The article classifies rural territorial entities using the systems approach, which is based on identifying their key subsystems—natural, social, and economic. The study aims to develop and implement a procedure for creating a multi-aspect assessment range of rural development levels relying on the combined use of multivariate statistical analysis and the computational and expert comparison of objective and subjective structured information. The grouping of rural territorial entities carried out on this basis is intended to identify a pattern representing their targeted development, taking into account both the existing social and economic situation in the territory and its perception by the population. Methodological approaches to classify territorial rural units according to their level of rural development usually lack a systemic perspective and a subjective dimension to include the rural inhabitant perspectives. Using only expert opinions does not allow it to be reflected adequately enough. The comparison between the objective and subjective assessments of the natural, social, and economic conditions of rural territorial entities serves as the basis for identifying three groups of development patterns. Results were obtained through the combined application methods—cluster analysis and multidimensional scaling. The first one was used for an objective ranking of municipal districts in the region using official statistical data, while the second method was used for structuring the rural survey results. The main study result is the procedure for the multi-aspect grouping of rural areas, which enables the objective and subjective assessment of their key subsystems—economic, social, and natural—to be integrated into a single assessment tool. Its application helps establish a range of general patterns representing rural development. The study results can be used in the creation and updating of object- and subject-differentiated programs for the development of rural territorial entities.

rural areas of Russia, rural development, multi-aspect grouping, cluster analysis, multidimensional scaling, development patterns

An implementation analysis of projects for engineering infrastructure develop­ment under the program for integrated rural development in 2018–2020 revealed the distortion of provided data in over 30% of Russian regions.¹ This fact has a negative impact on the effective use of budget funds, as well as reducing the effectiveness of the state rural development policy. The lack of complete data often leads to the failure of ongoing projects to meet the needs and problems of rural areas (Cherdantsev and Shakleina, 2016; Voroshilov, 2021). The inabili­ty to use available analytical tools and/or their insufficient capacity to provide adequate assessment generates unnecessary problems in rural development management, even in the presence of adequate information. The acquisition of quality data on the rural development level depends on the selected research method, its resource support, and the ability to use it by applying it to the issues under consideration. The relevance of designing and appropriately using new assessment methods is attributed to the high heterogeneity of rural development processes both within Russia and its regions (Liu et al., 2022; Leontieva, 2010). This problem is also relevant in a broader international context (Dong et al., 2022; Wang et al., 2023).

Three approaches are most common in assessing the social and economic development of rural areas. The first one relies on the use of a system of indices (indicators). Its universality consists in the capability of identifying the structure of the rural regional system (Wang et al., 2023), selecting and adapting groups of indicators to regional problems and their specifics (Kurochkina et al., 2021), as well as assessing the potential for rural development through the indicators of land use, industrial production, and rural standard of living (Dong et al., 2022). The selection of indicators is in general arbitrary without a comprehensive systemic approach to rural development.

The second approach involves ranking rural territories via a rating-based assessment of their social and economic development levels. The basic algorithm for producing a rating involves determining indicators and the impact of associated factors, as well as selecting a data processing method and rating group boundaries. A key step in rating-based assessment consists in selecting the structure and list of input data. For example, Cherdantsev and Shakleina (2016) performed an analysis of rural development using 60 indicators, which, in our opinion, is excessive because it complicates the analysis and interpretation of its results. Conversely, other authors narrow the number of used indicators, limiting them only to certain fields characterizing rural development (Voroshilov, 2021; Golubeva et al., 2014; Parkhomov, 2021). Despite the obvious­ advantages of rating-based assessment, its arbitrary character and the limitation of used statistical data often prevent a quality analysis of differen­ces in the levels of rural development.

