Vol. 40 (Number 10) Year 2019. Page 24
ARTEMOV, Vladimir A. 1; KONOREV, Alexander M. 2
Received: 01/11/2018 • Approved: 25/02/2019 • Published 31/03/2019
ABSTRACT: The authors of the article offer a special model to evaluate how financing of the investment processes can influence the results of the social sphere development. It is shown that closeness of the connections between the researched variables can be demonstrated within the offered model, which can be used by representatives of state governing institutions or regional authorities while managing the social investments financing and the social sphere development. Specific tendencies for development, which are typical of the social sphere of the regions in the Central Federal District, are shown. It lets speak about possibilities of successful management of social investments in the fast-moving society. |
RESUMEN: Los autores del artículo ofrecen un modelo para evaluar la influencia financiera de los procesos inversivos sobre los resultados del desarrollo social. Se muestra, que la relacion estrecha entre las variables estudiadas queda claramente demostrada por modelo propuesto. Los representantes de las autoridades o la administración pueden utilizar este modelo para gestionar la financiación de las inversiones sociales y el desarrollo de la esfera social. El artículo muestra que la esfera social de las regiones del Distrito Federal Central se caracteriza por tendencias de desarrollo específicas, que deja hablar sobre la posibilidad de gestionar con éxito las inversiones sociales en una sociedad que cambia dinámicamente. |
Nowadays, different issues connected with financing social investments are considered to be especially relevant in the Russian economic literature that is devoted to problems of management of the social sphere development. This fact is clear because the social sphere performs some key functions in the modern society: it provides the national economy with labour forces and renders various forms of life-support to different communities. Besides, interest in social investments have started increasing relatively recently in comparison with other social and economic categories even all over the world. Whereas in Russia, they started to consider social investments to be a developed category only at the beginning of the twenty-first century.
In fact, the authors of all the works known to us examine the social sphere in the context of more particular categories “social infrastructure” and “social relations”. These categories, in their turn, are connected with one another and influence each other. This fact cannot help but affect the development of the social sphere in general. Therefore the economic component of the social sphere should be examined within the categories “social infrastructure” and “social relations” (Ambarova P.A., 2017; Bessonova O.E., 2017; Vinokurova A. V., 2017).
In our opinion, the most pertinent interpretation of the social infrastructure is given by L.V. Bondarenko who thinks that “the social infrastructure can be defined as a territorial and sectoral complex, which gives social and spatial conditions for reproduction of labour forces, socialization and social protection of population, preserves and develops demographic, labour and spiritual potential of the society” (Bondarenko L.V., 2017).
Besides, it is important to say that E.V. Tishin thinks that the main part of the social sphere is not the social infrastructure, but “a goal-setting unit – real relations within the social space, which are, first of all, a subject of the social management, social policy in general” (Tishin E.V., 1994).
Due to these objective circumstances, social investments can be defined as “investments in objects of the social sphere in order to get profit and improve the standards of living and the quality of living of people by satisfying their material, spiritual or social needs” (Kolesov V.P., 2011). M.S. Нaraeva thinks that “the social investments are long-term investments in the social sphere in order to improve the living standards by creating new technologies and mechanisms of distribution of the received gross domestic product among different population groups paying attention to their needs” (Harayeva M.S.,2009).
The approach towards financing of the investment processes in the social sphere has been changing for some time. G.A. Akhinov thinks that the main technology in performing state social functions used to be the technology of “social transfers, which were paid to the most vulnerable categories of population, but now the active state support must include investments in people and human capital” (Akhinov G.A., 2011). An investment approach in performing state social functions usually means that the state tries to participate minimally in financing of the social programs that include compensation payments for needy people. The governments of some countries often interfere in activities of corporate business and private companies. The main direction of the state social policy is focusing on creation and development of “middle class”, which must be the main element in the development of the society. This kind of management of the state social policy has investment features but not consumer ones (Tikhonova N. E., 2015; Lapin N.I., 2018).
The main aim of financing of the social investment activity at the state level is reaching a more rational form of interaction between investors, the state and consumers in the process of improving the quality of living and the standards of living. Thus, when the state social function is performed, it is necessary pass from a consumer approach to an investment one (Birdsall N., 2014; Novokmet F., 2017; Tittenbrun J., 2017).
