.

Here, the potential (expression) of intangible assets and the level of SED in empirical models of rural development policy were assessed. The constructed system of indices and indicators confirms the qualitative analysis of empirical models. The responsible development model is the most successful among all the proposed models. Settlements that correspond to this model have the highest SED index and all intangible resources are most expressed. All other models are deficient in terms of intangible resources. They are approximately at the same lower level of socio-economic development, but they have growth potential. The second most expressed non-material resources model is the stagnant development one. Here, for now, relatively stable SED indicators are maintained by inertia. Such results are explained by the existence of long-established (back in the Soviet period) resources of local identity and strong social ties. Unlike the responsible development model, they are not supported by integration into political and management practices. Against the background of the reactivation of the potential of such intangible resources as local identity and social capital, the human potential is being leveled due to the ageing of the population. This, in turn, affects the population’s subjective vision of its future and well-being. The trend is rather negative. The fragmented development model is characterised by weak indicators of the expression of such intangible resources as local identity and leadership. This in turn prevents the activation of territorial development institutions, with a certain degree of expression of human resources. This is confirmed by the lowest indicators of personal participation in the development of settlements and authority confidence. The model of distant development is the most deficient in terms of the expression of intangible resources and their actualisation in political and managerial practice. This is explained by the lack of involvement of local communities in the processes of developing and implementing the RS development strategy.

Several groups of intangible resources have been distinguished both in terms of their classification quality for empirical models and their correlation with the socio-economic development of the settlement. The first group of intangible resources, which quantitative values of indicators confirm the classification of empirical models and have statistically significant correlations with the SED index, includes such factors such as “prospects for youth”, “leadership”, “institutions for territorial development”, and “personal engagement in territorial development”. These intangible resources reflect the current situation in rural settlements. The second group of factors also confirms the qualitative classification, but it has an insignificant correlation with the SED index. This group includes factors such as “local identity”, “network resources”, and “socio-psychological resources”, and it most likely reflects the development potential of rural settlements. The last group includes the resource “impact of migration”, which has no classifying property and does not correlate with the SED index, i.e., this material resource is insignificant within the settlements under consideration.

The diversity of practices for actualising intangible resources determines the specifics of designing rural development strategies based on various scenarios: (a) a scenario for the RS development as a “satellite” of a city in the system of an urban agglomeration; (b) a scenario for the development of “intersectoral partnership”, promoting development institutions and social capital in implementing cooperation projects; (c) the “ecological environment” scenario, in which the potential of natural resources is used for the development of social infrastructure and social entrepreneurship; (d) a scenario for the development of “cultural and historical heritage and revival of traditions” for folk crafts, lost crafts and the creation of places attractive for pilgrimage; (e) a scenario for the development of “rural tourism” as the promotion of agri-tourism of various forms for holiday makers with low and middle incomes; the scenario for the development of settlements of “rural city dwellers” is associated with the use of the human potential of people working remotely and summer residents. It should be noted that the success of the above scenarios will depend on the nature of the interaction between key actors in local development (government, business, civil society institutions, and local communities), and political and managerial practices that facilitate their integration both at the level of strategy design and implementation.

Prospects for further research were related to conducting large-scale comparative and cross-regional studies to identify successful policy and management mechanisms of intangible resource activation in different practical development models; developing practical recommendations for public administrators on strategic design of development policies based on intangible resources; and evaluating the effectiveness of implementation of these practices.