These problems can often be resolved through the use of the third approach—typological. The typology of rural areas constitutes an essential tool in demonstrating the heterogeneity and changes in their development. For ­example, in the typology of rural areas in Northeast China, the authors presented spatial differentiation through external factors, inner capacity, and area interaction with the environment (Liu et al., 2022). Grouping by spatial geographic and demographic characteristics (Naumov et al., 2021; Agibalov, 2022) as well as the sustainability of social and economic development (Nikitina, 2019) allows­ rural areas to be differentiated and their heterogeneity to be visualized. However, typological approaches provide no means to forecast future rural development, which limits their application in the development of relevant programs.

The approaches often used in assessing rural development have common disadvantages: use of knowingly incomplete statistical data that frequently fail to reflect the current condition of rural areas at the level of municipalities, and even more, of settlements. Thus, it makes impossible to carry out dynamic assessments in order to create a realistic future image of the territories.

Our study aims to develop and implement a procedure for creating a multi-aspect assessment range of rural development relying on the combined use of multivariate statistical analysis and comparison of objective and subjective information about rural territorial entities to identify a pattern of rural development. The procedure for conducting an objective and subjective assessment of conditions in rural areas presented in the article can provide a means to create high-quality and targeted programs for their development. The obtained results could be used for grounding agricultural policy.

The study proposes an original methodological framework to evaluate the conditions of rural development in low-order administrative territorial units, such as municipalities. An integrated approach was used, combining the analysis of qualitative and quantitative data.

The assessment of social and economic rural development was conducted using data on Stavropol Krai, a region characterized by a primarily rural (­agriculture-based) economy. The region is located in the south of Russia and has abundant natural resources for agricultural development and favorable climatic conditions for human life in rural areas. The share of rural population in the region­ significantly exceeds the national average: 40.7% as compared to Russia as a whole—25.2%. The choice of this region is due to the constant population growth that is not typical for most territories of Russia as well as the strategic importance of the development of agricultural sector for the country in conditions of ensuring food independence. The agrarian specificity­ of Russia’s southern regions suggests the relevance of the problems of rural development, where, in fact, commercial agriculture is concentrated.

The study took place from 2022 to 2023. In 2022, a social study was conducted in the form of a survey among rural residents of the region. The questionnaire survey was realized via an anonymous format with mandatory notification of respondents about the study aims and how the survey results would be used. Also, during this period, the required statistical data for 2021 on socio-economic situation of rural areas in Stavropol Krai was collected.

Using available statistical data, we selected indicators that adequately describe the functioning of rural areas in the region in terms of the specified subsystems. In order to ensure the comparability of rural areas, relative variables­ were used.

The clustering of districts in Stavropol Krai was carried out to determine their positions relative to average regional indicators and to identify their groups in terms of rural development in 2021. A general flowchart illustrating the algorithm behind the procedure for the multi-aspect grouping and the selection of rural develop­ment patterns is presented in Fig. 1. It comprises the following stages:

(1) Primary data collection using survey results and the statistical database. Supplementing statistical data characterizing the condition of rural areas with the rural survey results improves the reliability of the primary rural development assessment.

(2) Generalization of data in two datasets;

(3) Selection of input and calculation of relative indicators; their grouping by the three subsystems of rural areas (Table 1).

(4) Cluster analysis performed for the districts of the region (total of 26 in Stavropol Krai) according to nine variables (on the basis of statistical data) having­ standard values. To assess the homogeneity of clusters, the coefficients of variation for the studied indicators were calculated.

(5) Multidimensional scaling of the districts in the region using the survey results.

The number of dimensions was selected using the scree test (Fig. 2). On its basis, it can be concluded that both two-dimensional and three-dimensional spaces can be used. The latter option is selected here due to its higher informative value. Besides, the three-dimensional configuration of rural opinion in the corresponding districts of Stavropol Krai does not contradict the two-dimensional configuration.

The configuration of respondents’ opinions in 26 districts of Stavropol Krai in the three-dimensional space Dimension 1, Dimension 2, and Dimension 3 is presented in Fig. 3. The rural survey was conducted in order to reveal opinions on problems of various nature faced by rural residents. In this connection, the majority of responses characterize the severity of problems: a higher response rate indicates a worse situation in a certain area in the district. Thus, a higher value of the district coordinate on the scale implies a higher percentage of respondents who reported problems in the sphere represented by the (­characteristic) variables reflecting the essence of the scale, i.e., greater severity of these problems. This factor was taken into account by the authors in the interpretation of the study results.

