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Montenegrin Journal of Economics, Vol. 14, No. 4 (December 2018)

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Montenegrin Journal of Economics, Vol. 14, No. 4 (December 2018)

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Montenegrin Journal of Economics, Vol. 14, No. 4 (December 2018)

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ISSN 1800-5845

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Montenegrin Journal of Economics

Vol. 14, No. 4 (December 2018) ‘

C O N T E N T S

Structure of General Government Expenditure on Social Protection in the EU Member States RENATA HALASKOVA ...... 7

Methodical Approach to the Assessment of Risks Connected With the Legalization of the Proceeds of Crime NADIYA KOSTYUCHENKO, МYKOLA STARINSKYI, INNA TIUTIUNYK and IANA KOBUSHKO ...... 23

Formal Institutional Environment Influence on Social Entrepreneurship in Developed Countries EVGENY V. POPOV, ANNA Y. VERETENNIKOVA and KSENIA M. KOZINSKAYA ...... 45

Regional Development in Conditions of Limitation of Water Resources: Correlation Interconnections SVITLANA FEDULOVA, VITALINA KOMIRNA, NATALIIA NAUMENKO and OKSANA VASYLIUK ...... 57

Assessment of Fiscal Decentralization Influence on Social and Economic Development OLENA CHYGRYN, YURIY PETRUSHENKO, ALINA VYSOCHYNA and ANNA VORONTSOVA 69

A Comparative Analysis of Modern Performance Methods in Economic Practice VERONIKA CABINOVA, ERIKA ONUFEROVA, PETER GALLO, JR., PETER GALLO and JURAJ GALLO ...... 85

Employees Management: Evidence from Gamification Techniques SAIMA HUSSAIN, SARA QAZI, RIZWAN RAHEEM AHMED, DALIA STREIMIKIENE and JOLITA VVEINHARDT...... 97

Investment Activity of Insurers and the State Economic Growth GULNARA KAIGORODOVA, DARIA ALYAKINA, GUZEL PYRKOVA, ALFIYA MUSTAFINA and VIKTOR TRYNCHUK ...... 109

Investment Policy of Governance of Economic Security of Agrarian Sector of Ukraine on The Basis of Theory of Fuzzy Logics LIUDMYLA NIKOLENKO, EVGENIY JURAKOVSKIY, NATALYA IVANYUTA, OLENA ANDRONIK and SVITLANA SHARKOVSKA ...... 125

Startup Revenue Model Failures RICHARD BEDNAR, NATALIA TARISKOVA and BRANISLAV ZAGORSEK ...... 141

Efficiency of Innovation Activity Funding as the Driver of the State's National Economic Security LIUDMYLA ZAKHARKINA, IULIIA MYROSHNYCHENKO, DENYS SMOLENNIKOV and SVITLANA POKHYLKO ...... 159

Managament of the Sustainable Development of the Agrarian Sector of the Regions of Ukraine SERHII KOZLOVSKYI, VIKTORIIA BAIDALA, OLGA TKACHUK and TETIANA KOZYRSKA 175

The Becoming of Non-Linear Knowledge: New Risks, Vulnerabilities, and Hopes SERGEY A. KRAVCHENKO ...... 191

Economic Efficiency of Cultural Institutions: The Case of Museums in Slovakia MIRIAM SEBOVA ...... 203

Ukrainian Agricultural Contribution to the World Food Security: Economic Problems and Prospects NATALIA VASYLIEVA ...... 215

The Reactions of Post-Soviet Countries Employees to Changes Carried Out by Organizations in Higher Education: Cases of Lithuanian, Ukrainian and Belarusian State Colleges NATALIJA SEDZIUVIENE and JOLITA VVEINHARDT ...... 225

Criteria for Evaluation of Efficiency of Energy Transformation Based on Renewable Energy Sources VITALII NITSENKO, ABBAS MARDANI, JUSTAS STREIMIKIS, IRYNA SHKRABAK, IVAN KLOPOV, OLEH NOVOMLYNETS and OLHA PODOLSKA ...... 237

Author Guidelines ...... 249

Renata Halaskova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 007-021

Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 007-021 ‘

Structure of General Government Expenditure on Social Protection in the EU Member States

RENATA HALASKOVA1

1 Associate Professor, Department of Technical and Vocational Education, Faculty of Education, University of Ostra- va, Czech Republic, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 11, 2018 The subject of research are the main areas of social policy in rela- Revised from August 22, 2018 tion to general government expenditure on social protection. The Accepted November 10, 2018 article aim to evaluate the volume and structure of general govern- Available online December 15, 2018 ment expenditure on social protection, accentuating similarities and differences in EU member states. Research byl proveden for the period 2007-2016 using the method of cluster analysis and boxplot. JEL classification: In connection to the goal have been verified three research ques- E62, H53, H75. tions. RQ1: Are the largest differences in the volume of general government expenditure on social protection present between Nor- DOI: 10.14254/1800-5845/2018.14-4.1 dic countries compared to Eastern European countries and Baltic states? RQ2: Does expenditure on old age account for the most Keywords: significant share and on sickness and disability the second most significant share in the structure of general government expenditure general government expenditure, on social protection? RQ3: Do the differences in the volume and social protection, structure of general government expenditure on social protection in structure of expenditure on social EU member states depend on the character of welfare states and protection, the type of social protection? The results confirmed the largest EU member states differences in the volume of general government expenditure on social protection between Nordic countries compared to Eastern European and Baltic states (RQ1). The most significant share in the structure of general government expenditure on social protection is represented by expenditure on old age and sickness and disability in the majority of EU countries (RQ2). Research question (RQ3) confirmed the largest differences in government expenditure on old age and survivors (two countries of the second cluster, compared to the countries of the fourth cluster) and in government expenditure on sickness and disability (countries of the third cluster, compared to the countries of the fourth cluster). Nevertheless, these differences between groups of expenditure on social protection failed to confirm for all EU member states representing the respective welfare state and social model. Differences between countries expenditure levels partly reflect diverse levels of wealth, but also diversity in social protection systems, welfare policy, demographic trends, unemployment rates and other social, institutional and economic factors.

Renata Halaskova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 007-021

INTRODUCTION The variety of systems of social protection in the European Union comprises several aspects and depends upon the nature of welfare states. A welfare state is based on the idea that a country has a certain range of redistribution processes, defined by the amount of public expenditure on social protection, in order to set equal opportunities at the beginning of one’s life and to use social policy to create an environment of adequate welfare (Sinn, 1995; Adnet and Hardy, 2005; Pes- tieau, 2006; Leon, 2012; Diamond and Lodge, 2013; Kvist, 2013). All European systems of social protection implemented in EU countries contain certain elements from the basis of each system (Bismarck and Beveridge model). Differences between systems of social protection can be found in a) the cover of risks and the authorization to pay contributions, b) the structure of provided contributions, c) financing, and d) organization (Spicker, 2014). Social protection systems can be financed according to the European Commission (2015, p. 9) in two major ways: “through social contributions or general government contributions. Social contributions are payments by employers on behalf of their employees or by the protected persons themselves (employees, self-employed persons, retired persons and others) to secure entitlement to social benefits. General government contributions consist of a) the cost to general government of running government-controlled non-contributory schemes; as well as b) financial support provided by general government to other resident social protection schemes, and are broken down into earmarked taxes (the proceeds from taxes and levies which, by law, can be used only to finance social protection) and general revenue (general government contributions from sources other than earmarked taxes)”. According to the source of financing, European countries can be divided into countries with a strong tax structure, countries with strong contributions (payments), or countries with mixed financing. A number of studies have already dealt with social protection and sources of financing or the efficiency of social protection (Cornelisse and Goudswaard, 2002; Cichon et al., 2004; Pestieau, 2006; European Commission, 2015; Gavurova and Soltes, 2016; Kim and Kim, 2017), trends and evaluation of expenditure on social protection in a wider context were addressed by Izak and Dufkova, 2006; Adema et al., 2011; Clemente et al., 2012; Bontout and Lokajickova, 2013; Freysson and Wahrig, 2013; Sucur, 2016, expenditure on social protection in connection to redistribution effects were dealt with by Goudswaard and Caminada, 2010; Van Kersbergen and Hemer- ijck, 2012; Wang, et al., 2012. The subject of research are the main areas of social policy in relation to general government expenditure on social protection. The paper, with respect to our understanding, aims to analyze the structure of general government expenditure on social protection COFOG by selected groups (old age and survivors, sickness and disability, unemployment, housing, social exclusion) in the EU member states. The goal is to evaluate the volume and structure of general government expenditure on social protection, accentuating similarities and differences in EU member states. Three research questions are verified in the 28 EU member states in connection to the goal: RQ1: Are the largest differences in the volume of general government expenditure on social protection present between Nordic countries compared to Eastern European countries and Baltic states? RQ2: Does expenditure on old age account for the most significant share and on sickness and disability the second most significant share in the structure of general government expenditure on social protection? RQ3: Do the differences in the volume and structure of general government expenditure on social protection in EU member states depend on the character of welfare states and the type of social protection?

Renata Halaskova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 007-021

1. LITERATURE REVIEW Public expenditure, which include public social expenditure, are quite important for all countries considering its effects and functions in the economy. A number of authors deal with public expenditure in broader context in their research (Shelton, 2007; Ferreiro at al., 2013; Szarowska, 2014; Leitner and Stehrer, 2016; Halaskova and Haláskova, 2017). Authors Leitner and Stehrer (2016) analysed the changes in public spending structures in the EU Member States over the period 1995 to 2013 based on data on government expenditure by function (COFOG) with a focus on the social expenditure categories health, education and social protection. Halaskova and Halasko- va (2017) carried out research into public expenditure in EU countries according to selected COFOG functions, namely social protection, health, education, culture and recreation, in years 2005 and 2014. By using of method multidimensional scaling, dissimilarities and similarities in the volume of allocated public resources in EU countries were evaluated with respect to COFOG functions (% of GDP in 2005 and 2014). Public expenditure is also dealt with from the viewpoint of their effectiveness and efficiency. Selected categories of public expenditure and human development index were analyzed by Aysu and Bakirtas (2016), who carried out efficiency analysis focused on the public education, health and social protection expenditure as inputs and human development index as output in OECD countries. Merickova et al. (2017) analyzed the relation between the levels and structures of public expenditure and the Human Development Index using the Data En- velopment Analysis (DEA) to identify countries that effectively use public spending to achieve the highest socio-economic development of society. Also public social expenditure is dealt with from different perspectives (Izak and Dufkova, 2006; Clemente et al., 2012; Bontout and Lokajickova, 2013; Sucur, 2016). In relation to the development and trends of general government expenditure on social protection Bontout and Lo- kajickova (2013) reviewed social protection expenditure developments in the crisis, focusing on expenditure trends in volumes following the peak of the crisis (2009), on changes in the distribution of incomes and, notably, on the distributional impact of austerity packages. Also Sucur (2016) examined the role of social protection and social expenditure in the financial and economic crises and analyzed the trends in social expenditure developments in EU countries since the beginning of the last economic crisis (2008). His results confirmed that social protection expenditure has increased in almost all EU countries since the beginning of the crisis and that in the crisis most countries rely on redistributive effects of the so-called automatic stabilizers (Sucur, 2016). The evaluation of social protection expenditure in connection to redistribution effect was dealt with by Goudswaard and Caminada (2010) or Wang et al. (2012), who analyzed the redistributive effect of social expenditure with focus on public and private social programs and social transfer programs and taxes. De Simone et al. (2010) addressed public social expenditure. These authors applied a more traditional analysis of convergence (sigma and beta convergence) in public social expenditure for a sample of 16 European OECD countries plus the USA and analyzed public social expenditure allocation pro classification of the countries by means of a multivariate approach. Social expenditure in relation to selected socio-economic indicators were dealt with by, for instance, Molina-Morales et al. (2013); Rozensky (2014); Visser et al. (2014); Kim and Kim (2017). The analysis the economic and institutional factors influencing, to a greater or lesser degree, social spending in the 27 European Union countries was performed by Molina-Morales et al. (2013). The level of social expenditure in 30 European countries, (EU 27 states, Norway, Iceland and Switzer- land) was analyzed by Rozensky (2014), who explains the development of social expenditure levels in the period 1990-2010 a tries to estimate its sensitivity to basic economic, social, political and institutional determinants. His results confirm the theory of socio-economic determination of the level of social expenditure. Social expenditure levels seem to depend on unemployment, GDP growth, population ageing, GDP per capita and the openness of an economy (Rozensky, 2014). Visser et al. (2014) investigated to what extent macro-economic circumstances and social protection expenditure affect economic deprivation. Their results of linear multilevel regression anal- 9

Renata Halaskova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 007-021 yses indicate that in countries that perform worse economically, individual experiences of economic deprivation are more prevalent, the stronger the rise in the unemployment rate and the lower a country's wealth, the more economic deprivation individuals experience.

2. DATA AND METHODOLOGY

2.1 Data Data were collected from the available database Eurostat - Government finance statistics and General government expenditure by function (COFOG). The Classification of the Functions of Gov- ernment (COFOG) classifies government expenditure into ten main categories (divisions known as the COFOG I level breakdown). The COFOG categories classify government expenditure by function of government. At the most aggregate level ten different categories are identified (European Com- mission, 2011; Leitner and Stehrer, 2016). Regarding the structure of general government expenditure by function (COFOG), attention is paid to general government expenditure on social protection (GF10), which include contributions and support provided for individual services to individuals and households, and expenditure on collective services. The structure of general government expenditure on social protection (COFOG 2nd level) is seen in Table 1.

Table 1. General government expenditure on social protection by groups (COFOG)

GF 10 Social protection GF1005 Unemployment GF1001 Sickness and disability GF1006 Housing GF1002 Old age GF1007 Social exclusion n.e.c. (not elsewhere classified) GF1003 Survivors GF1008 R&D Social protection GF1004 Family and children GF1009 Social protection n.e.c. (not elsewhere classified

Source: Eurostat (2018).

For the purposes of the analysis of general government expenditure on social protection was selected the period 2007-2016, with 2007 as the default year and 2016 as the last year when data on all EU member states were available. The object of the quantitative analysis is a set of 28 EU Member States, comprising: Belgium (BE), Bulgaria (BG), the Czech Republic (CZ), Denmark (DK), Germany (DE), Estonia (EE), Ireland (IE), Greece (EL), Spain (ES), France (FR), Croatia (HR), Italy (IT), Cyprus (CY), Latvia (LV), Lithuania (LT), Luxembourg (LU), Hungary (HU), Malta (MT), the Netherlands (NL), Austria (AT), Poland (PL), Portugal (PT), Romania (RO), Slovenia (SI), Slovakia (SK), Finland (FI), Sweden (SE), the United Kingdom (UK). The structure of general government expenditure on social protection (COFOG II level) represents the basis for the analysis of (Table 1) a) sickness and disability (GF 1001), b) old age and survivors (GF1002 + GF1003), c) family and children (GF1004), d) unemployment, housing and social exclusion (GF1005+GF1006+GF1007). The correlation analysis (the Pearson correlation coefficient) was applied to test correlations of the observed variables (selected groups of expenditure on social protection) in the period 2007-2016 for the following cluster analysis (Table 2).

Renata Halaskova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 007-021

Table 2. Intercorrelation matrix of variables (expenditure by groups) in EU Member States in period 2007-2016

GF1005 + GF 1001 GF1002 + F1003 GF1004 GF1006+

GF1007 GF 1001 1 GF1002 + GF1003 -0.142 1 GF1004 0.405* -0.061 1 GF1005 + GF1006+ 0.644** -0.066 0.594** 1 GF1007

Note: * Correlation is significant at the 0.05 level (2-tailed), ** Correlation is significant at the 0.01 level (2- tailed). Source: Author

2.2 Methods Key methods of the scientific paper are analysis, comparison and abstraction, in creation of the theoretical framework and the review of literature, content analysis and synthesis and partial induction in drawing conclusions are used. The analysis of similarities and differences between EU member states by groups of general government expenditure on social protection was performed through cluster analysis. Cluster analysis is a multi-dimensional statistical method used for classification of objects. It sorts units (EU member states the present example) into groups (clusters) so that units belonging in the same group are more similar then objects from other groups. Cluster analysis can be performed on a set of objects each of which must be described using an identical set of signs worth observing in the particular set, as well as a set of signs characterized through a specific set of objects - carriers of these signs. Hierarchic clustering was applied, which generates a system of sub-sets: branching, softening of the classification. Hierarchic clustering brings about a multitude of alternative solutions how to cluster objects on the basis of their distance or similarity, its outcome can be expressed by means of a dendrogram (Garson, 2014). In this article, the furthest neighbor method (the determining factor is the maximum distance between objects) was applied. The distances of objects are measured by squared Euclidean distance (Rezankova et al., 2009). A dendrogram indicates that the larger the size on the horizontal axis (x), the less similar, in the present case, the EU member states are. Conversely the smaller the distance on the x axis, the larger the similarity between countries. Box plot is one way of visualizing numerical data by means of their quartiles. The middle "box" part of the diagram is delineated by the third quartile from the top, and the first quartile from the bottom, whilst the mean is expressed by a line in-between. Box plots can also contain lines beginning in the middle part of the diagram vertically up and down, the so-called whiskers, which express variability of data below the first and above the third quartile (Pavlik, 2005). The interquartile range is the difference between the 75th and 25th percentile and is depicted by the length of the box. An outlier (indicated by the circle) is a value that is found between the 1.5 and 3 quartile range from the end of the box. An extreme value (indicated by an asterisk) is higher than 3 quartile range from the end of the box. Cluster analysis was applied in papers by, for instance, Fenger (2007); Skuodis (2009); Draxler and van Vliet (2010). The calculations in the following part are the output of the SPSS Statistics 24.0 software.

Renata Halaskova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 007-021

3. RESULTS AND DISCUSSION In the present analysis, the focus is on 1) the position and structure of general government expenditure on social protection in EU28, 2) the volume and structure of general government expenditure on social protection in EU member states, 3) similarities and differences between EU member states according to selected groups of general government expenditure on social protection using cluster analysis.

3.1 Position and structure of general government expenditure on social protection in EU-28 in period 2007-2016 General government expenditure on social protection account for the largest share in the structure of general government expenditure by function (COFOG). General government expenditure on social protection for EU-28 in 2007-2016 as the average accounted for 18.9% GDP. The next most important areas were health (7.1%), general public services such as external affairs and public debt transactions (6.6%), education (5.0%) and economic affairs (4.5%). Public order and safety (1.8%), defense (1.4%), recreation, culture and religion (1.1%), environmental protection (0.8%) and housing and community amenities (0.8%) had more limited weights. However, these EU-level data mask significant differences between the Member States in the share of GDP dedicated to each function of general government expenditure (Figure 1).

Figure 1. Social protection in the structure of general government expenditure (COFOG) in EU28, the average of 2007-2016

Source: Author based on Eurostat (2018).

General government expenditure on social protection include benefits and support provided for services of individual nature to individuals and households and expenditure on services of collective nature. These divisions are further broken down into groups (COFOG II level). All benefits and support on services of individual nature are classified by COFOG into groups 10.1 through 10.7 (GF1001 Sickness and disability, GF1002 Old age, GF1003 Survivors, GF1004 Family and children, GF1005 Unemployment, GF1006 Housing, GF1007 Social exclusion n.e.c.). Expenditure on service of collective nature are classified in groups 10.8 and 10.9 (GF1008 R&D Social protection, GF1009 Social protection n.e.c.) (European Commission, 2011). Total general government expenditure on social protection and by groups (COFOG II level) in the EU over the period 2007- 2016 is captured in Table 3.

Renata Halaskova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 007-021

Table 3. General government expenditure on social protection total and by groups as % of GDP, EU-28

Government expenditure on social protection by groups (COFOG II level) 10

year Total ex- tection GF social pro- penditure on GF 1001 GF 1002 GF 1003 GF 1004 GF 1005 GF 1006 GF 1007 GF 1009

2007 17.0 2.5 8.9 1.3 1.7 1.4 0.4 0.6 0.3 2011 19.0 2.7 10.0 1.1 1.8 1.5 0.5 0.8 0.3 2016 19.1 2.7 10.2 1.0 1.7 1.3 0.5 0.5 0.3

Note: GF1001 Sickness and disability, GF1002 Old age, GF1003 Survivors, GF1004 Family and children, GF1005 Unemployment, GF 1006 Housing, GF1007 Social exclusion, GF1009 Social protection n. e. c., GF 1008 R&D Social protection is not stated in the table (null values). Source: Eurostat (2018).

The most significant group of general government expenditure on social protection (COFOG II level) in the EU average is the old age, with 10.2% of GDP in 2016. Other significant groups of social protection are sickness and disability, 2.7% GDP, and family and children, 1.7% GDP. Set be- side the expenditure on old age, expenditure on sickness and disability reach around 1/3 and on family on children around 1/5 of it. Other groups of expenditure on services of individual nature (unemployment, survivors, housing, social exclusion) and expenditure on collective services (social protection n.e.c.) accounted for 0.3 to 1.5% GDP, and showed a stable level over the period 2007- 2016.

3.2 VOLUME AND STRUCTURE OF GENERAL GOVERNMENT EXPENDITURE ON SOCIAL PROTECTION IN EU MEMBER STATES, 2007-2016 The form of financing social protection in EU member states is evaluated simultaneously from three perspectives each of which respects 1) the volume of public expenditure on social protection expressed in % of GDP, 2) the share of social financial contributions, 3) the structure of sources of financing (Spicker, 2014). The volume of general government expenditure on social protection and the share of general government expenditure by groups in 2007-2016 as average of the 28 EU member states is captured in Table 4.

Table 4. Total general government expenditure on social protection and by groups in the EU Mem- ber States as % of GDP, average 2007- 2016

Government expenditure on social protection by groups (COFOG II level) 10

country Total ex- tection GF social pro- penditure on GF 1001 GF 1002 GF 1003 GF 1004 GF 1005 GF 1006 GF 1007 GF 1009

BE 19.0 2.9 8.3 1.9 2.3 2.3 0.2 1.0 0.2 BG 12.4 0.6 8.8 0.0 2.2 0.1 0.2 0.1 0.5 CZ 12.8 2.4 7.4 0.6 1.3 0.3 0.2 0.4 0.2 DK 23.7 5.0 7.8 0.0 4.9 3.0 0.7 1.7 0.6

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DE 19.1 2.9 9.3 1.9 1.6 2.2 0.2 0.4 0.7 EE 12.6 2.1 6.9 0.1 2.1 1.1 0.0 0.1 0.2 IE 14.4 2.9 5.2 0.9 2.0 1.8 0.9 0.7 0.1 EL 19.2 1.6 14.1 1.6 0.7 0.8 0.1 0.1 0.1 ES 16.3 2.3 8.0 2.2 0.7 2.5 0.1 0.2 0.3 FR 23.6 2.7 13.0 1.5 2.5 1.8 0.9 1.0 0.2 HR 15.0 2.0 8.6 1.4 2.1 0.5 0.0 0.0 0.5 IT 20.0 1.8 13.2 2.6 1.1 0.9 0.0 0.2 0.0 CY 12.4 0.6 5.1 1.3 2.3 0.8 0.0 2.1 0.1 LV 11.6 2.0 7.1 0.2 0.9 0.5 0.1 0.4 0.3 LT 12.3 3.1 6.1 0.4 1.4 0.6 0.1 0.5 0.1 LU 18.0 1.7 10.3 0.0 3.5 1.6 0.0 0.7 0.1 HU 16.5 3.4 7.4 1.2 2.2 0.6 0.5 0.9 0.3 MT 13.4 1.5 7.7 1.7 1.1 0.5 0.2 0.3 0.4 NL 16.2 4.6 6.3 0.2 1.1 1.6 0.4 1.9 0.0 AT 20.9 1.9 12.5 1.5 2.5 1.4 0.1 0.9 0.2 PL 16.2 2.7 9.2 1.7 1.4 0.7 0.1 0.3 0.1 PT 17.4 1.4 11.1 1.7 1.2 1.2 0.1 0.3 0.6 RO 11.9 1.0 8.9 0.1 1.1 0.2 0.0 0.2 0.4 SI 17.4 2.4 9.5 1.5 2.1 0.7 0.0 0.7 0.4 SK 14.7 2.4 7.1 0.9 1.3 0.2 0.0 0.5 2.3 FI 23.2 4.2 11.0 0.8 3.2 2.3 0.3 0.8 0.6 SE 20.7 4.7 10.3 0.4 2.5 1.4 0.3 1.0 0.1 UK 16.2 2.4 8.4 0.1 2.0 0.3 1.3 1.5 0.3

Note: GF1001 Sickness and disability, GF1002 Old age, GF1003 Survivors, GF1004 Family and children, GF1005 Unemployment, GF1006 Housing, GF1007 Social exclusion, GF1009 Social protection n. e. c., GF 1008 R&D Social protection is not stated in the table (null values). Source: Author based on Eurostat (2018).

In EU member states, differences can be observed in not only the volume but also the structure of general government expenditure on social protection by groups (Table 4). The highest total government expenditure on social protection (over 23% GDP) was allocated by DK, FI and FR, as opposed to countries with the lowest general government expenditure on social protection, where belong LV and RO with less than 12% GDP followed by BG, LT (12.4% GDP) or EE (12.6% GDP). Consequently, RQ1 has been confirmed, namely that the largest differences in the volume of general government expenditure on social protection exist between Nordic countries (DK, FI) compared to Eastern European countries (BG, RO) and Baltic states (LV, LT, EE). RQ2 tested whether the dominant share in the structure of general government expenditure on social protection is represented by expenditure on old age, followed by sickness and disability. The results showed that the dominant in the structure of general government expenditure on social protection (COFOG II) in EU member states in the period 2007-2016 as average is the group old age (GF1002) in all EU countries, followed by sickness and disability (GF1001). The share of expenditure on old age and sickness and disability accounts for more than a half of government expenditure on social protection in all countries. Compared to other EU countries, it can be seen, 14

Renata Halaskova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 007-021 however, that DK, FI, SE, LU, FR, AT allocated also a higher share of expenditure as % of GDP on family and children (GF1004) and DK, FI, ES, BE, DE also a higher share of expenditure on unemployment (GF1005). First part of RQ2, whether expenditure on old age constitute the dominant part in the structure of general government expenditure on social protection, has been proven in all 28 EU member states, second part of RQ2, that expenditure on sickness and disability are the second most dominant in the structure of social protection, has been proved partially (in 19 out of 28 EU member states).

3.3 SIMILARITIES AND DIFFERENCES BETWEEN EU MEMBER STATES BY GENERAL GOVERNMENT EXPENDITURE ON SOCIAL PROTECTION USING CLUSTER ANALYSIS Applying the method of cluster analysis and box-plot, 28 EU member states are analyzed by selected groups of general government expenditure on social protection (sickness and disability, old age and survivors, family and children, unemployment, housing and social exclusion) in period 2007-2016 as average. The 28 EU member states by the evaluated groups of general government expenditure on social protection is seen in Figure 2. The dendrogram (Figure 2a) shows the results of the cluster analysis, and the box-plot (Figure 2b) presents the division of countries into four clusters by similarity (dissimilarity) of the evaluated groups of government expenditure on social protection.

Figure 2. EU member states by selected groups general government expenditure on social protection as average in 2007-2016

a) Dendrogram b) Box-plot

Source: Author

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Cluster 1 – comprises eleven countries (BE, DE, ES, HR, LU, HU, PL, SI, FI, SE, UK). Identical internal similarity is seen in the pairs of countries FI and SE, PL and HR, BE and ES, a lower internal similarity is seen between SI and LU, or HU and DE. In the structure of government expenditure on social protection, these countries attach primary importance to expenditure on old age and survivors as % of GDP, with the median 10.30. Among the most significant expenditure in the structure of government expenditure on social protection is expenditure on unemployment, housing and social exclusion (median 2.70). Compared to other clusters in the structure of government expenditure on social protection, these countries also have a higher share of expenditure on family and children (with the median value 2.10 as % GDP). Concerning expenditure on sickness and disability, SE, with 4.7% GDP, represents an outlier of expenditure on old age and survivors (outliers HU, 8.6% GDP and UK 8.5% GDP), and concerning expenditure on family and children, the outliers are ES, 0.7% GDP, and LU, 3.5% GDP. Cluster 2 – comprises nine countries (BG, CZ, EE, CY, LV, LT, MT, RO, SK). Identical internal similarity is seen between CZ and SK, EE and LT, MT and BG. A lower similarity is seen between LV and CY. These are countries with the lowest volume of general government expenditure. In the structure of government expenditure on social protection, the prominent group is expenditure on old age and survivors as % GDP (median value 8.0), followed by expenditure on sickness and disability (median 2.0 as % GDP). By contrast, the lowest share in the structure of social protection expenditure as % of GDP is expenditure on unemployment, housing and social exclusion (median 1.0), even when compared to other clusters of countries. The largest dispersion is seen in values of expenditure on sickness and disability as % of GDP, from 0.6 in BG, CY through 3.1 in LT, and expenditure on old age and survivors, from 6.4 in CY through 9.4 in MT. As regards the aggregately evaluated expenditure on unemployment, housing and social exclusion, CY represents the outlier, with 2.9% GDP. Cluster 3 – consists of three countries: DK, IE, NL. A higher internal similarity is seen between IE and NL, lower between IE and DK. In the structure of government expenditure on social protection, expenditure on old age and survivors have a significant share (median 6.50), expenditure on sickness and disability (median 4.60) and expenditure on unemployment, housing and social exclusion as % of GDP. The widest dispersion of values is seen in expenditure on family and children as % of GDP, ranging from 1.1 in NL through 4.9 in DK. Compared to other clusters, countries from the third cluster show the lowest volume of social protection expenditure on old age and survivors as % of GDP, but the highest representation of aggregately evaluated expenditure on unemployment, housing and social exclusion (median value 3.90 as % of GDP). Cluster 4 – is composed of five countries (EL, FR, IT, AT, PT). Identical internal similarity is seen between FR and AT, EL and PT, lower between FR and PT. These countries show the highest volume of government expenditure on old age and survivors as % of GDP (the median 14.50), along with a low volume of expenditure on family and children as % of GDP (median 1.20) compared to other clusters. The widest dispersion of values is seen in expenditure on old age and survivors as % of GDP, from 12.8 in PT through 15.7 in EL, and expenditure on unemployment, housing and social exclusion as % of GDP, from 1.0 in EL through 3.7 in FR. The outlier regarding expenditure on sickness and disability is FR (2.7% GDP). Table 5 shows median values of general government expenditure on social protection (COFOG II level) by selected groups in clusters EU member states. Results of the cluster analysis and box-plot, on dividing the EU member states into four clusters, showed similar features, but also marked differences by groups of general government expenditure on social protection (Figure 2, Table 5). If focusing on the similarities between EU member states in the structure of general government expenditure on social protection by the evaluated groups, the results showed that the largest similarity in the volume of general government expenditure on social protection is seen in countries of the second and fourth cluster in expenditure on sickness and disability (GF 1001), expenditure on family and children (GF 1004) and partially also in terms of aggregately evaluated expenditure on unemployment, social exclusion and housing. 16

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Table 5. Median of groups of general government expenditure on social protection in 2007-2016 as average of EU member states by clusters

Median /Expenditure on social protection by selected groups (as % of GDP) Country by GF1005+ GF1006+ cluster GF 1001 GF1002 + GF1003 GF1004 GF1007 cluster 1 2.70 10.30 2.10 2.70 cluster 2 2.00 8.00 1.30 1.00 cluster 3 4.60 6.50 2.00 3.90 cluster 4 1.80 14.50 1.20 1.60

Note: GF 1001 Sickness and disability, GF 1002 Old age, GF1003 Survivors, GF 1004 Family and children, GF 1005 Unemployment, GF 1006 Housing, GF 1007 Social exclusion. Source: Author

RQ3 verified whether the differences in the volume and structure of general government expenditure on social protection in EU member states depend on the character of welfare states and the type of social protection. From results of analysis of general government expenditure on social protection in the EU member states showed the most marked differences in the volume of expenditure on sickness and disability between countries of the third cluster (DK, NL - Nordic model) compared to the countries of the fourth cluster (EL, IT, PT - Mediterranean (Southern European) model). The most marked differences in the volume of expenditure on old age and survivors were proven between two countries of the second cluster (CY- Mediterranean (Southern European model), LT - Central/Eastern European model), compared to the countries of the fourth cluster (FR, AT - Continental (Bismarckian) model). Differences in the volume of aggregately evaluated general expenditure on unemployment, housing and social exclusion show also countries of the second cluster (BG, RO, SK, CZ - representing the Central/Eastern European model with the volume of expenditure below 1% GDP) compared to countries of the third cluster (DK, NL representing the Nor- dic model, with the volume of expenditure between 3.9-5.5% GDP). Table 6 captures results of cluster analysis of EU member states by selected groups of general government expenditure on social protection and the placement of EU member states by clusters into social welfare models - Continental (Bismarck), Anglo-Saxon, Nordic, Mediterranean (Southern European), Central/Eastern European. The division of countries by specific features into five European social models was also used in papers (Social Welfare Systems Across Europe (without year); Izquierdo and Gonzalez, 2017; Scaratti et al. 2018).

Table 6. EU Member States by general government expenditure on social protection (results of cluster analysis) and welfare state models

EU Member States by selected EU Member States by results of cluster analysis and by welfare groups of general government state models (European social models) expenditure on social protection

(results of cluster analysis ) Nordic model - FI, SE First cluster: BE, DE, ES, HR, LU, HU, Continental (Bismarckian) model – BE, DE, LU PL, SI, FI, SE, UK Central/Eastern European model - HR, SI, HU, PL Mediterranean (Southern European model) - ES Anglo-Saxon model - UK 17

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Second cluster: Central/Eastern European model - BG, CZ, EE, LV, LT, RO, SK BG, CZ, EE, CY, LV, LT, MT, RO, SK Mediterranean (Southern European ) - CY, MT The third cluster: Nordic model - DK, NL DK, IE, NL Anglo-Saxon model - IE Fourth cluster: Continental (Bismarckian) model - AT, FR EL, FR, IT, AT, PT Mediterranean (Southern European ) model - EL, IT, PT

Source: Author and Social Welfare Systems Across Europe (without year)

The results partially confirmed RQ3. Differences in the volume and structure of general government expenditure on social protection have been confirmed, but they do not represent a particular social model in all EU member states. It can thereby be argued that EU member states can not be clearly placed in a specific social model merely by the volume and structure of allocated public social expenditure. On the other hand, the same share of public social expenditure from GDP can have a different bearing in two different countries (priorities of countries in respective policy areas) and can also be an expression of another institutional structure of a welfare state. In relation to the results, Sapir (2006) claims the concept of the European social model does not apply everywhere in Europe. Regardless of any differences, the models are designed to protect people against the risks related to unemployment, parental responsibilities, health care, old age, housing and social exclusion. The Members states are responsible for organizing and financing their social protection systems. Also Mossuti and Asero (2012) argue that differences between countries expenditure levels partly reflect diverse levels of wealth, but also diversity in social protection systems, welfare policy, demographic trends, unemployment rates and other social, institutional and economic factors and specificities of each country. The differences that can be observed in these areas across Europe are often significant. By contrast, Buhigas Schubert and Martens (2005) argue that the social and welfare models applied in the European countries also have common characteristics (similarities): emphasis on social protection, ex-post benefits for traditional risks/needs, large role for ‘passive’ transfers during non-employment (pensions, unemployment, disability, sickness, maternity, family dependents etc.), residual safety nets (against poverty).

CONCLUSION Economists discuss the limits of the share of public social expenditure from GDP which is still bearable for national economies. The range of public social expenditure from GDP differs across countries depending on the rate of the public sector, range of tax burden and the level of redistribution. Public social expenditure is a sum of structured expenditure, where a marked increase of one item may not necessarily lead to significant changes in social policy. The article aimed to evaluate the volume and structure of general government expenditure on social protection, accentuating similarities and differences in the selected set of 28 EU member states. The results of the research into EU member states showed similarities and differences in the volume and structure of general government expenditure on social protection. On the basis of the defined research questions, the results confirmed the largest differences in the volume of general government expenditure on social protection between Nordic countries (DK, FI) compared to Eastern European countries (BG, RO) and Baltic states (LV, LT, EE). In terms of the structure of general government expenditure on social protection, the most significant share in all EU member states represent expenditure on old age and in most countries expenditure on sickness and disability. Results of the cluster analysis of EU member states revealed the most marked differences in the volume of government expenditure on old age and survivors between countries of the second

Renata Halaskova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 007-021 cluster (CY, LT- the lowest expenditure), compared to the countries of the fourth cluster (FR, AT- the highest expenditure), and the largest differences in the volume of government expenditure on sickness and disability between countries of the third cluster (DK, NL – the highest expenditure), compared to the countries of the fourth cluster (EL, IT, PT - the lowest expenditure). Nevertheless, these differences between groups of expenditure on social protection failed to confirm for all EU member states representing the respective welfare state and social model. Apart from the level and structure of expenditure on social protection, the topic for further research can also be an evaluation of expenditure on social protection with an accent on priorities of selected policies (pension, family, employment and housing policy) or evaluation of general government expenditure on social protection and selected indicators of welfare state (GDP per capita, income inequality, unemployment rate, poverty rate).

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Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043

Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 023-043 ‘

Methodical Approach to the Assessment of Risks Connected With the Legalization of the Proceeds of Crime

NADIYA KOSTYUCHENKO1, МYKOLA STARINSKYI2, INNA TIUTIUNYK3 and IANA KOBUSHKO4

1 Associate Professor of the Department of International Economic Relations, Sumy State University, Sumy city, Ukraine, e-mail: [email protected] 2 Associate Professor, Sumy State University, Head of the Department of Civil Law and Financial Law, Sumy city, Ukraine, e-mail: [email protected] 3 Senior Lecturer of the Department of Finance and Entrepreneurship, Sumy State University, Sumy city, Ukraine, e-mail: i.karpenko@ finance.sumdu.edu.ua 4 Senior Lecturer of the Department of Management, Sumy State University, Sumy city, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 07, 2018 The aim of the research is to develop a scientific and methodical Revised from August 21, 2018 approach, which consists in the timely identification of systemic Accepted November 12, 2018 risks connected with the legalization of the proceeds of crime, their Available online December 15, 2018 formalization, assessing and monitoring in order to prevent these transactions occurrence. The article analyzes the state of the Ukrainian economy in terms of its level of shadow economy, the JEL classification: corruption index, and the amount of illegal withdrawal of funds from G17; H30; K22. the country. The key ways and methods of money laundering in Ukraine are defined. The components of the process of illegal with- DOI: 10.14254/1800-5845/2018.14-4.2 drawal of funds abroad have been studied as well as key drivers and methods of shadow economy have been identified. The theoretical foundations of the development of the of formalization and Keywords: assessment model of systemic risks of hiding proceeds are high- withdrawal of funds, lighted. A model for assessment of the risk occurrence probability of money laundering, the legalization of proceeds of crime is developed. The risk occur- combating money laundering, rence probability of the legalization of proceeds with the participa- proceeds of crime, tion of Ukraine and partner countries was assessed. risk

INTRODUCTION To date, the problem of legalization of the proceeds of crime is a challenge for the financial and economic stability of many countries. Stable activity of the financial system of the country is possible through providing the effective mechanisms and measures on deterring and combating money laundering at the level of different agents of the national economy and financial institutions (Lyeonov at al., 2018; Vasylieva at al., 2018; Polko, 2016). Recently, there is the process of bringing the existing legislative and regulatory framework in compliance with the requirements of the international standards of the national system for combating the legalization of the proceeds of crime and terrorism financing. 23

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043 In order to determine the areas of activity and channels for the financial flows transferring, where the probability of money laundering and terrorism financing is the highest one, it is necessary to analyze and assess the risk of these transactions (Buriak at al., 2015). The risk assessment approach will involve identifying and monitoring of the potential risks of legalization of the proceeds of crime, that will allow to prevent the money laundering processes (Lazanyi et al., 2017). The implementation of a risk assessment approach is possible due to the cooperation of compe- tent authorities and financial institutions (Vasylyeva at al., 2014).

1. PROBLEM DEFINITION The financial and economic crisis of 2008-2009, which has affected its negative consequences all over the world, has demonstrated the need to improve the system of protecting from illegal operations (Alikariev et al., 2018). Based on the results of the analysis that was carried out by the International Anti-Money Laundering Group, the annual amount of money laundering is 700 billion - 2 trillion US$, that on average is equal to 2-5% of world GDP and is said to cause insignificant impact on the indicators of socio-economic development of the society (Report on money laundering methods typology in 2002-2003, 2013). However, despite the fact that the average world level of hiding proceeds does not exceed the conventionally safe value, this indicator is critical for some countries (Table 1) and reflects rather developed and extensive system of shadow relations.

Table 1. The shadow economy level of the countries of the world, 2016

Country The economy shadow level (% of GDP) Azerbaijan 60,04 Ukraine 52,2 Nigeria 48,37 The Russian Federation 39,07 Sri Lanka 37,76 Brazil 34,76 Pakistan 31,78 Spain 17,2 Germany 10,4 Australia 9,4 Canada 9,8 China 8,1 Switzerland 8,4 Japan 8,6 The USA 5,4

Source: Angelko, 2011

According to the above data, in Ukraine, as a result of deep economic and political crisis, the number and amount of the schemes used for illegal withdrawal of capital from the country has increased significantly, the level of economic crime has increased. According to the index of hiding proceeds, the country ranks second and has more than 50% of the total volume of added value of hidden proceeds (Figure 1). According to F. Schneider's (2015) calculations, the EU average shadow economy level in 2014 was 18.6 % of GDP, 10.8 % in France, 12.2 % in Germany, and 23.5 % in Poland respectively. At the end of the first decade of the 21st century, Ukraine's economy becomes one of 15 most shadow intensive economies (Vasylyeva at al.., 2014) with an indicator 17 % higher than the world average shadow economy level, 41.2 % higher than the minimum level of the shadow economy level in Switzerland, but 16.4 % less than the highest indicator of the shadow economy level in the world from 1999 through 2007, which was recorded in Bolivia.

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043

Figure 1. Integral indicator of the shadow economy in Ukraine (in % of the official GDP) and real GDP growth/reduction rate (in % of the corresponding period of the previous year)

Source: National risk assessment report on preventing and countering legalization (laundering) of proceeds of crime and financing of terrorism, 2016

The graph (Fig. 2) shows that Ukraine is on the right side of normal distribution (see the red vertical line), which is another illustrative example of the abnormal situation related to the national shadow economy. According to the experts of Global Financial Integrity, illegal financial flows are the illegally earned capital, transferred or used, which includes all unaccounted private financial flows that lead to the accumulation of foreign assets by residents that violates the existing regulatory framework and limits of capital controls (Dev and Sarah, 2011). However, a significant component of the hidden withdrawal of capital is formed through legal activity that is possible due to the gaps in legislation. In general, the illegal withdrawal of funds from developing countries made to 7,28 trillion dollars during the period from 2000 to 2008, with an average annual illegal withdrawal from 725 billion to 810 billion dollars per year for the period 2000-2008 (Leonov at al., 2013). Most of the illegal flows come from Asian countries - almost half a trillion dollars in 2008. For nine years, from 2000 to 2008, the cumulative flows were: China - 2.2 billion dollars, Malaysia - 291 billion dollars, Philippines - 109 billion dollars, and Indonesia and India - 104 billion dollars (Nelson, 2017). Ukraine is included in the TOP-20 countries, whose economy suffers from illegal withdrawal of capital.

Figure 2. World Economies’ Shadow Economy Level Probability Distribution Density

Source: National risk assessment report on preventing and countering legalization (laundering) of proceeds of crime and financing of terrorism, 2016 25

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043 The shadow economy index in Ukraine is at a sufficiently high level, which reflects the fundamental gaps in public policy. The largest share in the overall structure of money laundering is occupied by transactions of illegal withdrawal of funds abroad - 27 billion UAH or 60% of the total volume (Figure 3). According to the results of 2016, extractive industry, real estate transactions, recycling industry, financial and insurance activities are the main fields of the economy where the various schemes of hiding proceeds were used the most (Morscher et al., 2017).

Figure 3. The most widespread money laundering schemes in Ukraine (billion UAH)

Source: The most widespread money laundering schemes connected with the operations on the illegal withdrawal of money abroad and investment from offshore zones

Money laundering has both macro and micro levels. There are three main stages of money laundering on macro level: placement, layering and integration (Figure 3). Placement: Financial resources are deposited into financial institutions or converted into nego- tiable instruments for example money orders or traveler’s checks. The main aim of placement is to remove the cash from the location of acquisition in order to conceal from the authorities and transform into other asset forms. Layering: includes the separation of proceeds from the illegal source through the use of complex transactions designed to obscure the audit trail and hide the proceeds. This stage can include the transfer of money from one bank account to another, from one bank to another, from one country to another, or any combination thereof. For example, money can be moved into and out of various offshore bank accounts through Electronic Funds Transfers. Layering is the most international and complex step of the money laundering cycle because funds are typically moved from one foreign account to another. Considering the difficulty in obtaining account information to identify owner, using shell corporations and offshore banks is the most frequently way of money laundering at this stage. Integration: represents the conversion of illegal proceeds through financial or commercial operations. Integration creates the illusion of a legitimate source for criminally derived funds and involves techniques by legitimate businesses to increase profit and reduce tax liability. Integration includes producing false invoices for goods purportedly sold by a firm in one country to a firm in another country, using funds held in a foreign bank as security for a domestic loan, commingling in bank accounts of companies earnings and illegitimate income, and purchasing property to create the illusion of legal proceeds upon disposal (Tiutiunyk, 2017; Sroka and Vveinhardt, 2018) In recent years, a tendency of reducing the level of shadow economy has been observed. Ac- cording to the official data of the Ministry of Economic Development and Trade of Ukraine, the level of shadow economy is 34% of the official GDP (European Business Association). Despite the positive trend of reducing the shadow economy, there is a number of potential risks that may contrib- 26

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043 ute to growing of the shadow economy. First of all, they are associated with a possible recession in the EU and significant financial commitments of Ukraine in foreign markets, leading to a sharp slowdown in economic growth of Ukraine. There are a number of factors that influence on money laundering both directly and indirectly around the world. In general, they can be classified into 6 groups: political, social, technological, environmental, legislative, and economic. In more detail, their structure is shown in Figure 4.

Figure 4. The factors of money laundering

Source: Ayodegi, 2011.

Money laundering and the financing of terrorism are global problems that not only threaten security (Siemiątkowski, 2017), but also compromise the stability, transparency and efficiency of financial systems (Logan et al., 2017), thus undermine economic prosperity. It was considered as a threat to the integrity of financial and commodities markets, undermines fair inside and outside competition, distorts financial, material and nature resource allocation, destroys public trust and undermines the rule of law. If growth rates of shadow revenues outpaced the official GDP growth rate, this could lead to an underestimation of the real need of the economy in money, an increase in investment risks, a decrease in investment activity and, as a result, a reduction in the supply of investment resources (Bhowmik, 2018). Shadow activity limits the ability of the entrepreneurs to attract investment resources, especially foreign ones (Bobenic Hintosova, 2018). Expansion of the shadow sector stimulates the growth of speculative financial, trade and intermediary transactions to the detriment of 27

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043 the development of real production. The growth of the shadow sector is characterized by concealing the sources of investment. Thus, Ukrainian investors during the crisis insure against investment risks. It should also be noted that the “shadow” accelerates the process of concentration of capital, which is extremely important for countries with market economies. While analyzing the impact of money laundering on developing economies (Bartlett, 2002), five directions of money laundering flows in such economies were specified. First, domestic flow, in which illicit domestic funds are laundered within the country. Second, returned flow means criminal activities occurred in the developing country, fund placed outside the country and later on integration occurred in the developing country. Third, inbound funds, for which the predicate crime occurred abroad, is either initially laundered abroad or within the developing country, and ultimately is integrated into the developing economy. Fourth, outbound funds, constitutes laundered fund originated in the developing country and integrated outside the economy or capital flight. Fifth, flow-through funds originates and integrates in the developed country, by using the financial institution of developing country during the period of layering. The sources of illegal financial flows also include the proceeds in the form of tax evasion and manipulation of prices in foreign economic transactions (i.e. transfer pricing, which is the most significant component of such flows - Yakubovskiy and Rodionova, 2014). The shadow outflow of proceeds abroad is due to a violation of the current tax legislation (for example, without payment of relevant corporate taxes or violations of the rules of currency control and regulation, etc.).

Analysis of the data of the State Committee for Financial Monitoring 2004-2006 showed that popular methods of money laundering in Ukraine are as follows:

 operations on illegal withdrawal of funds abroad;  operations with use of lost passports;  operations with precious (banking) metals;  operations connected with illegal refunds of value added tax;  operations on illegal conversion of funds into cash (“conversion centers”);  smuggling and crimes against property;  drug-related crimes;  foreign economic operations (cargo customs declarations with signs of forgery, exports at in- flated prices or non-existent companies);  securities transactions;  real estate transactions (compiled by the authors based on the data of the State Financial Mon- itoring Service of Ukraine, Typology of legalization of proceeds of crime, 2003).

Despite the existence of a significant range of hiding proceeds channels, for Ukraine the most widespread ones are tax channels. Their purpose is to minimize the amount of tax liabilities. To date, there is significant number of ways to hide proceeds through tax channels in Ukraine, namely: the maintenance of two sets of books; the occurrence of accounting errors; understatement of profits; hiding part of assets. In addition, the operations on the withdrawal of profits abroad by distributing business processes between business entities of different countries are quite widespread. As a rule, the production processes are carried out on the territory of Ukraine, and financial operations and operations on assets management - at foreign enterprises abroad. The process of distribution of proceeds and expenditures of these enterprises is constructed in such a way that the maximum part of proceeds is accumulated abroad. In addition to the above mentioned scheme of hiding proceeds, some business entities use mechanisms aimed at obtaining VAT refunds through illegal operations with hiding proceeds (Fi- gure 5), which can result in increasing VAT gap in a country (Majerova, 2016).

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043

Figure 5. Algorithm of illegal VAT refunds during carrying out the operations on hiding proceeds through tax channels

Source: Typology of legalization of proceeds of crime, 2003

While studying the global competitiveness of 2017-2018, it is determined that the effectiveness of the tax system of Ukraine ranks 81 among 148 countries (World Economic Forum, 2017). The use of optimization schemes facilitates the development of channels for the withdrawal of funds from the national economy and it is a sufficiently flexible and universal tool for companies of various types of activities. Thus, the payment of interests for the use of loans from controlled foreign companies can become a convenient channel for domestic business entities for the hidden withdrawal of capital from the country. If foreign company is located in a country with which Ukraine has signed an agreement on avoidance of double taxation, and this country is not included in the list of offshore zones of the Cabinet of Ministers, the interests for using such loan belong to the expenditures of the Ukrainian company, which, accordingly, allows to minimize income tax liabilities. There are almost no barriers to the hidden withdrawal of capital in Ukraine and that is why the profits are withdrawn from the country almost without any interference. In some countries, in order to prevent illegal withdrawal of capital, there are created the conditions when the legal transfer of profits to “tax havens” is accompanied by additional payments to the budget and expenditures, and illegal – by risks of prosecution. Investment flows in Ukraine, in addition to their common purpose, are often a chain of the schemes of hidden withdrawal of capital. The prominent example is the large volume of investments from Cyprus, which amount to more than 32% of the total volume of foreign direct investment in Ukraine. According to the data of the State Statistics Service, Cyprus accounts for the largest share - 19 billion USD of 58,2 billion USD foreign direct investment attracted during all the years of Ukraine’s independence since 1991. Only in 2013 the investors, who were registered in Cyprus, invested 1.8 billion USD of 3.7 billion USD of foreign direct investment (3,9 billion USD of 4,1 billion USD in 2012). The experts’ evaluations of Tax justice network (The Financial Secrecy Index, 2018), certify that there are about 80% of the volume of trade in financial services in the world market per ten jurisdictions, which according to “The Financial Secrecy Index” have the highest indicators of financial secrecy. More than 50 % of bank assets flow through a jurisdiction that has a high degree of secrecy (Dheera-aumpon, 2017). According to the data of Stolen Asset Recovery Initiative almost all large multinational companies use jurisdictions with high degree of secrecy in order to minimize the tax base for corporate taxes (The Stolen Asset Recovery Initiative - StAR). Financial systems in developing countries are more likely to accumulate and implement the systemic risks, despite the small size and complexity of their financial systems in comparison with the developed countries. The level of the financial stability risk in the country depends on the behavior of many participants in the financial system (Sysoyeva and Buriak, 2014). 29

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043 Taking into account the constant systemic interaction of economic entities in the domestic and foreign markets of resources, goods and services and financial markets, and the consequences of these interrelationships in the form of synergistic explicit and implicit effects, we have determined the drivers and methods of shadow economy. The main components of the illegal withdrawal of funds abroad are shown in Figure 6.

Figure 6. Structural and logical scheme of formation of shadow financial flows

Source: compiled by the authors 30

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2. MODELS OF ASSESSMENT OF RISK OCCURRENCE PROBABILITY OF LEGALIZATION OF PROCEEDS OF CRIME: CRITICAL ANALYSIS AND PROSPECTS OF APPLICATION It is a difficult task to develop a unified approach to the assessment of the risk occurrence probability of the legalization of proceeds of crime, due to the multifaceted and multichannel phenomenon of the shadow economy. Since the factors and causes of the shadow economy differ significantly in each country, the determination of the scale of illegal activity depends on the features of the economic system functioning. So, if in the developed countries the shadow economy is a defined criminal activity and makes only a small share of the illegal operation, then in countries with transformational economy (including Ukraine) this is the share of the legal economy, without which the official economy functioning is impossible. The Bureau of International Narcotics and Law Enforcement Affair developed the classification of countries, depending on the effectiveness of measures to combat the legalization of proceeds of crime and terrorism financing:  countries that have “minor shortcomings” in systems of combating money laundering and try to eliminate them (Afghanistan, Austria, Burma, Great Britain, Canada, China, France, Germany, Iran, Italy, Nigeria, Russia, the USA, Turkey, Switzerland, Japan, Ukraine);  countries that have “minor shortcomings” in systems of combating proceeds legalization and do not take measures to minimize them (Azerbaijan, Belgium, Chile, Hungary, North and South Ko- rea, Moldova, Poland, Portugal, Serbia, Syria, etc.)  countries that have a lot of shortcomings and do not try to eliminate them (Cameroon, Cuba, Estonia, Macedonia, Nepal, Norway, Slovenia, Sweden, etc. - Koldovsky, 2008)

Table 2 shows the comparative characteristics of countries in terms of index of combating the legalization of proceeds of crime.

Table 2. Comparative characteristics of the countries in the context of combating the legalization of proceeds of crime

Country The USA Great Britain China Russia Ukraine The declared participation in combating money + + + + + laundering $10 000 cash payment, Limit amount of funds $100 000 ($500 000) for for carrying out the op- $10 000 $20 000 $11000 $3000 private persons/legal eration entities - non-cash Centralized management not all aspects + + - + at the state level are covered Approaches to the organ- Identification of the principle principle ization of the anti-money operations adding the control of control of “know your “know your laundering system principle “know your operations operations customer” customer” customer” The part of the anti- money laundering organ- FATF, MON- FATF, APG FATF EAG, ОGBS MONEYVAL ization EYVAL, EAG

Source: Koldovsky, 2008

The results of the table confirm the argument concerning a significant differentiation of tools and mechanisms on preventing the hiding proceeds. Taking into account the above mentioned, in our opinion, the development of a unified methodical approach and the development of mathemat-

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043 ical models for estimation of the shadow economy size and propositions for their substantial reduction are one of the priority areas of the research. To date, there is a significant variety of techniques for assessing the level of hiding proceeds, which differ in the different quantitative and qualitative composition of the indicators. The most common method for estimating the volume of hiding proceeds in countries with developed market economies is money-and-credit (monetary) methods. Their use is based on the thesis that only cash is used for the shadow schemes implementation. One of such methods is a method based on the analysis of the demand for cash (Turchinov, 1995). The increase in demand for cash during studied period, compared to the base one, indicates an increase of the level of the shadow economy. As a rule, the period with the lowest level of shadow economy in relation to the analyzed one is used as the base period. The most simple algorithm for using this method was developed by P. Gutmann (1982), who calculated the ratio of cash and deposits while studying the size of the shadow economy by the following formula:

, (1)

where – change in the ratio of cash and deposits during the studied period t relative to the base period t0; , – cash during studied period t and base period t0; , – deposits (time deposits, demand deposits) in banks during studied period t and base period t0. The size of the shadow economy is calculated by the formula

(2) where – size of the shadow economic activity in year t; – the volume of the official gross domestic product in year t.

The application of this group of methods has its own peculiarities that are connected with the following:  significant errors in the obtained results that are caused by the change in the time interval, during which the shadow economy phenomenon can be absent;  probabilistic nature of the assumption of the existence of a shadow economy in the country for a certain time;  the lack of reliable and confirmed data concerning the fact that any changes in the ratio of cash and deposits are caused by the influence of the shadow economy.

The Ministry of Economic Development and Trade of Ukraine proposed to estimate the size of the shadow economy on the basis of 4 methods: “expenditure of population - retail turnover”, unprofitability of enterprises, electricity consumption method and monetary method. The electricity consumption method is a common method of estimating the size of the shadow economy. Accord- ing to it, the dynamics of real GDP should correspond to the growth of domestic volumes of electricity consumption. If the growth rate of consumed electricity exceeds the analogous GDP indicator, then it is assumed that electricity is consumed in the production sphere of the shadow economy. The formalization of the electricity consumption method is based on the determination of index indicators of the studied and base periods by the formula:

(3) where –index of change in electricity consumption during the studied period compared to the base one; 32

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– index of GDP change during the analyzed period compared to the basic; t – studied period; t0 – base period (the index of changes in domestic electricity consumption and GDP is equal to 1). The assessment of the risk of hiding proceeds using the method of “expenditure of population - retail turnover” provides the monitoring and identification of the existence of an excess of consumer money expenditure for goods purchasing over the total volume goods sold to the population by all economic entities in the legal sector of the economy. Data on household expenditures are obtained through a sampling observation of their living conditions on a voluntary basis, and data on the total volume goods sold to the population by all business entities are received through statistical reporting. The method of unprofitability of the enterprises provides the determination of the minimum and maximum coefficients of the shadow economy as a share of GDP within which the level of the shadow economy lies. While using the method of unprofitability of enterprises, the following assumptions are used:  all unprofitable enterprises are actually profitable according to the official statistical data, that is considered as overestimation of the size of the shadow economy.  the return of unprofitable enterprises is equal to the return of profitable enterprises during the studied period.

In addition to the above analyzed methods, the risk occurrence probability of hiding proceeds is determined by use of soft modeling methods that involve the calculation of the relative size of the shadow economy through the determining of a set of factors that cause it. The peculiarity of this method is that it takes into account various factors leading to the formation and growth of the shadow economy, as well as the change in the shadow economy over time. This is important, since the shadow economy simultaneously influences production, labor and money markets. The structural method is based on the use of retrospective data on the scale of shadow economy in various industries. The expert method is based on the intuition and experience of qualified specialists, which determine the degree of data reliability, interrelations and relationships that cannot be quantified. Similarly, the latency of economic crimes is assessed. The method of comparing proceeds and expenditures uses the differences between statistical data on their volumes. In the system of national accounts, the volume of proceeds should be equal to the amount of expenditure. Thus, if an independent assessment of the expenditures of national accounts is available, then the difference between expenditures and proceeds can be used as an indicator of the level of shadow economy.

3. STUDY RESULTS According to the results of the conducted analysis, to date, the problem of illegal withdrawal of funds abroad and their subsequent legalization requires the development of a set of sequential measures aimed at both timely identification and punishment of participants of the shadow withdrawal of funds abroad and minimization of the risk of these transactions occurrence. At the same time, in our opinion, it is necessary to consider the risk of legalization of proceeds of crime as the possibility of a transaction related to the legalization of proceeds that were illegally withdrawn abroad. We propose to evaluate this risk by the following components: Authorities which are directly or indirectly involved in the operation. To date, in Ukraine, there is a significant level of corruption in most executive authorities at all levels. It is manifested both in concealing the facts of hiding proceeds, and in direct participation in these operations. Thus, according to the estimations of the international experts, for most authorities the level of corruption is above the average. The data on the level of corruption in Ukraine is shown in Fig. 7. 33

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043 Figure 7. Corruption level in different institutions of Ukraine

Source: National risk assessment report on preventing and countering legalization (laundering) of proceeds of crime and financing of terrorism, 2016

We propose to calculate the probabilistic value of the risk of proceeds legalization, that were illegally obtained by different government authorities, as a weighted average of the levels of corruption in a single institution with weighted coefficients from 1 - a high level of corruption (it is impossible to carry out any operations without a bribe) to 5 - a low level of corruption.  Countries involved in operations. Globalization phenomenon and changes in the main development determinants of industrialized countries lead to growing interdependencies among economies (Pietrzak, 2017). This criterion provides taking into account the risk occurrence probability of hiding proceeds in the partner countries, business entities or public authorities which are involved in operations. We propose to base the overall assessment of this risk on the value of the corruption index that was determined by the international organization Transparency International. The choice of this index is caused by the fact that, in our opinion, the country’s corruption index is a direct evidence of the possibility of legalizing the proceeds of crime in a particular country, since these transactions are always the result of the accumulation of proceeds of crime, as a result of the corruption schemes in the country. Transformation of this index into the probabilistic value, in our opinion, should be done by the following formula: (4) where Rml– probabilistic assessment of the risk of legalization of proceeds of crime of a partner country; α – weighted coefficient; Ik – corruption index of a particular country; h– limit value of the interval. To determine the optimal number of groups for ranking countries, we use the Sturgess formula: n = 1+3.322 *lg N, (5) where n – number of groups, N – sample size.

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043

After determining the number of groups, we calculate the upper and lower limits of the variable characteristics of each group. The length of the interval is determined by the formula: R h  , (6) n  1 where R = xmax – xmin – sample range; xmax, xmin– maximum and minimum value of characteristic. Based on the calculations, the grouping of countries in terms of the probability index of hiding proceeds is carried out at three levels: countries with a corruption index of 0-5 - have high risk of money laundering operations (limits of interval are 0.165-0.66) 5-6 have medium risk level , with the limits of 0-0,165; more than 6 – have low level of risk. The index of corruption of countries, according to the calculations of 2016 is shown in Table 3.

Table 3. Corruption index of the countries

Country Corruption Risk Country Corruption Risk or territory index assessment or territory index assessment Korea 0 high Latvia 4,8 high Iraq 1,5 high Lithuania 4,8 high Uzbekistan 1,7 high Slovakia 4,9 high Afghanistan 1,8 high Korea 5,1 average Venezuela 2 high TRepublic of South Africa 5,1 average Turkmenistan 2 high Italy 5,2 average The Central African Republic 2 high The Czech Republic 5,2 average Azerbaijan 2,1 high Cyprus 5,3 average The Republic of Belarus 2,1 high Hungary 5,3 average Kazakhstan 2,1 high Israel 6,1 low Tajikistan 2,1 high Portugal 6,5 low Nigeria 2,2 high Estonia 6,5 low The Russian Federation 2,3 high Slovenia 6,6 low Syria 2,4 high Spain 6,7 low Iran 2,5 high Chile 7 low Ukraine 2,7 high USA 7,2 low Moldova 2,8 high France 7,3 low Argentina 2,9 high Ireland 7,5 low Egypt 2,9 high Japan 7,5 low Bosnia and Herzegovina 3,3 high Germany 7,8 low Montenegro 3,3 high Austria 8,1 low Georgia 3,4 high Great Britain 8,4 low Saudi Arabia 3,4 high Luxemburg 8,4 low Serbia 3,4 high Australia 8,6 low India 3,5 high Canada 8,7 low China 3,5 high Norway 8,7 low Romania 3,7 high Netherlands 9 low Bulgaria 4,1 high Switzerland 9 low Turkey 4,1 high Iceland 9,2 low Croatia 4,1 high Sweden 9,3 low Poland 4,2 high Denmark 9,4 low Greece 4,6 high New Zealand 9,4 low

Source: constructed based on the data (Transparency International)

In addition, during the assessment process, it is necessary to take into account the fact that carrying out the operations involving offshore zones is characterized by an increased degree of risk. Thus, countries with high GDP indicators per capita are also world leaders in the volume of offshore financial services providing. In particular, 65% of the total volume of offshore financial services is concentrated in five countries with high GDP per capita (the USA, the UK, Luxembourg, Switzerland and Germany). According to the results of the Tax Justice Network survey, Ukraine is 35

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043 among the top twenty countries where residents have accumulated the largest offshore capital (almost 170 billion USD), that is more than triple the volume of foreign direct investment in Ukraine in 2014 (58,2 billion USD). The list of offshore zones is defined by the Decree of the Cabinet of Ministers of Ukraine No. 143-р of February 23, 2011. (On the list of offshore zones, 2011), is given in the Table 4.