We would like to describe the current situation in the area of financing of the main elements of the social sphere giving different regions of the Central Federal District as examples. The data are shown in Table 1. There is a great differentiation between the regions at financing levels of the social sphere. Moscow and Moscow Region are not present in the analysis to make the data more comparable.
Voronezh Region, Yaroslavl Region, Belgorod Region and Tula Region were the leading regions in the amounts of financing of the social sphere in 2015. The lowest levels of the social expenses were demonstrated in Kostroma Region, Oryol Region and Bryansk Region. Besides, there is a general tendency towards predominance of expenses on education in the financing structure and a lag in financing of housing and communal services. These facts were caused by a complicated situation in the budgets, growth of deficit and debt. Many regions chose the direction of their development, which was connected with optimizing expenses on housing and communal services.
The similar situation took place in 2016 and in 2017.
Table 1
Expenses on the social sphere of the consolidated budgets of the regions in
the Central Federal District from 2015 to 2017, millions of Russian roubles
Housing and Сommunal Services |
Education |
Health Care |
Social Policy |
|||||||||
2015 |
2016 |
2017 |
2015 |
2016 |
2017 |
2015 |
2016 |
2017 |
2015 |
2016 |
2017 |
|
Belgorod Region |
2944 |
2739 |
3294 |
23579 |
24586 |
26215 |
10552 |
10155 |
19976 |
9454 |
10025 |
15724 |
Bryansk Region |
1627 |
1777 |
2230 |
13493 |
14202 |
14972 |
6826 |
2300 |
13837 |
11632 |
15236 |
14813 |
Vladimir Region |
3511 |
3713 |
4050 |
17726 |
18291 |
20043 |
8375 |
8454 |
17828 |
10355 |
10875 |
15436 |
Voronezh Region |
4810 |
4242 |
4359 |
28415 |
28814 |
31259 |
17125 |
16461 |
28731 |
15977 |
16105 |
24769 |
Ivanovo Region |
3397 |
2573 |
3294 |
11032 |
11138 |
11358 |
5795 |
1679 |
10569 |
7259 |
11138 |
11418 |
Kaluga Region |
5879 |
5660 |
6714 |
15256 |
15665 |
15379 |
9141 |
4328 |
14208 |
9057 |
12578 |
12611 |
Kostroma Region |
1721 |
2161 |
1911 |
8853 |
8847 |
8889 |
4070 |
4006 |
7811 |
4252 |
4716 |
7001 |
Kursk Region |
1730 |
1812 |
2043 |
15965 |
16875 |
17053 |
6463 |
6549 |
13518 |
9228 |
9731 |
14759 |
Lipetsk Region |
2882 |
3272 |
3518 |
15305 |
15785 |
16896 |
9270 |
4350 |
14420 |
8611 |
13584 |
13595 |
Oryol Region |
1254 |
1008 |
976 |
10274 |
10236 |
11155 |
4966 |
5311 |
9510 |
6295 |
6639 |
10003 |
Ryazan Region |
2561 |
2552 |
1857 |
15261 |
15662 |
16495 |
6350 |
6834 |
13147 |
8343 |
8343 |
12680 |
Smolensk Region |
2430 |
2856 |
2428 |
12287 |
12369 |
11880 |
8439 |
6434 |
11360 |
7463 |
7842 |
11506 |
Tambov Region |
2565 |
2776 |
2946 |
11859 |
14036 |
14333 |
7662 |
6393 |
11684 |
7722 |
8145 |
11471 |
Tver Region |
4416 |
3220 |
3924 |
17299 |
16640 |
17560 |
10020 |
9919 |
16638 |
9587 |
10134 |
15242 |
Tula Region |
5935 |
6243 |
7245 |
21555 |
22858 |
24854 |
11009 |
12204 |
20917 |
15026 |
15106 |
22042 |
Yaroslavl Region |
4911 |
5030 |
4943 |
22260 |
21965 |
22463 |
8995 |
8994 |
17451 |
11090 |
12678 |
17053 |
The summarized data on financing of the social sphere per head in the context of the regions (taking into consideration the total regional rank in the Central Federal District) are shown in Table 2.