Figure 1.

Flowchart illustrating the algorithm behind the procedure for the multidimensional assessment of conditions in rural development. Source: Compiled by the authors.

Figure 2.

Scree plot (conventional units; c.u.). Source: Authors’ calculations.

Figure 3.

Configuration of respondents’ opinions in three-dimensional space. Note: 1—Alexandrovsky Municipal District (MD), 2—Andropovsky MD, 3—Apanasenkovsky MD, 4—Arzgirsky MD, 5—Blagodarnensky Urban District (UD), 6—Budyonnovsky MD, 7—Georgiyevsky UD, 8—Grachyovsky MD, 9—Izobilnensky UD, 10—Ipatovsky UD, 11—Kirovsky UD, 12—Kochubeyevsky MD, 13—Krasnogvardeysky MD, 14—Kursky MD, 15—Levokumsky MD, 16—Mineralovodsky UD, 17—Neftekumsky UD, 18—Novoalexandrovsky UD, 19—Novoselitsky MD, 20—Petrovsky UD, 21—Predgorny MD, 22—Sovetsky UD, 23—Stepnovsky MD, 24—Trunovsky MD, 25—Turkmensky MD, 26—Shpakovsky MD. Source: Authors’ calculations.

The success of multidimensional scaling is determined by the possibility­ of selecting the “correct name” for the scales, i.e., content identification. The study of rural areas as complex systems implies analyzing a large number of variables that affect them. Rural employment, provision and quality of social infrastructure, income level, migration attitudes, and natural conditions cannot be considered separately as these factors are correlated with each other to a different extent. Since the use of factor analysis in this case would not be quite correct, we adopted multidimensional scaling which does not exclude the multicollinearity of indicators. However, this fact raises the possibility that the same variable may affect different aspects of the problem under study. This is reflected in the interpretation of obtained scales: Dimension 1, Dimension 2, and Dimension 3.

The procedure for interpreting the essence of scales consists of the following steps.

The first step means arranging the districts with indicators in the ascending order of their scaled values on Dimension 1, Dimension 2, Dimension 3.

The second step involves determining the degree of correlation between each of the nine indicators and each of the three scales. A higher value of the positive­ correlation coefficient indicates a closer one-way relationship between this indicator and the Dimension.

The third step consists in visually assessing the correspondence of changes in characteristic variables to the position of the district on the scales.

The fourth step involves using multiple regression to assess the effect produced by the values of each variable defining the scale on the corresponding district coordinates for each of the three Dimensions.

The fifth step includes selecting indicators that represent the scale and its interpretation.

1. Grouping districts using the results of cluster analysis, followed by their division into segmented groups using the results of multidimensional scaling.

2. Comparison of two multidimensional (cluster and scaling) groupings of rural areas. The comparison was conducted by determining the difference between the position numbers of the districts in the ranked clusters and their place in the ordered groups according to the scaling results. This procedure allowed three groups of districts to be identified.

3. Identification of groups including the development patterns of districts by comparing their objective and subjective assessments:

• • structural patterns for districts exhibiting minor position deviations in both groupings;
• • behavior patterns for districts exhibiting significant positional discrepancies.

The survey results of rural residents were processed to obtain the required indicators using the SPSS Statistics software. The analytical part of the study was based on combining two methods of multivariate statistics: cluster analysis and multidimensional scaling. Cluster analysis is a multivariate classification method, i.e., it is useful for recognizing similarities between units and creating homogeneous groups. Multidimensional scaling is a multivariate method for reducing dimensions and ordering units in a space of two or more dimensions. It could be used for classification purposes, but the power of the method is the ordering of units considering several variables, where the distance represents similarities, among them (Soshnikova et al., 1999).