Table 4. List of offshore zones

Corruption Corruption Country Country level level Andorra 0 Anguilla (territory of Great Britain) 0 Antigua and Barbuda 0 Niue (territory of New Zealand) 0 Bahamas 0 The Turks and Caicos Islands 0 Barbados 6,9 The Netherlands Antilles 0 Bahrain 5 Montserrat 0 Belize 3 The Cayman Islands 0 Vanuatu 3,1 The Virgin Islands 0 Grenada 3,4 Puerto Rico 0 Dominica 5,6 Bermuda Islands 0 Liberia 2,1 Gibraltar 0 The Republic of Maldives 3,3 Alderney 0 The Marshall Islands 0 Isle of Man 0 Monaco 0 Isle of Jersey 0 Nauru 0 Isle of Guernsey 0 Samoa 4,5 Saint Kitts and Nevis 0 Seychelles 4,5 Saint Lucia 6,8 Saint Vincent and the Grenadines 6,1 Commonwealth of Puerto Rico 0 Aruba 0 The Cook Islands 0

Source: On the list of offshore zones, 2011

Formula 4 is a universal formula with help of which it is possible to transform relative indicators into probabilistic values. However, taking into account the uneven influence of different criteria on the final indicator of the probability of hiding proceeds in a particular operation, the use of a correction coefficient (α) will help to take into account these factors. Values of weighted coefficients for various risk components, which are determined by the expert assessments method, are given in Table 5.

Table 5. Values of weighted coefficient for the components of the probability of the hiding proceeds of an operation

Component Value of weighted coefficient Partner country 0,33 Country of origin 0,19 Type of economic activity 0,37 The authority involved in the operation 0,11

Source: own calculations

Thus, the formula for determining the probability of hiding proceeds by the criterion “partner country” has the following form:

(7)

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043

The type of economic activity is the next parameter in the model of qualitative assessment of the risk occurrence probability of hiding proceeds. During the process of risk assessment by this parameter, we propose to use the approach to the classification of economic activities defined by the National Classifier of Ukraine (Classification of types of economic activity, 2010). The score of certain types of economic activity in accordance with the National Classifier is given in Table 6.

Table 6. The score of certain types of economic activity

Title Score Duplication of recorded media 10 Jewelry production 10 Trade in motor vehicles 10 Retail trade by mail order firms 10 Activity of hotels and restaurants 10 Financial leasing 10 Extension of credit 10 Insurance 10 Advertising activity 10 Activity on organization of gambling 10 Operations on stock exchange with stock valuables 10 Activities of real estate agencies 10 Operations with real estate for third parties 8 Trade in motor vehicles and motorcycles, their maintenance and repair 7 Management of financial markets 7 Musical instruments manufacturing 6 Post and communication activity 6 Financial intermediation 5 Research and development 5 Management in the social sphere 5 Non-ferrous metals manufacturing 4 Precious metals manufacturing 4 Furniture production; manufacturing of other products 4 Operations with real estate 4 Activities in the field of compulsory social insurance 4 Activities in the field of transport 3 International activity 3 Activities in the field of military defense 3 Activities on the ensuring the safety of the population in emergency situations 3 Fire safety activities 3 Vocational-oriented education 3 Higher education 3 Extraction of fuel and energy minerals 3 Fur and fur products manufacturing 3 Publishing and printing activities, duplication of recorded media 3 Scientific and technical activity 3 Chemical industry 3 Construction 3 Agriculture, hunting and related services 2 Recycling industry 2 Food production 2 Textile industry 2 Metallurgy industry 2 Public administration 2

Source: systematized by the authors on the basis of (Classification of types of economic activity, 2010)

The application of the above mentioned approach will allow to obtain probabilistic indicators of the risk of illegal withdrawal of funds abroad by its individual components. The general formula for these components calculation is as follows: 37

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(8) where ) – probabilistic assessment of the risk of the non-legalization of the proceeds of crime by operation; – an integral index of the risk occurrence of legalization of the proceeds of crime by operation using the following factors: = { , ; } (9) The total number of scores by all components by the operation is calculated by the formula:

(10)

where ML – total number of scores by operation; – number of scores by “partner country” component; – number of scores by indicator “authority directly or indirectly involved in the operation”; – number of scores by indicator “country where the main operation participants are located ”; – number of scores by indicator “economic activity”; k – number of types of economic activity involved in operation; n – number of persons involved in the operation n=1; m – number of countries involved in operation. The above approach allows to assess the probability of legalization of proceeds of crime as a whole by all components of the operation. Effective implementation of any managerial activities requires the identification of the specific factors that caused the possibility of corruption schemes use and the development of mechanisms for their neutralization. That is why it is important to study the most risky components by determining the degree of excess of the calculated risk occurrence probability of the legalization of proceeds by a specific operation over its allowable value. Calculation of the deviation of these indicators, in our opinion, is advisable to carry out if the integral index of the risk occurrence probability is within the acceptable values, however, the analysis of the certain directions demonstrates the excess of the critically acceptable values. A rapid assessment of the level of risk for a specific country is shown graphically in Figure 8.

Figure 8. Graphical representation of the integral rapid assessment of the risk of the country’s hiding proceeds

Source: compiled by the authors 38

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043

4. STUDY RESULTS Practical approval of the proposed approach to the risk assessment of hiding proceeds will be carried out using the example of investment channels for withdrawal of funds. According to the data of the State Statistics Service of Ukraine for the period 2010-2016, the volume of direct foreign investment from Ukraine amounted to 39 billion USD. Cyprus and Great Britain were the main recipients. The share of countries in terms of direct investment from Ukraine is shown in Figure 9.

Figure 9. Direct investment from Ukraine in the economy of the countries of the world

Source: own calculations based on (State Statistic Service of Ukraine, 2017)

Taking into account the above, practical approval of the proposed approach to the determining the probability of occurrence of operations on legalization of proceeds of crime for Ukraine will be carried out using the example of Cyprus and the Virgin Islands, as the countries with the largest volumes of investments from Ukraine. As the largest volumes of funds was invested in scientific activities and recycling industry in Ukraine (according the types of economic activity) (Table 7), that is why these types of activities will be assessed in terms of the risk occurrence probability of hiding proceeds.

Table 7. Direct investments (share capital) from Ukraine by types of economic activity

Type of activity 2013 2014 2015 2016 Agriculture 19,0 19,3 16,7 15,9 Recycling industry 171,2 131,5 108,7 113,0 Manufacture of food products, beverages and tobacco 77,8 59,3 52,2 51,6 Manufacture of wood products, paper and printing 9,5 8,4 7,5 7,2 Manufacture of basic pharmaceutical products and pharmaceutical preparations 0,1 0,1 0,1 6,9 Manufacture of rubber and plastics products, and other non-metallic mineral products 0,6 0,7 0,8 0,9 Metallurgical production, manufacture of fabricated metal products, except machinery and equipment 39,5 25,0 18,0 21,4 Machine building, except repair and installation of machinery and equipment 37,8 31,2 24,8 19,9 Manufacture of furniture, related products; repair and installation of machinery and equipment * * 0,2 0,2 Construction 0,8 11,6 1,3 1,3 Wholesale and retail trade; repair of motor vehicles and motorcycles 128,0 112,1 80,1 88,0 Transportation, storage, postal and courier activities 24,2 22,7 24,7 26,6 Information and telecommunication 0,0 2,7 2,6 2,5 Financial and insurance activities 221,7 125,3 73,8 72,5 Real estate operations 66,5 50,7 45,8 44,7 Scientific and technical activity 6 030,6 5 968,6 5 953,2 5 966,4 Activities in the field of administrative and support services 22,4 1,0 0,4 0,4

Source: State Statistic Service of Ukraine, 2017. 39

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043 The results of the calculations are given in Tables 8 and 9 and they indicate the average level of risk of legalization of proceeds of crime while carrying out the operation of the recycling industry with the participation of Ukraine and the Virgin Islands. While carrying out these operations, we have identified local government and customs as authorities that are directly involved. However, this list is hypothetical, since, as a rule, much larger number of authorities from different institutions, both from one country and from another country, participates in these operations. While assessing the risk of laundering the proceeds of crime, the Virgin Islands, as an offshore zone, was given a high degree, and it caused a change from average to high degree of risk by the operation.

Table 8. The results of the assessment of the risk occurrence probability of legalization of proceeds of crime with the participation of Ukraine and the Virgin Islands

Assessment by Probabilistic Indicator Characteristics Score qualitative scale assessment Partner country The Virgin Islands high 0 0,27 Authorities directly or indirectly Local authorities high 22,13 0,13 involved in the operation Customs high 24,26 0,16 Country of origin Ukraine high 7,3 0,0085 Type of economic activity Recycling industry high 2 0,17 Total assessment high 55,7 (average) 0,74 (high)

Source: own calculations

The analysis of the results of the assessment of the risk occurrence probability of legalization of proceeds of by operations with the participation of Ukraine and partner countries is given in Ta- ble 9.

Table 9. The results of the assessment of the risk occurrence probability of the legalization of proceeds of crime with the participation of Ukraine and partner countries

The Virgin The Russian Type of activity Cyprus Austria Islands Federation Agriculture 0,59 0,74 0,61 0,73 Recycling industry 0,59 0,74 0,61 0,73 Manufacture of food products, beverages and tobacco 0,59 0,74 0,61 0,73 Manufacture of wood products, paper and printing 0,59 0,74 0,61 0,73 Manufacture of basic pharmaceutical products and 0,55 0,70 0,57 0,68 pharmaceutical preparations Manufacture of rubber and plastics products, and 0,59 0,74 0,61 0,73 other non-metallic mineral products Metallurgical production, manufacture of fabricated 1,06 0,74 0,61 0,73 metal products, except machinery and equipment Machine building, except repair and installation of 0,46 0,61 0,48 0,60 machinery and equipment Manufacture of furniture, related products; repair and 0,55 0,70 0,57 0,68 installation of machinery and equipment Construction 0,57 0,72 0,59 0,71 Wholesale and retail trade; repair of motor vehicles 0,55 0,70 0,57 0,68 and motorcycles Transportation, storage, postal and courier activities 0,43 0,58 0,45 0,57 Information and telecommunication 0,59 0,74 0,61 0,73 Financial and insurance activities 0,41 0,57 0,44 0,55 Real estate operations 0,43 0,58 0,45 0,56 Scientific and technical activity 0,33 0,72 0,59 0,71 Activities in the field of administrative and support services 0,59 0,74 0,61 0,73

Source: own calculations based on (State Statistic Service of Ukraine, 2017)

Nadiya Kostyuchenko, Мykola Starinskyi, Inna Tiutiunyk and Iana Kobushko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 023-043

CONCLUSIONS Summing up, we can note that the availability of an effective tool for estimating the volume of hiding proceeds creates favorable conditions for increasing the investment attractiveness of the country and increasing its financial potential. Timely prediction of the probability of carrying out the operations on the illegal withdrawal of funds abroad and the implementation of appropriate preventive measures are necessary preconditions for these goals achievement. The proposed approach allows to assess the risk occurrence probability of the legalization of proceeds of crime, both by individual components (type of economic activity, authorities involved in operations, country of origin, etc.) and by the operation as a whole. The basis of this approach development is the assumption that the use of corruption schemes for the withdrawal of funds abroad occurs at each level of the economic system and, therefore, should be assessed because it affects the overall degree of risk of the operation. This approach can be used as a tool for assessing the effectiveness of state anti-corruption policies, especially in the area of shadow flows minimization, both at the micro and macro levels. In addition, the proposed approach can be used to identify the main stabilizing and destabilizing factors of influence on the level of hiding proceeds, with purpose to study them more and transform into controlled ones in the short and long term. The research and assessment of the synergistic effect that can arise from the simultaneous negative influence of these factors on the risk occurrence probability of legalization of proceeds of crime require further development.

ACKNOWLEDGMENTS “This research was funded by the grant of the Ministry of Education and Science of Ukraine (No. g/r 0117U003930)”

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Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 045-056 ‘

Formal Institutional Environment Influence on Social Entrepreneurship in Developed Countries

EVGENY V. POPOV1, ANNA Y. VERETENNIKOVA2 and KSENIA M. KOZINSKAYA3

1 Professor, Corresponding Member of the Russian Academy of Sciences; Head of the Economic Theory Center, In- sti-tute of Economics of the Urals Branch of the Russian Academy of Sciences; Head of the Laboratory of Econo- mics of the Digital Society, Ural Federal University named after the First President of Russia B.N Yeltsin, e-mail: [email protected] 2 PhD Candidate, Researcher in the Economic Theory Center, Institute of Economics of the Urals Branch of the Russian Academy of Sciences; Assistant professor of the Department "Regional and Municipal Economy, Finance and Security", Institute of Public Administration and Entrepreneurship of the Ural Federal University named after the First President of Russia B.N Yeltsin, Ekaterinburg, Russia, e-mail: [email protected] 3 Student in the master's programme, Institute of Public Administration and Entrepreneurship of the Ural Federal University named after the First President of Russia B.N Yeltsin, Ekaterinburg, Russia, e-mail: ksush1@yandex.

ARTICLE INFO ABSTRACT Received August 12, 2018 The existence of social entrepreneurs is beneficial for the estab- Revised from August 25, 2018 lishment of a stable and just society dedicated to serving the needs Accepted November 09, 2018 of individuals and the creation of innovative market solutions. How- Available online December 15, 2018 ever, social entrepreneurs need an institutional environment of a proper quality to be able to function efficiently. On the one hand, the institutional environment should promote the development of social JEL classification: entrepreneurship, on the other, there is a need for designing direct- JEL: Н41, Н54. action social entrepreneurship institutions that would provide support and ensure the development of socially-oriented businesses, DOI: 10.14254/1800-5845/2018.14-4.3 and promote grassroots initiatives in this sphere. Despite the fact that researchers are interested in this issue, there are very few Keywords: quantitative studies assessing the impact of institutional environments for social entrepreneurs on a global scale. Furthermore, no institutional environment, attempts have been made to look at this phenomenon from an social entrepreneurship, economic development standpoint, by assessing it in developed developed countries, countries. The subject of the study is social entrepreneurship de- modelling, velopment and the role of formal institution environment on this formal institutions process. In this study, the formal institutional environment of social entrepreneurship for developed countries will be investigated econ- ometrically. From this aspect, this study aimed to empirically evaluate whether regulative and normative institutions affect social entrepreneurship growth. As a result of the hypothesis testing, it is determined that a normative institutional environment such as investment freedom or economic growth have a positive influence on the development of social entrepreneurship in developed countries. These findings indicate that social entrepreneurship for its development needs fundamental economic changes and sustainable development.

Evgeny V. Popov, Anna Y. Veretennikova and Ksenia M. Kozinskaya / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 045-056 INTRODUCTION Although social entrepreneurship is a new phenomenon worldwide, it is increasingly recognized by both private and public organizations as a source of solving various social problems of society: unemployment, social exclusion, the lack of affordable medical care. Several decades ago, the idea of social entrepreneurship was rarely described in research by scientists. Interest in social entrepreneurship developement in the USA, Latin America, European and Asian countries, however. A review of recent research on social entrepreneurship demonstrates the growth of interest in the academic setting as well. V. Friedman and H. Desivilya (2010) on the research "Integrating social entrepreneurship and conflict engagement for regional development in divided societies" noticed the importance of social entrepreneurship for regional development. F. Azmat (2013) found that the existence of social entrepreneurship has a positive impact on the economy in developing countries. Social-oriented entrepreneurship plays a vital role in society developing in emerging markets (Yiu et al., 2014; Kot et al., 2016; Bernat et al., 2016). According to the concept of the researcher E. McAnany (2012), social entrepreneurship carries a high potential for promoting social changes, the development of regional societies, economic growth, poverty reduction, environmental sustainability. However, the theoretical studies of social entrepreneurship lag behind the practice (Lepoutre et al., 2013). One of the promising directions that are able to clarify the patterns and features of the social entrepreneurship development is the analysis of the formal institutional environment and its influence on social entrepreneurship. The reasons, incentives, and conditions for evolution of the social entrepreneurship in developed and developing countries differ significantly. While in developed countries this type of activity is often supported by state institutions, creating favorable conditions for functioning, in developing countries it is a response to the failures of the public sector in the provision of social services. The interest in the problem lies in the attempt to apply an institutional approach to the study of social entrepreneurship in developed countries. The application of institutional theory relating to social entrepreneurship contributes to the development of both socially-oriented activities and institutional theory. The goal of the study is the modeling of the influence of the formal institutional environment in developed countries. This paper examines the importance of effective institutions (regulative and normative) for growth of social entrepreneurship, identifies factors influencing this type of activity in developed countries and builds an econometric model of the influence of the institutional environment on the social entrepreneurship.

1. RESEARCH FRAMEWORK: INSTITUTIONS AND SOCIAL ENTREPRENEURSHIP In theoretical reseach, the definition of “social entrepreneurship” is at the stage of formation. The lack of standard definition formes a problem in determining the universal characteristics in- herent in social entrepreneurship as a whole. A literature review on this subject has shown at least four science schools, such as The Innovation School of Thought, The Social Enterprise School of thought, the EMES approach and UK approach. The supporters of The Innovation School of Thought are J. Thompson, S. Alvord, J. Mair and J. Marti. The school is focused on establishing new and better ways to address social problems or meet social needs (Dees and Anderson, 2006). The subject of Social Enterprise school of Thought is the enterprise, described as an entrepreneurial, nonprofit venture that generates “earned-income” while serving a social mission. Within the framework of this school, scientists are engaged in the search for new ways of financing NPOs, and also introduce effective management methods in the activities of socially- oriented enterprises. However, there is no emphasis on innovation here. EMES approach representatives are U. Stephan and L. Uhlaner (2010), as well as R. Spear (2006) and I. Vidal (2005),

Evgeny V. Popov, Anna Y. Veretennikova and Ksenia M. Kozinskaya / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 045-056 see the goal of socially-oriented activity in satisfying the interests of society and conclude that the existence of entrepreneurship without strong state support is impossible. According to the UK approach (D. Turner, K. Lyming, S. Dixon and A. Clifford, L. Darby and J. Jenkins ), the solution of social problems should be entirely entrusted to business, while the revenue received is sent, first of all, to meet social needs (Hoogendoorn, 2010). In the study, the social entrepreneurship is interrelating activities in the aggregate aimed at solving social problems by developing the commercial activities of economic agents. In the definition, the focus is on the priority of public goals and the creation of social benefits over economic, thereby distinguishing social entrepreneurs from commercial. The modern theory of entrepreneurship is closely related to institutions. D. North (1991) defines institutions as “humanly devised constraints that structure political, economic and social interactions”. Institutions refer to deep aspects of social structure, which act as authoritative guidelines and constraints for behavior. Institutions are taken-for-granted rules that can be explicit- ly and consciously perceived by individuals or can act as implicit guidelines for individuals’ actions. Institutions consist of informal restrictions (values, norms, prohibitions, customs, traditions, and codes of conduct), and formal rules (constitution, laws, economic rules, property rights and contracts). According to O. Williamson (1975) formal institutions are divided into constitutional and regulatory, E. Ostrom (1994) calls it the “constitutional choice” and “collective choice”. Since state mechanisms regulate the actions of individuals and determine social norms, political structures play an essential role in perceiving the entrepreneurial activity as attractive or unattractive. Later, W. Scott (2001) added "supporting" institutions to Williamson's classification, dividing all institutions of social entrepreneurship into regulatory, normative and cognitive. The regulatory environment refers to “formal rules and incentives that limit and regulate entrepreneurial behavior.” The regulatory environment is responsible for establishing rules, rewards or punishments. While entrepreneurs in emerging markets meet with related to changes in the economic climate institutional changes, the level of state participation, ownership structures and enforcement of legislative norms, the regulatory environment has a significant impact on the activities of social entrepreneurs. C. Seelos et al. (2011) haveproved that regulatory factors control the processes and results of the socially oriented activity. According to M. Suchman (1995), the state is able to influence the growth and development of social entrepreneurship in three main ways: by creating a regulatory framework aimed at supporting social entrepreneurship, encouraging successful social entrepreneurship through partnerships, by developing and promoting a complete ecosystem of social entrepreneurship. S. Estrin et al. (2013) believes that social and entrepreneurial activity is more successful in an institutional environment that has robust legal system operates and suggests stimulating a regulatory environment for the development of socially-oriented activities. The creation of a regulatory framework should include legal forms that can provide social entrepreneurs with access to tax benefits, grants, subsidies and financial instruments. Also, the state is able to promote the development of socially- oriented organizations indirectly and act as a catalyst for the development of various components of the ecosystem that are necessary for the prosperity of social enterprises, such as education or social marketing. The existence of organizations depends on the consent of the society they embedded and this consent based on an understanding of how organizational form is able to solve problems. From this point of view, organizational legitimacy is a generalized perception that the actions of an economic entity are desirable, correct and appropriate in a particular system of norms, values, and beliefs. Legitimacy is a way thereby an organization receives and retains resources. Scientists consider social entrepreneurship as a complex of strategic measures to respond to exogenous problems that non-profit organizations face. Since non-profit organizations get financial support from membership fees, government grants, and grants, social entrepreneurship is a modern organiza- 47

Evgeny V. Popov, Anna Y. Veretennikova and Ksenia M. Kozinskaya / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 045-056 tional form and therefore needs the legitimacy of existing processes. Normative institutions are represented by accrediting systems, professional communities which include professional standards. Cultural-cognitive institutions are the most informal and constitute rules and beliefs established between individuals and their groups through social interaction. The application of this classification to the analysis of the institutional environment of social entrepreneurship is justified by the determining role of rules and norms governing the interaction between economic agents regarding the implementation of socially-oriented activity, as well as the need for a system of effective institutions that balance social and personal interests of social entrepreneurs and create favorable conditions for evolution of social entrepreneurship. In this paper, we consider regulatory and normative institutions relate more to a formal institutional environment. Institutions can have both direct and indirect influence on social entrepreneurship. For example, the complexity of regulating the formation of new socially-oriented organizations has a negative impact on social and entrepreneurial activities. From the institutional point of view, the environment is critical for the creation and socially-oriented organizations growing. From another point of view, an active state policy directed at supporting social entrepreneurs contributes to the development of socially-oriented activities.

2. REGULATORY AND NORMATIVE INSTITUTIONAL ENVIRONMENT. Property rights. According to De Soto (2000), assets themselves do not influence social behavior. Assets in nature are undivided into parts and form new combinations, that is, take any form required by market transactions. Inadequate legal and administrative system prevents most citizens of developing countries to use the real estate to create capital. The core element of effective business operations is secured property rights. As fixed property rights guarantee the growth of incentives for entrepreneurial activity, the institution of private property is one of the fundamental formal rules regulated at the state level. However, the functioning of this institution implies not only normative regulation but also control over its implementation. Coase in the article “The nature of the firm” (1998) notes that the internal firm control allows reducing the transaction costs significantly. Thus, the principle of the rule of law prevents the possible illegal expropriation of private property. According to D. North (1991) well-functioning legal and judicial systems are necessary for the economic growth of the country. Institutions of private property have a positive impact on the development of incentives for social companies, as they ensure entrepreneurship transparency, therefore lead to the predictabil- ity of the economic activity results. Without the functioning property rights institutions, any investment is at risk. Besides, the protection of property rights contributes to the innovative behavior of entrepreneurs and setting up new entrepreneurial opportunities. Hypothesis 1. Property rights institutions have a positive impact on social entrepreneurship. Government activism in solving social problems.The indicator of government activism in solving social problems of society is reflected through state spending. The indicator of government spending explains the ability of the state to provide public goods and with their help to solve the social problems of society. In the context of the study of social entrepreneurship, U. Stephan et al. (2015) use the term “institutional void,” defining it as limited government support for social programs. In case of active involvement of the public sector in solving social problems, the need for social services and goods decreases, hence, the motivation of entrepreneurs to participate in social entrepreneurship also decreases. An analysis of the activities of social entrepreneurs conducted by J. Mair et al. (2012) confirmed this point of view. Social entrepreneurs arise in places where the state is unable to provide all social needs of the population. Cross-national research conducted by S. Estrin et al. (2013) also demonstrates a negative correlation between state activity in solving 48

Evgeny V. Popov, Anna Y. Veretennikova and Ksenia M. Kozinskaya / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 045-056 social problems and the prevalence of social entrepreneurship.Following the phenomenon of institutional voids, the hypothesis follows: Hypothesis 2 (a). Government activism in solving social problems reduces the probability for development of social entrepreneurship. Grants, subsidies and other forms of financing are institutional support for social entrepreneurs, providing a partnership between the state on one hand and social entrepreneurs solving these problems on the other. Researchers G. Saxton and M. Benson (2005) found a positive correlation in the US between the activity of the government and the creation of non-profit organizations. Also, M. Marcuello (1998) discovered the same trend in Spain as in the USA. Thus, these studies demonstrate the importance of resource supportor social entrepreneurs. Hypothesis 2 (b). Active government activism in solving social problems has a positive impact on social entrepreneurship. Government Integrity.According to the researcher V. Tanzi and H. Davoodi (2001), corruption is a reflection of weak institutional development. The presence of corruption reflects the level of institutional development that has become an institutional norm. Corruption itself hampers the development of formal institutional reforms of an indirect nature, and it is difficult to change this formed norm. C. Bjornskov (2003) have found a correlation between the level of corruption and bureau- cratic regulation, which indicates that ineffective regulation generates corruption management practices. The problem of studying the institution of public administration efficiency is the question of which variables are considered endogenous. On the one hand, corruption can be regarded as an endogenous process that affects the effectiveness of management, on the other, it is the effect generated by excessive state regulation. In this study, we accept corruption as the result of inefficient public administration. There is a direct relationship between the extent of government intervention in economic activity and the prevalence of corruption. In particular, excessive and redundant government regulations provide opportunities for bribery and graft. In addition, government regulations or restrictions may create informal or black markets. For example, by imposing numerous burdensome barriers to conducting business, including regulatory red tape and high transaction costs, a government can incentivize bribery and encourage illegitimate and secret interactions that compromise the transparency that is essential for the functioning of a free market. Hypothesis 3. The quality of public administration affects on social entrepreneurship. Investment freedom. Restriction of investment freedom extends to a) business ownership by foreign citizens, b) increased requirements for foreign companies, and c) enterprises and companies openned for foreign investment. Moreover, investment freedom refers to the productive resources ownership by foreign agents. In countries where investment institutions are developed, rules and laws apply equally to local and foreign organizations, while foreign companies also receive equal access to finance. The restrictions imposed by the state on direct foreign investment reduces the inflow of capital and impedes the development of economic freedom. Foreign direct investment provides capital inflows and facilitates the transfer of intangible assets from other countries, such as experience, education, technology. Since social entrepreneurship has appeared relatively recently, the spread of successful experience is a prerequisite for its development and scaling. Hypothesis 4. Investment freedom has a positive impact on the development of social entrepreneurship. The level of economic development. S. Wennekers et al ( 2005), M. Carree et al. (2002), have noted the relationship between business and economic development. Researchers have found a 49

Evgeny V. Popov, Anna Y. Veretennikova and Ksenia M. Kozinskaya / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 045-056 link between economic indicators in the country and incentives to entrepreneurship, as a growing economy leads to an increase in the expected benefits of entrepreneurship. Z. Acs et al (1993) has proved the positive impact of economic growth on entrepreneurship. Hypothesis 5. The level of economic development pos y affects itivel social entrepreneurship.

3. DATA AND METHOD At present, there are 226 countries, significantly differentiated regarding living standards, political system, GDP and many other features. Different world organizations (UN, IMF, OECD) group states according to their criteria. Following the theory of Noutstain, there are: countries with appro- priating economies; developing countries; industrial countries; developed countries. For economic theory and its derived disciplines, the most important classification is the qualitative-quantitative criterion for developed and developing countries, and there is no common opinion about which countries are developed, which are developing. A complete picture countries groups is provided by the data of international organizations, of which the majority of the countries of the world are members - the UN, the IMF and the World Bank. The assessment by these organizations countries groups in the international economy is unusual since the number of member countries of these organizations is different, international organizations observe only the economies of their clients. The classification of the international monetary fund has two criteria: industrial countries, that is, developed or industrialized countries, and all other emerging and developing countries. Each of the groups is divided into subgroups. Thus, in the group of industrialized countries, consisting of 29 members, there are subgroups of the G-8 countries, the euro area countries, the newly industrialized countries of Asia. The “Other countries” group with the emerging market and developing are more numerous - 143 countries, in which 85% of the world population lives. According to the classification of the countries of the International Monetary Fund, these countries differ markedly in economic terms; therefore subgroups are formed according to geographical, analytical and financial criteria. Geo- graphically, countries are divided into Africa, the Middle East, the CIS, the Western Hemisphere, and Developing Asia. For the study, we used the classification of IMF. The list of observations included the following countries: Australia, Belgium, Estonia, Finland, Germany, Greece, Hungary, Ireland, Israel, Italy, Korea, South, Latvia, Luxembourg, Netherlands, Norway, Poland, Portugal, Czech Republic, Slovakia, Slovenia, South Africa, Spain, Sweden, Switzerland, Taiwan, the United Kingdom, The United States of America. Since the phenomenon of social entrepreneurship is new, there is a problem of quantitative measurement of this phenomenon. However, the researchers found three methods for measuring socially-oriented activities. PSED II (Panel Study on Entrepreneurial Dynamics), Global Entrepre- neurship Monitoring (GEM), as well as tax declarations of non-profit organizations around the world. PSED began in 1993 with adult research in the state of Visconti, and later, the University of Michigan, borrowing the approach, continued to study households in the US through a telephone survey of 64,000 Americans. The primary objective of this study is a representative sample of the adult population participating in the creation of the business. The PSED research project is a powerful resource for studying the establishment of a company and the development of new enterprises at an early stage of its growth. However, this technique was not aimed at studying exclusively social entrepreneurship and is conducted only in the US. The second method is the use of the GEM database aimed at studying social entrepreneurship in 2009 and 2015 more than in 50 countries of the world. This survey is conducted to determine the number of people not just hearing about this type of activity but directly engaged in social entrepreneurship. However, this approach has some shortcomings related to the complexity of verify- ing the data received, as well as the possibility of a different understanding of the word “social” in 50

Evgeny V. Popov, Anna Y. Veretennikova and Ksenia M. Kozinskaya / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 045-056 different countries of the world. In our study we used data from a survey of the population participating in social entrepreneurship at the operational stage of the organization's development in the broad sense of this meaning, that is, in any economic activity that has a social and environmental goal.

The latter method uses tax returns of non-profit organizations, despite a significant amount of data, the notion of a non-profit organization is able to vary in the tax code of different countries. Besides, the use of this method presents a challenge for studies regarding unifying all the data obtained. This method was used in the study of the development of social entrepreneurship in the United States from 1982 to 2002 (Kerlin and Pollak, 2011) and the impact of state support on its development. The second research (Caroll and Stater, 2009), also examines the impact of income diversification on socially-oriented activities in the United States. Statistical studies of GEM are the only statistical basis for the study of social entrepreneurship at the cross-country level. We used Indexes of the “Heritage Foundation” report for describing the institutional environment of social entrepreneurship as independent variables. For measurement government activism we used the size of government spending on social sphere. The score for the government integrity is derived by averaging scores for the following six sub-factors, all of which are weighted equally: Public trust in politicians, Irregular payments and bribes, Transparency of government policymaking, Absence of corruption, Perceptions of corruption, and Governmental and civil service transparency. The Index relies on the following sources for assessing government integrity: World Economic Forum, World Competitiveness Report; World Jus- tice Project, Rule of Law Index; Transparency International, Corruption Perceptions Index; and TRACE International, The Trace Matrix. The Investment freedom Index evaluates a variety of regulatory restrictions that typically are imposed on investment. The Index relies on the following sources for data on capital flows and foreign investment, in order of priority: official government publications of each country; U.S. Department of State, Investment Climate Statements; Economist Intelli- gence Unit, Country Commerce; Office of the U.S. Trade Representative, National Trade Estimate Report on Foreign Trade Barriers; World Bank, Investing Across Borders; Organisation for Economic Co-operation and Development, Services Trade Restrictiveness Index; and U.S. Department of Commerce, Country Commercial Guide. GDP five-year average annual growth is the average growth rate measured over a specified period. The five-year annual growth rate is measured using data from 2012 to 2016, based on real GDP growth rates. The primary source is the International Monetary Fund, World Economic Outlook Database. Secondary sources are the World Bank, World Development Indicators Online, and Economist Intelligence Unit, Data Tool. To identify the dependence of social entrepreneurship on formal institutions we used multifactor nonlinear regression analysis method. At the first stage, we carried out a preliminary analysis of the initial statistical data and identified the most appropriate type of functional dependence between the economic processes under consideration. At the second stage, a correlation analysis of the investigated factors was carried out, which made it possible to identify the factors eliminating from the model that formed such an unfavorable phenomenon as multicollinearity. At the third stage, a multifactor model was constructed directly; at the fourth stage - the quality of the constructed model was investigated. The fifth stage involved checking and eliminating the autocorrela- tion of the residuals in the model. At the data processing stages, software products such as MS Excel and E-views were used.

4. RESULTS To check the formulated hypotheses and further modeling the influence of the formal institutional environment on social entrepreneurship, we considered independent variables: 51

Evgeny V. Popov, Anna Y. Veretennikova and Ksenia M. Kozinskaya / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 045-056 X1 - property rights. X2 - Government Integrity. Х3 – Government Spending X4 - Investment Freedom ; Х5 - 5 Year GDP Growth Rate (%)

At the initial stage of data analysis, it was revealed that the distribution of random variables by the factors under test and the dependent variable Y is nonlinear. The distribution has the form of a power law. Therefore, the original data was transformed to a nonlinear form, and then a regression nonlinear model was constructed. The correlation matrix analysys has established multicollinearity in the model (Table 1).

Table 1. Correlation matrix

Y Х1 Х2 Х3 Х4 Х5 Y 1,00 Х1 0,15 1,00 Х2 0,27 0,89 1,00 Х3 -0,01 0,02 -0,08 1,00 Х4 0,46 0,51 0,49 -0,19 1,00 Х5 0,50 0,26 0,18 0,60 0,30 1,00

After elimination of multicollinearity, as well as factors that do not exert significant influence on the results of testing the hypothesis of the insignificance of regression coefficients, the dependence of the level of social entrepreneurship on factors X4 and X5 was established.The results of the regression analysis are presented in the Table 2.

Table 2. Regression analysis

After the conversion, this model has the following form: 67,7 07,2  XXY 47.0 4 5

Evgeny V. Popov, Anna Y. Veretennikova and Ksenia M. Kozinskaya / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 045-056

The importance of the coefficient of determination (F-statistic = 0.0024) allows us to conclude that the model as a whole is reliable, and also confirms the representativeness of the sample. The coefficient of determination R2 = 0,39 indicates that the variation of social entrepreneurship development indicators by about 39% depends on the indicators selected at the stage of modeling the matrix of paired correlation coefficients. Verification of the null hypothesis of the insignificance of regression coefficients showed that the selected factors do influence, their regression coefficients are statistically reliable and significant. The value of the F criterion and the significance level p demonstrate that the constructed model is significant at a significance level of α = 0.05. At the final stage, the fulfillment of the assumptions of the Gaus-Markov least squares method was carried out. In particular, the mathematical expectation of a random deviation of the remainders for all observations tends to 0. The Durbin-Watson test used to test the model for the presence of au- tocorrelation of the residues (d = 1.977) showed that there is no relationship between the residues, they are randomly distributed. The model was tested for heteroscedasticity using a visual analysis of the residue schedule. There is no evidence of variability in the variance, and also of the dependence of the residues, the model is homoscedastic.

5. DISCUSSION The first hypothesis was rejected in the process of hypothesis testing and building the described model. It should be noted that the negative impact of the property right institutions on social entrepreneurship development was not detected. In our opinion, these results could be explained by the pro-social goals of social enterprises above the purpose of making a profit. Hypotheses 2 (a) and 2 (b) that’s about the economic activity impact on solving social problems also had not been confirmed. It is attributable to the availability of alternative ways to solve social problems in developed countries, in particular by supporting the non-profit sector. As for Hypothesis 3, we didn't find support for a public administration quality with social entrepreneurship. This may be related to the problem of measuring the quality of the institution of public administration in general, and with the peculiarities of government policy in different countries. In assessing the quality of this institution, we used the corruption component, which is an institutional trap for the economies of different countries. (Polterovich, 2007). However, the inefficiency of public administration can also be observed due to the presence of institutions dysfunctions caused by special aspects of development or their inflexibility to the conditions of the external environment. The refutation of these hypotheses allows us to conclude that regulatory institutions play a less critical role in social entrepreneurship development. It may reflect that the process of social entrepreneurship formation is sufficiently lengthy and needs stability and favorable economic conditions more than regulatory initiatives. As predicted by Hypothesis 4 and, we found support for a positive interaction of investment freedom, level of economic development and social entrepreneurship. Since the investment climate and sustainable economic development belong to a normative institutional environment, it can be concluded that a favorable, rather than a regulatory environment has a significant impact on social and entrepreneurial activities. However, a favorable enabling environment is most likely a consequence of the effective operation of regulatory institutions, the development of social entre,preneurship is more dependent on the opportunities created in a particular country. Since social entrepreneurship is a new phenomenon, for its development it is necessary to adopt the successful experience of foreign colleagues and active economic, as well as social ties with other countries. Also, according to the Social Enterprise School of Thought, this type of activity

Evgeny V. Popov, Anna Y. Veretennikova and Ksenia M. Kozinskaya / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 045-056 must be scaled, and a favorable investment climate contributes to attracting new investors and the scalability of enterprises. Although the primary goal of social entrepreneurship is not an economic goal, but social, for successful functioning, this type of activity requires financial resources. Therefore, stable economic growth provides, on the one hand, with the resource support of social entrepreneurs, and on the other increases the solvency of consumers of social services.

CONCLUSION In order to model the influence of the formal institutional environment on social entrepreneurship in developed countries, the following conclusions were formulated in this article. First, the factors characterizing the institutional environment were identified and scientifically substantiated. This list of factors includes the rights of private property, the degree of government activity in solving social problems, investment freedom, and the level of economic development of countries. Secondly, a multifactorial regression model has been constructed, from the analysis of which it follows that the level of development of social entrepreneurship in developed countries is affected by the level of economic development in general, and also by the level of investment freedom. By the results obtained, we concluded the importance of the leading role of supporting institutional environment for the social entrepreneurship development. The practical significance of the research results is the possibility of their use in the formation of social policy at the regional and federal levels, as well as in confirming the leading role of support institutions for the development of social entrepreneurship in developed countries. In future researches, it is necessary to explore the influence of the formal institutional environment on developing countries and compare them with developed countries.

ACKNOWLEDGEMENTS The article was prepared in accordance with the state task of the The Federal Agency for Sci- entific Organizations (FASO Russia) for the Institute of Economics of the Ural Branch of the Ras- sian Academy of Science for 2018. The authors would also like to express their gratitude to the editor in chief and to the anonymous reviewers for the peer review.

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Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068

Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 057-068 ‘

Regional Development in Conditions of Limitation of Water Resources: Correlation Interconnections

SVITLANA FEDULOVA1, VITALINA KOMIRNA2, NATALIIA NAUMENKO3 and OKSANA VASYLIUK4

1 Associate Professor, Head of the department of Theoretical and Applied Economics, State Higher Educational Institution “Ukrainian State University of Chemical Technology”, Dnipro, Ukraine, e-mail: [email protected] 2 Associate Professor, Professor of the department of Theoretical and Applied Economics, State Higher Educational Institution “Ukrainian State University of Chemical Technology”, Dnipro, Ukraine, e-mail: [email protected] 3 Associate Professor, Department of Theoretical and Applied Economics, State Higher Educational Institution “Ukrainian State University of Chemical Technology”, Dnipro, Ukraine, e-mail: [email protected] 4 Postgraduate of the department of Theoretical and Applied Economics, State Higher Educational Institution “Ukrainian State University of Chemical Technology”, Dnipro, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 10, 2018 The main idea of the article is to study the problem of regional de- Revised from September 03, 2018 velopment in the conditions of limited water resources. The subject Accepted November 17, 2018 of the study is theoretical and methodological provisions and ap- Available online December 15, 2018 plied practical recommendations for the regulation of regional development in the conditions of limited water resources. The purpose of the article is to determine the connection between water supply JEL classification: (water potential) of Ukrainian regions and their specialization in pro- P11, P25, Q56, Q57, Q58, G18. duction and trade in the conditions of water resource constraints and determine the further prospects for regional development. In DOI: 10.14254/1800-5845/2018.14-4.4 the study have been used traditional and special methods of research, including: historical and logical method, method of abstrac- Keywords: tion and analogy, system analysis methods and the method of correlation analysis. For calculations was used the biserial correlation regional development, method based on the point of biserial relation coefficient of Pear- water resources, son. The article presents a hypothesis about the need to change the virtual water, paradigm of regulation of regional development regulation on the biserial correlation. basis of water-efficient water use and identified the territories of Uk- raine with the general water risk. It is determined that Ukraine is the world's largest exporter of food products, along with the USA, Aus- tralia and Russia, that requires significant water resources. It has been proved that there is no link between the water supply (water potential) of the regions and production and trade with grain and leguminous plants. It is possible to assume that such conditions have arisen due to the existence of land resources (black earth) on the territory of the country, and also that in Ukraine there are other more important problems of effective regional water use.

Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068 INTRODUCTION Over the past decades, the concepts of food security, energy security and access to natural resources have been widely discussed. However, for the moment, more researchers are recognizing that the environment and security are interconnected. In particular, on the example of water resources, it can be argued that the deficit of fresh water represents both a direct and an indirect threat to safety, since, on the one hand, this deficit creates a dangerous situation, and on the other hand, it is fraught with potential conflicts. The most serious security threat is associated with water deficit and it is not wars for water resources, but rather an aspect of human security that could endanger both state security and international security. However, it has to be taken into account that not only a water deficit generates a conflict, but the conflict itself can lead to a lack of water. Conflicts have direct consequences for water resources, for example, in the form of water pollution. So, the waters of the Danube river were polluted during the conflict in the former Yugoslavia, in Bosnia and even more so during the war in Kosovo. Also, dams and barrages, pumping stations and sewers can be damaged during war actions. In today's conditions it is necessary not to com- pete with each other, trying to provide ourselves with water resources but to cooperate. With the growth of water deficit in a number of countries, recently due to global warming, a number of strategies have been developed to overcome it, which include savings on water consumption, salt removal of saltish or salty sea water. Another alternative to minimize water consumption is to import wet industry products for agriculture, industry and energy. This thesis is explained with the concept of “virtual water”, which was developed in 1993. The creator of the "virtual water" concept which is related to measuring the volume of water embodied in the products and trade of food and other commodities is the professor of London University John Antony Allan (1998). He proposed a formula for calculating the amount of water which is required for production of a particular product. This concept helps to understand how much water is needed in order to create different goods and services. According to this concept, all virtual water can be divided into “blue” it is surface water, or groundwater, which is evaporated in the production of products, "green" it is rain water, which is usually evaporated in production and "grey" which is the volume water contaminated during the production process, that is determined by calculating the volume of water required for the dilution of pollutants entering to natural water systems during the production process up to obtaining water quality that meets the standards (based on the Official site of water footprint, 2017). The concept of virtual water set a start for additional research and development in this area. Later, the Profes- sor of University of Twente in the Netherlands, Arjen Y. Hoekstra proposed the concept of a "water footprint" in 2002. According to this theory, the water footprint represents the entire water consumed by the region, including the virtual one. The amount of virtual water can be calculated not only for individual products, but also for the person, enterprise, region and country as a whole. Since not all the products are consumed and produced in the same country, the concept of "water footprint" is used in the research. The water footprint consists of two parts: the use of inland water resources and the use of water resources from sources located outside of the country. The water footprint includes water intake from surface and underground water sources and the use of ground water in agricultural production. The concept of "virtual water" allowed to have a new look at issues of effective water use and water policy. Among the Ukrainian researchers who develop virtual water issues, the following scientists from the Institute of Natural Resources and Sustainable Development of the National Academy of Sci- ences of Ukraine can be mentioned: M. Khvesyk (2014), A. Sunduk (2014), L. Levkovska (2014), O. Melnik (2011), O. Matsenko (2011) and M. Khizhnyak (2011). Also such Ukrainian scholars as A. Yakymchuk and P. Skrypchuk are dealing with this issue. By definition of M. Khvesyk ("Virtual water: a myth or reality?"), stored in the product and its value, the water moves from the place of production to the final consumer. In the conditions of the global economy, the distances that goods overcome are thousands of kilometers. If to take into 58

Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068 account the trade turnover between countries and continents, trade performance will be very high. No less significant are the volumes of water components, as well as their monetary expression. Virtual water flows are formed on the basis of export-import operations. It should be noted that a large number of reports on the state of the environment and assessment of the state of water resources, which contain various materials and rich statistical information are published in Ukraine. At the same time, the analysis of all national and regional publications highlights the efficiency of water use shows that today there is not enough information available, and its relevance for effective political decisions is still low. The purpose of the article is to determine the connection between water supply (water potential) of regions of Ukraine and their specialization in production and trade in the conditions of water resource constraints and determine the further prospects for regional development.

1. BACKGROUND OFREGULATION PARADIGM CHANGE OF REGIONAL DEVELOPMENT FOR EFFICIENT REGIONAL WATER USE IN REGIONS Due to the non-homogenous geographical distribution of natural resources, demand for water and crude oil may not always be satisfied with the local supply. Water is usually not transported directly over long distances. Despite the fact that some cases of direct export of water are already fixed, there is neither a world market nor a standard global water price. Instead, the international trade in water-based goods, the so-called virtual water market, already exists (Oki, Yano and Hana- saki, 2017; Wichelns, 2010; Perelyot, 2010 and data of the Report of the High Level Panel on Fi- nancing Infrastructure for a Water-Secure World, 2015). As a rule, it is considered that trade with "real water" between territories without moisture is impossible due to long distances and associated costs, also because water as a production resource is required in a considerable volume (Oki, Yano and Hanasaki, 2017). International water trade through the construction of channels and the redistribution of river flows is very limited due to huge capital expenditures. It should be noted that the problems of export-import of "virtual water" are extremely worried by the Food and Agriculture Organization of the United Nations (FAO) (data of the Official site of Food and Agriculture Organization of the United Nations, 2018), the World Water Institute (SIWI) (data of the Official site of World Water Institute, 2018), the World Resources Institute (WRI) (data of the Official site of Institute of World Resources, 2018) and many European researchers. More we refresh on our memory the statement of the World Bank Vice-President I. Seragildina: "Wars of the XXI century will be wars for water," which he pronounced in 1995 (Dinar, 2007). For arid countries, the import of virtual water (first of all, in the form of agricultural products, which accounts up to 70-90% of water consumption, can be a good means of reducing domestic demand for water and, thus, mitigating the internal water shortage. It should be noted that there are many factors that affect the flow of water-based products, which are many times more important than water supply itself. Thus, the question becomes more important the issue about water availability determines the specialization of the region in the export or import of water-based products in the actual practice of international trade (Chuprov, 2016). A study conducted in 2003 by H. Yang and co-authors (Yang et al., 2003), who, after analyzing the data for countries in Africa and Asia, confirms that for most countries, the degree of water availability is not a significant factor affecting international trade. However, after reaching a certain threshold for a shortage of water, the country begins to demand the import of cereals, exponentially increasing with the reduction of water resources. Later, researchers conclude that reducing water availability is an important factor in the growth of net imports of virtual water by countries in the region (Yang et al., 2007). A study of H. Young with co-authors allows us to trace the dynamics of the relationship between water resources supply and its trade specialization. Regarding the Ukrain- ian territories, in the light of the problems of development in the conditions of limited water resources, the riskiness of the Ukrainian territories notes the Institute of World Resources (WRI) (Ta- 59

Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068 ble 1). According to the terminology of this Institute, the overall water risk determines areas where there is an increased risk associated with the use of water resources and represents the aggregate measure of all selected indicators by categories of physical, qualitative, regulatory and reputational risk (the explanation of these risks is given in Figure 1).

Figure 1. Components of general water risk according to the methodology of the World Resources Institute (WRI)*

Overal water risk    Regulatory and Reputation Phisical Risk Quantty Phisical Risk Quality Risk - Basic water stress, - Interannual shange, - Season shange, - Stream ratio, - Media coverage, - Flooda, - The protection of the land near - Access to water, - Dry conditions, water storages - Threat to amphibians - Water storage, - Stress of underground waters

*Source: Official site of Institute of World Resources: http://www.wri.org, 2018.

Table 1. Overall Water Risk for Ukrainian Regions according to the data of Institute of World Resources

Level of risk to Region of Ukraine methodology of WRI Low risk (0-1) --- Low to medium risk Kyiv, Chernivtsi, Zakarpattya, Lutsk, Zhytomyr, Chernihiv, (1-2) Cherkasy, Dnipropetrovsk (mostly), Zaporizhzhya (half) Medium to high risk Kharkiv, Poltava, Luhansk (most), Sumy, Kirovograd, Vinnytsya, Khmelnytsky, (2-3) Ternopil, Ivano-Frankivsk, Lviv, Rivne, Odessa (mostly), Zaporizhzhya (half) Crimea, Kherson, Nikolaev, Donetsk, Lugansk (small part), High risk (3-4) Dnipropetrovsk (small part), Odesa (small part)

Extremely high risk (4-5) ---

Source: Official site of Institute of World Resources: http://www.wri.org, 2018.

As can be seen from Table 1, the World Resources Institute (WRI) defines the territory of Ukraine with high water risk (3-4), including the temporarily occupied territory of the Autonomous Republic of Crimea, Kherson, Nikolaev, Donetsk, Lugansk (small part), Dnipropetrovsk (small part) and Odesa (small part) areas. At the same time, the researchers of the Institute for the Economy of Natural Resources and Sustainable Development of the National Academy of Sciences of Ukraine indicate that Ukraine, according to expert estimates, is distinguished by significant volumes of virtual water formation. In particular, the overall figure for exports is fixed at 19.5 billion m3, which exceeds the baseline volumes of water use in the country as a whole (Table 2). By import, the rate of virtual water is no longer so high and is limited to 1.84 billion m3 (Khvesyk, Levkovska and Sun- duk, 2014). Table 2 clearly demonstrates the predominance of virtual water exports over imports in Ukraine. 81.5% of this export is for agroindustrial complex, and only 18.5% refers to industry. 60

Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068

Thus, Ukraine, supplying grain abroad, outputs significant volumes of virtual water over its borders, which is not so much in imported goods. According to the researches of the Institute of Natural Resources of Economics and Sustainable Development of the National Academy of Sciences of Ukraine, in the regional dimension, the main load on virtual water falls predominantly on the southeastern regions of the country. The problem is the occupation of the territories of Donetsk and Luhansk regions and the annexation of the Crimea, as they created significant flows of virtual water. These leading regions serve as a kind of "global gate" for virtual water flows (Khvesyk, Lev- kovska and Sunduk, 2014).

Table 2. Export-import of virtual water and its characteristics for Ukraine (as of January 1, 2013)

Sphere of Internal prices, World prices, Million m3 virtual water formation million UAH million USD Agricultural Export 15900,4 6996,2 7950,2 complex Import 119,7 359,1 59,9 Export 3604,3 1587,0 3604,3 Industry Import 1728,1 5184,3 1728,1 Export 19504,7 8583,2 11554,5 General index Import 1847,8 5543,4 1788,0

Source: According to the data of Institute for the Economy of Natural Resources and Sustainable Develop- ment of the National Academy of Sciences of Ukraine, 2014.

According to researchers from the University of Leiden (the Netherlands), as of 2017, Ukraine is the world's largest food exporter, along with the United States (mainly South America), Australia and Russia. But the main importers of food products are Western Europe, Asia and Africa and the Middle East (Bacon, 2017). The same opinion is shared by researchers at the Institute of Natural Resources and Sustainable Development of the National Academy of Sciences of Ukraine, who claim that Ukraine is now among the three world grain exporters (after the USA) and has a significant place in the export of metallurgical products. Among the sectors of the economy of Ukraine, the largest consumers are energy, metallurgy, chemical and petrochemical industries, which simultaneously belong to the main polluting industries in the industrial sector of the economy. According to the State Agency of Water Resources and the State Committee of Statistics of Ukraine, Dnipropetrovsk, Zaporizhzhya, Donetsk, Kherson and Kyiv regions (Table 3) belong to the five largest consumers of water in Ukraine, which confirms the opinion that they are the main exporting regions of virtual water. The industrial need of these five regions (out of the 24 existing in Ukraine and the Autonomous Republic of Crimea) is almost 63.3% of the total use of fresh water by regions (Fedulova and Komirna, 2017). It should be noted that in Dnipro basin is created a diversified economic complex, which includes industry, agriculture, hydropower, communal services, water transport, fisheries. Here is concentrated about 43% of industrial production of Ukraine (Fedulova, 2017). Water content of gross national product is in 3-5 times higher than in industrialized countries of Europe, which testifies to inappropriate water use and low efficiency of existing production equipment. Today, the volumes of water use in river basins virtually reached the upper limit, resulting in a contradiction between the demand for water and the possibilities to meet it, not only in terms of quantity but also in quality (Khvesyk, 2013).

Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068 Table 3. Use of fresh water by regions of Ukraine, including freshwater and seawater, mln. m3*

Regions 1990 1995 2000 2005 2010 2011 2012 2013 2014 2015 2016 Ukraine 30201 20338 12991 10188 9817 10086 10507 10092 8710 7125 7169 Regions Dnipro 3599 2752 1756 1579 1361 1407 1429 1349 1359 881 1055 Donetsk 3419 2548 1751 1508 1467 1479 1445 1354 1135 936 926 Zapori- 4598 2635 1702 1076 1099 944 1186 1237 1146 1150 1081 zhzhya Kyiv 2131 1496 1132 812 902 925 1028 866 808 706 664 Kherson 2161 1131 639 610 770 963 1083 1074 1062 1037 990

* Prepared by the authors based on the source of State Statistics Service of Ukraine, 2014–2017.

But, returning to the assessment of the overall water risk by the World Resources Institute (Table 1) raises the question of if such water export loading on the virtual water could be endured by Kherson, Donetsk, and Dnipropetrovsk regions and the temporarily occupied territory of the ARC. These territories are classified as "High risk (3-4)". It should be noted that agricultural products are low value added goods. In our view, it’s necessary to have more balanced approach to the formation of strategic areas of economic activity and their forms. Thus, the conducted theoretical analysis of the functioning of regional socio-economic systems allows us to ascertain the change in the paradigm of regulation of regional development on the basis of water-efficient regional water use.

2. METHODIC APPROACH TO RESEARCH: CORRELATION INTERCONNECTIONS It is known that the largest volumes of water use in the world forms the production of grain and agricultural products. Data from the State Statistics Committee indicate that Ukraine can indeed be considered one of the largest exporters of grain and sunflower in the world (Figure 2).

Figure 2. Production of basic crops in Ukraine for 1990–2017, thou. tons

Source: prepared by the authors based on the source Agriculture of Ukraine. Statistical Collection, 2016. *Plant Growing in Ukraine. Statistical Collection, 2017. 1The data are given without taking into account the temporarily occupied territory of the ARC, the city of Sevastopol and the temporarily occupied territories of Donetsk and Luhansk. 62

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Compared to 1990 the highest rates of growth were observed in the production of grain and leguminous crops in Ukraine and sunflower, respectively, 121.38% and 475.94%, it means that over the years of independence, grain and legume production is increased in 1.2 times, while sunflower almost in 4.8 times If to investigate in the context of the last 10 years, then from 2007 to 2017 grain and legume production has already increased in 2.1 times, that is, we have a tendency for increase of this production. The studied dynamics since 1940 indicates that over the years, Ukraine has only increased grain and legume production from 26419.7 thousand tons in 1940 up to 61916.7 thousand tons in 2017 (in 2.3 times) and sunflower from 946.5 thousand tons in 1940 and up to 12235.5 thousand tons in 2017 (in 13 times) (Figure 3). This production burden falls on arid southeastern regions of Ukraine. As it was already described, some of these areas are referred by the Institute of World Resources to high risk water regions (High risk (3-4), the Crimea (temporarily occupied), Kherson, Nikolaev, Donetsk (part of the territory is occupied), Luhansk a small part of the territory is occupied), Dnipropetrovsk (a small part), Odesa (a small part). The peculiarity is that these regions have a critically low value of river runoff per year per person, Kherson has 0.13 thousand m3/year; Mykolaiv 0.49 thousand m3/year; Donetsk 0.24 thousand m3/year; Luhansk 0.66 thousand m3/year; Dnipropetrovsk 0.27 thousand m3/year; Odesa 0.15 thousand m3/year. In general, all south-eastern regions have a low value of river runoff per one person per year (Dnipropetrovsk, Donetsk, Zaporizhzhya, Luhansk, Mykolaiv, Odesa, Poltava, Kharkiv, Kherson) (from 0.13 to 0.66 thousand m3/year).

Figure 3. Production of grain and leguminous crops and sunflower in Ukraine for 1940–2017, thou. tons

Source: prepared by the authors based on the source Agriculture of Ukraine. Statistical Collection, 2008. *Plant Growing in Ukraine. Statistical Collection, 2017. 1The data are given without taking into account the temporarily occupied territory of the ARC, the city of Se- vastopol and the temporarily occupied territories of Donetsk and Luhansk.

So taking into account the increase of the agrarian potential described above, in Ukraine since 1990 and up to date there has been a significant reduction in water use (compared with 1990 almost in four times) and a corresponding reduction in the technogenic load on water objects (Fi- gure 4).

Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068 Figure 4. Use of fresh water from 1990 to 2016 in Ukraine (including freshwater and seawater), mln. m3*

* prepared by the authors based on the source of State Statistics Service of Ukraine, 2010–2017. 1The data are given without taking into account the temporarily occupied territory of the ARC, the city of Se- vastopol and the temporarily occupied territories of Donetsk and Luhansk.

There are several reasons for this: the one first is the reduction of national production, happened due to the reorientation of production to the domestic market during the crisis of 1991- 1996, and the second one is moral and physical aging of wastewater treatment equipment and systems, which also led to a decline in national production, and, of course, the third one, the use of water-efficient technologies in recent years has to be taken into account (technology of consistent recycled water supply in productions, closed technologies). However, the ecological state of surface and underground sources of water supply is not improving. There is a significant geochemical pollution of water with heavy metals, products of oil refining, mineral fertilizer residues. Every year, large amounts of inadequately cleaned municipal and industrial sewage are discharged into surface water objects of the country, which is the result of significant volumes of such waste and inefficiency of water treatment systems. Until the beginning of the armed conflict in 2014, there was another region in Ukraine that consumed water much more than other regions it was the Autonomous Republic of Crimea. By the end of 2013, the water consumption of the ARC was 7.62% of the total water use; at the same time, in Dnipropetrovsk region it was 13.37%; in Donetsk 13.42%; Zaporizhzhya 12.26%; Kiev 8.58%; Kherson 10.64%. For the rest of regions there was no more than 2-3% of the total water use, except Kyiv (5.76%). Thus, for 25 years, the most significant consumers in Ukraine are Dnipropetrovsk, Zaporozhzhya, Donetsk, Kherson and Kyiv regions and the Autonomous Republic of Crimea during when the territory was not occupied. From statistics it is clear that water consumption in Ukraine has decreased considerably, almost in 4 times. A marked increase in water use is observed in the Kherson region since 2003. Donetsk region consumes sea water as considerable part, it is almost the only area that uses seawater for production needs (practically 60% of regional water use). Sometimes seawater is still used by the Odesa region, they use 2-3% of the regional water use and Kherson region, that is 0.1%.According to statistical data Kyiv slightly behind for water use, but still consumes more than other regions. The described tendencies suggest that, indeed, in the regions of Ukraine, the degree of water availability (the water potential of the region) is not a significant factor affecting the trade with grain and legumes, which production requires significant water resources. In the fact that Ukraine is the poorest country in water resources in Europe, such trends are embarrassing and require 64

Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068 scientific substantiation of rational water use in regions. The proof of the author's assumption is the lack of a correlation between the average arithmetic values for 10 years of production of grain and leguminous crops by regions of Ukraine (thousand tons) and the value of average many years resources of river runoff in the regions (km3/year). The calculations were not used data on the Au- tonomous Republic of Crimea due to its temporary occupation and lack of data.

3. RESULTS For calculations was used such a method of correlation analysis of biserial correlation using a point biserial correlation coefficient of Pearson (Table 4).

Table 4. Calculation of the point biserial correlation coefficient of Pearson

Source: prepared by the authors

If the variable X is measured on a strong scale, and the variable Y is in the dichotomous, then the point biserial correlation coefficient is calculated by the formula

 xx 01 nn 01 rpb   , (1) Sx nn 1

where x1 – the average value for X objects with the value "unit" by Y; 65

Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068 x – the average value for X objects with a value of "zero" on Y; 0 sх – average square deviation of all values by X; n1 – number of objects "unit" by Y, n2 - number of objects "zero" by Y; n = n1 + n0–sample size.

The point biserial correlation coefficient can also be calculated using other equivalent expres- sions:  xx nn r  1  1 , (2) pb S nn 1 x 0 

 xx nn r  0  0 , (3) pb S nn 1 x 1  where x – total average value for variable X.

As per conditions of the task n1 = 14, n0 = 10. Sample size n = 24, df = 22.

We find the mean values for the variable X and the quadratic mean deviation sх and the value of the point biserial correlation coefficient:

x = 2273,778; x = 2223,507; x = 2344,158; S =1160,176. 1 0 x

 xx nn r   0101  ,05240 pb S nn 1 x   xx nn r  1  1  ,05240 pb S nn 1 x 0   xx nn r  0  0  ,05240 pb S nn 1 x 1 

The hypothesis of the significance of the point biserial correlation coefficient is verified using Student's criterion. The empirical value is equal to r pb  ,05240 t  n 2   ,245990224 1 r 2 2 pb  ,052401

We find the critical values of Student's criterion tα(df) in statistical tables for the number of degrees of freedom df = 22.

0,24599 2,074 2,819 t

Empirical value | t | = 0,24599 does not fall into the critical region, which allows us to accept the null hypothesis ρ = 0. Consequently, there is no link between the water supply (water potential) of the regions and the production and trade with grain and legumes, the production of which requires significant water resources. 66

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CONCLUSION However, there is still a question that, after reaching the threshold for water scarcity, Ukraine will begin to demand the import of water-based technologies and goods exponentially increasing with the reduction of water resources. Ukraine, having a total only 5.1 km3/year of total domestic renewable water resources, in no way cannot be compared with Brazil which is 5661 km3/year, the USA is 2818 km3 /year, China is 2813 km3/year, although it is the largest exporter of cereals in the world after the USA and Russia. Brazil, Russia, Canada, Colombia and Australia have the largest water potential in the world, which in future can create the most favorable conditions for economic growth. The analysis of the functioning of regional socioeconomic systems under the influence of global challenges for effective regional water use makes it possible to ascertain the existence of a problem of unbalanced regional development, and the country's economic complex is water unbalanced, and according to environmental parameters does not correspond to the possibilities of water resources restoration. Water supply of the territories does not determine the specialization of the regions of Ukraine in the production and trade with grain and leguminous plants. It is possible to assume that such conditions have arisen due to the existence of land resources (black earth) in the country. It is also possible to assume based on the abovementioned facts that there are other more important problems of effective regional water use in Ukraine than the reduction of river runoff, namely, the infrastructure provision of water management in the regions, which causes large water losses and their repeated pollution, as well as unauthorized and unaccounted water discharges .

