In this study, we present the computational method for risk assessment of the socio-economic development of regions. An attempt has been made to develop a method for the determination of integral risk indicators of socio-economic development based on the joint use of the methods of factor analysis and expert evaluation. This approach has increased the reliability of the calculations and made it possible to analyze the influence of socio-economic indicators on the risk level of socio-economic development. The integral risk indicator shows the efect of the inconsistency in the level of factor provision on the socio-economic development of the -th region (district) in comparison with the general situation in the country (regions). The closer the value of integral risk indicator is to 1, the higher the level of risk in this region. Using Kyiv region districts as an example, the process of risk assessment for regional socio-economic development has been considered. The results obtained in this investigation demonstrate that the presented computational method solves the problem of formalization of risk assessment for the socio-economic development of regions.
The market today is functioning in a turbulent environment facing continuous change because
of hyper-competition, changing demands of customers, regulatory changes, and technological
advancement [
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administration new approaches to the formation of socio-economic strategies, development
of adequate methodological solutions, and tools in the field of governance, especially it
concerns socio-economic development management of regions [
In socio-economic studies, to improve the reliability of the procedure for assess-ment of
socio-economic development using mechanism for determining the integral indicators based
on factor analysis, taking into account consider the knowledge and experience of experts
[
This section presents a method for determining the integral risk indicator of regional
socioeconomic development. The method is based on a model for determination of the socio-economic
development integral indicator which is described in detail in [
for all regions and the number of factors. In this case, the risk varies in the range from 0 to 1. The integral indicator shows the efect of the inconsistency in the level of factor provision on the socio-economic development of the -th region (district) in comparison with the general situation in the country (regions). The closer the value of is to 1, the higher the level of risk in this region. 1 stage. The entering of values of indicators of socio-economic development and expert evaluations in the form of the matrix of indicators and the matrix of expert evaluations of indicators, followed by its normalization to a single scale of measurements. 2 stage. The calculation of the pairwise correlations matrix and determination of its eigenvalues and eigenvectors. 3 stage. Obtaining a matrix of factors by multiplying the normalized matrix of indicators and the matrix of eigenvectors, and normalization of factors and calculation of their variance. 4 stage. Determining the number of factors included in the integral risk indicator (figure 1) on the basis of eigenvalues of the matrix of pairwise correlations of indicators and the given boundary value dispersion of normalized indicators or in other words – a sampling of the minimum number of factors with maximal eigenvalues is made, the sum values of which are not less than
[
In this section, we consider the process of risk assessment for regional socio-economic development in accordance with the developed computation method for integral risk indicators (figure 1) on the example of the Kyiv region districts. Listed in a single scale of measurements and normalized values of socio-economic indicators of districts are presented in table 1. According to the National State Statistics Service of Ukraine [24], one of the main indicators that characterize the level regional socio-economic development are (table 1): (P1) Number of cars per 1000 people; (P2) Services rendered per unit of population, UAH; (P3) Natural increase (reduction) of the population; (P4) Registered unemployment rate; (P5) Average monthly salary, UAH; (P6) Provision of housing by the population, 2 per person; (P7) The ratio of 2 of built housing to the population; (P8) Preschool establishments per unit of population; (P9) General educational institutions per unit of population; (P10) Number of crimes per 1000 people; (P11) Emissions of pollutants.
It should be noted that the list of indicators, depending on the goals and objectives of the risk assessing, may change, thereby changing its emphasis. Thus, for the Kyiv region we have a matrix of initial socio-economic indicators in the size of 25 × 11 (25 districts of the Kyiv region: Baryshivsky (D1), Bilotserkivsky (D2), Boguslavsky (D3), Boryspilsky (D4), Borodyansky (D5), Brovarsky (D6), Vasylkivsky (D7), Vyshgorod (D8), Volodarsky (D9), Zgurivsky (D10), Ivankivsky (D11), Kagarlytsky (D12), Kyiv-Sviatoshynsky (13), Makarivsky (D14), Myronivsky (D15), Obukhovsky (D16), Perejaslav-Khmelnytsky (D17), Polissya (D18), Rokytnyansky (D19), Skvyrsky (D20), Stavyshchensky (D21), Tarashchansky (D22), Tetiivsky (D23), Fastivsky (D24), Yahotynsky (D25)).