Absolute indeces disfigure the real situation in the regions a bit because there are great differences between the regions in the state of their social and economic sphere. Therefore, it is more reasonable to use statistic data per head to do a better analysis of financing of the social sphere. Differences between the regions in the social expenses of the budgets per head for the examined period of time changed slightly. However, the situation differs from the absolute statistics. Moscow and Moscow Region are the leaders in the amounts of financing of the social sphere. Yaroslavl Region, Kaluga Region, Tula Region and Lipetsk Region follow them in the list. The lowest levels of the social expenses were shown in Ivanovo Region, Bryansk Region and Voronezh Region.
Table 2
Dynamics of the total expenses on the social sphere of the consolidated budgets in the
regions of the Central Federal District and their ranks, thousands of Russian roubles per head
2015 |
2016 |
2017 |
Total Rank |
||||
|
Value |
Rank |
Value |
Rank |
Value |
Rank |
|
Belgorod Region |
30,0 |
9 |
30,6 |
11 |
42,1 |
9 |
11 |
Bryansk Region |
27,4 |
17 |
27,5 |
17 |
37,9 |
17 |
17 |
Vladimir Region |
28,6 |
14 |
29,7 |
14 |
41,6 |
10 |
13 |
Voronezh Region |
28,4 |
15 |
28,1 |
16 |
38,2 |
16 |
16 |
Ivanovo Region |
26,7 |
18 |
25,9 |
18 |
36,1 |
18 |
18 |
Kaluga Region |
39,0 |
3 |
37,7 |
4 |
48,3 |
5 |
4 |
Kostroma Region |
29,0 |
12 |
30,4 |
12 |
39,8 |
12 |
12 |
Kursk Region |
29,8 |
11 |
31,1 |
7 |
42,5 |
6 |
7 |
Lipetsk Region |
31,2 |
8 |
32,0 |
6 |
42,1 |
8 |
6 |
Moscow Region |
47,2 |
2 |
48,9 |
2 |
65,5 |
2 |
2 |
Oryol Region |
30,0 |
10 |
30,7 |
10 |
42,3 |
7 |
9 |
Ryazan Region |
28,8 |
13 |
29,6 |
15 |
39,4 |
13 |
14 |
Smolensk Region |
31,9 |
6 |
30,9 |
8 |
39,2 |
14 |
10 |
Tambov Region |
28,4 |
16 |
30,1 |
13 |
39,1 |
15 |
15 |
Tver Region |
31,7 |
7 |
30,8 |
9 |
41,6 |
11 |
9 |
Tula Region |
35,5 |
5 |
37,6 |
5 |
50,3 |
3 |
5 |
Yaroslavl Region |
37,2 |
4 |
38,3 |
3 |
48,9 |
4 |
3 |
Moscow |
75,5 |
1 |
83,7 |
1 |
113,6 |
1 |
1 |
Differentiation of the budget expenses on the social aims in the regions of the Central Federal District is rather great. It can be explained by different conditions, which influence financing of the social sphere (population size, development level of economy, regional social policy).
While modelling how different ways of financing of investment processes influence the results of the social sphere development it is reasonable to use the chosen criteria of efficiency (Kaplan R.S., 1996; Yuzvovich L.I.,2014). In this very situation we can use an integral detector “Social Sphere Development Index” (SSDI). This index describes the final results of the social investments precisely, it demonstrates dynamics of the process and the result of the social sphere development and lets evaluate quantitative indicators of the benchmarks, which can be reached due to financing social investments.
We use the integral SSDI detector, which is based on the model offered by W. Pluta, to create a model of a management system of the social sphere development in the context of financing of the social investments for the regions in the Central Federal District (Artemov V.A., Konorev A.M., Davydova L.V., 2017).
Financing of the investment processes in the social sphere performs a stimulating function and a reproducing function. Therefore, the SSDI indicator will show both the level of the development of the social relations and the efficiency of financing of the social infrastructure.