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Svitlana Fedulova, Vitalina Komirna, Nataliia Naumenko and Oksana Vasyliuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 057-068 420. Fedulova, S. O. (2016), “Formation of the market of water resources as a process of accumulation of capital in the regions of Ukraine on the way to sustainable development”, Baltic Journal of Economic Studies, Vol. 2, No, 2, pp. 176–183. Khvesyk, М. А. (2013), Scientific fundamentals of the national strategy of sustainable development of Ukraine, Institute of Economics of Natural Resources and Sustainable Development of the National Academy of Sciences of Ukraine, Kiev (in Ukrainian). Khvesyk, М.А., Levkovska, L.V., Sunduk, А.М. (2014), “Features of the economic evaluation of virtual water and possibilities of its use in Ukraine”, Finance of Ukraine, No. 6, pp. 83–96 (in Ukrainian). Melnyk, О. I., Matsenko, Е. I., Khyzhnyak, М. А. (2011), “Perspectives taking into account the concept of virtual water and water footprint in the economic relations of water use”, Mechanism of the economy regulation, No. 1, pp. 221–229 (in Ukrainian). Official site of Food and Agriculture Organization of the United Nations, available at: http://www.fao.org (accessed 14 January 2018). Official site of Institute of World Resources, available at: URL:http://www.wri.org (accessed 14 January 2018). Official site of World Water Institute (SIWI), available at: http://www.siwi.org (accessed 14 Janu- ary 2018). Perelyot, R. А. (2010), “Water scarcity and economics of water efficiency”, in Rational use of natural resources: international programs, Russian and foreign experience, Мoskow, Partnership of Scientific Knowledge КМК, pp. 168-181 (in Russian). Plant Growing of Ukraine,.Statistical Collection 2017 (2018), Ed. by O.M. Prokopenko, State Statis- tics Service of Ukraine, Kiev (in Ukrainian). Report of the High Level Panel on Financing Infrastructure for a Water-Secure World (2015), “Wa- ter: fit to finance? Catalyzing national growth through investment in water security”, Organisa- tion for Economic Cooperation and Development (OECD). Oki, T., Yano, S., Hanasaki, N. (2017), “Economic aspectsof virtual water trade”, Environ. Res. Lett. 12, available at: URL :https://doi.org/10.1088/1748-9326/aa625f (accessed 10 January 2018). Water footprint. Official site of water footprint, available at: http://www.waterfootprint.org. (accessed 27 December 2017). Wichelns, D. (2010), An Economic Analysis of the Virtual Water Concept in relation to the Agri-food Sector, Hanover college, United States of America, Indiana. Yang, H., Reichert, P., Abbaspour, K. C., Zehnder, A. J. B. (2003), “A Water Resources Threshold and Its Implications for Food Security”, Environmental Science and Technology, Vol. 37, No. 14, pp. 3048-3054. Yang, H., Wang, L., Zehnder, A. J. B. (2007), “Water scarcity and food trade in the Southern and Eastern Mediterranean countries”, Food Policy, Vol. 32, pp. 585-605.

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084

Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 069-084 ‘

Assessment of Fiscal Decentralization Influence on Social and Economic Development

OLENA CHYGRYN1, YURIY PETRUSHENKO2, ALINA VYSOCHYNA3 and ANNA VORONTSOVA4

1 Associate Professor of the Department of Economics, entrepreneurship and Business Administration, Sumy State University, Sumy, Ukraine, e-mail: [email protected] 2 Professor, Head of the Department of International Economics, Sumy State University, Sumy, Ukraine, e-mail: [email protected] 3 Senior Lecturer, Department of Accounting and Taxation, Sumy State University, Sumy, Ukraine, e-mail: [email protected] 4 Researcher of the scientific research work, International Economics Department, Sumy State University, Sumy, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 07, 2018 Transformation of the mechanism of responsibility redistribution Revised from August 23, 2018 between central and sub-central government resulted in expansion Accepted November 10, 2018 of fiscal decentralization (FD) ideas. The concept of fiscal decentral- Available online December 15, 2018 ization in general features was formed at the second half of XX century, however, key ideas appeared far more earlier. Neverthe- less, many controversial empirical research results are focused on JEL classification: the identification of fiscal decentralization influence on economic C12, C33, E62, H77, O47. and social development (different empirical researches confirmed its positive, negative or insignificant influence). Thus, because of DOI: 10.14254/1800-5845/2018.14-4.5 dynamic change of worldwide socio-economic trends, it becomes crucial to realize up-to-date empirical comprehensive research on Keywords: fiscal decentralization impact on different aspects of country social and economic development. This article is devoted to the analysis of fiscal decentralization, peculiarities, positive and negative consequences of fiscal decen- economic development, tralization and empirical testing of hypothesis that fiscal decentrali- social development, zation positively affected country social and economic development fiscal policy, aspects. For this purpose, evolution of fiscal decentralization con- taxation cept was analized. Main positive features (increase of transparency and accountability of sub-central government financial policy, amendment of public services quality etc.) and risks of its implementation (increase of macroeconomic instability due to lack of centralized control, along with high dependence of fiscal decentralization measures efficiency on competence of sub-central governments, decrease of large-scale social projects financing and others) were found. Thence, there is no single opinion about the impact of fiscal decentralization on country social and economic development, the main objective of the research is to test empirically the hypothesis that expansion of FD positively affected different measures of social and economic development (economic growth rate, inflation, 69

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employment rate, social contribution and others) (Simionescu et al., 2017; Lazányi et al., 2017). Testing the hypothesis of FD’s positive influence on different parameters of social and economic development was realized on the base of panel data analysis for the set of European countries (Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Macedonia, Moldova, Montenegro, Romania, Serbia, Slove- nia, Turkey and Ukraine). Methodologically it was realized through the using of panel data regression model (independent variables – measures of fiscal decentralization, dependent variables – measures of social or economic development) in Stata 12/SE. This research allowed to confirm the hypothesis on positive impact of different measures of fiscal decentralization on GDP, GDP growth rate, foreign direct investments, and social contributions. Neverthe- less, negative impact of fiscal decentralization on GNI per capita and export of goods and services was found. Besides, increase of public finance centralization stimulates inflation. There was identified controversial impact of different parameters of fiscal decentralization on dynamic of import of goods and services, accordingly.

INTRODUCTION Numerous debates concerning the role of centralized control in financial resources redistribution process in order to ensure economic development resulted the expansion of decentralization ideas and strengthened the role of local governments. Practically, formation of an effective fiscal system that would ensure balanced and sustainable development of the whole country and certain regions or municipalities at the same time dramatically depends on the level of centralization in the country. This is why the ability of local authorities being financially independent and functioning on the base of fair distribution of powers and resources became economically more viable, which causes attention of scholars to such a phenomenon as fiscal decentralization (Hybka, 2016; Seku- la and Smiechowicz, 2016; Sekula, 2017). Over the past three decades, many countries have started their work towards decentralization reform implementation in order to improve effectiveness of public finance management. In addition, such international organizations as the World Bank, the United Nations and the Organisation for Economic Co-operation and Development (OECD) are actively supporting the process of transformation towards decentralization in many developing countries (Mohamadi and Bohma, 2017; Morscher et al., 2017). The aim of our study is to analyze peculiarities, pros and cons of fiscal decentralization and realize empirical testing of hypothesis on positive effect of fiscal decentralization on social and economic development items. Main contribution of the paper lies in testing of the hypothesis of positive influence of fiscal decentralization on different parameters of social and economic development and it’s realization on the basis of panel data analysis for the set of European countries using the regression model in Stata 12/SE. The concept of decentralization in general features was formed at the second half of XX century, but its key ideas appeared far more earlier. The greatest contribution to the development of the fundamental foundation of decentralization concept has been made by C. M. Tiebout (1956), R. A. Musgrave (1959), M. Olson (1969) and W. Oates (1972). According to Tiebout hypothesis (1956), decentralization enhances competition among subnational governments in providing impure public goods according to consumer-voter’s preferences (“Tiebout sorting”). He states that it also contributes to the effectiveness of budgetary regulation of socio- economic development of regions (see also Ambroziak, 2016; Slusarczyk, 2018). R. A. Musgrave (1959) believed that the decentralization of intergovernmental relations contributed to the efficient allocation of resources or public goods.

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M. Olson (1969) introduced the principle of “fiscal equivalence”, which requires subnational governments make decisions about the size of necessary both public spending and mobilized public revenues. This principle is considered as one of the fundamental principles of decentralization concept. W. Oates (1972) proposed a decentralization theorem that formalizes the efficiency of decentralized decision on providing local public goods. It should be noted that the current stage of development decentralization reforms include political, administrative and fiscal decentralization. Each of the above-mentioned perspectives has its own specific features (Pimonenko, 2017; Pimonenko and Chigrin, 2014). Thus, political decentralization is aimed to transfer powers from central to local authorities, administrative decentralization is focused on transition of functional responsibilities from central to local governments, and fiscal decentralization is aimed at transforming financial relations between different levels of government (Sanusi et al. 2017). However, despite the existence of difference between these components of the concept of decentralization, as a rule, they are implemented together to ensure positive synergistic effect and maximum benefits for all economic agents. Fiscal decentralization usually involves the transfer of a significant amount of budget authority to subnational government level with the simultaneous expansion of their financial capabilities. This corresponds to fundamental principle of decentralization concept and European Union legislation – subsidiarity of public services provision, which reflects the transfer of authority and responsibility of decision-making process to the lowest level (closely to the citizens) (Melnyk et al., 2018; Pilia, 2017). Unless, it is more effective at national or regional level.

1. LITERATURE REVIEW Comprehensive analysis of FD consequences allows to summarize its positive and negative aspects. So, the most widespread positive results of FD can be summarized as follows: fostering accountability and transparency of local government activities (Feruglio and Anderson, 2008; Faguet, 2012; Vasylyeva et al., 2014), and improving quality of public service delivery through compliance the principle of subsidiarity (Martinez-Vazquez, 2011; Faguet, 2012; Zizlavsky, 2016). Despite broad support of fiscal decentralization measures, experts also emphasize the existence of certain risks of its implementation, such as: an increase of macroeconomic instability level due to lack of centralized control over local finances (Feruglio and Anderson, 2008; Siemiatkowski, 2017); high dependence of reform effectiveness on the competence of local authorities (Bahl, 1999; Hart and Welham, 2016; Bobenic Hintosova et al., 2018); lack of financial support for projects covering several jurisdictions (for example, repairing roads of that cover territory of several regions - Shah, 2004; Chygryn, 2016); deepening of horizontal imbalances and competition between municipalities for financial resources (Lyeonov et al., 2018). Therefore, taking into account all these risks and benefits of FD is an integral precondition of its effective implementation. The main issue to be solved by FD is transfer of a number of liabilities to the level of sub- central governments in order to ensure the principle of subsidiarity of the provision of public services, as well as strengthening the fiscal autonomy of local governments by expanding the range of sources of own revenues formation. For that reason, there are two perspectives of FD in the world of practice such as: cost decentralization and revenue decentralization. In addition, FD may also result in the transformation of intergovernmental transfers system and expansion the capacity of subnational governments to carry out the external borrowings. Despite a lot of positive sides of FD, there are many controversial empirical research results focusing on identification of FD influence on economic and social development (different empirical researches confirmed its positive, negative or insignificant influence).

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084 The study of relationship between the measures of decentralization and socio-economic indicators have begun in 90s of the last century and included both single-country and cross-country studies with various time period, methods and models basis. Many theoretical and empirical studies focused on the relationship between FD and economic growth (usually expressed through the Gross domestic product (GDP)). However, it should be noted that the researches had quite contradictory results. Thus, the positive and statistically significant cohesion between the indicator of decentralization and economic growth was confirmed by W. Oates (1993, 1995), A. Limi (2005), J. Martinez-Vazquez and R. McNab (2006), S. Leonov et al. (2012). In contrast, H. Davoodi and H. Zou (1998) proved a significant negative relationship, S. Yilmaz (2000), and T. Baskaran and F. Lars (2009) describe very weak and not statistically significant impact. Some group of scholars paid particular attention to the interconnection of decentralization indicators and macroeconomic stability of the country. For instance, J. Rodden and E. Wibbels (2002), T. Vasilyeva et al. (2018) proved positive and statistically significant impact of FD on the reduction of state budget deficit and level of inflation. In contrast, B. Neyapti (2010) described negative and statistically significant impact of decentralization on macroeconomic stability. Cohesion between decentralization and income inequality in academic circles is equally important (Nounamo Nguedie, 2018). Positive and statistically significant effect on the Gini coefficient was empirically confirmed by C. Sepúlveda and J. Martinez-Vazquez (2011), but B. Neyapti (2006) showed negative and statistically significant impact of income decentralization on household income inequality. Z. Scott (2009) researches relationships between decentralization and service delivery, economic development and social cohesion (in order to mitigate or exacerbate conflicts in society). This research reveales the next empirical results – lack of clear statistically significant impact of decentralization on economic development. However, targeting the local authorities in providing high-quality public services for their citizens could contribute to economic growth. This study also does not confirm positive / negative impact of statistically significant relationship of decentralization and conflict level in society. The issue of the relationship between the results of decentralization reform and the social sphere indicators in the country is also the subject of theoretical and empirical researches (Nelson, 2017; Krasnyak and Chygryn, 2015). Worthy of note is the I. Szarowska (2014) study, which dis- tinguishes such channels from decentralization to economic activity as government education spending, performance of the educational system, government capital spending, and human capital, that gives their theoretical substantiation. J. Faguet (2004), in his work, provided justification for the positive statistically significant impact of decentralization on providing higher quality educational and other social services at local level, at the same time more free access to population for these services (Faguet and Sanchez, 2014). There is also positive statistically significant relationship with regard to health services, but somewhat in comparison weaker than the same for educational services (Martinez-Vazquez et al., 2015). J. E. Ligthart and P. van Oudheusden (2015), T. Prince (2017) examine the relationship between the indicator of decentralization of public service provision and trust to government-related institutions (confidence in national and local authorities, government, political parties, etc.). The result of this study among 42 countries over the period 1994-2007 was a confirmation of hypothesis that FD contributes to increasing public confidence in government-related institutions and leads to lower perceptions of corruption in government. Thus, because of controversial above mentioned results and dynamic change of worldwide socio-economic trends, it becomes necessary to realize up-to-date empirical comprehensive research on FD impact on different aspects of country social and economic development.

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2. METHODOLOGY In order to realize the above-mentioned task we tested the hypothesis about positive influence of FD on different measures of social and economic development of the country by using panel data regression model. Technically, it was realized with Stata 12/SE. Traditionally, the regression equation can be described by the next formula:

(1)

where – dependent variable (measure of social or economic development); – independent variable (measure of fiscal decentralization); - error term.

We are interested in finding value of coefficient β, as it illustrates how changes on fiscal decentralization measures affect country social and economic development measures. Research is based on data collected for 12 European countries such as Albania, Bosnia and Herzegovina, Bulgaria, Croatia, Macedonia, Moldova, Montenegro, Romania, Serbia, Slovenia, Tur- key, and Ukraine. These countries were chosen because of geographical proximity and similarity of socio-economic development. Time horizon of the research is 2006-2015. There is no more recent data because the latest available period that allowed collecting balanced panel data was year 2015. All dependent variables (indicators of social and economic development) were collected from the World Development Indicators Database of the World Bank. The set of dependent variables include below:  GDP (current US$);  GDP growth (annual %);  GNI per capita, purchasing-power parity (current international $);  Exports of goods and services (% of GDP);  Imports of goods and services (% of GDP);  Inflation, GDP deflator (annual %);  Foreign direct investment, net inflows (balance of payments, current US$);  Employment to population ratio, 15+, total (%) (national estimate);  Unemployment, total (% of total labor force);  Social contributions (% of revenue).

All the above-mentioned indicators comprehensively describe different perspectives of country social and economic development. Key indicator is used to describe economic growth is GDP. Nev- ertheless, in some cases it is not informative to use absolute measure of GDP, so in numerous empirical researches it is often used GDP per capita or GDP growth rate. We added to the set of dependent variables both absolute and relative measures of GPD to increase feasibility of the results. We also used gross national income per capita indicator as a measure of country economic development, hence unlike GDP, it allows to assess somehow level of country residents’ prosperity, and i.e. it takes into consideration incomes gained by residents inside and outside of the country of residence also. Using of both (GDP and GNI) indicators helps to get more adequate empirical results. In turn, export and import to GDP ratios are used to characterize countries’ international trade activity that is an integral part of globalized economic relations. Employment and unemployment rates and social contribution to revenue ratio illustrate social perspective of country development. We used employment to population ratio to characterize labor force country capacity, while unemployment to total labor force ratio reflects somehow the level of ineffective usage of labor force country capacity.

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084 All independent variables (measures of FD) were collected from Fiscal Decentralization Re- ports of Network of Associations of Local Authorities of South-East Europe (NALAS), and indicators of fiscal decentralization for Ukraine were calculated according to NALAS methodology using statistical data from the reports provided by the State Treasury of Ukraine. Set of independent variables include the next indicators:

 Consolidated Public Revenues as % of GDP;  Local Government Revenue as % of Public Revenue;  Local Government Revenue as % of GDP;  Own revenue as % of local government revenue;  Shared taxes as % of local government revenue;  Local Government Investments as % of total public investment.

According to NALAS methodology, consolidated public revenues include budget revenues and financial resources of off-budget social security funds. Consolidated Public Revenues to GDP ratio illustrates centralization of public finance sector, i.e. how much money redistributes via state budget. Higher level of this indicator characterizes higher level of centralization. In turn, local government revenue consists of all financial resources gained by local government, including local borrowings but excluding intergovernmental grants from the state budget. Besides, own revenue of local governments include inflows from local taxes; gains from the sale or rental of municipal assets; fines, penalties, also interest; local user fees and charges; fees for permits, licenses, and the issuance of official documents. Finally, shared tax is one of fiscal equalization policy instrument that allows to increase local budget’s fiscal capacity throughout redistribution of revenues from collected national taxes. Besides, local government investment to total public investment ratio was chosen as a measure of fiscal decentralization indicator. Since as a rule local governments have more or less enough financial resources to cover provision of basic public services at local level. So, adequacy of financial resource in financing certain investment issues in addition to basic public services financing is one of the evidences of local governments’ satisfactory fiscal capacity and efficiency of fiscal decentralization implementation. Research consists of a few stages: the first stage identifies influence of each separate independent variable on each certain indicator of economic development; the second stage identifies influence of all independent variables by adding a new variable to each next equation. Both stages were realized through using of panel data regression analysis of Stata 12/SE.

3. EMPIRICAL RESULTS General information on the set of dependent and independent variables among 12 countries over the period 2006-2015 is presented in table 1. Data presented in the table allows to admit that quantity of observations is different for some variables (panel data set is slightly unbalanced), but this variance is not that much dramatic and does not affect the feasibility of empirical results. Besides, panel data regression instrument of Stata 12/SE has internal mechanisms, which help to deal with both balanced and unbalanced panel data. Absolute measure of GDP is one of the main indicators of economic growth. For the researched period, its value in average is 113 billion US $. In 2016 the largest amount of GDP was in Turkey (863.711 billion US $), the lowest in Montenegro (4.374 billion US $). In turn, better evidences of economic dynamics could be described by relative measure such as GDP growth rate. The average growth of GDP of analyzed countries is 2.32% with variation of 4.28 for the period or research. It varies from -14.8% (minimum value) for Ukraine in 2006 to 11.11% (maximum value) for Turkey in 2011. On the average, the GNI per capita for our observations was 13950.25 US $, 74

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084 which means that in this study was analyzed average upper-middle income countries (by classification of the World Bank). In fact, there were 16.7% of lower middle-income countries in 2015 (Mol- dova, Ukraine), and 83.3% of upper-middle income countries (Albania, Bosnia and Herzegovina, Romania, Macedonia, Montenegro, Bulgaria, Slovenia, Croatia, Turkey, Serbia).

Table 1. Descriptive statistics for the set of variables

Standard Variable Observations Mean Minimum Maximum deviation GDP 120 1.13e+11 2.14e+11 2.70e+09 9.51e+11 GDPg 120 2.32 4.28 -14.80 11.11 GNIpc 120 13950.25 6440.61 3570.00 30590.00 Exp 120 40.76 12.99 20.45 76.99 Imp 120 55.38 15.37 23.36 97.14 Deflator 120 5.71 5.85 -0.99 38.88 FDI 120 3.48e+09 4.75e+09 -3.46e+08 2.20e+10 Empl 115 45.51 7.29 30.70 60.20 Unempl 120 14.26 8.31 3.90 36.1 Soc 95 28.73 11.06 0.00 39.82 DEC1 120 35.81 6.38 22.23 50.00 DEC2 120 20.51 13.72 7.00 66.25 DEC3 120 6.92 3.15 2.10 16.04 DEC4 116 36.24 14.81 10.00 83.00 DEC5 115 24.75 17.57 0.00 57.00 DEC6 110 44.38 29.46 10.00 188.52

Note: GDP – GDP, US $; GDPg - GDP growth, %; GNIpc - GNI per capita (PPP), US $; Exp - Exports of goods and services (% of GDP); Imp - Imports of goods and services (% of GDP); Deflator - Inflation, GDP deflator, %; FDI - Foreign direct investment, net inflows (BoP), US $; Empl - Employment to population ratio, 15+, total (national estimate), %; Unempl - Unemployment, total (% of total labor force); Soc - Social contributions (% of revenue); DEC1 - Consolidated Public Revenues (% of GDP); DEC2 - Local Government Revenue (% of Public Revenue); DEC3 - Local Government Revenue (% of GDP); DEC4 - Own revenue (% of local government revenue); DEC5 - Shared taxes (% of local government revenue); DEC6 - Local Government Investments (% of total public investment). Source: author’s calculation on the base of World Development Indicators database and NALAS Fiscal decentralization reports.

Likewise, the average value for exports of goods and services to GDP ratio is 40.76%, at the same time for import of goods and services to GDP ratio - 55.38%; this tendency allows to admit a situation of trade deficit. As for GDP deflator as measurement of economy's inflation among analyzed 12 European countries it is averaged at the level of 5.71%, that allows to conclude that walk- ing type inflation existed in these countries. In 2015 the highest inflation level was in Turkey (8%) and Ukraine (17%), deflation was in Albania (-0.2%) and Croatia (-0.1%). Net inflows of foreign direct investment in average among analyzed countries is 3.179 billion US $. In 2015 the lowest level was in Moldova (90 million US $), the highest level in Turkey (12.307 billion US $). The average employment to population ratio is 45.51% with variation of 7.29, which means that this amount of population is employed. In 2015 the lowest level of employment rate was in Bosnia and Herzegovina (31.1%), the highest level was in Slovenia (52.1%) and Ukraine (56.4%). The average unemployment rate is fixed at the level of 14.26%. In 2015, the lowest level was in Moldova (4.1%) and Romania (95.9%), the highest level was in Macedonia (23.7%) and Bosnia and Herzegovina (26%). As we can see, those two indicators are not directly related. Unemployment rate indicates only the number of unemployed population who are looking for job. Social contribu- 75

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084 tions indicator among analyzed countries in average covers 28.73% of revenue. This indicator has the largest amount of missing values, and in 2015 was the lowest for Ukraine: 18% of revenue and the highest for Bosnia and Herzegovina (39% of revenue). More detailed country-specific characteristic on averaged fiscal decentralization indicators for 2006-2015 is presented in figure 1 and 2.

Figure 1. Average values of local government revenue to GDP ratio and consolidated public revenue to GDP ratio for the period 2006-2015, %

Note: ALB – Albania; BIH – Bosnia and Herzegovina; BGR – Bulgaria; HRV – Croatia; MKD – Macedonia; MDA – Moldova; MNE – Montenegro; ROU – Romania; SRB – Serbia; SVN – Slovenia; TUR – Turkey; UKR – Ukraine. Source: author’s calculation on the base of NALAS Fiscal decentralization reports.

According to the data (Figure 1) Ukraine, Albania, Macedonia, Romania and Turkey form the group of countries with less centralized public finance sector, because the level of consolidated public revenue to GDP ratio varies from 20% to 35%. In turn, the level of centralization in other countries can be characterized as moderate, because it does not exceed 50%, while value of the researched indicator in 50-60% illustrates a high level of centralization. It also should be noted that local government revenue to GDP ratio varies insignificantly across countries, and its average value for most of countries is in range between 4% and 10%. Nevertheless, there are extreme values of average local government revenue to GDP ratio in Ukraine (maximum value – 14.73%) and Albania (minimum value – 2.74%). Consequently, we can determine that Ukraine is the most decentralized country of the set, since it has the lowest level of centralization of GDP through state budget. However, at the same time it has the highest level of GDP redistribution through the local budgets. Other countries have moderate level of centralization both via state and local budgets. Not surprising, because it is one of the peculiarities of European model of GDP centralization via public finance system. 76

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084

Figure 2. Averaged values of own revenue to local government ratio and shared taxes to local government ratio for the period of 2006-2015, %

Data, presented in Figure 2, allows to subdivide countries according to the intensity of usage of such fiscal policy instruments as local taxes or shared taxes to ensure fiscal capacity of sub- central governments. Therefore, we can single out a group of countries (Ukraine, Macedonia, Bul- garia, and Albania), which have rather low expansion of shared taxes as fiscal instrument to ensure fiscal autonomy of sub-central governments and at the same time quite limited opportunity to accumulate own revenues, i.e. these countries have insufficient level of local budgets’ fiscal capacity. Bosnia and Herzegovina, Romania and Turkey have balanced usage of both above-mentioned fiscal instruments. Serbia, Slovenia and Croatia more apt to use shared taxes to form financial resources of local budgets in contrast to the first group of countries. Two countries, Montenegro and Macedonia, have extremely opposite tendencies of these fiscal instruments’ implementation. Mon- tenegro has quite low level of shared taxes usage, but far more higher own revenue to local government revenue ratio, which evidences to high level of fiscal autonomy of sub-central governments. Contrary, Macedonia has extremely low own revenue to local government revenue ratio and significantly high level of shared taxes to local government revenue ratio. Thus, it can be assumed that sub-central governments in Macedonia financially dependent on state budget, especially national taxes and fees. After a brief characteristic of key tendencies of dependent and independent variables dynamic, we can move to the core stage of the research – testing the hypothesis on positive influence of fiscal decentralization on country socio-economic development. All statistically significant results are presented in the following tables. It should be noted that most of these results were gained on the first stage (testing of cohesion between separate variables), except table 6, where results of multiple panel data regression are presented. 77

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084 Testing of the influence of FD indicators on GDP (table 2) allows to find out that increase of share of own revenue in local government revenue and shared taxes in local government revenue will likely result in increase of GDP (indicators are statistically significant at 90% confident interval). Thus, we can conclude that expansion of fiscal capacity of local governments via using of both fiscal instruments (optimization of local taxes system and efficiency of municipal property usage, and redistribution of national taxes) helps to increase the absolute measure of GDP. Result for the other independent variables on the first stage and whole results for the second stage are statistically insignificant.

Table 2. Influence of FD indicators on GDP

Variable Coefficient Standard error z P>|z| Dependent variable - GDP (current US$) DEC4 1.60e+09 9.20e+08 1.74 0.082 DEC5 1.46e+09 8.11e+09 1.80 0.071

Testing of the influence of fiscal decentralization on GDP growth (table 3) allows to identify that 1% increase of shared taxes in local government revenue will force 0.058% of annual GDP growth (coefficient is statistically significant at 99% confident interval). The rest of the results are insignificant.

Table 3. Influence of FD indicators on GDP growth

Variable Coefficient Standard error z P>|z| Dependent variable - GDP growth (annual %) DEC5 0.058 0.023 2.57 0.010

Results from table 2 and 3 argue that shared taxes is the most viable instrument of fiscal decentralization in order not just to ensure fiscal autonomy of sub-central governments, but also to boost economic growth. These findings can be useful for modeling the optimal model of fiscal decentralization and fiscal equalization framework development. Testing of the influence of fiscal decentralization on Gross National Income per capita (table 4) allows to identify that 1% increase of Local Government Revenue to GDP ratio will result in 357.54$ decrease of GNI per capita (results are significant at 90% confident interval). The other results are insignificant.

Table 4. Influence of FD indicators on Gross National Income per capita

Variable Coefficient Standard error z P>|z| Dependent variable - GNI per capita, PPP (current international $) DEC3 -357.54 212.24 -1.68 0.092

Testing of the influence of fiscal decentralization on Exports of goods and services to GDP ratio (table 5) allows to identify that 1% increase of Local Government Revenue to GDP ratio will result in 1.72% decrease of Exports of goods and services to GDP ratio (at 99% confident interval). The other results are insignificant. This effect can be explained by: from tables 1 and 2 it is obvious 78

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084 that increase of local and shared taxes helps to achieve fiscal autonomy of sub-central governments. Thus, expansion of fiscal decentralization (including increase of fiscal autonomy of sub- central governments) results in additional tax burden that can cause slight negative effect for export-oriented companies. But this problem could be partially solved through using of tax exporting (when tax burden increase for non-residents – for instance, taxes on tourism, rent payments for the usage of municipal property etc.)

Table 5. Influence of FD indicators on Exports of goods and services to GDP ratio

Variable Coefficient Standard error z P>|z| Dependent variable - Exports of goods and services (% of GDP) DEC3 -1.72 0.49 -3.51 0.000

Testing of the influence of fiscal decentralization on Imports of goods and services to GDP ratio (table 6) allows to identify statistically significant impact of both Consolidated Public Revenues to GDP ratio (DEC1) and Local Government Revenue to Public Revenue ratio (DEC2) (stage 2). 1% increase of Consolidated Public Revenues to GDP ratio will lead to 0.963% increase of Imports of goods and services to GDP ratio (at 99% confident interval), but 1% increase of Local Government Revenue to Public Revenue ratio will result in 0.207% decrease of Imports of goods and services to GDP ratio (at 95% confident interval). The other results are insignificant.

Table 6. Influence of FD indicators on Imports of goods and services to GDP ratio

Variable Coefficient Standard error Z P>|z| Dependent variable - Imports of goods and services (% of GDP) DEC1 0.963 0.260 3.71 0.000 DEC2 -0.207 0.106 -1.96 0.050 Cons 25.167 10.104 2.49 0.013

Therefore, we can summarize that centralization of public finance has positive impact on imports of goods and services to GDP ratio. Testing of the influence of fiscal decentralization on Inflation (table 7) allows to conclude that 1% increase of Consolidated Public Revenues to GDP ratio will boost 0.281% inflation per year (at 90% confident interval). The other results are insignificant.

Table 7. Influence of FD indicators on Inflation

Variable Coefficient Standard error z P>|z| Dependent variable - Inflation, GDP deflator (annual %) DEC1 0.281 0.148 1.90 0.058

Contrary to the previous indicator, centralization of public finance has negative influence on price stability. Testing of the influence of FD on net inflows of foreign direct investment (table 8) allows to find out that increase of Consolidated Public Revenues to GDP ratio and Local Government Revenue to 79

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084 GDP ratio will stimulate inflows of foreign direct investment (at 95% confident interval). The other results are insignificant.

Table 8. Influence of FD indicators on net inflows of foreign direct investment

Variable Coefficient Standard error z P>|z| Dependent variable – Foreign direct investment, net inflows (BoP, current US$) DEC1 2.27e+08 1.02e+08 2.21 0.027 DEC3 4.88e+08 2.29e+08 2.13 0.034

There is no statistically significant influence of FD measures on Employment to population ratio (15+, national estimate), but increase of all independent variables except Local Government Investments to total public investment ratio (DEC6) results in decrease of unemployment to total labor force) ratio (table 9).

Table 9. Influence of FD indicators on Unemployment, total (% of total labor force)

Variable Coefficient Standard error z P>|z| Dependent variable – Unemployment, total (% of total labor force) DEC1 -0.169 0.106 -1.60 0.110 DEC2 -0.141 0.041 -3.42 0.001 DEC3 -0.579 0.233 -2.48 0.013 DEC4 -0.086 0.049 -1.74 0.083 DEC5 -0.084 0.043 -1.94 0.053

There is also statistically significant (except shared taxes to local government revenue ratio) and dominantly positive (except Local Government Revenue to GDP ratio) influence of fiscal decentralization measures on social contribution to revenue ratio (table 10).

Table 10. Influence of FD indicators on social contribution to revenue ratio

Variable Coefficient Standard error z P>|z| Dependent variable – Social contributions (% of revenue) DEC1 0.395 0.159 2.48 0.013 DEC2 0.366 0.039 9.46 0.000 DEC3 -1.031 0.381 -2.71 0.007 DEC4 0.138 0.073 1.89 0.059 DEC6 0.043 0.018 2.36 0.018

CONCLUSION We can summarize that, in most cases statistically significant impact of fiscal decentralization measures on different perspectives of country socio-economic development was confirmed (except employment to population rate). It should be noted that, increase of Consolidated Public Revenues to GDP ratio positively affected import of goods and services, inflow of foreign direct investments, social contribution, and triggered moderate inflation and decrease of unemployment.

Olena Chygryn, Yuriy Petrushenko, Alina Vysochyna and Anna Vorontsova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 069-084

Increase of local governments’ fiscal autonomy (Local Government Revenue to Public Revenue ratio) stimulates expansion of social contributions, decrease of unemployment and impedes imports of goods and services. Otherwise, increase of Local Government Revenue to GDP ratio negatively affects gross national income per capita, social contributions, exports of goods and services, but stimulates foreign direct investments inflow and decrease of unemployment. Furthermore, dynamic of GDP is positively affected by expansion of shared taxes in local budget revenues. It was found that shared tax is the most effective fiscal instrument used under the fiscal decentralization implementation that also allows to achieve positive country economic dynamic. It is also notable that, increase of own revenue of sub-central government positively affected absolute measure of economic dynamic. In turn, expansion of local government revenue (in most cases with the help of expansion of local taxation) can negatively affect international trade (both export and import of goods and services), so it will be better to shift tax burden from residents to non-residents through using tax exporting. Generally, all the revealed tendency and cohesion confirmed a huge impact of fiscal decentralization on socio-economic development and might be taken into account to develop effective government economic policy.

ACKNOWLEDGMENT This research is realized under the project funded by the Ministry of Education and Science of Ukraine (№ g/r 0117U003935)

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Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 085-096 ‘

A Comparative Analysis of Modern Performance Methods in Economic Practice

VERONIKA CABINOVA1, ERIKA ONUFEROVA2, PETER GALLO, JR.3, PETER GALLO4 and JURAJ GALLO5

1 PhD. Student, University of Presov, Faculty of Management, Department of Finance, Presov, Slovakia, e-mail: [email protected] 2 PhD. Student, University of Presov, Faculty of Management, Department of Finance, Presov, Slovakia, e-mail: [email protected] 3 Assistant Professor, University of Presov, Faculty of Management, Department of Management, Presov, Slovakia, e-mail: [email protected] 4 Assiociate Professor, University of Presov, Faculty of Management, Department of Tourism and Hotel Management, Presov, Slovakia, e-mail: [email protected] 5 PhD. Student, University of Presov, Faculty of Management, Department of Management, Presov, Slovakia, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 09, 2018 The main objective of this paper was to analyse the performance Revised from September 07, 2018 and efficiency development of the Slovakia's spa sector over the Accepted November 14, 2018 years 2013 – 2017 by applying the selected modern methods of Available online December 15, 2018 business performance evaluation – the Economic Value Added, the Return On Net Assets, the Creditworthy Model, the Simplex Linear Programming Method and examine whether the application of these JEL classification: modern models provides an identical view of the financial perfor- C02, C51, C53, M31, M21. mance of the sector surveyed. The intention of the paper was also to reveal statistically significant interconnections between the key EVA DOI: 10.14254/1800-5845/2018.14-4.6 indicator and other modern methods. In the case of confirmation of a strong correlation we focused on evaluation the degree of identity Keywords: with the results of above-mentioned applied methods. Despite the fact that these models evaluate performance on the basis of differ- business performance, ent input data and indicators, year 2015 was identically identified business efficiency, as the best-performing one, vice-versa, the worst financial perfor- modern methods of performance mance of enterprises was recorded in 2016 within the application of evaluation, all methods. At the significance level of p < 0.05, a statistically spa enterprises significant correlation between all pairs of the analysed methods was revealed. However, the strongest positive correlation with the Economic Value Added was confirmed in the case of Simplex Meth- od of Linear Programming ( = 0.536842). This result was also confirmed by the identical assessment of the performance and efficiency development in individual years. Results of the study pointed to close interconnection between the dimension of performance (the EVA indicator) and efficiency (the Simplex Method of Linear Programming), which will be integrated into a new designed Enterprise Performance Model (EPM).

Veronika Cabinova, Erika Onuferova, Peter Gallo, Jr., Peter Gallo and Juraj Gallo / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 085-096

INTRODUCTION As a result of growing globalization and the internationalization of the business environment, the issue of business performance management is becoming more and more important, as constantly monitoring, analysing and increasing performance is a key tool in the current competitive struggle. As reported by Y. Bilan et al. (2017), new requirements for operation of enterprises in the globalized economic system characterized by processes of competition rise and development of information technology need to find new forms of cooperation with partners in the context of improving their enterprise performance. In this context, it is necessary to address issues of the enterprise performance analysis and management due to constantly changing global business environment bringing new, modern approaches to solve the issue in question. Comprehensive monitoring, quality management and continuous performance improvement is an important element in detecting and optimizing possible future business risks that can contribute to achieving the strategic goals set in all areas of business. Neither the Slovak spa enterprises are no exception, as thanks to them Slovakia belongs to one of the major spa countries in Europe with a rich history and representation of all types of healing mineral and thermal waters. Currently, the spa resorts and enterprises are integrated into health and medical system, but they do not represent a separate economic sector as it interferes with all spheres of economic and social life. In addition, spa tourism is one of the economy sectors with high growth potential and the main product line of tourism in Slovakia. Due to hectic way of life, unhealthy food, stress and many other factors that negatively affect our health, we can assume that its importance will increase even more. For this reason, we focused on the evaluation of the health spa sector in Slovakia by applying the selected modern financial methods over the last 5 years (2013 – 2017).

1. LITERATURE REVIEW The concept of enterprise performance takes an important place within the defining the company's activity essence in the business environment including its success and predictions of the future financial situation (Fibrova and Soljakova, 2005). The definition and evaluation of enterprise performance is addressed by many Slovak and foreign authors. According to the authors O. Fal- tejskova, L. Dvorakova and B. Hotovcova (2016), the enterprise performance can be most precise- ly defined as a success on the market, the ability to succeed in competition and to find the possibility of further growth in a constantly changing instable business environment. As reported by V. Soltes and B. Gavurova (2015), enterprises in today's highly competitive market must struggle to respond flexibly to changing conditions and regularly monitor and evaluate the level of their enterprise performance. However, managers solve problems how to measure the performance to prevent the improvement of one part of the business at the expense of another as increasing the implementation of performance measurement systems in companies is linked to many problems in need of answering (Striteska, Zapletal and Jelinkova, 2016). For qualified and correct answer how to achieve the higher efficiency and performance of business, it is firstly necessary to determine what the current and real business performance is and which appropriate and objective indicators use to measure this performance. The information obtained and values of the indicators are important factors for decision-making about future investment in companies. As reported by D. Miron and A. Petrache (2012), managers are still con- fronted with the decision, how to allocate limited company resources in a challenging and highly competitive environment. The enterprise performance according to Z. Niznikova et l. (2015) represents one of the crucial factors for responsible enterprise governance, especially from the perspective of valuation in the current competitive environment. For this reason, all enterprises have to dispose of a set of financial and non-financial indicators that can characterize its performance as accurately as possible. J. Wagner (2009) states that efficiency can be perceived as one of the enterprise performance dimensions (see Figure 1).

Veronika Cabinova, Erika Onuferova, Peter Gallo, Jr., Peter Gallo and Juraj Gallo / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 085-096

Figure 1. Basic dimensions of performance

Performance dimensions | | Efficiency Effectiveness   "Do the right things" – performance in "Do things right" – performance in terms of terms of choosing the activity we per- how we perform the activity. form.

Source: own processing according to Wagner, 2009

Nowadays, the scientific literature provides number of research studies devoted to measuring the business performance and each author has different point of view for measuring business performance. Aktan et al. (2018) explored the relationship between performance of the firms and corporate governance. The results suggested that board size, ownership concentration and audi- tor's reputation have a positive and significant impact on firm's Return on Assets, whereas the percentage of independent directors and the annual number of board meetings have negative and significant impact on firms' Return on Equity. Similar research was realized also by I. Todorovic (2013). Author's research problem was based on determining the impact of corporate governance implementation level on the enterprise performance. Results indicated that companies with higher level of implementation of corporate governance principles and better practice of corporate governance are more profitable and achieve higher performance. The impact of the financial structure on the enterprise efficiency was examined by T. Stevanovic et al. (2017). The paper findings confirmed that the growth of long-term debt and related financial expenses caused the reduction of efficiency of entrepreneurs. The influence of selected systematic and unsystematic risks to performance of the enterprises was analysed by D. Kiselakova et al. (2015). Based on the authors' results, the most significant impact on the enterprise performance has just financial risks. It was also observed that the unsystematic risks have a higher impact on performance of the enterprise as systematic risks. Z. Vir- glerova et al. (2016) studied the influence of selected factors of financial risk management on the enterprise performance. The performed research confirmed that there is an intensive financial risk in the business environment (e.g. poor access to external financial resources, poor payment discipline), which can consequently result in very serious financial difficulties and decrease in business performance. R. Rajnoha, P. Novak and M. Merkova (2016) analysed using of investment effectiveness methods within the business performance evaluation. The aim of research was to find out if using of certain investment valuation methods or some valuation approaches have the positive impact on business performance. Authors came to conclusion that the use of investment valuation methods is limited by foreign ownership of company and the use of investment controlling cause higher business performance. The analysis of transaction costs within the measurement of economic situation of institutions was emphasised by V. Draskovic et al. (2017). The analysis of types of transaction costs in the organisation allows the formulation of an algorithm of allocation of transaction costs as follows: determine key activities in the organisation; determine which types of resources are converted into which products within the core business; determine the type of process to which costs are allocated; determine whether the costs are costs of transactional areas and make the final decision about the type of transactional costs.

Veronika Cabinova, Erika Onuferova, Peter Gallo, Jr., Peter Gallo and Juraj Gallo / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 085-096

Each model for measuring performance and prediction is different and uses various financial or economic indicators. However, these models also have some common features. Most authors are focused on enhancing the predictive ability of original models by responding appropriately to the existing changed economic environment (Hyranek et al., 2018). In this regard, the above- mentioned authors classify these methods and approaches of analysing the financial performance into two large groups, namely:  The approaches that prefer to maximize the profits of an enterprise such as Return on Assets, Return on Equity, Return on Total Capital, etc. The Return on Equity, as one of the most important indicator for enterprise performance evaluation, was analysed by S. Jencova et al. (2018). Authors provided the practical application of the dynamic decomposition of Return on Equity through the Du Pont Pyramidal Model on the basis of the financial ratio indicators such as Return on Sales, Total Assets Turnover Ratio, Financial Leverage and Interest and Tax Profit Reduction.  The approaches that prefer the growth of the market value of an enterprise for its owners such as Return on Net Assets, Cash Return on Gross Assets, Economic Value Added and its modifi- cations, Cash Flow Return on Investment, etc. These performance assessment criteria measure the success of a business activity by its economic gain. Most of them have dynamic nature and also take into account the average cost of acquiring and binding the business' own external capital and interest-bearing debt capital.

The measurement and performance evaluation of an enterprise is nowadays a very topical issue, but also extensive and complex process. However, financial metrics systems help financial managers to generate the concept of development, to choose the right strategy as well as to plan all financial aspects in the short or long term. Therefore, the company's management should emphasize an increasingly implement financial models in its financial and economic analyses (Jen- cova et al., 2018). The increasing globalization and internationalization affects all countries, including Slovakia. For Slovakia, the transition to a new environment is much more challenging and influenced by various factors. In this context, it is necessary to focus on indicators not only financial but also non-financial. M. Draskovic et al. (2017) state that according to many non-economic indicators (political stability, democratization, liberalization and institutionalization of society, law, infrastructure development, safety, security, investment, compliance with environmental and social standards, efficiency of the legal system, human rights respect, etc.), as well as economic indicators (purchasing power, rate of economic growth, foreign trade balance, current account deficit, public debt, inflation rate, unemployment rate, public expenditure, investments, etc.), they are characterized by a long-term transitional crisis of structural type. Financial analysis involves comparing the firm's performance within the other firms in the same industry field and evaluating trends in the firm's financial position over the time (Vitkova and Semenova, 2015). In this regard, the classic approach to assessing the enterprise performance by indicators such as revenue, profit or market share is no longer sufficient in today's business environment. The traditional indicators, as stated by L. Svobodova (2015), indicate the level of the company's management, but only from a managerial point of view, which takes into account the return on capital or the rate of asset utilization, but not the risks accrued to owners by investing their available funds. Therefore, new financial management concepts are based on the shareholder value management, which is constructed on the modified financial indicators that enable better identification of the processes and activities increasing a long-term value for the shareholder as well as the overall value of the company. To conclude, E. Hyranek et al. (2014) emphasized that the latest approaches to enterprise performance are aimed at evaluating the level of production system functioning, where it is necessary to measure the effectiveness of the transformation process and to implement not only financial indicators but also the indicators of effectiveness and severity. Based on these empirical studies, 88

Veronika Cabinova, Erika Onuferova, Peter Gallo, Jr., Peter Gallo and Juraj Gallo / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 085-096 we focused on applying the selected advanced methods of enterprise performance evaluation and investigated mutual interconnections between them to reveal the identity of the results achieved. In their appropriate combination, we see the potential of creating a new model linking the dimension of enterprise performance and efficiency.

2. RESULTS AND DISCUSSION At first, the performance of the Slovak health spa enterprises during the years 2013 – 2017 was evaluated by applying the EVA indicator. The brief description of the input data entering the EVA calculation in individual years was described in the previous chapter. The average financial performance development of the Slovak spa enterprises quantified on the basis of the EVA indicator is shown in the following Figure 2.

Figure 2. The performance development of the health spa enterprises based on the EVA model application (in €)

Source: own processing based on the financial statements of analysed enterprises

As the EVA indicator level recorded the most significant negative decrease of € 277,841 in 2016 compared to previous period, this year can be considered as the worst-performing one throughout the monitored period. A more pronounced average increase in total costs (by 12.98 %) than total revenues (by 7.03 %) occurred despite of the repetitive decreasing in re indicator and is considered the main reason for the reduction of net profit in 2016, which subsequently influenced the creation of negative ROE as well as the high negative values of the EVA indicator. This fact together with the high value of the cost of equity (especially the Risk Free Rate of Re- turn at the level of 3.19%) was the main reason of reaching the negative EVA indicator also in year 2013 and the height of the cost of equity (re) recorded its highest level at all (4.74 %). Despite the significant net profit growth by 11.11 % on average in the following year 2014, analysed spa enterprises were not able to generate added value for shareholders. However, a positive change occurred in the following years and financial performance of enterprises started to show an appropriate development trend. Re-decreasing of the re indicator to the value of 2.23 % and increasing the ROE by 0.24 % on average in 2014 caused the improvement in the financial performance development of the Slovak health spa sector in the following year 2015 which was considered as the best-performing year throughout the analysed period. The second best-performing year was 2017, when the average value of the EVA indicator ranged around € 17,220 €. Despite the increase in cost of equity by 0.25%, this positive development was a result of a significant decrease of net profit within the health spa sector as a whole, 89

Veronika Cabinova, Erika Onuferova, Peter Gallo, Jr., Peter Gallo and Juraj Gallo / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 085-096 which was caused by progressively increasing total revenues (by 4.29%) and decreasing total costs (by 0.57%) compared to the previous year. The second modern indicator applied to evaluate the performance of enterprises within the Slovak medical spa sector over the years 2013 to 2017 was the RONA indicator. Its essence is the same as the EVA indicator with the difference that RONA is a relative ratio indicator that measures enterprise performance not in €, but in %. However, it is important not only to determine the level of RONA indicator, but to compare the difference between profitability quantified on the basis of RONA and WACC. The average performance development of enterprises assessed by above mentioned indicators illustrates the following figure.

Figure 3. The performance development of the health spa enterprises based on the RONA model application (in %)

Source: own processing based on the financial statements of analysed enterprises

In the first analysed year 2013, the RONA of enterprises operating in the Slovak health spa sector reached the average level of 2.37%. Compared to the WACC, the most noticeable and undesirable difference between these indicators was recorded, amounting to 2.13%. The rise in operating profit after taxes by 4.67% as well as an increase in the net asset value of € 221,525 in the following year 2014 caused the growth of RONA indicator level by an index of 1.0238. On the contrary, the WACC indicator dropped by 1.27% mainly due to declining the alternative cost of equity. This change caused a desired reduction in the difference between the RONA and WACC indicator values, even more than twice compared to the previous year. The positive development trend continued in 2015 as well, when the average net operating profit reached the value of € 264,542 and the net assets ranged around € 10,384,177. As a result, the level of RONA indicator re-increased and reached its maximum value of 2.55%. However, this positive development trend of all indicators was disrupted in 2016. The decline in net profit of the enterprises by € 244,171 compared to previous year 2015 caused the decline in RONA indicator by 2.35 % down to the level of 0.19 %. This sharp drop in operating profit, despite the re-reduction of the cost of equity by an index of 0.8634, did not ensure maintaining the desired relation RONA > WACC recorded in the previous year. Based on these results, we cannot identify these health spa enterprises as well-performing. The increase in total average revenues and decrease in total costs during the following year 2017 contributed to an increase in operating profit of € 240,740. As a result, the performance of spa enterprises came closer to the situation in 2015, but with less significant differences between the RONA and WACC (0.30%). 90

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The following partial analysis was focused on the performance evaluation of selected spa enterprises by means of the CWM compiled on the basis of the evaluation scoring table. The graphical performance development of these enterprises over the years 2013 – 2017 is presented in the next Figure 4.

Figure 4. The performance development of the health spa enterprises based on the CWM application (points)

Source: own processing based on the financial statements of analysed enterprises

Over the analysed period, the Slovak spa sector was located in the performance field of "Sub- standard" and "Watch". In this regard, the best-performing year was 2015, as the dimension of financial performance reached 46 points on average and the success rate reached even a bit higher score (50 points). This positive development was caused by an increase in net profit by 11.67%, which led to achieving more acceptable values of ratio indicators entering the first dimension of the model. In the following year 2016, a significant negative decrease of both dimensions was recorded. Due to insufficient values of profitability and activity ratio indicators, the financial performance evaluation got worsened by 4 points and success evaluation ranged around only 35 points. The dimension of success evaluation was negatively affected by the results of all predictive models based on which the average values of Slovak spa enterprises belonged to (except for a few exceptions) to the "grey zone of indefinite results" in all individual years. It is worthy to note that despite the fluctuating development of both dimensions of CWM, the success and financial performance evaluation of enterprises reached in the last analysed year the identical point rating as in the initial year 2013. According to the results of the CWM we can consider the overall financial performance of Slovak health spa enterprises as relatively standard during the analysed period. Furthermore, this sector continues to keep its performance level on the market stable and strives for its continuous growth. To conclude, we evaluated the effectiveness of Slovak spa enterprises by means of SLPM which focuses on solution of input-output transformations with the application of modified system of indicators. In the following Table 1 are stated initial values of the selected inputs and outputs 91

Veronika Cabinova, Erika Onuferova, Peter Gallo, Jr., Peter Gallo and Juraj Gallo / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 085-096 that represent the main basis for the efficiency quantification of the Slovak health spa enterprises. Individual values (in €) represent the average values throughout the health spa sector and indexes are quantified to overall revenues.

Table 1. Input data needed for application of SLPM

Inputs Outputs Personnel Material Net Added val- Total Total reve- costs (n2) costs (n3) profit (v2) ue (v3) Year costs (n1) nues (v1) Cost Wage Material In relation to In relation to In relation to of returns efficiency efficiency total revenues total revenues total revenues € 5,272,344 € 1,793,050 € 1,191,684 € 5,446,131 € 173,787 € 3,050,390 2013 0.9681 0.3292 0.2188 1.0000 0.0319 0.5601 € 5,342,795 € 1,861,158 € 1,187,809 € 5,535,894 € 193,099 € 3,249,967 2014 0.9651 0.3362 0.2146 1.0000 0.0349 0.5871 € 5,322,864 € 1,899,399 € 1,176,291 € 5,538,486 € 215,622 € 3,224,747 2015 0.9611 03429 0.2124 1.0000 0.0389 0.5822 € 6,013,584 € 2,060,184 € 1,211,600 € 5,927,758 € 85,825 € 3,408,810 2016 1.0145 0.3475 0.2044 1.0000 -0.0145 0.5751 € 5,979,006 € 2,235,240 € 1,217,483 € 6,182,136 € 203,130 € 3,195,759 2017 0.9671 0.3616 0.1969 1.0000 0.0329 0.5169

Source: own processing based on the financial statements of analysed enterprises

The SLPM analysis of efficiency consisted of weights (ui, tr) of selected indicators and devia- tions (wj), the sum of which had to be minimized. Based on the results processed in MS Excel, we can state that the highest weight of the selected inputs was proved in the case of material efficiency indicator (u3 = 2.03604) and wage efficiency indicator (u2 = 1.35644). No significant weight was identified for the other selected input and output items. In this regard, we can consider these two indicators as the most important determinants of efficiency, as well as the performance of the Slo- vak spa companies. On the basis of calculated weights, we subsequently quantified the efficiency of the selected sample of enterprises. Maximum efficiency (1.000) was reached in 2015, vice-versa, the lowest in 2016 (0.7889). In the other years of the analysed period, the efficiency ranged from 0.8514 (2013) to 0.9912 (2017). The overall average efficiency of the Slovak health spa sector reached the level of 0.9254. Despite this deviation from the required maximum efficiency at the level of 1.0000, the transformation efficiency of inputs to outputs within the analysed sector can be considered to be relatively high. The following table provides the final average ranking of the order of the spa businesses performance obtained based on the application of the selected modern methods used in evaluating the enterprises' performance during the examined years 2013 – 2017.

Veronika Cabinova, Erika Onuferova, Peter Gallo, Jr., Peter Gallo and Juraj Gallo / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 085-096

Table 2. The final order of the businesses' performance by the application of the methods selected

Spa enterprise Rank Spa enterprise Rank (SE) EVA RONA CWM SMLP (SE) EVA RONA CWM SMLP SE01 3 5 9 8 SE15 23 26 26 24 SE02 26 23 15 25 SE16 16 11 13 23 SE03 1 3 1 2 SE17 7 9 8 17 SE04 28 28 28 28 SE18 12 13 17 15 SE05 2 8 12 3 SE19 11 10 3 16 SE06 5 7 19 10 SE20 17 2 23 1 SE07 4 4 10 19 SE21 18 1 27 26 SE08 9 12 5 5 SE22 22 19 4 18 SE09 13 16 14 4 SE23 6 6 2 22 SE10 27 27 22 20 SE24 8 15 7 9 SE11 14 14 16 13 SE25 15 25 20 14 SE12 25 17 18 27 SE26 10 18 11 12 SE13 24 20 24 11 SE27 19 21 6 21 SE14 20 22 25 7 SE28 21 24 21 6

Source: own processing based on the financial statements of analysed enterprises

Based on the results above, it is obvious that most of the analysed spa enterprises did not reached the same position by application of all above mentioned modern performance evaluation methods. However, the most significant identical position was identified in the case of SE03 (Spa Bojnice) and SE04 (Spa Brusno) which were considered to be the best and the worst-performing ones within the entire Slovak health spa sector. As we have already mentioned, the calculation of these methods is based on different input data and indicators reflecting subsequently the different business performance assessment. Despite the fact that most enterprises did not reach identical performance position measured by the selected modern models, we have to emphasize the identification of the best and the worst-performing spa enterprise evaluated on the basis of all these models. To conclude, we focused on revealing the mutual interconnection between the currently most used modern EVA indicator and the CWM, RONA and SLPM within the sector surveyed. The results of correlation analysis by means of Kendall Tau test are presented in the following Table 3.

Table 3. The results of correlation analysis between the EVA indicator and the other ones

Kendall Tau correlations (SE 1-28 v PS1) Pair of variables Marked correlations are significant at p* < 0.05; p** < 0.01 Count Kendall Tau Z P-value

EVA & CWM 28 0.357895 2.206211 0.027369** EVA & RONA 28 0.431579 2.660431 0.007804**

EVA & SM LP 28 0.536842 3.309317 0.000935**

Source: own processing in Statistica

Veronika Cabinova, Erika Onuferova, Peter Gallo, Jr., Peter Gallo and Juraj Gallo / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 085-096

From all the pairs of variables representing the modern methods of enterprise performance evaluation, a statistically significant correlation (medium and strong) with the EVA indicator was confirmed in the case of CWM, RONA indicator as well as SLPM. It can be concluded that at the significance level p < 0.05, a statistically significant correlation was confirmed for all pairs of analysed methods. However, at the significance level p < 0.01, a statistically significant link was revealed between the EVA indicator in relation to RONA indicator and SLPM. In this regard, the highest positive dependency was identified in the case of SLPM ( = 0.536842).

CONCLUSION The paper was focused on the financial development analysis of the Slovak spa sector over the period of 2013  2017 using the selected modern methods of performance and efficiency evaluation – the Economic Value Added, the Return On Net Assets, the Creditworthy Model, the Simplex Linear Programming Method and to access whether the application of above-mentioned models reflects an identical financial performance of the enterprises analysed. The partial objective of this paper was to reveal statistically significant correlation between the EVA indicator and other selected modern methods and in the case of confirmation of a strong correlation to identify the degree of identity with the results of applied methods. The positive value of the EVA indicator was achieved only in the two years of the analysed period (2015 and 2017) and the overall average value reached the level of € 97,585. Therefore, spa companies were not able to generate added value for their shareholders, as the relation ROE > re was not respected for most of the analysed years. The average level of the RONA indicator was 1.99%, whereas the WACC value ranged around 2.80%. Even in this case, the desired relation RO- NA > WACC was not respected, which is a sign of non-performing enterprises. The RONA indicator exceeded the Weighted Average Cost of Capital only in 2015 and 2017; thereby the Slovak health spa sector could be identified as well-performing. The CWM's financial performance dimension reached 46 points on average, the dimension of success evaluation was a bit higher (47 points on average). In most of the analysed years, the Slo- vak health spa sector was located in the performance field of "Substandard". A point rating of at least one CWM dimension above the required average level (40 points) was recorded in each year of the analysed period, with only exception in 2016 when the success evaluation ranged only around 35 points. The highest weight of the selected inputs forming a precondition of SLPM application was confirmed for the case of material efficiency indicator (u3 = 2.03604) and wage efficiency indicator (u2 = 1.35644). In this regard, we can consider these two indicators as the most important determinants of efficiency, as well as the performance of the Slovak spa companies. No significant weight was identified for the other selected input and output items. Over the years 2013 – 2017, the overall average efficiency of the Slovak health spa sector reached the level of 0.9254. Despite this deviation from the required maximum efficiency at the level of 1.0000, the transformation efficiency of inputs to outputs within the analysed sector can be considered to be relatively high. Based on a more detailed analysis of the average financial performance of individual enterprises within the application of all models during the analysed years 2013 to 2017, the best- performing spas were Bojnice, Bardejov, Dudince, Nimnica and the Specialized Medical Institute Marína. On the other side, the worst-performing spas were considered Brusno, Číž, Sliač, Horezza and Trenčianske Teplice. Despite the fact that other input data and indicators influencing the differences in results are entering into the selected modern methods of business performance evaluation, year 2015 was identically identified as the best-performing one, vice-versa, the worst financial performance of enterprises was recorded in 2016 within the application of all methods. At the significance level of p < 0.05, a statistically significant correlation between all pairs of the analysed methods was re- 94

Veronika Cabinova, Erika Onuferova, Peter Gallo, Jr., Peter Gallo and Juraj Gallo / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 085-096 vealed. However, the strongest positive dependency with the EVA indicator was confirmed in the case of SMLP ( = 0.536842). This result was also confirmed by the identical assessment of the performance and efficiency development in individual years of the monitored period. The idea of comparing enterprise performance and efficiency was also surveyed by J. Horvathova and M. Mokrisova (2017). The authors examined whether the EVA indicator is an adequate alternative of measuring the enterprise performance by a matrix set of indicators in the form of a linear programming model using the Simplex method. The results of their study also revealed the statistically significant relationship between these pairs of methods, which was also confirmed by the results of our analyses. The findings of this study suggest that Slovak spa enterprises should continually evaluate their performance and efficiency by applying more modern methods, as each of them has its specific, informative value. In our opinion, companies should apply at least one of the above mentioned methods, since they are able to assess their performance and efficiency relatively the same. The application of other modern assessment methods and their appropriate combination in connection to Enterprise Performance Model (EPM) creation will be the subject of our further scientific research studies.

ACKNOWLEDGEMENT This article is one of outputs of project VEGA 1/0791/16 ″Modern approaches to improving enterprise performance and competitiveness using the innovative model – Enterprise Performance Model to streamline Management Decision-Making Processes″.

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Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107

Montenegrin Journal of Economics

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Employees Management: Evidence from Gamification Techniques

SAIMA HUSSAIN1, SARA QAZI2, RIZWAN RAHEEM AHMED3, DALIA STREIMIKIENE4 and JOLITA VVEINHARDT5

1 Assistant Professor, MS., Faculty of Management Sciences, SZABIST, Block-5, Clifton-90, Karachi, Pakistan; e-mail: [email protected] 2 Assistant Professor, MS., Faculty of Management Sciences, SZABIST, Block-5, Clifton-90, Karachi, Pakistan; e-mail: [email protected] 3 Full Professor, Ph.D., Faculty of Management Sciences, Indus University, Block-17, Gulshan, Karachi, Pakistan; e-mail: [email protected] 4 Full Professor, Ph.D., Chief Researcher, Lithuanian Sports University, Institute of Sport Science and Innovations, Innovations, Kaunas, Lithuania; e-mail: [email protected] 5 Full Professor, Ph.D., Chief Researcher, Faculty of Economics and Management, Vytautas Magnus University, Kaunas, Lithuania; e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 19, 2018 The purpose of this paper is to find out the effect of Gamification on Revised from September 03, 2018 employee motivation, employee engagement, employee retention, Accepted November 11, 2018 employee loyalty and organizational commitment. We want to know Available online December 15, 2018 this in order to check the viability of Gamification strategy for business development in Pakistan. Participants were 142 University students, who were working as well, we divided them into two JEL classification: groups, control group and Gamified group, we gave both groups pre- J24; M20; M50. test and post-test questionnaire. ANOVA and t-test were applied to pre-test and post-test data to analyze group difference and multivar- DOI: 10.14254/1800-5845/2018.14-4.7 iate GLM model was applied to analyze the influence of gender and gamification preferences on employee related variables. The Gami- Keywords: fication strategy used in the Gamified group was expected to influence the participants and increase their motivation, engagement, Gamification, retention, loyalty and organizational commitment. The Gamified employee motivation, group did show significance in employee engagement, employee employee engagement, retention and organizational commitment, whereby loyalty and employee retention, motivation were not significant for both gamified and non-gamified employee loyalty, environment. In addition GLM analysis indicates that if employees organizational commitment. are given playful environment that will lead to higher motivation, engagement and retention. Gamification strategy can help business development in Pakistan, as employees prefer a Gamified workplace over a non-Gamified one. Game elements in a work environment are likely to be highly positive for an employee's mental health, and allow employees to lead a healthier life with less stress. This study specifies predictors for business development through Gami- fication and emphasizes the importance of Gamification on all the variables.

Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107 INTRODUCTION Over the past two decades or so, there has been a substantial boost in the gaming industry because a majority of the population that were playing these video games had a firm belief that games do more good than harm. These ideals were contrary to the older generation (Generation X). After a while, all the people who enjoyed video games had grown up attached to the utility it provided and carried their ideals forward. Surveys found that the average age of a gamer is 31 years. The new generation, Generation Y, had been heavily influenced by games, even if they were not gamers because gaming had become a norm or even a subculture. This change in social acceptance and ideals has brought about a paradigm change in personal as well as professional approach (Raftopoulos, 2014). This research expects to understand the implications of Gamification of a business in the local context. The idea is to see how a Gamified business is better than a non-Gamified business and how it contributes towards business development. Particularly, this research aims to check employee engagement levels (Robson, Plangger, Kietzmann, McCarthy and Pitt, 2016), employee motivation(Kamasheva, Valeev, Yagudin and Maksimova, 2015), organizational commitment (Sips, Bozzon, Smit and Houben, 2015), employee retention(Dorling & McCaffery, 2012), and social loyalty (DuVernet, Asquer and Krachkovskaya, 2016) of the employees in a Gamified environment. Gamification is the process of applying game elements and game design to a non-game environment (Dorling and McCaffery, 2012). By using game logic to improve the business’s productivity and profitability, Gamification provides a method of business development (Fischer, 2017). The major findings, so far, have indicated that Gamification is a viable concept that shows promising results in many different contextssuch as consumer marketing (Huotari and Hamari, 2017), brand- ing (Gatautis, Banyte, Piligrimiene, Vitkauskaite and Tarute, 2016), tourism (Xu, Buhalis and Weber, 2017), innovation adoption (Müller-Stewens, Schlager, Häubl, & Herrmann, 2017) academia (Taspinar, Schmidt and Schuhbauer, 2016), healthcare(Hammedi et al., 2017) and human- computer interaction (Hamari, 2017), e-banking (Rodrigues, Oliveira and Costa, 2016) etc. In many situations Gamification has provided a structure which has boosted employee morale and efficiency. The concept of Gamification has sprung out in the early 2008 but hit a road block soon after due to the majority not accepting it as a relevant subject (Currier, 2008). Soon, researchers and business started to look into the potency of Gamification as a tool to improve employee and customer involvement (Fischer, 2017).

1. PROBLEM STATEMENT Through a Gamified structure an employee would be put in a setting where he/she can be re- warded and recognized for their efforts through a transparent reward management system (Werbach & Hunter, 2012). This design engages employees in experiencing their job in a more enjoyable setting. The business is creating a way to allow its employees to be creative and moti- vated to be more involved in the business processes. Gamification provides a transparent, exciting and fun environment which allows the employee to perform (or over perform) and see their progress as an individual and a group(Deterding, Dixon, Khaled and Nacke, 2011; Hamari, Koivisto and Sarsa, 2014; Harwood & Garry, 2015; Huotari and Hamari, 2017).

1.1 Objectives of the research The basic objective of this research is to experiment the significance of the Gamification strategy in a local market context. Gauging the difference in the employee’s engagement, motivation, commitment,retention, and employee loyalty levels in a Gamified environment. Overall, explaining the relevance of Gamification in business development. On the basis of previous literature we have 98

Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107 derived following research objectives:

 To understand the magnitude of Gamification on motivation levels.  To understand how Gamification can help in employee retention.  To determine the impact of Gamification on employee loyalty.  To understand how Gamification can help in improving employee’s commitment towards the organization.  To analyze how Gamification can influence the engagement level of employees in the organization.

2. SUBSTANTIATION FROM PREVIOUS LITERATURE Gamification is the term given to the concept of using game design elements and game logic in a non-game environment. By augmenting the environment to be more enjoyable and make work less monotonous, Gamification can control certain variables to enhance the work experience (Sarangi & Shah, 2015). The concept has been used for boosting employee morale by creating a transparent and fun environment helping them in problem solving (Hammedi et al., 2017). The same concept has been applied to show improvements by Gamifying intrinsic and extrinsic motivation making everyday tasks more exciting with special rewards behind them. The same idea has been used for ages but the method was unlabeled (Mekler, Brühlmann, Opwis, & Tuch, 2013). After enough material was circulating around, Gamification started getting recognition by corpo- rates and academics alike. One of the first official classes taught for Gamification started online and the professor had also published a book on the same. The course explained the relevance of Gamification of businesses and how it can be done using a “Tool-Kit” defined by the authors. The acknowledgment of the subject and disseminating its intelligence makes the concept applicable to any business (Werbach and Hunter, 2012). Keeping the definition of Gamification in mind, it is clear that the idea is to bolster positive stimulation, specifically to improve productivity. The concept has proven to bring improvements in many aspects of the business. The places which have adopted Gamification are enjoying healthy employee morale and increased productivity (Gears and Braun, 2013). Hammedi et al. (2017) further elaborates that with the understanding of the problems arising at a work place, Gamifica- tion can be used to create easier problem solving methods by introducing a more relaxed environment. This helps the business processes run smoothly and reduce the unwanted pressure from the employees looking for solutions. In retrospect, the Gamification theory states that all the different types of work places can be easily modified to be more fun and engaging (Fischer, 2017; Werbach and Hunter, 2012). These augmentations will allow the work to be more enjoyable, resulting in a boost in morale and employee commitment to the organization and the work. The overall cohesive bonding improved by this new environment will lead to better business development efforts with improved profitability and productivity (Hamari, 2017). There have been relatively few studies on Gamification but there are some ground breaking ones Hamari et al. (2014) creates a context to examine the influence of Gamification and offers affirmative impacts. However, the influence is hugely dependent on the framework in which the Gamification is being applied, as well as on the consumers using it. Another key study is Gears and Braun (2013) the paper investigates “Gamification”, and the historic roots of the terminology in connection to antecedents, alike perceptions and how Gamification is different from playfulness and suggested a Gamification technique improvement course, and role motivation interaction con- textpremeditated toenhance a problem condition in business. Game design patterns were custom- ized to offer employees a positive and engaging experience (Hamari, 2017; Hammedi et al., 2017). The operationalization of the concept was done by Gears and Braun (2013) explaining the concept of Gamification. The authors developed a leveling model including the pattern of game interface design, patterns of game and mechanics, game model, principles and heuristics of game, and de- 99

Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107 sign methods of game which helped in understanding that they are to be used for applying only the game related aspects to the real world rather than applying a full game.

2.1 Theoretical Framework In order to understand the relevance of Gamification on the business environment and the business development process, the effect of gamifying an environment must be measured. Gamifi- cation has been linked with improving productivity and profitability in many organizations across the globe that is adopting this strategy. To understand the improvement or change certain variables have been taken dependent to the application of the Gamification strategy (Landers and Armstrong, 2017). The first variable chosen is ‘Employee Motivation’ due to the observable improvements mentioned and theorized in many studies. Taking a closer look at the work environment, it can easily be said that the employee motivation plays a vital role in the productivity levels (Kamasheva et al., 2015; Mekler et al., 2013; Suh, Cheung, Ahuja and Wagner, 2017). Subsequently, the next variable chosen is ‘Employee Engagement’ which defines how much an employee is vested in his job. The motive behind this is to understand if Gamification lowers boredom levels by making the job more interactive and appealing (Hammedi et al., 2017; Harwood and Garry, 2015; Suh et al., 2017). To judge Gamification’s effect on an employee’s perception and attitude towards the company and/or job, the variables ‘Organizational Commitment’(Dorling and McCaffery, 2012) and ‘Employee Loyalty’ (Basten, 2017) were chosen. The former gauges the level of commitment and trust an employee has in the company and its goals while the latter measures the employees’ active loyalty to the firm about staying and not actively looking for other opportunities. The last variable was chosen based on the previous one’s ultimate effect, “Employee Retention” levels explains Gamification was completely successful in improving the work environment. All the dependent variables mentioned directly affect each individually and Gamification affects them (Baxter, Holderness and Wood, 2017). Organizations in Pakistan have not yet fully grasped or even considered the approach of business development through Gamification, this is so because there is an implied formality in the majority of our business structures which leads to close-minded working environments and due to the fact that many workers who belong to generation x would not be able to adapt this concept as well as those of generation y, as there is a majority of generation x in decision making positions in organizations throughout Pakistan, the concept of Gamification would seem foreign to them and might not regard it with importance (Aziz, Mushtaq and Anwar, 2017).

Figure 1.Variable relationship diagram

Motivation

Engagement Retention GAMIFICATION

Loyalty Commitment

Source: Dorling & McCaffery, 2012; Baxter, Holderness & Wood, 2017. 100

Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107

3. MATERIALS AND METHODS The Purpose of this Research is to experiment the significance of the Gamification strategy for employee engagement, motivation, turnover, organizational commitment and social loyalty levels. This research was a controlled experiment, collecting quantitativedata to find differences in the dependent variables: employee motivation, engagement, turnover, organizational commitment and employee loyalty. Participants for this research were invited through Facebook and university’s notice board for registration, total 160 registered divided in equal control and experiment groups through simple random sampling method. Experiment was conducted in two days for six hours. On day 1 Control group was given professional tasks without gamification and was judged by panel of professional judges for the completion and execution of assigned task. On day 2 Experiment group was given same tasks in gamified environment with leaderboard scores, badges, giveaways, token of appreciations and achievement levels for each employee based on judges’ evaluation for the completion and execution of assigned task. The data collection instrument adopted from developed scales including Motivation scale (Cerasoli, Nicklin and Ford, 2014), Engagement scale (Schaufeli, Bakker, & Salanova, 2006), Commitment (Mowday, Steers and Porter, 1979), retention (Bhatnagar, 2007) and Loyalty (Matzler and Renzl, 2006) were distributed to both groups before and after activities. The data was then coded in SPSS and analyzed through for MANOVA.

3.1 Hypotheses / Propositions

 A Gamified workplace has better motivational levels for employees than a non-Gamified workplace  A Gamified workplace has better engagement levels for employees than a non-Gamified workplace  A Gamified workplace has better organizational commitment levels for employees than a non- Gamified workplace  A Gamified workplace has better employee loyalty levels for employees than a non-Gamified workplace  A Gamified workplace has a better employee turnover rate than a non-Gamified workplace

4. ESTIMATIONS OF RESULTS AND DISCUSSIONS The data from questionnaire is coded in SPSS for hypothesis testing and analysis. The questionnaire had no missing values. Following section represents demographics, validity and reliability measures and results of hypothesis. Almost 100% participants were 21 to 25 age group with 53% males and 47% females.

4.1 Validity and Reliability Validity of the questionnaire was tested through face validity and convergent validity. Face validity was ensured by getting expert review on questionnaire and convergent validity was done through factor analysis. Statement with factor loading less than 0.4 were not considered for sum- mated scales (Hair, Black, Babin and Anderson, 2009). Reliability was tested through Cronbach alpha, for both gamified and non-gamified group responses (Sekaran & Bougie, 2016). Overall and inter-item reliabilities were above 0.7.

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Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107 4.2 Descriptive analysis The mean values are all above 5.5 (neutral value) on a scale of 1 to 10, indicates that the constructs are positively contributing in this research as exhibited by Table 1. Standard deviation values are all above 1, therefore it represents that there are many variations in the respondent’s given answers in the Gamification and non-Gamification experiments.

Table 1. Descriptive Statistics for all constructs

Control Group Experiment Group Mean SD Mean SD Motivation 7.6250 1.64874 7.1500 1.03923 Engagement 7.3600 1.36146 7.0167 1.26280 Retention 7.5844 1.31745 6.6438 1.57401 Commitment 6.7075 1.39291 6.2042 1.68554 Loyalty 5.8125 1.71683 6.3500 1.79066

Source: Authors’ estimations

4.3 MANOVA Assumptions Assumptions were tested for the data as indicated by Hair et al. (2009):

 Independence of observations was achieved through simple random sampling method adopted for assigning participants in control group and experiment group respondents.  Multivariate homoscedasticity was tested through Box’s M test and univariate was tested through Levene’s Test of Error Variances. Table 2 indicates no significant differences for equality of covariance in groups.  Test of Multivariate outliers was performed through calculating Mahalanobies for the given data. Maximum Mahal distance according to residual analysis was 19.092, which is less than 20.52 upper limit of mahal distances for five variables.  Normality was tested through Shapiro Wilk’s test and constructs were found to be normally distributed in both groups.  Multicollinearity was tested through correlation matrix and none of the correlations were found to be greater than 0.5.

Table 2. Homoscedasticity Assumption

Control Group Experiment Group Box’s M 21.994 Sig (.155) Box’s M 19.717 sig (.244) Levene’s Test of Equality of Error Variances Engagement f-value (1.987) sig (.164) f-value (.309) sig (.580) Retention f-value (1.205) sig (.276) f-value (2.241) sig (.132) Commitment f-value (2.009) sig (.160) f-value (2.842) sig (.096) Loyalty f-value (2.735) sig (.102) f-value (.145) sig (.704) Motivation f-value (1.205) sig (.276) f-value ( 1.795) sig (.184)

Source: Authors’ estimations

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Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107

4.4 MANOVA estimations

4.4.1 Multivariate analysis The data is satisfying sufficient number of assumptions for the application of MANOVA therefore GLM Multivariate technique was applied to the data of control group and experiment group as presented in Table 3.

Table 3. Multivariate Statistic

Effect Value F Sig. PSquared Pillai's Trace .183 3.320b .009 .183 Control Wilks' Lambda .817 3.320b .009 .183 Non-Game Hotelling's Trace .224 3.320b .009 .183 Pre and Post test Roy's Largest Root .224 3.320b .009 .183

Pillai's Trace .170 3.028b .015 .170 Wilks' Lambda .830 3.028b .015 .170 Experiment Gamified Hotelling's Trace .205 3.028b .015 .170 Pre and Post test Roy's Largest Root .205 3.028b .015 .170

Source: Authors’ estimations

4.4.2 Descriptive evaluation Table 4 presents the mean of all variables in control and experiment group for pre and post- test. It is evident from the table that Motivation and Retention is reduced posttest non-gamified monotonous environment in contrast Motivation, engagement, commitment and loyalty is increased in posttest Gamified environment.

Table 4. Descriptive Statistics

Std. Std. Variable Mean Mean N Deviation Deviation Pre Test. Non Pre Test. 8.1667 1.46371 6.9000 .97770 40 Gamification Gamification Motivation Post Test. Post Test. Non Gamifi- 7.0833 1.66196 Gamification 7.4000 1.05052 40 cation Pre Test. Non Pre Test. Gamification 7.4750 1.22511 Gamification 6.5250 1.987 40 Engagement Post Test. Post Test. Non Gamifi- 7.2450 1.49219 Gamification 7.4000 1.482 40 cation Pre Test. Non Pre Test. Retention 8.0563 1.03231 6.4063 1.74834 40 Gamification Gamification

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Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107 Post Test. Post Test. Non Gamifi- 7.1125 1.41189 Gamification 6.8813 1.35872 40 cation Pre Test. Non Pre Test. Gamification 6.9100 1.58451 Gamification 5.6042 1.71705 40 Commitment Post Test. Post Test. Non Gamifi- 6.5050 1.15558 Gamification 6.8042 1.43813 40 cation Pre Test. Non Pre Test. 5.9083 1.91558 5.8917 1.83723 40 Gamification Gamification Loyalty Post Test. Post Test. Non Gamifi- 5.7167 1.51074 Gamification 6.8083 1.63976 40 cation

Source: Authors’ estimations

4.4.3 Contrast analysis – K Matrix Furthermore significant differences are indicated by K-Matric results in Table 5 for Control and Experiment Group. All the tests indicate that the dependent variables do have significant differences in pretests and posttests of Control group responses and experiment group responses.

Table 5. Contrast Results (K Matrix)

Questionnaire Simple Control Group Experiment Group Contrast Motivation Retention Commitment Motivation Loyalty Engagement Contrast Estimate 1.083 .944 -1.200 -.500 -.917 -.875 Pre Sig. .003 .001 .001 .031 .021 .028 Test Lower Vs 95% Con- .386 .393 -1.905 -.952 -1.692 -16885 Post fidence Bound Test Interval for Upper 1.780 1.494 -.495 -.048 -.141 -.095 Difference Bound

Source: Authors’ estimations

4.5 Discussions The first dependent variable, Employee Motivation, shows significant difference in the non- Gamification/control group between pre and post-tests such that motivation is reduced significantly after eight hours of monotonous job and tasks. Whereas, the Gamification group showed significant increase in the average motivation levels of the participants (Cerasoli et al., 2014). Employee Engagement, the second dependent variable, has no significant changes in non-gamified environment in parallel gamificatiom significantly increases Employee engagement (Hammedi et al., 2017) in line with prolific literature of gamification and employee engagement (Bhatnagar, 2007; Narayanan, 2014; Robson et al., 2016). Employee Retention, however indicates significant reduction in the non-Gamified group whereby the retention behaviors are insignificant in gamified environments; thus monotonous, boring and routine work environment forces an employee to search for new job. Researcher further tested if retention in gamified environment is predicted by other variables and it was evident that employee retention is strong and positive with loyalty and raised commitment towards the organization. A healthy increase in employee Loyalty level was seen for 104

Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107 the Gamified post-test indicating that employee feel loyal towards workplace that makes the work fun and engaging (Lamm and Meeks, 2009; Mollick and Rothbard, 2014). Similarly Organizational Commitment, indicated a significant improvement in the Gamified group thus employees feel positive and committed towards the organization as indicated by (Robson et al., 2016). This movement indicates that Gamification of the environment does influence four main dependent variables but with different magnitudes. It is also important to state that the motivation levels were seen to be quite low for both the groups from the start. After being subjected to the workshop the changes, even if minimal, were not perceived as significant enough to be relevant because if the same study was carried out with a more expensive setting the results would have been more significant. With this in mind, the data gathered from the experiment was analyzed. The workplace simu- lated exercises/activities proved to be challenging and stimulating for the groups. The incentives given to the Gamified/experimental group resulted in better competition from all the groups as compared to the control group which had linear competition levels throughout, as observed during the workshop (Hamari et al., 2014). Making the work environment more fun and playful with changing the way work is done and introducing performance based reward system allows the employee to feel that the company is being just with them (Basten, 2017). Bottom line, Gamification brings a game changing model to the workplace and argues with the older generation that work should have a fun and playful feel to it rather than a stern seriousness which is generally offered (Werbach and Hunter, 2012).

CONCLUSIONS AND RECOMMENDATIONS

Conclusions This study seeks to create a stepping-stone for other research on the topic of the Gamification theory by conducting the first experiment in the country. The results of the entire experiment opened eyes to some key insights about the influence Gamification has over the dependent variables. The Gamification control experiment showed signs of relevance by being influential over the dependent variables; Employee Loyalty, Employee Motivation having a weaker connection and Employee Retention, Engagement and Organizational Commitment having the stronger link. Having established that the Gamification theory has some relevance in the improvement of productivity and profitability.Companies can add their own touches into creating a Gamified environment according to their specific structure as this tool can be generalized to affect a company in any industry. There is no hard and fast rule about how Gamification should be adopted; it is all based on the type of industry and the nature of the workplace. All the tools have been defined and the usage bear different fruits for each type of company. The significance has been defined now as well.

Recommendations & managerial implications On the basis of derived results and conclusions, we have recommended following items to the organizations and individuals. Particularly, the management of an organization can implement our gamification business model by integrating: -

 Leaderboard system: This system would be used to measure individual or group based performance on both projects and overall monthly tasks.  Appraisals/Scoring system:This system would work as judging criteria for individuals and groups, through assessing their performance and generating a score relative to its execution and affectivity on a periodic basis. This system can be performed through the line managers and bosses to evaluate the employees.  Leveling and progression system: This system would create a model that would be directly pro- portional to the performance of the given group or individual on respective projects or tasks,

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Saima Hussain, Sara Qazi, Rizwan Raheem Ahmed, Dalia Streimikiene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 097-107 they would be allotted badges for their effective performance through which they would achieve higher ranks amongst their colleagues.  Rewards and compensation system: This system enforces the redemption of badges earned by the respective employees and also had specific tiers that are unlocked by achieving certain ranks, badge redemption at each tier would give different unique benefits and rewards with higher rewards through badge redemption as employees pass along and succeed to the next tier.

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Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 109-123 ‘

Investment Activity of Insurers and the State Economic Growth

GULNARA KAIGORODOVA1, DARIA ALYAKINA2, GUZEL PYRKOVA3, ALFIYA MUSTAFINA4 and VIKTOR TRYNCHUK5

1 Associate Professor, Department of securities, exchanges and insurance, Institute of Management, Economics and Finance, Kazan (Volga region) Federal University, Russia, e-mail: [email protected] 2 Associate Professor, Department of securities, exchanges and insurance, Institute of Management, Economics and Finance, Kazan (Volga region) Federal University, Russia, e-mail: [email protected] 3 Associate Professor, Department of securities, exchanges and insurance, Institute of Management, Economics and Finance, Kazan (Volga region) Federal University, Russia, e-mail: [email protected] 4 Associate Professor, Department of securities, exchanges and insurance, Institute of Management, Economics and Finance, Kazan (Volga region) Federal University, Russia, e-mail: [email protected] 5 Associate Professor, Department of Finance, Accounting and Analysis, Institute of Entrepreneurship and Advanced Technology Lviv Polytechnic National University; Head of Insurance Department Institute of Postgraduate Education and Business, Kyiv, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 19, 2018 The purpose of the article is to summarize theoretical positions and Revised from September 03, 2018 practical experience regarding the existence of the relationship bet- Accepted November 11, 2018 ween the investment activity of insurers and the economic growth of Available online December 15, 2018 the state and identify the factors that have the greatest impact on the volume of insurance investments. As a hypothesis, the main factors influencing the level of investment of insurers in the econo- JEL classification: my are recognized: the profit of economic entities, investment in G22, P20. fixed assets, the rate of change in the volume of insurance premiums, the total authorized capital of insurers, GDP. Methods of corre- DOI: 10.14254/1800-5845/2018.14-4.8 lation-regression analysis, extrapolation and modeling were used. The subject of the study were the peculiarities and patterns of the Keywords: formation, use and regulation of the investment potential of Rus- sianinsurance companies in in modern economic conditions. In the insurer, course ofconducted researcha number of publications on the issues insurance market, of the relationship between the investment activity of insurers and investment activity, the economic growth of the state were worked out, investment real investment, potential of the insurance market of Russia was studied, the size of insurer's assets, real investments by insurers in the economy was determined, and insurance reserves the forecast of future trends in changes in their volumes was made. It is proved that between the volumes of insurance premiums, investments of enterprises in fixed assets, GDP and assets placed by the insurers in the real sector there is a high interdependence that allowed to determine the important role of insurers as participants of the insurance market for the economic growth of the state. The conclusion is made that, with a steady decline in the number of insurers, their aggregate capital increases, as well as growing insurance reserves. However, their insufficient part is invested in the real sector of the economy, and a significant amount of resources falls on investments with zero profitability of operations.

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INTRODUCTION Investment operations of the insurer allow providing the economy of the state with the necessary financial resources. At the same time, investment activity is one of the main factors for ensuring the effective functioning and financial stability of the insurance organization. First, investment activity determines the very possibility of providing insurance services by means of the formation of a sufficient insurance fund. Secondly, a well-organized investment activity determines the market status of the insurer, influencing the main characteristics of the insurance product. Thirdly, due to investment of insurance funds, there occurs an accumulation of resources to increase own funds without attracting external investments. One of the sources of profit for insurance organizations and factors of ensuring financial stability is investment activity. The problem for Russian insurers is the lack of the developed secondary securities market; there are problems of investment choice. Insurance companies must find their place in the investment process (Kucherenko et al., 2018; Mentel et al., 2017), which is the key to the recovery of the country's economic sectors. For example, in the European economy, insurance companies are the largest institutional investors; there are assets of 10 trillion euros under their management (Focarelli, 2017, pp. 348). The basis of interaction between enterprises and insurers should be the implementation of a complete investment program aimed at mutual increase in efficiency and scale (Ustinov et al, 2016; Grmanova and Strunz, 2017). However, at the present stage of development, Russian insurers do not view investment activity as the main source of profit, which is reflected in the low results from the placement of assets: the return on investment is below the level of inflation. The preferred range of investments is bank deposits, that is, low-yielding instruments that do not cover the level of inflation. In turn, it is necessary to streamline the interaction of insurers with the banking system and attract insurers when insuring credit risks of banks on a market basis, taking into account the analysis of the financial stability of the insurance organization. Therefore, the issue of managing the investment activities of insurance organizations in modern Russia is relevant.

1. LITERATURE REVIEW The issues of the insurers’ investment policy and its relationship with the economic growth of the state are studied by Russian and foreign scientists. Thus, Zweifel and Auckenthaler, (2008) analyze the dilemma arising in the formation of the investment strategy within the framework of life insurance, which would ensure the receipt of the income with a high level of profitability of current insurance payees and at the same time would guarantee the safety of funds for a long period of time for young insurers. The combination of such restrictions leads to a significant degradation in the investment policy of the insurer. Many researchers focus their attention on studying the interrelation between the insurer's investment policy, its solvency and state regulation of these aspects of activity. So, Y-L. Ma and Y. Ren (2012) consider the behavior of insurers at high rates of growth of stock indices and determine that in these conditions insurance companies are trying to attract ever larger amounts of funds to their business. At the same time, the influence of state regulation on the investment policy of insurers is taken into account. Thus, Focarelli, (2017), considering the role of insurers as pro- viders of long-term investment in the economy, is talking about the importance of prudential regulation. He states that insurers, especially at long-term types of insurance, use financial instruments characterized by a high level of issuer quality and at the same time ensuring a sufficient level of profitability. This is due to the need to fulfill liabilities, including those on profitability to holders of insurance policies, with a simultaneous guarantee of insurance payment. Insurers are interested in investing in segments where there is low volatility, predictable cash flows and low correlation with operations in financial markets (Focarelli, 2017; Palei et al., 2016).

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The issues of optimizing the investment policy of the insurer and its interrelationship with the policy of bonus pay for top management of the insurance company were studied in the work of D. Filipovic et al. (2015). It is determined that the limited to a certain extent, liability of the insurance company’s top management and huge resources control create an incentive for increasing the risk of assets and liabilities at the expense of policyholders. However, state regulation, requirements for the assets of insurance companies limit these risks. This is especially important in the light of the introduction of Solvency II standards. K. Fischer and S. Schlutter, (2015) study the issues of the formation of an optimal investment strategy of the insurer, taking into account the requirements of Solvency II. It is considered that higher stock market risk parameters should reduce risky investments, based on the requirements of the regulator. It is established that even a slight increase in the stock market risk leads to a more conservative placement of the assets by the insurer. However, the authors found that this is not always the case. We have found out that some insurers in these conditions tend to a higher risk allocation of their assets. These issues are actively studied by scientists. Thus, the distribution of insurers' assets on life insurance is analyzed according to the classical theory of the portfolio with the account of the requirements of Solvency II (Braun et al., 2017). They find serious drawbacks in this mechanism, which, in their opinion, impede economically reasonable decisions on asset management. It is believed that the introduction of the standard in its current form will have a negative impact on the European insurance sector. Many researchers conclude that the importance of the insurance sector for the economy is largely determined by the directions of the state regulation of insurance activities. Thus, W-Ch. Lin et al. (2014) in their study show that there is a threshold effect of the state interference in the investment policy of the insurer. The conclusion is drawn that often, strict regulation by the state has the opposite effect for the economy. R. Rees et al. (1999) study the limits of forms and spheres of the insurance activity regulation. It is determined that the regulation of insurers' solvency is an important direction of regulation, and one of its aspects is the investment activity of the insurance company. The conclusion is made that consumers should be informed about the existing insolvency risks of a certain insurer. The research in the field of systemic risk in the insurance sector is carried out M. Eling et al. (2016). It shows that classical insurance activities in the life and non-life sectors, as well as in reinsurance sector, does not entail an increase in systemic risks and does not increase the insurers' dependence on the stock market falls. At the same time, non-traditional types of insurance activity (underwriting of financial derivatives, provision of financial guarantees) increase the vulnerability of insurers against stock market falls and devaluation of their investments. These aspects should be taken into account when forming the state policy of regulating the investment operations of insurers. Many scientists carry out research in the field of studying the contribution of the insurance sector to the development of the economy. J. Outreville (2012) presents the result of a synthesis and analysis of 85 research articles that study the interrelation between insurance and economic growth. He concluded that research related to the later period revealed cause-and-effect relation between the development of the insurance sector and the economic growth of the state; at that, insurance in many countries is the determinant of economic growth. A very important thesis is formulated by D. Focarelli (2017). He says that even during economic recession, most insurers on long-term types of insurance continue to pay insurance premiums. This incoming cash flow allows insurers to acquire undervalued assets in a falling market, at the time when most collective investors are forced to sell them. That is, insurers have the opportunity to continue investing when other investors leave the market. He calls it an anti-cyclical and stabilizing effect for financial markets and for the economy. Z. Richterkova and P. Korab, (2013) carried out a combined assessment of the significance of certain statistics of the insurance sector on economic growth. As a measure of insurance activity, the volume of insurance premiums is used. The carried out mathematical calculations allowed the authors to conclude that there is a clear positive relationship between the development of the insurance market and the economy. They formulated recommendations for the purpose of more optimal regulation of insurance activity by the state. 111

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The relationship between insurance and economic growth was studied for six countries, both developing and developed (USA, UK, India, China, Malaysia, Pakistan - Ul Din et al., 2017b). They found that in the long term, there is a relationship between risk insurance, the development of the stock market and economic growth. At that in the long term, there is a negative correlation between foreign direct investment, the development of banking services and economic growth. The authors concluded that insurance is not only a service industry, it is a huge investment potential of the economy (Ul Din et al., 2017a). It is interesting to study the dynamic impact of financial institutions on economic growth based on the data from 13 European Union countries (Wu et al., 2010). They determined the long-term equilibrium relationship between the development of financial institutions and economic growth. H. Hou and S-Y. Cheng, (2017) presented a study of the impact of various segments of the financial market on economic growth in the long-term and short-term aspects. They came to the conclusion that the credit market has a negative impact on economic growth, the insurance and stock markets have a positive impact, but this impact is not sustainable. They found that this impact is largely determined by the level of development of the country, as well as the current state of the economy of the state. Russian scientists conduct the studies that take into account the specifics of the Russian financial market. Thus, E. Prokopieva, (2010) studies the issues of realization by insurers of the investment principles laid down in the legislation. It is concluded that often compliance with these principles is formal in character and does not lead to an increase of the effectiveness of the insurer’s investment operations. G. Kaigorodova (2013) defines the vulnerabilities of the investment policy of Russian insurers, explains its low efficiency by its subordination to far from market interests. A range of financial instruments not included in the investment portfolio of Russian insurers, but capable of bringing an average market yield is identified. Y. Nemtseva (2013) considers the issue of managing the investment activity of the insurer from the perspective of the impact of its profitability on the formation of tariffs, which will ensure its competitiveness. In his work, O. Khomchenko (2008) shows that focusing solely on the use of discounted methods for assessing the effectiveness of investments leads to the fact that long-term projects requiring significant start-up capital become unprofitable. He proposed a multi-factor model for assessing the investment attractiveness of the region from the point of view of insurers. The model includes general factors of the external environment (macroeconomic, political, legal and demographic), indicators characterizing the investment potential of the region in connection with the factors that determine the insurance potential. A number of researchers noted that the effectiveness of investment operations of the insurer has a direct impact on the process of formation of the insurance portfolio, since it is one of the factors ensuring the financial stability of the insurer (Kokh et al., 2016). The studies considering regional aspects of the investment activity in the economy are also relevant (Kvon et al., 2017). Obviously, in the context of the region development, all the opportunities for increasing the flow of financial resources to ensure the rates of economic growth are important (Mustafina et al., 2017).

2. METHODS Insurance companies have a significant investment potential, allowing the state to increase the rate of economic growth under the correct regulation of investment transactions. Obviously, not all the resources accumulated in the insurance reserves and the insurers’ own funds are used for investment in the economy. Therefore, from the actual statistics data it is necessary to identify the assets that are unequivocally regarded as investments of a real nature. Next, we identified a list of factors that affect the level of investment by insurers in the economy. As a hypothesis, we assumed that the main factors of influence are: the profit of economic entities, investments in the fixed capital of enterprises, the rate of change of insurance premiums, the total authorized capital of insurers, GDP. Our task is to identify the factors that have the greatest impact on the volume of investment by insurers. We will identify these factors through regression analysis. 112

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Thanks to the correlation analysis, we identified three main factors that affect the investment of insurers in the real sector of economy. We have singled out two of them (insurance premiums and enterprise investments in fixed assets) to build a model based on the function: VFC = f (IP; GDP ) (1), where VFC – volume of real investments of insurance organizations; IP - insurance premiums; GDP is the gross domestic product. Further, based on extrapolation, we will reveal what investment potential for the economy the insurance industry can form in the short-term perspective.

3. THE RESULTS AND DISCUSSION For the fifth year, the economy of the Russian Federation has been operating under difficult conditions. We outlined the following factors, which determine, among other things, the conditions for the functioning of the Russian insurance market in the current period:

 economic sanctions of the developed countries and Russia's countermeasures;  a small price stabilization in the world commodity markets after a long-term decline;  a growing level of risks, both external and internal;  the state budget deficit;  a lower standard of living of Russians;  growth in certain sectors of the economy associated with the defense capability of the state, as well as in industries whose products allow full replacement of goods that have fallen under the countermeasures of the Russian Federation.

It is necessary to continue the process of restructuring the economy in order to reach a more innovative level. The focus of the Russian economy on the innovative way of development brings to the fore the problem of reforming the existing mechanisms for satisfying the needs of economic entities in investments. The need for radical changes in this sphere is due to the role of investment in ensuring a stable high growth rate of the economy and increasing the efficiency of all the factors of production. While at the initial stage of development of the Russian insurance market there was potentially the opportunity to develop by expanding the scope of activity, opening new branches, modern conditions are characterized by increased competition, the need to find ways to optimize the financial activities of insurance companies, and intensive use of available resources. Accord- ingly, the importance of using the insurer's investment resources as the main source of profit increases. At the same time, the concentration of significant financial resources by insurers in their hands provides an opportunity to use investment resources as the most important factor in the development of the economy. The investment activity of insurance companies is limited by strict boundaries of the current legislation and is aimed at creating a balanced investment portfolio. When formulating an investment policy, it is necessary to use elements of a system approach that will ensure: – structuring of financial resources taking into account investment opportunities and conditions for their investment; – compliance with investment rules to achieve the main objective of investment activity; – development and use of the algorithm in the construction of the portfolio; – construction of the concept of interests balance of subjects of the investment project; – introduction of the method of situational analysis with the use of the system of evaluation indicators (Nikulina and Berezina, 2008, p. 228).

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Insurance companies are currently engaged in investing their own funds and insurance reserves as part of investment activities. An insurance fund before its use for insurance payments is temporarily free from insurance liabilities cash and in this capacity, it has a significant impact on the change in the size of the investment resources of the insurance company, depending on the availability and volume of insurance payments. From these positions, significant is the analysis of the dynamics of funds that can provide the economy with investment resources from the insurance sector. The main indicators in this section for insurance organizations are shown in Table 1.

Table 1. Key indicators characterizing the investment potential of insurance organizations in Rus- sia

Absolute Unit of Growth rate variation Indicator meas- 2014 2015 2016 2017 2017/201 2017/201 urement 4. в % 4 A number of insur- numbers 404 334 256 237 167 -41.3 ance organizations Growth rates of insurance organiza- % -0.8 -17.3 -23.4 -7.4 tions (compared to the previous year) Capital bln.rub. 389.9 395.5 462 598.6 208.7 53.5 Capital growth rate (to the previous % 4.2 1.4 16.8 29.6 year) Net profit bln.rub. 50.7 91.7 81.9 89.3 38.6 76.1 Profit growth rate (to the previous % 12.2 80.9 -10.7 9 year) Insurance funds bln.rub. 900.8 973.5 1136.1 1371.2 470.4 52.2 Insurance funds growth rate (to the % 20.6 8.1 16.7 20.7 previous year)

Source: Completed by authors

With negative trends in reducing the number of insurers for the period 2014-2017, the remaining indicators show a positive dynamics. Purification of the market is characterized by a simultaneous increase in the capital of insurers and insurance reserves. Based on the theory of insurance business, in general, the sources of funds for the period under study increased by 717.7 billion rubles, or by 53.5%. In general, the crisis state of the economy of the analyzed period for the insurance market manifested in a reduction in the number of insurers, an increase in the number of unprofitable insurance organizations. If in 2013 the share of unprofitable insurers in Russia was 7.2%, by the end of 2016 it was 21.1% of the total number of insurance organizations. Further, for the purpose of the analysis, we will trace the dynamics of the main indicators of the Russian economy for 2014-2017 (Table 2). Indicators are given in the current prices. The given data show that after the fall in the growth rate of commissioning the fixed assets in 2014-2015, the year 2016 observes the growth of this indicator. Unfortunately, at the moment there are no data on the fixed assets in the Rosstat (Russian Federal State Statistics Service) system of the Russian federation and their commissioning in 2017.

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Table 2. The main economic indicators characterizing investment activity in Russia

Absolute Unit of growth rate change in Indicator measure 2014 2015 2016 2017 for the whole 2017/2014. ment period, in % bln.rub. GDP (in current bln.rub. 79199.7 83387.2 85917.8 92081.9 12882.2 16.3 prices) GDP growth rate (to % 8.3 5.3 3 7.2 the previous year) Investments in fixed bln.rub. 10379.6 10496.3 11282.5 12025.6 1646 15.6 assets Growth rate of investments in the fixed assets (to the % 3.1 1.1 7.5 6.6 previous year)

Availability of fixed bln.rub. 147430 160725 183404 … 49882.5 37.4 assets Fixed assets growth rate (to the previous % 10.4 9 14.1 year) Commissioning of bln.rub. 10888 10721.1 13256.3 … 2095.8 18.8 fixed assets Growth rate of the fixed assets com- % -2.4 -1.5 23.6 missioning (to the previous year)

Source: Completed by authors

Realization of investments by Russian insurers is tightly regulated by the state. It is based on the principles of recurrence, urgency, diversification and profitability. The state determines the permitted directions of investment and the maximum amount of investments in each of the assets types. The investments of insurers’ own funds and insurance reserves are regulated separately. The structure of investments of Russian insurers is presented in Table 3.

Table 3. Structural relation of investments by insurers, in %

Changes in Types of assets 2014 2015 2016 2017 2014/2017 Bank deposit 18.3 24.7 26 23 4.7 Stocks 8.1 7.2 6.7 5.3 -2.8 Bonds (other than mortgage-backed 13.2 15.6 17.4 20.6 7.4 bonds and housing certificates) State and municipal securities 6.1 6.4 8.9 11.6 5.5 Share of accounts receivable 18.9 19 17.7 14.3 -4.6 Share of reinsurers in insurance reserves 10.4 8 8 6 -4.4 Investments in available funds 10.8 7.9 6.2 6.2 -4.6 Real property 5.7 5.1 4.2 3.6 -2.1 Promissory notes 1 0.4 0.1 0.1 -0.9 Investment shares of mutual funds 1.3 0.9 0.6 0.5 -0.8 Sundry assets 6.2 4.8 4.2 8.8 2.6 Total 100 100 100 100 – Source: Completed by authors 115

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For the period presented, the share of bonds in insurers’ investments has increased significantly; there has been an increase in investments in state and municipal securities. The interest of insurers in bank deposits is still high. The dynamics of assets and the effectiveness of their use is shown in Figure 1. The sharp increase in the total assets of insurance organizations in 2017 is connected with the need to take into account acquisition costs of future periods in the assets composition. That is, the accounting methodology has changed. Nevertheless, the growth of aggregate assets occurred in the insurance companies as a whole. It is advisable to analyze the segments of the insurance market to identify the segment that provides a significant increase. In 2016-2017, this type of segment was presented by the segment of life insurance on the insurance market of Russia.

Fig. 1 Aggregate assets of insurance organizations and their profitability

2500 7 6,2 2329,8 5,6 6 2000 1547,4 5 1871,4

. 1500 1626,6 4,2 4

3,3 % bln.rub 1000 3 2,6 2 500 1,99 2,01 2,17 1

0 0 2014 2015 2016 2017

Aggregate assets of insurance companies, bln.rub. Return on Total Assets, % Share of assets in GDP, %

Source: Completed by authors

In our opinion, investments in the economy by Russian insurers is currently determined by three areas - deposits in banks, investments in shares and corporate bonds. Based on the given analysis results, investments in these areas in 2015-2017 amount to about 50% of all the assets. In absolute terms, this amounts to 1139.3 billion rubles in 2017. Within the framework of the correlation analysis, we revealed the dependence of the volume of investments in the real sphere by Russian insurers on such factors as insurance premiums, GDP, net profit of insurance organizations, investments in the fixed assets by industrial enterprises of Russia, commissioning of fixed assets. The analysis showed a high degree of dependence of investment volumes in the real sphere on such factors as GDP (0.9927), insurance premiums (0.9826), enterprises’ investments in the fixed capital (0.9733). The degree of interdependence of insurers’ net profit and volumes of real investments is very low. This confirms our theoretical research in the sense that the Russian insurers’ interest in investment operations and their effectiveness increase is very low. Russian insurers have a relatively low level of a loss of insurance operations. Therefore, it is possible to profit from insurance operations, while in developed countries the profit center is investment activity. For this reason, the placement of Russian insurance companies' funds is carried 116

Gulnara Kaigorodova, Daria Alyakina, Guzel Pyrkova, Alfiya Mustafina and Viktor Trynchuk / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 109-123 out either in highly liquid but low-profit instruments (cash assets equivalents) or in financial instruments of customers or affiliated organizations that are low-liquid and low-yielding, which certainly does not lead to an increase in the efficiency of investment activities. Investments in affiliated structures can be implemented as follows: the owner of the insurance company pays his share in the authorized share capital of the insurer with his own shares or other securities of affiliated organizations, and such securities do not meet the conditions of liquidity and profitability. Thus, in the payment of the share in the authorized capital of the insurance organization there are low-quality financial instruments, which, obviously, negatively affects the financial state of the insurer. In addition, often characteristic is the regulation of financial flows and investing on the part of the owner, therefore, the share of affiliated organizations in the structure of the investment portfolio is significantly more prevalent. Based on the practice of investing own funds by Russian insurers, we can say that, as a rule, insurer's funds are used as quite cheap (and often free) sources of financing the activities of sub- sidiaries and affiliated entities. In general, the investment policy of Russian insurers is limited in a number of parameters, in particular: – a significant share of investments falls on assets that are not income-yielding; – companies use thin investment in debt and equity securities of enterprises, and it is the corporate securities market that currently allows getting an effective return on investment; – low diversification of the investment portfolio: a number of investment methods is practically not used.

The obtained model is shown in Figure 2.

Fig. 2. Model of dependence of the investment volume of insurance organizations in the real sector

1400

1200

1000

800

600

400

Insurance premiums (bln. Rub (bln. premiums Insurance 200

0 76000 78000 80000 82000 84000 86000 88000 90000 92000 94000 96000 GDP, in current price (bln. Rub.)

Insurance companies’ assets, allocated for real investments, bln. Rub.

Source: Completed by authors

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Assessing the prospects for economic growth in general and the insurance industry in particular, we assumed that further stabilization of the Russian economy, GDP growth will ensure further growth of the insurance market. This will help expand the investment opportunities of insurers. The studied interdependence between GDP (х) and assets of insurance companies allocated for real investments (y) allowed obtaining a trend line. The linear approximation gives us the following equation: y = 0.0415x - 2671.7 (2) Further, we base on the forecast of the socio-economic development of Russia. We take two estimates. According to the estimates of the Ministry of Economic Development of the Russian Federation, the GDP growth rate in the RF (according to the basic version of the forecast) in 2018 should be 2.1%, in 2019 2.2%, in 2020 2.3% (Ministry of Economic Development, 2017). The forecast of the Central Bank of the Russian Federation gives a level of 1.5 - 2.0 %% for the whole period. We take the averaged values between the minimum value of the Central Bank of the Rus- sian Federation and the forecast of the Ministry of Economics of the Russian Federation and get the following values, reflected in Table 4.

Table 4 Forecast values by volume of insurers’ investments in the Russian economy

Indicators 2018 2019 2020 GDP growth rate (forecast), in % 1.80 1.85 1.90 GDP, bln. Rub. 93739.4 95473.6 97287.6 Insurance organizations assets allocated for real 1218.5 1290.5 1365.7 investment, bln. rub. The growth rate of insurance organizations' assets 7.0 5.9 5.8 allocated for real investments, in %

Source: Completed by authors

Thus, the current trends make it possible to conclude that, if the country's socio-economic development forecasts for 2018-2020 are fulfilled, the insurance segment will allow increasing its investment in the real sector by about 6.0% annually. However, to ensure the implementation of the investment potential of insurers, it is necessary to overcome a number of problems of a qualitative nature. When considering the problems of managing the investment activities of Russian insurers, we have identified the following blocks: – statutory regulation of investment activities; – macroeconomic conditions for the functioning of the financial market; – low capable of paying demand for insurance services; – low interest of insurance companies in improving the management effectiveness of investment activities.

From the point of view of statutory regulation, it is possible to supplement the existing investment principles, in particular: – the auditability principle - implies ensuring the opportunity for insurers to monitor the allocated financial resources by participating in the boards of directors of enterprises in which funds are invested; obtaining information on management decisions taken, on the current financial state; – the principle of subordination - means that it is necessary to coordinate investments with insurance obligations, that is, to correlate the generation of investment income in place, amount 118

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and time agreed with insurance obligations. The necessity of this principle is explained by the fact that the investment portfolio should reflect the secondary nature of investment activity in relation to the insurance activity; – the principle of localization - means investing funds primarily on the territory of the Russian Federation.

The success of investment activities is largely determined by macroeconomic conditions. The problem here is the lack of a developed secondary securities market; insurers have problems of investment choice. This is due to the limited offers in the financial market, the lack of investment tools that meet the needs of conservative investors, such as insurance companies. Often, it is due to inadequate reliability and profitability of available tools. State support is necessary to create such tools. The second aspect is the need to take into account the investment risk factor, in case the necessary information is not sufficient. As a problem aspect, we can note a low level of demand for insurance services. For most types of property insurance, the number of contracts is reduced. The main share of reduction falls for insurance contracts concluded with legal entities. Thus, the sanctions policy against Russia, the fall in prices on the world energy market and the ensuing economic crisis have led to a reduction in the number of concluded insurance contracts with both legal entities and individuals. The volume of insurance of movable property of legal entities and individuals has significantly decreased, which is due to both hard times for enterprises and the population, and a significant decrease in the number of new cars sold, including on credit (which often implies the acquisition of the CASCO (fully comprehensive automobile insurance) policy). Since the introduction of American and European sanctions against Russia, the long-lasting deterioration of the situation on the world energy market for the Russian economy, the solvent demand for insurance has decreased. In general, the low level of demand for insurance services is due to a number of factors, such as: – low income level of most population groups in the regions; – low level of insurance culture of the population (Rotova & Trynchuk, 2004); – distrust of the population and business to financial institutions in general and to insurers, in particular; – neglect of the insurants' rights by a number of insurers; – high cost of insurance services often at their low quality.

The state regulator acting through the Central Bank of Russia considers it necessary to popu- larize insurance services for the population. Today, the insurant is initially placed in an unequal position, not being able to influence the formation and execution of the insurance contract, often without realizing what risks are involved in a certain insurance service or insurance company (Kozmenko and Oliynyk, 2015). It is necessary to focus on the consumer. It is worthwhile to study the experience of a number of countries to learn that the increase in the insurance culture of the population occurred through the introduction of a number of compulsory types of insurance. Russian insurers have traditionally focused their efforts on preserving and expanding the client base, agent and branch networks, without paying due attention to investment activities. Insurance operations, despite the high loss ratio for certain types of insurance, are the main source of income for the insurer. As for investment transactions, high efficiency is not typical. It is also necessary to state the lack of skills in the formation and management of the investment portfolio of most insurers, the lack of target units in insurance companies involved in the professional management of investments. However, the issue of wider involvement of the insurer's investment resources in the financial market should be considered jointly with other components of ensuring the financial stability of the insurer. Otherwise, we will face a situation where the insurers' investments in the real sector do not allow him to fulfill his obligations in time.

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Therefore, it is obvious that it is necessary to take into account all the aspects - both increasing the amount of absorption of the insurer's resources in the economy, and the importance of maintaining and ensuring financial stability of the insurer. From these positions, there is a number of problems in the Russian insurance market. Insurance companies reduce the volume of transfer of risks to reinsurance. Since the reinsurance system involved in the insurance company is one of the factors ensuring its financial stability, the fact of a decrease in the share of reinsurers is regarded as negative. This reduction occurs for two reasons. First, the low capacity of the insurance market. This prevents most insurers with the reinsurance license from accepting large volumes of risks for reinsurance protection. Secondly, during the crisis of the economy, the attraction of insurers' resources becomes difficult and the transfer of installments to reinsurance is regarded as unjustified expenses. A significant expense line for Russian insurers is the cost of conducting insurance operations. At that, a significant share falls on the salaries of management personnel and commission fees to insurance intermediaries. The success of the insurance intermediary depends on the receipt of insurance premiums and the volume of investment resources. However, it should be taken into account that the cost of conducting business affects the cost of insurance services. At the same time, the insurance intermediary is in no way involved in providing insurance policyholders with insurance protection, that is, his activities often end at the stage of contract conclusion. Low efficiency of investment operations. The contribution of investment operations in the formation of financial performance of insurance companies is extremely small. The reason for this is ineffective management of investment activities in the company, which does not use permitted and more profitable assets for investments. The task of optimizing the use of investment resources is closely linked with the task of forecasting the development of the insurance portfolio, solution of which, with acceptable accuracy for practical purposes, is possible only if there is sufficient statistical data and, most importantly, a significant number of homogeneous risks in the insurance portfolio. The heterogeneity of risks, due to their wide dispersion in insurance amounts, in the types of insured objects, as well as small sample sizes associated with underdeveloped and non-gross nature of insurance operations do not allow making forecasts confidently. Until recently, in the Russian insurance market, few companies could boast of a truly homogeneous and massive portfolio of basic types of insurance. At present, the situation is changing, more and more companies can forecast the development of the insurance portfolio, and as a result, they can plan investment activity and really assess their investment potential. Most insurance companies do not have a worked out investment policy. Under investment policy, they consider investing in tools of affiliated structures or in bank deposits - low-yielding instruments. Here can help the transfer of funds to trust management. The transfer of the portfolio to management companies is a worldwide practice. However, insurers are still not actively transferring funds to trust management. The reasons, in our opinion, are the following:

 the ability to "live" at the expense of profitability of insurance operations, not particularly caring about the investment income of the investment portfolio;  lack of a scientific approach to the construction of investment policy parameters;  the need to pay the management company a fee.

In the trust management, analysts see both positive aspects and negative ones. Among the positive aspects, they see the lack of the need to have specialists in the stock market in the staff, with minimum risk and for a small fee transfer the asset management functions to the professional manager; the profitability of the majority of management companies exceeds the profitability of insurers, high risk diversification. As for negative aspects, they include duplication of operations, as insurers have to repeat in their accounting records all transactions conducted by the management company.

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Trust management becomes relevant also because the progressive development of an insurance organization involves a gradual entry into the international market. In this regard, there are additional incentives to work with reliable and stable partners in the sphere of trust management of insurance reserves. In general, the investment activity of Russian insurers, under competition development, should increasingly become one of the main profit centers of the company, thereby increasing the sources of economic growth of the domestic economy. At the same time, a full-fledged insurance protection of enterprises is capable of indirectly increasing the economic opportunities of economic entities.

CONCLUSION Successful investment activity of insurance companies is important from two perspectives. First, it is the basis for ensuring the financial stability of insurers. Secondly, investment resources accumulated by insurers are one of the sources of the state economic growth. The process of the state regulation of investment activity is based on the need to ensure the safety of the insurance fund reserves and insurers’ own funds, since they will be the sources of loss cover for the insured (first - the insurance fund, with its lack - the insurer's own funds). Therefore, based on the regulatory measures of the state and the borrowed nature of the funds of insurance reserves, the investment policy of the insurer must be conservative. It should be kept in mind that there is another interdependence between the insurance market and the state economy, in addition to the investment operations of insurers. Full-fledged insurance protection of economic entities against risks allows them to focus on other aspects of their activities. This allows them to release resources and allocate them for expanding business. That is, the expansion of proposals on the part of insurers, the emergence of insurance products that meet the current needs of economic entities can also ensure the forward movement of the economy. The possibilities of the insurance market are limited by its low capacity. Reinsurance abroad becomes an expensive service. Nevertheless, we believe that only with full protection against insurance risks economic growth and increased volumes of direct investment will be ensured. This is especially important in terms of sanctions risks escalation. Perhaps, it is necessary to study the issue of increasing the requirements for the minimum amount of authorized capital of insurers. The carried out analysis allows us to conclude that, with a steady trend of reducing the number of insurers, their aggregate capital increases, and insurance reserves grow as well. However, their insufficient part is invested in the real sector of the economy. A significant amount of resources is allocated for investments with zero profitability. The study showed that between the volumes of insurance premiums, investments of enterprises in the fixed assets, GDP and assets placed by insurers in the real sector, there is a high interdependence, which allows us to determine the importance of the insurance market for the state economic growth. However, only about 50% of the insurers’ resources go to the real sector. The constructed model of interconnection made it possible to forecast the volume of investments in the economy from the insurance sector for the next three years. However, it should be noted that when considering the need for and the possibility of investing in the real sector, it is required to study not only the quantitative indicators, but also the qualitative ones. It is necessary to consider the existing model of state regulation, macroeconomic conditions of insurance and investment activities, as well as factors determining the demand for insurance products. The calculated forecast indicators show that, under the current conditions and in the implementation of the planned indicators of Russian socio-economic growth, the next three years will ensure the investment increase in the economy from the insurance sector by 6% per year. Howev- er, it is necessary to focus on creating conditions that will allow insurers to increase the share of investments in the real sector in the total amount of funds they place.

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Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 125-140 ‘

Investment Policy of Governance of Economic Security of Agrarian Sector of Ukraine on the Basis of Theory of Fuzzy Logics

LIUDMYLA NIKOLENKO1, EVGENIY JURAKOVSKIY2, NATALYA IVANYUTA3, OLENA ANDRONIK4 and SVITLANA SHARKOVSKA5

1 Professor, Donetsk Law Institute of the Ministry of Internal Affairs of Ukraine, Head of Law Department, Mariupol, Ukraine, e-mail: [email protected] 2 Post-graduate student, Vasyl’ Stus Donetsk National University, Vinnytsia, Ukraine, e-mail: [email protected] 3 Associate Professor, Donetsk Law Institute of the Ministry of Internal Affairs of Ukraine, Law Department, Mariupol, Ukraine, e-mail: [email protected] 4 Associate Professor, Vasyl’ Stus Donetsk National University, Department of Entrepreneurship, Corporate and Spatial Economics, Vinnytsia, Ukraine, e-mail: [email protected] 5 Post-graduate student, National University of Life and Environmental Sciences of Ukraine, Kyiv, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 29, 2018 In modern globalized world the issue of ensuring economic security Revised from September 23, 2018 is a priority task for the protection of the national interests of the Accepted November 07, 2018 state against all kinds of threats that are of particular importance to Available online December 15, 2018 Ukraine. The different economic potential of the Ukrainian regions, the level of their development, the recent economic and financial problems in the country, the unresolved armed conflicts in the East- JEL classification: ern part, the large number of settlers from the occupied territories C 45, С 53, О13. and other adverse conditions faced by the Ukrainian economy in early 2014 require a special approach to solving the problem ensur- DOI: 10.14254/1800-5845/2018.14-4.9 ing economic security of the agrarian sector as a leading industry in Ukraine. The purpose of this article is to develop a system for in- Keywords: vestment managing the economic security of the agrarian sector of the region of Ukraine (Vinnytsia region) based on the innovative investment, economic and mathematical model for estimating and forecasting management, the level of economic security of the agrarian sector built by the economic security of agrarian sector, means of the theory of fuzzy logic. The subject of the research is a forecasting, set of theoretical, methodological and practical aspects of invest- Ukraine ment managing of the economic security of the agrarian sector of Ukraine. The innovative mathematical model of estimation and prediction of economic security of agrarian sector of Ukraine in the region (namely, Vinnytsia region) was first developed based on the theory of fuzzy logic. The forecast of the level of economic security of the agrarian sector of the region of Ukraine up to 2022 was made. The structural diagram of the process of decision-making and support with the developed economic-mathematical model is proposed.

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INTRODUCTION The changing dynamics of modern life creates new problems, intensifies methodological quest research and creates new paradigms of economic processes. In their numerical list issues of investment management of economic security of agrarian sector of the region of Ukraine are high- lighted. The issue of ensuring the country's economic security as a priority task of protecting the national interests of the state from all kinds of threats is of special urgency. One method of evaluating the current situation and ways to increase the level of economic security of the agrarian sector is the use of information technology that will improve the efficiency and quality of management decisions. How world’s experience shows, the agrarian sector can’t develop effectively and be competitive without attracting investments. Accumulation of investment capital not only can provide economic security of the agrarian sector but also ensure the development of other sectors of the Ukrainian economy. In addition, investment is the tool of macroeconomic stabilization of the agrarian sector which also allows to solve other problems of the state development in general. To address the issues it is needed to develop an economic and mathematical model of a new type, allowing a problem-oriented search, to analyze information, and to provide factual information to the user in an easily accessible form. Resolving the problems of managing the economic security of the agrarian sector of Ukraine, attracting investment in this sector can be solved by developing an economic and mathematical model that will determine the level of economic security of the agrarian sector of Ukraine. On the basis of this model, it is possible to develop a decision support system that will allow managing effectively and determining the problems of the development of this industry. The use of such models and economic systems can provide accelerated economic growth of the leading - agrarian sector of Ukraine.

1. LITERATURE REVIEW As is known, the problems of achieving economic security of the state, regions, separate branches of the economy, as well as research questions, investment management, are reflected in the scientific works of many foreign and Ukrainian scientists. According to the Law of Ukraine “About Investment Activity” (1991), an investment activity is a set of practical actions of citizens, legal entities and the state regarding the implementation of investments. The rational use of investment resources determines the effective implementation of investment activity in any industry, including in the agricultural sector. Formation of investment resources is an important component of the investment and general financial strategy of production structures, as well as the initial condition for the implementation of the investment process at all its stages. According to F. Vajinskiy et al. (2016), without the states regulatory role investment activity in the agrarian sector cannot exist. State regulation of investment activity of the agricultural sector is based on three groups of methods: economic, organizational and legal. Economical methods consist of a combination of depreciation, tax, lending, innovation, pricing policies and policies in the field of human capital formation. According to I. Zlelko et al. (2016) - the development of innovation activity in the agrarian sector of the economy is restraining by the following factors: - the im- perfection of the legislative framework and the lack of state incentives for innovation; the limited internal and external sources of funding for innovation and the impossibility of their rapid mobiliza- tion; the low level of investment attractiveness of the industry. V. Kirilenko (2005) devoted his work to the issues of investment as a method of securing the economic security of the agrarian sector. He states that the investment component is sufficiently important in the system of economic security, in particular, investment security. Considering the concept of economic security in relation to the agrarian sector, its particular importance should be 126

Liudmyla Nikolenko, Evgeniy Jurakovskiy, Natalya Ivanyuta, Olena Andronik and Svitlana Sharkovska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 125-140 taken into account, since the main purpose of the agrarian sector is to provide the population of the country with affordable food in the required volume and quality, as well as to create jobs for the inhabitants of the countryside. Therefore, as stressed by I. Ushachev (2006), the state of the agrarian sector determines not only food security but also social stability in society. However, despite the presence of a vast number of scientific developments, some issues of a comprehensive assessment of the state of the agrarian sector and its impact on ensuring the country's economic security requires clarification and further research. O. Vlasuk (2008) and Kharlamova et al. (2016) considers the economic security of the agrarian sector has a complex structure that should be considered regarding the functional and structural aspect as a set of interconnected security systems that reflect the functioning of individual “blocks” or spheres of the state's economic system. B. Bisultanov (2008) offers the following interpretation: “The Economic security of the agro-industrial complex is a state of economic, financial, social, legal and ecological conditions for functioning and achievement of competitiveness with providing the necessary level of protection of vital interests of enterprises of the complex from internal and external threats”. N. Kulagina (2012) considers the economic security of the agrarian sector as a system of economic interests consists of finding mechanisms for a compromise between ensuring the national benefit of the country, food security and risks, as a result of which the stable functioning of the agroindustrial complex is ensured. Thus, V, Kurgan (2008) believes that “the Economic security of the agrarian sector of the economy should be considered as an effective use of enterprise resources and existing market opportunities, which helps to prevent internal and external threats, ensures its long-term survival and sustainable development by the chosen mission”. M. Lychik (2013) considers the Economic security of the agrarian sector is ultimately the food saturation and balanced nutrition of the Ukrainian population, the availability of resources for production and the ability to the dynamic economic development of rural areas. A. Svetlakov (2010) believes that “the Economic security of the agro-industrial complex is a complex of economic, social, legal and environmental conditions and factors for the functioning, development and achievement of the competitiveness of the industry with the aim of self-sufficiency of high-quality food products of the population of the territory, stimulation of individual commodity producers”. Analysis of the statements shows that the economic security of the agrarian sector has a complex structure. For example, O. Vlasuk (2008) proposes to consider economic security as a set of interconnected security systems that reflect the functioning of individual “blocks” or spheres of the economic system of the state and suggests to consider the following functional components of the economic security of the agrarian sector: political and legal; financial; production-technological; investment; innovative; ecological; marketing; intellectual-framing; social foreign economic activity. Ensuring economic security of the agrarian sector of Ukraine can be achieved by applying modern methods of economic-mathematical modeling, namely, the theory of fuzzy logic. As emphasize K. Bilovsky and O. Matkovska (2013) in the report “Application of fuzzy logic to solve economic problems”, exactly the uncertainty of information makes it necessary to replace the traditional mathematical methods of fuzzy logic simulation. Methods of fuzzy logic allow simulating any socioeconomic processes under conditions of insufficient information and quantitative uncertainty of input data. The advantages of models based on fuzzy sets are the possibility of using numerical and linguistic data, the possibility of obtaining a generalized assessment in the case of the use of non-interconnected input and output data, the possibility of taking into account the specifics of the object or process under study and the ability to adapt the model to dynamic conditions of the economy.