In the first stage, the rural areas of 26 municipal districts in Stavropol Krai were grouped according to nine (standard) indicators (see item 3 of the procedure) via k-means clustering using the STATISTICA software. In determining the number of clusters, cluster distributions were analyzed for different numbers of clusters ranging from three to seven. As a result, it was decided to use five clusters, which was justified by their quantitative composition and the expert analysis of their homogeneity.

The second stage involved conducting the multidimensional scaling of districts in Stavropol Krai according to the standard survey indicators. The scaling was carried out using the STATISTICA software by calculating the corresponding Euclidean distances. As a result, the distribution of districts was obtained within the three Dimensions.

The validity of the used procedure, including the interim and final results of its application, is based both on monitoring the use of multivariate statistical methods and verification expert analysis.

The procedure for conducting an objective and subjective assessment of rural development at the municipal level presented in the article can establish conditions for the creation of higher-quality and targeted development programs. The obtained results can be used to justify choosing certain areas of territorial development and update some of them.

We hope that our study has achieved the stated aim. The combined use of multivariate statistical analysis methods yielded assessment data combining the objective and subjective perspectives on the subject matter of the study, which served as a ­basis for the multi-aspect grouping of districts in Stavropol Krai. The establishment of the matrix of indicators was based on the systems approach to the study of rural areas, enabling their multidimensional assessment from the combined perspectives of the economic, social, and natural subsystems of rural areas. The proposed procedure complements existing approaches to the assessment of rural development processes, expanding the used information and analytical support as well as providing a means to obtain multidimensional assessment data under the systems approach. It is applicable not only for Stavropol Krai and the rest of Russia, but also for rural areas of other countries, while allowing for the expansion or narrowing of the information base used, both from the standpoint of accessibility of its formation and taking into account local specifics.

The study compares the results of objective and subjective assessment of the social and economic situation in rural areas, which was not presented in earlier­ works. As compared to the index assessment method, the procedure of multi-aspect assessment expands the capabilities to use the obtained results in practice while maintaining the universality and adaptability in the selection of indicators. In common with ranking or typologization, the approach proposed in this article enables a comparison of rural areas, significantly improving the quality of assessment results due to the multi-aspect grouping and the use of information support. In addition, the procedure provides a means to create a future image of rural areas and use relevant patterns to achieve it.

While cluster analysis is a more conventional and proven method, the application of multidimensional scaling to this problem is understudied. Despite the subjective-objective approach used in the article to determine the essence of scales, we realize the possibility of improving the application of this toolkit using correlation and regression analysis, in particular, a closer examination of the applicability scope of the multiple regression toolkit.

Another aspect that requires further study is the procedure for selecting and validating the patterns of rural development as well as their adaptation to specific conditions, including within individual subsystems of the territory. Working with patterns, in turn, implies conducting a dynamic assessment and building forecast scenarios for the rural development pattern adaptation or change. This area of research involves constructing the cluster trajectories of rural areas for a certain time period as well as vectors of changes in perceiving the social and economic situation by their population. The identification of rural development trends and a multi-aspect prognostic study intended to create a future image (Baydakov, 2022) of rural areas will help improve the validity in identifying areas of focus and timely update development programs.

The creation of a set of patterns and the choice of one of them for a particular rural area should be agreed upon with governing bodies, which are multi-actor in nature. These issues require a separate study due to the problems related to actors’ conflicting efforts in the management of rural areas.

The obtained results of the multi-aspect rural development assessment provide a means to study rural territorial entities in greater depth. The study results help improve the quality of information and analytical support to rural development manage­ment. They can be used in the preparation of programs and projects for the development of territories. Further analysis of the dynamics and management features of rural develop­ment will make the reasoning behind rural development decisions more grounded.

The study has been funded within the framework of the strategic academic leader­ship program “Priority­ 2030” and the joint project “Spatial analysis as a key tool for managing rural development in the region: Principles, methods, practical ­recommendations” of Stavropol State Agrarian University and HSE University (part of the program “Mirror Labs”).