The first stage of creating an integral index requires paying attention only to those particular variables, which are characterized by the greatest changeability (where it is possible to see the maximum dispersion of the objects). We analyzed a set of initial particular indeces and chose the following five of them as input variables: birth rate per 1000 persons, average cash income per capita, total square of living quarters per one inhabitant, the number of hospital beds per 10000 persons, the number of registered crimes per 100000 persons. The values of the mentioned particular indeces are published in collections of statistics. The initial data used for calculations of SSDI in 2017 are shown in Table 3 as an example.
Table 3
Initial indeces used for calculations of SSDI in 2017
Regions |
Birth Rate |
Average Cash Income per capita |
Square of Living Quarters |
The Number of Hospital Beds |
The Number of Registered Crimes |
The Russian Federation |
11,6 |
31591 |
24,9 |
81,6 |
98527 |
The Central Federal District |
10,6 |
39903 |
26,5 |
76,4 |
98710 |
Belgorod Region |
9,8 |
29747 |
29,9 |
72,7 |
99116 |
Bryansk Region |
9,6 |
26332 |
28,7 |
74,9 |
98707 |
Vladimir Region |
9,8 |
23928 |
27,8 |
84 |
98627 |
Voronezh Region |
9,7 |
29974 |
28,8 |
84,1 |
98528 |
Ivanovo Region |
9,8 |
24399 |
25,7 |
82 |
98597 |
Kaluga Region |
10,9 |
28177 |
28,8 |
79,3 |
98400 |
Kostroma Region |
10,9 |
23765 |
26,9 |
95,6 |
98649 |
Kursk Region |
9,7 |
27649 |
29,3 |
85,1 |
98913 |
Lipetsk Region |
10,2 |
29001 |
29,2 |
83 |
98930 |
Moscow Region |
12 |
40895 |
33,7 |
74 |
98802 |
Oryol Region |
9,6 |
23187 |
27,6 |
92,6 |
98770 |
Ryazan Region |
9,9 |
24960 |
29,5 |
79,3 |
99156 |
Smolensk Region |
9,2 |
24367 |
27,9 |
97,1 |
98569 |
Tambov Region |
8,7 |
27240 |
27,7 |
80,6 |
98835 |
Tver Region |
9,9 |
25761 |
30,8 |
93,9 |
98386 |
Tula Region |
9,1 |
27350 |
27,7 |
85,8 |
99201 |
Yaroslavl Region |
10,6 |
27052 |
26,6 |
92,6 |
98553 |
Moscow |
10,9 |
59762 |
19,1 |
65,2 |
98593 |
The fact that it is rather difficult to detect any trends in the development of the social sphere in the regions of the Central Federal District (even using a relatively small amount of initial particular indeces) demonstrates the necessity of integral indeces.
The next stage of the performance audit of the social investments is an indication of the level of how initial particular indeces influence an integral SSDI detector with the help of weighting factors, which can be detected with the help of the method of paired comparison. Counted values of the weighting factors are given in Table 4.
Table 4
Values of weighting factors for calculations of SSDI
Values |
Birth Rate |
Average Cash Income per capita |
Square of Living Quarters |
The Number of Hospital Beds |
The Number of Registered Crimes |
Abbreviation |
BR |
ACI |
SLQ |
HB |
RC |
Weighting Factors |
0,2513 |
0,3317 |
0,0804 |
0,1608 |
0,1759 |
The biggest value of each particular index of all the regions during the analyzed period is chosen as a standard. A set of standard values of particular indeces is given in Table 5.
Table 5
A set of standard values of indeces for calculations of SSDI
|
X0J |
||||
X01 |
X02 |
X03 |
X04 |
X05 |
|
Abbreviation |
BR |
ACI |
SLQ |
HB |
RC |
Name of Index |
Birth Rate |
Average Cash Income per capita |
Square of Living Quarters |
The Number of Hospital Beds |
The Number of Registered Crimes |
Unit |
Birth Rate per 1000 Persons |
Russian Roubles |
Square Metres |
The Number of Hospital Beds per 10000 Persons |
The Number of Registered Crimes per 100000 Persons |
Region |
The Russian Federation |
Moscow |
Moscow Region |
Smolensk Region |
Tula Region |
Year |
2014, 2015 |
2015 |
2017 |
2013 |
2017 |
Scaled Value |
25,12 |
33,17 |
8,04 |
16,08 |
17,59 |
Calculated values of SSDI in the regions of the Central Federal District from 2013 to 2017 are shown in Table 6.