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2. METHODOLOGY Despite the urgency of the problem of granting the economic security of the agrarian sector, today there is no universally accepted, approved methodology for assessing the level of economic security. Economists directed their studies to study the issues of regional economic security and security of the economy using the methodology sponsored by the state and by comparing the values of macroeconomic statistical indicators with their threshold values determined the level of economic security of the country. However, in our opinion, this approach is rather general and approximate, because it does not allow determining the real level of economic security of a particular region and its branches because of the failure to take into account the specifics of the territories, available resources, economic conditions, etc. Also, this approach does not take into account the dynamics factor. Thus, in order to assess the state and level of economic security of the agrarian sector of the region, it is necessary to form a system of indicators that fully characterize it, and based on the selected indicators, using one or another method of analysis, carry out an assessment of the state of economic security. The ultimate goal of assessing the state and level of economic security of the agrarian sector is the development of measures to improve the functioning of the agrarian sector in order to ensure the food independence of the country as a whole and its regions on the basis of harmoniza- tion of goals and objectives of the agrarian policy of the state at all levels of management through the use and analysis of scientifically based quantitative and qualitative indicators that characterize the state of economic security. The initial stage in the assessment of the condition and level of economic security of the agrarian sector is the establishment of adverse factors that may be prerequisites for the emergence of internal and external threats to economic security. This can be done through the use of a systematic approach, which involves the separation of interrelated subsystems of economic security, the qualitative state of which directly affects the level and prospects for the development of agribusiness of the region and the country as a whole. We propose the following subsystems: the peculiarities of the agrofood business; level and quality of life of the population; the state of ecology and nature management; quality management of agroindustrial complex. The methodology for assessing the level of economic security of the agrarian sector should take into account the interdependence of economic categories: “economic security of the agrarian sector” → "food security of the country” → “national security as well as regional features of the territories”. This is necessary for the case when it is necessary to adjust the indicators and criteria of economic security depending on changes in the natural and climatic conditions in which the agricultural sector operates. In our opinion, the most well-known methods for assessing the state and level of economic security include the methodology by S. Yashin (2006). This methodology is based on the use of indicators published by state statistics authorities. The assessment of the level of regional economic security of the agrarian sector is carried out by comparing predefined indicators with their average values in the region. Based on the results of the analysis, we believe that the indicator method with the use of threshold values may be the most optimal and effective for assessing the economic security of the agrarian sector at the regional level. In this regard, we propose to consider the economic security of the agrarian sector on the basis of the analysis of such components (see figure 1).

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Figure 1. Components of the economic security of the agrarian sector

Components of the economic security of the agrarian sector

economically- productively tech- natural-ecological socio-intellectual financial nological

institutional political-integration

Source: compiled by the authors based on (Kozlovskyi and Jurakovskiy, 2016)

At the same time, it should be emphasized that the methods of economic modeling have recently become widely used. The advantages of these methods are following: shortening the timing of the analysis, the full coverage and taking into account the factors affecting the results of the object under study, the replacement of approximate or simplified calculations by exact calculations, the possibility of formulating and solving complex multidimensional problems. However, classical methods of economic modeling are not quite suitable for analyzing and forecasting the development of systems operating in conditions of significant uncertainty. The traditional methods of economic modeling are based primarily on binary logic, which is not correct for dealing with inaccuracies and uncertainty occurring in financial and economic systems. For this purpose, the need to develop new methods that would be based on "soft" computing, mainly on fuzzy logic and neurocomputing occurred. It is the fuzzy logic that represents the system of calculations in which objects of calculations are objects with vague boundaries. In the areas of finance and economics, such modeling techniques are rather common than exceptional. The advantages of the theory of fuzzy logic in comparison with other mathematical methods are given in (S. Kozlovskyi and V. Kozlovskyi, 2005), which one more time confirms the effectiveness of the use of the theory of fuzzy logic to solve the research problem – Table 1.

Table 1. Methodological analysis of the choice of mathematical apparatus for solving the research problem

Approaches to considering factors of uncertainty

Characteristics considered logic val mean val tive probabilities theory of subjec- theory of probability theory multitasking logic the theory of fuzzy of the theory the theory of inter- of the theory the theory of errors of the theory 1. Considering the physical numerical - + + + + + 2. uncertainty 2. Considering the physical non-numerical + + - + + + uncertainty 3. Considering non-numeric linguistic + - - - + + 4. uncertainty 4. Dependence of the error of the final result unac- Increases does not increases on the accuracy of the input data ceptable rapidly surpass 5. Ability to take into account the semantic + + - - + + modality of information 129

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6. Ability to take into account the level (quantification) of uncertainty - + - - + + 7. Taking into account expert qualifications + - - - - + (more than, significantly, very, etc.) 8. Ability to take into account the contradiction + - - + + + between accuracy and uncertainty 9. Efficiency of formalization of complete + - + + + + ignorance 10. Absence of a requirement for a clear task of a complete list of events + - + + - + 11. Possibility of effective consideration of the mutual influence of uncertainties in the + - - - - + processing of information 12. Possibility of simultaneous reception of pessimistic and optimistic assessments and - + - + + + the level of trust in them 13. Unified approach to presenting accurate, uncertain, incomplete, fuzzy parameter values - - - - - + 14. Ability to implement algorithms for + + + + + + processing information 15. Ability to work in the professional language + - - - - + of the user 16. Simplicity of obtaining expert opinions + - + + - + (statements) 17. Ability to work with uncertain information based on small statistical samples + - + + - + 18. Visibility of the results obtained for the - - - - - + calculation and risk assessment 19. Effective and rapid adjustment of the - - + + - + model

Source: compiled by the authors based on (S. Kozlovskyi and V. Kozlovskyi, 2005)

The theory of fuzzy logic allows to consider and analyze both qualitative and quantitative indicators. The use of this mathematical apparatus has been successfully tested in solving similar economic problems (S. Kozlovskyi et al., 2018; Shvedovsky et al., 2016) and can be applied to achieve the purpose of this work. In the process of research, the following methods were used:  expert assessments – in assessing the factors affecting the economic security of the agrarian sector;  systemic – in studying the principles of assessing economic security;  synthesis, analysis, grouping – in determining the factors of influence and justifying the choice of factors that affect the economic security of the agrarian sector;  modeling – in studying the processes of development of the agrarian sector;  abstraction – in developing a model for assessing the economic security of the agrarian sector;  generalization – when constructing analytic dependencies for choosing the method of constructing membership functions in models of management of economic security.

The purpose of this article is to develop a system for investment managing the economic security of the agrarian sector of the region of Ukraine (Vinnytsia region) based on the innovative economic and mathematical model for estimating and forecasting the level of economic security of the agrarian sector built by the means of the theory of fuzzy logic.

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3. RESULTS Considering the classification mentioned above of signs of "economic security of the agrarian sector", we will develop an economic-mathematical model for estimating and forecasting the level of economic security of the agrarian sector of the region of Ukraine with the theory of fuzzy logic. The classical approach to modeling based on the theory of fuzzy logic involves a step-by-step solution to the following problems (Kozlovskyi, 2005): the isolation of the main factors of influence on the level of economic security of the agrarian sector; formalization of the interconnections between the factors of influence in a generalized form; definition and formalization of linguistic assessments of the factors of influence; construction of a fuzzy knowledge base on the interconnections between the factors of influence; the output of fuzzy logic equations based on linguistic estimates and fuzzy knowledge base; optimization of fuzzy model parameters. Considering the necessity of observing the basic principles of simulation (Jurakovskiy et al, 2016) of the level of economic security of the agrarian sector of the region and the current conceptual apparatus of the theory of fuzzy logic the input factors of the model for assessing the level of economic security of the agrarian sector of the region are given in table 2.

Table 2. Input factors (variables) of the model and their linguistic estimation

Input factor Name of the input parameter Input parame- Linguistic evaluation of input parameters (variable) (variable) ter range (terms) Classification of economic and financial factors (e) x1 Average prices of grain and legumi- 1-5 Low, 1-2, (Н) nous crops in the region thousand UAH Average, 2-3, (С) / tonne High, 3-5, (В) x2 Average prices of animal production 2-10 Low, 2-3 thousand UAH / tonne, (N) in the region thousand UAH Average, 3-6 thousand UAH / tonne, (C) / tonne High, 6-10 thousand UAH / tonne, (B) x3 Net profit enterprises of the agrari- 10-50 Low, 0-10 billion UAH (Н) an branch of the region billion UAH. Average, 10-20 billion UAH (С) High, 20-50 billion UAH (В) x4 The balance of the foreign -30-50 Negative -30-0 million dollars USA, (НН) trade of Vinnitsa region million dollars Low, 0-10 million dollars USA, (Н) USA Average 10-30 million dollars USA, (С) High, 30- million dollars USA, (В) x5 The number of subsidies to agricul- 50-500 Low, 50-100 million UAH, (Н) ture from the state budget million UAH. Average 100-300 million UAH, (С) High, 300-500 million UAH (В) x6 The level of profitability of all activity 3-50 % Low, 3-5%, (Н) of enterprises of the agrarian sector Average, 5-20%, (С) of the region High, 20-50%, (В) x7 Inflation rate in Ukraine 0-100 % Low, 0-8 %, (Н) Average, 9-15 %, (С) High, 15-100 %, (В) Classification of production and technological factors (v) x8 Gross output of agriculture of the 10-40 Low, 10-15 billion UAH (Н) region billion UAH. Average, 15-30 billion UAH, (С) High, more than 30 billion UAH., (В) x9 Number of agrarian enterprises in 1-3 Low, 1-1,5 thousand pcs/year, (Н) the region thousand Average, 1,5-2 thousand pcs/year, (С) pcs/year High, 2-3 thousand pcs/year, (В)

x10 Number of agrarian machinery in 5-80 Low, 1-20 thousand pcs., (Н) the region thousand pcs. Average, 20-40 thousand pcs., (С) High, 40-80 thousand pcs., (В) Classification of natural and ecological factors (p) x11 The volume of sown areas of the 1-2 Low, 1-1,2 million hectares, (Н) 131

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main crops in the region million hec- Average, 1,2-1,7 million hectares, (С) tares High, 1,7-2 million hectares, (В) x12 Grain and legume yield cultures in 30-70 Low, 30-40 quintal per hectare, (Н) the region quintal per Average, 40-50 quintal per hectare, (С) hectare High, 50-70 quintal per hectare, (В)

x13 Rape yield in the region 10-40 Low, 10-15 quintal per hectare, (Н) quintal per Average, 15-30 quintal per hectare, (С) hectare High, 30-40 quintal per hectare, (В)

x14 Emission volume pollutants in the 30-200 Low, 30-80 thousand tonnes, (Н) atmosphere of the region thousand Average, 80-120 thousand tonnes, (С) tonnes High, 120-200 thousand tonnes, (В) x15 Natural disasters in the region 0-100 Low level, 0-25, (Н) points Average level, 25-50, (С) High level, 50-100, (В) Classification of social and intellectual factors (s) x16 Average number of employed work- 25-50 Low, 20-30 thousand persons/year, (Н) ers in the agrarian production of the thousand per- Average, 30-40 thousand persons/year, (С) region sons/year High, 40-50 thousand persons/year, (В)

x17 Average salary in the region 1-7 Low, 1-3 thousand UAH/ month, (Н) thousand UAH/ Average, 3-4 thousand UAH/ month, (С) month High, 4-7 thousand UAH/ month, (В) x18 Intellectual potential (index of hu- 0-1 Low, 0-0,5 (Н) man development) of the country unit. Average, 0,5-0,7 (С) High, 0,7-1 (В) x19 Innovative potential of the region 0-100 Low, 0-30, (Н) points Average, 30-60, (С) High, 60-100, (В) Classification of institutional-political-integration factors (i) x20 Institutional potential of the region 0-100 Low, 0-30, (Н) points Average, 30-60, (С) High, 60-100, (В) x21 The level of political stability in the 0-100 Low, 0-30, (Н) country points Average, 30-60, (С) High 60-100, (В) x22 International integration status of 0-100 Low, 0-30, (Н) the country points Average, 30-60, (С) High 60-100, (В)

Source: developed by the authors

Denote the linguistic variables of factors e, v, p, s, and by the following relationships:

e = fe(x1, x2, x3, x4, x5, x6, x7), (1) v = fv(x8, x9, x10), (2) p = fp(x11, x12, x13, x14, x15), (3) s = fs(x16, x17, x18, x19), (4) i = fi(x20, x21, x22). (5) where x1…x7 – economic and financial factors; x8…x10 – production-technological factors; x11…x15 – natural and environmental factors; x16…x19 – social and intellectual factors; x20…x22 – institutional-political-integration factors.

The output value, that is, the level of economic security of the agrarian sector of the Vinnytsia region Z, can be determined by the formula (6): 132

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Z = fz(e, v, p, s, I, t), (6) where e, v, p, s, і are linguistic variables that describe economically-financial, industrial- technological, natural-ecological, socio-intellectual, institutional-political-integration factors of influence and period of forecasting accordingly. The prediction period t will be encoded in two char- acters in the future: (6M, 1P, 2P, 3R, where the letters M and P are designated month and year).

Using the recommendations of experts (Official website of the World Food and Agriculture Or- ganization of the United Nations) and according to the particular economic situation in the agrarian sector, the level of economic security of the region of Ukraine (namely, Vinnytsia region) can be characterized by the following levels (on a scale from 0 to 100):

 Z1 (85-100) – excellent economic security (class А or 1);  Z2 (66-84) – good economic security (class В or 2);  Z3 (51-65) – satisfactory economic security (class С or 3);  Z4 (31-50) – unsatisfactory economic security (class D or 4);  Z5 (0-30) – absolute economic danger (class Е or 5).

The structure of the economic model for estimating and forecasting the level of economic security of the agrarian sector of the region of Ukraine (namely, the Vinnytsia region) will be presented in the form of the so-called "tree of logical conclusion". A logical conclusion tree is a graph that shows the logical connections between the predictive index Z and the factors {x1 ... x22} that affect this predictive Z in compliance with the relationships given in formulas (1) to (5). Structural models of estimation and forecasting of the level of economic security of the agrarian sector of the region of Ukraine (namely: Vinnitsa region) will have the form shown in figure 2. Presented structural analysis of the model of estimation and forecasting of the level of economic security of the agrarian sector of the Vinnytsia region shows that this model actually consists of five other interrelated models:  the model of economic and financial factors providing economic security of the agrarian sector of the region;  models of production-technological factors providing economic security of the agrarian sector of the region;  models of natural and ecological factors ensuring economic security of the agrarian sector of the region;  models of social and intellectual factors ensuring economic security of the agrarian sector of the region;  models of institutional and political-integration factors ensuring economic security of the agrarian sector of the region.

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Figure 2. Structural models of estimation and forecasting of level economic security of the agrarian sector of the region of Ukraine

х9 х8 x7 x6 x5 x4 x3 х2 x1 100 x 10 Z1

fe x11 fv Z2 x12 e v

x13 fp Z3 p Z

x14 fZ Z4 х15 s i fs t f i t x16 Z5 0 x17 x18 x19 x20 x21 x22

Source: compiled by the authors based on (Jurakovskiy et al, 2016)

It should be noted that when constructing the model, we operated with incoming quantitative and input qualitative parameters at the same time. Input parameters {x1 ... x14, x16 ... x18} are quantitative, and we used statistical data for their description. The parameters {х15, x19 ... х22} are qualitative, therefore for their description we used a scale of grades from 0 to 100 points. The theory of fuzzy sets involves determining the levels (terms) of changes in the original index (Kozlovskyi et al, 2018). Each term is given by a fuzzy set with the corresponding membership function. To describe the terms, we use the method shown in (S. Kozlovskyi and V. Kozlovskyi, 2005). In this case, the terms will be presented in the form of fuzzy sets, using the model of the membership function (MF): T(x) = , (7) where b and c – membership function parameters (MF); b – coordinate of the maximum of function; с – coefficient of the concentration of stretching.

The values of the coefficients b and c for the variables x1 are presented in table 3 (as an example). 134

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Table 3. The values of the parameters b and the functions of the membership variables x1

Input variables The name of the input variable Linguistic estimation of b c (parameter) (parameter) input variables (terms) Н 1 1 Average prices for grain and legu- x1 С 2,5 1,5 minous crops in the region В 4 1,5

Source: developed by the authors

The choice of the membership function of this type (see formula 7) is explained by the fact that this feature is sufficiently flexible and simple, since it is given only by two parameters, and is more convenient for further adjustment of the model. The next step in modeling the level of economic security in the agrarian sector of Vinnytsia region is the compilation of a hierarchical knowledge base. To build the knowledge base, we used information from experts from the Department of Agrarian Development and the Department of Regional Economic Development of Vinnytsia Re- gional State Administration and the Main Department of Statistics in Vinnytsia region, factual information of central executive authorities of Ukraine and information from specialists in this field (Kaletnik et al., 2011). Let’s consider the relation (6). To estimate the value of linguistic variables that show the caus- al link between the level of economic security of the agrarian sector of the Vinnytsia region Z and economically-financial, industrial-technological, natural-ecological, social-intellectual, institutional and political-integration factors of influence we use the system of term-sets, which is proposed above. Then the knowledge base for the variable Z, which characterizes the level of economic security of the agrarian sector of the region of Ukraine (namely, the Vinnytsia region), will look like on table 4.

Table 4. Knowledge base of the variable Z

e v p s i t Z w

Н Н Н Н Н t1 Z5 w1

Н C C Н Н t2 Z5 w2

С Н С Н Н t4 Z5 w3

Н С Н С С t2 Z4 w4

С С Н Н С t3 Z4 w5

С Н С С Н t1 Z4 w6

С С С С С t4 Z3 w7

ВС Н С ВС ВС t1 Z3 w8

В Н В Н Н t2 Z3 w9

С ВС ВС ВС Н t3 Z2 w10

ВС С ВС С С t2 Z2 w11

В В В Н С t1 Z2 w12

В В В В В t3 Z1 w13

В ВС ВС ВС С t4 Z1 w14

ВС В В ВС В t2 Z1 w15

Source: developed by the authors 135

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Each knowledge base rule is an “IF-THEN” statement. Rules that have the same output parameter are merged in the table rows with the “OR” logical statement. The weight of the w rule expresses the personal confidence of the expert in this rule. At the stage of formation of the structure of the fuzzy model, we take the weight of all rules of the knowledge base equal to unity (Ko- zlovskyi and Grynyk, et al, 2017). To implement a fuzzy logical conclusion, it is necessary to make a transition from logical statements to fuzzy logic equations (Zadeh, 1976). Such equations can be obtained by replacing linguistic values  with the values  of membership functions, and the operations “YES” and “OR” are fuzzy logic operations of intersection and union. The weight of rules in the knowledge base is considered by multiplying the fuzzy expression corresponding to each line of the base to the corresponding value of weight (Rotshtein, et al, 1998). Then listed in the table 4 linguistic statements will correspond to the following fuzzy logic equations (see formulas 8-12):

Z5 ННHНН t1 1 )]t()i()p()s()e()a([w)Z(  (8) ННССН t 2 2 )]t()i()p()s()e()a([w 

ННCНC t 4 3  )];t()i()p()s()e()a([w Z4 CCHCН t 2 )]t()i()p()s()e()a([w)Z(  4 (9) СННСC t3 5 )]t()i()p()s()e()a([w 

НСCНC t1 6  )];t()i()p()s()e()a([w Z3 CCСCC t 4 )]t()i()p()s()e()a([w)Z(  7 (10) ВC СН ВС ВС t1 8 )]t()i()p()s()e()a([w 

ННВНВ t 2 9  )];t()i()p()s()e()a([w Z2 C ВC ВС ВC Н t3 )]t()i()p()s()e()a([w)Z(  10 (11) ВC С ВС СС t 2 11 )]t()i()p()s()e()a([w 

СНВВВ t1 12  )];t()i()p()s()e()a([w Z1 ВВВВВ t3 )]t()i()p()s()e()a([w)Z(  13 (12) В ВС ВС ВС С t 4 14 )]t()i()p()s()e()a([w 

ВС ВВ ВС В t 2 15  )].t()i()p()s()e()a([w

The significance of the degrees of membership functions in equations (8) - (12) is determined by fuzzy knowledge bases that characterize economic-financial, production-technological, natural- ecological, social-intellectual, institutional-political-integration factors of influence. Fuzzy logic equations (8) - (12) are a mathematical implementation of the model of estimation and forecasting of the level of economic security of the agrarian sector of the Vinnytsia region. Defuzzification is the last stage of the simulation and represents a reverse transformation of the fuzzy logical statement (conclusion) found in the output estimating or predictive parameter (variable), which is subject to modeling and prediction. There are various methods of defuzzification, the choice and application of which depends on the object of modelling (Rotshtein, 1999). Proceeding from the characteristics of the modelling purpose and the nature of the initial parameter (variable) to solve the logical equations we select the method of defuzzification, which is called "method of the center of gravity extended" (S. Kozlovskyi and V. Kozlovskyi, 2005). In our case, when the output parameter (variable) has "n" terms, the calculation of the center of gravity is reduced to the solution of equation (13):

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n   ZZ EA  Zi   E )1i(Z    1i  1n  (13) Z  n ,  Zi 1i where n – is the number of (discrete values) of the terms of the variable «Z»; AE )Z(Z – lower (upper) limit of the range of variable “Z”;  Zi – function of the variable “Z” belonging to the fuzzy term “Zi”. In the mathematical package Matlab (Pratar, 1999) an experiment was conducted using the above-mentioned method. The figure 3 shows the results of the estimation and forecasting of the level of economic security of the agrarian sector of the Vinnytsia region up to 2022.

Figure 3. Results of estimation and forecasting of the level of economic security of the agrarian sector of the region of Ukraine (namely: Vinnytsia region)

Source: developed by the authors

Analyzing the results of modeling the level of economic security of the agrarian sector of the Vinnytsia region for the period 2018-2022, one can draw the following conclusion: in 2018, the level of economic security of the agrarian sector of the region can be classified in class D – “unsatisfactory economic security”. In 2019-2022, the predicted level of economic security of the agrarian sector will improve to class C – “satisfactory economic security”. To improve the reliability of the forecast of the level of economic security of the agrarian sector of the region it is possible to optimize (adjust) this model. The advantage of economic and mathematical models built with the use of fuzzy logic is the ability, when constructing a model, to take into account the input variables given by the linguistic conclusions of the experts, which largely compensates the lack of analytic relationships between the input and output variables of the prediction object. However, the use of fuzzy logic makes it possible to realize another advantage of this method, namely – it gives an opportunity to influence the process of forming linguistic statements (conclu- 137

Liudmyla Nikolenko, Evgeniy Jurakovskiy, Natalya Ivanyuta, Olena Andronik and Svitlana Sharkovska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 125-140 sions) of experts that they make on certain issues during the expertise. That is, models built with the use of a fuzzy logic have the ability to self-study. That is, the ability to flexible adjustment to reflect changes in the structure of causality between input and output parameters (variables) or according to changes in external factors. That provides an opportunity to build an interactive decision support system for managing the economic security of the agrarian sector of the Vinnytsia region. Considering the above-mentioned facts, the process of making and supporting investment decisions with the help of the developed model can be presented in form of the diagram shown in figure 4.

Figure 4. The block diagram of the process of acceptance and support of investment decisions with the help of the developed economic-mathematical model

Statistic and expert information

Experts Impact factors Knowledge

Vector of input variables Generation of Х1 … X22 knowledge base rules

Definition of MF values Knowledge base

Weight rules

Generation of debug parameters Solving fuzzy logic MF base b,c parametrs (optional) equations

Dephasing and obtaining the initial value of the Z-level of economic security of the agrarian sector of the region

Manager / Investor (Transition to functional management structure)

Source: developed by the authors

We agree with the expert’s opinion that the process of “decision-making2 by complexity and character is comparable directly with the thinking process. By making decisions, we understand the one-off act of choosing a certain alternative of managerial influence from their plurality. Among the input parameters of our economic and mathematical model of estimation and forecasting of the level of economic security of the agrarian sector of the region of Ukraine are parameters that take into account the process of human thinking. There are reflexive parameters x19-x22, which reflect the result of human thinking. 138

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СONCLUSION The situations in which decisions regarding the economic security of the agrarian sector of the Ukrainian regions are made aren't always clearly defined. The main difficulties in making decisions in uncertain situations are the impossibility of obtaining a completely reliable forecast or estimation of the probability of occurrence of specific events in one or another economic situation. The choice of an alternative solution is done with the help of the reflexive (intellectual) processes that we have taken into account in the developed model of estimation and prediction of the level of economic security of the agrarian sector of the region of Ukraine. The developed economic-mathematical model of estimation and forecasting of economic security of the agrarian sector of the region of Ukraine can be considered as typical for this class of objects, and the modeling methodology developed on its basis can be used for modeling of any economic processes characterized by a fuzzy connection between the input and output parameters, the ability to draw linguistic conclusions of experts for the construction of models, etc. The reported system allows predicting the level of economic security of the agrarian sector of the region of Ukraine and determining its state (class) based on analysis of expert statements. The developed system of support and decision-making will enable the management of central and regional authorities to make optimal decisions on the development and implementation of a program for the development of the agrarian sector, both for individual regions and for the country as a whole. The developed economic-mathematical model allows an investor to determine the level of economic security of Ukrainian agrarian sector and make decisions based on the received information about the possibility of implementing investment policy in this sector of the economy. Also, an investor can monitor the level of economic security of the agrarian sector and find ways to improve it.

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Kulagina, N. (2012), Theory and methodology of economic security of the agro-industrial complex, Moscow (in Russian). Kurgan, V. (2008), The economy of agrarian enterprises, University Book, Sumi (in Ukrainian). Law of Ukraine «About Investment Activity», (1991), avaible at: http://www.rada.gov.ua Lychik, M. (2013), “Methodological and methodological approaches to ensuring economic security of the agrarian sector”, Economic sciences, Vol. 10(3), pp. 455-463 (in Russian). Nikolenko, L. (2014), „Definition of legal forecasting“, Modern technologies of tourism and hotel and restaurant business management, Vol. VI, pp. 144-145. Nikolenko, L., Ivanyuta, N. (2013), „Protection of public property in the economic judicial proceedings“, Economics and Law, Vol. 1, pp. 47-50 (in Ukrainian). Official website of the World Food and Agriculture Organization of the United Nations, avaible at: http://www.fao.org Official site of the Institute for Economic Research and Policy Consulting, avaible at: http://www.ier.com.ua/ Pratar, R. (1999), Getting started with Matlab 5. A quick introduction for scientists and engineers, Oxford University Press, Oxford. Rotshtein, A. (1999), Intellectual identification technologies: fuzzy logic, genetic algorithms, neural networks, Universum-Vinnytsia, Ukraine (in Ukrainian). Rotshtein, A., Teodorescu, A. (1998), „Design and Tuning of Fuzzy Rule – Based Systems for Medi- cal Diagnosis“, In Fuzzy and Neuro – Fuzzy Systems in Medicine, CRC Press, Israel. Shvedovsky, V., Standrik, A., Bilan, Y. (2016), “Economic and Social Institutions: Modelling the Evo- lution Paths for the Archaic Society”, Economics and Sociology, Vol. 9, No. 2, pp. 137-147. DOI: 10.14254/2071-789X.2016/9-2/9 Svetlakov, A. (2010), Economic security, FGOU, Perm, Russia (in Russian). Ushachev, I. (2006), „Development of the agro-food sector: main directions and problems“, Agri- business: Economics, Management, Vol. 6, pp. 6-11 (in Ukrainian). Vajinskiy, F., Lalakulich, M. (2016), „Activation of investment activity of agricultural enterprises“, Scientific Bulletin of Mukacheve State University, Vol. 2(4), issue 2, pp. 56-61 (in Ukrainian). Vlasuk, O. (2008), Theory and practice of economical baking in the science of economics, National university problem of international security, Kiev (in Ukrainian). Yashin, S. (2006), „Indicators of comprehensive comparative assessment of the region's potential in the framework of economic security monitoring“, Finance and credit, Vol. 5, pp. 39-44 (in Russian). Zadeh, L. (1976), The concept of a linguistic variable and its application to making approximate decisions, Mir, Moscow (in Russian). Zlelko, I., Plotnikova, M. (2016), „Formation of innovation and investment policy in the agrarian sector“, Innovations and fundamental sciences in the conditions of technogenic economy, Vol. 1, pp. 40-42 (in Ukrainian).

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Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 141-157 ‘

Startup Revenue Model Failures

RICHARD BEDNAR1, NATALIA TARISKOVA2 and BRANISLAV ZAGORSEK3,

1 Assistant Professor, Department of Management, Faculty of Business Management, University of Economics in Bratislava, Slovakia, E-mail: [email protected] 2 Assistant Professor, Department of Management, Faculty of Business Management, University of Economics in Bratislava, Slovakia, E-mail: [email protected] 3 Assistant Professor, Department of Management, Faculty of Business Management, University of Economics in Bratislava, Slovakia, E-mail: [email protected]

ARTICLE INFO ABSTRACT Received september 02, 2018 Start-up is a fast-growing business and its revenue model, as Revised from September 18, 2018 conceptual framework for revenue, income and above average Accepted November 17, 2018 return on investment generation, can be considered to be the heart Available online December 15, 2018 of the business model and the key to its success.The subject of the study are the specifics of startup monetization models in Slovakia. The main goal of the study is to define the types of revenue models JEL classification: used on Slovak startups market place and to answe the question M13, M21, O52. whether there is a significant difference in startups’ revenue failures due to the revenue model, pricing model and time of payment. The DOI: 10.14254/1800-5845/2018.14-4.10 methods used in gathering data were regular one on one in person interviews with startup founders and the ANOVA and Tukey’s honest Keywords: significant difference methods. We considered the results to be statistically significant if they were on level p < 0.05. The results we startup, found is a significant difference between focusing on a concrete business model, revenue model compared to not having one and there was not a revenue streams, significant difference among models itself. failure

INTRODUCTION Startup is an enterprise for fast growth. Everything else associated with the startup environment results from the need for rapid growth. Startup is therefore an enterprise designed to search for a repeatable and scalable business model, "unconventional thinking, creativity and originality". Paul Graham extends the definition by saying that startups are trying to do something that customers really want, and therefore offer better technology solutions than they now have and spend the least money on it. What revenue model represents the startup success? Regardless of any categorization, certainly the amount of money, and capital spent on the operation ultimately does not exceed the volume of money earned from them (positive cash flow). Otherwise, business efforts are condemned to insolvency. Similarly, an equation can be noted, which also applies to the acquisition of new customers who are transformed in an ideal model from non-paying users using free, trial and initial use of products and services. If the customer acquisiton cost is lower than the cus- 141

Richard Bednar, Natalia Tariskova and Branislav Zagorsek / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 141-157 tomer lifetime value that new customers bring to the startup business, the startup will not survive in the near future for more than a few months. The revenue model is then characterized as a conceptual framework for income generation, profit making, and generating higher than average return on investment. In other words, the revenue model provides answers to two basic questions: a) What is the value for the startup money, to whom and for how much? and b) What is the source of income at startup? We are focusing on the results of the state survey, structure and development of startup monetization in Slovakia in the period from October 2015 to June 2017. It has never been so easy to set up a business and barriers to entry, or at least because of the arrival of cloud computing services such as Amazon EC2 and Rackspace, businesses spend just a few hundred dollars on server infrastructure, databases and their processing. Several years ago, entrepreneurs spent tens of thousands for the same services and thousands more for maintenance and operations. Businesses thrive when they make a profit. For any business, profit can be measured by total revenue and deduction of total costs. Since the total startup costs are much lower due to the aforementioned cloud services, it makes it easier for them to prove that their business is successful and trusted when they try to claim venture capital. And as far as costs are concerned, at least it is necessary to cover the expenses for employees, which is one of the reasons why the startup slim and has fewer than 10 employees. What is the best way to generate revenue? There are a lot of launches, like Instagram, a photo sharing service that has no revenue generation model from users. Is this business model sustainable? Successful investors discuss about five key determinants of startup investing: market, product, team, customers, and revenue (Civelek et al., 2016; Virglerova et al., 2017; Zwilling, 2016). Can startups work without a revenue model, if they compensate for a perfect product, a functioning team, focus on the right market and attract many users? 99% startups need a reliable and efficient way to make money, otherwise they will end up. But the remaining 1%, which has an excellent product and an excellent team, can resist the principle of microeconomics and succeed even without an explicit revenue generation model. It is very probable that Instagram does not intend to monetize its services at all and instead uses its product as a springboard for much better, bigger things. Anyone in Silicon Valley can produce a product or provide services, but very few people can deliver as excellent service as Insta- gram. Beluga was a startup who specialized in mobile interviews. Later, he bought Facebook, his team was incorporated into the business and helped develop Facebook Messager for mobile applications. GroupMe was another solution to social communication that was later purchased by Skype. Both launches have been bought for several million dollars. Startup launched for multi-million-dollar redeeming is a legitimate strategy despite the risk that it is unclear whether anyone will be interested in buying in the future. However, if a startup creates a large user base, larger businesses will take this fact into account and buy a startup just to expand their users' advice. The chance of this model is high, but the inability to pay employees, serv- ers, and operations over the five to ten year horizon until the startup matures on the market is the biggest barrier to a happy ending.

1. LITERATURE REVIEW S. Armstrong et al. (2011) examined the behavior and determinants of market-to- revenue ratios in public and private capital markets. They analysed three groups of data. “All publicly traded stocks listed at some time on the New York Stock Exchange/American Stock Ex- change/National Association of Securities Dealers Automated Quotation System in the 1980— 2004 period; sample of over 300 so-called ‘internet companies’ in the 1996—2004 period; and over 5500 privately held venture capital-backed companies in the 1992—2004 period.” They found that company size and the most recent revenue growth rate can explain the “significant 142

Richard Bednar, Natalia Tariskova and Branislav Zagorsek / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 141-157 variation across companies in their market-to-revenue multiples, where smaller companies and companies with higher recent revenue growth rates had higher multiples. They also documented how the capital market appears to use a broad-based information set when setting market-to- revenue multiples for companies with negative revenue growth rates — transitory revenue growth components appeared to be identified (in a probabilistic sense) by the capital market. Contrary to much anecdotal comment, they presented evidence that the capital market behaved directionally along the lines predicted by capital market theory in the pricing of internet stocks in the 1996— 2004 period.” T. Kohler (2018) argued that innovative companies are building their business upon the participation of crowds. They write “these crowdsourcing business models are powerful because they are scalable. By encouraging external innovators to contribute to value creation, crowdsourcing innovation platforms have the capability to grow significantly in size and revenue without equally increasing its costs. However, quickly and sustainably scaling crowdsourcing platforms is a daunt- ing challenge.” In his work, he analyzed over 20 leading crowdsourcing ventures to identify challenges and strategies of scaling. He then presents a framework on how to build a scalable crowdsourcing platform. M. Walske and D. Tyson (2015) deal with the issue of scaling social enterprises. They argue that“ of late, much credit has been given to design innovation, design thinking, fast pivots, and the Business Model Canvas. But what if we are placing too much emphasis on design innovation and pivoting, and missing other key important factors that help social enterprises scale quickly? While innovation is important, and arguably a necessary baseline, the authors' interviews with successful social entrepreneurs have pointed more to the importance of sourcing financial capital, building out their supply chain – both in production and distribution – and obtaining early media recognition. These three factors created a virtuous cycle, allowing these firms to increase their revenues, employees and impact each year substantially during their first four years after founding.” M. Fochler (2016) investigates the relationship between research and innovation policy and new institutional spaces at the interface of academia and business. “High-tech startups founded by academic entrepreneurs have been central to these policy imaginaries. These companies offer researchers new possibilities beyond and between academia and larger industry. However, the field of science and technology studies has thus far shown only limited interest in understanding these companies as spaces of knowledge production.” Fochler studies “how researchers working in small and medium-sized biotechnology companies in Vienna, Austria, describe the cultural characteristics of knowledge production in this particular institutional space. It traces how they relate these characteristics to other institutional spaces they have experienced in their research biographies, such as in academia or larger corporations. It shows that the reasons why researchers decide to work in biotechnology companies and how they organize their work are deeply influenced by their perception of deficiencies in the conditions for epistemic work in contemporary academia and, to a lesser degree, in industry.” J. Zinger at al. (2003) did a study of 145 new venture start-ups. They explored a model of growth momentum as measured by sales. They considered their primary interest to be the “relationships among pre-startup activities, intended and actual business expansion activities, and early stage performance. Results indicated that the sales level achieved in the second year had a positive correlation with both the breadth of pre-startup activities and the range of expansion activities. They further found that business performance had a negative correlation with the firm's relative dependence on the technical skills of the owner-manager. Also, the study revealed a consistent gap between owner-managers' expansion intentions and actual expansion.” J. Langemeyer (2005) studies the commercialization of startups. He argues that “While the National Research Council of Canada has generated many start-ups over its 88-year history, the creation of a formal entrepreneurship programme in the mid-1990s dramatically accelerated the pace at which they were created. Many factors come into play in the decision to select an appropriate commercialization vehicle. While the strength and scale of the business opportunity are key factors, as are the 143

Richard Bednar, Natalia Tariskova and Branislav Zagorsek / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 141-157 people involved and the status of the intellectual property, many other internal and external factors must be taken into consideration.” While previous research can be found that highlight the importance of business failure as a forerunner to transformational learning there is a deficit of studies exploring the conditions under which such learning can occur or of the content of the resulting knowledge. B. Mueller and A. Shepherd (2016) “explored several cognitive moderators of the relationship between failure experiences and a specific type of opportunity identification knowledge—the use of structural alignment processes. Results indicate that learning from failure is facilitated for entrepreneurs who possess a cognitive toolset that consists of opportunity prototypes and an intuitive cognitive style. Moreover, they found that prior professional knowledge negatively moderates this relationship.” D. Smallbone at al. (2002) studies a high-growth start-up programme launched in 1999/2000 as a part of a shift in small and medium-sized enterprise policy in the United Kingdom. The policy had to shift the narrow focus on supporting established firms, to include start-ups and other types of small and medium-sized companies. D. Smallbone at al. (2002) further argue that “some of the policy issues surrounding the design, development, and implementation of the new programme, with the aid of results from in-depth research in the East Midlands region. After a brief description of the new enhanced support programme for high-growth start-ups, and the policy context in which it was introduced, Smallbone et al. review the support needs of this type of business and how the new programme might contribute to addressing these. In the final section Smallbone considered some of the wider policy issues raised by the programme in terms of the extent to which: first, effective regional models can be developed to encourage widespread participation by appropriate private sector organisations; second, access to appropriate finance, including seedcorn and venture capital, can be increased for high-growth-potential start-ups; third, universities are able to contribute to the generation of new business activity and become integrated into regional business support infrastructures; and fourth, the enhanced support programme is tuned to the needs of the target group and is effectively delivered.” There is also the T. Clement‘s (2018) case study. In the case study, he applies the lean startup methodology to the failed Excelsior-Henderson Motorcycle Manufacturing Company. He writes that “Company founder Dan Hanlon and his family pursued a dream to start a new American motorcycle company based largely on market leader Harley-Davidson’s inability to meet customer demand. Excelsior-Henderson’s founders followed a path of crafting and executing an elaborate business plan filled with goals and assumptions, with no customer discovery, validation, or itera- tive testing performed. The result was misjudging the complexities and financial demands of the motorcycle industry and an ultimate lack of customer response which led to a loss of US$100 million in the capital and eventual bankruptcy. The case study is relevant to students of business administration and management and will learn to evaluate the challenges start-ups face through the lens of the lean start-up method, including customer discovery and the consideration of a minimum viable product (MVP).” The view that new product innovations can be attributed to new ventures is held by E. Diakanastasi at al. (2018). Further, they argue that the reason for many new ventures quitting before they fulfill their potential is mainly because of the team quitting its effort. In their research, they study several factors that “affect the dynamics of an entrepreneurial team along the different stages of the new venture creation process. Given such direction, primary data from 12 entrepreneurial teams are undertaken within an incubator environment, which is an underresearched context. An in-depth study of these entrepreneurial teams suggests various determinants (variation in the intentions and expectations, improper leadership, ineffective communication, engagement, team structure) to have a serious effect on team dynamics and team cohesiveness. Following a grounded theory approach, specific research propositions are formulated, and theoretical insights are drawn. Moreover, the results of his research assist nascent entrepreneurs in assessing their potential, recognizing critical team dynamics, and basing their choice on moving to a sustainable 144

Richard Bednar, Natalia Tariskova and Branislav Zagorsek / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 141-157 new venture creation.”

The relationship between internationalization and technology is studied by J. Banioniene and L. Dagiliene (2017). They investigate a recent phenomenon of technological catch up at domestic- foreign company‘s level. Their paper evaluates the relation between investment in technology, technological progress, and macroeconomic indicators. They then estimate opportunities of investing in technology to level the playfield at the macro level with advanced countries. They apply “theoretical framework of neoclassical growth theory to explore the relationship between investment in technology, technological progress and macroeconomic indicators in different countries.” In their paper they show that “regardless income level, countries can increase economic growth rates and catch up higher income countries by making appropriate decisions of investment, changing the structure of investment in technologies by funding sources and spheres.”

2. METHODOLOGICAL APPROACH The main goal of the research is to clarify and deepen the understanding of income models, their structure, meaning and effectiveness, and based on the theoretical backgrounds, analyze models in selected startups, identify main trends, extremes, tendencies and typology. Partial goals are derived from the main goal and take it from a number of perspectives:  Analyze the selected income models from a quantitative point of view and determine the fre- quency of occurrence of their properties.  Determine whether there is a significant difference in startups’ revenue due to the revenue model, pricing model and time of payment.  Based on an analysis of scientific literature, quantitative and qualitative analysis, group the models in several respects and create a typology of income models. This goal is essential in two respects. First of all, it systematises the facts and makes them more transparent, thus providing insight into the existence and functioning of startups. At the same time, this oversight should help science and business practice to apply any of these forms of income to the startup practitioners.  Identify the level of business model development and startup performance in the period 2015- 2017, based on the performance of startups, which is expressed by the number of users, paying customers, structure and revenue levels. Identify the causes of product and service failure in the preparation, implementation, distribution, identify the causes of startup failures that originate from funding sources, partners, and the startup team.

To analyze the impact of revenue model, pricing model and time of payment on startup’s revenues we used the ANOVA method. We considered the results to be statistically significant if they were on level p < 0.05. To investigate further the differences, we used the Tukey’s honest significant difference (HSD) test, also at the significance level p<0.05.

3. CONDUCTING RESEARCH AND RESULTS

3.1 Data analysis During 2015, 2016 and 2017, 76 startups in Slovakia were analyzed in three stages, and their business models were questioned. The analysis included a review of their revenue streams, which were compared in two periods in 2015 and 2016. Financial data was drawn from Finstat's public sources.

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The data on the research sample is processed by the descriptive statistics methods. By descriptive statistics, we have dealt with the answers to questions 2.8 Income flows of the canvas business model in quantitative or qualitative terms. Startup revenue streams are recorded in the structure of the subjective value that customers are willing to pay for. The level of the concrete idea of the amount of the product / service price has been recorded in the range: 1 - none, 2 - the first concept, 3 - a complete concept, 4 - implementation, 5 - complete or almost definitive. The degree of concrete idea of the quantity of products / services sold was recorded in the scale: 1 - none, 2 - first concept, 3 - integrated concept, 4 - implementation, 5 - complete or almost definitive. The measurable startup performance was recorded in the number of users on the scale: 1 - none, 2 - several, 3 - several tens, 4 - several hundred, 5 - several thousand and more. The number of paying users (customers) has been recorded on the scale: 1 - none, 2 - several, 3 - several dozen, 4 - several hundred, 5 - several thousand or more. Revenues were recorded on a scale of: 1 - none, 2 - cover current costs from 0 to 25%, 3 - cover current costs from 25 to 75%, 4 - cover current costs from 75 to 100%, 5 - of costs, 6 - bring profit from 25% to 50% of costs, 7 - bring profit more than 50% of costs. The causes of non-revenue generation have been monitored by a 100% scale, which has been divided into product-related causes: the product is in the production phase, the product's failure, the manufacturing / manufacturing base of the product is in the preparation phase, the product failure, the product distribution is in the preparation phase, distribution of the product. If there were other circumstances behind the startup failure, 100% of the scale was divided into causes: funding failure, team failure, partner failures, and other causes.

3.2 Structural overview – industry, users, customers, revenue The bulk of startups in the research sample according to SK NACE categorization is in the Infor- mation and Communication (24%). This industry is typical for Industry 4.0 and allows for the scalability that the startups are looking for. More than 1/5 startups are focused on administrative and support services that help streamline business processes to save costs and increase profitability. Industry is the third most represented sector (14.7%). These companies develop and produce products of the most demanding technological nature. The overview is shown in Table 1.

Table 1. Sectors according SK NACE

Sector % Information and telecomunication 24,0 Administration services 21,3 Industrial production 14,7 Art and entertainment 9,3 Wholesale and retail (including car spare parts) 5,3 Financial and insurance services 5,3 Health and social care 5,3 Waste water treatment and waste disposal services 4,0 Public services 2,7 Education 2,7 Building industry 2,7 Agriculture, forestry and fishing 1,3 Other services 1,3 Total 100,0

The products and services offered by the startup are mostly intended for the final customer (B2C, 53.3%). More than 1/3 of the startups sell their products to other businesses (B2B, 37.3%). Only 9.3% startups combine end customers and corporate clients. The essence of business startup 146

Richard Bednar, Natalia Tariskova and Branislav Zagorsek / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 141-157 success is the high interest of customers in the real market. Therefore, especially at the outset, businesses prefer recruiting new customers to obtain sales revenue. Startups believe that if they get enough users, they will generate revenue streams later on. Nearly 1/5 of businesses have no customers, but the number of startups without customers is declining in the year-to-year comparison. In these cases, businesses are developing or frozen for business, and their users have been shut down for some time from their product or service. Most businesses have several tens to hundreds of users. Almost ¼ of the startup analyzes have several thousand users, with a year-on-year increase of more than 4.5% in the last survey period. The fastest growing number of users in the year-to-year comparison in the category by several hundreds. The overview is in Table 2.

Table 2. Number of users as followed by each of three research phases (in %)

Number of users phase 1 (%) phase 2 (%) phase 3 (%) None 17,9 12,0 13,3 Several 13,4 17,2 8,0 Several tens 28,4 29,3 26,7 Several hundreds 14,9 15,5 21,3 Several thousands and more 25,4 25,9 30,7 Total 100,0 100,0 100,0

The main business goal is to generate income. Startups that can generate money from users are able to cover their fixed and variable costs and thus generate profit for the owners. The rate of growth in the number of paid customers in the category of one thousand and more increases year- on-year and is about the same percentage of the growth of new users. The overview is in Table 3.

Table 3. Number of customers as followed by each of three research phases (in %)

Number of customers phase 1 (%) phase 2 (%) phase 3 (%) None 32,8 24,1 33,3 Several 13,4 17,2 9,3 Several tens 26,9 31,0 26,7 Several hundreds 14,9 15,5 13,3 Several thousands and more 11,9 12,1 17,3 Total 100,0 100,0 100,0

Table 4. Sales in 2015 a 2016 (in %)

Revenue range 2016 2015 Growth factor 0 37,3 44 0,8 1 - 1 000 8 8 1 1000 -10000 9,3 9,3 1 10 000 - 50 000 14,7 17,3 0,8 50 000 - 100 000 10,7 10,7 1 100 000 - 500 000 10,7 8 1,3 500 000 and more 9,3 2,7 3,4 Total 100 100 1

One of the most important indicators of business success is revenue generation. More than a third of the startup analyzes do not generate any sales, but it is positively perceived that their share is decreasing (Table 4). Compared with the previous year, the number of startups with reve-

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Richard Bednar, Natalia Tariskova and Branislav Zagorsek / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 141-157 nue and sales of more than half a million have risen. In startups that have not reached sales, we have analyzed the reasons for not making them. The most important reason is the finalization of distribution channels. The second problem is that the product is not yet ready and can not be placed on the market. Another important determinant is the failure of the team, which respondents cited as the most serious reason, except for the causes of product development. The overview is shown in Table 4.

3.3 Analysis of revenue streams In a deeper study of revenue streams, we analyzed several ways of generating revenue. In terms of value creation, most startups generate revenue by selling the service (40.00%). Most often it is a one-time purchase of the customer. Startups try to create re-purchase themes in this model. The second most commonly used form of income generation is the sale of products produced by the enterprise itself (17.11%). The third largest share of the sample has startups that have not yet found a suitable income model (17.3%). They are at the stage of looking for a form of income, or they are not considering any income model. The principle is that what they do not look at, it can not be upgraded, scale and grow.

Table 5. Causes of non-revenue generation

Causes – product Average value (%) The distribution of the product is in the preparation phase 48,8 The product is in the preparation phase 38,6 Failure of the product distribution 29,6 The production base of the product is in the preparation phase 24,5 Product failure 10,6 Failure of the product realization 6,7 Causes – other than product causes Average value (%) Failure of the team 14,6 Failure of the partners 5,8 Failure of the financing 4,7

Some of the startup analysts have voluntarily decided not to target revenue streams and are focused on the development phase only on building a business model, product formation, and building a customer base. An example is Vectary, according to which monetization slows down the development and increase of the startup, and therefore the founders will do monetization only later. Similarly, the revenue model is represented by a commission when the startup mediates the sale of the service (16.0%) or the product (4.0%), thus obtaining a share in the transaction. The overview is in Table 6. Another division is revenue models in terms of income generation. The standard revenue model, which is represented in almost half of the startups (49.3%), is based on a simple sale of a product or service in exchange for money. The freeware (freem) model (29.3%), which has multiple forms, is among the popular and popular startup models. For all, there is a common provision of basic service for free. Money is generated, for example, through the sale of premium services. The second form of a free model is to generate revenue from another entity, such as a service user. Minority used is a premium model. Unlike the free model in this case, everyone is paid a standard, larger, lower amount. They pay for each premium service. The overview is shown in Table 7.

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Table 6. Revenue models based on value creation

Revenue models % Sale of the service 40,0 Sale of the product 22,7 No model 17,3 Mediation of the sale of the service 16,0 Mediation of the sale of the product 4,0 Total 100,0

Table 7. Revenue models based on income generation

Revenue model % Standard 49,3 Freeware 29,3 None 17,3 Premium 4,0 Total 100,0

The last measure is the timing of payment (Table 8). In contrast to a one-time sales of services (41.3%), the form of sale via subscription (29.3%) is creating and constantly growing. It is a re-sale of services that will ensure startups even smaller (compared to one-time sales) but stable revenue streams, allowing them to work more efficiently with funds and investments. In some cases, the startup gets paid only after the service has been executed and charged (12.0%).

Table 8. Revenue models based on the timing of payment

Revenue model % One time sales 41,3 Subscription 29,3 Payment after invoicing 12,0 None 17,3 Total 100,0

The main economic indicators of success include money generation, ie sales. In 2016, startups were the highest revenue in the information technology and administrative and support services sectors. The highest growth was recorded in sales in the administrative and support services sector (Table 9).

Table 9. Sales based on sector

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3.4 Profit analysis The main goal of every entrepreneur is to make a profit. It's a reward for business owners. This is the main reason why investors are interested in putting their money into risky projects. In 2016, 16% of the startup analyzes achieved, which is 1.3% less than in 2015 (Table 10).

Table 10. Profit in 2015 and 2016

Profit range 2016 (%) 2015 (%) Growth factor Less than 250 000 4,0 1,3 3,1 250 000 -100 000 9,3 9,3 1,0 100 000 - 50 000 8,0 4,0 2,0 50 000 -10 000 10,7 10,7 1,0 10 000 – 1 10,7 16,0 0,7 0 41,3 41,3 1,0 1 - 10 000 10,7 8,0 1,3 10 000 - 50 000 4,0 8,0 0,5 50 000 - 100 000 1,3 1,3 1,0 Total 100,0 100,0 -

3.5 Value added analysis In the next part of the research, we analyzed the generating of profit in terms of value creation. The most effective way is to sell the product. Startups that can produce a product are most likely to make a profit. This is how QuakeResQ launches a quest to launch an earthquake that causes $ 106 billion worldwide in damage, causing more than 4 billion lives and killing 63,000 people. This unhappiness can only be avoided by early warning. QuakeResQ created a Qbox device that can capture the initial seismic waves that the human body does not feel. He then sends a signal to the connected mobile phones via the server and alerts when the earthquake will come, what will be strong and what to do. Founder Martin, in his analysis of competition, found that there are only a few companies in the world that produce seismic detectors and sell them for more than € 50,000. They are technologically demanding, they have to land at least 12 m deep into the earth and can detect the earthquake when it occurs. It was therefore decided that its competitive advantage would consist of simple production, size of the instrument (carton box), minimal energy consumption and low production costs. Martin wants to sell the device at 1% of the competition price for 59 € via his website www.quakersq.com. The largest share of the start-ups is made up of those who sell or mediate the sale of the service (Table 11). The most successful revenue-generating model is mainly startups that sell their own services. They are more effective than selling the product or selling sales (Table 12).

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Table 11. Economic results according to value creation

2015 2016 Value creation (%) Loss 0 profit Loss 0 Profit Sales of the product 2,7 12,0 8,0 4,0 12,0 6,7 Sales of the service 22,7 10,7 6,7 21,3 12 6,7 Mediation of the sale of the product 2,7 1,3 0,0 2,7 1,3 0,0 Mediaton of the sale of the service 12,0 2,7 1,3 10,7 2,7 2,7 No model 1,3 14,7 1,3 4,0 13,3 0,0

Table 12. Sales according to value creation

2015 2016 Value creation (%) 50,000 1 -50 50,000 0 1 - 50,000 0 and more 000 and more Sales of the product 12,0 6,6 4,0 8,0 9,3 5,3 Sales of the service 10,6 16,0 5,3 9,3 13,3 17,3 Mediation of the sale of the product 1,3 1,3 1,3 1,3 0,0 2,6 Mediaton of the sale of the service 4,0 9,3 2,6 2,7 8,0 5,3 No model 16,0 1,3 0,0 16 1,3 0,0

The most profitable model with regard to revenue generation is startup, which is sold by default (Table 13). An example is Speekle. It is a game software that complements the role of a par- ent, logoped and entertainingly works with children. It also records the progress and potential problems of the child's pronunciation. One of the exercises is a language-enhancing game in which the child controls the screen on a screen, or a game in which the child dries the dragon by pronouncing shush. It is therefore an aid that is an integral part of therapy and to help children with speech disorders around the world. It employs innovative technology when children can play games by language or pronounce whistles and practice pronunciation exactly as they need. Startup sells these games one-time or in a subscription form.

Table 13. Economic results based on revenue generation

2015 2016 Revenue generation (%) Loss 0 profit loss 0 Profit Standard 18,6 20,0 6,6 18,6 20,0 10,6 Freeware 18,6 6,6 1,3 17,3 6,6 5,3 None 2,6 0,0 1,3 2,6 1,3 0,0 Premium 1,3 14,6 9,0 4,0 13,3 0,0

As with profits, the form of the standard income model appears to be most advantageous (Ta- ble 14). In this way, revenue generates Amena's startup, which offers the rental of LED-based light boards that make up the mobile navigation system for cultural and social events. Quick info, short texts, symbols, or arrows can be displayed on the charts to guide visitors in real time. The boards are battery-powered, so the system is flexible. It is used for crowd management, that is, to direct a large number of people to different events. Amena uses a rental model and therefore leases the facility to the organizer of the event at a pre-arranged time.

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Table 14. Sales based on revenue generation

2015 2016 Revenue generation (%) 50,000 50,000 0 1 -50,000 0 1 -50,000 and more and more Standard 20,0 18,7 10,7 14,6 18,6 16,9 Freeware 8,0 12,0 9,3 5,3 10,6 13,3 None 0,0 2,7 1,3 1,3 1,3 1,3 Premium 16,0 1,3 0,0 16 1,3 0

Choosing a payment method and its timing may also affect the profitability of the monetization model. Payment on sales is the most used form in startups that make a profit (Table 15). Such a form is used, for example, by Power Coffee. Startup sells coffee that, thanks to the slow roasting technology and the resulting, neither hot nor acidic, that is, full taste, reaches high sales. Coffee trade shows high profits, mainly due to high margins. Startup, in addition to coffee, sells coconut oil, MCT, high quality bio chocolate Fino de Aroma and delicious almond butter. By customer feedback, founder Dušan Plicht found that, thanks to improved food, it was really possible to change the eating habits of households. That is why he decided to implement the Powerlogy concept. The concept of improved food for active people has been created, which aims to gradually improve the eating habits of at least 100,000 thousand households in Slovakia and the Czech Republic.

Table 15. Economic results based on payment methods

2015 2016 Payment method (%) Loss 0 profit loss 0 profit One time sales 17,3 5,3 6,6 20,0 6,6 2,6 Subscription 14,6 17,3 9,3 12,0 17,3 12,0 Payment after invoicing 8,0 2,6 0,0 5,3 4,0 1,3 None 1,3 16,0 1,3 5,3 13,3 0,0

Unlike earnings, the most common way to pay for a sale is the most common. There is only a slightly lower representation of subscription sales (Table 16). An example is Piano Media, a payment system that combines Internet portals and allows easy pricing of their content. It was created as a joint venture between Etarget and Next Big. In Slovakia, it was put into operation on May 2, 2011. The customer receives access to premium content and services on selected Slovak web- sites for a single payment. The system has offered 60 content services on 12 different sites since its inception. Customers can order Piano services via the www.pianomedia.sk portal, filling out a simple form. Once they have been paid, they receive the login information and can use the system. The flat fee is graded according to the length of the subscription (€ 1.39 / week, € 3.90 / month, € 39 / year). It is a transfer, a deposit on account, a payment card, a sms, a Paypal system, and a news item is a mail order to the customer where the customer pays for receipt of the shipment (s). Revenue from revenue is broken down as follows: 40% of the payment remains the payment medium, 30% is divided between the media the user visits (the key for the time is spent on the appropriate medium), and the remaining 30% remains Pianu for running the system. Money is divided between the media that the client actually reads. Decides on what they will support and then they will get more money to improve their content.

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Table 16. Sales according to payment method

2015 2016 Payment methods (%) 50,000 50,000 0 1 -50,000 0 1 -50,000 and more and more Subscription 5,3 12,9 12,0 5,3 10,6 13,3 One time sales 16,0 17,3 8,0 12,0 16,0 13,3 Payment after invoicing 5,3 4,9 1,3 4,0 4,0 2,6 None 17,3 1,3 0,0 16,0 1,3 1,3

3.6 Discussion In our research, we conducted the analyses of variance to determine whether there is a significant difference in startups’ revenue due to the revenue model, pricing model and time of payment. We conducted a one-way between-subjects ANOVA to compare the effect of revenue model on revenues in product, service, mediating product, mediating service and no model scenarios. We found a significant effect of revenue model on revenues at the p<0.05 level for the five conditions [F(4, 70) = 4.94, p = 0.001].The highest revenue level was achieved for mediation of products, followed by the mediation of services, selling services, selling products and no defined model. Among the startup, the studied companies achieved higher revenues by mediating then by selling directly. Regarding mediation, it was better to mediate products, however, when selling it was better to sell services. It seems that it is harder to sell products so when mediating there is higher margin, meanwhile when selling directly, it is easier to sell services. Next, we conducted a one-way between-subjects ANOVA to compare the effect of pricing model on revenues in standard, freemium, premium, and no model scenarios. We found a significant effect of pricing model on revenues at the p<0.05 level for the four conditions [F(3, 71) = 6.07, p = 0.001]. Among startups, in our sample, the highest revenues were achieved by startups with freemium pricing model followed by premium, standard and no model. It seems that freemium model is well suited to burst revenue in startups. Finally, we studied by a one-way between-subjects ANOVA the effect of time of payment on revenues in subscription, paying at check out, invoice and no model scenarios. We found a significant effect of time of payment on revenues at the p<0.05 level for the four conditions [F(3, 71) = 5.27, p = 0.002]. Most revenues were realized through subscription method. Followed by payment by check out, invoice and no model. After conducting the Tukey’s HSD test in all three cases, we were able to identify a significant difference between using a model and not using one. There was not a significant difference among models itself. That is why we conclude that companies seem to be better off if they have a model chosen. We could not identify one model that is better than others. We think that because of the high variability in the real world there cannot be one size fits all approach and the right models have to be chosen contingently. Also, we think that companies need to focus on a particular model. Otherwise, they are not effectively using their resources. A typical startup in the analyzed sample is a business and communications business whose target customer is a physical person (B2C). Despite serving a few dozen customers, they are in most non-paying clients. This business model does not receive any sales, especially because the distribution of the product or service is in the preparatory phase. From the research we defined the majority income model (Table 17), whose properties are most represented in our sample.

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Table 17. Majority income model

Income model Specification Sales of the service One time sale of the service to customer Standard selling model Sale in exchange for money One time sales Payment for goods / services

The majority revenue model has monetization set in a similar way to standard businesses that do not fall into the startup definition. Startupists therefore use ways that they know confidentially from other business models, are trusted and known to customers. In the next part of the research, we analyzed the forms and ways of monetization through which startups most often generate sales, taking into account the years 2015 and 2016 (Table 18).

Table 18. The most revenue-generating model

Income model Specification Sales of the service One time sale of the service to customer Standard selling model Sale in exchange for money One time sales Payment for goods / services

Startup, which is the most common in sales, is in the field of information and technology, sells the service directly through the customer's exchange, which is immediately applicable to the purchase. On the other hand, start-ups that do not reach sales are doing business in the arts, entertainment and recreation sectors, but they also plan to sell the product in a standard form but have not yet found the right way to timely payment (Table 19).

Table 19. Model that is not earning revenue

Income model Specification Sales of the product One time sale of the product to customer Standard selling model Sale in exchange for money None Have not found a suitable way yet

The main goal of doing business is to make a profit, so we also analyzed the forms and ways of monetization through which startups achieve the highest profit taking into account 2015 and 2016 (Table 20).

Table 20. Most profitable model

Income model Specification Sales of the product One time sale of the product to customer Standard selling model Sale in exchange for money Payment on sale Payment for goods / services

Unlike generating revenue, the salesperson is typically a profitable startup. Other forms of monetization are identical. The products that make up the blue ocean are typical of the industry, so they can not compare the prices with similar products and startup can give the margin they want and need to satisfy the investor as well as to cover the cost of further research and development. 154

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We have also analyzed income models that are the least scalable in our sample (Table 21).

Table 21. Income models that are the least scalable

Income model Specification Sales of the service One time sale of the service to customer Standard selling model Sale in exchange for money Subscription Subscription to service / product usage

The difference between the most profitable and the most shaky startup of the sample lies in the sale of the product / service as well as in the timing of the payment. However, the analysis can not be said to be the loss caused by these two factors. In our opinion, the loss is mainly due to the amount of technology investment in the sale of services. Startup typically creates complicated and technologically demanding services that require long-term development, investment in IT staff whose wage costs are the highest, and the cost of multiple service testing. Another important factor in services is the fact that many companies offering services are only focused on recruiting in the first years. Just a large customer base is helping you later to sell a high-price startup or generate revenue from another group of customers. It is natural for startups that they do not reach sales in the early stages of development, and their economic result is negative. Most founders are primarily focused on analyzing the problem with a potential customer and then creating value in the form of a product or service that will help solve the problem's problem. In creating an entrepreneurial model, more attention should be paid to revenue generating options. Income model research should further analyze the effectiveness of product and service monetization. Such research could serve both professionals and entrepreneurs to make better choices about choosing how to create revenue channels and monetize their business idea. What is the value of the startup’s offering to the business, and what resources and support will the startup need so the customers can actually obtain its offering? Many startups are not very good at communicating their customer value proposition in a way that helps the customer firm making such assessments. M. Wouters et al. (2018) recommends that startups construct two sequential value propositions: the Innovative Offering Value Proposition and the Leveraging Assis- tance Value Propositon. The Innovative Offering Value Proposition communicates how the startup’s offering creates superior value for the customer. It answers the question: What is ex- traordinary about the startup’s offering that will enable the customer to solve a significant problem it has or achieve a top priority it has? The Leveraging Assistance Value Proposition conveys what the customer firm will get in return for providing support and resources to the startup. It answers the question: What will make it worthwhile from the customer’s perspective to support the startup to realize its innovative offering? When it comes to agility, startups have an edge over large corporations. Large corporations sit on resources which startups can only dream of. The combination of entrepreneurial activity with corporate ability seems like a perfect match, but can be elusive to achieve. Large corporations from the tech industry have begun to tap into entrepreneurial innovation from startups. While corporate equity is the key mechanism behind more established models, newer approaches replace equity with shared technology to connect both worlds with fewer organizational costs and greater speed and agility. T. Weiblen and W. Chesbrough (2015) presents a typology of corporate mechanisms to engage with startups that balance speed and agility against control and strategic direction, to map the ways companies can bridge the gap between themselves and the startup world.