On the basis of the normalized matrix of socio-economic indicators (table 1), the pairwise correlations matrix of indicators is dimensioned 11 × 11. For the pairwise correlations matrix of indicators, we determine eigenvalues (table 2) and eigenvectors X. The matrix of factors is obtained by multiplying the normalized matrix of socio-economic indicators (table 1) into the matrix of the eigenvectors of the pairwise correlations matrix. The obtained factors are normalized. The normalized factor matrix is used to calculate the matrix of correlations between factors and indicators of socio-economic development that is required for the interpretation of factors. the given threshold of the dispersion for normalized socioeconomic indicators (table 1), the formula (4) determines the number of factors in the integral risk indicator. In this case, the number of main components (factors) must be used, which exhaust at least 60-70% of the variance of the initial random variables. For example, at a given threshold of 0,6 from table 2 it is necessary to select factors with maximal eigenvalues, the sum of values of which is not less than 0, 6 × 11 = 6,6. The sum of the first three eigenvalues is 7,49 that is the integral index consists of the first three factors ( = 3) that explain approximately 68% (see formula 4) of the variance of the initial data (table 2). The calculate matrix of correlations between the normalized socio-economic indicators and the factors shows, which indicators are included in the given three factors (with the value of the variance of the indicators should not be less than the given limit value of 0,6). Table 3 shows the structure of factors: the coeficient of correlation between the indicators and factors in which they are included, statistical and expert weights coeficients and weighted average weight coeficient of factors. The first factor included the first four indicators: 1) the number of cars per 1000 people; 2) services rendered per unit of population; 3) natural increase (reduction) of the population; 4) the level of registered unemployment. The second factor included the eleventh indicator – emissions of pollutants. The third factor entered the seventh indicator – the ratio 2 of the built housing to the population.
By multiplying the obtained factors by the corresponding weighted average weight coeficients
of factors, by formula (2) we obtain the values of integral risk indicators according to formula (3)
that allow ranking the regions in terms of their risk assessment of socio-economic development
(figure 2). In table 4 calculations results of integral risk indicators of social and economic
development of Kyiv region districts are presented. It is also worth noting that for a better
understanding of the calculation procedures should be carefully study the model that presented
in [
The obtained results demonstrate that the presented computational method solves the problem of formalization of risk assessment for the socio-economic development and can be used to analyze and predict the socio-economic situation in the region. In the framework of the presented method by changing the values of socio-economic indicators, it is possible to analyze and model the socio-economic situation in the region, which undoubtedly provides management with valuable information on possible risks and directions of efective strategies for socio-economic development. The management receives not only an adequate assessment of the risk level of socio-economic development of the region, but also the opportunity to determine the immediate causes and consequences that shape the current and future socio-economic situation in the region. The results of modeling the process of assessing the level of socio-economic development of Kyiv region showed that the main advantages of the method of determining the integrated risk indicators are the possibility of studying correlations between socio-economic indicators, between indicators and factors, interpretation of factors, determining negative and positive characteristics of socio-economic situations in context of research objects. The proposed method for risk assessment makes it possible to implement a unified approach to data analysis and to ensure the eficiency of constructing integral risk indicators.
This study was supported by the Ministry of Education and Science of Ukraine, Project No. 0112U000635, “Development and implementation of the modern information systems and technologies in the socio-economic activities”. [17] G. Di Franco, A. Marradi, Factor analysis and principal component analysis, FrancoAngeli, 2013. [18] W. H. Finch, Exploratory factor analysis, SAGE Publications, 2019. [19] S. Farhady, Expert Evaluation Game Factors, LAP Lambert Academic Publishing, 2013. [20] J. J. Phillips, P. P. Phillips, Handbook of training evaluation and measurement methods,
Routledge, 2016. [21] L. Ustinovieius, Determining integrated weights of attributes, Journal of Civil Engineering and Management 7 (2001) 321–326. doi:10.3846/13921525.2001.10531743. [22] M. Rausand, S. Haugen, Risk Assessment: Theory, Methods, and Applications (Statistics in
Practice), Wiley, 2020. [23] A. A. Kuklin, M. S. Pecherkina, A. N. Tyrsin, A. A. Surina, Methodological tools for the detection of risks to the welfare of the individuals and the territory of residence, Economy of Region 13 (2017) 1030–1043. doi:10.17059/2017- 4- 5. [24] Statistical yearbook of Ukraine for 2017, 2017.