Table 6
Values of Social Sphere Development Index in
the regions arranged by years (and their ranks)
Regions |
2013 |
2014 |
2015 |
2016 |
2017 |
Total Rank |
|||||
Value |
Rank |
Value |
Rank |
Value |
Rank |
Value |
Rank |
Value |
Rank |
||
The Russian Federation |
0,333 |
4 |
0,331 |
4 |
0,336 |
4 |
0,331 |
4 |
0,341 |
4 |
4 |
The Central Federal District |
0,457 |
2 |
0,455 |
3 |
0,491 |
2 |
0,488 |
3 |
0,475 |
3 |
3 |
Belgorod Region |
0,277 |
6 |
0,267 |
7 |
0,275 |
8 |
0,282 |
7 |
0,258 |
8 |
7 |
Bryansk Region |
0,213 |
11 |
0,204 |
11 |
0,215 |
12 |
0,191 |
13 |
0,189 |
13 |
12 |
Vladimir Region |
0,185 |
16 |
0,173 |
18 |
0,184 |
15 |
0,153 |
20 |
0,156 |
19 |
18 |
Voronezh Region |
0,244 |
9 |
0,272 |
6 |
0,314 |
5 |
0,286 |
5 |
0,275 |
5 |
6 |
Ivanovo Region |
0,180 |
18 |
0,177 |
17 |
0,164 |
20 |
0,167 |
18 |
0,162 |
17 |
19 |
Kaluga Region |
0,280 |
5 |
0,276 |
5 |
0,284 |
6 |
0,284 |
6 |
0,261 |
7 |
5 |
Kostroma Region |
0,179 |
20 |
0,166 |
20 |
0,172 |
18 |
0,193 |
12 |
0,183 |
15 |
17 |
Kursk Region |
0,230 |
10 |
0,234 |
10 |
0,231 |
10 |
0,215 |
11 |
0,230 |
10 |
10 |
Lipetsk Region |
0,245 |
8 |
0,264 |
8 |
0,266 |
9 |
0,270 |
9 |
0,267 |
6 |
9 |
Moscow Region |
0,447 |
3 |
0,459 |
2 |
0,475 |
3 |
0,525 |
2 |
0,523 |
2 |
2 |
Oryol Region |
0,181 |
17 |
0,168 |
19 |
0,170 |
19 |
0,164 |
19 |
0,143 |
20 |
20 |
Ryazan Region |
0,202 |
14 |
0,202 |
14 |
0,184 |
16 |
0,184 |
15 |
0,175 |
16 |
15 |
Smolensk Region |
0,209 |
12 |
0,202 |
13 |
0,199 |
13 |
0,181 |
17 |
0,160 |
18 |
14 |
Tambov Region |
0,179 |
19 |
0,187 |
15 |
0,177 |
17 |
0,185 |
14 |
0,188 |
14 |
16 |
Tver Region |
0,202 |
15 |
0,186 |
16 |
0,184 |
14 |
0,183 |
16 |
0,206 |
12 |
14 |
Tula Region |
0,206 |
13 |
0,204 |
12 |
0,222 |
11 |
0,231 |
10 |
0,207 |
11 |
11 |
Yaroslavl Region |
0,246 |
7 |
0,255 |
9 |
0,277 |
7 |
0,276 |
8 |
0,244 |
9 |
9 |
Moscow |
0,757 |
1 |
0,740 |
1 |
0,734 |
1 |
0,690 |
1 |
0,653 |
1 |
1 |
The calculated data show a great differentiation in the regions of the Central Federal District at the efficiency level of financing of the social investments. Moscow and Moscow Region are the doubtless leaders at the efficiency level of the social investments. This fact is caused by the significant superiority in the development of the social sphere of these regions. Kaluga Region, Voronezh Region and Belgorod Region use the financial resources of the social investments rather effectively. The lowest efficiency level of the social investments is demonstrated in Ivanovo Region and Oryol Region.