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3.7 Limitations Our study design also has some limitations that should be considered in the interpretation of our results. The sample selection was not random in the strictest sense because startups asked to cooperate were from a specific geographic area, they were usually from an accelerator, so they have already shown some success and they self-selected themselves by agreeing to cooperate with us. Despite this, we think our research is of great value to startups, investors, and academics as it provides much information about the startups and shows a real image of their processes.

CONCLUSION Startup is an enterprise built on the principle of searching, constantly testing and improving a scalable business model. The startup specificity lies in the fact that it is not possible to predict the development in the distant future of the future, but it is necessary to constantly balance what can be done today on the milestones achieved and what can be done tomorrow on the basis of the generated resources. The life of any attractive, practical or modern startup without the ability to calculate initial costs, estimate the annual budget and update the occurrences on a monthly or monthly basis (burn rate) to the capital intensity will not save even a perfectly designed business model. The startup life is a movement between two worlds. One is dreamlike and full of digital im- agery, customer needs, expectations, plans and qualitative milestones, and the other is real, quantified - in terms of sales and profits. If the startup fails to generate money, its existence is only temporary and the investment is lost. In our research, we did not identify significant differences between different revenue models what suggests that there is not one better way how to create revenue streams. However, we found a significant difference between have a concrete revenue model and not having one. In future research, we would like to continue to collect data on success and failure of startups to create a representative sample and find whether there are patterns of success and failure depending on revenue models.

ACKNOWLEDGEMENT The authors are thankful to VEGA project of Ministry of Education of Slovak Republic No. 1/0019/15 „ Business models and business startup strategies“ for financial support to carry out this research.

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Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 159-173 ‘

Efficiency of Innovation Activity Funding as the Driver of the State's National Economic Security

LIUDMYLA ZAKHARKINA1, IULIIA MYROSHNYCHENKO2, DENYS SMOLENNIKOV3 and SVITLANA POKHYLKO4

1 Associate Professor, PhD, Department of Finance and Entrepreneurship, Sumy State University, Sumy, Ukraine, e-mail: [email protected]; 2 Associate Professor, PhD, Department of Management, Sumy State University, Sumy, Ukraine, e-mail: [email protected]; 3 Senior Lecturer, PhD, Department of Management, Sumy State University, Sumy, Ukraine, e-mail: [email protected]; 4 Associate Professor, PhD, Department of Finance and Entrepreneurship, Sumy State University, Sumy, Ukraine, e-mail: [email protected].

ARTICLE INFO ABSTRACT Received August 27, 2018 Purpose. The article is devoted to an analysis of the impact of inno- Revised from September 29, 2018 vative enterprises activity on ensuring the indicators of state eco- Accepted November 15, 2018 nomic security. The main emphasize was made on the innovative Available online December 15, 2018 component analysis of the economic security in Ukraine. Place and role of stimulating innovative activity to achieve sustainable socio- economic development and improve competitiveness of the econo- JEL classification: my is analyzed in the article. Methodology. In the article economic F52; O31 security of Ukraine analyzed by using two methodological approach: Methodological Recommendations on calculating the level of eco- DOI: 10.14254/1800-5845/2018.14-4.11 nomic security of Ukraine, approved by the order of the Ministry of Economic Development and Trade of Ukraine and the Global Com- Keywords: petitiveness Index. Findings. The authors determined which sectors Innovation activity, innovation activity can increase the growth rates of economic secu- national economic security, rity and which state support methods should be used to maximize funding, the growth of innovation activity. The conducted research made it innovation policy possible to find out that the activation of innovation activity as a factor in the economic security formation of the state, which operates in developed countries, is not used properly in Ukraine. Such trends in innovation development of Ukraine are connected, first of all, with low (least critical) level of science funding by the state (primarily applied character, innovation and technology transfer) and low level of funding by economic entities themselves.

INTRODUCTION The problem of ensuring economic security of the country is one of the key aspects in the macroeconomic policy of developed and developing countries of the world (Bednar and Halaskova,

 This work was supported by the Ministry of Education and Science of Ukraine (Project No. 0117U003922 «Innovative drivers of national economic security: structural modeling and forecasting») 159

Liudmyla Zakharkina, Iuliia Myroshnychenko, Denys Smolennikov and Svitlana Pokhylko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 159-173 2018; Bhowmik, 2018; Fashina et al., 2018; Lyeonov et al., 2018; Milovic and Jocovic, 2017; Nguedie, 2018; Soylu et al., 2018; Vasylieva et al., 2018). The economic security of the state characterizes the condition of the national economy, which protects national interests, resistance to internal and external threats, the ability to develop and protect the vital interests of people, society, and the state. Vital interests represent a set of needs that ensure the existence and progressive development of personality, society and state (Kouassi, 2018; Vasilyeva et al., 2018; Wachowska, 2018). The importance of considering innovation in the context of ensuring economic security of the state is explained by the fact that innovative activity can bring a qualitative leap of the national economy due to realizing full scientific and technical potentials as well as define state from external and internal threats (Ginevicius et al., 2018; Ilie et al., 2017; Karaoulanis, 2018; Khan, 2018; Meyer and Neethling, 2018). The issue of the national economic security formation through the innovation development of economic entities, as well as the link between them, was investigated by numerous scholars and scientists. H. Barnett (1960) by accepting the fact of existing correlation between research and development and economic growth, examined the paths open to the government in selecting its policy. T.C.R. van Someren and S. van Someren-Wang (2013) conducted a comparative analysis of the structure and innovation policy extent of the state in China, the USA and the EU and its impact on economic growth and development of countries. The hypothesis that the high innovation activity of the state's economy is ensured by the leading role of the state in the scientific and technical field and by determination of national priorities and the active influence of the state on the innovation development process of the economic entities through a balanced system of economic stimulation instruments is put forward and confirmed. A broad retrospective analysis (1945-2012) of the innovation impact on the US economy growth has been conducted by L. Weiss (2014). Author showed link between technological leadership, domestic manufacturing and skilled employment growth as well as way of transformative innovation to the strength of its national security state by example of USA. D. Leyden (2016) developed a National Systems of Entrepreneurship-based theoretical model of the entrepreneurial environment that explain the mutual influence of the public and private sectors in national economic growth through innovation development. Impact of innovative enterprises activity on ensuring the indicators of state economic security is highly discussed in Ukrainian scientific literature. O. Pabat (2012) developed theoretical and methodological bases for assessing the impact of innovative factors on the state economic security. V. Loyko (2015) formed a system of indicators for the determining an innovative component level of the economic security of the national economy as a modeling and prognosticating objects. Author determined factors that influence the formation of an innovative component of national economic security (innovation infrastructure, venture investment, technology transfer, intellectual potential of the nation, innovation risk insurance at the state level). The scholars O. Sergienko et al. (2015) developed a set of economic and mathematical models for assessing the competitiveness and social and economic development of the state in order to ensure effective strategic economic security management.

O. Lyulyov and H. Shvindina (2017) studied the problems of industries’ instability, how clusters and states influences on the countries’ economy. On this bases they proposed model that has proven its efficiency as at the public administration levels so at the micro level management. T. Vasylieva and V. Kasyanenko (2013) presented the technique for the integral assessment of innovative potential of national economy (IANE) based on the theory of sets and carries out the analysis of the dynamics of its structure-forming components for Ukraine during 2004-2011. T. Kolmykova et al. (2013) analyzed the role of government in stimulating the development of the national industry. Made conclusion that structural reforms must be directed to support basic research in priority areas of science and technology, the creation of high technology, the introduction of innovations in different sectors of the economy, and especially in the industrial complex.

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Literature review shows, the issues of economic security of the state and the innovation development trajectory of enterprises are considered mostly separately and declaratively. The lack of formalized research on the impact of innovative enterprises activity on ensuring the indicators of state economic security leads to a lack of a clear national strategy for providing economic security through an innovative breakthrough. There are no clear formalized studies that will give an idea in which sectors innovation activity will increase the growth rates of economic security, which sectors need to be stimulated and which state support methods should be used to maximize the growth of innovation activity. The present paper is structured as follow. In the next section we make an innovative component analysis of the economic security (on the example of Ukraine). In the section 2, using econometric models, we analyze the innovation development trends of economic entities in Ukraine. The section 3 and 4 details scientific and scientific and technical activity funding trends in Ukraine and its effectiveness.

1. THE INNOVATIVE COMPONENT ANALYSIS OF THE ECONOMIC SECURITY IN UKRAINE In accordance with the Methodological Recommendations on the estimation of the economic security level of Ukraine, approved by the order of the Ministry of Economic Development and Trade of Ukraine (2013), economic security is a condition of the national economy, which preserves resilience to internal and external threats, ensures high competitiveness in the global economic environment and characterizes the ability of the national economy to achieve sustainable and balanced growth. The components of economic security are: industrial, demographic, energet- ic, foreign economic, investment and innovation, macroeconomic, food, social, financial security, and therefore economic security is an integral indicator. We agree with Djalilov et al (2015), that economic security of Ukraine can be calculated not only in accordance with the Methodological Recommendations on calculating the level of economic security of Ukraine, approved by the order of the Ministry of Economic Development and Trade of Ukraine (2013), but also according to the Global Competitiveness Index. According to the ranking of countries based on the Global Competi- tiveness Index of the countries, the rating of Ukraine is constantly changing and occupies the worst positions in recent years (Figure 1). The indicators deteriorated especially in 2016-2017 (it is 6 positions below in comparison with 2015-2016).

Figure 1. Ukraine's rating according to the Global Competitiveness Index of countries

Source: Сompiled by the authors on the basis of data from reports of the World Economic Forum (The Global Competitiveness Report, 2012-2013; 2013-2014; 2014-2015; 2015-2016; 2016-2017; 2017-2018). 161

Liudmyla Zakharkina, Iuliia Myroshnychenko, Denys Smolennikov and Svitlana Pokhylko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 159-173 The Global Competitiveness Index of countries consists of a large number of indicators grouped into 3 main sub-indexes: "Basic requirements", "Efficiency enhancers" and "Innovation and sophistication factors". The main sub-indexes, innovation indicators of place and technological development in them are represented in the Table 1.

Table 1. The Global Competitiveness Index of Ukraine

2014-2015 2015-2016 2016-2017 2017-2018 The Global Competitiveness Index (out of 144) (out of 140) (out of 138) (out of 137) 76 79 85 81 Index Component 1. BASIC REQUIREMENTS 87 101 102 96 1.1 Institutions 1.2 Infrastructure 1.3 Macroeconomic environment 1.4 Health and primary education 2. EFFICIENCY ENHANCERS 67 65 74 70 2.1 Higher education and training 2.2 Goods market efficiency 2.3 Labor market efficiency 2.4 Financial market development 2.5 Technological readiness 85 86 85 81 2.5.1 Technological borrowing 114 103 97 111 Availability of latest technologies 113 96 93 107 Firm-level technology absorption 100 100 74 84 FDI and technology transfer 127 117 115 118 2.6 Market size 3. INNOVATION AND SOPHISTICATION FACTORS 92 72 73 77 3.1 Business sophistication 99 91 98 90 Local supplier quantity 80 61 62 63 Local supplier quality 83 80 79 67 State of cluster development 128 124 125 108 Value chain breadth 79 70 97 94 Control of international distribution 82 86 91 95 Production process sophistication 95 68 71 72 Extent of marketing 79 81 80 74 3.2 Innovation 81 54 52 61 Capacity for innovation 82 52 49 51 Quality of scientific research institutions 67 43 50 60 Company spending on R&D 66 54 68 76 University-industry collaboration in R&D 74 74 57 73 Gov't procurement of advanced tech- 123 98 82 96 nology products Availability of scientists and engineers 48 29 29 25

Source: Сompiled by the author on the basis of data from reports of the World Economic Forum (The Global Competitiveness Report, 2014-2015; 2015-2016; 2016-2017; 2017-2018).

According to the data on the sub-index "Basic requirement" there is a slight improvement (on 6 positions from 102 to 96 out of 137 countries). We observe a deterioration according to two other sub-indexes. Analyzing the technological availability indicators of the “Efficiency enhancers” sub- index and the indicators of the “Innovation and sophistication factors” sub-index, it can be noted that the most problematic are the factors of the latest technologies availability and development, the factor of foreign direct investment attraction, the state of cluster development, and government procurement of advanced technology products. Only the indicator of the scientists’ and engineers’ availability is quite high and is improving every year. Ukraine has lost a small number of 162

Liudmyla Zakharkina, Iuliia Myroshnychenko, Denys Smolennikov and Svitlana Pokhylko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 159-173 positions in terms of "Capacity for innovation" (51 position out of 137 countries in 2017-2018 compared to 49 positions out of 138 countries in 2016-2017). Thus, the analysis of the Global Competitiveness Index values has shown that Ukraine loses its positions in a global competitiveness. The innovation factor that works in developed countries is not used to the proper extent in Ukraine, which provokes deterioration of the remaining indicators. Bogma (2016) based on data Ministry of economic development and trade of Ukraine and Methodological Recommendations on calculating the level of economic security of Ukraine (2013) considered the level of economic security on Ukraine (Figure 2).

Figure 2. The level of economic security of Ukraine by components, %

Changes 2014 up 2011 2012 2013 2014 to 2013 Economic security as a whole 50% 48% 49% 45% -4% Energy security 32% 34% 39% 45% 6% Financial security 48% 46% 50% 36% -14% Production security 57% 53% 53% 52% -1% Macroeconomic security 47% 38% 40% 33% -7% Investment and innovation 36% 37% 35% 30% -5% security Social security 59% 62% 64% 57% -7% Food security 92% 93% 86% 94% 8% Demographic security 52% 45% 46% 46% - Foreign economic security 35% 30% 32% 35% 3% Levels of economic security, % 0-19% – critical level of economic security 20-39% – dangerous level of economic security 40-59% – unsatisfactory level of economic security 60-79% – satisfactory level of economic security 80-100% – optimal level of economic security

Source: Bogma O.S. (2016).

As we can see, the overall level of economic security and the majority of its components has a negative tendency to decrease. The overall level of economic security is an unsatisfactory one. The level of the "Investment and innovation security" component decreases every year and has dangerous level. Thus, the represented data correlates with the mentioned above indicators values of sub-indexes of the Global Competitiveness Index of the countries (see Table 1) and reaffirms the thesis that innovation, which is the locomotive of development in the developed countries, does not work at an adequate level in Ukraine.

2. INNOVATION DEVELOPMENT TRENDS OF ECONOMIC ENTITIES IN UKRAINE Since 2000, the Ukraine has declared that the purpose of modern development is implementation of an innovative model of development. However, the regression analysis of time trends development of scientific, scientific and technical activity and innovation activity indicators of enterprises allowed to draw conclusions about the reduction of innovation processes in Ukraine (Figure 3). 163

Liudmyla Zakharkina, Iuliia Myroshnychenko, Denys Smolennikov and Svitlana Pokhylko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 159-173 Figure 3. The innovation activity of industrial enterprises research results in 2000-2016

Source: Calculated by the authors on the basis of data from State statistics service of Ukraine (Scientific and innovative activity of Ukraine, 2016, 2017). Note: The data is given without taking into account the temporarily occupied territory of the Autonomous Republic of Crimea, the city of Sevastopol and a part of the antiterrorist operation zone.

The trend of the acquired innovative types of products volume changes (Qinnov) over the period from 2000 to 2016, can be characterized by the equation (1): (1) where t is time characteristics of innovation development (in the analysis this is a year), t=1,…,n. Based on the above equation, it can be noted that the volume of acquired innovative types of products is reducing every minute. Analyzing the dynamics of changes in this indicator over 2009- 2016 (Figure 3), it can be noted that during 2009-2016, there is a slight increase, then the decrease of the acquired innovative types of products volume. There is no stable growth trend, but the forecast trend for the next 2 years still has a positive dynamic. As a result of the study of the expenditures trends volume for scientific and scientific and technical work (Qexpenses), representing the "Sum of expenses for innovation activities" over the period from 2000 to 2016, the following equation (2) was obtained: (2)

In this model, the expenses of innovation development are generated by the expenses on the research and development, the acquisition and acquirement of new technologies, the preparation of production for the innovations implementation, the purchase of machinery and equipment, which are associated with the innovation implementation and other expenses. Based on the above equation, it can be noted that the expenses of innovation development increases by 906,16t every 164

Liudmyla Zakharkina, Iuliia Myroshnychenko, Denys Smolennikov and Svitlana Pokhylko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 159-173 next moment of time. The research of personnel potential of innovation development is characterized by such equation (3): (3) Based on the above equation, it can be noted that the number of scientific and scientific and technical works performers (doctors and candidates of science, researchers, technicians, auxiliary staff) decreases by a value of 3313,4t every next moment of time.

3. SCIENTIFIC AND SCIENTIFIC AND TECHNICAL ACTIVITY FUNDING TRENDS IN UKRAINE The above-mentioned trends are determined by the low level of governance in innovation development in Ukraine. Thus, funding of scientific and scientific and technical activities in Ukraine is extremely low and has a negative tendency to decrease. The indicator of the specific weight volume expenses of scientific and scientific and technical works funding in 2000-2010 fluctuates at the level of 0.82-0.99% and did not exceed 1.12%, and from 2010 it is basically decreased to 0.62-0.75% (Figure 4), while it has been proved that the innovation development model requires expenses on science funding at least 2% of GDP, according to the European strategy for economic development "Europe 2020: strategy of reasonable, stable and comprehensive growth " and not less than 3% of GDP (Fedulova and Androshchuk, 2014). But a number of Ukrainian researchers proved, that this indicator should be even higher in Ukraine, due to the following reasons: first, due to the low level of GDP per capita, secondly, due to the deformed structure of the economy and industry, and thirdly, due to the extremely low level of innovation funding in the last ten years (My- roshnychenko and Matvieieva, 2017; O. Zakharkin, 2014; Vasilyeva, et al., 2013).

Figure 4. Volume dynamics of scientific and scientific and technical works funding in GDP

Source: Calculated by the authors on the basis of the data from State statistics service of Ukraine (Scientific and innovative activity of Ukraine, 2016) and data from Ministry of Education and Science of Ukraine (The state of science and technology development, the results of scientific, scientific and technical, innovation, technology transfer for 2015, 2016). Note: The data is given without taking into account the temporarily occupied territory of the Autonomous Republic of Crimea, the city of Sevastopol and a part of the antiterrorist operation zone.

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Liudmyla Zakharkina, Iuliia Myroshnychenko, Denys Smolennikov and Svitlana Pokhylko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 159-173 The data on expenditures for the scientific research and development implementation over 2010-2015 were recalculated in 2016, to compare the domestic data with global indicators in accordance with the new methodology of organizing and conducting state statistical observation "Implementation of scientific research and development", which is introduced since 2016 (excluding expenses for implementation of scientific and technical services). Trends according to the new data are shown in Figure 5.

Figure 5. Specific weight of research and development expenses in GDP (according to Eurostat)

Source: Calculated by the authors on the basis of the data from State statistics service of Ukraine (Scientific and innovative activity of Ukraine, 2017) and data from Ministry of Education and Science of Ukraine (The state of science and technology development, the results of scientific, scientific and technical, innovation, technology transfer for 2016, 2017).

Thereby, according to the new data (compared for an adequate comparison with world the data), funding of scientific and scientific and technical activities in Ukraine is approximately 30% lower than relying on the data, calculated according to the old methodology. At the same time, the research intensity of the GDP countries of the EU-28, according to the data in 2015, averaged 2.03%. It was higher than average in Sweden – 3.26%, Austria - 3,07%, Denmark – 3,03%, Finland – 2,90%, Germany – 2,87%, Belgium – 2,45%, France – 2.23%. The scientific research funding in most countries is a function of the state. It is legally established that the share of budget expenditures on various stages of scientific and scientific and technical work funding should amount to at least 1.7% of GDP in Ukraine (paragraph 2 of the Arti- cle 48 of the Law of Ukraine " On Scientific and Scientific and Technical Activity "(2015), however, the volume of such funding in 2000-2010 fluctuated within 0.4% of GDP, which is 4 times less than the fixed rate of budget funding of science, and in 2010-2015 (according to new data, Figure 5) even within the range of 0.33-0.16% of GDP, which is almost 9.5 times less than the fixed rate of budgetary funding of science. Thus, science funding in Ukraine is always low compared to the world leaders and does not correspond to the practice of most developed countries. The funding research of innovation activity in Ukraine has shown that it is even lower than the science funding (Figure 6, Figure 7).

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Figure 6. Dynamics of innovation activity funding in GDP

Source: Calculated by the authors on the basis of data from State statistics service of Ukraine (Scientific and innovative activity of Ukraine, 2016). Note: The data is given without taking into account the temporarily occupied territory of the Autonomous Republic of Crimea, the city of Sevastopol and a part of the antiterrorist operation zone.

While analyzing trends, it is necessary to mention the growth of the total amount of innovation activity funding in 2015 and 2016 (Figure 6), in this case this increase is ensured by the enterprises expenditure growth. The state budget funding of innovation activity in 2000-2010 fluctuates from 0.002-0.036% of GDP (Figure 7) and in 2015 it was 0.003% of GDP, in 2016 it increased to 0.008% of GDP.

Figure 7. Dynamics of state budget funding of innovation activity in GDP

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Liudmyla Zakharkina, Iuliia Myroshnychenko, Denys Smolennikov and Svitlana Pokhylko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 159-173 Figure 8. Innovation activity funding structure of industrial enterprises by the technological sectors in 2014 and 2015, %

Source: Calculated by the authors on the basis of data from Ministry of Education and Science of Ukraine (The state of science and technology development, the results of scientific, scientific and technical, innovation, technology transfer for 2015, 2016).

Thus, compared to 2014, the funding share of low-tech sector decreased by 62% (from 47% to 18%), however, the funding share of high-tech and medium-tech sectors also decreased by 45% (from 44 % to 23%). The significant share of funding (59%) falls on the middle-low-tech sector, which is not a positive phenomenon.

4. EFFECTIVENESS OF INNOVATION ACTIVITY FUNDING In developed countries, the ratio of scientific works and innovation funding is 1:5 (Volkov et al., 2007). The distribution of funds between different stages of the innovation process is unbalanced in Ukraine (Figure 9). This is explained by the fact that the majority of funds (mainly state-owned), which are used for scientific and scientific and technical developments funding are used ineffi- ciently. Their results do not have the proper practical application, and the cost of implementing scientific developments far exceeds the investment opportunities of enterprises.

Figure 9. The ratio of scientific and scientific and technical works funding to the amount of innovation activities funding

Source: Calculated by the authors on the basis of data from State statistics service of Ukraine (Scientific and innovative activity of Ukraine, 2016). 168

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The data regarding the implementation state of scientific and technical products (STP), created in 2016 in the context of state funding sources and in the context of priority areas given to confirm the above-mentioned thesis. The results of scientific and scientific and technical developments do not have the proper practical application in the Table 2 and on Figure 10.

Table 2. The implementation state of the created STP in 2016

At the expense of general and special funds Including priority areas Scientific and of the State Budget of Ukraine technical products (STP) Implementation, Implementation, Created Implemented Created Implemented % %

Types of prod- 910 394 43,30 368 142 38,59 ucts including 528 316 59,85 247 112 45,34 equipment Technologies 1337 934 69,86 808 541 66,96 Material 724 201 27,76 451 113 25,06 Plant species and animal 226 128 56,64 215 121 56,28 breeds Methods, theo- 5025 3029 60,28 3953 2161 54,67 ries Other 9541 6915 72,48 5176 3532 68,24

Total 18291 11917 65,15 11218 6722 59,92

Source: Summarized by the authors on the basis of data from Ministry of Education and Science of Ukraine (The state of science and technology development, the results of scientific, scientific and technical, innovation, technology transfer for 2016, 2017).

Table 2 shows that on average only 65.15% of the created STP is implemented. The share of implemented STP in priority areas is about 60%. In the structure of the STP, in general, and in priority areas, in particular, fundamental scientific research is prevailing. In accordance with world practice it's a must. However, today, taking into account the macroeconomic situation in Ukraine, consumers of STP and the significant share of these consumers are subjects of economic entities and it requires applied research. Figure 9 shows that the share of applied research implementation is 92.4%. Therefore, it is necessary to revise the structure of budget expenditures in priority areas with an increase in applied research funding.

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Liudmyla Zakharkina, Iuliia Myroshnychenko, Denys Smolennikov and Svitlana Pokhylko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 159-173 Figure 10. Distribution of the established and implemented STP amount in priority areas in 2016.

The main problem of innovation development governance in Ukraine is the lack of system management of the innovation process by the state. The scattering of managerial functions between a large number of government bodies leads to the absence of common goals, objectives, incoherence and inconsistence.

Thus, the management of scientific and scientific and technical activities is actually carried out by separate units of two ministries: The Ministry of Education and Science of Ukraine (the Scientific and Technological Development Department), the Ministry of Economic Development and Trade (the Intellectual Property Department). To date, the central executive body has not actually been identified, which would have taken measures to implement a unified innovation policy as such, and a unified innovation policy in the production sector, in particular. Today, two central executive bodies are obliged to take part in the formation and implementation of the state innovation policy: The Ministry of Education and Science of Ukraine (the Innovation and Technology Transfer Department) and indirectly the Ministry of Economic Development and Trade (the Economic Strategic and Mac- roeconomic Forecasting Department, the State Investment Projects and Development Support Department, the Investment Involvement Department). Additionally, on October 25, 2017, the In- novation Development Council was created, which is a "temporary consultative and advisory body of the Cabinet of Ministers of Ukraine, formed to study the problematic issues related with the implementation of state policy in the innovation development field, ensuring effective co-operation between the Cabinet of Ministers Ukraine, bodies of executive power, civil society, economic entities and innovation activity entities with the purpose of developing, organizing, coordinating and implementing measures, mechanisms and conditions for innovation development of the national economy, the creation of innovation infrastructure and the reforms implementation in the innovation activity field”.

CONCLUSIONS

The conducted research made it possible to find out that the activation of innovation activity as a factor in the economic security formation of the state, which operates in developed countries, is not used properly in Ukraine. Thus, the analysis of the Global Competitiveness Index values has shown that Ukraine is losing its position in global competitiveness, although the ability to innovate 170

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(Ukraine is in 51 positions out of 137 countries) and the human resources availability (Ukraine in 25 positions out of 137 countries) is still at a high level. This is also confirmed by the Global Inno- vation Index data (innovation potential is available, but it is not used properly). The analysis of the innovation and investment component of Ukrainian economic security has also shown that investment and innovation security is decreasing every year and is in a danger zone. Such trends in innovation development of Ukraine are connected, first of all, with low (least critical) level of science funding by the state (primarily applied character, innovation and technology transfer) and low level of funding by economic entities themselves. It has been found that mainly the areas of development and projects of the medium-tech sector are funded. It has been established that on average only 65.15% of the created STP is implemented. The share of implemented STP in priority areas is about 60%, while the share of applied research implementation is 92.4%. Therefore, it is proposed to reconsider the structure of budget expenditures in priority areas, with increased funding for applied research. The analysis of governance of innovation process has shown that there is no systematic management of the innovation process in Ukraine. There is no coordination and coherence between the majority of authorities. In return, there is no growth level roadmap of economic security through an innovative breakthrough of economic entities. Thus, the further research will be devoted to the mathematical formalization and modelling of the most effective priorities structure of innovation development of enterprises and optimization of the state instrument system stimulation of innovation activity, which will allow to ensure the growth of economic security of the state in the shortest possible time.

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Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 175-190 ‘

Managament of the Sustainable Development of the Agrarian Sector of the Regions of Ukraine

SERHII KOZLOVSKYI1, VIKTORIIA BAIDALA2, OLGA TKACHUK3 and TETIANA KOZYRSKA4

1 Professor, Vasyl’ Stus Donetsk National University, Department of Entrepreneurship, Corporate and Spatial Economics, Vinnytsia, Ukraine, e-mail: [email protected] 2 Associate Professor, National University of Life and Environmental Sciences of Ukraine, Department of Economic Theory, Kyiv, Ukraine, e-mail: [email protected] 3 Professor, Vinnytsia Institute of Trade and Economics of Kyiv National University of Trade and Economics, Department of Economics and International Relations, Vinnytsia, Ukraine, e-mail: [email protected] 4 Research Assistant, National University of Life and Environmental Sciences of Ukraine, Department of Patent-Licensing, Inventive and Innovative Work, Kyiv, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 17, 2018 Nowadays there is no single generally accepted definition of "sus- Revised from August 29, 2018 tainability development of the agrarian branch (sector)", which is Accepted November 05, 2018 caused by underdevelopment and the controversy of sustainable Available online December 15, 2018 development concepts, lack of information for the quantitative measurement of the sustainability degree. To develop a management model of the sustainable development of the agrarian sector JEL classification: in the region it is necessary to define the interpretation of the con- O13, Q11. cept of "economic sustainability of the region" and "economic sustainability of the agrarian sector." The search for new directions and DOI: 10.14254/1800-5845/2018.14-4.12 ways of agrarian activities development, which reduce its negative impact on the environment, defined the emergence of a new inter- Keywords: pretation of agrarian production sustainability as production, based on the quality of food, quality of life and environmental safety, sustainability, preservation of conditions for sustainable food ensuring of humanity agrarian sector, in the long term. In this general ecological approach to the concept management, of "sustainability of agrarian sector development of the region" the government, following categories are distinguished: ecological, productive, eco- development nomic and social. It is appropriate to supplement them in an intellectual and innovative component. The experience of Ukrainian regions shows that one of the most effective instruments of state regulation of the agrarian sector development of the region could be indicative planning, methodology, technology and organization of which should be built based on the market conditions. The development of indicative plan of agrarian sector development of the region should work together with the approved concept of regional development, based on which a strategic development plan is formed. It should be based on forecasting, which is rightly regarded as part of the indicative planning. It is necessary to rationally combine the actions of all subjects of management of agrarian relations, namely government regulation, market self-organization and management of agrarian sector on order to build an effective system of sustainability management of agrarian sector development of the region and to ensure its sustainable growth. 175

Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 INTRODUCTION In conditions of increasing urbanization in many countries, including Ukraine, the problem of ensuring sustainable development of the agrarian sector becomes more actual. The most pressing issues of sustainable and integrated development stand out in agrarian regions of the country where this form of territorial organization of population and production prevails. Considering that the priority of the national policy of Ukraine is providing the population with a wide range of high quality food in sufficient quantity, the scientific problematic development of the agrarian sector is very important. Its solution is largely dependent on the state and conditions of functioning of agriculture, which has undergone a significant transformation, changes as a result of reforms, carried out during the 1990s. Despite this fact, the state is still in a difficult financial and economic situation. The current stage of the national economic development is characterized by the intensification of the process of the transition to a social-market type of regional policy, focused on accelerating the pace and improving the quality of economic growth. Regional specialties of the farmer’s production and strengthening social and economic importance of the agrarian sector serve to encourage the necessity of development and justification of methodological positions aimed at solving multifaceted and different issues of ensuring sustainable development of the agrarian sector at the level of regional structures. Recognizing the scientific and practical value of the research carried out by Ukrainian and foreign researchers, it should be noted that several aspects of sustainable development of the agrarian sector requires further and more systematic study. In today's conditions, it is necessary to ex- pend the format of the studying of this problem from the strategic view. It allows a comprehensive study of not only economic, but also social, scientific and technical, institutional, innovational, investment problems that arise in the food chain “product-intermediary-producer-consumer” of agrarian products. This explains the need for comprehensive research that will form the agrarian policy, adapted to the new economic situation of the Ukrainian agrarian policy in general as well as for individual regions. There are still some problems of ensuring the sustainability of functioning of the agrarian production industries of these regions that remain unresolved on the regional level.

1. LITERATURE REVIEW The necessity of research is determined by the fact that in the global economy, beginning with the end of the twentieth century, the transformational processes have been expanding both in the systemic and in the structural-branch nature. In this regard, ensuring the sustainability of the development of economic systems gains a prime importance both in scientific research and in practical developments. Therefore, the issue of determining the sustainability of economic systems is relevant and crucial for current activity and for the future both for the economy as a whole, and for its industries, regions, individual enterprises, etc. Analyzing the concept of “sustainability” it is worth noting that for the first time the term “sustainable development” appeared in the “World Strategy for the Protection of Nature”, which was developed by the International Union for the Na- ture Conservation and published in 1980 (World Nature Conservation Strategy, 1980). The concept of the sustainability of economic systems development is fundamental, because it is not possible to provide either economic growth or sustainable development of society without it. For further analysis expanded interpretations of the concept of “sustainability of development” made by scholars for the macro, meta- and micro-levels of economy are presented. V. Bugay (2008) offers the following interpretation: “Sustainability of development is the ability of an enterprise to absorb external and internal destabilizing factors through the efficient use of its resources by improving economic potential”. O. Vasilenko (2005) considers the sustainability of development from the point of view of the system's efficiency and believes that “the sustainability of development is the ability of the system to maintain its operative condition to achieve the planned results 176

Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 when there are various changing impacts”. More complete is the definition of the category of sustainable development, which was proposed by V. Ivanov (2005): “Sustainability is the ability of the economic system not to deviate from its state (statistical or dynamic) because of various internal and external destabilizing influences through effective formation and use of financial, production and organizational mechanisms”. Analyzing the concept of “sustainable development of the agrarian sector of the economy” it should be noted that there are different approaches to its definition. According to I. Suray (2003), the agrarian sector in the broad sense covers all enterprises of Ukraine regardless of the form of ownership and the organizational-legal form of management, which produce agrarian products and products of its primary processing, as well as related service enterprises and organizations (institutions) engaged in the development and implementation of state agrarian policy. Thus, N. Kulagina (2012) believes that “the economic security of the agrarian sector as a system of economic interests is the discovering mechanisms for a compromise between ensuring the national interests of the country, food security and risks. These mechanisms result in the stable functioning of the agroindustrial complex”. From the standpoint of agrarian economic systems, the definition by O. Baltremus (2017) is: “The economic sustainability of the agrarian sector is the ability of the agrarian sector to counteract external and internal influences, to maintain a stable equilibrium for a sufficient amount of time”. M. Udovichenko (2012) considers the concept of sustainability from the point of achieving the state of equilibrium: “…means the change of equilibrium states that guarantee the achievement of strategic and tactical goals at specific time intervals and ensure the compliance of parameters and results of the course of internal processes with the changing requirements of the internal environment”. A number of scientists, including O. Moroz (2008), tend to interpret the sustainability of the economy as its ability to counteract external negative influences and burdens. The list of factors that determine sustainability is quite diverse. In some sources, the procedure for determining the sustainability of the economic system is reduced to the calculation of certain financial indicators, that means calculating financial sustainability. In other words, methods that allow taking into account a large number of both quantitative and qualitative indicators influencing the sustainability of the system have been developed. There are approaches that differentiate external and internal sustainability factors. However, it would be more appropriate to divide these factors into those that can affect sustainability and those that provide it. R. Popelnukhov (2009) notes that the sustainability of the economic system reflects its ability to effectively counteract adverse internal and external influences, adequately and quickly change its internal structure in accordance with changing conditions. The more stable the system is in adverse events, the more viable it is. T. Shovgenov (2007) reckons that the sustainability of the socio- economic system differs significantly from technical and physical sustainability, since the main characteristic of the socio-economic system is a certain equilibrium state and the ability to return to it in the case of harassing actions or preservation of a given course of development in the case of the impact of opposing forces, as well as the ability to effectively use and independently modify the resources of its development without increasing or minimizing the cost of basic, non-renewable resources. The condition of economic security of the state can be studied from the standpoint of sustainability. Thus, S. Kozlovskyi, I. Khadzhynov, I. Vlasenko and L. Marynchak (2017) consider that the economic sustainability of the system is the ability of the economic system after a certain perturbation (changes in the parameters of the economic system, its indicators) to quickly return to the state not worse than the previous one, to maintain its condition as long as desired, as well as to improve its state to the size of the perturbation in case of positive changes in the economic parameters of the system. Thus, the researchers of the problems of sustainable development of the economy tried to solve the problem rather from one side. Some of them proposed creation of an 177

Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 effective system of material wealth’s distribution of the country. Others tried to find an efficient economic sector that would allow sustainable development of society and its prosperity. Later researchers, using the method of a systematic approach, proposed the search for a single, general- important branch of the economy, which would ensure sustainable economic development of the country (Serban et al., 2017; Ruzic and Demonja, 2017). Thus, it can be argued that the concept of sustainable economic development of the agrarian sector of Ukraine focuses on internal resources of the system, modernization of production, etc. It is especially important for most regional economic systems of Ukraine, the main components of which are agrarian and industrial segments of the real economy sector. The abovementioned analysis of the research problems helps to formulate the purpose of this work. The purposes of the article are to investigate the theory and nature of the concept of “sustainability of the agrarian sector in the region,” to interpret the terms “economic sustainability of the region” and “economic sustainability of agrarian sector in the region,” to improve the category of “development sustainability of the agrarian sector in the region”, to single out its levels, to develop of its management model, to define the state’s functions in ensuring the development sustainability of the agrarian sector, to form a functional model of management of the sustainability and to develop the agrarian sector of the region.

2. METHODOLOGY A systematic approach to the interpretation of the concept of “sustainability of the agrarian sector” consists in analyzing and justifying the choice of the most effective ways to achieve the balanced production, economic, social and environmental goals that the agrarian sector is facing. In this regard, there is an objective need to determine the basic methodological principles of the study of the concept of “sustainability of the agrarian sector”. All research principles can be grouped into three groups: general, specific and instrumental. Compliance with these principles will allow more skilful and purposeful formation of a management mechanism that would ensure the sustainable development of the agrarian sector (or agrarian economic system). It should be emphasized that there are two main existing types of models for building a system for managing the sustainability of the development of the agrarian sector (Kozlovskyi et al., 2017), differing in principles of construction and functioning. According to the first type, the sustainability of the development of agrarian production is presented as an unregulated system, which operates only on the principles of self-organization and self-adjustment. Such a model operates on the principle of operation of a system with negative feedback, based on the mechanisms of market self- regulation of production processes. In the other model, the principle of negative feedback is realized, using the regulatory influence on the sustainability of the agrarian sector. The difference between supply and demand for resources, services, products, etc. leads to an imbalance in the economic system and causes a need for the implementation of regulatory influences by the state and activating mechanisms of market self-regulation, which together eliminate this imbalance. It should be noted that the first model of the stability of the development of agrarian production has more theoretical and methodological significance, since it is practically not found in the classical form. Even in developed economies, the sustainability of the agrarian industry in the country and the region cannot be a self- regulating system. The state and regions retain a wide range of regulatory functions. In order to ensure sustainability economic development of the agrarian sector of Ukraine it is expedient to use the following methods:  Observation of the magnitude of the main macroeconomic indicators and their comparison with the threshold values for which the values of indicators are not lower than in the average world. An indicator approach that is used here allows you to analyze the state of the agrarian sector, taking into account its peculiarities. 178

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 Comparison, that is, the calculation of the rates of economic growth by the main macroeconomic indicators and determining the dynamics of their changes.  Expert evaluation, which enables to describe the qualitative characteristics of the investigated process.  Scenario, which gives an opportunity to estimate the most probable course of development of events and possible consequences of the decisions taken on the basis of the expert's consideration of the smallest details of the current state of development of the phenomenon under study.  Discriminant analysis, which involves establishing the essence of an unknown object based on the study of differences between several classes of objects in one or more parameters.

In the current conditions, achieving sustainable development of the agrarian sector can be carried out on its own reproductive basis. This is justified by the availability of significant natural resources, that are enough for production the required amount of all major types of food products, and by the large industrial potential of the agricultural sector, which was accumulated during many years of functioning. At the same time, the problem of ensuring the sustainable development of agrarian production in the regions is exacerbated by the influence of various internal and external factors, which necessitates its further research and development. The research of the problem was conducted in three stages:  at the first stage there was the theoretical analysis of existing methodological approaches in economic scientific literature and also theories and research methods in this field; the problem, the aim, and the research methods are identified.  at the second stage the conceptual model of sustainable development of the agrarian sector was developed, this model is based on achievement of the main and basic targets in economic, social, ecological, institutional spheres of development of the agrarian sector whilst its rational interaction with the external environment by means of ensuring its ability to self- development, efficiency of functioning, flexibility, adaptability and safety.  at the third stage the teoretical-experimental work was completed, the most important criteria for assessment of prospects evaluation of sustainable development of agrarian sector and their social, ecologic and economic levels were identified.

The following methodological steps were offered:  to create a model of sustainable development of the agrarian sector of the regions of Ukraine.

To manage this task, it is necessary to identify the problems of the development of the agricultural sector of Ukraine, to analyze the economic state - the output (GDP) of the agrarian sector of Ukraine:  to identify and to classify the problems of the development of the agricultural sector at the macroeconomic level;  to conduct an analysis of the concepts of the category "sustainable development of the agricultural sector". To identify the key factors affecting the development of the agricultural sector;  to modernize the existing concept of sustainable development of the agricultural sector in the direction of considering the factors of international influence;  to structure the levels of ensuring the sustainability of the agrarian sector in the regions of Ukraine, considering the identified problem factors;  based on these conceptual conclusions, to develop a structural model for ensuring sustainable development of the agricultural sector in the regions of Ukraine.

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Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 3. RESULTS The sustainability of the agrarian sector plays a special role in ensuring the economic development. K. Marks wrote: “Food production is the first condition of living of direct producers and all production in general". Moreover, this process should be continuous and steadily growing, not only because people cannot stop consumption of products, but also because of the need to increase the volume and to improve the quality of produced products due to the growing demand and population. Solving this problem is very actual, as starvation is a constant world problem during the history of humanity. The analysis of macroeconomic indicators of Ukraine's economy shows that currently its agrarian sector is probably the only one that maintains the whole economy of the country. According to the IMF, in 2013 Ukraine's GDP per capita was US $ 3862, which is lower than in Albania or Mon- golia. Overall, it shows a critical state of the national economy, especially considering the events that took place at the beginning of 2014 and led to the outflow of investment capital from Ukraine, devaluation of national currency, reduction of real incomes and termination of production capacity because of supply contracts cancelling, increasing inflationary processes and so on. At the same time, in 2013 the domestic agro-industrial complex accounted for the largest share in total export performance: Ukraine exported agri-food products worth US $ 17 billion., while import increased by almost 9% and amounted to US $8.2 billion; 2/3 agro-food exports goods consisted of 5 types of products: corn - 13%, wheat - 11% rape seed - 7%, sunflower oil - 19% and sunflower meal - 5%. According to the State Information and Analytical Center of External Commodity Markets Moni- toring, priority direction for Ukraine in the structure of export and trade turnover in 2013 was the CIS countries, where about 36% of goods were exported, while 10% less products went to the EU. Among the leading importers of Ukrainian products were also countries of the Near and Far East, which imported 18% and 8% of goods respectively. The structure of imports to Ukraine was relatively similar: its largest share of products (37%) was imported from the CIS. The share of imports from the EU accounted for 35%, that was followed by the Far East and the Near East with 15% and 3% of the total correspondently. The main indicator of the development of the agrarian economic system of the country is the indicator of gross agricultural production in Ukraine (Figure 1). In 2013, the change in the gross agricultural output in all categories of farms compared to 2012, which was calculated as the base one, was 29.5%. In 2014 compared with the base year of 2012 the difference accounted for 34.8%, while in 2015 – for 22.9%. At the same time, in 2016, compared with the base year of 2012, this indicator stood at 38%, which indicates that the Ukrainian agricultural sector is in a state of development and there is not enough data on the decline of the basic economic index of its activities (State Statistics Service of Ukraine, 2018). At the same time, the study of the pace of growth of the agrarian sector is considerably interesting, since its low growth rates may indicate the presence of the influence of certain negative factors, which in turn may jeopardize the economic security of the entire agrarian sector. The negative side of this process is low added value of the exported goods. It is appropriate to note that in the years 1660-1685 in England at the time of King Charles II a law forbidding export of raw materials was passed. It happened because after processing them abroad they were then imported into the country at higher prices. Ukraine also has to diversify its export structure of agro- food products and to increase export of goods with high added value. As a result, the importance of the agrarian sector in the economy will grow. The domestic agrarian sector will become "a locomotive" for the modernization of state and the source that will stimulate the national economy through the multiplier effect of its various spheres (food industry, machinery, etc.). The problem of ensuring the sustainable development of the agrarian sector is not new, but its implementation directions under conditions of modern economic relations are significantly different (Figure 2).

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Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190

Figure 1. Volume of gross agricultural production (in comparable prices in 2012, billion UAH) in Ukraine

Source: compiled by the authors based on (State Statistics Service of Ukraine, 2018; Vdovenko et al., 2018)

Firstly, agriculture continues to be a major food producer and a major source of human activity. Any violation of its development leads to destabilization and imbalance of production and consumption. Structural changes in the sector under conditions of transformational economy is mainly focused on the production of the products, which provide the maximum profit. In the agrarian sector it is not always justified in terms of sustainability of production, ensuring food security in society and preserving soil fertility.

Figure 2. The problems of ensuring sustainability of the agrarian sector

The problems of ensuring sustainability of the agrarian sector

Agricultural Infrastructure production (underdeveloped infrastructure of the (aimed at obtaining maximum profit, agroindustrial complex does not allow to it disturbs the equilibrium minimize production costs on products in the sector) sales)

Narrow specialization of The state agroenterprises (government regulation and (leads to underutilization their management are not always directed to productive potential) the optimal development of the agroindustrial complex sectors) Influence of natural factors, ecological state Global, integration processes

(destructive industrial technologies lead (determine the problems of to degradation and agroindustrial enterprises land output from circulation) competitiveness)

International quotas for export of agricultural products of Ukraine

Source: compiled by the authors based on (S. Kozlovskyi, V. Kozlovskyi, and Burlaka, 2014; Kolesnyk, Samborska, Talavyria and Nikolenko, 2018) 181

Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 Secondly, market conditions determine the processes of narrow specialization of agrarian enterprises, which basically means production that gives the highest profit. However, changeable competition and world market conditions align prices and the profitability of producers, which eventually leads to underutilization of production potential of the agrarian sector, decline in production and sustainability, aggravation of social problems (Vol’vach et al., 2002). Thirdly, environmental factors significantly affect all processes in agriculture. Their partial regulation by melioration, chemicalization, mechanization, seed production organization on a scientific-substantiated level requires large investments, while agrarian producers and the state have an insufficient amount of investment capital, that causes instability in the main produce. Fourth, the developed infrastructure of agro-industrial complex and agrarian market is an important condition of the agrarian sector sustainability. With the transition to a market economy its formation and normal functioning are complicated, which increases the costs for promoting products to consumers, causes significant price variations, which also generates instability in agrarian development. Fifth, the sustainability of agrarian production and interrelated sectors of the economy largely depends on the regulatory role of the state. However, its impact currently is insufficient. It disrupts the normal course of the reproduction process. Also, the damage caused by instability in the agrarian sector significantly exceeds the losses in other sectors. Sixth, there is a threat to sustainability of Ukraine’s agrarian sector under conditions of world integration processes that is determined by the inability of most companies to produce products that meet international standards. All of this enhances the attention of modern researchers (Aksaeva, 2002; Dolishnii, 1998) to the problem of production sustainability and agrarian development. At the same time, basic approaches to the disclosure of this concept and development measures that will ensure dynamic development are determined primarily by the need to overcome crisis state of agrarian production. A retrospective review of theoretical aspects of the agriculture sustainability problem shows that over many years it has been among the most urgent for the state. However, despite the significant number of scientific publications on improving the sustainability of production in general and agriculture in particular, it should be noted that the sustainability of the agrarian sector development is a new and still not enough disclosed category in terms of both the essence and research methodology. Nowadays, there is no single generally accepted definition of “sustainability development of the agrarian branch (sector)”, which is caused by underdevelopment and controversy of sustainable development concepts, lack of information for the quantitative measurement of the sustainability degree. Some authors believe that sustainability, of farming in particular, is the ability to withstand negative influences, mainly elemental forces of nature, to prevent or weaken decline in production (Zagaitov and Polovinkin, 1984). Others consider it as sustainability of average level of dynamic row (Yastremskii, 1961). The third group interpret it as sustainability of evolution, development of investigated phenomenon (Chetverikov, 1963). However, this concept is not limited to these definitions. Not only does sustainability of agrarian development provide an opportunity to overcome the adverse effects for agriculture, but it also gives the ability to use them with the greatest effect. To develop a management model of the sustainable development of the agrarian sector in the region it is necessary to define the interpretation of the concept of "economic sustainability of the region" and "economic sustainability of the agrarian sector." Considering the research conducted by individual scholars (Kozlovskyi et al., 2017; Kaletnik, et al., 2012), it is accepted that: ̶ the economic sustainability of the region is the ability of its economy after some disturbance (changes in external or internal factors of development) to quickly return to a state not worse than the previous, to maintain it for an arbitrarily long time, and to improve its subject to positive changes in the economy of the region; ̶ the economic sustainability of the agrarian sector is the ability to withstand external and internal influences and to save stable equilibrium for a sufficient time.

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The transition to the sustainable development of the regional agrarian sector is quite a long process that requires solving complex economic tasks. With promotion towards the sustainable development this very idea will change and clarify, people's needs will be rationalized according to existing constraints, while the means for meeting those needs are improved. Therefore, implementation of the principles of sustainable development should be considered in stages. Moreover, only for a relatively early stage some programs and forecast documents can be developed. On the one hand, the region is a complex socio-economic system, internal environment of which consists of economic, social and ecological subsystems. On the other hand, it is a subsystem of a higher hierarchical level. A variety of approaches to the definition of sustainability in the agrarian sector in the region is caused by the multifaceted problem, extreme complexity of the object and a set of tasks being solved by this sector as a whole and its components in particular. The search for new directions and ways of agrarian activities development, which would reduce its negative impact on the environment, defined the emergence of a new interpretation of agrarian production sustainability as production, based on the quality of food, quality of life and environmental safety, preservation of conditions for sustainable food provision of humanity in the long term. In this general ecological approach to the concept of "sustainability of agrarian sector development of the region" the following categories are distinguished: ecological, productive, economic and social. It is appropriate to supplement them in an intellectual and innovative component – Figure 3.

Figure 3. The concept of categories "sustainability of agrarian sector development of the region"

Categories of sustainability of the agrarian sector development of the region

Ecological Productive Economic Social

Sustainable Sustainable Sustainable Agriculture provides agriculture prevents agriculture agriculture forms higher living the use of renewable provides consistent financial and standards resources at a pace increase in the economic to the rural population that is faster than volume opportunities for both thanks to the growth their recovery pace of agricultural simple and advanced of its incomes and environmental products, on the basis reproduction. It also through effective pollution in volumes of which contributes to the less employment exceeding the ability it is possible to dependence on of ecosystems to achieve stable purchased means of Intellectual - assimilate it provision of food to production and its innovative population larger diversification, which makes it less vulnerable to International and conjuncture political fluctuations on the factors markets of certain Source: compiled by the authors based on (Burlaka et al., 2014; Kolesnyk et al., 2018)

Currently there is no clear definition of "intellectual potential". Scientists consider “intellectual potential” as unity of creative and individual labor potentials of employees, which characterize their ability to the production of material goods using materialization of knowledge and their adequacy of management requirements.

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Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 Innovative potential is the totality of all kinds of informational, intellectual, technological, and scientific-production resources, including technical documentation, patents, licenses, business plans, and innovative programs and so on. Choice of one or another development strategy depends on the state of innovative potential. Innovative potential in this case can be considered as "degree of readiness" of the economic system to fulfill the goals of development. The system of sustainability of the agrarian sector of regions is the structural element of the economic system at the national level and the main link in the complex of measures to provide the population has food. Considering agriculture (agrarian sector of economy) as unified economic, ecological and social system that has certain goals, structurally united features and functional relationships, in the modern period it is not only just the actual growth of agrarian production what is important, but also the increase of its economic efficiency, which provides sustainability in general. Thus, the increase in the volume of livestock production without radically enhancing its profitability (which is much lower than in crop production) can lead to aggravation of reproduction problems in the agrarian sector. At the same time, efficiency gain, which is not accompanied by an increase in production, threatens to strengthen the country's dependence on food imports and may cause growth of unemployment in rural areas and the expansion of poverty. The approach to achieving social goals of sustainable development of the agrarian sector requires an explanation. In fact, the improvement in living conditions of agrarian workers extends beyond the agrarian sector and is largely connected with a complex development of rural areas (regions). Therefore, there is a clear interdependence of sustainability in the agrarian sector and the level of rural development. The close relationship of agriculture development with rural development as a social and territorial subsystem of society, where the sector dominates, is the most important component of the research methodology of agrarian sector sustainability problems. This approach was declared at the session of FAO in Rome in 1996. It was mentioned in the materials: "The main task of the program of sustainable agriculture and development is a stable raising the level of food production and ensuring food security". Thus, the essence of a systematic approach to the interpretation of sustainability of the agrarian sector development of the region is a balanced combination of productive, economic, social and ecological goals. Using a systematic approach and relying on identified essential features of the investigated category, it can be said that sustainability of development of the regional agrarian sector is a dynamic transition process of the system to a qualitatively new innovation level, aimed at ensuring economically grounded, ecologically safe, socially oriented expanded reproduction, as well as at increasing the level and improving the quality of life of rural population living under the influence of internal and external environment factors. The process of transition of agrarian sector to sustainable development involves the implementation at several levels – Figure 4. The first one is the lower level. This is the level of development, which involves scientific compliance of crop rotation, the use of windbreaks and modern land melioration, optimally conditioned observance saving technologies in crop growing, forming productive herd, transition to biological methods of crop protection from pests, reducing the use of agrochemicals and pesticides. The second, average, one is the level of agriculture sustainability, which involves, along with above- mentioned characteristics, a number of additional measures that follow: improving soil fertility and natural resources of rural agrarian landscapes, the use of advanced diagnostic soil cover analysis and the health of farm animals, constant monitoring of pests and diseases of plants and animals. On the third, higher level, attention should be given to the formation and organization of farms producing ecologically clean crop and livestock production, focused on implementing the most progressive, safe and non-waste technologies in the field of immune, genetic engineering and remote sensing of agrarian complex objects. Particular importance should be given to practical use of the agro-marketing principles and entrepreneurship. The following complex measures are also envisaged: mitigation and prevention of unemployment in rural areas, employment regulation in the context of all types of business entity, creating conditions for increasing incentives for highly productive labor in agriculture production, including through the extensive development of 184

Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 different types of farms in non-agrarian production. Ultimately, this will facilitate sustainability of development in the agrarian sector in terms of improving the economic, social and labor sector in rural areas, increasing incomes and social protection of the rural population.

Figure 4. The levels of sustainability of the agrarian sector of the region

Ensuring the sustainability of development of the agrarian sector of the region

Top level

International and political factors

Higher level

Implementa- Using the A sufficient Employment Stimulation of The use of tion of principles of amount of regulation highly intellectual progressive agro- domestic food for all forms of productive and innovative and safe marketing of high quality; farming labor in component of technologies (including the Permanently the agrarian the economy in the field of formation and increasing the sphere immune, distribution of level of genetic cooperative incomes of the engineering type of population business)

Average level

Solving the major Stable functioning The use of modern Improvement of soil problems of quality of of the social sphere technologies for fertility and safe use of agricultural products and infrastructure that diagnosis of soil cover natural resources will help to reduce the and health of outflow of population agricultural animals from rural territories

Lower level

Achievement of the Adherence to Optimal use of Transition to biological required volume and progressive agro-chemicals and methods of crop assortment of technologies, pesticides protection from pests agricultural products crop rotation, and diseases and modern land reclamation

Source: developed by the authors based on (Burlaka et al., 2014)

To ensure the sustainability of development of the agrarian sector a great importance must be given to the management of processes, which occur in its system forming elements. Indeed, any 185

Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 existing organizational structure must comply with an adequate system of state management, because no country can exist without it. In Ukraine such a management body is the Ministry of Agrari- an Policy and Food of Ukraine and its regional divisions. For the implementation of management impacts on the agrarian sector of the region the application of a general cybernetic method is appropriate, proposed by the founder of cybernetics Norbert Wiener (Wiener, 2002). It has been successfully used in agriculture by A. Chudnovskyi (Chudnovskyi, 1965). Using this approach for management of sustainability of the agrarian sector development it is expedient at first to determine the following features: input and output information flows of agrarian economic system of the region; the principles of construction and functioning of the management model of sustainability of agrarian economic system development of the region; the objective function, types and tasks of management of sustainability of the agrarian economic system development of the region. From this point of view the sustainability of the agrarian sector development of the region can be considered as a system formed by the following interrelated components: processes of production and recycling of agrarian products, the market of production means and services, the market of agrarian products and foodstuffs and the current economic mechanism of management – Fig- ure 5. The relationship of these components forms a so-called sustainability management model of agrarian sector development of the region.

Figure 5. Sustainability management model of development on the agrarian sector

Economic mechanism of management

The state

Agrarian sector

The market of production means of resources and services.

1. Means and items of labor. 2. Resources and potential: labor, material, financial, intellectual.

3. Services: productive, marketing, staff training. market The of agrarian products

Agricultural products:grain, bread and bakery prod- fish, products, dairy milk, products, meat meat, ucts, eggs, fruits, and vegetables potatoes, products, fish etc. sugar Source: developed by the authors

The transition to the sustainability management model of agrarian sector development of the region means, on the one hand, the existence of cash flows, consumer demand for agrarian products and foodstuffs. On the other hand, it creates an offer of economic entities of two main types of commodity groups: resources and services and means and items of labor. Resources and services are presented by a qualified labor force, productive and technical services (repair and maintenance of agrarian machinery, transportation, electricity, gas supply, etc.) staff training, scientific support, advertising and information services and so on. The second commodity group includes: industrial buildings, agrarian machinery and equipment, vehicles, etc., and also fertilizers, combined feed, fuel, basic and auxiliary materials and so 186

Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 on. The output of the model is, on the one hand, the aggregate supply of agrarian enterprises, stor- ing and recycling enterprises, and also personal peasant households in the form of direct agrarian foodstuffs and commodity products of food industry. On the other, it is their demand for means of production and labor items, a variety of resources and services. The balance of aggregate demand and aggregate supply of business entities with an aggregate consumer demand for foodstuffs determines the equilibrium prices of the means of production, resources and services and also for certain kinds of food products. Central place in the proposed sustainability management model of the agrarian sector development of the region take the market of agrarian products and foodstuffs, while the market of production means, resources and services has secondary importance. Indeed, the sale of means and items of work, material, labor, financial and other resources, provision of different services, only creates conditions for sustainability of the agrarian sector development, production growth and agrarian products sales. It should be noted that the proposed model of sustainability of agrarian production development of the region has more theoretical and methodological than practical significance. Even in countries with developed economy sustainability of agrarian sector development of both the country in general and separate region cannot be a self-regulating system. State and regions retain quite a wide range of regulatory function: ̶ regulation of land operations and control of the structure of land use; ̶ stimulation of expedient concentration of agrarian production, its horizontal and vertical integration with related industries; ̶ regimentation of legal, organizational and economic conditions of rural producers activity; ̶ control of the ratio of market prices for means of production and agrarian products; ̶ control of the dynamics of production costs in agriculture, observance of established by the state standards of product quality; ̶ fixation of market prices for agrarian products, bringing their level to the level established by law or governmental resolutions; ̶ fines for failure to comply with the State established standards of product quality, quotas for its production and rules of land use; ̶ economic stimulation of new forms of organization of farming, social security and creating acceptable conditions for the life of the villagers; ̶ using quotas for products sales as an instrument of planning and regulation of government procurement of agrarian products; ̶ domestic and foreign economic protectionism in relation to rural producers; ̶ payment of subsidies for exports, what will cover losses of rural producers due to the lower world prices compared to the prices in the domestic market (without breaking the principles of the WTO); ̶ long- and medium-term planning of the development of agrarian production and agrarian science under the scheme "planning - budgeting".

According to the authors (Zinovchuk, 2001; Mazur et al., 2018), which suggest that government regulation of sustainability of the agrarian sector development must have a program-targeted character and include a set of legal, economic, organizational and administrative measures with necessary resources, an effective mechanism for implementing the goal and corresponding administrative apparatus that would provide direct and feedback relations of the State (regions) with producers and consumers of agrarian products. The State and regions should, on the one hand, guarantee agrarian producers such profitability that would be enough for expanded reproduction and not destroy at the same time the market mechanisms of self-regulation. On the other hand, they should implement measures for protecting the interests of consumers and the population in purchasing at affordable prices and requiring food products of the set quality.

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Serhii Kozlovskyi, Viktoriia Baidala, Olga Tkachuk and Tetiana Kozyrska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 175-190 СONCLUSION The experience of Ukrainian regions shows that one of the most effective instruments of state regulation of the agrarian sector development of the region could be indicative planning, methodology, technology and organization of which should be created considering the market conditions. Indicative planning system must meet the following requirements:  be formed, using the existing powers of regional executive authorities;  to contain information of interest to entrepreneurs and potential investors, considering the direction of social and economic development of the region, the benefits of the economic policy of regional authorities, the factors that determine the investment climate, etc.  to have heredity and connection with the long-term strategy of socio-economic development.

The development of indicative plan of agrarian sector development of the region should be linked to the approved concept of regional development, based on which a strategic development plan is formed. It should be based on forecasting, which is rightly regarded as part of the indicative planning. For building an effective system of sustainability management of agrarian sector development of the region and ensuring its sustainable growth, it is necessary to rationally combine the actions of all subjects of management of agrarian relations, namely government regulation, market self-organization and management of agrarian sector. The interaction of these three elements is implemented in the following way: the state regu- lates and stimulates the development of agrarian sector, contributes to the organization of branch management; market self-regulation forms the economic interests of the agrarian market subjects; branch management "complements" the state, indicates goals, direction and development prospects of the agrarian business. To improve management of the agrarian sector development of the region, it is necessary to ensure a reasonable balance of economic interests between the state, market self-regulation and branch management, to introduce public control over the activities of state and local government. Studies conducted in Vinnitsa region, convincingly proved the importance of public control and civil initiatives for adoption of substantiated management decisions. It should be emphasized that the importance of the agrarian sector for economic development requires a targeted effort, not only by the state but also by private business. An important instrument in this case could be the developing of forecasting scenarios of agrarian development in the long term, the use of which will allow agrarian producers better orientation in the agrarian market situation and an efficient implementation of modern achievements of science and practice in its activities, thus, contributing to the improvement of production, social and economic components of sustainability of the agrarian sector development.

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Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202

Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 191-202 ‘

The Becoming of Non-Linear Knowledge: New Risks, Vulnerabilities, and Hopes

SERGEY A. KRAVCHENKO1

1 Professor, Head of the Department of Sociology, MGIMO-University, Moscow, Russia, Moscow State University of International Relations, MFA of Russia, Institute of Sociology RAS. Moscow, Russia, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 27, 2018 In the article it is stated that non-linear knowledge is being born and Revised from September 29, 2018 it has come into life due to ‘arrow of time’ effect (I. Prigogine) ac- Accepted November 15, 2018 cording to which all the matter is being developed increasingly Available online December 15, 2018 quicker and in a more complex way. The author extends this effect to the dynamics of knowledge showing that the modern knowledge acquires the quality of reflexivity, takes on a completely new vector JEL classification: of non-linear development within ‘turns’ in sciences. The passage D81; D83; I23. from linear to non-linear knowledge produces, correspondingly, more complex manufactured risks including risks of dehumanization DOI: 10.14254/1800-5845/2018.14-4.13 that are specially analyzed. The monitoring of these risks implies that value oriented non-linear knowledge should be taken into ac- Keywords: count that includes not only the achievements of natural, technical and social sciences but also of the humanities. Among new chal- Non-linear knowledge, lenges to humanity there appeared modern vulnerabilities mani- risks, fested in increasing structural dysfunctions of a social complex vulnerabilities, system and/or a techno-natural system that take forms of ‘normal institutional regulation, accidents’, ‘collateral damage’, etc. It is stated that main challenges humanistic turn of vulnerabilities lie in the domination of pragmatic values of modern knowledge. Along with changes a new kind of development in the form of metamorphosis, presupposing non-linear transformations, has come into our life that adds much to the complex character of risks and vulnerabilities. The answer to these challenges the author sees in the humanistic turn that might give as a valid knowledge of complex risks and vulnerabilities as well as grounds for better futures that people want.

