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Vol. 40 (Number 10) Year 2019. Page 24

Modelling the management system of the social sphere development in the context of financing for social investments

Modelado del Sistema de Gestión del Desarrollo Social en el contexto del financiamiento de inversiones sociales

ARTEMOV, Vladimir A. 1; KONOREV, Alexander M. 2

Received: 01/11/2018 • Approved: 25/02/2019 • Published 31/03/2019


Contents

1. Introduction

2. Methodology

3. Results

4. Conclusions

Acknowledgments

Bibliographic references


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.
Keywords: financing, social sphere, social investments, development

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.
Palabras clave: financiación, esfera social, inversiones sociales, desarrollo

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1. Introduction

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).

2. Methodology

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.

3. Results

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)

 

4. Conclusions

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.

Acknowledgments

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


Revista ESPACIOS. ISSN 0798 1015
Vol. 40 (Nº 10) Year 2019

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