We are going to do a correlation and regression analysis of indeces of financing levels of the social sphere and an analysis of an integral index of the social sphere development in 2017 at the next stage of modelling of the management system of the social sphere development in the context of financing for social investments. The initial data for the analysis are given in Table 7.
Table 7
Initial data for a correlation and regression analysis
SSDI |
Housing and Сommunal Services |
Education |
Health Care |
Social Policy |
|
Y |
Х1 |
Х2 |
Х3 |
Х4 |
|
Belgorod Region |
0,341 |
3293,7 |
26214,9 |
19976 |
15724,4 |
Bryansk Region |
0,475 |
2229,8 |
14971,7 |
13837,2 |
14812,5 |
Vladimir Region |
0,258 |
4050,4 |
20042,6 |
17828,2 |
15436,3 |
Voronezh Region |
0,189 |
4358,9 |
31258,9 |
28731,1 |
24768,6 |
Ivanovo Region |
0,156 |
3294,4 |
11357,6 |
10569,1 |
11418 |
Kaluga Region |
0,275 |
6714 |
15379 |
14208,3 |
12610,7 |
Kostroma Region |
0,162 |
1910,8 |
8889,1 |
7811,1 |
7000,8 |
Kursk Region |
0,261 |
2043 |
17053,3 |
13517,6 |
14759 |
Lipetsk Region |
0,183 |
3518 |
16896,4 |
14420,2 |
13594,6 |
Moscow Region |
0,230 |
48252,5 |
172924,4 |
154899,8 |
115241 |
Oryol Region |
0,267 |
976,3 |
11155,2 |
9510,4 |
10003 |
Ryazan Region |
0,523 |
1857,2 |
16495,4 |
13147 |
12680,2 |
Smolensk Region |
0,143 |
2427,9 |
11880,3 |
11360,3 |
11506,4 |
Tambov Region |
0,175 |
2945,9 |
14332,8 |
11684,3 |
11471,1 |
Tver Region |
0,160 |
3924,1 |
17559,7 |
16638,2 |
15241,7 |
Tula Region |
0,188 |
7244,8 |
24853,7 |
20916,5 |
22042,4 |
Yaroslavl Region |
0,206 |
4943,2 |
22463,2 |
17451 |
17052,9 |
You can see correlation factors in Table 8. These factors demonstrate a very close (almost equal) connection between the variables.
Table 8
Correlation factors (r), characterizing the
connection between the analyzed variables
|
у |
у |
1 |
х1 |
0,901273604 |
х2 |
0,912628633 |
х3 |
0,908726501 |
х4 |
0,906097262 |
It is necessary to scale the variables beforehand to do a regression analysis. The results of the regression analysis let create the following mathematical function (1), which characterizes how financing of the investment processes can influence the results of the development of the social sphere:
y=17,3+0,15x1+0,78x2-0,57x3-0,17x4 (R2=0,92) (1)
State governing institutions or regional authorities should implement the policy of the social investments based on equalizing social and economic development of regions by redistribution of financial flows and paying attention to levels of the social development of the regions in dynamics. The program of social and economic development in low-performing regions must include an increase in the social development level and a creation of programs, which connect actual financial resources with planned social and economic results. Removal of inequality in living standards in different areas is a long process. It requires the development of the social infrastructure and productive forces in those areas where the standards of living are lower. It is reasonable to point out that the civil society must be the main participant of the social regulation. The society must direct its efforts towards a gradual growth of the social responsibility of business based on the social partnership. Whereas the offered model of the management system of the social sphere development in the context of financing for social investments can be used in carrying out the financial control of the investment processes in the social sphere.
The study is supported by the Russian Foundation for Basic Research (RFBR), Grant 17-32- 01189 “Methodology of influence of financing of investment processes on results of development of the social sphere”.
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1. Kursk State University, Kursk, Russia. ava_fkn@mail.ru
2. Kursk State University, Kursk, Russia. konorev04@mail.ru