INTRODUCTION D. Bell (1973, p. 212) argues that it is characterized by two basic phenomena: a) “the sources of innovation are increasingly derivative from reach and development (and more directly, there is a new relation between science and technology because of the centrality of theoretical knowledge”; b) “the weight of the society – measured by a larger proportion of Gross National Product and a

 Acknowledgements. The research was supported by RSF, project № 16-18-10411

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Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202 larger share of employment – is increasingly in the knowledge field”. This situation is due to great changes in the nature of knowledge that took place within the development of human civilization. The Nobel prize-winner I. Prigogine (1997)has worked out the theory of ‘arrow of time’ according to which all the matter (this concerns as material as well as bio and social worlds) is being developed increasingly quicker and in a more complex way. He speaks about emergent, dynamic, and self-organizing systems interacting in ways that heavily influence the probabilities of later events. In my opinion, the effect of ‘arrow of time’ that interprets the complex development of all matter should be extended to the dynamics of knowledge that becomes a great factor of change. Knowledge is characterized as a “generalized capacity to act and as a model for reality” (Adolf and Stehr, 2017, p. 18). In ancient times indigenous knowledge was mainly transferred by oral communications in the form of traditions and rituals. So, social changes were slow and mainly linear in character. Nowa- days we have a globally circulating knowledge (Giddens, 1990) whose uneven dynamics becomes more complex, includes points of bifurcation, gaps, and traumas. As a result a new kind of non- linear knowledge is being produced. Modern knowledge guides skilled reflexive actors whose main goal is wealth achieved through changes in society and nature while choosing options from many alternatives. In the long run it presupposes risks as chances with hope and danger. It also produces vulnerable realities in the form of ‘new catastrophes’ and ‘normal accidents’ as side-effects of self-realization of knowledge in technical innovations – so, catastrophic futures gained wider acceptance. But other social goals and practices are possible: value-added knowledge of humane character may become a major factor of the passage to a new trend of human development with the hope for better and secure futures.

1. THE ESSENCE OF THE NON-LINEAR KNOWLEDGE: POWER TO ACT WITH REFLEXIVE AND COMPLEX REALITY Perhaps, the first evidence of the birth of the non-linear knowledge is expressed in the claim of scientists for critical non-linear reflection. The famous American sociologists R. Merton (1993, p. xix)acknowledged that science advances by standing “on the shoulders of giants” and at the same time he proposed the adoption of “the non-linear”. Using this approach the scientist reflected “the course taken by history in general, by the history of ideas in particular, and, in a way, by the course taken in scientific inquiry as well”. He argued for re-reading, in fact, constant reinterpretation of masters of Science in the context of time and correspondingly accumulation of innovative non- linear knowledge. “I have long argued that the writings of classical authors in every field of learning can be read with profit time and time again, additional ideas and intimations coming fleshly into view with each re-reading. What is to be found in writings of the past is anything but fixed, once and for all. It changes as our own intellectual sensitivities change; the more we learn on our own account the more we can learn by re-reading from our freshly gained perspective” (Ibid., p. 45). He continued to develop ideas of non-linear development of scientific knowledge via such concepts as unanticipated consequences, ambivalences (Merton, 1936; 1976), codependence of functions, non-functions and dysfunctions, manifest and latent functions (Merton, 1996) that are up to now useable – they give human power to deal effectively with many challenges of ‘arrow of time’ effects. A valuable contribution to the non-linear scientific knowledge was made by T. Kuhn who put in- to question the traditional notion of the development of science in a linear accumulation of knowledge stating the ideas of gaps of knowledge and scientific revolutions (Kuhn, 1970). Accord- ing to him, in a specific period of time the content and essence of science is defined by a paradigm as a set of categorical principles recognized by a group of scientists that during a certain historical period constitute the particularity of the science’s subject matter. However, within some time the scientists begin to face an increase of anomalies that cannot be explained by the existing theoreti-

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Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202 cal and methodological tools which consequently causes the crisis manifesting itself in gaps of knowledge and ending in a scientific revolution – a transition to a new paradigm takes place. The passage to the non-linear development of scientific knowledge via paradigms evoked additional constant criticism of existing knowledge and the acceleration of movement towards non-linear knowledge. One more step towards the non-linear development of knowledge is connected with the formation of reflexive social sciences that meant a more valid understanding of the becoming complex realities taking into consideration both the reflexivity of objective structures and human agency. It gave a start to investigate improvisations and game strategies that are typical of non-linear knowledge. According to P. Bourdieu, scientists begin to play certain strategies in the academic field struggle over the truth seeking to achieve victories in the game that influences the knowledge and produces competing social conditions for better scientific results (Bourdieu, 1984). To succeed, one must non-linearly innovate in working out new empirical, theoretical and methodological tools. No wonder, the reflexive sociology enhanced a higher integrated synthesis of empiricism and theory (Bourdieu and Wacquant, 1992). Bourdieu argues that agency produces both intentional and unintended consequences that become a factor of unsynchronised emergence. If indigenous or traditional knowledge results on the whole in intentional consequences and the establishment of synchronized structures and functions, the modern knowledge due to its reflexive nature produces uncertainties and thus side-effects that are usually non-linear in character. Another representative of the reflexive social science is A. Giddens who describes the non- linear changes in knowledge in the contest of ‘institutionalized reflexivity’ and ‘manufactured uncertainties. According to him, social actions become knowledge-dependent both on the previous social practices and scientific and expert recomendations. As a result, on the one hand, people are liberated from structures, but on the other – they meet more complex emergent uncertainties. He puts it: “What I call ‘manufactured uncertainties’ is bound up more with the advance of knowledge than with its limitations” (Giddens and Pierson, 1998, p. 105). So, the ‘manufactured uncertainties’ force scientists to rely on instruments based on principles of non-linear knowledge. Great advances in non-linear knowledge are connected with ‘turns’ in sciences. British sociologist J. Urry was one of the first who laid the foundations of non-linear knowledge within ‘complexity and resource turns’. According to him, great changes are taking place in the material world, especially the climates are actually changing. Heavy environmental pressures appear due to innovative technologies that presuppose the production and transportation of much carbon energy. This manifests that the society and the material world are utterly intertwined constituting a unique socio-environmental reality with new risks and vulnerabilities that have never existed before – they are the product of global carbon networks that affect the social, material world, including climate change, and humanitarian spheres of human activity. The solutions to these challenges cannot be developed within a single science. This encourages Urry for a ‘complexity turn’ based on a new synthesis of scientific knowledge: “I embed society, and hence sociology, as a subject within the analyses of climate change, and more generally within a world of objects, technologies, machines and environments. A strong claim is made here that the social and the physical/material worlds are utterly intertwined and the dichotomy between the two is an ideological construct to be overcome” (Urry, 2011, p. 8). He also argues for a ‘resource turn’ presupposing that “societies should be examined through the patterns, scale and character of their resource-dependence and resource-consequences” (Ibid., p. 16). These turns imply that in order to develop a valid non-linear knowledge under the conditions of a complex socio-natural reality it is necessary to take into account the potential of even ‘insignificant factors’ – they could be studied on the basis of new synthesis of sciences. The latest contributions to the non-linear knowledge have been made by U. Beck who puts forward ‘the theory of metamorphosis of the world’. According to him, modern turbulences cannot be conceptualized in terms of change which implies that some phenomena change but others re-

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Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202 main the same. Metamorphosis presupposes non-linear transformation in which “old certainties of modern society are falling away and something quite new is emerging”. The very theory of metamorphosis is non-leaner in character even if compared with his own previous theorizing as it “goes beyond theory of world risk society: it is not about the negative side effects of goods but about the positive side effects of bads” (Beck, 2016, pp. 34). I think the process of ‘metamorphization’ has already began, but it should not be overestimated: there might be different types of metamorpho- ses in the form of as negative side effects of goods as well as positive side effects of bads – their codependence may be a special theme for further investigations. In any case non-linear realities have come into our life and, correspondingly, non-linear knowledge is demanded in order to produce people’s power to adequately act. Thus, one can see a dramatic shift in scientists’ thinking that focuses on acceleration of the production of innovative knowledge and corresponding innovations: now they have not to clarify or improve the existing scientific tools, but from the standpoint of innovative theorizing to constantly ‘rediscover’ as social and environmental realities as well as ways of their transformation. The ongoing need to interpret daily the appearing anomalies is caused by ‘arrow of time’ effects in societies and nature that are global occurrences.

2. COMPLEX RISKS: DEMAND FOR VALUE ORIENTED NON-LINEAR KNOWLEDGE In reflexive modernity the development of knowledge is accompanied by the production of risks. U. Beck (Beck, 1992, p. 21) defines risk as a systematic way of dealing with hazards and insecurities induced and introduced by modernisation itself. In its turn modernization is linked with exclusive use of technical innovations that are based on permanent renewing of knowledge. So, “knowledge implies the risk of change. It confronts people without concern for their wants or what they believe are their needs. It throws the established intellectual and social world into turmoil” (Crozier, 1982, p. 126). There appears a tendency: the passage from linear to non-linear knowledge produces more complex risks that are staged in character. “’Staging’ here, – U. Beck notes, – is not intended in the colloquial sense of the deliberate falsification of reality by exaggerating ‘unreal’ risks”, that leads to non-linearity between anticipation and reality: “It does not matter whether we live in a world that is ‘objectively’ more secure than any that has gone before – the staged anticipation of disasters and catastrophes obliges us to take preventive action” (Beck, 2010, pp. 10-11). Mass- media, mainly expressing everyday knowledge, dramatizes events and adds performances facilitat- ing the staging of risks. J. C. Alexander (2017, p. 7), the author of the theory of performance, notes: “Internet technologies are a means of symbolic production, devices that allow for rapid circulation of performance and drama”. That means that the risk script and its performative staging are absorbed too deeply adding to the complexity of risks. An important component of the non-linear knowledge is scientific non-knowledge. Speaking about the catastrophe in Chernobyl U. Beck (2010, p. 116) argues: “The nuclear explosion was accompanied by an explosion of non-knowledge… What used to count as knowing is becoming non-knowing, and non-knowing is acquiring the status of knowledge”. Similar ‘explosions’ occur more or less regularly in all sciences – yesterday's ‘universal’ knowledge in the form of a ‘true’ paradigm nowadays is ‘ageing’ and due to added innovations produces scientific non-knowledge. Here the non-knowledge is not ignorance but a kind of higher knowledge presupposing the existence of hypothetical risks. In real life we have a paradoxical combination of various kinds of knowledge and non-knowledge making the dispersion of ‘old’ and modern risks. These new knowledge phenomena were a significant factor of the birth of complex risks that are different from ‘old’ ones. U. Beck (2010, p. 52) states that the new types of risks that are promoting the global anticipation of global disasters and catastrophes exhibit three characteristic 194

Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202 features:  “delocalization: their causes and consequences are not limited to one geographical location or space”;  “incalculability: their consequences are in principle incalculable; at bottom they involve ‘hypothetical’ risks based on scientifically generated non-knowing and normative dissent”;  “non-compensatability: …the logic of compensation is breaking down and is being replaced by the principle of precaution through prevention”.

 Nevertheless, Beck’s (1998) approach fails to take into special consideration the complexities of dehumanized effects of risks. Practically all risks are ambivalent and manifest both as positive as well as negative forces. A. Giddens argues that active risk-taking is a core force in the creation of innovative society, renewal of social life and democracy. Wealth and achievements are eventually due to the people’s choice of knowledge alternatives and values. Thus, professional risks are usually portrayed as heroic and socially important. But some risk-choices are made within the values of well-being that increases growth and consumption by all means. As growth is a “function of inequality” (Baudrillard, 2017, p. 73) there appeared complex risks of new segregation. In any case risks are determined by knowledge within dominant values. The risks of modern societies have come into conflict with the orientations of people towards industrial socio-cultural values and the logic of formal rationality, according to which the pursuit of welfare is rational. However, a further increase in growth of the production and consumption of goods will inevitably lead to even more complex manufactured risks that might undermine the bonds of human relations. The monitoring of these risks implies that linear knowledge with a pragmatic goal to create wealth and comfort should be replaced by value oriented non-linear knowledge that includes in itself not only the achievements of natural, technical and social sciences but also knowledge of the humanities. This synthesis might give not only more valid knowledge of complex risks of dehumanization but hopes for a better risk-management.  There have already appeared shifts in our thinking about how people might resist the inhu- mane aspects in modern knowledge. Z. Bauman (2011, pp. 18-19) pays attention to the fact that some human actions lack their proper humane characteristics. In the essay As the birds do he metaphorically compares human actions and reflexivity with the birds’ one arguing that ‘Twitter’ is what birds produce when they tweet. Tweeting plays two roles in the life of birds: it allows them to keep in touch with each others, and to prevent other birds from transgressing on the territory they’ve made their own. Human Twitter has the same functions: “Once face-to- face contact is replaced by a screen-to-screen variety, – he writes, – it is the surfaces that come in touch. Courtesy of Twitter, ‘surfing’, the preferred means of locomotion in our hurried life of instantly born and instantly vanishing opportunities, has finally caught up with interhuman communication. What has suffered as a result is the intimacy, the depth and the durabil- ity of human intercourse and human bonds” (Ibid.). The lack of humane phenomena is also expressed in manufactured risks of ‘moral insensitivity’, ‘heartless kind of behaviour’, ‘simulating friendship’.  Z. Bauman and L. Donskis (2013, pp. 13-14) state that “the function of pain to be an alert, a warning, and a prophylactic tends to be all but forgotten, however, when the notion of ‘insensitivity’ is transferred from organic and bodily phenomena to the universe of interhuman relation, and so attached to the qualifier ‘moral’. The non-perception of early signals that something threatens to be or is already wrong with human togetherness and the viability of human community, and that if nothing is done things will get still worse, means the danger is lost from sight or played down for long enough to disable human interactions as potential factors of communal self-defence…”. These complex risks of dehumanization have appeared not by chance – they are the product of human actions and, in my opinion, they are due to the lacks of the proper role of the humanities in the mainstream knowledge.

I’ll also mark two risks of dehumanization evoked by ‘arrow of time’ effects: 195

Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202  the speed of acquiring formal and practical knowledge performing the function of change to create wealth is much greater than the development of humanistic orientation in our knowledge. Without the proper human ethics the knowledge as a model for better reality is very often opposed to major principles of the civil society and even destroys them (the Occupy protests, the social uprising in the Arab Spring, Black Lives Matter, etc. were determined by knowledge linked with staging performances about ‘happy life’ and ‘real justice’). As one can see choreographed agency may be based on the rapid speed of acquiring dispersed knowledge of unsynchronized individual ‘liberation’ without human ethics;  in the knowledge of individual actors there is still little understanding that we are dealing with the management of the non-linear developing social-natural systems that function on different principles. It means that “the complex systems world is a world of avalanches, of founder effects, self-restoring patterns, apparently stable regimes that suddenly collapse, punctuated equilibria, ‘butterfly effects’ and thresholds as systems tip from one state to another” (Kravchenko and Perova, 2017, pp. 449-459).

Order and chaos, he notes, are in a certain state of balance “where the components are neither fully locked into place but yet do not dissolve into anarchy. They are ‘on the edge of chaos’” (Urry, 2003, pp. 237-238). This is a fundamentally new view on social order where the criteria of true and false, norm and anomie are subjected to diffusion. So, humane approaches are needed while dealing with complex risks. One should consider that the situation has radically changed: under the conditions of the complexity risk-taking to restore the social order almost always generate further unsynchronised emergences and unanticipated consequences pushing as the society as well as the material world away from the equilibrium. The humanity might have come to the threshold of actual human capacity of reflection transient events that is to act adequately, rationally, and most importantly – to make decisions based on humane purposes.

3. VULNERABILITIES OF COMPLEX SYSTEMS AS CHALLENGES TO HUMANITY Until recently scientists tended to find causes of disasters among external forces such as natural processes or human activities. Now disasters and catastrophes might be also caused by internal factors generated by ‘normal’ interactions of people with complex social, technical and environmental systems. In such complex systems internal factors may start to function out of the men’s control producing vulnerabilities. In the most general sense, modern vulnerability means an increasing structural dysfunction of a social complex system (socio-cultural order of a city) and/or a techno-natural system (nuclear power plant, water and food production) that under the influence of the external agency of people including high-scale knowledge and technology may cause internal forces of a system to express its own ‘will’ as a sort of reflexivity that is destructive for the society. In real life, this phenomenon manifests itself in a potential threat of a catastrophe and social fears of people about emerging uncertainties in systems (Kravchenko, 2017, pp. 3-13). It should be stressed that these complex systems couldn’t have been born before rather a high level of non- linear scientific knowledge came into life (Georgesku and Spatariu, 2017). This knowledge carries within itself a kind of pressure towards practical materialization in advanced technologies, enterprises, infrastructures, thus forming complex systems mainly as socio-technical-environmental hybrids that change the character of modern societies. “Scientific knowledge has been accumulat- ing for a very long period, and had a consistent if frequently unperceived effect in shaping the fundamental character of human societies” (Fukuyama, 1992, p. 72). Societies with these complex systems become turbulent and vulnerable that presupposes the catastrophe’s uncertainty in time and space. Whether it happens or not depends on lots of factors including the potential of the system's ability/inability to withstand the external and internal burdens of emergent nature. There appeared “new” catastrophes that are facilitated by non-linear knowledge accumulation, the production of non-knowledge and knowledge explosions. They manifest themselves in permanent staging of disasters and ‘liquid fears’. The very discourse of vulnerability means a great challenge 196

Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202 to the humankind’s security.

American sociologist Ch. Perrow (1999, p. 5) metaphorically named modern vulnerabilities ‘normal accidents’. By this concept he means accidents and disasters caused not by false management of personnel, but by everyday functioning of complex technical systems that periodically fail ‘normally’. That is, as he states, given the system complex characteristics, the “multiple and unexpected failures are inevitable” even with the best management. In the new book The Next Catastrophe: Reducing our Vulnerabilities to Natural, Industrial, and Terrorist Disasters he demonstrates that vulnerabilities become increasingly complex in their nature: “concentrations of hazardous materials, populations, and economic power in our critical infrastructure make us more vulnerable to natural disasters, industrial/technological disasters, and terrorist attacts” (Perrow, 2011, p. vii). At the same time Perrow (Ibid., p. xxii) stresses that a potential catastrophe is not caused by human errors but by nature of complex systems – to minimize their risks people shoud avoid construction such systems. “Normal Accident Theory (NAT) argued that if we had systems with catastrophic potential that might fail because of their complexity and tight coupling, even if everyone played as safe as humanly possible, these systems should have been abandoned. Catastrophes would be rare, but if inevitable, we should not run the risk”. The scientist calls for stopping the population growth in ecologically and technologically dangerous areas where industrial extraction of natural resources is carried out simultaneously with the development of agriculture, fisheries, creating social and cultural infrastructure. To prevent vulnerabilities from terrorist threats he advocates closing “all the holes in our open society” (Ibid., p. 127) However, he is sure that it is impossible to totally eliminate ‘normal accidents’ which are attributes of complex socio-technical systems: “We are not safe. Nor can we ever be fully safe, for nature, organizations, and terrorists promise that we will have disasters evermore. Let us minimize their consequences by minimizing the size of our vulnerable targets” (Ibid., p. 325). In my oppinion, complex systems in the forms they exist nowadays produce great challenges to humanity. But we find the idea utopian to abandon them as such or even limit their functions: the process of the self-realization of knowledge makes it impossible. Risks of ‘normal accidents’ should be minimized not by the way of abandonment of complex systems but rather through their comprehensive humanization that would let them aquire new forms – humane-based development on the basis of the integrity of social, natural, technical and the humanities. To this synthesis traditional and even religious knowledge should be added as it preserves routine social practices and conservative ways of thinking that might balance or prevent pragmatic risky innovations (the production of knowledge in human genetics, new means of conducting wars that go out of the men’s control). I also believe that main challenges of ‘normal accidents’ lie not so in potentially possible catastrophes, but in the domination of formal rationality and pragmatic values in modern knowledge. There might be complex systems that are based on a kind of high-level knowledge oriented on substantial rationality and existential people’s needs. Essential contribution to understanding of vulnerabilities of the climate system as a complex socio-environmental hybrid was made by A. Giddens. According to him, climate change is the result of pragmatic, mercantile agencies of people who exploited nature within the knowledge of Man as ‘the measure of all things’ for a considerable period of time without taking into account environmental sensitivity. As a result man-made challenges to humanity were produced. He interprets them in the context of the effect, which he called honoring his name, ‘Giddens’s paradox’. The essence of it is that the traditional division of the natural and social environment is now losing its sense. In traditional and even industrial societies challenges in the form of different disasters used to come from nature. Now they come mainly from the people themselves. Meanwhile, linear thinking and knowledge of climate continue to dominate. In The Policy of Climate Change he argues that it leads to a false, distorted perception of contemporary activities of people: they “find it hard to give the same level of reality to the future as they do to the present” (Giddens, 2009, p. 2). It concerns as everyday problems as well as global environmental problems: the representatives of busi-

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Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202 ness and political elite are well aware of the postponed negative side-effects of their policy regarding nature, but under the influence of pragmatic values in modern knowledge they do not take necessary efforts to transform the situation for the better – ‘organized irresponcibility’ enables climatic changes that are possibly irreversible. As one can see linear knowledge keeps producing challenges to humanity. The vulnerabilities of the climate system were also analyzed by J. Urry (2011, p. 23) who proposed a valid interpretation of ‘global warming’. In his opinion, ‘warming’ is “a simplifying term since what may happen in different parts of the world may be very different, with possibly significant cooling occurring in some places. Indeed the problem of the term warming stems from the sheer difficulty in predicting long-term future climates”. It seems that this is actually a non-linear interpretation of the vulnerabilities of the climate system, the one which emphasizes turbulence, unpredictability of climate change, as well as possible unintended consequences arising from the self-realization of knowledge and the innovative activity of the humanity. The sociologist also notes the interdependence of climate change and the destiny of civilizations, advocating the study of the complex causes of the emerging vulnerabilities in the climate system within “a framework which emphasizes non-leanerity, thresholds and abrupt and sudden change” pointing to the limits of the linear knowledge on climate: “it is noteworthy that historical analysis and science did not consider that climate played much of a role in the rise and fall of civilizations. Climate was typically viewed as immutable, not changing much and no being of great consequence for the ways in which special societies develop and change” (Ibid., pp. 21-22, 24). Really, non-linear and humanistic approaches to the vulnerabilities of the climate system are demanded. New vulnerabilities have also emerged in the food system which is also a complex socio- environmental hybrid. The functionality of global-network agribusiness is much predisposed to ‘normal accidents’ in the form of an ever increasing production of genetically modified products and extension of ‘dead land and water’ (Sassen, 2014, pp. 149–210; Galnaitytė et al., 2017; Czyzewski and Smedzik-Ambrozy, 2015). I believe the minimization of these vulnerabilities can be achieved by the rejection of the obsolete dogma – the more food, the better. In fact people need qualitative nutrition that would create conditions for their physical and spiritual health, empower them with adequate energy to prevent culture-bound syndromes – anorexia nervosa, addiction on fat and sweat food, etc. Z. Bauman (2011a, pp. 65-66, 68) states that there appeared new vulnerabilities in the form of ‘collateral damage’ of man’s activity. This term was used in the vocabulary of military expedi- tionary forces but under the consequences of ‘liquid modernity’ it denotes unintended, unplanned effects of human actions in general. Thus, while producing knowledge and wealth people didn’t take in account the possibility of existential insecurity that accompanies the life in the ‘liquid modern’ world. This type of insecurity is determined by the very life of a big city and expressed in mix- ophilia (attractions it can support and offer) and mixophobia (fears that force people into ‘gated communities’). The side effects of these wall-off developments are vulnerabilities to existential needs of communicating with one another: people pay a lot of money “in order to liberate themselves from unwanted company to be left alone. Inside the walls and the gates live loners” (Ibid.). The need for security can become addictive: “Once you start drawing and fortifying borders, there is no stopping”. So, one can see ‘collateral damage’ in the self-realization of knowledge – unpre- dictable consequences appear. I argue that in order to overcome these vulnerabilities people need decisions and actions with humane character. There are at least three basic grounds for them:  up to now the institutional regulation of scientific knowledge production is supported by many societies allowing not overstepping the edge of chaos. Institutional control should be more directed toward ‘manufactured uncertainties’ and existential people’s needs;  the ‘arrow of time’ effects bring a fundamentally new interdependence of scientists manifested in a variety of communities whose content may be expressed as in strong ties suggesting their 198

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intensive interactions as well as in weak ties that are of particular importance in networks of ‘invisible colleges’ (Crane, 1969). The total effectiveness of them depends not only on increasing the number of arithmetic nodes included in it, but on increasing the social space in which people are involved. This confirms the truth that in complex systems weak ties can have a great impact on the very humanization of scientific and technological innovations as they presuppose particular network private insurance;  the subject matter of many sciences is being changed in humane direction: more scholars pass over to the problems of new vulnerabilities treating them as a matter of life, as a need to move in the direction to existential security of humanity.

4. HOPES FOR BETTER FUTURES: SEARCHING FOR KNOWLEDGE BASED ON SOLIDARITY VALUES Nowadays scientists rediscover futures on the basis of non-linear knowledge taking into account the demand for its humanization. The general opinion is that there are no simple linear ways to the ‘universal common’ future of mankind. If not so long ago the future was seen as an optimistic ‘progress’ of all mankind, the theme of the Third ISA Forum (Vienna, 2016) was: “The Futures (plural!) We Want: Global Sociology and the Struggles for a Better World”. Much attention was given to overcoming of ‘global humanitarian crisis’ while working out the essence of the future – could it be ‘human’, ‘posthuman’ or trunshuman’ (Fuller, 2016, p. 85). I believe that the most important goal is to elaborate valid and humane ways to possible futures, so that their models could be both desired and realized. The 13th Conference of the European Sociological Association (Ath- ens, 2017) stressed the necessity of new solidarities in Europe that demands the overcoming the limits of the existing knowledge: “The project of questioning reality began in Greece, and sociology from the start shared in this task of highlighting dominant forms of understanding in societies (and science) that limit knowledge, by working towards more fitting kinds of understanding” (Welz, 2017, p. 9). To fulfill this task we need to pass to a humane vector in the production of knowledge based on culture and solidarity values which are already being born producing hopes for better futures. M. Castells (2015, pp. 2-3, 14) has made great contributions to understanding modern movements that were provoked by “the cynicism and arrogance of those in power, be it financial, political or cultural, that brought together those who turned fear into outrage. And outrage into hope for a better humanity…Hope projects behavior into the future. Since a distinctive feature of the human mind is the ability to imagine the future, hope is a fundamental ingredient in supporting goal-seeking action”. The results of the study undertaken by a collaborative international research network on alternative economic practices and their cultural foundations that investigated between 2011-2015 allowed to come to the conclusion that there began a social change in the direction of another economy that embodied alternative values to formal rationalism, pragmatism and mercantilism: “the value of life over the value of money; the effectiveness of cooperation over cutthroat competition; the social responsibility of corporations and responsible regulation by governments over the short-term financial strategies... we saw the blossoming of multiple experiences of innovation in organizing work and life: cooperatives, barter networks, ethical banking, community currencies, time sharing banks, alternative means of payments, etc” (Castells, 2017, p. 1). At the same time many people fear the alarm discourse of knowledge about the future and they turned toward the past. According to Z. Bauman (2017, pp. 14, 16, 25, 44) there began a ‘global epidemic of nostalgia’. In Retrotopia he argues that its major factors lie in the optionality of human choices among which are as follows: the ‘civilizing process’ was designed as “a reform of human manners, not human capacities, predispositions and impulses”; “in the course of civilizing process, acts of human violence were shifted out of sight, not out of human nature”; “we seem to be settling for a prospect of a continuous and never conclusive war-to-exhaustion between ‘good

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Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202 violence’ and ‘bad violence’”; “we live in a world in which pragmatism is the topmost rationality”; “our world – the world of weakening human bonds”. As the scientist considers, in order to overcome the global epidemic of nostalgia people should reject the dogma ‘there is no alternative’ and realize “chances of success and defeat”: “More than at any other time, we – human inhabitants on Earth – are in the either/or situation: we face joining either hands, or common graves” (Ibid., p. 167). The humanity has the hope to make a choice of a humane model for future. U. Beck (2016, p. 26) applying to his theory of ‘metamorphosis of the world’ proposes the idea of ‘emancipating catastrophism’ which takes into account the ‘positive side effects of global risks’. From this point of view he criticizes the pessimism of representatives of the linear approach to modern disasters. “We all know that the caterpillar will be metamorphosed into a butterfly. But does the caterpillar know that? That is the question we must put to the preachers of catastrophe. They are like caterpillars, cocooned in the worldview of their caterpillar existence, oblivious to their impending metamorphosis. They are incapable of distinguishing between decay and becoming something different. They see the destruction of the world and their values, whereas it is not the world that is perishing, but their image of the world. The world is not perishing, as the preachers of catastrophe believe, and the rescue of the world, as invoked by the optimistic advocates of progress, is not imminent either. Rather, the world is undergoing a surprising, but understandable, metamorphosis through the transformation of the reference horizon and the coordinates of the action” (Ibid.). I argue for a humanistic turn in all sciences that proceeds from the premise that the synthesis of natural, social and humanitarian knowledge would result in a humanistic paradigm of complexity presenting a value-added non-linear knowledge that is needed to analyze the complex social, technical, and natural realities. This implies a newer type of scientific knowledge, whereby societies and all matter should be analyzed through the patterns and character of their complexity- dependence and human agency-consequences (Kravchenko, 2013, pp. 3-12; Chuprov, 2016). As it is known, the representatives of the Enlightenment (Sh. Montesquieu, K. Helvetius, and others), in contrast to religious humanism founded secular humanism asserting the self-worth of worldly existence, the dominant role of reason and rationality as the main values. They advocated the need to overcome all forms of unfreedom, considering a person to be the yardstick of all things whose omnipotence of intelligence can produce only goodness and morality. In fact, pragmatic principles of humanism of the Enlightenment latently prepared the grounds for anthropocentrism producing nowadays complex risks and vulnerabilities. The anthropocene as the current period of geological history is characterized by “soaring carbon dioxide levels, a quantum step upward in erosion, wide-spread species extinction, ecosystem disturbance and acidification of the oceans” (Urry, 2016, p. 45). These risks and vulnerabilities could be overcome or minimized only by passing to a new trend of developing knowledge and its self-realization. At the most general level, the humanistic turn deals with the ‘arrow of time’ effects of socio-cultural and environmental dynamics, synergetically takes into consideration risks and vulnerabilities of society and environment, searching for new forms of humanism adequate to the becoming complex systems of the contemporary world that are economic, physical, technical, political and social (Vancea et al., 2017). At the same time the humanistic turn, rejecting the ideas and practices of anthropocentrism, aims to study the problems of humanization of scientific and technological innovations to maintain the balance between them and key environmental processes, so that the advance of non-linear knowledge would produce as much as possible controlled ‘manufactured uncertainties’. Through the humane practices, the development of scientific ethos it is possible to establish the principles of substantial rationality in modern non-linear knowledge that would make a realistic basis of hopes for futures people want.

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Sergey A. Kravchenko / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 191-202 Kravchenko, S. A. (2017), “The coexistence of riskophobia and riskophilia - An expression of "normal anomie”, Sotsiologicheskie Issledovaniya, No. 2, pp. 3-13 (in Russian). Kravchenko, S. A., Perova, A. E. (2017), “New catastrophism and the future: The demand for non-linear knowledge”, RUDN Journal of Sociology, Vol. 17, No. 4, pp. 449-459. Kuhn, T. (1970), The Structure of Scientific Revolutions, University of Chicago Press, Chicago. Merton, R. (1996), On Social Structure and Science, University of Chicago Press, Chicago. Merton, R. (1993), On the Shoulders of Giants, University of Chicago Press, Chicago and London. Merton, R. (1976), Sociological Ambivalence and Other Essays, Free Press, New York. Merton, R. (1936), “The Unanticipated Consequences of Purposive Action”, American Sociological Review, Vol. 1, No. 6, pp. 894-904. Perrow, Ch. (2011), The Next Catastrophe: Reducing our Vulnerabilities to Natural, Industrial, and Terrorist Disasters, Princeton University Press, Princeton. Perrow, Ch. (1999), Normal Accidents: Living with High-Risk Technologies, Princeton Univercity Press, Princeton. Prigogine, I. (1997), The End of Certainty, Free Press, New York. Sassen S. (2014), Expulsions: Brutality and Complexity in the Global Economy, Belknap Press, Harvard University Press, Cambridge. The Futures We Want: Global Sociology and the Struggles for a Better World (2016), 3rd Forum of Sociology, 10-14 July 2016, Vienna. Urry, J. (2016), What is the Future?, Polity Press, Cambridge. Urry, J. (2011), Climate Change and Society, Polity Press, Cambridge. Urry, J. (2003), Global Complexity, Polity Press, Cambridge. Vancea, D. P. C., Aivaz, K. A., Duhnea, Ch. (2017),” Political Uncertainty and Volatility on the Financial Markets- the Case of Romania”, Transformations in Business & Economics, Vol. 16, No. 2A (41A), pp. 457-477. Welz, F. (2017), The President’s Welcome, Programme book, 13th Conference of the European Sociological Association, (Un)Making Europe: Capitalism, Solidarities, Subjectivities, Athens, 29.08 – 01.09.

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Montenegrin Journal of Economics

Vol. 14, No. 4 (2018), 203-214 ‘

Economic Efficiency of Cultural Institutions: The Case of Museums in Slovakia

MIRIAM SEBOVA1

1 Associate Professor, Technical University of Košice, Faculty of Economics, Department of Regional Science and Management, Slovakia E-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 22, 2018 The efficiency analysis is widely applied in public institutions, but Revised from September 21, 2018 they are still rarely used in case of cultural institutions. The paper Accepted November 11, 2018 deals with the technical efficiency of museums that are character- Available online December 15, 2018 ized by diverse activities and multiple objectives. This multidimen- sionality complicates the evaluation of efficiency by using traditional economic methods. The paper evaluates the technical efficiency of JEL classification: museums in Slovakia using the method Data Envelopment Analysis. Z11. The objective of the paper was to identify key indicators suitable for analyzing efficiency based on available cultural statistics. The DOI: 10.14254/1800-5845/2018.14-4.14 appropriate indicators were selected using multivariate statistical techniques - Principal Component Analysis. The findings of the anal- Keywords: ysis point to significant differences in the efficiency of slovak museums and enable defining the characteristics of the most effective Economics of Culture, museums. As this is the first analysis of the efficiency of Slovak museums, museums, the findings can be helpful in practice for improving the efficiency, statistical surveys about museums and their efficiency in Slovakia. data Envelopment Analysis, Slovakia

INTRODUCTION Economists started to examine the efficiency of cultural institutions relatively late. Analysing of culture and cultural institutions from a financial perspective have until recently been uncommon. The economic studies in this area have blossomed since Baumol and Bowen (1966) published their seminal work about economic dilemmas in the American cultural sector 60’s of the 20th century. A possible reason for such lateness includes the uniqueness of cultural goods, which makes it difficult to evaluate the results of the performance of cultural institutions. Despite the specific character of cultural goods, evaluating the efficiency of theatres, museums, libraries, and galleries is equally relevant as for other institutions of the public sector such as in the area of education or 203

Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214 health service. Providing cultural goods and services requires public resources which are scarce, and their allocation is subject to public choice. Assessment of the optimal allocation of public resources to public institutions requires as much information on the efficiency of their functioning as possible. Another fact is that, although services provided by cultural institutions are intangible and non-market, it is possible to relatively objectively determine the efficiency of their production and the organisation’s management. (Herrero-Prieto, 2013) Methodologies used to evaluate the performance of cultural organisations are essential equally in terms of „fundraising“. The survival of cultural institutions often depends on the availability of not only public but also private funding. The more efficient an organisation appears to be, the more likely it is to qualify for the public as well as private funding and grants (Basso and Funari, 2004). At present, an ever-growing number of studies analyze the efficiency of, for example, and Wet- zel, 2010); libraries (Miidla and Kikas, 2009); (Hemmeter, 2006) and museums (Carvalho, et al., 2014). The boom of these academic studies has been made possible by improvements in data availability, which allowed the more detailed quantitative analyses. Slovakia, as other countries EU, higher attention focuses on the cultural sector. The effects of substantial investment in cultural infrastructure and cultural activities mainly driven by European structural funds need to be regular evaluate. A Eurobarometer survey from 2013 found that the the number of visitors to museums and galleries in Slovakia declined in comparison with the year 2007. Whereas in 2007 as many as 40% of people visited a museum or a gallery at least once a year, in 2013 this number was 9% lower. An average decline in the number of visitors to museums and galleries in the EU for the same period of time was only 4% (TNS, 2013). The countries that manage to attract a higher number of visitors to their museums include e.g. Sweden where as many as 76% of inhabitants visit a museum or a gallery at least once a year. The efficiency of museums is interlinked with the management skills in museum management. We do not know any study in Slovakia that would analyse the efficiency of cultural institutions. It might be due to the complicated acquisition of data and their complex processing. Data about cultural institutions is collected in state statistical survey yearly. The collected data are evaluated only summarily by using descriptive statistics. The available data provide room for more sophisticated analyses that will enable deeper insight into the economy and management of cultural institutions. This paper aims to analyse the technical efficiency of Slovak museums. The latest available data come from 2015. The study was conducted in cooperation with the statistical unit at the Min- istry of Culture which grant us permission for access to individual data from the statistical surveys. Our objective was to perform a quantitative analysis of the efficiency of museums and identify the factors that influence their performance. As these are institutions funded from public resources, it is essential to deal with the optimisation of their performance. In this study, we address the following research questions: What indicators on the side of inputs and outputs are suitable for evaluating the technical efficiency of museums? How many museums in Slovakia are efficient and how many are non-efficient? Why are there differences in the performance of museums? The methodological approach to our research involves two techniques. We used multivariate statistical analysis to construct the evaluation indicator system and DEA to calculate the efficiency scores. The structure of the paper follows this order. The first part of the paper gives a review of the literature and briefly introduces the method DEA. In the next part were identified input and output indicators using Principal Component Method. Secondly was analysed the technical efficiency using two basic DEA models. The last section offers some concluding remarks about results of the provided empirical investigation of Slovak museums which may be of interest in practice. . 204

Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214 1. THEORETICAL FRAMEWORK The museum provides a variety of services to the society. The Act of the National Council of the Slovak Republic No. 115/1998 on museums and galleries defines the objectives and functions of museums, particularly as regards the specialist care of objects e. g. the recording and documenting of collections, classification and presentation. The traditional functions of museums conclude collecting of cultural objects, preservation, presentation, research and education. These functions have evolved with time and new functions emerge continually. With the increasing role of museums in society, the economic impact of their activity becomes more visible. Museums provide employment, attract tourist, generate economic activity, contribute to urban regeneration and favour the growth of creative industries. (Carvalho et al., 2014) Some of the so-called big star museums have a substantial economic impact on a town or region where they are located. An iconic example is The Guggenheim Museum in the Spanish town of Bilbao with 380 thousand inhabitants. The localisation of the museum, which visits more than 1 million tour- ists annually, kick-started the development of tourism and revived the economy in the formerly declining post-industrial town (Plaza, 2000). Academics discuss a diversity of topics related to the economy of museums. The papers range from managerial studies aimed at the assessment of different types of management in public, private or non-profit museums (McCall and Gray, 2013) to studies concerning marketing strategies of museums (vom Lehm & Heath, 2016). Other studies examine the potential of museums as drivers of heritage tourism (Smith, 2018) commonly interlinked with the research about benefits for a local economy in term of increased income and employment (Bowitz & Ibenholt, 2009). Other studies deal with the museum´s value using different methods of measurement of economic and social values of non-market goods. (Whelan, 2015). Studies considering museum efficiency may be divided into two groups. The first group focuses on measuring a museum´s performance by drawing up a set of performance indicators, the second group of studies examine the efficiency of a set of museums using so-called frontier techniques (Barrio et al., 2009) The earliest study aimed at measuring the economic efficiency of museums comes fromcomes from the USA (Jackson, 1988) and it introduced a simple production function for a selected group of North-American museums. Later, American authors developed a simple model of measuring the technical efficiency of museums and identified a basic set of indicators on the side of inputs and outputs. (Basso andFunari, 2004) The public sector was interested in measuring the efficiency of museums and there were proposed a set of indicators for museums funded by local governments in Great Britain in 1991. Collection and interpretation of the proposed indicators proved to be complicated and poorly applied in practice. As a result, the British Department for Digital, Culture, Media & Sport (DCMS) developed a new framework for evaluating museums. The new study focused on the efficiency and effectiveness and identified as many as 365 indicators to measure the efficiency of various types of activities of museums and galleries. (Mairessse and Vanden Eeckaut, 2002) Further analyses performed in different countries of Western Europe started to use more sophisticated methods: analysis of British museums (Bishop and Brand, 2003 ), analysis of the efficiency of French museums, Belgian museums (Mairessse and Vanden Eeckaut, 2002), Finnish museums, Italian museums (Basso and Funari, 2004). (Camarero, et al., 2011) published a study of the efficiency of 491 museums in Great Britain, France, Italy and Spain. The latest studies are mainly from southern countries – on the efficiency of Portuguese museums (Carvalho, et al.,

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Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214 2014), Spanish museums (Barrio, et al., 2009) (Herrero-Prieto, 2013) and Iranian studies (Taheri and Ansari, 2013). From the literature review, it is obvious that this research area is still emerging in Central and Eastern Europe where studies have begun to appear only recently. One of them is a study about the efficiency of cultural sites from the Czech Republic written by (Placek et al., 2016). Herrero-Prieto (2013) summarises the difficulties of measuring the performance of museums as follows. Firstly the museums deal with a wide range of resources which are complicated to measure due to their qualitative or heterogeneous nature. Secondly, the objective of museums is to provide complex and multiple products, which is not always tangible or commercial. Thirdly the results of the museums as public or non-profit organisations may not be measured in solely financial terms. These obstacles have to be taken into account by choosing the appropriate method to measure the efficiency. As museums have varied objectives which cannot be measured using only financial indicators, their assessment can be best performed using methods that allow comparing various inputs and outputs. These are mainly nonparametric methods such as Data envelopment analysis - DEA, whose variations are most often used for the analysis of the museums‘ efficiency, e.g. (Carvalho et al., 2014; Mairessse and Vanden Eeckaut, 2002; Basso and Funari, 2004; Barrio et al., 2009; Herrero-Prieto, 2013; Taheri and Ansari, 2013). An overview of the applied methods and indicators examined in the studies on museums‘ efficiency was summarized by (Sebova, 2016). DEA was initially developed for evaluating the efficiency of public organisations where using parametric models would not be suitable due to the necessity to set a mathematical ratio between inputs and outputs. The term relative effectiveness refers to the efficiency achieved by the evaluated production unit within a homogenous group of production units which perform the same or very similar activity through applying the defined input and output criteria. (Cooper et al., 2007) Efficiency can be simply defined as a ratio between input and output. More outputs relative to an input unit indicate a higher efficiency. If the production unit achieves the highest possible performance of output unit relative to an input unit, then it obtains the optimal efficiency and it is impossible for it to become more efficient without implementing new technologies or other changes to processes. (Cooper et al., 2011) Thus, DEA analysis enables to assess the technical efficiency of a museum which can be defined as museum’s capacity to maximize outputs for determined inputs or as museum’s capacity to produce the same volume of outputs along with maximizing outputs (Basso and Funari, 2004). DEA can be used for museums if perceived museum as a production unit. DEA method allows to compare the efficiency of museums and identify the most efficient ones (with efficiency equal 1) and inefficient museums (with efficiency less than 1). DEA is used to determine the so-called relative efficiency as it can to identify a museum with the highest performance only in the observed group. DEA compares each museum with all other museums and optimizes the weights of individual indicators towards the highest possible efficiency of a museum using the process of linear programming.

Basic DEA models include the CCR and the BCC model named after their authors. The difference between these models is that the CCR model (Charnes, Cooper and Rhodes) assumes constant returns to scale whereas the BCC model (Banker, Charnes and Cooper) allows for variable returns to scale. (Cooper, et al., 2007) The difference between the CCR and the BCC model is in the adjunction of the convexity condition , which eliminates the restriction from the CCR model that DMU must achieve efficiency to scale. The BCC model divides the efficiency derived from the CCR model into pure technical efficiency and scale efficiency (Cooper et al., 2007). 206

Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214 Both CCR and BCC models can be oriented either to inputs or outputs. Input-oriented models help to identify a minimum input level necessary to reach maximum efficiency for the outputs. Out- put-oriented models identify the maximum output level necessary to reach maximum efficiency for outputs in comparison with other units in the assessed group. Both models not only evaluate the DMU efficiency rate but also calculate how a DMU can improve its performance on the inputs or outputs to be efficient. Both forms of the DEA model (input and output-oriented) can be significant for the assessment of the museums‘ efficiency. The practice of management plays an essential role when deciding on the DEA form. If is not possible to change the volume of inputs within a short period, then it is better to use an output form of the model. DMUs are investigated in order to maximize outputs so that they do not require more inputs. (Basso and Funari, 2004). Output-oriented models are mostly used for the analysis of museums that inform non-efficient ones on the outputs that should be changed in order to be more efficient. For that reason, we also used the output DEA model in our experimental analysis. Generally, the output-oriented BCC- efficiency can be expressed as follows (Cooper et al., 2011)

i=1,2,...,m

i=1,2,...,s

j=1,2,...,n

2. DATA AND METHODOLOGY Our analysis evaluates the efficiency of museums in Slovakia in 2015. The data stem from the national statistics on museums for the year 2015. In 2015, 111 museums in Slovakia provided performance reports. We had to exclude 11 museums from the analysis because of the missing data. Museums are obviously the emblematic representations of cultural heritage in the region or in the city. After public administration reform in 2001 Slovak museums are classified according to the ownership as state-owned museums, local-owned museums, regional-owned museums and private-owned museums. Over 90 % of the objects displayed in the museums and galleries of the Slovak Republic are the property of the State or the regional or local administration. Only a fraction is in possession of private owners (Zorjanova, 2004). There are two models of financing the public museums. If all revenues and expenditures of the museum´s budget are linked to the the budget of subordinary institution (state, municipality or region) it is a budgetary model. If the museum covers its expenses (till 50%) by its own revenues it is called contributory model. The museums differ in their characteristics by specialization, size and locality. In generally most of the studies e.g. (Barrio et al., 2009; Carvalho et al., 2014; Wang et al., 2016) indicate that the choice of variables for the measurement of inputs and outputs is the most demanding task due to the differences in the character of museums. The variables in DEA processing have to reflect the varios features of museums.

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Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214 Table 1. Descriptive analysis of the basic indicators of analysed Slovak museums

Indicator Maximum Minimum Mean Standard deviation Staff 96 0 19,5 20,9 Size (number of objects) 56 1 2 9,4 Size (number of expositions) 17 1 4,8 5,3 Number of exhibitions 63 0 11 10,8 Number of events 573 0 80,4 119,9 Number of visitors (in 2015) 375000 201 40280 62659,2

Source: Own elaboration

In order to make an appropriate choice of indicators, we looked at the indicators used in published studies in this field. We found out that the most common input indicators which describe the size and availability of museums i.e. the number of opening hours of museums and their exhibition areas (m2) are not collected in surveys. For that reason we decided to include in our analysis as many indicators as possible to provide the best description of the variety of a museum’s character and activities that could affect its efficiency. Our analysis was performed in two steps. In the first step we used the principal component analysis (PCA) to choose appropriate indicators for DEA analysis. In a second step we provided efficiency analysing using DEA models.

3. CONSTRUCTING AN INDICATOR SYSTEM DEA results depend decisively on the set of input and output variables that are used in the analysis. From the perspective of using DEA method a large number of input and output indicators lead to an increase in the number of effective DMUs, which reduces the evaluation effectiveness of DEA method (Wang et al., 2016). The rules for the selection of variable in DEA was discussed deeply by (Wagner & Shimshak, 2007). As they stated, any resource used by a DMU should be treated as an input variable. The output variables come from the performance and activity measures that result when a DMU con- verts resources to products or services. There is the rule that the number of indicators should not exceed one third of DMUs. (Wagner and Shimshak, 2007) Our statistical database about museums includes 94 indicators. This list of potential variables for DEA is too large. Therefore we selected 24 indicators with impact on the efficiency of the museum as the “feasible indicator system”. These indicators reflecting the various functions of museums are shown in Table 2. Indicators from the reports were selected in such a way that on the side of inputs they would provide a description of : - museum´s size (number of branches, their own facilities, expositions, etc.), - equipment of museums (collection), - human resources in the museum (structure and salaries of the staff) - the museum’s funding (costs, etc.).

On the side of outputs, indicators describe how a museum fulfils its functions:  presentation (new exhibitions, new collections, maintenance, digitization, and exhibitions),  communication (publishing activities, events).

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Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214 It describes also how many visitors visit it and what revenues it manages to generate (including actual revenues indicator, grants and donations). We included the financial indicators into the analysis because of the absence of important volume indicators (m2, opening hours etc.). Next, we reduced the set of 24 theoretically possible indicators from the list using PCA to 8 cumulative indicators (Table 2) which were used in the second step in DEA analysis. Using PCA in the selection procedure of indicators for DEA in museums was used for example by Barrio et al. (2009) and Wang, et al. (2016). PCA is a procedure traditionally used in the multivariate analysis. The main idea of the technique is to reduce the dimension of a data set which includes a number of mutually interconnected variables while preserving the maximum number of possible variances of initial data. It is achieved by the transformation of the set to new variables called main components which are not intercorrelated and arranged in such a way that the first several components account for most of the variance. (Hendl, 2009). In our case the PCA method brought together initial indicators according to the inner variability of data and the obtained cumulative indicators represented their combination. The method proved to be suitable for our data because the factors obtained well represented the initial ones and could be logically interpreted. These factors were sufficiently intercorrelated with new factors (Table 2) named according to their focus.

Table 2. Construction of indicator system

Interpretation of cummulative indicators Set of theoretically possible indicators based on PCA Number of branches Own facilities Factor of a museum’s size Number of expositions Collection (number) Factor of growth Inputs Capital expenditures Total expenditures Operating costs Employees Factor of operation Expert staff Average salaries of employees Digitalization New expositions Factor of digitalization and exposition Maintenance of collections New collections (number pcs of collection objects) Factor of the growth of the collection Exhibitions Out- Own exhibitions Factor of activities (exhibitions and puts Events events) Visitors Foreign visitors Non-paying visitors Factor of visit rate Editorial activities Grants and donations Revenues Factor of revenues Earned returns Source: Own elaboration 209

Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214 The Kaiser-Meyer-Olkin measure (KMO) of Sampling Adequacy was higher than 0,6 implying that our sampling is adequate for factor analysis (Table 3).

Table 3. Principal Component Analysis: KMO statistics and the Bartlett coefficient

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. ,701 Bartlett's Test of Sphericity Approx. Chi-Square 831,353 df 45 Sig. ,000

Source: Own elaboration

The communality of the variables is quite high, which indicate that they are well represented through the factors (Table 4). With the factorial scores for every museum we performed DEA analysis.

Table 4. Principal Component Analysis: Communalities

Inputs Extraction Outputs Extraction 1 0,760658 1 0,787349 2 0,340488 2 0,8415 3 0,821437 3 0,300572 4 0,690197 4 0,781812 5 0,833296 5 0,910008 6 0,962069 6 0,847791 7 0,8336 7 0,605903 8 0,583055 8 0,765969 9 0,863345 9 0,871402 10 0,796418 10 0,933087 11 0,382069 12 0,691853 13 0,727823 14 0,723827

Source: Own elaboration

EFFICIENCY ANALYSIS OF MUSEUMS In the DEA analysis, we evaluated two most frequently used simple DEA models, i.e. the BCC and the CCR model. As mentioned above, the difference between the models is that the CCR model considers a constant return to scale (thus, a change by a unit on the side of inputs leads to the same change by one unit on the side of outputs). The BCC model considers a variable return to scale (thus, a change by a unit on the side of inputs results in a bigger or smaller change than by a unit). Therefore, the results of the BCC model 210

Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214 take into account the museum’s size. As we compare museums of different sizes, the BCC model is more suitable for the analysis. The DEA analysis was performed using DeaSolver software. Summary results of the overall technical efficiency and its two components (pure technical efficiency and efficiency to scale) are given in Table 5.

Table 5. Results of DEA analysis

Overall technical efficiency Pure technical efficiency

(CCR model) (BCC model) Number of efficient museums 11 20 Number of inefficient museums 89 80 The highest efficiency ratio 1 1 The lowest efficiency ratio 0,78 0,81 Mean of efficiency ratio 0,92 0,93

Source: Own elaboration

The overall technical efficiency can be divided into two components. We identified the sources of inefficiency using the decomposition of efficiency to identify if the inefficiency is caused by the museum’s activities (rate of pure technical efficiency), unfavourable conditions (rate of efficiency to scale) or both. The first component is the rate of technical efficiency measured by the BCC model. The average BCC efficiency of the museums analyzed in 2015 was 93%, and the model identified 20 efficient museums (Table 5). The average BCC rate of efficiency showed the potential saving for the analyzed museums in order to increase their efficiency. It therefore suggests that the analyzed museums required on average only 92% of the inputs used when producing their outputs. The increase of outputs on average by 7% should have helped inefficient museums to reach the efficiency level. The second component of the overall technical efficiency is an efficiency to scale that determines how efficiently a museum behaves in its size group. Based on the BCC and CCR rate of efficiency it is obvious that within the period evaluated, only 11 museums operated efficiently under conditions of constant returns to scale. These museums operate in the so-called most productive size range. Their combination of inputs and outputs allows them to be efficient under conditions of both constant and variable returns to scale. Other 9 museums indentified as BCC efficient, but not CRR efficient were only efficient at the local but not at the global level due to their size. Looking at the highest and lowest score of efficiency it is obvious that the differences between the efficiencies of the museums compared are small. The average efficiency of museums was 92, 6%. It would seem that on average museums in Slovakia are highly effective. This result is biased due to the PCA analysis performed in the first step. The advantage of the output-oriented DEA analysis is that apart from measuring the level of the achieved efficiency it gives recommendations on the number of outputs required for the given inputs to achieve efficiency. Using vectors of optimal values of variables and input values of effective production units it is possible to calculate the recommended values for individual outputs of non-efficient museums. As the indicators were modified by the PCA method for the purpose of their analysis, we do not quote any calculations, only their description. The deeper look on the results of efficiency analysis showed that the efficient museums are mostly museums attractive for visitors wich could be classified into three categories:

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Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214  Museums of the „superstar“ type situated in castles and manor houses which are the most attractive for visitors  Specialised museums devoted to one specific topic, such as the Museum of Slovak National Uprising, the Postal Museum.  Museums in Bratislava benefit from being located in the capital city, e.g. the Historical Museum Bratislava, the Natural Science Museum in Bratislava.

Besides these categories, there were also efficient museums in smaller towns. On the other hand, museums included in the previous three categories were not among the most efficient ones. The analysis thus showed that the size, attractive exposition of locality is not an essential assumption of the efficiency. The group of efficient museums included also smaller museums which, although they do not have economies of scale, are capable to „produce“ higher outputs from the available resources. It indicates the influence of the management and marketing of a museum on its efficiency. Smaller museums in minor towns can also be efficient if they manage to provide attractive events and exhibitions for their visitors. In fact, the number of visitors was the most fre- quent indicator of the DEA results which non-efficient museums should improve on the side of inputs.

CONCLUSION The aim of this paper was to estimate the level of the relative technical efficiency of museums in Slovakia for the year 2015. Efficiency was measured by output CCR and BCC model using the PCA method in the first step to defining input and output indicators. The study showed that it is possible to assess the efficiency of museums and compare their efficiency with each other based on the data obtained from the surveys of the Ministry of Culture of SR. Our analysis showed the necessity to revise these surveys. The collected indicators should be properly amended or modified in such a way that it will be possible to carry out deeper analyses of the efficiency of cultural institutions. To perform better efficiency analysis they should include for example the information on the size of exhibition areas and the number of opening hours at a museum. Our findings showed that in 2015, 80% of Slovak museums did not reach the level of efficiency – thus, they could be more efficient in terms of the events they organized to attract more visitors. 20% of the Slovak efficient museums proved that it was possible to perform better with the resources available to them. According to our case study the average technical efficiency in the year 2015 was 92% where pure technical efficiency amounted to 92% and efficiency to scale was 93%. This high efficiency ratio was caused by the previous step of the PCA analysis. This counts also for the limitation of our study, that we can not interpret this result. The general weakness of DEA is that the results are sensitive to the number and character of the indicators in the analysis. Therefore we will continue with efficiency analysis using another system of indicators (for example only volume indicators) or other DEA models to improve the findings. An important contribution of our study is the recommendations for practice in cultural statistics and cultural management in Slovakia. Comparison of the museums’ efficiency can have a practical influence on the improvement of the museums’ management by providing information on the indicators which could be improved compared to similar institutions. Museums carry out numerous various activities that are not visible to visitors such as registration or maintenance of their collections, research, and digitization of collection items as well as publishing activities. The number of these activities is growing annually which is confirmed by long- term statistics of their growth. The analysis showed that the biggest gaps were on the side of outputs, i.e. working with visitors and potential for increasing their numbers. The study therefore con- 212

Miriam Sebova / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 203-214 firmed that Slovak museums should use more intensively different promotional tools to draw in public and build relationships with visitors.

ACKNOWLEDGEMENT This work was supported by the grant VEGA 1/0806/18.

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Vol. 14, No. 4 (2018), 215-224 ‘

Ukrainian Agricultural Contribution to the World Food Security: Economic Problems and Prospects

NATALIA VASYLIEVA1

1 1 Professor, Department of Information Systems and Technologies, Dnipro State Agrarian and Economic Universi- ty, Dnipro, Ukraine, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 29, 2018 Ukrainian agriculture belongs to the world Top-10 exporters of cere- Revised from September 27, 2018 als and oilseeds. The option of increasing their contributions to the Accepted November 15, 2018 global food security by means of extensive expanding sown area in Available online December 15, 2018 Ukraine has been already exhausted. To meet the contemporary challenges it is necessary to arrange intensive productions of wheat, barley, maize, sunflower, and soybeans with high yields. To monitor JEL classification: the regional dynamics over growing cereal and oilseed crops this C13; C43; O13; Q17. research offers the aggregate rank assessments. They incorporate regional yields, shares of arable lands under wheat, barley, maize, DOI: 10.14254/1800-5845/2018.14-4.15 sunflower, and soybeans, as well as shares of their productions by agricultural enterprises. To explore the existing disproportions be- Keywords: tween the used agricultural lands and obtained harvests this study recommends applying Lorenz curves and a set of inequality indica- food security, tors such as Hoover and Theil indices, Gini coefficient, and also effectiveness of production, Ratio 20:20. Short-term and long-term prospects in enhancing cereals and oilseeds, Ukrainian contributions to the cereal and oilseed segments of the regional inequality, global food security were evaluated according to results of their export profile leading exporters. To reach the levels of the chosen target countries, this research substantiates implementing optimal fertilizers application, rational land use, balanced pricing, developed transport infrastructure, as well as improved innovative, technological and market components in Ukrainian economy.

INTRODUCTION Ukrainian agriculture holds the dominant world positions in growing cereal and oilseed crops. The national exporters belong to Top-10 Lists at wheat, barley, maize, sunflower, and soybeans markets with the total values of US$38.9 bln., US$6.9 bln., US$29.6 bln., US$7.2 bln., US$58.2 bln. (World’s Top Exports, 2018). Demands for cereals and oilseeds continue to increase. It is an

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Natalia Vasylieva / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 215-224 expected trend since these crops have a triple use: firstly, for food purposes, secondly, for feeding in animal husbandry, and thirdly, as raw materials for manufacturing bio-fuel. The main world importers of  wheat are Indonesia, Egypt, Algeria, Italy, Japan, and Nigeria;  barley are China, Saudi Arabia, Iran, the Netherlands, Belgium, and Japan;  maize are Japan, Mexico, South Korea, Egypt, Spain, and Vietnam;  sunflower seeds and oil are India, Turkey, China, Spain, Indonesia, the Netherlands, and Ger- many;  soybeans are China, Mexico, Japan, Spain, Indonesia, and Thailand.

Agriculture of each country is committed to providing food security. In view of the expected population’s growth up to 9 bln. by 2050, this social and economic task becomes even more important in the global scale (Kavallari et al., 2015; Grafton et al., 2015; Candel and Biesbroek, 2018). Its solution requires involvements of all farmers around the world, especially the ones with the relevant experiences and results in this activity. Scientists in agricultural economics should not skip the issue in question. It entails conducting a topical analytical study over Ukrainian problems and prospects of agricultural participation in the cereal and oilseed segments of the global food security system. Sustainable development of agrarian sector belongs to the prime themes of the contemporary scientific research. In particular, Mykhailova et al. (2018) shaped necessary institutional transformations in Ukrainian agriculture. Kolesnyk et al. (2018) focused on providing its ecological, productive, economical, social, intellectual-innovating, and globally integrative features. Katan et al. (2018), Khalatur et al. (2018), Babenko et al. (2017) grounded that being the most stable branch in Ukrainian economy, agrarian sector, to a large extent, defines macroeconomic growth, environmental health, financial security, and international integration for the whole country. Agricultural development in the global scale is responsible for supporting the world food security. It demands a restricted resource use and sustainable technological intensification followed by the food growth (Norton et al., 2014; Godfray and Garnett, 2014; Meyers and Kalaitzandona- kes, 2015; Serban et al, 2017). Ukrainian farmers are not ready to participate in providing food security with animal products since crisis issues in animal husbandry don’t allow saturating even the domestic market (Vasylieva, 2015; Vasylieva and Velychko, 2017). At the same time, Ukrainian agriculture has well known achievements in cereal and oilseed segments analyzed by Schroeder and Meyers (2015), Kuzmenko et al. (2016). Ongoing world and domestic challenges induced by the market volatility, unstable production, strong competition, unequal export opportunities among the regions, high demands for crops (Rude and An, 2015; Hudym and Khalatur, 2016; Moroz et al., 2017; Malyarets et al., 2018) require a new investigation which should address options over increasing Ukrainian contributions to the cereal and oilseed components of the global food security.

1. THEORETICAL FRAMEWORK Cereals and oilseeds count for above 25% and 20% of the total agricultural production in Ukraine. They bring the highest incomes with the average profitability of 26.7% and 49.3% for the last 5 years (State statistics service of Ukraine, 2017). Sown area under wheat, barley, and maize was 48.7%, the one under sunflower and soybeans was 29.2% of the total arable land in Ukraine in 2017. At present, Ukrainian share of the total territory conveyed to arable lands is 44.2%. Evi- dently, the national agriculture almost exhausted all possibilities of the extensive increase in cereal and oilseed harvests by means of expanding sown areas. However, the yields of these crops are barely 30-50% of the varieties’ potentials. 216

Natalia Vasylieva / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 215-224 Moreover, fluctuations of wheat, barley, maize, sunflower, and soybeans yields amounted to 24.2%, 41.5%, 19.8%, 15.5%, and 25% in 2013-2017. Thus, the depicted agricultural background substantiates the research starting hypothesis about exploring options of rising Ukrainian agricultural contribution to the global food security by means of stable high yields of cereals and oilseeds. Hence, the goal of this research was to carry out a quantitative analysis over problems and prospects of enhancing exports of wheat, barley, maize, sunflower, and soybeans from Ukrainian producers. The set objective meant accomplishing 3 tasks such as:  to specify regional rank in cereal and oilseed segments;  to evaluate inequality in growing wheat, barley, maize, sunflower, and soybeans at the state level;  to offer target profiles of developing cereal and oilseed productions in Ukraine in comparison with the indicators of the prominent world exporters.  The obtained results to the first task were connected with the aggregate rank assessments including  regional yields which characterize effectiveness of land use;  shares of sown areas under wheat, barley, maize, sunflower, and soybeans among the regions that depict levels of concentration at these crops;  shares of agricultural enterprises in growing cereals and oilseeds that confirm innovative activity and better maintenance with production resources (Phillips et al., 2012; Shorikov and Babenko, 2014; Velychko and Velychko, 2017).

Mathematical tools for obtaining results to the second task was a set of inequality indicators, containing Lorenz curve, Hoover and Theil indices, Gini coefficient and Ratio 20:20 (Vasylieva, 2017). In particular, Lorenz curve facilitates a visual monitoring over unbalances between sown areas and collected harvests of wheat, barley, maize, sunflower, and soybeans. Hoover index evaluates the highest level of these disproportions. Theil index determines the corresponding average distributions. Gini coefficient calculates a total measure of inequality. Its top and bottom quintiles are compared with Ratio 20:20. The obtained results to the third task were dedicated to creating target profiles on increasing yields of cereals and oilseeds for the remote and near future dealing with indicators of Top-10 exporters. Implementations of the chosen agricultural practices, concerning land use, fertilizers’ application, development of infrastructure, and price competitiveness would benefit Ukrainian agricultural contribution to providing the global food security.

2. RESULTS OF CALCULATIONS Assessments of wheat, barley, maize, sunflower, and soybeans productions in 24 Ukrainian regions were calculated on the samples of data from State statistics service of Ukraine (2017). Table 1 and Table 2 comprise regional ranks over the analyzed indicators and display the aggregate assessments grouped in the order of the decreasing effectiveness of cereal and oilseed productions. It should be noted that the similar mathematical approach is successful at both micro- and macroeconomic levels (Dovgal et al., 2017).

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Natalia Vasylieva / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 215-224 Table 1. Rank assessments of regional contributions to cereal production

Share in regional Yield sown area Share of agri- Region cultural enter- Aggregate rank prises’ produc- assessment tion Wheat Barley Maize Wheat Barley Maize Ternopil 23 25 23 15 20 11 14 131 Khmelnytskiy 24 24 24 12 14 13 20 131 Vinnytsya 19 21 18 13 11 17 18 117 Sumy 21 17 20 9 3 21 23 114 Chernihiv 17 20 21 7 1 23 24 113 Lviv 18 23 17 14 15 4 8 99 Odesa 9 13 6 22 24 7 13 94 Rivne 15 19 16 11 17 9 7 94 Kyiv 6 16 13 10 7 18 22 92 Kharkiv 16 13 7 18 16 12 10 92 Ivano-Frankivsk 22 22 14 5 13 10 5 91 Cherkasy 8 12 12 8 5 22 21 88 Zhytomyr 13 13 19 3 4 15 19 86 Volyn 14 11 22 19 8 3 3 80 Poltava 11 9 9 2 6 24 17 78 Donetsk 11 8 2 21 18 5 11 76 Chernivtsi 19 18 10 1 10 16 1 75 Kherson 5 3 15 23 22 2 4 74 Dnipropetrovsk 7 4 5 16 21 14 6 73 Kirovohrad 3 6 8 6 12 19 15 69 Mykolayiv 4 5 3 17 23 6 9 67 Zaporizhya 2 2 4 24 19 1 12 64 Luhansk 9 1 1 20 9 8 16 64 Zakarpattya 1 7 11 4 2 20 2 47

Source: author’s calculations based on the data of State statistics service of Ukraine (2017).

Table 2. Rank assessments of regional contributions to oilseed production

Share in re- Yield gional sown area Share of agri- Aggregate cultural enter- Region rank assess- prises’ pro- ment duction Soybeans Sunflower Sunflower Soybeans Khmelnytskiy 24 18 9 22 21 94 Ternopil 22 22 7 16 19 86 Rivne 21 19 3 18 23 84 Sumy 20 11 14 20 16 81 Vinnytsya 23 12 11 13 15 74 Zhytomyr 12 17 8 23 13 73 Chernihiv 15 13 12 14 19 73 Kyiv 14 9 10 21 17 71 Ivano-Frankivsk 16 20 6 10 18 70 Cherkasy 18 8 13 17 14 70 218

Natalia Vasylieva / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 215-224

Chernivtsi 19 10 5 24 10 68 Volyn 17 15 2 9 22 65 Lviv 9 16 4 12 23 64 Poltava 13 7 15 19 7 61 Zaporizhya 2 21 23 5 4 55 Kherson 1 23 17 11 1 53 Kirovohrad 7 5 20 15 3 50 Odesa 8 14 16 4 8 50 Zakarpattya 10 24 1 8 6 49 Kharkiv 11 3 18 7 9 48 Dnipropetrovsk 6 6 19 3 12 46 Luhansk 3 2 24 1 11 41 Mykolayiv 4 4 22 6 2 38 Donetsk 5 1 21 2 5 34

Source: author’s calculations based on the data of State statistics service of Ukraine (2017).

The assessments of disproportions between the harvests of the considered crops and their sown areas were performed on the samples of data from State statistics service of Ukraine (2017). Lorenz curves in Figure 1 and Figure 2 reveal inequality in growing cereals and oilseeds.

Figure 1. Lorenz curves to inequality in production of: a – wheat, b – barley, c – maize.

100 100 100 80 80 80 60 60 60 40 40 40 Production, % Production, 20 % Production, 20 Production, % Production, 20 0 0 0 0 20406080100 0 20406080100 0 20406080100 Lands, % Lands, % Lands, % a b c

Source: composed by the author based on the data of State statistics service of Ukraine (2017).

Figure 2. Lorenz curves to inequality in production of: a – sunflower, b – soybeans.

100 100

80 80 60 60 40 40

% Production, 20 Production, % Production, 20

0 0 0 20406080100 0 20406080100 Lands, % Lands, % a b Source: composed by the author based on the data of State statistics service of Ukraine (2017). 219

Natalia Vasylieva / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 215-224

The calculated quantitative indicators of inequality in production of the considered crops are collected in Table 3.

Table 3. Inequality indicators to production of cereals and oilseeds

Crop Hoover index Theil index Gini coefficient Ratio 20:20 Wheat 0.074 0.014 0.095 1.55 Barley 0.094 0.025 0.123 1.78 Maize 0.138 0.054 0.179 2.46 Sunflower 0.089 0.023 0.121 1.78 Soybeans 0.113 0.038 0.153 2.17

Source: author’s calculations based on the data of State statistics service of Ukraine (2017).

3. DISCUSSION As distinct from studies of Rude and An (2015), Schroeder and Meyers (2015), Kuzmenko et al. (2016), the accomplished research proposes a uniform approach dealing with a qualitative analysis of problems and prospects in cereal and oilseed segments of Ukrainian agriculture. Let us start the discussion from the results regarding an intensive development of the respective regional productions. Data in Table 1 convince that Top-5 cereal producers with the aggregate rank assessments over 100 had high ranks by all considered crops. The reason for such result was their preferential growing by competitive agricultural enterprises. In contrast, 5 off-track regions with the aggregate rank assessments below 70 had poor ranks of yields of all considered crops and uneven distribution of their sown areas. Data in Table 2 clarifies that Top-3 oilseed producers with the aggregate rank assessments over 83 demonstrated high yields of both considered crops. Their growing was concentrated in strong agricultural enterprises. They preferred soybeans which less ex- haust soil fertility. On the contrary, 2 off-track regions with the aggregate rank assessments below 40 had poor ranks of yields of both considered crops. Their dominant producers were small-scale households. They focused on growing sunflower which essentially ruins soil fertility. Let us proceed to discussing results concerning an extensive development of the respective regional productions at the expense of sown areas under cereals and oilseeds. An average normative monetary value of arable land in Ukraine amounts to US$2583 per ha. In particular, it varies between US$1719 per ha in Zhytomyr region and US$3324 per ha in Cherkasy one. Figure 1 revealed that maize production happened to be the most unbalanced among cereals. In contrast, wheat growing appeared to be in the most balanced state. Inequality indicators in Table 3 mean that the total disproportions in wheat, barley, and maize productions are 9.5%, 12.3%, and 17.9%. Their average levels of the inequality entropy achieve 1.4%, 2.5%, and 5.4%. The gaps between top and bottom quintiles of wheat, barley, and maize growers reach 1.55, 1.78, and 2.46 times. An alignment over Ukrainian cereal production supposes the corresponding rearrangements by 7.4%, 9.4%, and 13.8%. Figure 2 convinced that soybeans production was more balanced than sunflower growing. Inequality indicators in Table 3 mean that the total disproportions in sunflower and soybeans productions are 12.1% and 15.3%. Their average levels of the inequality entropy achieve 2.3% and 3.8%. The gaps between top and bottom quintiles of sunflower and soybeans growers reach 1.78 and 2.17 times. An alignment over Ukrainian oilseed production supposes the corresponding rearrangements by 8.9% and 11.3%.

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Natalia Vasylieva / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 215-224 At last, Ukrainian prospects for increasing wheat, barley, maize, sunflower, and soybeans contributions to the global food security might be determined via achievements of their Top-exporters. Among them Ukraine was ranked at the 6th, 4th, 4th, 1st, 7th positions with 7.1%, 10.2%, 10.1%, 47.5%, 1.8% at the corresponding markets in 2017. According to the research hypothesis, the closest higher yields collected by Top-10 exporters of the considered crops should be Ukrainian aim of development in the short-run. The corresponding target profile was displayed in the form of linear diagram in Figure 3. The highest yields collected by Top-10 exporters of wheat, barley, maize, sunflower, and soybeans could be the strategic benchmarks in the long-run. The corresponding target profile was depicted in the form of linear diagram in Figure 4. The achieved results in target countries suppose applications of contemporary technological and economic practices. They provided the 1st and 2nd positions to the USA in maize and soybeans exports (market shares of 32.3% and 37.2%), 3rd position to Argentina in maize export (market share of 13.1%), and also 5th position to Canada in soybeans export (market share of 3.3%). All target countries apply optimal quantity of fertilizers. In particular, Bulgaria, Canada, Ger- many, Hungary, and the USA use on average 107.9, 105.2, 198.8, 113.4, and 133.7 kg of fertilizers per ha of cropland (FAO, 2018). Target countries prevent irrational land use restricting arable shares in the cultivated areas. In particular, these indicators in Argentina, Bulgaria, Canada, Ger- many, and the USA reach 26.4%, 70%, 69.6%, 70.8%, and 37.5%. On the contrary, the similar values in Ukraine are 42.3 kg of fertilizers per ha and 78.8% of arable lands. Such irrational approach is explained by the prolonged ban over market turnover of agricultural land in Ukraine (Koroteyev et al., 2017).

Figure 3. Short-run target profile to production of cereals and oilseeds.

Crop/Country 23 26,6

22,4 Ukraine beans/Canada y 22,9 Top-Exporters So ulgaria B 66

a 74,4 unflower/ S ntin ge 33 e/Ar iz a 37,2 Ma d 42,1 arley/Cana c/ha B 47,5 ia

020406080 heat/Bulgar W

Source: composed by the author based on the data of FAO (2018).

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Natalia Vasylieva / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 215-224 Agriculture like the whole Ukrainian economy lacks Innovations, Technological readiness, and Goods market efficiency (ranks 61, 81, and 101) as distinct from Canada (ranks 23, 23, and 18), Germany (ranks 5, 8, and 11), and the USA (ranks 2, 6, and 7). Export development also demands advanced transport infrastructure (Lugovskyy and Skiba, 2016; Atkociuniene and Kiausiene, 2017). Canada, Germany, Hungary, and the USA were ranked 16, 10, 56, and 9 by the World Eco- nomic Forum in 2017. Unfortunately, Ukraine lags behind in estimates of automobile roads and port facilities (ranks 130 and 93) (The Global Competitiveness Report, 2017).

Figure 4. Long-run target profile to production of cereals and oilseeds

Crop/Country 23 35

22,4 Ukraine

Soybeans/USA 25 Top-Exporters ry ga un 66

A 109,6 US Sunflower/He/ iz 33 Ma 66,9 any rm /Ge 42,1 ey rl 76,4 c/ha Ba rmany Ge 0 20 40 60 80 100 120 Wheat/

Source: composed by the author based on the data of FAO (2018).

At the same time, average producers’ prices in Ukraine were US$128.7, 117.7, 138.2, 333.3, and 348.1 per tonne of wheat, barley, maize, sunflower, and soybeans. They appeared to be lower than the prices of US$155.5 and 154.9 for wheat in Bulgaria and Germany, US$165.4 and 139.4 for barley in Canada and Germany, US$134 for maize in the USA, US$394.2 and 375.9 for sunflower in Bulgaria and Hungary, US$349 for soybeans in the USA (FAO, 2018). It could be a promising signal about the existing options for updating technological and economic practices in Ukraine to increase its cereal and oilseed contributions to the global food security.

CONCLUSION It was clarified that the rising yields and harvests from agricultural enterprises should be the key priorities of increasing the regional productions and exports of cereals and oilseeds. According to the calculated aggregate rank assessments it was recommended to transfer technological and economic practices in wheat, barley, maize sunflower, and soybeans growing from Ternopil and Khmelnytskiy to Zakarpattya, Mykolayiv, and Donetsk regions.

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Natalia Vasylieva / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 215-224 The burning issue of increasing Ukrainian contributions to the cereal and oilseed segments of the global food security is irrational use of agricultural land. The highest imbalances of 13.8% and 11.3% between sown areas and harvests were found to be in growing maize and soybeans. Compliance with the practices of the target countries among Top-10 exporters of wheat, barley, maize, sunflower, and soybeans would result in rising the corresponding harvests by 12.8%, 12.7%, 12.7%, 2.2%, and 15.7% in the short-run as well as by 81.5%, 102.7%, 66.1%, 11.6%, and 52.2% in the long-run. Ukrainian agriculture would reach these increments due to the optimal fertilizers’ application, rational land use, developed transport infrastructure, balanced pricing, and also improved innovative, technological and market environment.

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The Reactions of Post-Soviet Countries Employees to Changes Carried Out by Organizations in Higher Education: Cases of Lithuanian, Ukrainian and Belarusian State Colleges

NATALIJA SEDZIUVIENE1 and JOLITA VVEINHARDT2

1 Associate Professor, Siauliai State College, Lithuania, e-mail: [email protected] 2 Professor, Chief Researcher, Department of Management, Faculty of Economics and Management, Vytautas Mag- nus University, Lithuania, e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 30, 2018 This paper presents the scale created by authors for determining Revised from September 22, 2018 employees' attitudes towards changes (employees’ attitude towards Accepted November 11, 2018 changes - EATC) and its psychometric characteristics. The results of Available online December 15, 2018 the research, which includes staff from three state colleges from three countries (N = 222) are also analyzed. The aim of this research is by comparing the attitudes of employees of different post- JEL classification: Soviet state colleges to identify the main challenges that heads of M12, M14, M19. organizations face. The results of the research show that there is a certain gap between the benefits of changes which employees per- DOI: 10.14254/1800-5845/2018.14-4.16 ceive, and their coworkers expressed perception of changes. It should be emphasized that the internal opposition, created by direct Keywords: (linear) heads, emerges as a significant organizational problem that should be considered by the organizational leadership implementing Higher education, the changes. state colleges, Attitudes of employees, Lithuania, Ukraine, Belarus

INTRODUCTION Relevance and level of problem exploration. Colleges training competitive specialists are faced with the task of maintaining maximum openness to trends in the national and international markets, be capable to anticipate them and flexibly change. In this context, it is reasonable to assess the experience of countries that need to follow similar paths of transformation. Researchers studying development trends of higher education in Eastern and Central Europe draw attention to the influence of historical experience in the long-term transformational perspective (Kwiek, 2001; Gaw- 225

Natalija Sedziuviene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 225-235 licz and Starnawski, 2018; etc.), but their results are still ambiguous (Dakowska, 2017). In recent years, considerable attention has been devoted to various external challenges related to the quality of higher education and response to the needs of society (Tarlea, 2017; Dobbins and Kwiek, 2017; Dakowska, 2017; etc.). On the one hand, higher education sector is currently undergoing major structural changes, and insufficient financing of the sector remains an important factor (Dlouha et al., 2017), while on the other hand, specific problems of human resource management in organizations also have to be taken into account, which affects employees' reactions to changes because success of the implementation of reforms are crucial to that (Van Emmerik et al., 2009; Dasborough et al., 2015). In this context, the greatest negative impact on the attitude towards changes arises from bad workplace relations and stress (Vakola and Nikolaou, 2005), while Nitta et al. (2014) emphasizes that difficulties always arise when employees do not understand the goals of reorganization and how to achieve them, they are not empowered to act following the vision of change, and heads do not know how to communicate the vision of change to staff. These are just some of the challenges related to employees' attitudes that heads of organizations face, especially considering that the region is not homogeneous according to both macroeconomic and educational policy characteristics (Gawlicz and Starnawski, 2018), and in this context there is a lack of research of management of change in higher education. Therefore, research problem is formulated using questions how employees respond to changes and what are the main challenges for the heads implementing changes in colleges in Lithuania, Belarus and Ukraine. The aim of the research is to identify the main challenges that heads of organizations face by comparing attitudes of employees of different post-Soviet state colleges to changes. Objectives of the research: a) Analyze theoretical aspects of employees' attitude towards changes, b) Identify the attitude of employees towards changes in the view of their own, colleagues and activities, and c) Identify personal employee's attitude to the current changes in their workplace and how they consider their head's position on the issue of change. Limitations of the research. Only employees' attitudes influenced by organizational factors were analyzed, and therefore psychological, cultural or political determinants were not analyzed. Research was conducted in three state colleges in different countries, therefore, to make broader generalizations in the future, the research should be repeated in a larger sample, taking into account the scale of the changes implemented in different countries, which may affect attitudes of respondents. Further research directions. It would make sense in the future to compare cultural determinants and their impact on the attitude of employees to changes. The research should also include staff of other European colleges in a larger sample by producing questionnaires in the languages of those respondents.

1. THEORETICAL BACKGROUND Studies dealing with the implementation of changes in higher education organizations single out several factors influencing implementation of decisions. B. Stensaker et al. (2014) surveyed 26 decision makers of European universities. The study demonstrated that changes were perceived as highly dependent on leadership, decision-making procedures, communication and assessment. Another study confirmed that lack of communication and participants’ low involvement during changes were emphasized as important factors, contributing to resistance and that they were related to organizational culture (Canning and Found, 2015). In addition, information about changes was positively related to the psychological implementation of agreements and the attitude to changes (van den Heuvel et al., 2017), as well as to greater confidence, enabling to achieve the set results (Vosse and Aliyu, 2018). 226

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In other words, internal quality of communication, the ability to clearly report changes and their benefits reduce resistance to changes and ensure employees’ support. In this case, high requirements are raised to the management personnel of the organizations. Managers must be alert to express loyalty more. They should also acknowledge their role generating positive experience of the process, which is a precondition for developing change possibilities at the employees’ level (Sten- saker and Meyer, 2011). In higher education, the manager’s support, work control and professional development possibilities are related to a more favourable assessment of organizational changes (van Emmerik et al., 2009). In addition, the heads of higher education institutions should perceive that employees react to changes differently and for this reason they should not be perceived as one single group (Dasborough et al., 2015), therefore, higher education policy makers are ad- vised to evaluate different emotional responses (Tran et al., 2017). Employees’ attitude can be described as a diagnostic tool that collects a lot of information under controlled conditions. It works as a communication tool enabling employees to express their satisfaction and ideas for improvement as well as to propose unique and practical information about management that helps to solve problems (Anderson, 1974). In this case, it is important what emotional reactions are caused by changes implemented in the organization and how these reactions are managed. It was found that persons who experienced greater levels of stress showed lower commitment and greater reluctance to accept organizational change interventions (Vakola and Nikolaou, 2005), while positive climate increased the motivation of teachers of higher education institutions (Specht et al., 2018). The greatest negative impact on the attitude towards changes arises due to bad working relationships (Vakola and Nikolaou, 2005), the more so that some studies demonstrate that stress is also often experienced by middle managers of higher education institutions, and the source of this stress is the leadership of the organization (Chan et al., 2018), while leaders’ high level emotional intelligence contributes to preparation of employees for future changes (Gelaidan et al., 2018). Another study emphasizes that emotions can become an obstacle to change, and how people who cannot mobilize themselves and behave differently become less inclined to discuss changes (Eriksson, 2004). When the management implements mutual commitments and builds confidence, this enables to respond to organizational changes more construc- tively compared with the focus on management of organizational changes as an independent event (van den Heuvel et al., 2016). Under conditions of economic transition, employees who are more satisfied with their job are more inclined to be take part in the organizational change process than employees whose job satisfaction is lower (Alas and Wadi, 2006). According to the authors, the employees who assessed their organizational culture as being stronger are more willing to participate in the implementation of organizational changes and are more satisfied with their jobs and managers.

2. RESEARCH METHODOLOGY The instrument used for the research was created based on the analysis of scientific literature and by selecting publications using keywords "higher education", "changes", "attitudes of employees" and by focusing on magazines indexed in SCOPUS and WOS databases, which quartile is not lower than Q3. In this way, 15 publications that were published between 2004 and 2018 were selected (Eriksson, 2004; Vakola and Nikolaou, 2005; Alas and Vadi, 2006; Armenakis et al., 2007; Walker et al., 2007; van Emmerik et al., 2009; Stensaker and Meyer, 2011; Hyde, 2012; Carter et al., 2013; Stensaker et al., 2014; Dasborough et al., 2015; Canning and Found, 2015; Fu et al., 2016; van den Heuvel et al., 2017; Vosse and Aliyu, 2018).

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Natalija Sedziuviene and Jolita Vveinhardt / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 225-235 The scale created by authors includes of 3 sub-scales, which in total consist of 16 statements. The first sub-scale "Changes for the Employee" consists of 9 statements (two statements are re- versed). This sub-scale measures employee's attitude to the personal benefit he or she expects (or not) to receive, appropriateness of changes methods implementation and usefulness of the investments used. The two negative statements aim at identifying the employee's stress and fear of losing job. The purpose of second sub-scale "Changes for Colleagues" (3 statements) is to determine how the employee participating in the survey sees his or her colleagues in the changing environment with regard to the age and responsibilities of employees, as well as when comparing "what was before" and "what is now". Third sub-scale "Changes for Activities" (4 statements) aims at identifying employee's attitude to the impact of change on the activities undertaken at the state level, at the organizational level, at the head's level, at the colleagues' level and at the employee's own level.

Table 1. Psychometric scale characteristics (N = 222)

Explained spread Factor weight (L) Number of Cronbach's Spearman Sub-scales percentage statements Alpha Brown min. mean max. % of variance Changes for the 9 0.85 0.84 34.38 0.37 0.66 0.82 Employee Changes for 3 0.61  57.59 0.54 0.74 0.87 Colleagues Changes for 4 0.82 0.77 65.68 0.68 0.81 0.87 Activities

Source: composed by authors

Examples of each of the three sub-scales statements and their internal consistency are given in Table 1. Closer Cronbach's Alpha coefficient value to 1 indicates the greater scale measurement accuracy. Values below 0.60 are acceptable when the scale / sub-scale is composed only by a few statements (Hair et al., 2009) or it is a newly created instrument (Nunnally, 1978). In the case of this scale, the Cronbach's Alpha coefficient of one sub-scale is not high, but higher than 0.60. The Cronbach Alpha coefficient depends on the length of the scale - the more individual statements make up the sub-scale, the higher coefficient can be. Therefore, the validity of the scale in regard of repeated execution was verified by the Spearman Brown statistical correlation coefficient of the two variables measured in the rage of a sub-scale. Spearman Brown coefficient value of 0.60 is acceptable when using questionnaires which are still being developed (Brace et al., 2004) and it should be noted that the Spearman-Brown value is always lower than the Cronbach Alpha coefficient (Vveinhardt and Gulbovaitė, 2018) Explained factor spread must be higher than the permis- sible 10% minimum limit, i.e. if the factor explained spread in the sub-scale is lower than the specified allowed limit, then this indicates that the analyzed sub-scale contains statements that reduce the spread. The percentage of explained spread in the analyzed scale meets the requirements. Minimum factor weight (L) can not be lower than 0.3. If it is less than 0.3, this indicates that an incorrect statement is in the sub-scale. In this case the minimum factor weight is from 0.37 to 0.68 in all sub-scales (Vveinhardt, 2012). Table 2 shows intercorrelations of sub-scales which are strong and very strong. All sub-scales are statistically reliable, because p is 0.000. Additionally, besides already presented sub-scales, respondents were given two open type questions: “Please, finish the thought: Changes in my workplace for me are: ...” and “Please, finish the thought: "In my opinion, changes for our organization's 228

Natalija Sedziuviene and Jolita Vveinhardt/ Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 225-235 head are: ...”. These questions were aimed to identify personal employee's attitude to the current changes in their workplace and how they view their head's position on the issue of change.

Table 2. Intercorrelation among sub-scales

Changes for Changes for Changes for Sub-scales the Employee Activities Colleagues Pearson Correlation 1 .829** .707** Changes for the Em- Sig. (2-tailed) .000 .000 ployee N 222 222 222 Pearson Correlation .829** 1 .725** Changes for Activities Sig. (2-tailed) .000 .000 N 222 222 222 Pearson Correlation .707** .725** 1 Changes for Col- Sig. (2-tailed) .000 .000 leagues N 222 222 222 **. Correlation is significant at the 0.01 level (2-tailed). 0,8

Source: composed by authors

3. RESEARCH RESULTS The survey was conducted using an online survey platform, guaranteeing anonymity of respondents and limiting the possibility to fill out a questionnaire more than once. Authors have developed, adapted and verified questionnaire in different languages (Lithuanian, Ukrainian and Russian) for this research. The survey was conducted with the consent of the college' heads. The survey involved 222 respondents (76 in Lithuania, 75 in Ukraine and 71 in Belarus) from three similar size state colleges. Socio-demographic characteristics of respondents are presented in Ta- ble 3.

Table 3. Socio-demographic characteristics of respondents

Lithuania Ukraine Belarus Country, sample N=76 N=75 N=71 % % % Age of employees 23-30 14.5 5.3 14.1 31-40 26.3 30.7 38.0 41-50 30.3 46.7 23.9 51-60 26.3 16.0 21.1 61 and over 2.6 1.3 2.8 Gender of employees Male 19.7 21.3 42.3 Female 80.3 78.7 57.7 Position of employees Administrative staff 38.2 2.7 9.9 Administrative staff and lecturer 11.8 2.7 14.1 Head of unit 5.3 1.3 8.5

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Lithuania Ukraine Belarus Country, sample N=76 N=75 N=71 % % % Head of unit and lecturer 9.2 10.7 9.9 Lecturer 35.5 82.7 57.7

Source: composed by authors According to age the majority of respondents were individuals aged from 31 to 60. In this context, the Ukrainian group stood out with the largest number of respondents 41-50 years old (46.7%). The age percentage of respondents who participated in the research, in range from 31 to 60 years make similar groups, i.e. in the case of Lithuania it is 82.9%, in the case of Ukraine 93.4%, and Belarus - 83%. According to the gender, the distribution of respondents is quite similar in Lithuanian and Ukrainian colleges, because in the education system, according to the Statistics Department, more women than men are employed. However, in the case of Belarus, the sample number of respondents in regard to age is similar. Analysis according to position showed that working only as a lecturer and working as a lecturer while holding position of unit's head / administrative staff, group percentage distributed as follows: 56.5% (Lithuania), 96.1% (Ukraine), 81.7% and (Belarus). The results of the research show (Table 4) that the biggest change-related optimism is demonstrated by the Belarusian respondents (the average is 50.7%, in Lithuania respectively - 34.6% and in Ukraine - 24.4%). Similar trends persist when dividing respondents according to self-perceived benefits (Belarus - 78.4%, Lithuania 63.7%, Ukraine -39%) and benefits for colleagues as well as organization sub-scales (Belarus - 80.6%, Lithuania - 45.1%, Ukraine -29.9%). However, unlike Lithuanian and Ukrainian respondents, Belarusian respondents tend to attribute greater benefit of changes for the organization. On the other hand, respondents in the latter country are least likely to associate the changes with the stress and fears of losing their job (respectively 9.9% and 4.2%), unlike in Ukraine (68% and 41.3% respectively) and in Lithuania (34.2% and 44.7% respectively), which has a significant impact on employees' resistance to change and the success of their implementation, because more stressed individuals show less commitment and greater reluctance to take organizational change interventions (Vakola and Nikolaou, 2005). In addition, researchers point out that organization heads must be particularly attentive to their behavior and express higher loyalty. They should also take responsibility for their role in generating a positive process experience, which is a prerequisite for creating positive change possibilities at the employee level (Sten- saker and Meyer, 2011). Our research results show that in this respect the situation is most favorable in Belarus, where 94.3% respondents say that the direct head accepts changes in the organization, unlike in Lithua- nia and Ukraine (44.7% and 25.3% respectively). Similar situation is with the criterion of the awareness of change. 94.3% of respondents in this country state that "the goals of the changes currently being implemented in the workplace are clear to me", while it is 37.4% in Ukraine and 54% in Lithuania. A significant part of respondents (especially in Lithuania and Ukraine) is not sure about the fact that there takes place break in the quality of work. That is, either this change is not actualized in the internal communication or does not cover the entire organization's units. In addition, strong doubts about the appropriateness of investment and the methods used by organizations was expressed in all three cases, which is most clearly reflected in cases of Ukraine and Lith- uania. The overall psychological "notion" of supporting / not supporting change is important in an organization, which in the research not only varied considerably, but also raised a number of questions for further research. This notion includes not only the attitudes of respondents themselves, but also their reflected attitudes of co-workers. Thus, the tendency emerges that in all three cases the informants demonstrate greater acceptance of the changes than their counterparts do. The 230

Natalija Sedziuviene and Jolita Vveinhardt/ Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 225-235 biggest difference between individual and reflective evaluation of co-workers is in the case of Ukraine, where the difference is 18.7 percentage points (in the case of Lithuania 6.6%, and Bela- rus - 11.3%).

Table 4. The attitude of employees towards changes in the view of their own, colleagues and activities

Country Lithuania Ukraine Belarus Sample N=76 N=75 N=71 Statements No Doubts Yes No Doubts Yes No Doubts Yes Answer groups The changes currently taking place in my workplace 6.6 51.3 42.1 38.6 24 37.4 2.8 8.5 88.7 will benefit me as an employee personally My peer colleagues believe in benefits of changes that 6.5 57.9 35.5 25.4 56 18.7 5.6 16.9 77.4 are currently taking place in our workplace I believe that proposed organizational changes will 6.6 46.1 47.4 34.7 29.3 36 1.4 9.9 88.7 have a positive effect on our activities We now must work differently in our organization than 18.4 17.1 64.5 10.6 12 77.4 4.2 15.5 80.3 we used to These changes will result in 27.6 59.2 13.1 22.6 57.3 20 11.3 50.7 38 a higher salary for me Change of my job assign- ments will increase my 10.6 44.7 44.7 20 25.3 54.7 1.4 9.9 88.8 professional achievements My direct head agrees with organization's ongoing 3.9 51.3 44.7 21.3 53.3 25.3 0 5.6 94.3 changes The goals of changes that are currently being imple- 10.5 35.5 54 48 14.7 37.4 0 5.6 94.4 mented in my workplace are clear to me The methods of implementing changes are acceptable 10.5 52.6 36.8 53.3 16 30.7 0 11.3 88.8 to me I have no doubt about the expediency of my organiza- 9.2 22.4 68.4 10.7 30.7 58.6 1.4 40.8 57.8 tion's investments Sufficient funds are allocated for improvement of the 17.1 23.7 59.2 36 44 20 1.4 22.5 76 staff in my workplace New organizational requirements do not exceed 5.2 21.1 73.7 26.7 20 53.3 2.8 11.3 85.9 my abilities Ongoing changes in my workplace are causing me a 28.9 36.8 34.2 13.3 18.7 68 78.8 11.3 9.9 lot of additional stress I am afraid that, due to the 26.3 28.9 44.7 20 38.7 41.3 84.5 11.3 4.2 current changes, I can lose 231

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Country Lithuania Ukraine Belarus Sample N=76 N=75 N=71 Statements No Doubts Yes No Doubts Yes No Doubts Yes Answer groups my job Colleagues in the same positions as I am believe in the benefits of changes that 11.8 57.9 30.2 20 68 12 2.8 28.2 69 are currently taking place in our workplace The ongoing reform of higher educational institutions 9.2 61.8 28.9 36 44 20 0 21.1 78.8 will improve our organization's position

Source: composed by authors

When analyzing respondents' answers to the open questions presented to them, it becomes meaningful to group them into three groups according to understanding the meaning for oneself, relationship with the organization and at a national level, i.e. educational system (distinguishing the most characteristic descriptions). The number and variety of isolated terms indicate differences between the dominant tendencies in the countries. According to the personal relationship of employees in response to changes in the case of Lithuania, the following terms can be distinguished: routine ("bored", "constant phenomenon"), uncertainty ("not defined, not explained", "acceptable, but their effectiveness is doubtful", "uncertainty for the future"); the need for personal development ("promotes learning, improvement", "related to my work and good work results"); challenge ("challenge", "new test"). According to relationship with the organization: the attractiveness of the organization to the market ("responding to the needs of the region"), operational efficiency ("increasing the quality and efficiency of work"). At national level: "possibility to improve and grow together with the processes in the country, to become part of those processes", "changes at the national level" can affect my organization, but they are still ambiguous, and their consequences are more frightening"). Thus, although there is a perception that changes are necessary and can be beneficial to both the individual and the organization, there remains a strong sense of ambiguity and uncertainty about the future, weak understanding of oneself as an organizational system, both at the organizational and at the state level. In the case of Ukraine according to a personal relationship in response to changes following components can be distinguished: denial ("these changes are undesirable to me", "trouble", "extra work", "changes are unjust"); personal development ("changes are needed", "personal growth", "stimulus for professional growth"); uncertainty ("I doubt it will be better", "it is not clear what's go- ing on"). In organizational context following components can be distinguished: prestige of the organization ("opportunity to raise the prestige of institution", "stable wages and development of our institution"). At national level: damage to the optimal system ("destruction of technical schools and colleges", "doubtful attempts to reform the education system", "without a logical final result"), improvement of the system, competitiveness ("possibility to access the European educational area", "need to reform the education system", "better education for children"). In the case of Belarus according to personal relationship: actual ("necessary, dictated by the time", "clear and necessary"), improvement ("innovative process in which I participate", "expanding possibilities for development", "motivating to review methods of work"), uncertainty ("there will be more uncertainty than positive results"), distrust ("necessary for leadership only", "unnecessary, we just need to take in mind that we are working with a new generation", "new things not always lead to perfection"). Organizational context: improvement of activities ("positive effect on our activities", "will improve work of the college"). The relationship with state policy remained unexpressed. 232

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In short, it is worth noting that concern for individual perspective is dominating and in individual cases, relating that to the activities of the entire organization, and only in the case of Ukraine, a specific dimension has emerged - concern for the need for improvement of the whole educational system. It is significant how employees interpret the attitude of the organization's head towards changes and how this attitude is clear to them and how it can be viewed from the perspective of the organization. In the case of Lithuania, although statistically significant doubts have emerged about the personal benefits of change and the lack of loyalty of linear heads, responses to the open question relate efforts of a head to improve effectiveness of the organization's activities and increase competitiveness. Similar attitudes emerged in the case of Ukraine, but for a significant part of respondents' organization head's attitudes remain unclear. A certain emotional connection is also noticeable, understanding that changes are as stressful for employees as they are for heads, and together with that goes a belief that implementation of changes is "imposed from above". In the case of Belarus, stood out the head's behavior as a motivating factor, which could explain an exceptionally favorable attitude to changes.

CONCLUSIONS This study draws attention to patterns and disturbances of changes which colleges in three different countries face. Research shows that employees' attitudes to changes depend on a wide range of organizational factors. That includes the quality of the organization's internal communication, employee awareness, a trust-based psychological climate, engagement and involvement in the process of changes, perception of the benefits for the individual and organization, and the leadership's loyalty to the organization implementing changes. The general tendencies lead to conclusion that there is a certain tension between the benefits of changes which respondents perceive, and their coworkers expressed attitudes to changes. That is, the general notion (which is different in different countries) shows less optimism than the respondents themselves are demonstrating. This becomes evident both according to the age and position of coworkers whose attitudes are reflected. Identification of reasons of this contradiction requires additional research, taking into account the specifics of interpersonal communication, cultural aspects of organizations and climate of organizations, which may affect the way employees trust and how openly they express their attitudes on the changes that are being implemented. On the other hand, distrust of the expediency of investments, poorly perceived benefits of changes, encountered stress and uncertainty regarding the future can be a major disruptor that should be addressed both by government representatives who initiate reforms as well as by heads of individual organizations. In this case, it would be insufficient to raise employees' awareness of personal and organizational benefits of changes, because if there is no culture of trust which is consolidated by linear heads, and changes can only remain a beautiful declaration that does not guarantee significant changes. The internal opposition, created by direct (linear) heads, emerges as a significant organizational problem that should be considered by the organizational leadership implementing the changes. Although it can be argued whether direct heads demonstrate greater support for changes, but their loyalty remains a relevant issue that is particularly acute in cases of Lithuania and Ukraine. This also determines workers' faith in qualitative changes of work. However, not only the employee's attitude to changes is important in the context of personal interests, but also how a person perceives oneself in the context of organization and the whole system transformations. These interpretations are influenced not only by the position of linear heads, but also by how clearly the head of the organization presents a position. 233

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Criteria for Evaluation of Efficiency of Energy Transformation Based on Renewable Energy Sources

VITALII NITSENKO1, ABBAS MARDANI2, JUSTAS STREIMIKIS3, IRYNA SHKRABAK4, IVAN KLOPOV5, OLEH NOVOMLYNETS6 and OLHA PODOLSKA7

1 Department of Accounting, Analysis and Audit, Odessa I. I.Mechnikov National University, Odessa, Ukraine, e-mail: [email protected] 2 Azman Hashim International Business School, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia, e-mail: [email protected] 3 Lithuanian Institute of Agrarian Economics, Vilnius, Lithuania, e-mail: [email protected] 4 Department of Management, Donbas State Engineering Academy, Kramatorsk, Ukraine, e-mail: [email protected] 5 Department of Economic of Enterprise, Donbas State Engineering Academy, Kramatorsk, Ukraine, e-mai: [email protected] 6 Vise-Rector, Сhernihiv National University of Technology, Сhernihiv, Ukraine, e-mail: [email protected] 7 Department of Organization of Production, Business and Management, Kharkiv Petro Vasylenko National Technical University of Agriculture, Kharkiv, Ukraine,e-mail: [email protected]

ARTICLE INFO ABSTRACT Received August 12, 2018 In the context of shortage of natural resources in Ukraine, the prob- Revised from September 14, 2018 lem of evaluating efficiency of the implementation of renewable Accepted November 17, 2018 energy sources (RES) becomes of particular importance and comes Available online December 15, 2018 in first place, since its solution will reduce the energy dependence of the state. Theoretical, methodological and practical issues of diver- JEL classification: sification of energy sources and evaluation of their effectiveness have found their development in modern works of domestic and Q1, Q20; Q48 foreign scientists. However, methods proposed by the scientists for evaluating efficiency of the transformation of energy consumption DOI: 10.14254/1800-5845/2018.14-4.17 based on RES are purely theoretical and do not fully allow to determine the current state and directions of diversification of energy Keywords: sources. The research is aimed at improving methodological support Efficiency, and developing criteria for evaluating the efficiency of energy con- energy consumption, sumption transformation based on RES. The article considers the diversification of energy sources, main methods of evaluating efficiency of involving RES in the energy renewable energy sources, consumption of the state, region and enterprise. Advantages, draw- energy potential. backs and perspectives of diversifying energy sources based on RES are shown. The system of criteria for evaluating effectiveness of energy consumption transformation based on RES is proposed. It is based on economic, environmental and social efficiency.

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Vitalii Nitsenko, Abbas Mardani, Justas Streimikis, Iryna Shkrabak, Ivan Klopov, Oleh Novomlynets and Olha Podolska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 237-247 INTRODUCTION In times of the crisis of national economy it is impossible to reach a stable economic growth without implementing and using a complex of innovation techniques of using renewable and alternative energy sources; it makes it possible to reduce dependence of national economy on the world prices for energy resources, to avoid irretrievable loss of money for purchasing the imported energy sources, to provide with the reduction of energy intensity of output of industrial products and to increase competitiveness of national industrial products. It influences positively energy intensity of GDP and favours the increase of energy efficiency of national economy and, as a result, provides economic development of the state. At the same time, it is impossible to determine an appropriateness of using renewable or alternative sources of energy supply in the process of economic activity of any economic entity without implementation of an effective tool and methodological support for evaluating efficiency of energy consumption. In the context of shortage of natural resources in Ukraine, the problem of evaluating the efficiency of implementation of renewable energy sources (RES) becomes of particular importance and comes in the first place, since its solution will reduce the energy dependence of the state.

1. LITERATURE REVIEW Most of the present foreign studies in the field of diversification of energy resources supply is devoted to evaluation of technological, social and ecological changes in the field of energy generation and use, to identification of their influence on the social and ecological development and its stability (e.g. Jacobson, 2009; Carley et al. 2012; Markard et al., 2012; Negro et al, 2012; Terrapon-Pfaff et al., 2014). At the same time, as G. Mao et al. (2015), note, there is a significant geographical heterogeneity in the forming a range of research problems and their results. Besides, the literature analysis shows that a peak of interest was observed in different periods of time: at first there was an interest to solar and wind energy, and then – to the use of biofuel. Research in the field of incentives policy of alternative sources energy development and use of “green energy” (Schmalensee, 2011; Mundaca and Richter, 2015) was widely-spread. There is a growing interest to the organizational and managerial aspects of implementation of diversification tools of energy sources supply, development of applied projects for certain types of renewable energy, as well as energy based on the use of recoverable resources, including the ways of solution of risks evaluation of implementation of such projects, approaches to integration of technologies of renewable and alternative power engineering and information control system etc. (e.g. Hitzeroth and Megerle, 2013; Chappin and Friege, 2014; Rasool, Ehsan and Shahbaz, 2015; Lee and Zhong et. al., 2015). It should be noted, that although the present variety of scientific research in the field of alternative and renewable energy has certain methodological basis in the form of sustainable development concept (Meadows et al, 1972; Belousov, 2013), but the latter has a great number of transformations and variety of interpretations, that situationally appeared as a result of the necessity to solve specific and practical tasks, and which are characterized by the relative eclecticism and a lack of system. At the same time the mentioned groundworks are fragmentary and don’t have rather developed methodological apparatus for designing evaluation models of efficiency of energy supply transformation in terms of renewable energy sources and explanation of methodological approaches to the formation of organizational and economic supply of the efficiency of their use.

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Vitalii Nitsenko, Abbas Mardani, Justas Streimikis, Iryna Shkrabak, Ivan Klopov, Oleh Novomlynets and Olha Podolska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 237-247 2. RESEARCH METHODOLOGY Methods proposed by scientists for evaluating efficiency of transformation of energy consumption based on RES are purely theoretical and do not fully allow to determine the current state and directions of diversification of energy sources. The research is aimed at improving methodological support and developing criteria for evaluating efficiency of energy consumption transformation based on RES. Presentation of baselines. Analysis of the scientific research relating to efficiency of energy supply transformation shows that there is a number of calculating methods and efficiency rate of energy production on the whole and based on renewable sources in particular. I. V. Belov (1983) points out in his research that when choosing an alternative from a number of presented engineering solutions with a single capital investment and little-changeable operating costs over the years, a minimum of annual unit expenditure is a choice criterion for project implementation and an indicator of comparative economic efficiency of capital investment. Besides, the scientist notes, that the comparison of alternatives should be done according to the unit expenditure over the calculating period. In the opinion of some authors (Barabaner, Nikitin and Klokova, 1989), the recoupment ra- tion for evaluating and choosing the project (supply system) is used as the unknown, which is calculated by the equation, linking up money flow of the whole term of the project and the unit capital investment for its implementation. So, a recoupment ratio is the indicator, which is set within a specific project. The value of the ratio for the person who makes a decision is not applied to other projects, as the proposed indicator is a maximum possible level of investment return for the specific project. A group of scientists, H. Z. Barabaner, S. M. Nikitin and T. I. Klokova (1989) in their paper “Methodological issues of energy development of rural districts” consider the following indicators as production efficiency criteria: gross and net revenue, reduction of labour inputs and material resources, growth of funds supply, cost of a working place of fixed kilowatt, growth of output volume, reduction of unit expenditures. In our opinion, the most complete criteria systems of efficiency evaluation of energy transformation based on renewable energy sources (RES) are offered by the following scientists: V. S. Simankov, P. Yu. and Buchatsky (2012), and G. B. Osadchy (2014). V. S. Simankov, P. Yu. and Buchatsky (Ibid.) single out the following criteria among the main criteria of efficiency of RES involvement: resources significance; economic significance; social significance; non-energy significance; budgetary significance; environmental significance; energy significance; they make examples of their own indicators of efficiency.

3. RESEARCH RESULTS Depending on the purposes and tasks of efficiency evaluation of energy supply transformation based on renewable energy sources, one or another indicator given above may be used. Nevertheless, considered methods have a significant drawback – they are theoretical and over- burdened with indicators. To our mind, the model of efficiency evaluation of energy supply transformation based on renewable energy sources should include three components: economic, environmental and social efficiency. Choice of stated components is explained by the following. Facilities of renewable power engineering have different economic features, which determine in turn an economic significance of the facility. Economic evaluation makes it possible to determine profitability of construction of renewable power engineering facility as for society on the whole so for specific economic entities implementing the projects. It also enables to compare efficiency of investments expenditures for construction of traditional and renewable power engineering facilities. Taking into account the constant growth of energy cost, understanding of reasonability and economic efficiency of use of renewable energy sources by the households is not less important thing.

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Vitalii Nitsenko, Abbas Mardani, Justas Streimikis, Iryna Shkrabak, Ivan Klopov, Oleh Novomlynets and Olha Podolska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 237-247 Considering the incomplete electrification of the state and large problems with connection of distant sectors to electrical supply network, the problem of efficiency of renewable power engineering technologies in case of full lack of electrical supply network is urgent. Power engineering facilities influence the environment in different ways. Different ecological compatibility is determined by environmental damage, which its construction and operation makes. Comparison of the influence of different type power stations on the environment makes it possible to determine expenditures necessary for maintaining the environment in acceptable state. Energy production based on renewable sources takes place without hydrocarbon raw material burning and, as a result, without the release of greenhouse gases and other atmosphere pollutants. So, we may assert so-called “the effect of zero СО2 emission”. However, the study of the whole life cycle of production, beginning with the preparatory stage, makes it possible to reveal side effects in the process of energy production. It is necessary for energy generation to manufacture and to install energy equipment, to create infrastructure and to provide its working conditions, to prepare raw materials, to utilize used materials and equipment after expiration date. It needs operation of metallurgical, engineering, agricultural and other plats, use of extractive sources of energy and means “nonzero emission”. Transition to renewable power engineering doesn’t always lead to the decrease of the environmental pollution, including decrease of СО2 emission and other greenhouse gases. So, it needs more detailed and thorough study. Comparing the alternatives of development of renewable energy facilities, important features defining their social significance are not considered enough. A number of unemployed for 9 months of 2016 was 1 662.2 persons, compared to the appropriate period in 2015 it went 24.6 thousand up. Unemployment rate increased from 9.0% to 9.2% of economically active population. Development of mechanical engineering and scientific and technical potential of renewable power engineering may result in the increase of working places in Ukraine and provide the economic growth. So, the social effect of the development of renewable energy sources is beyond any doubt and needs further study.

3.1 Economic Efficiency It is reasonable to consider economic efficiency from four standpoints: efficiency of investments expenditures, fixed cost of electricity, technology efficiency of the households, and technology efficiency in case of full lack of electrical supply network. To give an example, we calculate measure of effectiveness of investments expenditures for three types of power stations: gas thermal power plant (TPP), wind-driven power station (WPS) and solar power-station (SPS). The following data are used:  Rated capacity, MW – 620 MW (for TPP), 100 MW (for WPS), 150 MW (for SPS).  Ratio of rated capacity use (RRCU), % – 87% (for TPP), 35% (for WPS), 25% (for SPS).  Investments expenditures, $/1 kW – 917 $/1 kW (for TPP), 2213 $/1 kW (for WPS), 3873 $/1 kW (for SPS).  Fixed operating costs, $/kW – 13.20 $/kW (for TPP), 39.55 $/kW (for WPS), 24.69 $/kW (for SPS).  Variable operating costs, $/kW·h – 0.05 $/kW·h (for TPP), 0 $/kW·h (for WPS), 0 $/kW·h (for SPS).

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Vitalii Nitsenko, Abbas Mardani, Justas Streimikis, Iryna Shkrabak, Ivan Klopov, Oleh Novomlynets and Olha Podolska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 237-247 It should be noted that power stations must generate an equal amount of electricity. Calcula- tion of the main factors and their comparison are given in the Table 1.

Table 1. Calculation and comparison of the main economic factors of TPP, WPS and SPS

Factor Gas TPP WPS SPS RRCU, % 87% 35% 25% 1541 2158 Rated capacity, MW 620 620 MW × 87 % / 35 % 620 MW × 87 % / 25 % = 1541 MW = 2157 MW 4725144 4725144 4725144 Electricity generation 620 MW × 8760 h × 87 1541 MW × 8760 h × 2158 MW × 8760 h × per year, MW·h % = 4725 GW·h 35 % = 4725 GW·h 25 % = 4725 GW·h Investments expendi- 917 2213 3873 tures, $ / 1 kW 569 3411 8356 Investments expendi- 620 MW × $ 917 thou- 1541 MW · $ 2213 2157 MW · $ 3873 tures, mil $, total sand / 1 MW = $ 569 thousand / 1 MW = $ thousand / 1MW = $ mil 3411 mil 8356 mil Fixed operating costs, 13,20 39,55 24,69 $ / kW 8 61 53 Fixed operating costs, $ 13,2 × 620 000 kW = $ 39,55 × 1541000 kW $ 24,69 × 2158000kW mil $ $ 8 mil = $ 61 mil = $ 53 mil Variable operating 0,05 0 0 costs, $ / kW·h 236 Variable operating $ 0,05 × 4725 GW·h = 0 0 costs, mil $ $ 236 mil per year 244 Operating costs, mil $, $ 8 + $ 236 = $ 244 61 53 total mil per year Exceeding the invest- 2842 7788 ments expenditures $ 3411 mil – $ 569 mil - $ 8356 mil – $ 569 mil relative to gas TPP, mil = = $ 7788 mil $ $ 2842 mil Saving in operating 183 191 costs relative to gas - $ 244mil – $ 61 mil = $ $ 244 mil – $ 53 mil = TPP, mil $ per year 183 mil $ 191 mil Common term of re- 15,5 40,7 coupment for gas TPP, - $ 2842 mil / $ 183 mil $ 7788 mil / $ 191 mil years per year = 15.5 times per year = 40.7 times

Source: The study was performed by the authors on the base of source Degtyarev (2015, 2017.

According to the results of calculations we can conclude that power stations using the renewable energy sources still need more sizable investments than power stations using traditional sources of energy. But too high term of recoupment of power stations using RES in comparison to the traditional ones is unattractive for investors. When comparing the cost price of electricity, generated at the expense of RES to the electricity, generated at the expense of extractive fuel, such indicator as fixed cost of electricity is used (FCOE).

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Vitalii Nitsenko, Abbas Mardani, Justas Streimikis, Iryna Shkrabak, Ivan Klopov, Oleh Novomlynets and Olha Podolska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 237-247 Fixed cost of electricity is such cost for kW·h during the whole period of power station operating, which equates the unit cost of proceeds from the energy generation and sale with the unit cost of expenditures for construction and operation of the power station. As for the components of the given above factor for investment projects in Ukraine, it is unfortunately possible to get only the common volume of expenditures for construction of power stations and their capacity. Herewith, it is usually impossible to understand what the expenditures for construction consist of: whether the delivery and equipment installation was included, or the expenditures for the whole equipment or only crucial components were taken into account. In the available sources, there is also lack of data about equipment used at RES power stations; it additionally makes worse the comparison of the received results. So, to carry out further study and comparison, we use calculated fixed cost price of electricity in the USA, the EU and China (Figure 1).

Figure 1. Evaluation of the average fixed cost price of electricity of power stations in 2010 and power stations being put into operation in 2018, $/MW·h.

Source: Bezrukikh,2010; 2015.

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Vitalii Nitsenko, Abbas Mardani, Justas Streimikis, Iryna Shkrabak, Ivan Klopov, Oleh Novomlynets and Olha Podolska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 237-247 As we see, power stations using wind energy yield only to natural gas-fired plants in the fixed cost, leaving behind only coal and nuclear power stations. According to the trends of 2018 wind power stations also leave behind water power plants as opposed to 2010. However, in spite of positive trends most of power stations using RER are outsiders – solar power stations, “Offshore” wind power stations. Efficiency of the technology for household defines position of the customer, who chooses between energy purchasing at retail price (in a number of cases with the expenses for technological connection to the network) and installing private autonomous system enabling to turn off the system or to reduce expenses for energy purchasing. In this case we also make calculations of efficiency. So, a solar power station of low capacity (5 kW) being assembled, which includes solar cell panel, accumulator, inverting element, is proposed, according to online shop data (https://alteco.in.ua), at the price of about 188270 UAH (or $6 724) (without installation), the generation per year is 5348 kW. With RRCU 17.0%, electricity generation per year is: 5348-(5348×17.0%) = 4438 kW·h. With retail prices for electricity at the level 1.68 UAH/kW·h (for volume used above 100 kW⋅h electricity per month), the use of the complex enables the customer to save 4438×1.68=7455.84 UAH per year (or $266.3 per year). Thus, a common recoupment period for it is 188270/7456 = 25 years. There is no point for user from the standpoint of economic effect, because it exceeds a predicted lifetime of the complex. One of the most influential factors of competitiveness of autonomous power stations based on RES is lack of electricity network. In such case a consumer has additional expenditures for technological connection to the network. It is especially urgent for rural areas and distant economies. Cost of manufacturing equipment is often lower than the cost of connection, particularly subject to the laying the power lines (PL). According to our own calculations cost of laying 1 km of PL is 5.0-6.0 mil UAH (or $178 571-$214 286) (without the cost of transformer substation); incidental cost at the rate of about 20.0%, i.e. 1.0-1.2 mil UAH (or $35 715-$42 857) are additionally expected. So, total investments expenditures, without the cost of transformer substation, for 1 km of PL constitute 6.0-7.2 mil UAH (or $214 286-$257 143). Cost of solar power station with the capacity of 32 kW is 726 000 UAH (or $25 929). If the average RRCU is equal to 17.0%, the power station can generate 26 500 kW⋅h of electricity. It can be compared with the yearly electricity consumption by 10-12 households. Besides, further economic effect is reached at the account of lack of payment for electricity. From the mentioned above we can conclude the following: the more the distance of the consumer from the central electricity supply, the more the economic effect of the use of autonomous power stations based on RES.

3.2 Environmental Efficiency Power stations using traditional sources of energy emit yearly sulphur dioxide, carbon dioxide, nitrogen oxide, solid particles of ash, and also toxic heavy metals. Comparison of the influence of different types of power stations on the environment is given in the Table 2. As it is seen from the table, renewable sources of energy are more environmentally friendly than traditional sources according to all indicators.

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Types of influence RES Atom Coal Natural gas Global warming – – + + Water pollution by the thermal – + + + or harmful emissions Air pollution – – + limited Mercury emissions – – + – Mining activity, fuel output – + + + Solid waste – + + – Habitation environment limited limited + +

Source: Annual Energy Outlook, 2017.

However, as the opponents of the development of renewable power engineering note, СО2 emission occurs during the production process of components for “green power engineering”. Main indicator, from the standpoint of greenhouse gases emission, is connected with energy generation – an amount of СО2 gram-equivalent for the unit of generated energy, namely, kW⋅h for power engineering, i.e. gСО2eq/kW⋅h. It is obviously that distribution of greenhouse gases emission according to the stages of production life cycle is different for different types of energy. It also concerns power stations using RES. Life cycle analysis gives the following emission indicators for different types of electricity generation [gСО2eq/kW⋅h]: wind – 10 (according to the life cycle stages: preparation – 8.6; production – 0.5; finishing – 0.9); tidal – 15.0; water – 20.0; ocean wave – 22.0; geothermal – 35.0; solar batteries – 40.0; solar concentrators – 10.0; bioenergetics – 230. However, it is significantly less than the values given for power stations using traditional sources of energy: coal – 1000 (according to the life cycle: preparation – 10; production – 980; finishing – 10); gas – 490. One of the most important features of environmental efficiency is irretrievable water consumption at the power stations. Irretrievable water consumption at different types of power stations is given in the Table 3.

Table 3. Irretrievable water consumption at the power stations of different types

TPS Type of oil the power WPS SPS APS prod- gas coal station ucts Unit water 0,004 0,11 2,3 1,6 0,95 1,9 loss, l/kW·h 575 times < than APS; 21 times < than APS; 400 times < than TPS 15 times < than TPS using oil products; using oil products; 238 times < than TPS 9 times < than TPS using gas; using gas; 475 times < than TPS 17 times < than TPS using coal using coal.

Source: Annual Energy Outlook, 2017.

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3.3 Social efficiency Except the reduction of harmful emissions, which influence negatively the health of population, job creation in the region of production of equipment for renewable power engineering and operation of RES facilities is considered to be a significant social effect. Working places connected with renewable power engineering (direct) and related fields (indirect) are considered as well. According to the results of the previous year there were 9.8 mil of working places in renewable power engineering of the world (Fig. 2). Solar photoelectric technologies provided the greatest number of working places (3950000), bioenergetics took the second place (2800000), big and small water power engineering took the third place (1700000).

Figure 2. Working places in renewable power engineering

Source: REN 21 (2012, 2013, 2017)

So, positive dynamics of the growth of the number of people working in the field of renewable power engineering is observed at present. According to IRENA predictions, doubling of the part of renewable energy sources in the world energy balance results in more than 24 mil of working places all over the world by 2030. It may be the chance for employment growth and development of scientific and technical potential of Ukraine. In addition to production capacities, scientific and research, technical secondary and higher educational institutions will be developed.

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Vitalii Nitsenko, Abbas Mardani, Justas Streimikis, Iryna Shkrabak, Ivan Klopov, Oleh Novomlynets and Olha Podolska / Montenegrin Journal of Economics, Vol. 14, No. 4 (2018), 237-247 CONCLUSIONS The system of criteria developed and tested by the authors allows to cover the main areas that receive a positive effect due to the transformation of the structure of energy supply sources based on renewable energy sources, namely: economic, environmental and social. At the same time, the calculation of the economic efficiency of the diversification of energy supply sources is complicated by the unreliability of data on the average normed cost of electricity produced by various types of energy supply sources and the efficiency of renewable energy sources based technology for households, especially in the absence of complete network power supply. These directions of further research should be considered promising, their results will increase the validity of managerial decisions on the transformation of the structure of energy supply sources in Ukraine with the use of renewable energy sources.

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 Classification codes - Please provide up to 6 standard JEL codes. The available codes may be accessed at JEL: http://www.aeaweb.org/journal/jel_class_system.html

 Article structure - The structure of article should comprise: the title, abstract, key words, introduction, subtitles, conclusion and bibliography. Articles can also be structured in the following way: introduction, starting hypotheses, solutions, discussion, conclusion and bibliography. Divide your article into clearly defined and numbered sections (1, 2, 3 ...). Subsections should be num-bered 1.1 (then 1.1.1, 1.1.2 ...), 1.2, etc. (the abstract is not included in section numbering).

 Abstract - The abstract must include sufficient information for readers to judge the nature and significance of the topic, the adequacy of the investigative strategy, the nature of the results and the conclusions. The abstract is not an introduction, it summarizes the substantive results of the work, not merely listing the topics that are discussed in the paper. The abstract should contain the main idea of the paper, the subject and the goal of the research, methods used, hypotheses, research results and a brief conclusion. It must have 200 to 250 words.

 Technical presentation - Main body of the text should be printed in Times New Roman, 12pt with single line spacing. Subtitles must be short, clearly defined and numbered, except for Introduction and Conclusion. All tables and figures need to support your research findings. They should be clearly referred to and numbered consecutively in Arabic numerals. They should be placed in the text at the appropriate paragraph, immediately below their name. Below them, the source should be listed. All tables and figures must have captions. In all tables and figures taken or adapted from other sources, a brief note to that effect is obligatory, below the figure

 Footnotes - Footnotes should be used as least as possible, and only for the necessary explanations, with the continuous use of Arabic numbers.

References SSCI reccomends that self-citation for the best journals in the field goes around 10%. Ac- cordingly we encourage authors to pay attention to this and cite their own works accordingly. Literature is not to be numerated. It is to be arranged in alphabetic order of authors and chronologically for the articles of the same author. Literature is to be quoted according to the examples for books, magazines and other sources. References to other publications must be in Harvard style and carefully checked for com- pleteness, accuracy and consistency. You should cite publications in the text: (Ilic, 2009) using the first named author's name or (Ilic and Tot, 2009) citing both names of two, or (Tot et al., 2009), when there are three or more authors. At the end of the paper a reference list in alpha- betical order should be supplied:

 For books - Surname, Initials (year), Title of Book, Publisher, Place of publication. e.g. Bagdikian, B. H. (1983), The Media Monopoly, Beacon Press, Boston.

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 For book chapters - Surname, Initials (year), “Chapter title”, Editor's Surname, Initials, Title of Book, Publisher, Place of publication, pages. e.g. Picard, R. G. (2005), “Money, Media, and the Public Interest”, in Overholster, G., Ja- mieson, K. H. (Ed.), The Press, Oxford University Press, Oxford, pp. 337-350.

 For journals - Surname, Initials (year), “Title of article”, Journal Name, volume, number, pages. e.g. Thacher, D., Rein, M. (2004), „Managing Value Conflict in Public Policy”, Governance, Vol. 17, No. 4, pp. 457-486.

 For published conference proceedings - Surname, Initials (year of publication), "Title of paper", in Surname, Initials (Ed.), Title of published proceeding which may include place and date(s) held, Publisher, Place of publication, Page numbers. e.g. Draskovic, V., Grego, Z., Draskovic, M. (2011), “Media Concentration, Neoliberal Para- doxes and Increase in Virtuality”, in Media Concentration proceedings of the international conference in Podgorica, Montenegro 2011, Elit, Podgorica, pp. 33-45.

 For working papers - Surname, Initials (year), “Title of article”, working paper [number if available], Institution or organization, Place of organization, date. e.g. Draskovic, V. (2007), “Specificities and problems of Montenegrin transition”, working paper, Leeds University Business School, TIGER, Warsaw, September.

 For newspaper articles (authored) - Surname, Initials (year), “Article title”, Newspaper, date, pages. e.g. Miller, M. C. (1997), “The Crushing Power of Big Publishing”, The Nation, 17 March, p. 10.

 For newspaper articles (non-authored) - Newspaper (year), “Article title”, date, pages. e.g. Vijesti (2011), „The New Media“ 2 December, p. 5.

 For electronic sources - If available online, the full URL should be supplied at the end of the reference, as well as a date that the resource was accessed. e.g. Compaine, B. M. (2005), „The Media Monopoly Myth: How New Competition is Expand- ing our Sources of Information and Entertainment”, available at: http://www.NewMillennium Research.org//archive/ final_Compaine_Paper_050205. pdf (accessed 10 december 2011).

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